WP2 Part A - Final Report “Off-line Capability Assessment”
UoM-ENWL_CLASS_WP2T1_FRv10
23rd September 2015
Title:
Synopsis:
Document ID:
Date:
Prepared For:
WP2 Part A - Final Report “Off-Line
Capability Assessment”
This document covers the methodology, application and findings from
the work carried out by Work Package 2 Part A “Off-Line Capability
Assessment” as part of the Customer Load Active System Services
(CLASS) project. Considering residential load models based on
literature data, the methodology is first demonstrated using individual
primary substations as well as constraints resulting from the
interactions across voltage levels (including the effects on LV
customers). The half-hourly quantification of the potential residential
demand response is then extended to whole Electricity North West
Limited area and to the whole UK. In addition, load models based on
measurements from the trials (catering for both residential and nonresidential loads), are also adopted to produce a more
comprehensive quantification.
UoM-ENWL_CLASS_WP2T1_FRv10
23rd September 2015
Kieran Bailey
CLASS Future networks engineer
Electricity North West Limited, UK
Steve Stott
CLASS technical engineer
Electricity North West Limited, UK
Prepared By:
Andrea Ballanti
The University of Manchester
Sackville Street, Manchester M13 9PL, UK
Revised By:
Dr Luis(Nando) Ochoa
The University of Manchester
Sackville Street, Manchester M13 9PL, UK
Contacts:
Dr Luis(Nando) Ochoa
+44 (0)161 306 4819
luis.ochoa@manchester.ac.uk
The results and discussions provided in this report are outcomes of preliminary analyses and ‘proof of concept’
performed at selected subset of substations of a particular electricity distribution network under specific
operating conditions and is not guaranteed to be the same for other sites or other networks. The readers should
use this document as guidance and at their own responsibility. Any omissions or errors, if identified, should be
reported to the authors.
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Executive Summary
This document presents the methodology, application and findings from the work carried out by Work
Package 2 Part A (WP2-A) as part of the Low Carbon Networks Fund Tier 2 project “Customer Load
Active System Services (CLASS)” run by Electricity North West Limited. The aim of this work package
is to provide Electricity North West Limited with the half-hour demand response (DR) unlocked by
reducing voltages through on-load tap changer actions at primary substations.
The DR quantification was carried out per primary substation, per grid supply point, for the Electricity
North West Limited area and for the whole UK considering four typical days. The developed
methodology caters for the constraints imposed by customers in real LV networks as well as for
features of real high voltage (HV) and extra high voltage (EHV) networks. This, in turn, allows
quantifying in a realistic manner the extent to which the voltage can be changed (i.e., voltage
capability) without affecting customers or violating any network limitation. In addition, load models (i.e.,
the relationship between voltage and demand) based on literature data (residential loads only) as well
as on measurements (both residential and non-residential loads), produced by Work Package 1
(WP1), were adopted.
Key aspects of the developed methodology and associated results are described as follows.
1. Load Modelling
The load model provides the mathematic relationship between voltage and demand, essential to
quantify, in conjunction with the voltage capability, the volume of DR that can be unlocked. In
WP2-A the time-varying load model based on literature data for residential customers is
developed by a bottom-up approach in which each single appliance load model and demand
was aggregated at primary substation level. Given the limited information on commercial and
industrial customers, the non-residential component of the demand was considered as nonresponsive to voltage changes. In this context, the resulting literature-based load models allow
for an initial conservative estimation of the potential DR considering only residential consumers.
More comprehensive aggregated load models were produced by WP1 using the measurements
from 60 primary substations part of the trial, i.e., catering for both residential and non-residential
substations. A comparison between the literature and measurement-based load models
revealed, as expected, that the former underestimates the total load responsiveness given that
the non-residential component is neglected. Indeed, the discrepancies are larger for primary
substations identified as mainly non-residential. However, when considering the average
literature and measurement-based load models from the mainly residential primary substations
(also known as representative models), the resulting values showed good consistency
especially at peak time (6 pm).
2. HV Network Modelling
To assess the voltage capabilities of a given primary substation, a realistic representation of the
corresponding HV network is required. For this purpose, a methodology is developed to model
HV networks from the provided DiNIS export files into the adopted power distribution system
simulator (OpenDSS). Among the 15 HV networks modelled only three (Romiley, Fallowfield
and Ashton Golborne) were adopted for further power flow analysis given the high confidence
on the developed models. From the LV perspective, realistic settings for the off-load tap
changer (typically unknown) are considered.
3. LV Network Constraints
To assess the extent to which the voltage at primary substations can be reduced, the
constraints that LV customers might impose were quantified. For this purpose, a Monte Carlo
approach was applied to 57 LV feeders considering 200 simulations per typical day. The
outcome is the average number of BS EN 50160 compliant customers per LV busbar voltage.
Results show that the minimum LV busbar voltage needed to maintain the number of compliant
customers above 99 % is 234 V (1.017 p.u.) in winter, 231 V (1.004 p.u.) during spring and
autumn, and 230 V (1.0 p.u.) in summer. As expected, given the high demand in that
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characterise winter period a lower tap capability (or a higher LV busbar voltage) was found. On
the other hand, no significant difference can be noticed between autumn and spring due to their
similar demand level.
4. Voltage Capability
Based on the developed HV network models and LV network constraints, power flow studies
were performed to quantify the extent to which the voltage at the primary substation can be
reduced, i.e., the voltage capability. The potential voltage fluctuations at the primary side of the
primary substation are also catered for carrying out a simplified analysis using one EHV network
model. As expected during winter peak hours (6 pm) all HV networks showed the lowest voltage
capability (2.86 % or 1.43 %). The highest voltage capability (4.29 %) was found during summer
nights (9 pm to 7 am) and it was mainly limited by the available OLTC tap step positions (i.e.,
OLTC headroom) rather than by the LV network constraints.
5. Demand Response Quantification: Electricity North West Limited Area
To quantify the DR that a primary substation can provide for which network models are not
available the voltage capability results obtained from the three analysed HV networks were
generalized. More precisely, two scenarios were produced: conservative and optimistic. With
the optimistic scenario it was found that a voltage reduction of 2.86 % (i.e., 2 OLTC tap steps)
can always been introduced without affecting customers. Moreover, a reduction of 4.29 % (i.e.,
3 OTLC tap steps) was found to be feasible throughout a significant part of the year.
The measurement-based load models and their statistical variability obtained by WP1 was also
considered. In addition, the contribution of the EHV network was taken into account and found
to be typically below 2 % of the DR (resulting from the reduction of losses). This quantification
process was carried out considering all primary substations, the aggregated demand response
per GSP, and the whole Electricity North West Limited area.
Considering the literature-based models i.e., only the residential component of the demand, and
the optimistic voltage capability scenario it was found that the DR that the Electricity North West
Limited area can provide varies between 15 MW during summer (3 am) to 170 MW during
winter (around 8 pm). However, considering the measurement-based load models (in which
both residential and non-residential components are accounted for), a much higher DR was
found: between 65 MW during summer (around 5 am) to 235 MW during winter (around 8 pm).
No significant seasonality effects were noticed between autumn and spring due to the similar
loading level in these seasons.
6. Demand Response Quantification: Whole UK
Information such as number, size and main type of customers corresponding to primary
substations outside the Electricity North West Limited area was not available. Consequently, for
the UK quantification, the number of houses nationwide as well as the aggregated demand data
from National Grid were adopted.
Adopting the optimistic capability scenario and the literature-based load model (only the
residential component is considered), it was found that for the whole UK the DR can vary from
about 63 MW during summer (around 5 am) to more than 1.2 GW in winter evening (around 8
pm). During winter peak hours (around 6 pm) more than 1 GW of DR can be unlocked. This,
however, represents a conservative quantification of the potential benefits from CLASS.
To understand the full extent to which CLASS can contribute, the non-residential component
was incorporated in the quantification, aligned with measurement-based models produced by
WP1. Adopting the optimistic voltage capability scenario, it was found that the whole UK can
provide a DR varying from 1.2 GW during summer (around 5 am) to more than 3.3 GW during
winter evening (8 pm). Finally, during winter, autumn and spring peak hours (6 pm) almost
3 GW of potential DR were found.
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Table of Contents
Executive Summary ............................................................................................................................... 2
1
1.1
1.2
1.3
1.3.1
1.3.2
1.3.3
1.3.4
1.4
1.5
Load Modelling and Demand Profiling ............................................................................. 6
Primary Substation Demand Profile...................................................................................... 6
Load Models: Basic Concepts .............................................................................................. 8
Literature-Based Load Model ............................................................................................... 9
Single Appliance Demand Profile ......................................................................................... 9
Single Appliance Load Model ............................................................................................. 10
Representative Load Models for Residential Customers ................................................... 11
Primary Substation Load Model.......................................................................................... 12
Measurement-based Load Model: Primary Substation ...................................................... 13
Load Model Comparison ..................................................................................................... 15
2
2.1
2.1.1
2.1.2
2.1.3
2.2
2.3
2.4
2.5
2.6
HV Network Modelling ...................................................................................................... 18
Structure of DiNIS Export Files ........................................................................................... 18
Table ST40 ......................................................................................................................... 19
Table ND108 ....................................................................................................................... 19
Table LN135 ....................................................................................................................... 20
Structure of the OpenDSS Files ......................................................................................... 20
Translation Process ............................................................................................................ 21
HV Network Selection ......................................................................................................... 22
Primary Substation: On-Load Tap Changer Modelling ....................................................... 23
Secondary Substation: Off-Load Tap Changer Modelling .................................................. 24
3
3.1
3.2
LV Network Constraints ................................................................................................... 26
Methodology ....................................................................................................................... 26
Results ................................................................................................................................ 29
4
4.1
4.2
4.3
4.4
Voltage Capability ............................................................................................................. 31
EHV Network Influence ....................................................................................................... 31
Time-Varying Load Allocation ............................................................................................. 32
Time-Varying Voltage Capability Assessment.................................................................... 33
Demand Response Quantification ...................................................................................... 35
5
5.1
5.2
Demand Response: Primary Substation ........................................................................ 37
Tap Capability Generalisation............................................................................................. 37
Demand Response Quantification ...................................................................................... 38
6
6.1
6.2
Demand Response: Electricity North West Limited Area ............................................. 41
EHV Network Contribution .................................................................................................. 41
Demand Response Quantification ...................................................................................... 42
7
Demand Response: United Kingdom ............................................................................. 46
8
Conclusions ...................................................................................................................... 48
9
References......................................................................................................................... 51
Appendix 1 - Load Model per Appliance ............................................................................................ 53
Appendix 2 - Aggregating Load Models ............................................................................................ 55
Appendix 3 - HV Network Selection: Filtering Process .................................................................... 57
Appendix 4 - Off-Load Tap Changer Modelling ................................................................................. 59
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Appendix 5 - Time-Varying Load Allocation ...................................................................................... 60
Appendix 6 - Conservative Tap Capability Scenario ........................................................................ 63
DR in the Electricity North West Limited area ........................................................................................ 63
DR in the United Kingdom ...................................................................................................................... 64
Appendix 7 - One Primary Substation Dashboard ............................................................................ 66
Appendix 8 - Electricity North West Limited Dashboard ................................................................. 68
Appendix 9 - UK Dashboard ................................................................................................................ 69
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1 Load Modelling and Demand Profiling
In order to quantify the demand response that is possible to unlock in a given primary substation, the
aggregated load model needs to be produced. A literature-based approach is proposed here by which
the load models of individual residential appliances are aggregated at a primary substation. For this
purpose, the aggregated residential and non-residential demand profiles are first produced
considering the customer types according to ELEXON. Thereafter, the aggregated time-varying load
models are produced for the residential demand. Although the response, i.e., load models, from nonresidential demand is neglected in this approach, it allows quantifying the potential contributions from
the residential sector alone. Finally, these literature-based load models are compared with models
produced by Work Package 1 that were derived from measurements and embed both residential and
non-residential responses to voltage changes.
Primary Substation Demand Profile
1.1
In this section the methodology to estimate the aggregated demand per primary substation is
discussed. This includes the breakdown per type of demand, residential and non-residential, for any
primary substation.
In the UK the balancing and settlement code company (ELEXON), by comparing how much electricity
generators and suppliers produce and consume, has classified customers, based on their
consumption, in 8 categories (profile class, PC) as defined below [1]:








Profile Class 1 (PC1) Domestic Unrestricted Customers
Profile Class 2 (PC2) Domestic Economy 7 Customers. These customers are usually
equipped with storage space heating appliances typically activated at night as their tariff
guarantee lower electricity price during these hours
Profile Class 3 (PC3) Non-Domestic Unrestricted Customers
Profile Class 4 (PC4) Non-Domestic Economy 7 Customers
Profile Class 5 (PC5) Non-Domestic Maximum Demand (MD) Customers with a Peak Load
Factor (LF) of less than 20 %
Profile Class 6 (PC6) Non-Domestic Maximum Demand Customers with a Peak Load Factor
between 20 % and 30 %
Profile Class 7 (PC7) Non-Domestic Maximum Demand Customers with a Peak Load Factor
between 30 % and 40%
Profile Class 8 (PC8) Non-Domestic Maximum Demand Customers with a Peak Load Factor
over 40 %
For each PC and day throughout the fiscal year, ELEXON produces half-hourly consumption profiles.
These profiles can be modified so as to create representative demand profiles by considering the total
number of customers per PC. An example of this is shown in Figure 1.
For every primary (and secondary) substation in the UK the number of customers per profile class is,
in general, known. Consequently, the aggregated yearly profile for a primary substation can be
obtained by adding up the profiles resulting from the direct multiplication of the number of customers
per PC and the corresponding representative profile. PC0 customers, characterised by a maximum
demand above 100 kW and equipped with Half-Hourly (HH) meter, are neglected due to lack of data.
Given that PC0 customers are neglected, the resulting aggregated profile is scaled up in order to
match the actual maximum demand. For illustration purposes, the load profiling methodology is
applied to Egremont primary substation. The corresponding data is presented in Table I and Table II
The final aggregated demand profile for the peak day (13th December) for Egremont primary
substation is shown in Figure 2. The demand of PC1 and PC2 customers (dominant in this primary
substation) provides the residential component of the aggregated demand, useful for the application of
the literature-based load model explained in the next chapters.
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16
PC1
14
PC2
PC3
PC4
PC5 to PC8
Demand (kW)
12
10
8
6
4
2
0
00:00
03:00
06:00
09:00
12:00
15:00
24 hours - 30 minutes
18:00
21:00
Figure 1 ELEXON profiles per customer category in a winter day
Table I Egremont primary substation available data: Number of customers
N°
PC0
PC1
PC2
Profile classes
PC3
PC4
PC5
24
8.891
644
445
135
8
PC6
PC7
PC8
7
6
8
Table II Egremont primary substation available data: Peak Demand
Primary peak
Peak demand 2012/2013
Power factor
12.25 MW
0.97
16
14
PC5 to PC8
PC4
PC3
PC2
PC1
Demand (MW)
12
10
8
6
4
2
0
00:00
03:00
06:00
09:00
12:00
15:00
24 hours - 30 minutes
18:00
21:00
Figure 2 Aggregated demand per customer type for the peak day (13th December) on Egremont
primary substation
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16
14
Winter
Autumn
Spring
Summer
Demand (MW)
12
10
8
6
4
2
0
00:00
03:00
06:00
09:00
12:00
15:00
24 hours - 30 minutes
18:00
21:00
Figure 3 Estimated aggregated demand on Egremont for 4 representative weekdays
The same process can be repeated for any other day of the year. Figure 3 shows the profiles for four
typical days1 (identified using a clustering technique). The highest demand of around 11 MW is
experienced in winter at 6pm and no significant difference can be noticed between spring and autumn
as previously discussed. During summer at night the lowest demand (3.4 MW) is experienced.
Caveats
It should be noted that the estimated aggregated demand does not take into account PC0 customers
(i.e., those with a declared maximum demand above 100 kW) as no data was made available.
Consequently, in the effort to compensate this approximation the estimated ELEXON demand was
scaled up to match the (yearly) monitored maximum demand. In this way both residential and nonresidential components have been scaled by the same factor not reflecting the real (and unknown)
load composition. This, in turn, may lead to potential under or overestimation of the residential share in
the aggregated demand.
In addition, it should be noted that the ELEXON profiles for high-consuming customers such as PC7PC8 are obtained based on a lower number of monitored customers compared to the much more
numerous PC1 and PC2. Consequently, those profiles may not provide a trustworthy representation.
This is important in cases with large commercial or industrial customers.
1.2
Load Models: Basic Concepts
Throughout this report terminology related to load modelling is used. Hence, it is important to highlight
that all models discussed hereafter are for steady-state studies.
It is widely known and accepted that each electric load is characterised by its own voltage/power
dependency. In particular, the following classification of loads is the most common:
1. Constant power: These loads are insensible to voltage variation (within acceptable limits).
This kind of load is also very common in most load flow studies where a constant P-Q is
assumed [2] for steady-state studies. In practice, only Switch Mode Power Supply (SMPS),
generally known as electronic devices, belong to this category.
2. Constant current: A linear relationship between voltage and power is considered.
3. Constant impedance: A quadratic relationship between power and voltage is defined.
Resistive loads without thermal control (e.g., space or water heaters) belong to this category.
1
Winter (26th January) – Autumn (2nd October) – Spring (13th April) – Summer (22nd June)
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A recent growing interest in load modelling has highlighted the inaccuracies brought by load models
based only on the previous approach [2]. A more sophisticated model, called “exponential”, is shown
in Eq. 1.
V 
P  P0  
 V0 
np
Eq. 1
The key element in Eq. 1 is the np coefficient. It can adopt fractional values between 0 and 2 thus
increasing the flexibility of the model – a feature not possible with the previous classification where np
becomes 0, 1 or 2, respectively.
However, the accuracy of this model has been improved by the so-called polynomial (or ZIP) model
(shown in Eq. 2).
  V 2

