Mitigating Power Fluctuations from Renewable Energy Sources
Sean Leavey
Supervisor: Dr Stefan Hild
March 2012
Abstract
Government legislation, new technology and efforts to reduce carbon emissions will result
in renewable energy sources making up a higher proportion of the National Grid’s electricity
supply in the future. A grid using a higher proportion of renewable energy sources will suffer
from larger supply fluctuations than presently experienced due to the unpredictable natures
of the underlying phenomena from which renewable sources derive their energy. Current mitigation techniques are outlined, then a model is created to demonstrate a grid powered solely
from renewable energy sources. This model is used to predict the fluctuations that could be
experienced in such a grid. With this information in mind, a prototype of the world’s first
smart charging laptop is built and demonstrated. This involves sensing the grid frequency
in real time and controlling the laptop’s charger accordingly. This system is tested with two
different laptop computers, and it is found that rates of charge and discharge and battery
life are the dominant factors in the computers’ performances. Further insight is gained from
simulations of battery levels. Finally, the large scale effect of the smart charging technique is
modelled.
1
Contents
1 Introduction
1.1 Legislation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 The National Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Supply and Demand Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Power Fluctuations
2.1 Supply Fluctuations from Renewable
2.1.1 Periods of Low Wind . . . . .
2.2 Demand Fluctuations . . . . . . . .
2.3 Dealing with Power Fluctuations . .
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3 Characterising Supply/Demand Mismatch
3.1 Grid Frequency Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4 Current Methods for Mitigating
4.1 Spinning Reserve . . . . . . . .
4.2 Short Term Operating Reserve
4.3 Frequency Response . . . . . .
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Energy Sources
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Power Fluctuations
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5 Mitigating Power Fluctuations in the Future
5.1 A 100% Renewable Energy Grid . . . . . . .
5.2 Demand-Side Management . . . . . . . . . .
5.2.1 Smart Electric Cars . . . . . . . . . .
5.2.2 Smart Fridges . . . . . . . . . . . . . .
5.2.3 Consumer Incentives . . . . . . . . . .
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6 Project Aims
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7 Prototyping, Testing and Characterisation of a Smart
7.1 Measuring the Grid Frequency . . . . . . . . . . . . . .
7.2 Frequency Measurement Technique . . . . . . . . . . . .
7.3 Verifying the Frequency Measurement Technique . . . .
7.4 Laptop Battery Performance . . . . . . . . . . . . . . .
7.5 Laptop Battery Simulations . . . . . . . . . . . . . . . .
8 Large Scale Impact of Smart Charging Laptop
8.1 Quantifying UK Grid Demand . . . . . . . . . . . . . .
8.2 Entirely Renewable Grid . . . . . . . . . . . . . . . . . .
8.2.1 Demand Model . . . . . . . . . . . . . . . . . . .
8.2.2 Wind Model . . . . . . . . . . . . . . . . . . . .
8.2.3 Solar Model . . . . . . . . . . . . . . . . . . . . .
8.2.4 The Combined Model . . . . . . . . . . . . . . .
8.3 Entirely Renewable Grid with Dynamic Demand Laptop
8.3.1 Large Scale Laptop Model Assumptions . . . . .
8.3.2 Simulation Mechanism . . . . . . . . . . . . . . .
9 Discussion
9.1 Grid Models and Simulations . . . . . . . . .
9.1.1 Wind and Solar Models . . . . . . . .
9.2 Laptop Controller Experiment . . . . . . . .
9.2.1 Long Term Effect on Laptop Batteries
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Charging Laptop
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Charging
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Project as a Whole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
10 Conclusions
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References
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Appendices
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A Interconnector Power Imports/Exports Sample
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B National Grid Website Logger Program
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B.1 Initial Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
B.2 Refined Frequency Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
C Battery Logging Shell Scripts
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C.1 Ubuntu Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
C.2 Mac OS X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
D Laptop Grid Model Assumptions
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List of Figures
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Average UK weekly demand 2011 . . . . . . . . . . . .
Typical UK weekday demand pattern . . . . . . . . .
Irish wind power fluctuations, Apr-May 2011 . . . . .
Example of a future UK power production scenario . .
A typical demand TV pickup . . . . . . . . . . . . . .
Grid frequency spectrogram . . . . . . . . . . . . . . .
A dynamic demand refrigerator . . . . . . . . . . . . .
Control system output and grid frequency comparison
Dynamic demand laptop system flowchart . . . . . . .
Control system input/output signal comparison . . . .
Dell laptop charge time series . . . . . . . . . . . . . .
Apple laptop charge time series . . . . . . . . . . . . .
Full Dell smart charging system . . . . . . . . . . . . .
Real and simulated laptop charge comparison . . . . .
Twenty-one day laptop charge simulation . . . . . . .
Laptop charge simulation histograms . . . . . . . . . .
Three day solar model simulation . . . . . . . . . . . .
Power surplus/deficit using renewable grid model . . .
Histogram of renewable grid model’s periods of deficit
A rare period of very long deficit . . . . . . . . . . . .
Power surplus/deficit using renewable grid model, with
Example of power mitigation using 20 million laptops
Interconnector imports/exports sample . . . . . . . . .
Histograms of laptop grid model assumptions . . . . .
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mitigation
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1 Introduction
independent body responsible for maintaining
the grid on the government’s behalf, are tasked
Increasing the use of renewable energy sources with keeping the supply and demand of elecwill bring about a reduction in the reliability of tricity in balance.
electricity generation being able to meet electricity demand, due to the inherent variability 1.3 Supply and Demand Pattern
of the physical phenomena renewable energy
sources derive their power from. It is therefore Electricity demand in the UK follows a reaimportant to find ways in which to mitigate sonably predictable pattern. National Grid
these fluctuations to prevent them from caus- PLC forecast demand using a number of meaing shortcomings in supply during periods of surements such as weather patterns, time of
low prevalence of the underlying physical pro- year, day of the week and time of day. Naturally, demand for electricity is greatest in
cesses.
the colder months (see Figure 1) when people
need more heat, and particularly in the day1.1 Legislation
light hours when offices are lit and heated, and
The UK Government is working towards pro- when people use productive equipment such as
viding a greater proportion of the National computers (see Figure 2). The whole counGrid’s electricity via renewable energy sources try’s demand throughout the day will tend to
in order to reduce the UK’s greenhouse gas fluctuate only gradually, as localised occasions
emissions from fossil fuel power plants. Un- where power is required tend to be balanced
der the Climate Change Act signed into law by other simultaneous occasions where power
in 2008, the government set out targets to cut is no longer required, such as a kettle being
CO2 equivalent emissions by 34% compared to switched on in one household as another house1990 levels by 2020 [1]. In Scotland, the gov- hold’s kettle switches off having finished boiling
ernment has been considering plans ”to meet some water [4]. This is known as diversity of
an equivalent of 100% demand for electricity demand.
from renewable energy by 2020” [2]. The EU
also has a directive which legislates that the
UK should increase production of electricity
from renewable energy sources by 15% over the
same period [3].
1.2 The National Grid
Electricity is supplied to homes and businesses
in the UK from the National Grid, a series of
interconnected power stations, substations and
other infrastructure connected to homes and
businesses. Instantaneously, there is a specific
demand (typically of the order 35 GW) created by the electrical devices connected to the
grid, and a specific power being supplied by the
various interconnected generators from power
plants. Keeping these two factors in balance
is extremely important because the electricity
grid does not have the ability to store any energy, so the demand must match the supply
to a high degree at all times otherwise devices
connected to the grid across the country will receive too little or too much electricity. National
Grid PLC (Publicly Limited Company), the
Figure 1: Average weekly demand in the UK for
2011 (data sourced from National Grid website).
