Simulating Active and Reactive Energy Markets
Antonia Nasiakou
Manolis Vavalis
Dimitrios Bargiotas
Department of Electrical Engineering
Technological Educational Institute of Central Greece
Halkida, Greece
bargiotas@teilam.gr
Department of Electrical and Computer Engineering
University of Thessaly and IETETH/CERTH
Volos, Greece
{adnasiak,mav}@inf.uth.gr
Abstract—The aim of this paper is to present our initial efforts
on understanding, mainly through models and simulations, the
practical interactions between reactive and active energy
markets in a power grid. The reactive power markets, regardless
the obvious importance, have not received significant research
and development attention so far. Recent studies concern the
characteristics of such markets focusing almost exclusively on
the producers’ viewpoint. In this paper we propose to study the
effect of reactive power at the consumer side and the price that
has to pay. Experiments exhibiting the interaction of the reactive
and active energy markets are presented and analyzed through
a simple IEEE bus configuration and a case study of a power
grid with a total of approximately 30000 households with
varying energy requirements and distributed energy producers.
Keywords -- Active Power, Demand Respond, Energy Markets,
Reactive Power, Simulation, Smart Grid.
I.
INTRODUCTION
It is known that the power quality is deteriorating due to
reactive power and harmonic wave in the electrical power
grids. The control of voltage is crucial for the operation of
power systems and the reactive power is essential for this. It is
responsible for the flow of the active power through the
transmission and distribution network. In other words, reactive
power is required to maintain the voltage within the
appropriate limits in order to ensure the reliability of the
power system and deliver the demanded active power through
transmission lines. Voltage control in an electrical power
system is important for the proper operation for electrical
power equipment. It prevents damages such as overheating of
generators and motors, it reduces transmission losses and
maintains the ability of the system to withstand and prevent
voltage collapse. As it is well known, increasing reactive
power causing voltage to fall while decreasing it causing
voltage to rise.
Congestion is one of the most important factors that will
be affecting the operation of a power system. Two significant
Work supported in part by the GSRT Excellence Research Grant entitled
Towards Next Generation Intelligent Energy Systems and the EPEATH Kripis
Grant.
factors raise congestion in power systems: thermal limits and
voltage limits. A simple quadratic DC-Load model, which
relies on active power and assumes that losses are relatively
small, is sufficient in the case of congestion due to thermal
limits [5]. However, in the case of congestion related to
voltage limits, DC-Load models fail and AC-Load models
take their place where the power is divided into active and
reactive. Modern energy markets almost exclusively concern
active power. The concept of active power is more intimate
than the reactive power, with the latter playing a vital role as
ancillary service in the operation of power systems.
Utility power purchase agreements only base payments on
real power generation and not reactive power generation and
perhaps this will be the case in the near future. Nevertheless,
we argue the necessity of an investigation on separating the
energy markets into two interrelated, one associated with
active and the other with the reactive power. This separation is
motivated by the emerging energy market mechanisms and
dynamics, the information available through smart grid and
the distributed local energy generation, in particular through
renewable source which bring additional stochasticity on the
system. In particular we believe that it is worth to start our
study with the simple case in which each consumer and
producer bid into the market for its demanded power.
The rest of this paper is organized as follows. In the next
section we briefly describe the landscape of the currently
available simulation systems and models of reactive power
market that are closely related to our study. In section III we
present the implementation and the experimental
configurations. Section IV contains the simulation results. Our
future prospects and conclusions can be found in Section V.
II.
BACKGROUND AND STATE OF THE ART
The main characteristics of a reactive power market are
based on the facts that:
•
Reactive power cannot be transmitted over long distances
due to its excessive losses, so the local production of
reactive power is desired.
•
A reactive power market is usually monopsony – only
one buyer, the system operator that actually takes the
charges from the customers.
Recent research efforts have been recently devote on
reactive power and in particular on issues related to its
management and pricing and the coupling of reactive and
active power markets. Specifically [1], [9] and [3] concern
reactive power management mechanisms. In particular,[9]
assumes that the operation of reactive power market is
decoupled from the active power market and in order to
formulate the reactive power dispatch, reactive power reserve
management associated with “Voltage Control Area” and
payments are considered. In the mechanism that is proposed in
[3], a must-run capacity of a generator through an index
called, reactive must-run index is used. This index is utilized
for the reactive power calculation based on network topology,
reactive power compensators and reactive power producers.