V 
P  P0  Z P    I P    PP 
  V0 

 V0 
Eq. 2
Z P  I P  PP  1
Given that each of the three polynomial coefficients (ZP, IP and PP) can be defined, this model allows a
more precise modelling [2]. Indeed, when it is possible to define the coefficients, it is the preferred
model for in-depth analysis [3, 4].
In WP2-A both exponential and ZIP load models were extensively adopted to shows main results and
compare the outcomes of two different approaches (i.e., literature and measurement based)
developed to characterise the voltage dependencies of the aggregated demand (at primary substation,
Electricity North West Limited area, and the whole UK).
1.3
Literature-Based Load Model
In this chapter the bottom-up approach developed to produce a “time-varying” load model for the
residential component of the demand (PC1 and PC2) is described. This model was adopted in the
early stage of CLASS project whilst the measurement-based model was under development.
By defining the load model and demand profile for every single appliance typically found in a UK
dwelling the load model of any primary substation can be produced adopting a bottom-up approach.
This process is summarised in Figure 4. First, a literature review has been carried out in order to
associate a ZIP model with each single appliance. Thereafter, by adopting the freely available CREST
tool [5], the load profile for each residential customer (and its appliances) has been stochastically
produced. Then, the daily ZIP model for each residential customer is calculated by aggregating each
single appliance model and profiles. The load model for a generic residential customer is then
obtained by averaging the load model of 500 residential customers. The non-residential demand is
considered as non-responsive to voltage changes, i.e., constant power load model. Finally, by
adopting the ELEXON profiles to estimate the aggregated residential and non-residential component
of the demand, the aggregated load model for any primary substation can be produced.
1.3.1
Single Appliance Demand Profile
For the specific UK residential sector, Loughborough University, by analysing the UK national Time of
Use survey, produced a freely available VBA tool [5] (“CREST tool”) able to stochastically reproduce 1
min resolution demand profile per dwelling and down to every single appliance that can be found in it.
For this purpose, the tool initially simulates how people in a dwelling are interacting with electrical
appliances (e.g., watching TV, cooking, ironing etc.). Based on the yearly energy consumption per
appliance, it then stochastically decides whether a specific appliance is being turned on at a given
moment in time.
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1 Appliance
Load model
literature review
Demand profile
CREST tool
Aggregation Process
for all appliances in a dwelling
1 residential
customers
Load model
1 residential customer
Demand profile
aggregated
Aggregation Process
for 500 customers
500
Residentialcustomers
Representative load model
for residential customer
Demand profiles
residential and non-residential
ELEXON
Load model
for non-residential customer
(Not responsive)
Aggregation Process
for all customers in a primary
substation
Primary
Substation
Load model
aggregated
Demand profile
aggregated
Figure 4 Flowchart of the literature-based load model for a primary substation
The overall dwelling power is obtained summing up the power of each appliance used at that moment.
This operation is repeated each minute for the whole day, finally obtaining the corresponding demand
profile. Thanks to the adoption of Markovian chains (that summarise the time of use survey) the
CREST tool is able to provide realistic load profiles in a stochastic manner.
An example of the dwelling profile that can be produced is shown in Figure 5 (two people, weekday,
winter). As evident in Figure 5 the power consumption of each appliance within a dwelling was
generated. This particular feature makes it possible to associate each single appliance the ZIP model
found in the literature (Appendix 1).
1.3.2
Single Appliance Load Model
A total of 35 appliances commonly found in a UK dwelling are considered to produce the aggregated
load model of a single residential customer. The corresponding individual load models have been
produced based on 29 papers and reports. Different measurement approaches, countries, regulations
and even the obvious technological evolution of appliances made the load modelling a complex task.
The following criteria (in order of relevance) were adopted to select the most relevant papers [6-8]:
1.
2.
3.
4.
5.
Measurement-based models
Year of publication
Agreement of models with other studies
Production of models via simulations
V/P-Q dependency within practical intervals (i.e., ±5 % of V0)
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The adopted polynomial coefficients (adopting the ZIP load model) for each of the 35 appliances are
shown the Appendix 1. The latter also presents further details related to the selection process.
4.5
4
3.5
Power (kW)
3
2.5
2
1.5
1
0.5
0
00:00
03:00
06:00
09:00
12:00
15:00
24 hours - 1 minute
18:00
21:00
CHEST FREEZER'
FRIDGE FREEZER'
FRIDGE
UPRIGHT FREEZER'
ANSWER MACHINE'
CD PLAYER'
CLOCK
PHONE
HIFI
IRON
VACUUM
FAX
PC
PRINTER
TV(CRT)
TV(LCD)
TV(Plasma)
VCR DVD'
RECEIVER
HOB
OVEN
MICROWAVE
KETTLE
SMALL COOKING'
DISH WASHER'
TUMBLE DRYER'
WASHING MACHINE'
WASHER DRYER'
DESWH
E INST'
ELEC SHOWER'
STORAGE HEATER'
ELEC SPACE HEATING'
GIS Light
CFL Light
Figure 5 Example of dwelling profile and breakdown per appliance (CREST tool)
1.3.3
Representative Load Models for Residential Customers
Considering the availability of demand profile per single appliance within a dwelling (CREST tool, an
example is shown in Figure 5) and associated load model found in the literature (section 1.3.2) the
time-varying load model per single domestic customers can be produced as detailed in the Appendix
2. However, in the context of CLASS it is more important to quantify the aggregated demand response
provided by thousands of customers typically connected to a primary substation.
Given the stochastic nature of the CREST tool the procedure can be repeated to produce the required
thousands of load models (and profiles) for each one of the domestic customers connected to a
primary substation and finally aggregate them for the whole substation. However, it was noticed that
for any number of customer above 300 (far below the number of residential customers typically
connected to a primary substation) the aggregated time-varying load model does not change
significantly. Consequently, it was decided to define a representative time-varying load model for
residential customers that is realistic when thousands of them are aggregated. This representative
load model was obtained by aggregating the load models of 500 customers (more than 300 to capture
potential outliers). The aggregated demand (and breakdown per appliance) for 500 PC1 customers for
a winter weekday is shown in Figure 6. The resulting representative load models for PC1 and PC2
customers are shown in Figure 7.
The time-varying nature of such model allows capturing the variability in the demand composition as
evident in Figure 6 where for instance during night hours cold appliances (e.g., fridge, refrigerator) are
dominant while at peak time (6 pm) light appliances are one of the most important component.
Although the resulting PC1 load model varies in time (Figure 7), it is consistent with aggregated timeindependent models found in the literature [4, 9-11] where np values around 1.1 and 1.4 were found.
In terms of PC2 customers, as it can be noticed from Figure 7, they are characterised by a high
responsiveness at night hours. This is due to the presence of storage space heating appliances which
is the main difference from PC1 customers. Representative PC1 and PC2 load models were produce
for 4 typical days adopting the same procedure presented here.
Finally, it is important to highlight that the CREST tool is only used to produce the time-varying load
models. The demand profiles for PC1 and PC2 customers are produced using the methodology
described in section 1.1.
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450
400
350
Power (kW)
300
250
200
150
100
50
0
00:00
03:00
06:00
09:00 12:00 15:00
24 hours - 30 minutes
18:00
21:00
CHEST FREEZER
FRIDGE FREEZER
FRIDGE
UPRIGHT FREEZER
ANSWER MACHINE
CD PLAYER
CLOCK
PHONE
HIFI
IRON
VACUUM
FAX
PC
PRINTER
TV(CRT)
TV(LCD)
TV(Plasma)
VCR DVD
RECEIVER
HOB
OVEN
MICROWAVE
KETTLE
SMALL COOKING
DISH WASHER
TUMBLE DRYER
WASHING MACHINE
WASHER DRYER
DESWH
E INST
ELEC SHOWER
STORAGE HEATER
ELEC SPACE HEATING
GIS Light
CFL Light
Figure 6 Demand profile and breakdown per appliance during winter weekday for 500 PC1
customers (CREST tool)
Figure 7 Representative literature-based load models for PC1 and PC2 customers in winter
weekday
1.3.4
Primary Substation Load Model
A literature-based load model was developed for residential customers (PC1 and PC2) whilst the nonresidential ones (PC3 to PC8) were considered non-responsive (i.e., np=0) as no data on type and
load composition were available. In addition, for every customer category, the estimated aggregated
demand was also quantified (section 1.3). Consequently, following the aggregation process described
in the Appendix 2 the literature-based load model for any primary substation can be produced.
To demonstrate the methodology the aggregated load model for Egremont primary substation is
shown in Figure 8-a. In this case it can be noticed that the load model coefficient np varies between
0.3 and 1.1. This implies that 1 % voltage reduction can trigger, in function of the time, a DR from
0.3 % to 1.1 % of the aggregated demand.
During summer night hours the lowest np coefficient was found. Such low responsiveness can be
justified by the dominance of refrigerators (Figure 6), typically characterised by a low np [12]. In
addition, as expected, the primary substation load model (Figure 8-a) is less responsive than it would
be if it was 100 % residential (Figure 7). Indeed, the residential component in this primary substation is
estimated to vary between 65 and 85 % of the total demand (Figure 9-a).
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a)
b)
Figure 8 Literature-based load model for a) Egremont, b) Hyndburn Road
a)
b)
Figure 9 Estimated residential share in a) Egremont, b) Hyndburn Road
For primary substations with higher industrial share as Hynburn Road (Figure 9-b) a lower np (Figure
8-b) was found. This is an expected outcome as the literature-based load model aims to quantify the
demand response provided by the residential component only of the demand. Hence, the smaller
residential component the smaller the demand responsiveness quantified with the literature-based
load model.
Caveats
Although the literature-based load model shows strong consistency with findings in the literature [13],
it should be noticed that it may not capture atypical load compositions such as higher than average
penetrations of space heating appliances among residential customers.
Moreover, given that the literature-based load model relays on number of customer per type (i.e., PC1
to PC8) it provides similar outcomes (i.e., np coefficient) for substations with similar customer
composition. However, this might not be the case in practice as customers belonging to the same
category (particularly PC5 to PC8) may not provide the same response to voltage variations due to
their different types of loads and consequently load composition.
1.4
Measurement-based Load Model: Primary Substation
WP1 produced measurement-based time-varying load model for 60 primary substations by analysing
one year of monitoring data from CLASS trials. More specifically, for every substation 5 values (i.e.,
mean, 25th percentile, 75th percentile as well as lower and upper np) are provided half-hourly for
representative days. An example is shown in Figure 10 for Egremont primary substation for a summer
weekday.
Given the need to generalise the measurement-based load models so they can be applied to other
primary substations, representative measurement-based load models were produced considering the
average values for those substations within the same category, i.e., with similar demand compositions
at peak time. Adopting the approach detailed in [14], the 60 primary substations were divided into
three categories: mainly residential (40), mixed (16) and non-residential (4).
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Figure 10 Measurement-based load model for Egremont primary substation in a summer
weekday
Figure 11 Representative measurement-based load model per category in winter, weekday
The results of this averaging process (considering only the mean values) are shown in Figure 11 for
each of the three categories during winter (weekday). As it can be noticed the mainly non-residential
primary substations present the highest responsiveness to voltage variation with an average np
coefficient that varies from 1.3 to around 1.7.
Figure 12 shows the representative measurement-based load model for main residential primary
substations considering different seasons (weekday). The same pattern in the demand
responsiveness is followed across the year: highest at night hours and the lowest at around 15:00.
During spring and autumn quite close results can be noticed due to the general small difference in
demand (and load composition) during these seasons.
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Figure 12 Representative measurement-based load model for mainly residential primary
substations, weekday
1.5
Load Model Comparison
The literature-based described the voltage-demand relationship of mainly residential primary
substations as the non-residential component of the demand was assumed non-responsive.
Consequently, to verify the accuracy of this model the comparison with the measurement-based will
involve only mainly residential primary substations.
The measurement-based load model of a substation embeds the specific features of the downstream
demand that might be different from that of another primary substation belonging to the same
category. This is clearly shown in Figure 13 where the measurement-based load model of the 40
mainly-residential primary substations involved in the trial show differences (even significant) one from
another. However, this features might not be captured by the literature-based load model where only
number of customers, average load profile and load models are considered.
For instance, Figure 14 shows literature and representative measurement-based load model (of two
primary substations belonging to the same category (mainly residential). In particular, for Egremont the
two models show consistency (Figure 14-a) whilst a significant difference can be noticed in the case of
Fallowfield (Figure 14-b). Indeed, in the latter some (unknown) features of the downstream load are
likely to justify this difference. Indeed, even among the mainly residential substations Fallowfield
shows significant differences as evident in Figure 13.
Figure 13 Measurement-based load models for mainly residential subs in winter, weekday
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a)
b)
Figure 14 Load model per primary substation in winter, weekday for: a) Egremont b) Fallowfield
In order to compare the two load models per substation category, the same process undertaken to
generalise the measurement-based load model is repeated adopting the literature-based load models
(for the mainly residential category). The resulting representative literature-based load model is shown
in Figure 15-a together with the measurement-based one.
a)
b)
Figure 15 Representative load models for mainly residential substations: a) Winter (weekday)
b) Summer (weekday)
From Figure 15-a it can be noticed that the literature-based load model underestimates the load
responsiveness (i.e., lower np) compared to the measurement-based. This is expected given that the
former assumes the non-residential demand as non-responsive (i.e., np=0). Nonetheless, the
literature-based load model provides comparable results to the measurement-based at peak time
(6pm) when the non-residential component is expected to represent a small share of the demand.
Conversely, at around noon the highest mismatch can be noticed. Indeed, commercial and industrial
loads (non-residential component) are likely to be more significant in the central hours of the day [15].
This can also be seen in Figure 9-a where the residential load share drops from 85 % at 6pm to 60 %
at noon.
During summer, as shown in Figure 15-b, the literature-based load model presents slightly lower np
coefficients compared to its winter counterpart (Figure 15-a black line). This is due to the expected
reduction in space heating appliances consumption especially during at night. However, this is in
contradiction with the measurement-based findings where the responsiveness at night hours (np≈1.4)
is even higher than during daylight hours (np≈1.2). Street lights (that might represent an important
fraction of the total demand at night) or non-residential customers that might use resistor-based
devices may justify such outcomes.
Representative literature-based load models can also be obtained for mixed and mainly nonresidential primary substations. However, as shown in Figure 16, the representative literature-based
load model is not able to represent the demand responsiveness of these two categories. As
mentioned previously, this is due to the fact that the non-residential component, which is dominant in
these categories, was neglected.
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a)
b)
Figure 16 Representative load model for winter, weekday for: a) mixed b) mainly nonresidential primary substations
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2 HV Network Modelling
To quantify the volume of voltage-led DR that CLASS can provide the whole distribution network
(Figure 17), from the transmission-distribution interface (Grid Supply Point, GSP) down to residential
customers, needs to be modelled. In addition, only the adoption of real network models can provide
confidence in quantifying the extent to which the voltage at primary substation can be changed without
exposing downstream connected customers to unacceptable voltages.
Figure 17 Distribution network in the UK
However, due to the complexity and dimension of the corresponding modelling task, the methodology
proposed here considers a limited number of real representative network models for every voltage
level (i.e., EHV, HV and LV) as detailed below:
1. EHV network modelling: 1 EHV network (corresponding to Kearsley GSP) fully modelled into
OpenDSS [16] by the REACT project [17]. This network allows realistically considering the
voltage changes on the primary side of the primary substations and the corresponding impact
on the on load tap changer;
2. HV network modelling: 3 accurate HV network models produced using the raw data provided
by Electricity North West Limited for CLASS. With these networks the extent to which the
voltage can be changed at primary substations is investigated; and,
3. LV network modelling: 57 residential LV feeders fully modelled into OpenDSS by the “Low
Voltage Network Solutions” project. These networks allow quantifying the extent to which
voltages can be changed at LV busbars without affecting customers.
As EHV and LV network models have been already developed in the context of other projects, only
the HV network modelling is discussed here.
Given that the state-of-the-art distribution network analysis software package OpenDSS [16] will be
adopted for further analysis, the DiNIS2 export files provided by Electricity North West Limited for the
15 HV networks need to be translated accordingly. In this section, the structure of the available DiNIS
export files and the required output (i.e., translated files) are briefly described. Finally, the developed
translation process is explained adopting one network (Romiley) for illustration purposes.
2.1
Structure of DiNIS Export Files
The HV network used to illustrate the translation process presented in this report is fed by a primary
substation called Romiley and supplies the urban area of the Metropolitan Borough of Stockport,
Greater Manchester. The CSV DiNIS export files provided by Electricity North West Limited for
Romiley corresponds to a table of 687 rows and 202 columns, as shown in Figure 18.
2
DiNIS (Distribution Network Information System) is a network analysis product developed by Fujitsu in 1987 and
adopted mainly by distribution network operators in UK, USA and Australia
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Figure 18 Snapshot of the provided DiNIS export file containing data of Romiley HV network
These data lack of any regularity in their structure. Indeed not only the number of columns changes for
every row but also the kind of information that can be found in the same column changes in function of
the row.
Based on the “Export manual” of DiNIS it was found that the provided table is in reality the
concatenation of 3 different tables here identified as: ST40, ND108 and LN135. The structure of those
tables (for Romiley primary substation) is described in the following sub-section. The qualitative
aspects are applicable to all DiNIS export files provided by Electricity North West Limited.
2.1.1
Table ST40
This table of 24 rows with a variable length of up to 40 columns reports the main information about
each “site” associated with Romiley primary substation (such as GIS coordinates, primary substation
name and number, etc.). A “site” in DiNIS is just a collection of other objects (e.g., transformer,
protections, etc.) that are physically in one place (e.g., a primary substation). It was the user's choice
(at the moment of creation of such table) which components go on a given site. This represents a
problem as the data on this table is partially dictated by the (unknown) user’s personal choices.
After an analysis of the available data the main conclusions, in the context of the HV network
translation process, are summarised below:
1. The table also contains data of other primary substations despite the fact that it is supposed to
contain exclusively information on the 11kV network feed by Romiley primary substation.
2. The Grid Supply Point (GSP) and a Bulk Supply Point (BSP) data have also been found. After
internal discussions with Electricity North West Limited it was clarified that due to an export
problem data associated to part of the upstream network was also included in the export file.
2.1.2
Table ND108
This table of 300 rows with a variable length up to 158 columns contains the information of every
“node” in the HV network. A “node” might be a transformer, a generator, a protection device or a
motor. Every element is associated with a field identifier. Specific combinations of elements (and
associate field identifiers) might also be present.
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After a first analysis of the available data the main conclusions, in the context of the HV network
translation process, are summarised as follows:
1. Only the field identifiers for loads (LD138), transformers (TR146-147), generators (GN129)
and text (TX118) will be considered. No switch or protection devices will be included in the HV
network model.
2. Importance is given to the field TX118 given that it provides the name of the corresponding LV
substation and reference number. This information is critical as it can be used to link the LV
substations to another database where the number of customer per class type (i.e., from PC0
to PC8) is provided.
2.1.3
Table LN135
This table, of 314 rows of variable lengths up to 197 columns, contains the information of every line in
the network. The terms used in Figure 19 will be adopted to describe the data. A line element in DiNIS
can by a single segment or a multi-segment as shown in Figure 19. A segment is part of a line and
has the same type of conductor (identified by a unique name called linecode). Every segment is given
by one or more sub-segments that are part of a line specified by two GIS points.
After a first analysis of the available data (and associated information) the main conclusions, in the
context of the HV network translation process, are summarised as follows:
1. Data related to the DiNIS graphical output (i.e., one-line diagram) has been neglected given
that the GIS references can provide a more effective visualisation of the network.
2. The length and linecode are associated with every segment but not with each sub-segment.
Consequently, this information has to be passed on accordingly.
3. The status of the extreme points of a line is valuable information to understand whether LV
substations are indeed connected to the HV network. If the extreme of a line is open hereafter
this point will be called an “open point”.
Figure 19 Definition of line, segment and sub-segment introduced in LN135 table
Structure of the OpenDSS Files
2.2
The structure of the txt files required by OpenDSS to perform a power flow (that are also the output file
of the translation process) is described as follows:
•
Lines.txt. This file describes the network topology. Each row of code describes one line (o
part of it) of the network with data such as bus connections, line type (also called linecode)
and length. An example is provided below:
New Line.LINE1 bus1=1 bus2=2 Length=183.24 unit=m LineCode=11kV300ACAS
•
LineCode.txt. This file associates a linecode (i.e., a unique name that identifies a specific
cable/overhead line type) with its electric parameters. An example is provided below:
New Linecode.11kV300ACAS R1=0.120 X1=0.077 R0=29.7 X0=25.6 nphases=3 units=km
•
Load.txt: Every row of code in this file describes a load by phase connection, bus connection,
nominal rate and power factor. An example is provided below:
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New Load.YY phases=3 bus1=537 kV=11 kW=100 PF=0.95
It is important to note that Electricity North West Limited has provided all the electric parameters (i.e.,
R, X, R0, X0) corresponding to overhead lines and underground cables adopted in their HV networks.
2.3
Translation Process
Once the desired output format has been defined (section 2.2) and the input data provided (i.e., one
table for every HV network) an automatic procedure can be coded to carry out the translation process
from the provided DiNIS export files to the required OpenDSS ones. This has been done using MS
Visual Basic for Applications (VBA). The detailed description of the implemented VBA code goes
beyond the objective of this report. However, a brief overview of its capability is provided as follows.
1. Automatic subdivision of the DiNIS export file (CSV) into the three main tables ST40, ND108,
and LN135 (section 2.1).
2. Automatic generation of separate and ordered tables for lines, loads, open points, and
generators present. Elements not part of the HV network are automatically removed.
3. Automatic association of a unique number to every bus (export files only provide coordinates).
4. Automatic connectivity check to verify whether in the translation process electric islands have
been accidentally generated.
5. Automatic primary substation cleansing. The export files detail every connection in a busbar
(even for a couple of centimetres). An automatic procedure is created to ‘clean’ this
unnecessary use of nodes.
6. Automatic generation of the OpenDSS files.
7. Automatic network topology visualisation. For debugging purposes every element in the
network (i.e., lines, loads, primary substation, LV substations and open points) and their
information (e.g., name, length, bus connection) can be visualised. In Figure 20 the whole
network is shown whilst in Figure 21 the main features of the selected element (a line in this
case) are depicted.
Figure 20 Visualisation of Romiley HV network
It is important to highlight that several assumptions have being considered in the translation process
given that some input data necessary for the conversion process were missing. The main are
described as follows:
1. Missing LineCode. The electric features (e.g., impedances) of some line codes from the DiNIS
export files (LN135 table) are not provided. For this purpose, considering the available name,
a thorough check in the provided Electricity North West Limited Code of Practice has been
carried out in order to define its likely characteristics.
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2. Unknown phase connection of a PMT. In the UK, especially in rural areas, a pole mounted
transformer could be found to supply a small number of customers. It is usually fed by two
phases of the upstream HV network. However, in some cases, the DiNIS export files do not
provide the corresponding information. As a consequence the connection is randomly
assumed in the generation process (in order to guarantee a uniform connection among the
phases).
3. Secondary substation data missing. Sometimes a SS that appears in DiNIS does not exist in
the Electricity North West Limited database containing the information of number of customers
per profile class. In this case, these SS have been excluded from the model.
The OpenDSS models of 15 HV networks have been so produced.
Figure 21 Description of a selected element (line) in Romiley HV network
2.4
HV Network Selection
Due to different levels of accuracy in the input data (missing line parameters, imprecise secondary
substation reference numbers, etc.), the confidence on the 15 OpenDSS network models cannot be
considered the same. Consequently, a four-step filtering process was developed to select the more
accurate HV network models on which carry out further network analysis necessary for the voltage
capability assessment, Figure 22. The details of the process are discussed in the Appendix 3.
ENWL other
database
Translation
process (VBA)
15 network
models
1st Filter:
Translation accuracy
2nd Filter:
LV topology
3rd Filter:
LV visibility
4th Filter:
Mainly domestic
3 network
models
DiNIS
export files
Figure 22 HV network modelling and conversion overview
Only 3 HV network models, whose main features are report in Table III, were finally chosen: Romiley
(Figure 23), Fallowfield and Ashton Golborne.
Table III Main electric feature of the selected 3 HV networks
HV network
Romiley
Fallowfield
Ashton Golborne
Line
length
(km)
46.08
29.91
60.07
# LV
transformers
89
50
83
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#
custom
ers
11,977
9,981
10,705
Peak
demand
(MW)
14.52
14.03
17.41
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Romileyv4 network
393
392.5
392
Y (km)
391.5
391
390.5
390
Disconnected SS Transformer
SS Transformer
Open point
Primary substation transformer
389.5
389
388.5
391
392
393
394
X (km)
395
396
397
Figure 23 Topology of Romiley HV network
Primary Substation: On-Load Tap Changer Modelling
2.5
The tap capability of a primary substation is defined as the maximum number of tap steps that can be
changed (from normal operation conditions) without affecting customers. However, the actual tap
capability may be constrained by the physical limit of the On Load Tap Changer (OLTC). Indeed, as
characterised by a finite number of tap steps it is important understanding if, from normal operation
condition, the available tap headroom is large enough to allow CLASS actions.
For this purpose, a realistic representation of the on load tap changer was embedded in the HV
network model by considering:
1.
2.
3.
4.
Typical lowest-highest OLTC tap step position;
Typical voltage amplitude per tap step;
Typical nominal tap position: tap position when the primary side voltage is 1 pu
Typical voltage target (i.e., the voltage that the OLTC tries to maintain on the secondary side
of the primary substation) and deadband (tolerance around the voltage target).
A typical on-load tap changer, installed on the primary side of the primary substation transformer, is
characterised by 17 tap steps. Every tap step is in general 1.43 % in voltage amplitude with a
deadband settings (Vs%) of ±1.5 % and time delay of 120s as shown in Figure 24-a.
a)
b)
Figure 24 OLTC in a UK primary substation: a) settings, and b) tap steps
The disposition of the tap within the primary winding of the primary substation transformer is illustrated
in Figure 24-b where the purple squares indicate the OLTC tap positions and Vp and Vs indicate
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primary and secondary side voltage respectively. For this particular numbering, a tap step reduction
(e.g., from position 4 to 3) implies a reduction of the secondary side voltage, Vs. The nominal tap
position adopted in the Electricity North West Limited area varies according to the year of
commissioning of the primary substation as follows:


Position 4 for primary substations commissioned before 1960-1970; and,
Position 5 for primary substations commissioned after.
From internal information it was found that most of the transformers in the Electricity North West
Limited area are more than 43 years old making them quite likely to be equipped with OLTC in
nominal tap position 4, Figure 25.
Figure 25 Number of transformer per age of installation (2012)
Consequently, in the context of this report the OLTC is always assumed to have a nominal tap position
4. This, in turn, implies that if the supplied voltage on the 33 kV side of the primary transformer is
within 1 pu (± deadband) the OLTC will be in position 4. As such, for an eventual CLASS action (i.e.,
voltage reduction), a headroom of 3 tap steps will be available (i.e., from 4 to 3, 3 to 2 and 2 to 1).
Based on information made internally available by the “Reactive Power Exchange Application
Capability Transfer” (REACT) project [17] it was found that the most common voltage target value
across primary substations is 1.00 pu (Vs in Figure 24-a).
All the aforementioned settings have been applied to both parallel transformers making them act
simultaneously. This is not the case in reality where, due to mechanical and control delays, one
transformer will always trigger a tap change earlier than the other. This phenomena was not captured
in the analysis as it evolves in a much shorter time scale (seconds) than the simulation resolution (10
min). For the same reason, tap delays (2 minutes versus 10 minute time stamp in the simulation) was
not included neither. However, because of their short duration, they are not expected to affect the
robustness of the results of this report as tap capabilities and demand response are meant to be
provided for much longer periods (such as half hour or 1 hour).
2.6
Secondary Substation: Off-Load Tap Changer Modelling
Every secondary substation in
installed on the primary side
11/0.433 kV or 6.6/0.433 kV)
deployable and cost effective
feeders.
the UK is generally equipped with an off-load tap changer. This device,
of the transformer, by modifying the nominal transformer ratio (i.e.,
boosts or reduces the LV busbar voltage. This represents a fast
solution for DNOs to solve voltage problems on the downstream LV
The off-load tap changer setting is defined by its “Tap position”, Table IV, at which a voltage
boosts/decrease on the LV side (compared to the natural transformer ratio) is associated. Tap position
3 (also called nominal position) does not modify the nominal transformer ratio (i.e., 1 p.u on the
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primary side will result in 433 V on the LV side3). Tap positions 4 and 5 are adopted to increase the LV
busbar voltage (needed if the end customer voltage is too low) and vice-versa for tap positions 1 and
2. As the off-load tap changer position is not commonly a recorded feature within DNOs a survey was
carried out on 80 secondary substations (SSs) to verify whether tap position 3 is the only one adopted
as typically assumed.
Table IV Off-load tap changer survey: Positions, voltage boost/reduction, and percentage
Tap
position
5
4
(nominal) 3
2
1
LV busbar voltage
variation
+5%
+2.5%
0%
-2.5%
-5%
% of the surveyed secondary
substations*
0%
0%
52%
29%
9%
*For 10% of SS it was not possible to identify the tap position
The results shown in Table IV (last column) highlight that 38% of secondary substations are equipped
with an off-load tap changer in position different from 3, particularly positions 1 or 2. This is likely to
have been adopted in SSs that feed LV feeders with photovoltaic systems. Nevertheless, tap positions
4 or 5 were not found (i.e., voltage boost on the LV side) it was concluded (after internal discussion
with Electricity North West) that up to 10 % of the total population are likely to be in position 4 or 5.
Consequently, due to the likelihood that different tap settings are adopted, a methodology to define the
tap step position for every secondary substation across a given HV network model has been defined
as discussed in detail in the Appendix 4. HV network models and monitored aggregated demand are
needed for this purpose
For illustration, Figure 26 shows the off-load tap changer positions so found for the SSs in Romiley HV
network. It can be noticed that 22 out of 89 off-load tap changers have positions higher than 3 (i.e., 4
or 5). This high number of SSs (more than 10 %) is due to the numerical nature of the methodology by
which the typical voltages at the LV transformer busbar (above 415V line-to-line) must be met. Indeed,
a change in the SS tap position is usually triggered by a customer complaint due to malfunctioning
appliances. However, modern appliances are designed to work properly within wider voltage ranges
[18]. Consequently, the LV supplied voltage may drop below the statutory limit just for few hours a
year without compromising the adequate functioning of appliances. This situation in reality will not
affect the nominal tap position (3) while in the simulation this will trigger a tap step increase.
Final off load tap changer settings for Romiley
5
Tap postion
4
3
2
1
0
0
10
20
30
40
50
SS number
60
70
80
90
Figure 26 Off-load tap changer positions for the LV transformers in Romiley HV network
3
Neglecting internal transformer voltage drop.
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3 LV Network Constraints
The benefits that the voltage-led demand response scheme proposed in CLASS might deliver are
limited to the extent to which the voltage at the primary substation could be changed without
negatively affecting the voltages of LV customers. Consequently, power flow studies should be carried
out in HV and downstream LV networks simultaneously in order to evaluate the limit of any voltage-led
demand response scheme. However, due to the complexity and dimension it is not practical to
analyse every single LV feeder. For instance, the 60 HV networks involved in the CLASS trials feed
around 350,000 customers by an estimated 7,000 LV feeders (assuming 50 customers per feeder).
The influence of LV customers is catered for by a statistical analysis on 50+ LV residential feeders
modelled in the context the Low Carbon Networks Fund (LCNF) project “LV Networks Solutions” [19].
The outcome of this analysis is the percentage of BS EN 50160 non-compliant customers per LV
busbar voltage. These values, will allow quantifying the voltage capability in further HV network
studies without the need to explicitly model thousands of LV feeders.
3.1
Methodology
Prior any statistical quantification of the LV network constraints a preliminary deterministic analysis on
the available 73 feeders from the “LV Network Solutions” project is carried out. In particular, the
highest voltage drop (from LV busbar to customer) is evaluated in order identify eventual anomalies in
the original data-set. A residential customer demand of 0.9 kW (i.e., annual diversified maximum
demand, based on ELEXON 2012) was adopted and the results are shown in Figure 27. The voltage
drop so calculated shows a great variability from value as high as 22 V (i.e., 9.6 %) to as low as
fraction of volts.
Feeders with less than 1 V of voltage drop are characterised by small cables length and a low number
of customers. An example is shown in Figure 28-a. On the other hand, those with the highest voltage
drops connect more than 150 customers with more than 1 km of cables (Figure 28-b).
In the context of the statistical assessment feeders with less than 5 customers have been removed as
likely to supply commercial or small industrial users and not representative of the residential
population. Consequently only 57 feeders out of 73 will be considered.
Figure 27 Highest voltage drop (LV busbar to customers) per feeder (0.9 kW per customer)
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a)
b)
Figure 28 Voltage drop heat map per customer for a feeder with: a) more than 100 customers b)
less than 5 customers
A Monte Carlo approach has been applied to the selected 57 LV feeders considering 200 simulations
for every typical day (winter, summer, autumn and spring) adopting randomly generated 10-min
residential load profiles [5] from the CREST tool (section 1.3.1). To study the impact that voltage in the
HV network might have on customer the LV busbar voltage were kept constant throughout the day.
This will provide more severe outcomes in terms of voltage headroom assessment as in the context of
CLASS voltage changes at the primary substation will be carried out for short periods rather than for
the whole day.
Then, by averaging the results from the 200 simulation the percentage of BS EN 50160 non- LV
customers per LV busbar voltage is quantified. Ultimately, it is this percentage that can be used to
understand the limits to which a primary substation can change voltage by adopting these results in
conjunction with future HV network studies.
The proposed Monte Carlo approach for one feeder can be summarised as follows:




Step 1. A feeder is selected from the 57 available (Figure 29)
Step 2. Its secondary side voltage is considered constant for the whole day. Then a random
produced load profiles is allocated to each customer in the analysed feeder (Figure 30).
Step 3. A load flow is performed in OpenDSS and metrics such as compliance to
BS EN 50160 are obtained (Figure 31). This procedure (for the same feeder and LV busbar
voltage) is repeated for 200 simulations.
Step 4. Once this process is finished, the LV busbar voltage is increased by 1 V (i.e., 0.43 %).
This resolution was chosen as best compromise between accuracy and required
computational time.
At the end of step 4 the percentage of non-compliant customers, given a LV busbar voltage value, is
obtained for every of the 200 simulations. To obtain one single value per LV busbar voltage the
average of those 200 percentage values (excluding the 5% highest i.e., above the 95th percentile) is
averaged. The so obtained average percentage of non-compliant customer per feeder and LV busbar
voltage is shown in Figure 32. It can be seen that a voltage range between 230 and 252 V leads
mostly to 0% of customers with voltage issues
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Figure 29 Voltage constraint assessment methodology: Step 1
Figure 30 Voltage constraint assessment methodology: Step 2
Figure 31 Voltage constraint assessment methodology: Step 3
.
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100
90
80
70
60
50
40
30
20
10
0
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
Feeder Number
Average of customer with problem excluding the 5% highest values
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
VLV - LV busbar voltage (V)
Figure 32 Average percentage of non-compliant customer per feeder and LV busbar voltage in
a spring day (weekday)
It is important to highlight that the assumptions below have been adopted throughout this analysis and
hence are also limitations of the findings.
1. Every feeder has been considered individually. Consequently, the effect on a secondary
substation that feed completely diverse feeders was not take into account.
2. The BS EN 50160 was used considering the daily simulations instead of a week-long
assessment. This provides a more conservative quantification of the LV network constraints.
3. The voltage at the LV busbar has been assumed constant throughout the day. However, the
application of voltage-led demand reduction is likely to be applied only for small periods during
the day.
3.2
Results
The Monte Carlo approach has generated 21664 values as shown in Figure 32. Each corresponds to
the average percentage of non-compliant customers per feeder and LV busbar voltage. For instance,
for feeder 1 (1st row from the top in Figure 32) and LV busbar voltage of 225 V (10th column from the
left in Figure 32) the average percentage of customers with problems is 6.12 %.
It is worth noticing that the number of customers with voltage drop problems (below the statutory limit)
varies significantly from feeder to feeder. For instance, feeder number 57 (the longest and with more
than 180 of customers, Figure 28 right) starts experiencing voltage issues at 235 V, whilst feeder
number 1 (with only 70 customers, Figure 30) at 227 V pu, Figure 32.
The results in Figure 32 (for spring) are further summarised in Figure 33 that reports also the results
for the other seasons. This is done by averaging, for the same LV busbar voltage, the percentage of
non-compliant customers of all feeders. The blue area in Figure 33 defines the “Low Risk Scenario”
i.e., the LV busbar voltage range at which more than 99 % of customers are BS EN 50160 compliant
(or less than 1% non-compliant).
4
2166 = 57 feeders over 38 LV busbar voltage values (from 216 to 253 V with 1 V step)
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Legend: % of non-compliant customers
>10%
5-10%
1-5%
<1%
Wint.
Aut.
Spr.
Sum.
80
60
40
20
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
0
LV busbar voltage (V)
Winter (26th January) – Autumn (2nd October) – Spring (13th April) – Summer (22nd June)
Figure 33 Average % of BS EN 50160 non-compliant customers per LV busbar voltage
In particular, the lowest value of this region for every season (i.e., 234 V in winter, 231 V in autumn
and spring and 230 V in summer), defines the LV network constraints for voltage reduction purposes.
If a less conservative figure of 5 % (green area) is considered, a wider voltage range could be
adopted.
Interesting to notice is that the LV network constraints are less severe (i.e., lower voltage values can
be accepted in the LV busbar) during summer. This is due to the reduction in the demand level, and
consequently in the voltage drop in the network. No significant difference can be noticed between
autumn and spring as similar demand levels (and consequently voltages) are experienced.
Figure 33 represents valuable information in deciding the extent to which a voltage reduction can be
implemented at primary substation. Indeed, once the corresponding HV network has been analysed, it
is possible to verify whether or not the voltages at the LV busbar lay within the acceptable range
presented in Figure 33. If so, the voltage reduction of the analysed primary substation can be
considered as “low-risk” for the downstream connected LV customers.
It is important to highlight that the voltage ranges defined above (i.e., blue, green, orange and red) are
obtained after two “averaging” processes. Consequently, every percentage of non-compliant
customers associated with a LV busbar voltage value (Figure 33) is the summary of 11,400
simulations5. Hence, caution should be taken when reading these initial results. For instance, for
spring season a LV busbar voltage of 232 V does not expose more than 1% of customers (in average)
to voltage issues (Figure 33). However, some feeders might experience much larger issues. This is
the case of feeder 57 with an average of 12.09% (last row, 17th column from left in Figure 32). Indeed,
feeder 57 represents a “particular” case that, when included in the whole population, does not have
great influence in the overall results. Nonetheless, it highlights the fact that certain voltages might not
necessarily be adequate for “particular” feeders.
Finally, it should be noted that due to the mathematical nature of the LV network constraints
assessment a customer is defined as non-compliant even when its supply voltage violates the
statutory limits for fraction of Volts. However, in these cases, customer’s appliances are unlikely to be
negatively affected as they are designed to work within a wider voltage range than those defined by
the statutory limits (some of them are even able to operate in other countries with very different
nominal voltage levels). Consequently, in practice, the voltage range to which customers are not
negatively affected is expected to be larger than the obtained here.
5
11,400 = 200 scenarios per LV feeder * 57 LV feeder
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4 Voltage Capability
This chapter aims to quantify the voltage capability and demand response for the 3 HV networks (i.e.,
Fallowfield, Romiley and Ashton Golborne) for which detailed models and monitored data are
available. For this purpose, first the potential voltage influence of the upstream EHV network is
considered. Thereafter, the LV busbar voltage profiles in normal operation condition are quantified by
power flow analysis performed in OpenDSS for the 3 HV networks. In addition, the effects on the LV
customers are catered for by the LV network constraints that allow calculating the voltage (and tap)
capability (section 4.3). Finally, the DR was quantified (section 4.4).
4.1
EHV Network Influence
For the quantification of the voltage capability (i.e., the extent to which the voltage can be changed in
the primary substation) it is key to quantify voltages across the HV networks in normal operation
condition (i.e., prior to any CLASS action).
For an accurate quantification the influence of the voltage variations in the upstream EHV network
need to be catered for. The models of the EHV networks that feed the analysed 3 HV networks were
not available. Only the model of one real EHV network (Figure 34) from the North West of England
developed in the context of the REACT project [17] was adopted.
The network presents a total of 17 Bulk Supply Points (BSP, 132/33 kV) and 91 primary substations
(33/11 kV or 33/6.6 kV) connected by almost 400 km of 132 kV and 33 kV lines. The Grid Supply Point
(GSP) secondary busbar voltage is assumed to be 1.02 p.u while the target voltage on the secondary
side voltage Bulk Supply Points (BSPs) and primary substations is assumed to be 1 p.u. These values
reflect the typical DNO and TSO practice in the UK. According to the monitoring data this network
experiences a yearly peak demand of 561 MW in winter and supplies a total of 263,653 customers.
Figure 34 Schematic of the modelled EHV network [17]
The influence of this EHV network on the downstream primary substations is given by the voltage on
the primary side of each of the 91 primary substations. However, in order to provide a general profile
to be applied to the 3 HV networks (whose EHV network is not Kearsley) the average was obtained,
Figure 35 for the 4 typical days. This profile will be adopted as primary side voltage at the primary
substations of the 3 HV networks.
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Primary side voltage (pu)
23rd September 2015
1.03
1.02
1.01
1.00
Winter
Autumn
Spring
Summer
0.99
0.98
0.97
00:00
03:00
06:00 09:00 12:00 15:00 18:00
24 hours - 10 min resolution
21:00
Figure 35 Representative primary side voltage adopted for the primary substation
4.2
Time-Varying Load Allocation
The benefits that CLASS can provide are limited to the extent to which the voltage at primary
substation can be changed without affecting downstream LV customers.
This requires the evaluation of voltages on the HV network in normal operation condition (i.e., prior
any voltage control action) by mean of power flow studies. However, the demand profiles at secondary
substations, needed for this purpose, are not available given that the only monitored point (power)
across the HV network is at the primary substation. Consequently, a simple load allocation technique
to estimate the aggregated demand profile at every secondary substation, based on the few available
monitoring data and HV network model, was developed as discussed in the Appendix 5.
15
Allocated demand for every SS and PMT
ACTIVE power demand for every SS and PMT in the HV network
1.8
Estimated aggregated domestic component
Estimated aggregated non-domestic component
Monitored aggregated demand
Simulated aggregated demand
Allocated power (MW)
1.6
Power (MW)
10
5
1.4
1.2
1
0.8
0.6
0.4
0.2
0
00:00
03:00
06:00
09:00
12:00
15:00
Time 10-min
18:00
21:00
a)
0
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
Time 10-min
b)
Figure 36 Load allocation results for Romiley on January 26th: a) Aggregated demand
b) allocated demand per secondary substation
For Romiley primary substation on a winter day (January 26th) the results of such process are shown
in Figure 36. As can be seen the aggregated demand profiles per secondary substation (Figure 36-b)
are as such that no mismatch can be noticed between the monitored active power demand at primary
substation (purple line in Figure 36-a) and aggregated simulated counterpart (blue line in Figure 36-a).
It is interesting to remark how the allocated residential component dominates the demand at peak time
(black line in Figure 36-a, at around 18:30); a further proof of the mainly residential nature of Romiley
HV network.
As it can be noticed from Figure 36-b the power demand of one substation reaches very high value
but always below 1.2 MW. This is because the transformer has a rating of 1.2 MW that cannot be
overcome in normal operation condition. This highlights that the algorithm has successfully found a
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solution that satisfy the monitored data (Figure 36-a) and adopted constraints (such as the transformer
rating).
It is worth noticing that the allocated profiles represent only a set of possible solutions that satisfies the
network constraints (transformer size or highest monitored demand) and match the monitored values
(i.e., primary substation demand). However, no indication on the accuracy of the allocated profiles can
be provided as no secondary substations power measurements are available for validation.
Nonetheless, high confidence is expected at least for mainly domestic secondary substations given
the residential component is more predictable (in the aggregate) than its non-residential counterpart.
4.3
Time-Varying Voltage Capability Assessment
In this section the voltage capability is quantified for the 3 HV networks for which network models and
monitored data are available. This is one of the core findings of WP2-A as the voltage capability
affects directly the volume of demand response that can be provided.
The voltage capability is obtained by comparing the voltage limit imposed by the analysis of LV
network with the LV busbar voltage of every secondary substation in the HV networks in normal
operation condition. For such quantification power flow studies were performed adopting the modelling
framework developed in the previous sections. This is summarised as follows:
1. EHV network influence: The voltage profile on the primary side of the HV networks varies to
mimic the likely voltage influence of the upstream EHV network (section 4.1)
2. On-load tap changer modelling: The real features such as number of tap steps, amplitude and
nominal position of the OLTCs on the primary substations are considered (section 2.5)
3. Off-load tap changer modelling: tap position is estimated for every secondary substation
(section 2.6)
4. Load allocation profile per secondary substation: the aggregated demand profiles per
secondary substation are considered (section 4.2)
More precisely, for Romiley primary substation on January 26th, the LV busbar voltage for every
secondary substations during normal operation are shown in Figure 37 [20].
Daily voltage profile for all the SS in Romiley for 26/01/2015
260
LV busbar voltage (V)
255
250
245
240
i-th VLV
235
Voltage capability
Lowest VLV
230
00:00
03:00
LV network constraints
06:00
09:00
12:00
15:00
Time 10-min
18:00
21:00
Figure 37 LV busbar voltage per secondary substation in normal operation condition, Romiley
on January 26th
Assuming that the average percentage of non-compliant customers should be maintained below 1 %
(i.e., low risk scenario, section 3) then the voltage level on the LV busbar should be kept above 234 V
(dashed line in Figure 37). The difference between the lowest LV busbar voltage (dashed red line in
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Figure 37) and the limit imposed by network constraints represents is the voltage capability (green
area in Figure 37).
Finally, knowing that one tap step will reduce the voltage by 1.43 % of any downstream connected
secondary substation (i.e., around 3.5 V) the tap capability can be obtained by dividing the voltage
capability over 3.5 V (i.e., 1.43 %).
The results for Romiley on January 26th are shown in Figure 38-a with a blue line. However, when also
the constraints given by the finite number of tap step on the OLTC are considered, the tap step
reduction that can actually be introduced is lower (red line in Figure 38-a). Consequently, the physical
limits of the OLTC do influence the overall demand response that can be provided; especially on the
early hours of the day. For instance, at 3:00 am a reduction up to 4 tap steps is allowed as no
significant voltage problem will arise (blue line in Figure 38-a). However, because at that moment the
OLTC is in tap position 4 (black line in Figure 38-b) only 3 tap steps reduction are possible (i.e., 4 to 3,
3 to 2 and 2 to 1). Consequently, only 3 tap steps reduction, considering both LV network and OLTC
constraints, are
allowed
at 3:00
as shown by the red line in Figure 38-a.
Allowable
# tap reduction
foram
day 26/01/2015
considering OLTC headroom limit
8
Without considering OLTC limit
Considering OLTC limit
5
7
6
4
Tap position
Allowed tap step reduction
6
3
2
Lowest acceptable tap step position
Tap position normal operation
OLTC minimum tap step position
5
4
3
2
1
0
00:00
Tap position in 26/01/2015 with and without CLASS
1
03:00
06:00
09:00 12:00 15:00
Time 10-min
a)
18:00
21:00
0
00:00
03:00
06:00
09:00 12:00 15:00
Time 10-min
18:00
21:00
b)
Figure 38 Romiley on January 26th: a) Tap capability with and without OLTC limit b) Tap
positions
In particular, it was found that during summer period and night hours, due to higher voltage on the
primary side of the primary substation, the main limit to tap step reduction is given by the OLTC
headroom rather than by the LV network constraints. This highlight the importance in modelling the
influence of the EHV network.
In addition, it is worth noticing that a voltage reduction on the primary substations will reduce the LV
busbar voltage close to the LV network constraints limit only for the furthest secondary substations
and only for the period of time in which CLASS is meant to be applied. This, in turn, means that the
percentage of non-compliant customers is much lower than 1 % if evaluated in aggregate at primary
substation level. To understand the concept let’s assume that, for peak reduction purposes, a CLASS
action is undertaken from 6 pm to 6:30 pm. In this case, considering the tap availability, a reduction of
3 tap steps is introduced. The daily voltage profiles on the LV side after such actions are shown in
Figure 39.
As it can be seen, in the considered half hour, only few LV busbar voltages are close to the limit of
234 V (winter limit) whilst for the rest the voltages vary between 240 and 235 V. This implies that most
of the secondary substations are characterised by a percentage of non-compliant customers (see [20])
much lower than 1 %. In addition, the CLASS action will last for a finite period of time (half hour for
instance) making even more remote the possibility of voltage violations.
The LV busbar voltage of PC0 customers (characterised by a maximum power above 100 kW) has not
been considered for the voltage capability assessment in this analysis, Figure 37. Indeed, a PC0
customer most of the time owns the secondary substation that it is often located within its property (or
in general very close to the demand). Consequently, the LV network constraints cannot be applied to
these cases as the supplied voltage of a PC0 customer can be practically assumed coincident with its
secondary substation LV busbar voltage.
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In addition, the LV network that supplies a PC0 customers will not have a “residential topology” as
those used to define the LV network constraints in [20] making the adoption of the LV network
constraints limits inappropriate for PC0 customers.
Figure 39 LV busbar voltages assuming one CLASS action for peak demand reduction purpose
in Romiley on January 26th
4.4
Demand Response Quantification
In the previous section the tap capability based on detailed HV network and load models was
obtained. Hence, based on these results, the demand response is quantified in this section. In
particular, for Romiley primary substation on January 26th assuming a tap reduction (from normal
operation condition) equal to the tap capability a daily power flow is performed in OpenDSS. The
demand response was obtained by the difference between the aggregated primary substation demand
before and after the voltage reduction. It was found that the demand response varies between 100
and more than 600 kW Figure 40. The daily pattern in the primary substation aggregated demand and
the time-varying nature of the load composition (and therefore in the load model) justify these wide
variations.
Demand response (kW)
700
Achievable power demand reduction in Romileyv4 in 26/01/2015
600
500
400
300
200
100
00:00
03:00
06:00
09:00
12:00
15:00
Time 10-min
18:00
21:00
Figure 40 Estimated demand response for Romiley on January 26th
Repeating the whole procedure it is possible to quantify the demand response for the other typical
days. The results for Romiley are shown in Figure 41-a. It is of interest to remark that during summer
peak hours (i.e., 6 pm) the absolute demand response that can be provided is smaller than its winter
counterpart. Indeed, in summer not only the demand is 30-40% lower but also a lower tap capability
mainly due to the limited on load tap changer headroom, Figure 41-b.
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It was also found that night hours are characterised by the same tap capabilities throughout the whole
year, Figure 41-b. This is due to the limit imposed by the on-load tap changer headroom as explained
in section 4.3.
Estimated maximum demand reduction typical days: Romiley
600
500
400
6
26-01-2015
23-06-2014
02-10-2014
13-04-2015
5
Tap capability
Demand Response (kW)
700
300
200
26-01-2015
23-06-2014
02-10-2014
13-04-2015
3
2
1
100
0
00:00
4
N tap reduction for voltage reduction actions WITH OLTC constraints
Romiley
03:00
06:00
09:00
12:00
Time 10 min
15:00
18:00
21:00
0
00:00
a)
03:00
06:00
09:00
12:00
Time 10 min
15:00
18:00
21:00
b)
Figure 41 Romiley on the four typical days: a) Demand response b) Tap capability
The whole methodology has been extended to the other two primary substations (i.e., Fallowfield and
Ashton-Golborne) for which detailed HV network models are available. However, for the DR
quantification in the whole Electricity North West Limited area it is necessary to adopt similar voltage
capabilities for those primary substations for which no detailed network models are available. Such
quantification is explained in the next chapter.
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5 Demand Response: Primary Substation
In the previous chapter the voltage capability and demand response (DR) were quantified adopting
both detailed network models and monitoring data for the selected 3 HV networks (i.e., Fallowfield,
Ashton Golborne and Romiley). However, given that such data are not available for the majority of the
HV networks, another approach is proposed here. More specifically, the tap capability of the 3
representative HV networks are generalised by introducing two scenarios (section 5.1). These tap
scenarios are then adopted in combination with aggregated demand profiles and load models (section
1) to quantify the volume of DR of any primary substation (section 5.2).
5.1
Tap Capability Generalisation
In chapter 4 the voltage capability and demand response were quantified for three mainly residential
primary substations for which network models and monitoring data were available. The maximum
number of tap step reductions that is possible to introduce (i.e., tap capability) in these substations for
a typical winter day are shown in Figure 42. Differences among HV networks are due to topology and
load level characteristics. For instance, for Ashton Golborne at peak demand only 1 tap step reduction
is feasible as affected by higher voltage drop due to is larger than other line extension (Table III)
Tap capability in winter day for 3 primary sub.
5
Romiley
Fallowfield
Ashton Golborne
Tap capability
4
3
2
1
0
00:00
03:00
06:00 09:00 12:00 15:00 18:00
24 hours - 10 min resolution
21:00
Figure 42 Tap capability for the three HV primary substations on a winter weekday
The tap capability can be obtained exclusively by carrying out power flow studies, not possible for
those HV networks (the majority) for which models are not available. Consequently, the results of the
considered 3 HV networks have to be generalised. For this purpose, the highest and lowest tap
capability among the three primary substations at any moment in time (i.e., the highest and lowest
envelope of the three curves) are selected to produce optimistic and conservative scenarios. These
Tap capability extreme on
are shown in Figure 43 for a winter weekday.
26-01-2015
Tap capability
5
4
3
2
1
0
00:00
Conservative
Optimistic
03:00
06:00 09:00 12:00 15:00 18:00
24 hours - 10 min resolution
21:00
Figure 43 Conservative and optimistic tap capability scenarios on a winter weekday
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The optimistic tap capability scenarios for the four seasons (half-hourly resolution) are shown in Figure
44 where a “CLASS reference” scenario is also defined (red dotted line in Figure 44). This was the
voltage capability (i.e., 3 % voltage reduction, approximately 2 taps) considered at the proposal stage
of the project. It can be noticed that a voltage reduction of 2.86 % (2 taps) is always possible
throughout the year while a reduction of 4.29 % (i.e., 3 taps) was found to be feasible throughout a
significant part of the year without affecting customers. The conservative scenarios can be found in
the Appendix 6.
Figure 44 Optimistic tap capability scenarios (one tap step=1.43% voltage reduction)
Caveats
The described methodology extrapolates the results of three mainly residential primary substations.
Although these HV networks show variety in topology and numbers of customers they might not cover
the whole range of variability that can be seen in the whole Electricity North West Limited area.
Consequently, the real tap capability for some primary substations might be substantially different.
In addition, although the tap capability scenarios will be applied to all primary substations (residential
and non-residential) for a wider-scale demand response quantification. These figures should be
considered more reliable when applied only to mainly residential primary substations as the 3 HV
networks from which these results have been produced belong to this category. On the other hand,
HV networks dominated by non-residential demand might have different topologies and consequently
different tap capabilities.
Finally, it should be noted that the voltage capability scenarios here evaluated (both conservative and
optimistic) have been produced by adopting the LV network constraints previously introduced. As
already discussed in chapter 3, due to the mathematical nature of the adopted quantification, these
voltage capabilities flag a customer as non-compliant when its supplied voltage violates the statutory
limits even for a fraction of Volts. However, such situations are, in practice, unlikely to result in
problems to customers as the appliances are designed to work for a wider range of voltages.
Consequently, the true voltage capability is expected to be higher than here considered. Indeed, this is
aligned with the findings of CLASS trials where 3 % and 5 % voltage reduction at peak winter time
(6 pm) in 14 primary substations have been introduced without noticing any negative impact on
customers.
5.2
Demand Response Quantification
For the demand response (DR) quantification (adopting both literature and measurement-based load
models) the following data have been adopted:
1. Aggregated demand for any chosen primary substation in the Electricity North West Limited
area P0(t) from section 1
2. Tap capability scenarios (Figure 44) from which the corresponding voltage reduction can be
obtained (V(t))
3. Load model (np(t)) per primary substation category (both literature and measurement-based)
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In particular the demand response (DR(t)) for a primary substation can be obtained from Eq. 3.
V 
P(t )  P0 (t )  ( t ) 
V 
 0(t ) 
np ( t )
 V  V( t ) 