2 Power Fluctuations
Power fluctuations, where the supply does not
match the demand, need to be dealt with properly to ensure that electrical devices connected
to the grid don’t receive too little or too much
power. These fluctuations can arise from variations in supply, such as when a power plant
stops producing output, or from variations in
demand, when consumers require more or less
power than is being supplied.
4
Over the period of a week at the start of
June 2011, wind turbines in the Republic of
Ireland (for which there is information readily available) produced an average output of
315 MW [5]. The average UK power demand
over a typical week is about 35 GW [6]. If the
UK were to use only wind turbines to power
the grid, the power output of the wind turbines would need to match the power demand.
In addition, there would have to be enough of
a buffer to cover periods where there is not
enough wind power to match demand. This
buffer can be formed from energy storage facilities and methods of reducing demand, as
outlined later in Section 4.
Figure 2: Typical UK weekday demand pattern.
Demand is greatest around dinner time, and lowest
during the night (data from demand on 23/10/11,
sourced from National Grid website).
2.1 Supply Fluctuations from Renewable Energy Sources
Renewable energy sources rely on highly complex physical processes which are hard to predict to a high degree of accuracy. Electricity
generation from wind, for instance, relies on
physical processes involving the interactions of
innumerable air molecules, the Coriolis force,
the Moon’s gravitational pull and thermodynamic quantities. As such, providing a predictable power output from these sources is extremely tricky (an example of the fluctuations
can be seen in Figure 3). Instead, it is necessary to evolve processes to cope with the natural fluctuations from these renewable energy
sources.
Figure 4: A simulation of UK power production
over the course of a day for a grid powered solely
from wind turbines, and a typical UK power demand profile for the same day (where the average
power generation matches the average power demand). Unscaled data taken from Republic of Ireland wind power production on 23/10/11.
Figure 4 shows a potential power production
profile for the UK, using a grid powered solely
from wind turbines. Weather fluctuations result in a varying power output, whereas power
demand remains reasonably stable. In this instance, a buffer of about 15 GW (equivalent to
the output of about 30 typical large nuclear
power reactors1 ) would be required to cover for
Figure 3: Fluctuations of the power output of wind
the worst case scenario of the lowest wind pefarms in Ireland in April and May 2011.
riod coinciding with the highest demand (occurring around 9am).
In reality, the buffer figure would need to be
even higher in order to cover longer periods
2.1.1 Periods of Low Wind
without wind, higher winter demands, periods
When wind power production takes place of low wind coinciding with peak demand, and
across a large area, localised instances of low to allow for failures in wind farm transmission
or high wind speeds somewhat average out.
1
Nuclear power plants usually have two or more reHowever, there can still occasionally be peri- actors, meaning their output level is typically around
ods of low average production when large scale 1 GW. Coal fired power plants, of which there are 18
weather patterns have an impact.
in the UK, have a typical output of about 1.5 GW.
5
devices and other parts of the National Grid.
UK demand is also likely to increase in the future as consumers move to using electric transport, heating and other uses of electricity as
dwindling oil and gas reserves push these fuel
prices higher.
a television demand pickup of the order 2 GW,
the three large pumped storage hydroelectric
facilities in the UK have a combined output
just large enough to cover this [9]. Occasionally
it is necessary to purchase power from France
and the Netherlands during this period to cover
the gap in supply [10] (see Appendix A).
An indirect way of dealing with a demand
spike from one part of the grid would be to
decrease the demand in another part of the
grid to compensate. The National Grid have
a program called Frequency Control Demand
Management which allows large consumers of
electricity such as industrial works to take part
in a system where they will scale back or halt
their electricity use by at least 3 MW for a period of 30 minutes during times of high demand [11]. The consumers are paid a premium [12] for the time they spend on a reduced
consumption. In September 2011, the cost
to the National Grid in utilising commercial
frequency control demand management contracts amounted to £7.32M for a reduction of
369 GWh (£19.82/ MWh) [13].
Figure 5: A typical TV demand pickup on Thursday 20/10/11. The left bump occurred after EastEnders finished at 8pm (a 400 MW pickup) and the
right bump occurred after Coronation Street finished at 9pm (a 200 MW pickup).
2.2 Demand Fluctuations
Other than fluctuations in supply, there can be
fluctuations in demand in addition to the reasonably predictable day to day demand variation. Occasionally, demand will undergo a
sudden change when the level of electricity consumption across the country is somewhat synchronised. So-called demand pickups can occur after popular television programmes such
as soaps or football cup final matches finish,
where demand increases by as much as 2.8 GW
[7–9] as the viewers switch their kettles on for a
post-programme cup of tea. The effect can be
seen in Figure 5 for a typical Thursday evening.
3 Characterising
Mismatch
Supply/Demand
The mismatch between supply and demand on
the grid can be characterised by the frequency
of the AC (alternating current) voltage it provides. In the UK and most of Europe the grid
is designed to run at 50 Hz [14,15]. When there
is too much power on the grid, the frequency
will be higher than this value, and when there
is too little power on the grid the frequency will
be lower [6, 12]. Typically, the frequency will
deviate within ±0.2 Hz of that value, which is
a fluctuation limit deemed small enough so as
to not damage electrical equipment plugged in
to the grid [6].
Dangers arise when the frequency drops
lower than 49.5 Hz or increases above 50.5 Hz,
as this suggests that substantially too much
or too little power is being transmitted over
the grid. National Grid PLC has statutory
obligations to maintain the frequency within
50.0 ± 0.5 Hz [6, 14] in the absence of extraneous circumstances.
Figure 6 shows the grid frequency as a spectrogram over a 10 hour period. The grid fre-
2.3 Dealing with Power Fluctuations
In order to make effective use of renewable energy sources, technologies must be developed
which can mitigate their power fluctuations.
One of the main ways the National Grid controllers can directly deal with a power fluctuation is by changing output to meet the demand,
but this can be costly and of limited use and
economy for shorter periods of increased or decreased demand. Power stations can raise or
lower their electricity production temporarily,
but this conflicts with their ideal output level
and production efficiencies can suffer [7]. For
6
than the ideal 50 Hz, there is too much energy
in the system, and when it is lower than 50 Hz,
there is too little energy in the system. It is
on this basis that the frequency can be used as
a guideline as to the current balance of supply
and demand on the grid.
The grid frequency is regulated by power
plants measuring the current grid frequency
and having their generators try to match that
frequency. For a coal plant, this might involve
regulating a steam inlet valve so as to converge
upon the current grid frequency; whereas for
a hydroelectric plant this might involve regulating the flow of water through the turbines.
In the event of a power plant going offline or a
large surge in demand creating a supply deficit,
power plants will initially provide the extra
power required by using their flywheels’ kinetic
energy. Once this is no longer sustainable, the
power plant’s frequency will drop unless measures to compensate for the supply deficit have
been put in place by grid controllers. An analogy to this is with a car using cruise control
starting to go up a hill: initially the car will
match the speed it was travelling at before the
hill, but eventually it will have to slow down if
its torque cannot match the force required to
oppose gravity.
Figure 6: A spectrogram of the mains frequency
as measured at the University of Glasgow, between
00:00 and 10:00 on 01/11/2011.
quency varies around the 50 Hz mark, keeping within the National Grid’s own target of
50 ± 0.2 Hz for the large majority of the time
in this instance.
3.1 Grid Frequency Mechanics
The mechanics of the voltage frequency produced by a power plant arises from the angular
velocity at which its generator’s rotor is spinning [15]. When the demand is higher than the
supply, the deficit is met by extracting kinetic
energy from the rotational inertia attributed
to the generator [12]. Typically, for a power
plant class generator, the demand can be met
in this way for a period of 2 to 8 seconds [16].