Furthermore, [1]concerns about several issues associated with
existing provision policies for reactive power services like
procurement, energy price volatility, optimal provision for
reactive power and payment mechanisms(auction, bilateral
and long term agreements). The proposed mechanism is based
on two levels, associated with long-term (seasonal) and shortterm management (real time). The necessity of incorporating
in addition to the conventional generic power generators, into
the network reactive power providers that reduce market
prices is also discussed. Moreover, in [6] the production costs
and the reactive power capacity are considered and analyzed.
The coupling of reactive and active market is considered
several studies. In [12], the design of the reactive power
market that is based on the clearing type of market (first price,
uniform auction), the monopsony market and the long-term
contracts is proposed. The market is built on the providers’
“Expected Payment Function” in which each component is
associated with a region of the reactive capability curve of a
generator. The proposed approach is also associated with (a)
the minimization of total payment, (b) the minimization of
transmission losses and (c) the minimization of deviations
from contracts.[7] and [8]are based on the above mentioned
study in order to develop their market model. They study a
coupled energy and reactive market based on power system
security and voltage dependent load models respectively.
Methods for reactive power payments and reactive pricing
schemes like “Total Payment Function”, “Loss Minimization”,
“Load Served” and “Voltage Stability Enhancement Index”
are discussed in detail in [8]. Furthermore, [7] presents a
coupled day-ahead energy and reactive power market based on
the settlement mechanism, which is cleared through an
optimal power flow problem.
As regards to the pricing methods, in [4] a non-linear
programming method for reactive power pricing based on
marginal costs in competitive electricity market is presented
which encourages the producers to procure reactive power by
ensuring retrieval of their costs.
The above reactive market related studies concentrate almost
exclusively on the producers. To the best of our knowledge
the only study that investigates the role of the consumers in
reactive power markets is presented in [10] where it is shown
that the “Locational Marginal Prices” could be used as price
signals to the consumers in order to compensate for reactive
power by introducing compensation devices. It is shown that
the insertion of such devices leads on decreasing of the
electricity cost.
III.
IMLEMENTATION AND EXPERIMENTAL
CONFIGURATIONS
A. Design and Implementation
The aim of our study is to develop, implement, and analyze a
coupled power market. We design and implement a related
simulation platform that is based on GridLAB-D [2] utilizing
five of its modules. We have extended the functionality of
several components of these modules to support the operation
of the proposed market model.
Specifically, we used
• The residential module, which implements houses with
various appliances.
• The climate module which provides weather data.
• The tape module, which allow us to specify (and modify)
various parameters of a model and to collect the
simulated data.
• The module that solves the power flow equations.
• The auction based market module, which accepts offers
and bids either from stub bidder objects or controller
objects (controllers for devices and generators). The bid
period is 15 minutes and the clearing price and quantity
is obtained after an insignificant latency interval.
In our study, the local reactive power sources are either diesel
engines or wind turbines (see Table 2). They are connected to
the grid through a meter and bid through a controller, both
objects of GridLAB-D. The consumers (the residential
module of GridLAB-D) are connected to the distribution
network through the object triplex meter, object of GridLABD. As usual, producers\generators and consumers bid into the
market which is organized as an auction. The producers offer
power (active and reactive) at a single price irrespective the
fact that they bid into the market only for active power. The
reactive power is produced anyway. Therefore, the price that
the consumers take, after the clearing of the market, is both
for reactive and active power. The producer bids its produced
active power and the market operator aggregates the reactive
power from all the producers that are included after the
clearing into the market. The reactive power that each
producer offers depends on its capability curve. Moreover,
the bid price of each producer is associated with the reactive
power that it could give. If the producer reduces the
production of active power to produce more reactive power,
then the bid price will be higher in order to balance the losses
of producing more reactive power. Similarly, consumers bid
for active power and the market operator aggregates the
reactive power needed from all the consumers. The
consumers don’t bid for reactive power. After the market
clearing, the market operator collects the supplied reactive
power and the demanded reactive power, and compares those
quantities in order to decide if more reactive power is needed.
If the producers, that are included in the clearing of the
market, cannot satisfy the demanded reactive power then the
Experiment II connects 20 IEEE-13 feeders, with some
modifications regarding the load that can be supported, and
accounts to a total of 30,000 houses .Each house may contain
a water heater, an HVAC system, a refrigerator, a light
system and a microwave. Note that only the HVAC system
requires reactive power. Different house configurations have
been used as this is depicted in Table 1.