 P(t )  P0 (t )  0 (t )


V
0(t )


np ( t )
 DR(t )  P(t )  P0 (t )
Eq. 3
Where P(t) indicates the power after the voltage reduction while V0(t) the voltage initially applied to the
aggregated demand (assumed to be nominal for simplicity). The comparison between the DR obtained
with measurement and literature-based load models in Egremont primary substation is shown in
Figure 45. A winter weekday and the optimistic tap capability scenarios were adopted for this purpose.
Figure 45 DR for Egremont in winter day: optimistic tap capability scenario
As presented in section 1.4, for the measurement-based load model, mean, upper bound and lower
bound values were adopted. This, in turn, provides a range (within blue lines in Figure 45) of potential
DR that the substation is able to deliver by taking into account the statistic uncertainty in the demand
responsiveness based on monitoring data. The corresponding dashboard (i.e., the table with halfhourly DR estimates that Electricity North West Limited may use for the roll-out of CLASS) reporting
the mean, maximum and minimum values can be found in the Appendix 7.
The strongest agreement between the DR volume quantified with the literature-based load model and
measurement-based load model (mean value) was found at peak hours (around 6 pm) whilst the
largest discrepancies occur in the central hours of the day. The maximum DR (mean of circa 0.7 MW)
is reached in evening hours as during this period the highest tap capability is also available.
Finally, the demand response quantified adopting the measurement–based load model with the
optimistic tap capability scenario across the 4 seasons is shown in Figure 46 (adopting the mean
value for simplicity). The highest demand response was found in winter (circa 0.7 MW) at around 8 pm
while at peak time (i.e., at around 6 pm) due to the lower voltage capability only 0.6 MW were
available. The lowest volume of demand response is reached during summer at around 5 am (circa
0.18 MW) mainly due to the low demand value in this period of time.
The above DR can also be presented as a percentage of the corresponding aggregated demand each
season and each half hour so as to understand the proportion that can be reduced. This is shown in
Figure 47. A DR varying from 3.8 % to 7.5 % was found for Egremont primary substation. In particular,
due to similarities in load models (Figure 15) and tap capability (Figure 44) among seasons no
significant differences can be noticed in the relative demand response along the year except at late
evening where load level and voltage capability change significantly .
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With the above generic process, the DR can be quantified for any primary substation and for any day
of the year considering either of the tap capability scenarios (conservative or optimistic) without the
need of network models.
Figure 46 Absolute DR for Egremont: optimistic tap capability scenario - mean measurementbased load model
Figure 47 Relative DR for Egremont: optimistic tap capability scenario - mean measurementbased load model
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6 Demand Response: Electricity North West Limited Area
In the previous chapter the methodology developed to quantify the demand response of a generic
primary substation for which no network models are available, considering both literature and
measurement-based load model, was presented. In particular, Egremont primary substation was used
to illustrate the process.
In this chapter, the methodology will be extended obtaining the demand response (DR) that all the
350+ primary substations in the Electricity North West Limited area can provide considering also the
contribution of the EHV network.
6.1
EHV Network Contribution
For the provision of ancillary services the demand response at the interface transmission-distribution
(i.e., Grid Supply Point, GSP) should be quantified given that this might be different from the simple
aggregation of the demand response per single primary substation. For this purpose, considering the
available EHV network model (section 4.1) and assuming all downstream primary substations will
have the same voltage reduction (equal to the tap capability scenarios previously discussed, Figure
44), the demand response at the GSP was quantified. In addition, this value was compared to the
aggregation of every single primary substation DR. The results, for the winter day, are shown in Figure
48 by the EHVDR_Ratio calculated as the DR at the GSP (DRGSP) over the sum of the DR of every single
primary substation (DRSubi-th), Eq. 4.
DRGSP
EHVDR _ Ratio 
N
 DR
GSP DR over Summation Primary sub. DR on 26/01/2015 with higher tap
i 1
Eq. 4
Subi th
EHV
DR Ratio
1.020
1.010
1.000
00:00
03:00
06:00
09:00
12:00
15:00
24 hours - 10 min resolution
18:00
21:00
Figure 48 EHVDR_Ratio in a winter weekday for the considered EHV network
It could be noticed that the EHVDR_Ratio is bigger than 1. This implies that the DR provided at GSP level
(DRGSP) is higher than the demand response provided by the aggregation of all primary substations.
This is due to the decrease in the EHV network losses that in turn contributes to the overall demand
reduction. This contribution peaks to ~1.5 % of the DR (i.e., EHVDR_Ratio≈1.015) at around 6 pm.
Similar patterns can be identified for the other typical days.
It should be noticed that, due to lack of modelling data of the EHV network, the iron losses in the
primary substation transformers were neglected. Only cable losses and transformer copper losses
were considered. Consequently, the contribution to the DR provided by the EHV network is in reality
expected to be slightly higher (i.e., higher EHVDR_Ratio). Indeed, the iron losses are known to decrease
with the voltage.
The time-varying values of EHVDR_Ratio obtained above are considered when quantifying the demand
response in the Electricity North West Limited area as well as nationwide.
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6.2
Demand Response Quantification
Once the DR for every primary substation is evaluated following the procedure discussed in chapter 5
and considering also contribution due to the EHV networks (i.e., EHVDR_Ratio factor), the DR per GSP
can be obtained. The results are shown in Figure 49 and Figure 50 (for a winter weekday) adopting
the literature and measurement-based load model respectively.
Figure 49 Aggregated DR per GSP: literature-based load model - optimistic scenario - winter
weekday
Figure 50 Aggregated DR per GSP: measurement-based load model - optimistic scenario –
winter weekday
The GSP that shows the highest (in absolute) demand response with the measurement-based load
model is “Harker/Hutton” with 28 MW in winter at 8 pm. By adding up the contribution of every single
GSP the results for the whole Electricity North West Limited area are shown in Figure 51. The DR that
can be unlocked varies from 15 MW during summer at night hours to almost 170 MW in winter at
around 8 pm. It can also be seen that the highest DR is not coincident with the peak in demand (winter
at 6 pm) due to the reduced tap capability (3 taps rather than 4 as shown in Figure 44).
Figure 52 and Figure 53 show the DR estimated for winter and summer periods respectively adopting
the optimistic tap capability scenario and both load models.
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Figure 51 Aggregated DR for the Electricity North West Limited area: all seasons – optimistic
tap capability scenario – literature-based load model
Figure 52 Aggregated DR for the Electricity North West Limited area: winter weekday –
optimistic tap capability scenario
Figure 53 Aggregated DR for the Electricity North West Limited area: summer weekday –
optimistic tap capability scenario
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The same conclusions obtained for Egremont primary substation can be drawn here. More precisely,
during winter the highest difference (around 50 %) between the DR estimated with the two load
models is noticed at around noon. This is due to absence in the literature-based load model of the
non-residential component of the demand that is likely to have its maximum in the central hours of the
day. On the other hand, a significant lower mismatch (20 %) can be noticed at peak time (i.e., 6 pm)
as the two load models show strong consistency (Figure 15).
During summer, a significant difference between the two load models can be noticed at night (midnight
to 6 am) as the DR volumes estimated by the literature-based are much lower than the measurementbased. As already discussed, this is due to the high demand responsiveness found with the
measurement-based load model during night hours (section 1.5).
The demand response for the Electricity North West Limited area across the four seasons obtained
with the measurement-based load model (adopting the mean values) and optimistic tap capability
scenario is provided in Figure 54. The DR as a percentage of the half-hourly aggregated demand per
season is also presented in Figure 55.
Figure 54 Aggregated DR for the Electricity North West Limited area: all seasons –
measurement based load model - optimistic tap capability scenario
Figure 55 Relative aggregated DR for the Electricity North West Limited area: all seasons –
optimistic tap capability scenario – measurement based load model
The DR was found to reach up to 235 MW (8 % of the total demand as shown in Figure 55) during
winter at around 8 pm and 200 MW (almost 6 %) at peak hours (6 pm). The minimum was found
during summer night hours (around 5 am) when only 65 MW (less than 6 %) can be provided. An
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almost constant DR of around 170 MW (around 6 %) can be provided during winter period from
8:30 am to 3:30 pm due to the steadiness in the aggregated demand and voltage capability.
Finally, it can be of interest to investigate how the literature-based load model can be enhanced by
including the non-residential component of the demand. In particular, a load model coefficient of
np=1.3 (rather than np=0) was adopted for the non-residential component of the demand as in this
case the lowest mismatch with the measurement-based results was found. The quantification for the
Electricity North West Limited area is carried out again and the results for the winter case with
optimistic tap capability scenario are shown in Figure 56.
Figure 56 Aggregated DR for the Electricity North West Limited area: winter weekday –
optimistic tap capability scenario – enhanced literature and measurement based load models
In this case the enhanced literature-based load models results in more similar values to those from the
measurement-based. Indeed, for a significant part of the day the difference between the two models is
between ±5 %. This demonstrates that a load model coefficient of np=1.3 can realistically describe the
non-residential component of the demand. This value is similar to those reported in the literature
(np=0.4 and np=0.8 for large and small commercial respectively [21], np=0.2-1.4 [18]). However, for
industrial customers the literature reports much lower values (np=0 [21], np=0.8 [18]). This, in turn,
may imply that the non-residential component, in the aggregate, is mainly dominated by small
commercial activities than large industrial ones (whose load model, in general, is lower than 1.3).
However, the limited number of studies on this subject makes it difficult to be conclusive at this stage.
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7 Demand Response: United Kingdom
The potential DR that CLASS may provide is demonstrated by a nationwide quantification. The
methodology discussed in the previous chapters cannot be applied for this purpose as no information
is available on primary substations on other DNO’s license areas.
The aggregated residential demand and non-residential in the UK is estimated considering the number
and type of customers according to national statistics. More specifically, the data considered includes:




Number of dwellings in the UK (Ndw) [22];
Residential customer proportion in the UK (PC1%, PC2%) [23];
Average demand profile per single dwelling in the UK (PdwPC1(t), PdwPC2(t)) [24]; and,
UK demand (P_UK(t)) [25].
Eq. 5 is used to combine the above data and estimate the UK residential demand PResidential_UK(t), and
non-residential PNON_Residential_UK(t).
PRe sidential _ UK (t )  N dw  PC1%  PdwPC 1 (t )  N dw  PC 2 %  PdwPC 2 (t )
PNON _ Re sidential _ UK (t )  PUK (t )  PRe sidentialUK (t )
Eq. 5
The resulting values are presented in Figure 57 for a winter day. The UK residential component (blue
area in Figure 57) was estimated to vary between 5 (at around midnight) to approximately 19 GW at
peak hours (6 pm) or from 20 % to 40 % of the demand. This, in turn, highlights that the nonresidential component is expected to be dominant.
Although the entire nationwide non-residential component is considered here, only the fraction
connected downstream the voltage control point, i.e., primary substations, would in practice be able to
provide demand response in the context of CLASS. However, due to the lack of information on the
proportion of demand upstream primary substations, it is assumed that this component is affected by
voltage changes at primary substations. Consequently, the quantification proposed here is expected to
overestimate the absolute DR that the non-residential component can effectively provide.
a)
b)
Figure 57 Estimated UK demand share per customer type on January 26th 2015: a) absolute
b) relative
Once the residential and non-residential demand component have been quantified, the literaturebased load model was adopted and the results shown in Figure 58. The DR was found to reach
around 1.2 GW in winter evening (around 8 pm) while during peak hours (around 6 pm) more than
1 GW of DR can be unlocked. The minimum was found in summer at around 5 am when only 63 MW
of DR were available. These results, considering the literature-based load model (np=0 for the nonresidential), provide the volume of demand response that CLASS can unlock if only the residential
component of the demand is considered.
However, to quantify the full potential of CLASS the non-residential component needs to be taken into
account. For this purpose, a load model coefficient np=1.3 (rather than np=0) was adopted for this
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component, producing the so called “enhanced” literature-based load model. This value has provided
the closest agreement with the measurement-based load model findings previously discussed and
shown in Figure 56 for the Electricity North West Limited area. The results corresponding to the DR for
the UK are shown in Figure 59. The associated dashboard can be found in the Appendix 9.
Figure 58 Aggregated DR for the whole UK: literature-based load model – optimistic tap
capability scenario
Figure 59 Aggregated DR for the whole UK: enhanced literature-based load model – optimistic
tap capability scenario
It was found that up to 3.3 GW of DR can be unlocked during winter evening (8 pm) while an almost
constant demand response of 2.5 GW is available from 8:30 am to 3:30 pm. During spring, winter and
autumn peak hours (6 pm) almost 3 GW are available. As previously discussed the lowest volume of
DR (1.2 GW) was found during summer night hours (around 5 am).
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8 Conclusions
This document presented methodology, application and findings from the work carried out by Work
Package 2 Part A (WP2-A) as part of the Low Carbon Networks Fund Tier 2 project “Customer Load
Active System Services (CLASS)” run by Electricity North West Limited. The aim of this work package
is to quantify the half-hour demand response (DR) that Electricity North West Limited can provide by
reducing voltages (i.e., voltage-led demand response) through on-load tap changer actions at primary
substations.
The DR quantification was carried out per primary substation, per grid supply point, for the Electricity
North West Limited area and for the whole UK considering four typical days. The developed
methodology caters for the constraints imposed by customers in real LV networks as well as for
features of real high voltage (HV) and extra high voltage (EHV) networks. This, in turn, allows
quantifying in a realistic manner the extent to which the voltage can be changed (i.e., voltage
capability) without affecting customers or violating any network limitation. In addition, load models (i.e.,
the relationship between voltage and demand) based on literature data (residential loads only) as well
as on measurements (both residential and non-residential loads), produced by Work Package 1
(WP1), were adopted.
Key aspects of the developed methodology and associated results are described as follows.
1.
Load Modelling
The load model provides the mathematic relationship between voltage and demand, essential to
quantify, in conjunction with the voltage capability, the volume of DR that can be unlocked.
In the context of WP2-A a time-varying load model based on literature data was developed to
represent domestic customers only. The “time-varying” nature of such model captured the daily
and seasonal changes in the demand reflecting the typical pattern in the residential customers’
behaviour. This is essential for an accurate quantification of DR in the context of the provision of
ancillary services.
Given the limited information on commercial and industrial customers, the non-residential
component of the demand was considered as non-responsive to voltage changes (i.e., load
model coefficient np equals to zero). Consequently, the resulting-literature based load models
allow for an initial conservative quantification of the DR. The extent of such underestimation is
function of the actual responsiveness and level of the non-residential demand.
More comprehensive aggregated load models were produced by WP1 using the measurements
from 60 primary substations part of the trial, i.e., catering for both residential and non-residential
components. A comparison between the literature and measurement-based load models
revealed, as expected, that the former underestimates the total load responsiveness given that
the non-residential component is neglected. Indeed, the discrepancies are larger for primary
substations identified as mainly non-residential. However, when considering the average
literature and measurement-based load models (called here as representative models) from the
mainly residential primary substations, the resulting values showed good consistency. In
particular, a strong agreement was found at peak demand during winter days with np
coefficients equal to 1.1 and 1.3, for the representative literature and measurement-based
models, respectively although at noon less consistency was found due to the neglected nonresidential component of the demand that peaks at around those hours.
2.
HV Network Modelling
To assess the voltage capabilities of a given primary substation, a realistic representation of the
corresponding HV network during normal operation is required. For this purpose, a methodology
is developed to model HV networks from the provided DiNIS export files into the adopted power
distribution system simulator (OpenDSS). Among the 15 HV networks modelled only three
(Romiley, Fallowfield and Ashton Golborne) were adopted for further power flow analysis given
the high confidence on the developed models. In addition, realistic settings for the off-load tap
changer (typically unknown) are considered.
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3.
LV Network Constraints
To assess the extent to which the voltage can be reduced the constraints that LV customers
might impose were quantified. For this purpose, a Monte Carlo approach was applied to 57 LV
feeders considering 200 simulations per typical day. The outcome is the average number of
BS EN 50160 compliant customers per LV busbar voltage.
Results show that the minimum LV busbar voltage needed to maintain the number of compliant
customers above 99 % is 234 V (1.017 p.u.) in winter, 231 V (1.004 p.u.) during spring and
autumn, and 230 V (1.0 p.u.) in summer. The higher value found in winter is due to the higher
voltage drop in the LV network in this period. No significant difference can be noticed between
autumn and spring as similar demand levels were found in these seasons.
It should be noted that, in practice, the LV busbar voltage for which no negative impact can be
seen on customers is likely to be higher than obtained here. The analysis flags a customer as
non-compliant whenever its supply voltage violates the statutory limits even for a fraction of
Volts. However, in practice, no negative impact will be noticed as the appliances are designed
to work within wider voltage ranges.
4.
Voltage capability
Based on the developed HV network models (Romiley, Fallowfield and Ashton Golborne) and
LV network constraints power flow studies were performed to quantify the extent to which the
voltage can be modified (i.e., the voltage capability). The influence of the EHV network, in term
of potential voltage fluctuations at the primary side of the primary substation, are also catered
for carrying out a simplified analysis using one EHV network model.
As expected during winter peak hours (6 pm) all HV networks showed the lowest voltage
capability (2.86 % or 1.43%). The highest voltage capability (4.29%) was found during summer
nights (9pm to 7am) and it was mainly limited by the available OLTC tap step positions (i.e.,
OLTC headroom) rather than by the LV network constraints.
5.
Demand response quantification: Electricity North West Limited area
To quantify the DR that a primary substation can provide for which network models are not
available the voltage capability results obtained from the three HV networks were generalized.
More precisely two scenarios were produced: conservative and optimistic. With the optimistic
scenario it was found that a voltage reduction of 2.86 % (i.e., 2 OLTC tap steps) is always
possible throughout the whole year without affecting customers. Even a reduction of 4.29 %
(i.e., 3 OTLC tap steps) was found to be feasible throughout a significant part of the year. Even
with the conservative scenario a voltage reduction of 2.86 % can be introduce throughout the
year except during peak time in winter weekdays at around 6 pm.
The measurement-based load models and their statistical variability obtained by WP1 was also
considered. In addition, the contribution of the EHV network was taken into account and found
to be typically below 2 % of the DR (resulting from the reduction of losses). This quantification
process was carried out per primary substations, per GSP, and for the whole Electricity North
West Limited area.
Considering only the literature-based models, i.e., only the residential component of the
demand, and the optimistic voltage capability scenario it was found that the DR that the
Electricity North West Limited area can provide varies between 15 MW during summer (3 am) to
170 MW during winter (around 8 pm). However, considering the measurement-based load
models (in which both residential and non-residential components are accounted for), a much
higher DR was found: between 65 MW during summer (around 5 am) to 235 MW during winter
(around 8 pm). No significant seasonality effects were noticed among winter and spring due to
the similar loading level in these seasons.
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7. Demand response quantification Whole UK
For the UK quantification the previous technique cannot be adopted given that information such
as number, size and main type of customers corresponding to primary substations of other
DNOs were not available. To overcome this limitation, the number of houses in the UK (from
national statistics) and aggregated national demand (from National Grid) were adopted to
estimate the residential component of the demand at national level.
The quantification for the whole UK, adopting the optimistic capability scenario and the
literature-based load model, shows a DR varying from 63 MW during summer (around 5 am) to
1.2 GW autumn evening (around 7 pm) or winter (around 8 pm). This represents a conservative
quantification of the benefits that CLASS can provide as the non-residential response was not
considered.
The non-residential component was incorporated in the quantification by adopting the
measurement-based models produced by WP1. The DR resulting from considering the
optimistic voltage capability scenario was found to vary from 1.2 GW during summer nights to
up to 3.3 GW during winter (around 8 pm). A significant volume of DR of around 3 GW is also
available during spring, winter and autumn at peak hours (around 6 pm).
Even adopting the conservative voltage capability scenario significant volumes of DR were
found. More precisely, the whole UK from 1.2 GW during summer (midnight to 5 am) to 2.5 GW
of DR during winter (around 9 pm and from 10 am to 3:30 pm) were estimated.
CONFIDENTIAL
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9 References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
ELEXON, "Load Profiles and their use in Electricity Settlement," 2013.
"Modelling and aggregation of loads in flexible power networks," 2014.
J. V. Milanović, "On unreliability of exponential load models," Electric Power Systems Research,
vol. 49, pp. 1-9, 2/15/ 1999.
L. M. Korunovic, D. P. Stojanovic, and J. V. Milanovic, "Identification of static load
characteristics based on measurements in medium-voltage distribution network," Generation,
Transmission & Distribution, IET, vol. 2, pp. 227-234, 2008.
R. Ian, "Integrated high-resolution modelling of domestic electricity demand and low voltage
electricity distribution networks," Doctor of Philosophy, Electronic,Electrical and Systems
Engineering, Loughborough University, 2011.
C. Cresswell, "Steady State Load Models for Power System Analysis," Doctor of Philosophy,
School of electrical and electronic engineering, The University of Edinburgh, 2009.
A. Bokhari, A. Alkan, R. Dogan, M. Diaz-Aguilo, F. de Leon, D. Czarkowski, et al., "Experimental
Determination of the ZIP Coefficients for Modern Residential, Commercial, and Industrial
Loads," Power Delivery, IEEE Transactions on, vol. PP, pp. 1-1, 2013.
G. Tsagarakis, A. Collin, and A. Kiprakis, "A statistical survey of the UK residential electrical
loads," De Gruyter, vol. 14, pp. 509-523375-523, 2009.
"EPRI Smart grid demonstration initiative 5 Year update," 2013.
J. L. Aguero, M. B. Barbieri, and M. C. Beroqui, "Voltage depending load models. Validation by
voltage step tests," in Power Engineering Society General Meeting, 2006. IEEE, 2006, p. 6 pp.
Northwest Energy Efficiency Alliance, "Distribution Efficiency Initiative," 2007.
A. J. Collin, G. Tsagarakis, A. E. Kiprakis, and S. McLaughlin, "Development of low-voltage load
models for the residential load sector," IEEE Trans. on Power Systems, vol. 29, pp. 2180-2188,
Sep. 2014.
A. Ballanti, "WP2 Deliverable 1: Progress Report of Task 1.1, Task 1.1 and Task 1.3," 2014.
Electricity North West, "Trial Substation Selection Methodology," 2013.
R. Wardle and P. Davison, "Initial load and generation profiles from CLNR monitoring trials,"
2012.
R. C. Dugan and T. E. McDermott, "An open source platform for collaborating on smart grid
research," in Power and Energy Society General Meeting, 2011 IEEE, 2011, pp. 1-7.
(2015). Reactive Power Exchange Application Capability (REACT). Available:
http://www.smarternetworks.org/Project.aspx?ProjectID=1460
A. Bokhari, A. Alkan, R. Dogan, M. Diaz-Aguilo, F. de Leon, D. Czarkowski, et al., "Experimental
determination of the ZIP coefficients for modern residential, commercial, and industrial loads,"
IEEE Trans. on Power Delivery, vol. 29, pp. 1372-1381, Jun. 2014.
(2014). LV Network Solutions. Available: http://www.enwl.co.uk/about-us/the-future/lcnf-tier-1projects/low-voltage-network-solutions
A. Ballanti, "WP2 Deliverable 2: Progress Report of Task 1.1, Task 1.2 and Task 1.3," 2014.
M. Diaz-Aguilo, J. Sandraz, R. Macwan, F. de Leon, D. Czarkowski, C. Comack, et al., "FieldValidated Load Model for the Analysis of CVR in Distribution Secondary Networks: Energy
Conservation," Power Delivery, IEEE Transactions on, vol. 28, pp. 2428-2436, 2013.
N. Higgins, "Dwelling Stock Estimates: 2014, England," National Statistics2015.
G. Owen and J. Ward, "GB Electricity Demand - Context and 2010 Baseline Data," 2011.
I. Richardson, M. Thomson, D. Infield, and C. Clifford, "Domestic electricity use: A highresolution energy demand model," Energy and Buildings, vol. 42, pp. 1878-1887, Oct. 2010.
N.
Grid.
Available:
http://www2.nationalgrid.com/UK/Industry-information/Electricitytransmission-operational-data/Data-explorer/
F. L. Quilumba, L. Wei-Jen, H. Heng, D. Y. Wang, and R. L. Szabados, "Load model
development for next generation appliances," in Industry Applications Society Annual Meeting
(IAS), 2011 IEEE, 2011, pp. 1-7.
Z. Bing, T. Yong, Z. Wen-chao, and Q. Wang, "Modeling of common load components in power
system based on dynamic simulation experiments," in Power System Technology
(POWERCON), 2010 International Conference on, 2010, pp. 1-7.
CONFIDENTIAL
Copyright © 2015 A. Ballanti & L. Ochoa - The University of Manchester
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WP2 Part A - Final Report “Off-line Capability Assessment”
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[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
F. Lamberti, D. Cuicai, V. Calderaro, and L. F. Ochoa, "Estimating the load response to voltage
changes at UK primary substations," in Innovative Smart Grid Technologies Europe (ISGT
EUROPE), 2013 4th IEEE/PES, 2013, pp. 1-5.
K. P. Schneider and J. C. Fuller, "Detailed end use load modeling for distribution system
analysis," in Power and Energy Society General Meeting, 2010 IEEE, 2010, pp. 1-7.
L. M. Hajagos and B. Danai, "Laboratory measurements and models of modern loads and their
effect on voltage stability studies," Power Systems, IEEE Transactions on, vol. 13, pp. 584-592,
1998.
G. Tsagarakis, A. J. Collin, and A. E. Kiprakis, "Modelling the electrical loads of UK residential
energy users," in Universities Power Engineering Conference (UPEC), 2012 47th International,
2012, pp. 1-6.
CIGRÉ, Modelling and Aggregation of Loads in Flexible Power Networks: CIGRÉ, 2014.
Electricity North West, "CLASS Trail design and associated schedule," 2014.
V. Miranda, J. Pereira, and J. T. Saraiva, "Load allocation in DMS with a fuzzy state estimator,"
IEEE Trans. on Power Systems, vol. 15, pp. 529-534, May 2000.
D. Youman, H. Ying, and Z. Boming, "Branch-estimation-based state estimation for radial
distribution systems," in Power Engineering Society Winter Meeting, 2000. IEEE, 2000, pp.
2351-2356 vol.4.
CONFIDENTIAL
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Appendix 1 - Load Model per Appliance
No consistency has been found in literature as quite often several different load models could be
found for the same appliance.
In order to associate a set of reasonable ZIP values for the 35 defined appliances in a common
dwelling in UK the following criteria have been adopted:
1.
2.
3.
4.
5.
Higher importance to model based on measurement
Higher importance to model developed in recent literature
Higher importance to model validated by other measured models (from other literature)
Higher importance to model validated by simulations
Higher importance to the V/P-Q dependency only for practical interesting voltage interval (i.e.,
±5 % of V0)
Although a high number of studies have been consulted the majority of the adopted models come from
[6-8] as shown in Table V. The 35 considered appliances come from [5] with some minor adaptations.
Table V Adopted ZIP model per each appliance in a common UK dwelling
active power
reactive power
N
Name
Z
I
P
Ref.
Z
I
P
Ref.
PF6
1
Chest freezer
1.17
-1.83
1.66
[7]
7.07
-10.94
4.87
[7]
0.920
2
Fridge freezer
1.17
-1.83
1.66
[7]
7.07
-10.94
4.87
[7]
0.920
3
Refrigerator
1.17
-1.83
1.66
[7]
7.07
-10.94
4.87
[7]
0.920
4
Upright freezer
1.17
-1.83
1.66
[7]
7.07
-10.94
4.87
[7]
0.920
5
Answer machine
0.00
0.00
1.00
[6]
3.63
-9.88
7.25
[6]
-0.990
6
CD Player
0.00
0.00
1.00
[6]
3.63
-9.88
7.25
[6]
-0.990
7
Clock
0.00
0.00
1.00
[6]
3.63
-9.88
7.25
[6]
-0.990
8
Cordless
telephone
0.00
0.00
1.00
[6]
3.63
-9.88
7.25
[6]
-0.990
9
Hi-Fi
0.00
0.00
1.00
[6]
0.45
-1.44
1.99
[6]
0.970
10
Iron
1.00
0.00
0.00
-
0.00
0.00
1.00
-
1.000
11
Vacuum
1.18
-0.38
0.20
[7]
4.10
-5.87
2.77
[7]
0.970
12
Fax
0.00
0.00
1.00
[6]
3.63
-9.88
7.25
[6]
-0.990
13
PC
0.00
0.00
1.00
[6]
0.45
-1.44
1.99
[6]
0.970
14
Printer
0.00
0.00
1.00
[6]
0.00
0.00
1.00
[6]
1.000
15
TV 1-LCD
0.00
0.00
1.00
[26]
0.45
-1.44
1.99
[26]
0.997
16
TV 1-CRT
0.16
-0.15
0.99
[27]
0.45
-1.44
1.99
[26]
0.970
17
TV 1-Plasma
0.00
0.00
1.00
[28]
[29]
0.00
0.00
1.00
[28]
[29]
0.997
18
VCR / DVD
0.00
0.00
1.00
[26]
3.63
-9.88
7.25
[26]
-0.990
19
TV Receiver box
0.00
0.00
1.00
[26]
3.63
-9.88
7.25
[26]
-0.990
20
Hob
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
21
Oven
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
22
Microwave
1.39
-1.96
1.57
[7]
50.07
-93.55
44.48
[7]
0.950
6
Negative PF indicates a capacitive load
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23
Kettle
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
24
Small cooking
(group)
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
25
Tumble dryer
0.96
0.05
-0.01
[30]
0.00
0.00
1.00
[30]
1.000
26
DESWH
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
27
E-INST
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
28
Electric shower
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
29
Storage heaters
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
30
Other electric
space heating
1.00
0.00
0.00
[8]
0.00
0.00
1.00
[8]
1.000
31
GIS
0.47
0.63
-0.10
[7]
1.48
-1.29
0.81
[7]
0.995
32
CFL
-0.01
0.96
0.05
[6]
-0.10
0.73
0.37
[6]
-0.910
Appliances like dish washer, washing machine and washer dryer machine cannot be defined by a
unique set of ZIP values due to different stages (and electrical behaviour) during their operation cycle.
For this reason a more complicated modelling, following the indication in [31], has been considered as
detailed in Table VI.
Table VI Appliances with their own time variant cycle
33
Dish washer
Zip cycle
[31]
Zip cycle
[31]
1.000
34
Washing machine
Zip cycle
[31]
Zip cycle
[31]
0.995
35
Washer dryer
Zip cycle
[31]
Zip cycle
[31]
-0.910
CONFIDENTIAL
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Appendix 2 - Aggregating Load Models
The processes necessary to aggregate numerous load models for the development of the literaturebased load models (Figure 4) are detailed as follows.
1. Aggregation of numerous appliance load models: The adopted procedure is illustrated in
Eq. 6 and Eq. 7. One set of ZIP coefficients per single residential customer (rather than per
appliance) is the main outcome (Eq. 7).
In Eq. 6 first the total dwelling demand (Pdwelling) is obtained by adding up the power required
by each single appliance (only two for sake of simplicity, P1 and P2 in Eq. 6). Then the
definition of ZIP model definition is introduced. Finally, only 3 ZIP coefficients (Zdwelling, Idwelling
and Pdwelling) have been defined to model the whole customer. For a general N number of
appliances the findings in Eq. 6 are generalised in Eq. 7.
Pdwelling (t )  P1 (t )  P2 (t )
  V 2