After this period, additional power generators
will have had to have been brought online to
cover the demand, or the demand will have had
to have been reduced to compensate.
All of the power plants synchronised to the
grid at any given moment can been empirically
represented as one generator with a flywheel
with output power and frequency equivalent to
that of the grid as a whole [17]. The flywheel’s
moment of inertia can be described as I, and
so if one knows a value for the inertia, one can
estimate the electrical energy available on the
grid through the use of the highly generalised
Equation 1.
1
E = Iω 2
2
ω = 2πf
4 Current Methods for Mitigating
Power Fluctuations
Current methods for mitigating power fluctuations primarily involve the use of additional
so-called ’peaking’ power plants – fast start
(typically gas turbine) generators which can be
turned on to meet short term spikes in demand
– and by increasing the output power provided
by power plants already in operation. In addition, the grid can purchase power from France,
Northern Ireland and the Netherlands via underwater cables [18].
(1) 4.1
Spinning Reserve
(2) A number of power plants generating electricity for the National Grid are running at only
From this we can see that the kinetic en- a fraction of their total capacity. This extra
ergy is proportional to the square of the fre- capacity is kept available in order to use when
quency f , for a flywheel with moment of iner- needed, to cover supply shortages that the base
tia I. Therefore when the frequency is higher load cannot handle. However, running power
7
plants at less than 100% capacity typically results in losses of efficiency, as generators are
usually optimised to run at full load. A typical coal plant might have a thermal efficiency
of 40% at full output, but only 35% at half
load [7]. The full efficiency is usually only
achievable after a long period of warming up
(around 8 hours), so this method is not ideal
for handling shorter term fluctuations.
electricity in its entirety from renewable energy
sources.
5.1 A 100% Renewable Energy Grid
If the UK were to move towards a state where
the power supplied to the National Grid was
solely provided by renewable energy sources
such as wind, wave and solar, a great deal of
care would need to be given towards handling
the power fluctuations these renewable forms
would produce.
The diversity of supply from renewable energy in the UK might mean that a lack of
wind in Scotland might not necessarily impact greatly on the overall power supply from
wind in the UK, as the geographical diversity
of other wind farms in the UK such as those
in Wales would ensure that adequate power is
supplied from the wind [9]. However, there can
be periods where weather patterns contribute
to low wind and low temperature across the
whole country, such as in winter. In this situation, there can be little power generation from
wind farms and high energy consumption from
domestic heating, which would create a problem in a grid designed to maximise wind power
generation. Using weather reports from January 2005, a theoretical installed capacity of
25 GW of wind turbines (just under half the
peak demand in the UK) would have placed
demand on other sources of power such as fossil
fuel plants at between 6 and 56 GW at various
points throughout the month [19].
Clearly, there is scope for a form of power
fluctuation mitigation which involves a reduction in demand rather than an increase in
supply from conventional power plants. Over
shorter periods of time, this reduction can be
met through the use of smart household appliances and a temporary decrease in the power
consumption of industrial consumers. Over
longer periods of time it would be necessary
for behaviours to change with respect to power
consumption when there is a lack of power from
renewable energy. A study by Oswald et al [19]
discusses these issues.
4.2 Short Term Operating Reserve
Certain types of electricity generators such
as gas turbine plants or hydroelectric dams
(termed ’pumped storage’) are very quick to
produce electricity when needed. The National Grid keeps a selection of these plants on
standby for use at short notice. The use of gas
turbine plants is expensive, as they substitute
fuel efficiency for a quick reaction time, a bit
like a supercharger in a race car. A gas turbine plant may only have a thermal efficiency
of around 30% [7]. A hydroelectric dam has an
operation time limited to the amount of water
available to produce electricity from, and so is
only available after a suitable resupply period
after its last use, and for a limited amount of
time.
4.3 Frequency Response
The National Grid has commercial agreements
with large electricity consumers to limit their
use in times of high demand or low supply
where spinning reserves are unable to cope [12].
Consumers willing to take part in this are most
likely to have electricity use patterns which
aren’t adversely affected by short periods of
loss of supply, such as steel works (where the
furnace can take days to heat up with an induction heater) or cold storage facilities.
5 Mitigating Power Fluctuations in
the Future
The use of fossil fuels for providing power is
ultimately unsustainable as increasing populations and energy usage put more strain on nat5.2 Demand-Side Management
ural resources. This fact, along with government legislation, means that there might come Methods for reducing demand involve the use
a time when the National Grid must supply of buffers in order to temporarily prevent
8
devices that consume electricity from taking
power from the grid, instead continuing their
operation by using its own internal supply of
energy. Such buffers can take the form of flywheels (such as those used in kinetic energy
recovery systems in turbines), pumped storage using gravitational potential energy and
also even batteries in laptops – an application
which will be investigated later. Devices with
buffers can be designed to use grid electricity
only when there is a supply surplus, and not
when there is a deficit.
Managing demand in this way is called
demand-side management.
A number of
demand-side management techniques exist,
such as:
grid frequency, such that periods of grid deficit
increase the temperature at which they start to
cool the contents. Typically, these fridges can
maintain the internal temperature within safe
limits for up to an hour before being forced to
switch back on again regardless of the state of
the grid. This window gives the National Grid
controllers some time in which to get reserve
power systems online. If every fridge in the
UK (estimated to be around 40 million [23])
were to use this kind of technology, savings of
between 500 MW [24] (roughly equivalent to a
hydroelectric power plant) and 1200 MW [23]
could be achieved for up to an hour at short
notice. Figure 7 shows the possible effect this
deferral could have.
• Smart appliances, such as air conditioners [20], which either monitor the grid frequency for an instantaneous measurement
of the supply-demand balance or receive
information as to the current grid load via
radio, internet or other means. They use
this information to limit their power use
in times of high demand or low supply.
• Flexible tariffs, smart electric cars
and fridges, as outlined in the following
sections.
5.2.1
Figure 7: An example of the effect that dynamic
demand management in fridges could have on the
electricity demand over the course of a few hours.
During a period of high demand, fridge energy consumption is deferred until a later time. Image taken
from Stabilization of Grid Frequency Through Dynamic Demand Control (Short et al. [12]).
Smart Electric Cars
Various proposals exist for the charging of electric cars using systems which sense the availability of electricity on the grid [21]. Some systems even propose using electric cars as electricity supplies, releasing power back into the
grid when required, leaving enough stored for
5.2.3
the owner’s own usage [22].
5.2.2
Consumer Incentives
A number of demand-based tariffs have been
trialled around the world, from a lower ’Economy7’ tariff used in Europe for 7 hours during
the night [25] to an hour-by-hour rate used in
parts of Florida [26].
In France, the high proportion of nuclear
power plants, which are best utilised in providing a constant output over long periods of
time, has led to incentives for consumers to use
more of their electricity during the night. A
higher proportion of French homes are heated
with electricity than gas, using night time storage heaters, due to the incentive created by
Smart Fridges
Refrigerators with built-in controllers monitoring the grid frequency in order to decide when
to initiate refrigeration have been tested and
have shown positive results [12]. Fridges typically have two threshold temperatures: one
temperature above which the fridge will turn
on its compressor to cool the contents, and
another temperature below which the fridge
will turn off its compressor to stop cooling the
contents too much. Smart fridges scale these
threshold temperatures by the inverse of the
9
cheaper night time electricity [9]. This helps
to move some of the peak load during the day
into the night.
A study conducted in a German town [22,27]
showed that electricity tariffs which incentivise
use of electricity outside of peak times can have
an impact in reducing peak load during the
day, but failed to fully mitigate large spikes
in demand. In the study, a larger than normal spike occurred around the border between
a low tariff and a medium tariff time, which
had the opposite effect of mitigation in that
instance; though the use of electricity during
the day when demand is provided by the base
load was lower.
its internal battery, it must know the state of
the grid, as reflected in its frequency.
A real time digital control system running a
Linux operating system was used to make this
decision. The mains alternating current was
measured by using an AC transformer, which
stepped the voltage down from 230 V to 9 V.
This allowed the mains input to be safely measured by the control system.
A set of frequency time series values collected over the course of 24 hours using the
system was compared against the corresponding 24 hours worth of frequency measurements
as reported by the National Grid website, using data obtained by the Java program written
primarily to log the grid demand earlier. The
data matched up well, meaning that the mains
6 Project Aims
signal received by the digital control system
was unperturbed by other equipment nearby
This project will focus on the development of
and the data was being logged correctly and
concepts for mitigating power fluctuations usinstantaneously. The results can be seen in
ing demand-side techniques, to allow for a reFigure 8.
duction in the amount of standby fossil fuel
power plant capacity required.
To investigate methods for mitigation, a system to control the charging of a laptop which is
responsive to changes in grid frequency will be
investigated, using mains frequency sensitive
hardware and software methods. The reason
for this is that laptops are widespread already
and so would not need major capital investment, and the potential impact is significant. Figure 8: A comparison between the frequency as
To assist in quantifying the effect that such measured by Fourier transforms conducted on data
a system might have on a future grid, a model collected by the digital control system and the frewill be developed to provide information about quency as reported by the National Grid website.
the forms and magnitudes of the fluctuations The National Grid website reports frequency values
that might be present in such an environment. which may be up to three minutes out of date, so a
systematic correction has been applied to this data
in order for it to overlay the corresponding control
system measurements correctly.
7 Prototyping, Testing and Characterisation of a Smart Charging
Laptop
7.2 Frequency Measurement Technique
The frequency of the input signal could have
been measured directly using Fourier transforms (like that which has been done in Figure 8). However, these operations are computationally expensive and require a lot of memory. For the purposes of this project, it is
7.1 Measuring the Grid Frequency
also true that it is only necessary to know
In order for a laptop to effectively judge when whether the input frequency is above or below
to use power from the mains and when to use the threshold in question, 50 Hz. Therefore, it
The simple case was considered of a laptop
which charges when the grid frequency is above
50 Hz and uses its battery when the grid frequency is below 50 Hz.
10
was more practical to use a differential band
pass filter method in order to obtain such information.
changing sign too quickly when the grid
frequency is quickly fluctuating above and
below 50 Hz
• The signal is input into a comparator,
which outputs from the control system a
high signal (in this case, 4 V) if the signal from the previous step is negative, or
a low signal (in this case, 0 V) if the signal
is positive
The Method Used in order to Measure the State
of the Signal
• The voltage signal from the National Grid
is stepped down from 230 V to 9 V
• This signal is passed through an analogueto-digital converter, then passed into the
digital control system, where the signal is
sampled a frequency of 4096 Hz
• The signal is passed through a band pass
filter with range 45-55 Hz to eliminate
any frequencies well outside the possible
range2 , such as higher frequency harmonics and background noise
• The signal is split into two copies, with
the first copy passing through a band pass
filter with a range of 48-50 Hz, and the second copy passing through a band pass filter with a range of 50-52 Hz
• Both signals are saturated so as to become
square waves
• The signals undergo a half wave rectification and the absolute value is taken, so as
to result in the signal being directly proportional to the signal the bandpass filters
pick up
• The signals are passed through a low pass
filter with corner frequency 1 Hz, so as to
remove any fast transients
• The signal that passed through the higher
bound band pass filter is then subtracted
from the signal that passed through the
lower bound band pass filter, thereby giving a value which, if positive, represents
an underlying frequency below 50 Hz, and,
if negative, represents an underlying frequency above 50 Hz
• The signal is passed through a low pass
filter of 0.1 Hz to prevent the signal from
2
The National Grid frequency very rarely falls below
the 49.5 Hz or rises above 50.5 Hz, so this limit is very
lenient.
• This output control signal is amplified by
a factor of 6
• The amplified signal is input into a Form
A (’normally open’) relay requiring a control signal potential difference of 20 V to
switch on, which drives the laptop charger
circuit
Notes: The frequency filters used are digital versions; and the output signal from the
control system has to be amplified because of
constraints on the ability of the control system
to output larger voltage signals.
The differential band pass filter method is
effectively comparing a signal with frequency
components below 50 Hz with a signal with
frequency components above 50 Hz. At any
given time, depending on the frequency of the
grid, one of these signals should be zero. This
means that, upon subtracting one signal from
the other, the polarity of the resulting signal
will represent whether the frequency of the grid
is above or below 50 Hz.
A flowchart of the process can be seen in
Figure 9. Characterisation measurements conducted with a calibrated frequency generator
showed that this method has a resolution of
about ±1 mHz.
7.3 Verifying the Frequency Measurement
Technique
In order to check that the frequency was being correctly measured, the input signal and
the output control signal were measured for a
period of 24 hours. A frequency spectrogram
was then produced using Fourier analysis performed on the input signal data, in order to
show the frequency components present in the
signal. In order to retrieve the underlying grid
frequency from this spectrogram, the frequency
11
harmonic with the strongest amplitudes was
chosen (in this case 250 Hz) and the maximum
value at each time interval was obtained. This
maximum value represents the grid frequency
signal, or in this case, the 5th harmonic. The
frequency was obtained simply by dividing the
harmonic by 5.
Figure 10 shows the input and output signals from the control system alongside the frequency of the input signal as produced from
performing spectrogram analysis. It is clear to
see that the control system output the correct
control signal for each input measurement.
Figure 10: A comparison between the control
system’s input signal frequency (as inferred from
Fourier analysis) and the control system’s output
signal. It can be observed that when the input signal frequency is above 50 Hz, the output signal is
high, and when the input signal frequency is below
50 Hz, the output signal is low.
7.4 Laptop Battery Performance
Figure 9: The control flowchart for the dynamic
demand laptop system. The signal is taken from
the National Grid, stepped down from a voltage of
230 V to 9 V, converted to digital form and analysed
by the control system. A control signal is then output by the control system which in turn operates
the relay. The relay then governs the charging of
the laptop from the mains. The area shaded blue
is part of the control system and the area shaded
red is the National Grid.
The system would be useless unless it was able
to allow the laptop of interest (we have arbitrarily chosen a Dell Latitude E5510 provided
by the university) to be used in a way it would
have been used without the dynamic demand
system. It was therefore necessary to measure
the laptop’s battery charge as a function of
time when using the smart charging technique.
In order to make such a measurement, a shell
script was written to continuously run using
a terminal within the Ubuntu Linux operating system installed on the laptop, logging the
state of the battery obtained from a special
battery monitoring program to a text file.
The results of the 24 hour experiment can
be seen in Figure 11. The laptop managed to
retain some level of charge for 23 hours, until a particularly long period of grid frequency
below the 50 Hz threshold caused the laptop’s
12
battery life to eventually drain. A full comparison of the whole system in operation can be
seen in Figure 12.
For the sake of comparison, the same test
was also performed on an Apple laptop for a
period of 7 hours using a modified terminal
script for Mac OS X. It was reasoned that the
Apple would give a useful comparison as its
battery life is much longer than that of the first
laptop. The results from this experiment can
be seen in Figure 13. Clearly, the Apple laptop’s ability to charge much more quickly than
it discharges and its larger buffer size helped it
remain on throughout the test, but a longer period of measurement would have proved these
outcomes more conclusively.