  V 2

V
V
Pdwelling (t )  P01 (t )  Z1    I1  P1   P02 (t )  Z 2    I 2  P2 
V0
V0
  V0 

  V0 

2
V 
V 
Pdwelling (t )    P01 (t ) Z1  P02 (t ) Z 2    P01 (t ) I1  P02 (t ) I 2   P01 (t ) P1  P02 (t ) P2 
 V0 
 V0 
Eq. 6
 P (t ) Z  P (t ) Z  V  2 P (t ) I  P (t ) I  V  P (t ) P  P (t ) P 
2
   01 1 02 2    01 1 02 2 
Pdwelling (t )  P01 (t )  P02 (t ) 01 1 02
P
(
t
)

P
(
t
)
P01 (t )  P02 (t )  V0 
P01 (t )  P02 (t ) 
 01
02
 V0 

2


V 
V 
Pdwelling (t )  P0 dwelling (t )  Z dwelling (t )   I dwelling (t )   Pdwelling (t )


 V0 
 V0 
N
 P0i (t )Z i
Z dwelling (t ) 
i 1
N
P
i 1
0i
N
; I dwelling (t ) 
(t )
 P0i (t ) I i
i 1
N
P
i 1
0i
N
; P0 dwelling (t ) 
(t )
P
i 1
N
0i
P
i 1
(t ) Pi
0i
Eq. 7
(t )
2. Aggregation of N customer load models: This aggregation process can be applied even if
the customer belongs to different ELEXON categories (i.e., PC1 to PC8). The adopted
approach is identical to the one previous introduced and repeated in Eq. 8 for clarity.
N
PNcustDemand (t )   Pi
i 1
  V 2

V
PNcustDemand (t )   P0i (t )  Z i    I i
 Pi 
V0
  V0 

i 1
2


V 
V
PNcustDemand (t )  P0 NcustDemand (t )  Z Ncust    I Ncust
 PNcust 
V0


 V0 
N
N
Z Ncust (t ) 
N
P
i 1
N
(t ) Z i
0i
P
i 1
0i
(t )
; I Ncust (t ) 
N
P
i 1
N
0i
P
i 1
Eq. 8
(t ) I i
0i
; PNcust (t ) 
(t )
CONFIDENTIAL
Copyright © 2015 A. Ballanti & L. Ochoa - The University of Manchester
P
i 1
N
0i
P
i 1
(t ) Pi
0i
(t )
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Although the ZIP form was adopted [32], the aggregated load model is expressed in the exponential
form (i.e., np(t) coefficient) Eq. 9. This is a necessary step in order to compare the aggregated
literature-based load model developed here with the measurement-based.
V 
P( t )  P0 (t )  ( t ) 
V 
 0(t ) 
np ( t )
Eq. 9
Where P0(t) and P(t) indicate the demand pre and post voltage variation (i.e., V(t)=V0(t)-V(t)). The
conversion from Z-I-P to np coefficient is illustrated in Eq. 10 [12].
np(t )  2Z (t )  I (t )  P(t )
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Eq. 10
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Appendix 3 - HV Network Selection: Filtering Process
Among the 15 HV network models translated into OpenDSS only 3 have been selected based on the
following 4 criteria:




1st Filter: Translation accuracy: From the developed OpenDSS model it is possible to obtain
the number of customers per primary (N1). The same information is also available in a EWNL
database (N2). If the translation process has been carried out without significant
approximations the two figures (N1 and N2) should match or, at most, be very close. If a
secondary substation has been “lost” or some open points have been not considered properly
in the translation process the condition N1=N2 will not be met anymore. The difference
between N1 and N2 is an indicator of how accurate the OpenDSS model can be. If the
difference is above 5% the network model is considered not acceptable.
2nd Filter: LV topology. The availability of OpenDSS model (from “LV Network Solutions”
project) of LV networks supplied by an analysed HV network is considered an advantage for
future studies in which LV and HV network might be analysed in an integrated manner.
3rd Filter: LV network visibility. Those HV networks whose LV feeders present both LV busbar
and end point monitors are preferred to those with only one monitoring point.
4th Filter: Mainly residential. Based on the number of customers per profile class and ELEXON
associated profiles it is possible to estimate the contribution of the residential component on
the peak demand (Peak Load Sharing, PLS, [33]). Only primary substation with a PLS>75%
(i.e., the estimated residential component is above 75% of the peak demand) have been
considered “mainly residential”.
It is worth noticing that the 4th Filter has been previously adopted to categorise 15 primary substations
involved in Trial 17 at the beginning of the project [33] as shown in Table VII.
Table VII Primary substation category in Trial 1 [33] and updated PLS (within brackets)
Electricity North West Limited
mainly non-residential
(PLS should be <25%)
Electricity North West Limited
mainly residential
(PLS should be >75%)
Electricity North West Limited
mixed
(25% <PLS should be<75%)
Trafford Park North (0.1%)
Dickinson Street (58.9%)
Kitt Green (87.5%)
Avenham (22.3%)
Central Manchester (64.31%)
Fallowfield (85.8%)
Romiley (89.1%)
Wilmslow (66%)
Egremont (85.5%)
Ashton (87.9%)
Buckshaw (57.5%)
Victoria Pk (53.3%)
Hyndburn Rd (48.5%)
Blackfriars (77.7%)
Bridgewater (75.3%)
However, after adopting the latest available database and fixing some inaccuracies from previous
analyses (Deliverable 3) a few inconsistencies in the proposed Trial 1 classification can be noticed
(highlight in red in Table VII). More precisely, the primary substation called Wilmslow defined as
“mainly residential” in [33] presents, adopting the latest data, a PLS below 75 % (within brackets in red
Table VII). On the other hand, Dickinson, Kitt Green and Central Manchester, cannot be defined
anymore “large commercial” as their PLS is above the predefined threshold of 25 %. In addition,
Blackfriars and Bridgewater, previously classified as mixed, they were found to belong to the mainlyresidential category as their PLS is above 75 % as shown in Table VII.
The results of the four filtering actions are shown in Table VIII. Different colours indicate the empirical
degree of acceptability of the HV network model, from dark green (optimal) to red (not acceptable). For
instance, the HV network model of “Dickinson” cannot be accepted for the 1st filtering (i.e., “translation
accuracy”) as the mismatch in term of number of customers (N1 and N2) is above 5 %.
It is important to highlight that the peak load share estimation is based only on one instant (peak time).
However, residential and non-residential components are likely to change throughout day as well as
7
Trial 1 in CLASS project aims to investigate the relationship voltage/demand by introducing controlled and
monitored voltage variation on 15 primary substations
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throughout seasons. Consequently, this methodology provides a simple indication of those primary
substations that are likely to supply mainly residential demand as the actual time-varying peak share is
unknown.
Table VIII Filtering actions adopted to define the most reliable HV network models
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Appendix 4 - Off-Load Tap Changer Modelling
The methodology adopted to identify the off-load tap changer position of every secondary substations
across a generic HV network requires the HV network model and the aggregate demand profile per
secondary substation in the most loaded day. In addition some outcomes from the Low Carbon
Network Fund project “LV network solutions” have also been adopted. More precisely, the analysis of
millions of monitored voltage sample undertaken in “LV network solution” project has highlighted that
for around 90 % of the time (at 10 min resolution for one year) the LV busbar voltages of 100
secondary substations in the Electricity North West Limited area are above 240 V. This figure also
agrees with the outcomes of another Low Carbon Network Fund projects (“LV Network Templates”).
In addition, it shows consistency with the common design criteria followed by Electricity North West
Limited by which a voltage drop from LV busbar to customer should not overcome 7 % (i.e.,16 V) for a
typical feeder. This, in turn, implies a customer voltage no lower than 224 V (assuming 240 V as
lowest value), close to nominal voltage (i.e., 230 V in UK). Consequently, assuming that the LV busbar
voltage of a secondary substation should never drop below 240 V, as this would not reflect normal
operation condition, the criteria illustrated in detail in Figure 60 is defined to assess the tap position
per secondary substation.
Figure 60 Adopted criteria to define the off-load tap changer position of a secondary substation
First, the most loaded day for the defined primary substation is identified from CLASS monitored data
(iHost data). Then, in a first attempt, the off-load tap changer position of every LV transformer is fixed
at 3. The load allocation technique explained in Appendix is adopted to quantify the likely aggregate
demand per secondary substation.
Then, in such condition, power flow is performed in OpenDSS [16]. For those secondary substation
that experience a LV busbar voltage below 240 V the respective off-load tap changer position is raised
(i.e., from 3 to 4 or 3 to 5) until the LV busbar voltage is above 240 V at all times. The settings so
obtained have been incorporated in the HV network modelling, generating the “updated HV network
model” Figure 60, adopted throughout this report.
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Appendix 5 - Time-Varying Load Allocation
To evaluate the voltage levels across the HV network, necessary for the voltage capability
quantification, the aggregated demand profiles per secondary substation are needed. However, as not
monitor device are installed at this level, a load allocation technique to estimate the aggregate
demand profile per secondary substation is proposed here.
This belongs to a category of “state estimation” techniques where the state of the system (i.e.,
demand profiles in this case) is quantified in order to match the few available measured variables,
Figure 61-a. For this purpose, power flow equations are modified (“rearranged” Figure 61-b) in order to
obtain the unknown secondary substation demand profiles to match the monitored measurement.
A key role in the load allocation is played by the so called “pseudomeasurements”. These are usually
historical data adopted to make the network observable (i.e., it is possible to know voltage and power
in every point). These can be considered, in the load allocation perspective, as a reliable “first guess”
of what the demand of the LV substations might be. In the context of this work the
pseudomeasurements per secondary substations are obtained by multiplying the number of
customers per profile class (PC) with the associate ELEXON and CREST profiles as detailed in
section 1.
Nevertheless several sophisticated techniques to solve the load allocation problem might be found in
the literature [34]-[35] a simpler approach has been developed here.
a)
Load profile: First guess
(Pseudomeasurement)
Network
topology
Rearranged
load flow
equations
Simulated
variable (Vs)
Error=
Vm-Vs
Minimise
error
Error<
tolerance
YES
Load
profile:
allocated
Monitored variable
(Vm)
NO
b)
Figure 61 Load allocation: a) problem b) approach
Indeed the very low number of monitoring points in power (only one in this case, P and Q at the
primary substation8,) and high number of LV transformer demand profile to allocate (from 50 to +100)
makes the problem greatly undetermined. Consequently, the benefits arising from the adoption of
complex techniques has been retained negligible.
8
The voltage on the secondary side of the primary substation transformer is also provided. However only a
minimal contribution on the load allocation procedure can provide as it is weakly correlated with the load
downstream.
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The adopted methodology is summarised in Figure 62. Once the HV network model is defined the
number of residential (PC1 and PC2) and non-residential customers (PC3 to PC8 and PC0) is known
for every secondary substation in the network. The demand profile per customer class depends on the
day; hence this information should also be defined. Once customer number and associated (ELEXON
and CREST) profiles are defined it is possible to associate a pseudo-measurement to every
secondary substation.
P
# of PC3 to
PC8 & PC0
customers
START
Define
primary
substation
&
Day
ELEXON
load profile
CREST
# of
PC1 & PC2
customers
Calculate
nondomestic
component
for every SS
Calculate minimum
variation (P) on the
non-domestic
component only to
eliminate the error
Run power
flow
Calculate
domestic
component
for every SS
HV network
in OpenDSS
Primary sub.
simulated
demand (Ps)
NO
E=Ps-Pm
E
<5kW
YES
END
Primary sub.
monitored
demand (Pm)
Figure 62 Load allocation: adopted methodology
For the residential component the CREST tool-based profiles have been preferred to the ELEXON
ones [13]. The main advantage lies on the possibility to generate one different profile for every single
residential customer (providing diversity in the among secondary substation aggregate demand).
Once load profiles have been estimated (in this 1st iteration) a power flow determines the total demand
at the secondary side of the primary substation. Ideally, if the first attempt in allocated the demand per
secondary substation has been quite successful, the difference (E, Figure 62) between monitored
and simulated primary substation demand will be below the tolerance. However this is hardly ever the
case.
Hence, the mismatch is used as feedback in a linearised version of the power flow equations that
correlates the variation of every secondary substation demand (active and reactive) with the power on
the secondary side of the primary substation (the monitored variable). This is a single equation with
one known term (the monitored demand) and as many unknown variables as the demand profiles to
allocate.
Due to the infinite solutions that this undetermined system might provide (as there is only one equation
with as many unknown as secondary substations in the HV network) an objective function has been
adopted to reduce the search space: the variation introduced in the first guess (P, Figure 62) should
be the smallest possible. Indeed the first guess demand (the pseudo-measurements) represents the
best of the knowledge on the secondary substation demand profiles. Ultimately this process will
provide, given the mismatch (E), the update (P) to apply at the demand of every secondary
substation. As the linearised version of the system is only an approximation of the real one few
iterations are needed to find the correction values (P) of the secondary substation demand that
brings the mismatch below the defined threshold.
It is worth noticing that, given the reliability of CREST tool in simulating residential demand profiles the
residential component of the pseudo-measurements is not modified by the load allocation process. In
other words, only the non-residential component of the demand is updated (by P), iteration by
iteration, in order to reduce the mismatch between simulated and monitored primary substation
demand (the mismatch E).
In order to provide more realistic outcomes a constraint has been introduced: the allocated demand
per secondary substation should never overcome the biggest value between Maximum Demand Index
(i.e., the highest recorded demand in the site) and LV transformer nominal rating.
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1.5
REACTIVE power demand for every SS and PMT in the HV network
Estimated aggregated domestic component
Estimated aggregated non-domestic component
Monitored aggregated demand
Simulated aggregated demand
Power (Mvar)
1
0.5
0
00:00
03:00
06:00
09:00
12:00
15:00
Time 10-min
18:00
21:00
Figure 63 Load allocation results for the reactive power for Romiley on January 26th (at primary
substation level)
The methodology has also been applied to estimate the reactive power component of the nonresidential demand. In this case the power factor (PF) per secondary substation is the variable to
allocate (as the active power is known from the previous steps). It has been decided to limit the power
factor between 1 and 0.85 p.u. No capacitive behaviour has been allowed as no information is
available.
For the reactive power not always a perfect match has been obtained between monitored and
simulated aggregated demand as can be noticed in Figure 63 for few instants through the day in which
monitors and simulated reactive demand do not overlap perfectly. The problem happens when the
monitored reactive power is lower than estimated residential reactive power (generated by CREST
and not modified in the load allocation process). Hence, as no capacitive behaviour has been
introduced in the non-residential component it is not possible to have an aggregated reactive power
lower than the residential one making impossible to find an exact solution. In this case the best
solution is to set the reactive power of the industrial component to zero as the algorithm does (Figure
63).
A work around may be to consider the industrial customers able to inject reactive power (i.e., being
capacitive). Nonetheless, as no information is available on the matter, such modification has not been
implemented.
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Appendix 6 - Conservative Tap Capability Scenario
Figure 64 shows the conservative tap capability scenario obtained by considering the lowest tap
capability (for every instant) among those found in the detailed network analysis carried out for
Fallowfied, Ashton Golborne and Romiley (section 5.1). In addition, a “CLASS reference” scenario is
also defined (red dotted line). This was the voltage capability (i.e., 3 % voltage reduction,
approximately 2 taps) considered at the proposal stage of the project. Even with the conservative
scenario it was found that a voltage reduction of 2.86 % (2 taps) is indeed possible throughout the
year (except during peak time in winter weekdays, around 6 pm). A voltage reduction of 4.29 % (i.e., 3
taps) was also found to be feasible throughout a significant part of the year without affecting
customers.
Figure 64 Conservative tap capability scenarios
The aggregated DR for both Electricity North West Limited area and the UK adopting the literature and
measurement-based load models with the conservative tap capability scenario are provided in the
following sections.
DR in the Electricity North West Limited Area
Adopting the literature-based load model it was found that the DR varies from 15 MW during night
summer hours (around 3-4 am) to 120 MW in autumn peak time (6 pm) and winter in late evening
(9 pm). It can also be noticed that, due to the adopted conservative tap capability scenario, the peak in
the DR (in winter at 9 pm) is not coincident with the annual peak in the demand (in winter at 6 pm).
Indeed, during the highest demand, the tap capability is lower and so the voltage capability.
The DR for the Electricity North West Limited area across the four seasons adopting the
measurement-based load model and conservative tap capability scenario is shown in Figure 66. The
minimum of almost 65 MW was found during summer early morning hours (around 6 am) whilst the
maximum of around 170 MW happens in winter at around 9 pm (as well as between 10 am and
3:30 pm). At around winter peak hours (6 pm) only 65 MW were found. This is due to the lower tap
capability found during this season (i.e., 1 tap or 1.48 % voltage reduction).
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Figure 65 Aggregated DR for the Electricity North West Limited area: all seasons - literaturebased load model - conservative tap capability scenario
Figure 66 Aggregated DR for the Electricity North West Limited area: all seasons measurement-based load model - conservative tap capability scenario
DR in the United Kingdom
Considering the residential-component only of the demand and adopting the conservative tap
capability scenario it was found that the DR that CLASS may unlock at national level varies between
63 and 930 MW throughout the year. The maximum is reached at peak hours (6 pm) in autumn and
the minimum during summer night (3 am).
However, to assess the full potential of CLASS the non-residential component response was
embedded by describing its voltage-demand relationship with a load model coefficient of np=1.3
(rather than np=0) as shown in Figure 68. This value has provided the closest agreement with the
measurement-based load model outcomes for the DR quantification in the Electricity North West
Limited area (Figure 56). As expected, the estimated demand response is higher when the nonresidential component is included. In particular, it reaches its maximum of 2.5 GW during winter
(around 9 pm). The same volume of DR is also available from 10:00 to 15:30 due to the steadiness in
the aggregated nationwide and voltage capability. Finally, at peak winter time (6 pm) only 1 GW is
available as only 1 tap step reduction is considered in the conservative tap capability scenario (Figure
64).
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Figure 67 Aggregated DR for the whole UK: all seasons - literature-based load model conservative tap capability scenario
Figure 68 Estimated DR for the whole UK: all seasons - enhanced literature-based load model conservative tap capability scenario
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Appendix 7 - One Primary Substation Dashboard
Table IX and Table X report the demand response that is possible to unlock in Egremont primary
substation by considering the mean, minimum and maximum load model values provided by the
statistical analysis carried out in WP1 (Section 1.4). The data are provided at half hour resolution for
the four typical weekdays adopting the optimistic tap capability scenario.
Table IX Dashboard: Egremont (MW) weekday: a) Winter, b) Autumn
a)
Time
00:00
00:30
01:00
01:30
02:00
02:30
03:00
03:30
04:00
04:30
05:00
05:30
06:00
06:30
07:00
07:30
08:00
08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30
Min.
0.184
0.209
0.201
0.198
0.192
0.205
0.195
0.182
0.178
0.181
0.178
0.193
0.206
0.233
0.264
0.179
0.282
0.285
0.29
0.284
0.276
0.277
0.271
0.267
0.274
0.26
0.242
0.236
0.251
0.264
0.256
0.27
0.29
0.21
0.231
0.364
0.364
0.354
0.35
0.339
0.439
0.432
0.307
0.301
0.379
0.359
0.236
0.206
b)
Mean
0.301
0.311
0.31
0.301
0.297
0.315
0.308
0.301
0.295
0.3
0.304
0.321
0.35
0.387
0.453
0.304
0.486
0.475
0.473
0.464
0.449
0.448
0.446
0.446
0.454
0.449
0.434
0.426
0.424
0.439
0.449
0.465
0.491
0.353
0.384
0.584
0.589
0.579
0.559
0.533
0.687
0.652
0.479
0.47
0.592
0.552
0.373
0.335
Max.
0.366
0.419
0.411
0.396
0.384
0.403
0.4
0.382
0.375
0.39
0.39
0.426
0.46
0.519
0.596
0.407
0.627
0.597
0.608
0.602
0.586
0.582
0.575
0.574
0.59
0.589
0.581
0.57
0.562
0.589
0.622
0.643
0.687
0.504
0.545
0.832
0.833
0.808
0.778
0.755
0.979
0.918
0.644
0.622
0.777
0.728
0.45
0.403
CONFIDENTIAL
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Time
00:00
00:30
01:00
01:30
02:00
02:30
03:00
03:30
04:00
04:30
05:00
05:30
06:00
06:30
07:00
07:30
08:00
08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30
Min.
0.04
0.036
0.035
0.142
0.137
0.134
0.13
0.13
0.125
0.122
0.121
0.129
0.146
0.168
0.198
0.217
0.229
0.22
0.219
0.216
0.211
0.21
0.213
0.209
0.211
0.208
0.199
0.2
0.202
0.202
0.208
0.219
0.228
0.244
0.261
0.269
0.374
0.382
0.383
0.379
0.354
0.338
0.083
0.08
0.077
0.069
0.044
0.045
Mean
0.213
0.193
0.185
0.215
0.208
0.207
0.203
0.203
0.201
0.202
0.202
0.217
0.249
0.289
0.343
0.378
0.4
0.388
0.388
0.381
0.37
0.365
0.366
0.364
0.361
0.361
0.349
0.34
0.34
0.343
0.349
0.366
0.381
0.401
0.417
0.431
0.594
0.601
0.603
0.592
0.576
0.552
0.532
0.518
0.498
0.452
0.29
0.24
Max.
0.298
0.269
0.259
0.293
0.279
0.27
0.263
0.265
0.265
0.261
0.256
0.276
0.315
0.365
0.425
0.465
0.495
0.49
0.509
0.502
0.497
0.493
0.502
0.506
0.518
0.529
0.53
0.518
0.528
0.579
0.583
0.608
0.635
0.665
0.691
0.708
0.97
0.978
0.971
0.938
0.919
0.869
0.843
0.809
0.758
0.68
0.433
0.344
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Table X Dashboard: Egremont (MW) weekdays: a) Spring, b) Summer
a)
Time
00:00
00:30
01:00
01:30
02:00
02:30
03:00
03:30
04:00
04:30
05:00
05:30
06:00
06:30
07:00
07:30
08:00
08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30
Min.
0.158
0.142
0.135
0.172
0.167
0.163
0.161
0.159
0.151
0.151
0.148
0.156
0.176
0.198
0.22
0.242
0.264
0.252
0.254
0.251
0.241
0.239
0.239
0.239
0.242
0.246
0.229
0.215
0.213
0.218
0.216
0.228
0.241
0.256
0.357
0.364
0.371
0.357
0.362
0.365
0.371
0.372
0.373
0.369
0.358
0.345
0.217
0.173
b)
Mean
0.244
0.221
0.208
0.262
0.259
0.255
0.252
0.248
0.241
0.241
0.236
0.246
0.28
0.311
0.347
0.385
0.422
0.398
0.399
0.395
0.384
0.378
0.381
0.38
0.38
0.383
0.372
0.353
0.345
0.351
0.352
0.364
0.373
0.396
0.554
0.565
0.579
0.56
0.563
0.56
0.565
0.571
0.577
0.577
0.563
0.528
0.34
0.272
Max.
0.338
0.301
0.277
0.352
0.352
0.346
0.337
0.333
0.321
0.32
0.32
0.336
0.384
0.432
0.485
0.546
0.603
0.574
0.56
0.559
0.544
0.533
0.529
0.529
0.537
0.545
0.523
0.497
0.489
0.478
0.47
0.484
0.494
0.536
0.752
0.795
0.82
0.797
0.801
0.814
0.813
0.815
0.834
0.822
0.805
0.749
0.474
0.381
CONFIDENTIAL
Copyright © 2015 A. Ballanti & L. Ochoa - The University of Manchester
Time
00:00
00:30
01:00
01:30
02:00
02:30
03:00
03:30
04:00
04:30
05:00
05:30
06:00
06:30
07:00
07:30
08:00
08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30
Min.
0.119
0.108
0.098
0.105
0.102
0.1
0.098
0.097
0.096
0.096
0.094
0.099
0.114
0.136
0.164
0.189
0.201
0.203
0.211
0.206
0.204
0.202
0.201
0.204
0.208
0.208
0.202
0.198
0.198
0.198
0.196
0.2
0.207
0.216
0.22
0.222
0.228
0.22
0.211
0.2
0.197
0.189
0.19
0.198
0.198
0.189
0.167
0.142
Mean
0.223
0.201
0.185
0.202
0.197
0.193
0.187
0.182
0.182
0.179
0.172
0.186
0.21
0.244
0.287
0.327
0.348
0.348
0.352
0.34
0.336
0.332
0.329
0.332
0.333
0.33
0.31
0.3
0.295
0.294
0.293
0.305
0.323
0.338
0.351
0.351
0.358
0.353
0.341
0.328
0.325
0.322
0.331
0.352
0.353
0.341
0.305
0.266
Max.
0.453
0.409
0.376
0.411
0.398
0.325
0.314
0.309
0.308
0.304
0.259
0.281
0.305
0.341
0.404
0.466
0.496
0.501
0.471
0.456
0.45
0.446
0.439
0.446
0.453
0.454
0.433
0.425
0.421
0.42
0.416
0.525
0.549
0.574
0.593
0.589
0.599
0.613
0.585
0.557
0.547
0.533
0.619
0.73
0.73
0.699
0.624
0.537
67
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23rd September 2015
Appendix 8 - Electricity North West Limited Dashboard
Table XI reports the half hourly dashboard of the aggregated demand response for the whole
Electricity North West Limited area in four typical weekdays. For this purpose, the mean
measurement-based load model values were adopted with the optimistic tap capability scenario.
Table XI Dashboard: Electricity North West Limited area (MW) –Weekdays
Time
00:00
00:30
01:00
01:30
02:00
02:30
03:00
03:30
04:00
04:30
05:00
05:30
06:00
06:30
07:00
07:30
08:00
08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30
Winter
108.19
115.33
116.37
114.19
112.81
120.86
118.31
115.23
112.63
113.89
115.18
120.42
129.28
141.79
165.03
108.8
175.76
174.4
175.3
173.65
170.08
169.62
170.22
169.66
171.78
169.63
164.4
160.65
158.77
163.91
166.19
171.53
180.02
127.26
136.06
203.36
203.31
198.27
191.06
182.44
235.29
224.39
164.59
160.87
202.43
191.96
130.16
118.02
Autumn
76.91
70.94
69
81.5
78.94
78.73
76.94
76.78
76.14
76.16
76.09
81.76
91.7
104.96
123.43
136.4
146.54
143.36
145.42
144.67
142.3
141.03
141.45
140.67
139.66
139.07
133.85
130.5
130.2
130.33
132.05
137.31
140.52
146.46
149.51
152.29
207.97
208.98
209.53
206.2
201.61
192.85
186.41
181.73
173.42
158.09
102.66
85.87
CONFIDENTIAL
Copyright © 2015 A. Ballanti & L. Ochoa - The University of Manchester
Spring
85.95
79.37
75.88
99.47
98.39
96.87
95.35
93.43
90.15
89.96
87.8
90.8
101.11
111.48
124.19
137.18
151.74
144.5
146.25
146.22
143.52
141.73
142.92
142.45
141.75
141.42
137.97
131.38
128.09
129.48
130.05
133.82
135.72
141.99
195.43
196.84
199.29
192.36
192.7
192.18
194.04
196.11
198
199.67
194.53
182.7
118.57
95.34
Summer
79.44
72.57
67.56
76.42
73.53
72.37
70.65
69.15
68.87
67.76
65.71
70.93
78.49
90.58
106.11
121
129.86
131.68
134.98
132.09
131.39
130.45
129.85
130.44
130.37
129.26
120.99
116.69
114.16
112.68
112.33
115.19
120.01
123.82
126.91
125.53
126.37
123.91
119.36
114.83
114.19
112.72
115.69
122.3
122.14
117.33
106.14
92.83
68
WP2 Part A - Final Report “Off-line Capability Assessment”
UoM-ENWL_CLASS_WP2T1_FRv10
23rd September 2015
Appendix 9 - UK Dashboard
Table XII reports the half hourly dashboard of the aggregated demand response for the UK for four
typical days. For this purpose, also the non-residential component of the demand have been modelled
assuming for this component a load model np=1.3, aligned with the measurement findings. The
optimistic tap capability scenario was adopted.
Table XII Dashboard: UK (MW) –Weekdays
Time
00:00
00:30
01:00
01:30
02:00
02:30
03:00
03:30
04:00
04:30
05:00
05:30
06:00
06:30
07:00
07:30
08:00
08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30
Winter
1562.4
1802.1
1824.1
1808.7
1785.9
1782.0
1781.4
1766.1
1751.1
1756.1
1765.5
1773.6
1811.4
1931.9
2168.3
1582.6
2419.5
2413.3
2452.6
2460.7
2435.6
2407.9
2414.8
2410.1
2423.1
2418.7
2415.0
2358.7
2344.4
2374.9
2378.1
2421.3
2540.8
1787.3
1909.9
2903.7
2872.1
2784.1
2718.8
2605.1
3330.9
3195.8
2311.5
2158.0
2707.2
2547.6
1770.0
1687.7
Autumn
1377.0
1574.8
1559.1
1547.9
1545.7
1537.3
1519.0
1533.1
1558.3
1612.9
1688.5
1839.7
2010.8
2083.9
2125.9
2141.1
2151.7
2150.7
2147.9
2137.2
2112.3
2097.9
2083.6
2083.6
2066.0
2053.6
2026.0
2006.6
2001.8
1993.7
2051.6
2085.2
2132.4
2160.7
2168.8
2239.4
3110.6
3082.4
2949.8
2796.1
2662.4
2482.2
2346.4
2157.1
2011.3
1870.0
1367.7
1309.4
CONFIDENTIAL
Copyright © 2015 A. Ballanti & L. Ochoa - The University of Manchester
Spring
1407.2
1631.4
1637.8
1627.5
1609.1
1601.1
1600.1
1601.4
1633.0
1662.0
1722.0
1831.4
1958.2
2022.8
2093.9
2106.6
2115.4
2102.6
2062.5
2045.7
2013.6
1901.3
1958.0
1954.2
1939.1
1909.7
1894.8
1883.5
1852.9
1868.5
1935.8
1976.9
2036.9
2055.3
2772.1
2815.8
2805.2
2810.5
2845.9
2852.7
2754.9
2602.6
2464.4
2280.3
2127.8
1967.0
1428.2
1392.8
Summer
1244.0
1244.8
1219.2
1199.5
1185.0
1192.8
1177.1
1161.3
1161.0
1164.5
1256.1
1414.8
1597.5
1776.5
1902.9
1970.4
2026.3
2030.6
2030.2
2017.0
2026.3
2028.2
2042.1
2034.3
2034.0
2027.1
2018.2
1979.8
1967.8
1983.9
2032.4
2046.3
2086.3
2083.6
2067.0
2030.2
2000.3
1962.6
1878.0
1830.6
1827.3
1784.9
1797.3
1706.5
1609.8
1482.6
1403.8
1363.6
69