Both shell scripts can be seen in Appendix C.
Figure 11: The charge level of a Dell laptop during
a 24 hour experiment using the dynamic demand
control system. The laptop manages to keep some
charge for 23 hours before a long period of low grid
frequency results in the battery draining.
Figure 13: The charge level of an Apple Mac laptop during a 7 hour experiment using the dynamic
demand control system. The laptop manages to remain operational throughout the test, though the
test might not have been long enough to conclusively prove that this will always remain so.
Rather than comparing the two laptop simulations in terms of their rates of charge and
discharge percentage points per second, which
would inherently rely on each laptop’s battery
capacity, a more reliable figure for comparisons
is the ratio between the relative rates of charge
and discharge. This value denotes how quickly
the laptop charges with respect to how quickly
it discharges (see Equation 3).
R=
Rate of Charge
Rate of Discharge
(3)
The Apple laptop charges more than twice
as quickly as it discharges, whereas the Dell’s
rates of charge and discharge are lower. This
means that, during the experiment, the Dell
was more susceptible to running out of charge
during prolonged periods of below-threshold
frequency.
It is clear that the value chosen as a threshold frequency has a big impact on the lifetime
of a laptop plugged into the control system.
While a threshold of 50 Hz should theoretically
allow for the same amount of time spent charging as time spent discharging in the control system (so long as the National Grid controllers
manage to maintain an average frequency of
50 Hz), the individual periods in which the
grid frequency is above or below 50 Hz can last
longer than the battery lifetime of a laptop like
the Dell. In a situation where a long period of
frequency lower than the threshold occurs, this
can result in the battery draining and the laptop becoming non-operational.
For a laptop that is similar to the Dell, it
is reasonable to make a compromise by lowering the threshold used to make the decision
of whether to charge the laptop or not. A
lower threshold frequency means that, in principle, the controller will output a high signal
for longer periods than it will output a low signal. This in turn means that the laptop should
spend more time charging than it does discharging, ensuring that it retains a high level
of buffer capacity. In contrast, laptops which
can charge much faster than they discharge,
like the Apple Mac, will probably not need to
have the threshold lowered as far (or at all).
7.5 Laptop Battery Simulations
In order to find a value for the threshold which
is adequate in terms of keeping the laptop functional but which is also still high enough to provide a reasonable amount of mitigation, it was
easier to simulate the charge level of the laptops. This prevents having to run experiments
13
Figure 12: Combined version of Figures 10 and 11, related to the smart charging system using the
Dell. The top graph shows the grid frequency during the 24 hour test, the middle graph shows the
corresponding control signal output and the bottom graph shows the laptop’s charge level for the same
time series. The laptop manages to remain operational until 23 hours into the experiment.
Laptop
Dell
Mac
Ratio
1.8522
2.2857
reached this threshold in order to help compensate for this effect.
Finally, the charge level simulation was
undertaken using the same set of frequency
Table 1: A list of the rates (percentage points
data collected in the experiment.
The
per second) of charge, RCharge , and discharge,
charge/discharge
rates
were
perturbed
until
RDischarge , and the ratio between the two.
the simulated charge levels closely matched the
real levels. The result can be seen in Figure 14.
with different thresholds, which, if to be conducted thoroughly, would have taken weeks. It
also allows for better comparability of results,
as the same frequency measurement time series can be used for many different variations
on the same experiment.
The first step in creating a simulation was
to measure the rates of charge and discharge
of each laptop, by approximating the gradients
of the charge rates from the logs obtained in Figure 14: A comparison between the real and simSection 7.4. These approximations are listed ulated charge levels during the 24 hour test. As
can be seen, the simulation successfully recreates
in Table 1.
the charge levels experienced in reality to a good
However, it can be seen from the charge degree. The points at which the simulation over
graph that the Dell laptop appears to slow or underestimates the charge can be attributed to
down its rate of charging as the charge level slightly incorrect charge/discharge gradients which
surpasses 90-95%. This might have been due might arise from effects governed by the real batto a mechanism built into the battery itself, tery’s microcontroller.
or from a software controller, in order to preserve long term battery performance. RegardThree weeks of frequency data was then obless, this change affected the naive assumption tained using a purpose built Java program,
of linear charge and discharge rates. A correc- developed by modifying the previous demand
tion factor was applied to charge rates as they logger program to instead log frequency data
RCharge
0.0213
0.0128
RDischarge
0.0115
0.0056
14
to higher (15 second) resolution (see Appendix B.2). This data was used in the simulations so as to provide a conclusive result as to
whether or not a particular threshold frequency
would allow a laptop to stay operational at all
times.
Simulations using different threshold frequencies below 50 Hz were then undertaken.
Histograms of five of these thresholds are
shown in Figure 15. From these histograms it
is easy to see which threshold frequencies will
allow the laptop to remain operational, and details of the range of expected charge percentages are also readily apparent. For instance, a
threshold frequency of 49.955 Hz always keeps
the laptop in the charge range 60-100%.
The best compromise with the Dell seemed
to be to use a threshold of 49.975 Hz. Figure
16 shows a time series of the charge level in
the laptop, simulated over a period of 21 days
using this particular threshold. It is clear to see
that the laptop charge does not fall below 40%,
and typically stays within the 70-100% charge
level, allowing the user to disconnect from the
mains if required and still have an adequate
charge level for laptop use on the move.
data obtained using this method was useful for
quantifying the demand on the scale of a week
(the length of time the program was operational), but it was not ideal for measuring the
effects of longer demand variations due to the
need to log data in real time. It was therefore
favourable to obtain historical data so as to be
able to demonstrate the long term variations
in demand.
At the beginning of 2012, the National Grid
made available demand data to half-hour resolution for all of 2011. A visualisation of this
data can be seen in Figure 1. From this it was
possible to then obtain a figure for the average power demand over the course of the year,
which could be used to calibrate future models.
Figure 16: A time series of the simulated charge
levels over a period of 21 days using a threshold
frequency of 49.975 Hz. The charge level does not
drop below 40% and typically stays in the 70-100%
region.
8.2.1
8 Large Scale Impact of Smart
Charging Laptop
8.1 Quantifying UK Grid Demand
Initially, in order to quantify the UK demand,
a computer program was written which logs
the UK demand to five minute resolution (and
UK grid frequency to 3 minute resolution) as
reported by the National Grid website. The
8.2 Entirely Renewable Grid
Due to the lack of substantial, high resolution
data available regarding the fluctuations that
would be present in a grid powered solely from
renewable energy sources in the UK, such fluctuations can only be simulated.
In order to simulate this future grid, some assumptions must be made as to the form such a
grid would take. The arbitrary assumption was
made that the grid would be powered solely
from wind and solar energy sources, and that
the split would be equal between the two, such
that the average production from each source
equalled half of the average demand.
Demand Model
A model of the demand in 2011 was produced
using the data obtained from the National
Grid, with a resolution of 30 minutes. It was
possible to use 2011 data as long as it can be
assumed that demand will not differ greatly in
2020 from that in 2011.
From Figures 1 and 2 it is clear to see that
demand is greatest during the day around dinner time and lowest during the early hours of
the morning. Throughout the year, the demand is greatest in winter and least in summer.
On average, demand for 2011 was 35 GW.
At its peak it was 55 GW, and at its lowest it
was 19 GW.
15
Figure 15: A set of histograms of laptop charge simulations over 21 days, each with a different frequency
threshold. This shows the amount of time spent in each charge percentage range for each threshold value,
in steps of 10 percentage points. It is clear to see that a threshold of 49.975 Hz or less would ensure that
the connected laptop keeps at least 40% of its battery charge.
8.2.2
Wind Model
8.2.3
Information with regards to the wind power
production in the United Kingdom was not
readily available. The Irish electricity grid operators, EirGrid, however, provide details of
Irish power production from wind on their website and so it was possible to obtain from them
an entire year’s worth of data to 15 minute resolution for 2011.
Making the assumption that weather patterns affecting wind, particularly from the Atlantic Ocean, are broadly similar in the UK and
Ireland, it was possible to scale the wind power
production values obtained from EirGrid to a
level at which they would represent the power
production required to meet 50% of the average
demand in the UK grid (i.e. 17.5 GW). This is
equivalent to about 65 GW3 of installed capacity. This assumption is likely to represent the
worst case scenario, as one would expect better
averaging of wind fluctuations due to the larger
land mass in the UK with respect to Ireland.
3
This is equivalent to about 10000 of the largest current wind turbine designs.
Solar Model
It became apparent that comprehensive solar
production data was not available for the UK,
and so it was necessary to develop a simulation
of such data.
Data was obtained for the times of sunrise
and sunset on a daily basis for 2011. From
this information it was possible to calculate the
time of solar noon—the time at which the Sun
is highest in the sky directly above the line of
longitude on Earth in question—for each day.
Additionally, information about the angle the
Sun made with respect to Edinburgh on a daily
basis was also used in order to correct for the
fact that the latitude being considered was not
on the equator (and thus receiving the greatest power density), and to account for the seasonal variation of this angle. Both sets of data
were obtained from the UK Hydrographic Office [28], and the Sun angle data included corrections for atmospheric extinction.
The solar intensity for a given day was
then modelled using a Gaussian curve centred
around the solar noon. The full width at 5%
of maximum was calculated such that it coincided with sunrise and sunset, resulting in the
intensity being very low during the night, as
16
intended. It was assumed that sunset and sunrise provide less than 5% of the maximum solar
noon value, with a small amount of light coming from atmospheric refraction, moonlight, artificial light and other sources.
The effect of clouds was then modelled by
introducing ten intensity correction factors of
varying frequency, which had the effect of reducing the intensity of light incident on the
ground on the scale of minutes, hours, days and
weeks. Each ’cloud’ had an associated intensity correction factor between 0 and 1, which is
multiplied with the intensity value associated
with the solar model for a given time step to
produce the actual intensity of light through
the cloud. Each cloud had a different correction factor, had an effect for different lengths
of time and recurred on a different frequency.
The result was that it was clear to see significant drops in intensity throughout the day as
these ’clouds’ covered the Sun in the sky, which
allowed the model to better represent reality.
Finally, the actual power generated P from
a square meter A of solar panels was calculated using Equation 4. This used the model’s
calculation for solar intensity for a given time,
I, along with the power of sunlight incident
on the equator (�, the solar constant, roughly
1.3×104 kW/m2 ) and the efficiency of the solar
cells, η, which was assumed to be 40%4 . The
area of panels was scaled in the same way as
the wind model so that the average power produced in a year was equal to half of the average
demand for a year.
P = η�IA
(4)
As can be expected, the intensity of the
Sun as reported by the solar model is greatest
during the summer months, when the Northern Hemisphere is tilted towards the Sun, and
greatest during solar noon each day when the
sunlight is as close to perpendicular as it can be
to the ground it is incident upon. In any particular day, clouds affect the power production
to different degrees. A typical three day period modelled for September 2011 can be seen
in Figure 17.
4
Current commercial solar panels only offer an efficiency of about 10%, but prototypes have been shown
to demonstrate an efficiency upwards of 40% [29], which
is why this figure was used for the 2020 model.
Figure 17: Solar model of three days in September 2011 using data regarding the angle of the Sun
with respect to Edinburgh, with the effect of different clouds factored in. The intensity axis is with
respect to the intensity at the equator.
8.2.4
The Combined Model
The wind and solar models combined would
provide, on average, enough power to exactly
meet demand for a year like 2011.
In order to combine the two energy sources
in the renewable grid model, and to then have
a discrete value for the surplus or deficit supply
power for each time interval throughout 2011,
it was necessary that each set of data used the
same resolution. The solar model was created
in such a way that it could generate a data
set with any resolution necessary as long as
the set of cloud frequencies were recalibrated
to remain realistic. The wind data available
from EirGrid was of 15 minute resolution, and
the demand data from National Grid was of 30
minute resolution.
Both the demand and wind models could be
interpolated so as to fit the 15 or 30 minute resolution of the other, but it was decided that it
would be better to use as high a time resolution
as possible5 . It was also reasoned that artificially increasing the resolution of the demand
model would result in less loss of information
than in decreasing the resolution of the wind
model by averaging neighbouring values, due
to the diversity of demand, and because the
various supply ’spikes’ in the wind supply ’signal’ appeared to be of higher frequency than
the spikes in the demand ’signal’. This is to
say, the wind supply model is more stochastic
than the demand model.
The demand model was interpolated by a
5
A 15 minute resolution would allow more short term
fluctuations to be seen than a 30 minute resolution
would.
17
factor of 2 in order to achieve the required resolution. The time vector used to specify the
30 minute intervals for the demand model had
extra time values inserted in between the existing values. A linear interpolation was then
used to calculate the respective demand values
for the new time intervals.
The resulting set of wind, solar and demand
models were then combined into one model,
with the wind and solar supplies being subtracted from the demand for each time interval. This provided a new set of power values
denoting the deficit or surplus of power supply
for each time interval. This new model can be
seen for a particular day in Figure 18.
ure 20. In this situation, low winter solar power
production coincided with a period of low pressure weather, causing a lack of wind along with
cold temperatures, creating a huge deficit in
supply.
Figure 19: A histogram of the periods the renewable grid model spends in deficit (i.e. not enough
supply to cover demand). For the most part, these
periods are of the order an hour, but there are some
periods of significantly longer deficit which must be
considered as part of the solution to the problem of
mitigating power fluctuations in a renewable grid.
Figure 18: An example of the power surplus and
deficit throughout a typical day in February 2011
using the combined renewable grid model. The
green shaded areas are times when there is more
production than demand, and the red shaded areas
are times when there is too little production for demand. The right hand side of the image shows a
power axis in terms of conventional 500 MW power
plants, thus around 7:30pm on this day, the deficit
would require 40 of these power plants in the ab- Figure 20: An example of a rare period where the
sence of mitigation techniques.
renewable grid model is at a deficit for a period
greater than around 5 hours. The deficit here lasts
The largest single deficit in the model was 47 hours, from around 2pm on the first day to
61.9 GW which occurred on 21st Jan 2011 at around 1pm on the third. The deficit region in
5pm. The wind and solar power production at question is shaded red. A few hours after the deficit
period ends, the renewable grid model is back in
that time was 0.87 GW and 0.08 GW, respec- deficit again as solar noon comes and goes, which
tively. Occasions like this where peak demand would leave little time for buffers to replenish.
coincides with darkness and lack of wind are
common so long as sunset occurs before peak
demand. On this particular day, sunset occurred at 4:22pm.
A histogram of the periods of deficit is shown
in Figure 19. This shows that deficits typically
last less than 2 hours. However, there are some
longer periods, and there are two occasions in
the year where the period of deficit lasts significantly longer. The longest deficit lasted
46h45m, occurring between January 16th and
January 18th 2011. This can be seen in Fig-
8.3 Entirely Renewable Grid with Dynamic
Demand Laptop Charging
In order to quantify the effect that the dynamic
demand laptop control system would have on
an entirely renewable grid as a whole, it was
necessary to simulate the effects of a large number of laptops using this system.
To achieve this, it was necessary to combine
the parts of the project as presented in Sections
18
8.2 and 7. Ideally, in a situation like the one
shown in Figure 18, the area under the curve
during periods of supply surplus would cancel
the area under the curve during supply deficit,
as shown in Figure 21.
Figure 21: An example of the situation shown in
Figure 18, but with some form of mitigation being
used to cancel the surplus and deficit, just like in
Figure 7.
8.3.1
Large Scale Laptop Model Assumptions
8.3.2
Simulation Mechanism
In the model, 1000 entities represent 20000 laptops each, so as to simulate 20 million. The
model was designed in this way for performance reasons6 .
For each time step, which took the form of
each 15 minute period in 2011 as defined by the
renewable grid model, each of the 1000 laptops
had its battery charge level calculated based
on its previous value and the state of the grid
(i.e. whether or not the frequency was below
or above 50 Hz). If the battery had to charge,
then the power the laptop used in doing so was
added to the renewable grid model’s total demand for the next time step. Otherwise, the
value was subtracted (representing an occasion
where the laptop uses its buffer).
Finally, the new grid model was obtained by
running this model using the desired data, either for the whole year or for individual days.
Figure 22 shows the effect that the laptop grid
model has on a particular day, in terms of the
power successfully mitigated from periods of
supply deficit. At about 6:30am, the grid was
running at a surplus and so this extra power
was used to charge the 20 million laptops’ batteries. This resulted in a reduction in the
amount of surplus being produced, as of course
the laptops had used some of this power. After
about 45 minutes, the grid stopped running at
a surplus and instead entered a period of power
deficit. The 20 million laptops then started using their batteries, removing their power demand from the grid, and so the total amount
of power deficit was reduced. This whole process repeated three more times later in the day,
and to a larger degree. The slopes at the top of
the mitigation peaks are due to some of the 20
million laptops running out of charge. Clearly,
longer periods of deficit would result in almost
all of the 20 million laptops running out of
charge, so this technique cannot mitigate all
occurrences of grid deficits, particularly those
lasting longer than about 4 hours.
Notice in Figure 22 that the power scales on
the vertical axes are of different orders of magnitude. Clearly, the effect of 20 million laptops
In order to create a large scale laptop use
model, some assumptions had to be made. It
was assumed that there were around 20 million
laptops connected to the mains and capable of
using this system in the United Kingdom, and
that each laptop had a certain usage pattern
throughout the day. Laptops have different
charge and discharge rates, and different battery lives, which was also taken into account.
The assumption was also made that at the start
of the simulation, laptops will have a random
level of charge between 0 and 100%.
The charge and discharge rates, battery
lives and laptop use times were calibrated using pseudorandom numbers which, on average,
form a Gaussian distribution. The Gaussian
waist and standard deviation for the laptop
use times were defined so as to have a mean
of 6 hours, but with a range of time lengths
between 3 and 9 hours falling within one standard deviation, to represent the different ways
in which people use laptops. Similarly, for the
operational times, the time half way through
the laptop’s use in one day was again defined
using a Gaussian distribution, with the midtime falling around midday. The charge rates
6
The size of the matrix representing the model is
were distributed around the value for the Dell about 1 × 108 (about 800 megabytes of double precision
laptop. Histograms of these calibration factors floating point variables) as opposed to about 2 × 1012
can be seen in Appendix D.
(about 15 terabytes) by doing this!
19
Figure 22: Power mitigated using the laptop grid model during a day in July. Periods of grid surplus are
used to charge the laptops’ batteries (regions shown in green), and periods of grid deficit are mitigated
by making the laptops use their batteries instead of the mains (regions in red).
’unplugging’ is small in comparison to the entire grid. However, the amount of power mitigated in this case was still significant—about
500 MW at its peak—equivalent to a large
power plant. Over the whole year, the greatest effect of mitigation was 764 MW, equivalent
to about 1.5 power plants. The average power
mitigated when the grid was in a supply deficit
was 237 MW, equivalent to about half a power
plant.
9 Discussion
9.1 Grid Models and Simulations
9.1.1
Wind and Solar Models
The assumption of 50% production from wind
power in the wind production model is equivalent to about 65 GW of installed capacity.
David MacKay [30] estimates the power per
unit land area of a wind turbine to be about
2.2 W/m2 . Therefore, to achieve this installed
capacity, the area of land required would be
about 29000 km2 , or about 12% of the total
land area of the UK (244000 km2 ). Whether
this figure sounds reasonable or not is up for
debate. While land containing wind turbines
can be used for other purposes, such as farming, there are objections along the lines of
noise, aesthetics and other reasons. A discus-
sion of these issues can be found in the MacKay
book.
The area of solar panels required to produce the 50% equivalent supply would total
about 693 km2 . This figure is reliant upon the
cloud simulation characteristics, and is thus
only an empirical estimate. The model was created with the assumption that the solar panels
would have an efficiency of 40%, which is equivalent to the performance of the most cutting
edge prototypes today, and not the standard
mass production designs in 2012. A more realistic figure might be about 10%, meaning the
area required for the same production would
be quadruple. However, there is little to stop
the use of solar panels on the same land as
wind turbines, except to remove the ability
to use the land for farming and other means.
Wind turbines cast little shadow and solar panels don’t affect the wind above a few metres
off the ground. The MacKay book estimates
the area of south facing roof space in the UK
available for solar panels to be about 10 m2 per
person, or about 600 km2 in total. Therefore,
covering every roof in the country and using
some dedicated spaces for solar panels would
theoretically allow for this production. Again,
whether this is plausible is up for debate.
In the solar model, the difference between
the average production in summer and winter
is huge—about 40 GW, equivalent to the aver-
20
age demand in the year. The purpose of this
project was to look at ways of mitigating power
fluctuations without having to resort to methods such as those outlined in Section 4. If the
long term difference between summer and winter output from solar necessitates the use of
a set of standby power plants to make up the
difference, this would counteract the effect that
the mitigation techniques would have in reducing the need for these power plants. Clearly a
renewable grid would need to be carefully designed in order to prevent the need to resort to
using the ’old ways’ for parts of the year, and
relying so much on solar power is probably not
the way forward.
The two renewable energy source models –
the wind and solar models – were produced using some assumptions as to how they would
perform as an energy source. These assumptions are accurate enough to obtain ’ball park
figures’ for the kind of outputs these sources
might produce throughout a year, but they are
not accurate enough to be used for any sort of
precision modelling. The models can be improved in many respects by using higher resolution underlying data, more accurate calibration factors such as cloud effects by basing them on real observations, and by factoring
in regional geographical variations which affect
the ability to produce electricity.
9.2 Laptop Controller Experiment
The differential band pass filter method used
to obtain the state of the grid is very effective,
with accuracy to within about 1 mHz. Noise
present in the signal is also suppressed in the
stage which subtracts one copy from the other.
Although the method provides information as
to whether the frequency is above or below the
chosen threshold, it does not give an estimate
as to how far away the frequency is from this
point. Such information would be useful for
differentiating between slight and major shortfalls in demand. If this information was available, it would be possible to vary the threshold
at which the laptop system would stop taking power from the grid. For instance, if the
laptop’s battery was below, say, 50% full, the
control system could decide to only stop the
laptop from taking power from the grid when
the threshold fell below a lower threshold, ef-
fectively helping the grid only when the grid
really needs the help.
The results regarding the laptop controller
are proofs of concept. The best frequency
threshold for a particular laptop depends on a
number of variables, including the battery life,
rates of charge and discharge, and whether or
not the laptop has a high CPU load when in
use. A desirable situation would be where the
user decides the threshold they wish to use,
taking account of their personal needs. A laptop that stays ’plugged in’ to a wall socket at all
times can safely use a high threshold, whereas a
laptop that is frequently used on the move will
need to retain a high level of battery charge
level at all times for such circumstances. Similarly, a fail safe mechanism would need to be
built in to end user systems to prevent long periods of frequency below threshold from draining the laptop battery entirely, with the potential for data corruption and loss of productivity.
9.2.1
Long Term Effect on Laptop Batteries
An issue that has not been considered in this
study is the long term effect frequent charge
and discharge cycles can have on laptop batteries.
When a lithium-ion cell charges, typical of
those used in modern laptops, deposits are
formed inside the electrolyte, which increases
the internal resistance of the battery and decreases its ability to store energy over time.
This means that frequent charging will limit
the life of the battery.
Tests of a lithium-ion cell showed that the
battery life is influenced mainly by the conditions in which the battery is charged, and
not so much the discharge conditions [31]. In
particular, over-charging, where the battery is
charged beyond its design capacity, is a large
factor in the degradation [32]. This means that
it would be reasonable to build some logic into
the controller which takes into account the conditions in which the laptop would be charging.
These conditions could be elements such as the
ambient temperature, current charge level (for
instance, whether or not to charge if the battery is already almost full), or other factors.
21
9.3 Project as a Whole
For certain periods of power deficit of the order
a couple of hours long, it is not inconceivable
to suggest that this kind of deficit could be
dealt with through some forms of demand-side
management such as the laptop control system
outlined as part of this study. The particular laptop system developed in this study was
able to mitigate an average of 237 MW of demand when used by 20 million devices, which
represents about 0.6% of the total average UK
demand. If all of our electronic devices, such
as lights, heating and cooling systems, electric
cars, ovens, desktop computers, televisions and
so on implemented a similar form of demandside management, the power available for mitigation at any one time would become even
greater. A combination of these devices shutting down in tandem might be enough to mitigate some of the fluctuations that a grid powered from renewable energy sources might create, and thus there would no longer be the need
to build so many cost intensive ’peaking’ power
plants7 .
However, demand-side management techniques are not quite so useful for longer periods
of supply deficit, and in a grid powered from
a high proportion of volatile renewable energy
sources such as wind, such periods of supply
deficit will occasionally occur. Again, if the
grid is to involve a greater amount of energy
sources such as wind, models must be in place
to deal with the loss of production during periods of low output. In particular, it is worth
focusing on the scenarios where long periods of
low wind coincide with high periods of demand,
such as in winter and in the evening.
9.4 Future Work
crocontroller could also be used in other applications.
The models developed as part of this study
can be improved as outlined in the discussion. With higher resolution data, it could be
possible to undertake different forms of analysis, such as looking at the fluctuations from
a frequency domain perspective. This method
would have the potential to provide information about the magnitude and frequency of
different fluctuations present in the renewable
grid model, which would be useful for better
coordinating mitigation techniques.
10 Conclusions
The concept for power fluctuation mitigation
using laptops as outlined in this study shows
promise. Although the simulations by no
means fully represent reality, which is vastly
more complex than the basic assumptions
made in the model allow, the concept has been
clearly demonstrated to have a positive effect.
This technique, along with some of the conventional methods of mitigation, could stand
the National Grid in good stead to face the
challenges of the future, in terms of quality of
electricity service, environmental impact (the
potential for a reduction in traditional gas and
coal fired plants) and cost (not having to pay
upwards of £7M a month for industries to temporarily cease electricity consumption).
In the future, the concepts developed as part
of this study can be applied to other forms of
buffers, which, working in tandem, can provide
a valuable means of demand reduction during
times of supply deficit.
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In the future, it would be worth exploring ways
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to the charger’s transformer box. Such a mi7
A typical new 500 MW nuclear power plant costs of
the order £1.5B to build [33].
22
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24
Appendices
A Interconnector Power Imports/Exports Sample
The UK grid has interconnectors with neighbouring grid networks. Within the British
Isles, there are interconnectors between Northern Ireland and Scotland, Northern Ireland
and the Republic of Ireland, the Isle of Man and England, Scotland and England, and
North England and South England. Outwith the UK, there are interconnectors between
the UK and France (2 GW), and the UK and the Netherlands (1 GW). There are also
preliminary plans to create an interconnector between the UK and Norway (see http:
//en.wikipedia.org/wiki/Scotland-Norway_interconnector and http://en.wikipedia.
org/wiki/HVDC_Norway-Great_Britain) by 2020. Figure 23 shows the French and Dutch
interconnectors in use.
Figure 23: National Grid interconnector imports and exports on 24th October 2011. Throughout the
day the UK buys power from France and the Netherlands, except for a period in the morning when the
UK sells power in the other direction. Interestingly, there is a period during the day when the UK grid
imports power from the Netherlands and exports power to France at the same time.
B National Grid Website Logger Program
B.1 Initial Program
Real time data is published on the National Grid’s website, on this page: http://www.
nationalgrid.com/ngrealtime/realtime/systemdata.aspx. The data available is demand,
frequency, and system transfers (electricity export/import) between Scotland/England and
North England to South England, and transfers from the interconnectors (Northern Ireland,
France and the Netherlands). This data is of limited resolution.
The program8 developed uses regular expressions in order to extract the figures for demand
and frequency from the HTML source code behind this web page.
8
SVN Path: /trunk/GridAnalyticsProgram/
25
B.2 Refined Frequency Program
High resolution frequency data, along with a wealth of other information, is published by the
National Grid on their dedicated statistics website, BM Reports (http://www.bmreports.
com/). The frequency data is published in both XML and CVS format, and can be found in
the ’Electricity Data Summary’ page within the ’General’ tab.
The program9 developed downloads an XML file periodically and adds the data it doesn’t
already have to its logs.
C Battery Logging Shell Scripts
C.1 Ubuntu Linux
Install the battery-charge-graph package from the official Ubuntu repository, either using the
Software Centre or by typing the following in a terminal:
sudo apt-get install battery-charge-graph
Then start the logging by typing the following in a terminal:
sudo battery-stats-collector -o batterylog.txt
Where batterylog.txt is the name of the file you wish to use for the log, in the current working
directory. Leave the terminal running the command open for as long as you wish to log the
battery charge level.
C.2 Mac OS X
Create a script file such as batterylog.sh and type the following into it:
#!/bin/bash
cd ~/Desktop/
date >> battery.txt;
while true; do
ioreg -w0 -l | grep Capacity >> battery.txt
sleep 15
done
Save the file and make sure it has execute permissions, either by altering its properties in the
GUI or by running the following terminal command:
chmod +x batterylog.sh
Where batterylog.sh is the name of the script file you created.
The script will log some information regarding battery state from the Mac OS X kernel to
a file called battery.txt. Dividing the value for ’current’ with the value for ’capacity’ will
yield the current battery charge level between 0 and 1. Note that the program will sleep for
15 seconds during each iteration. During subsequent analysis, it will be necessary to add a
correction factor to this 15 second interval due to the small but significant time it takes to
execute the program on each iteration. During the analysis in this experiment, the correction
factor was found to be about 0.5 s.
9
SVN Path: /trunk/FrequencyLoggerProgram/
26
D Laptop Grid Model Assumptions
(a) Charge Rates
(b) Initial Charge Levels
(c) Times of Mid-Use
(d) Operation Times
Figure 24: Histograms of laptop grid model assumptions
27