As regards the generator side, we assume that there are both
local generators and transmission generators. The latter feed,
through the transmission lines, the distribution substation of
the distribution network, which offers active and reactive
power as well. The generators have appropriate properties,
which are associated with the active and reactive power at
each particular simulation instance. The controller uses these
3
2
3
2
2
1
5
Microwave
2
4
4
2
Water Heater
1
1
3
1
2
2
Refrigerator
2
2
7
1
3
1
1
Devices per house
Lights
1
2
Diesel Generators
1
6
Large-GE
Medium
6 11500
4 3600
1 1600
1
840
1
850
1
900
1
540
1 1040
1 1135
1
874
1
650
1 1350
Wind
Turbines
Small
C1
C2
T1
T2
T3
T4
T5
T6
T7
T8
T9
T1
0
Local generators
HVAC
Experiment I consists of one IEEE-37 feeder realizing a town
with 790 houses each accommodated with an HVAC
(heating, ventilating, and air conditioning) system and a water
heater. Two small wind turbines, a diesel generator, with max
capacity approximately 0.011MW are the local distributed
generators.
Table 1– Configuration of Experiment II.
Number of Houses
B. Experimental Configurations
For the needs of our experimentation we have designed
several case studies that are as realistic as possible and of
significant large scale. Based on IEEE distribution feeders we
generate through various scripts the associated glm
(GridLAB-D Model) (see [2]) files according to our various
experimental design decisions. For our present paper we
have selected the following two.
The GE_25MW, LARGE, MID and SMALL wind turbines
models found in the GridLAB-D platform have been used.
Their characteristics and the bid prices for local generators
are given in Table 2.The transmission generator bids on 0.40
$\KVA (for both active and reactive). In our experiments, for
both diesel and wind turbine, we set the power factor in 0.85,
0.99 and 0.85, 0.97 respectively. We have to mention here
that the price of the generators regarding the power factor
changes is based on the capability curve of the generator.
IEEE Feeders
The clearing price of active power is calculated via the
auction procedure which is part of the simulation framework.
The price of reactive power that consumers have to pay when
the reactive power is satisfied from the producers which are
included in the market is the clearing price. On the other
hand, the price of reactive power that the consumers have to
pay, when the producers, that are participated in the market
clearing don’t offer the needed reactive, is calculated through
a simple procedure. Market operator is responsible for that
procedure. Actually, that procedure is taken place after the
clearing of the market and calculates the price of reactive
power as the average price of producer’s bid price that is not
included in the market clearing. This procedure does not
affect the simulation and it’s is not part of the simulation
procedure. It is worth to be mentioned that there is no bidding
operation for reactive power. Consumers ask for active power
but they have to pay for reactive as well. Similarly, producers
offer active and reactive power at a single price but they bid
only for active power and are paid for reactive as well.
properties in order to calculate its bid (price and quantity) for
the market. We have not considered solar panels since due to
the particular weather data selected, theWA-SEATTLE.tmy2
in the climate module (a tmy2 file is a weather data file for a
particular location of a given day and hour) resulted in the
low contribution on total supply. Table summarizes the
configuration of Experiment II while Table 2 presents the
characteristics of the local generators involved.
Cities and Towns
producers that are not included in the clearing of the market
offer only their reactive power at their bid price. The
consumers in that case have to pay (a) the clearing price for
both active and reactive power and (b) an additional price per
KVAr for each additional unit of reactive power. The
calculation of the additional price is described below.
1
1
1
1
1
1
1
1
1
1
1
1
0
0
1
1
0
1
0
0
1
0
0
0
0
1
1
1
0
0
1
0
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
0
1
0
0
1
1
0
0
0
Table 2–Capacities-prices of the local generators in Experiments I
and II.
Max capacity
Active (MW)
Reactive (MVAr)
Price ($/KVA)
Price(($/KVA)
(pf=0.85)
Small
.0058
.0025
.40-.45
.60
Wind Turbine
Medium
Large
.15
1.50
.045
0.65
.40 -.45
.40-.45
.60
.60
Diesel
2.50
1.09
.678
.72
The reactive power from the transmission nnetwork is offered
at 0.75$/KVAr when it is supplied from wind turbines or
synchronous condensers. The price is highher because these
sources will give only reactive power andd because of the
disadvantage of reactive power to be transsmitted over long
distances due to its excessive losses. Thhe price for bulk
transmission generators is 0.40$/KVA. Thhe devices bid on
average 0.50$/KW for the refrigerator, 0..45$/KW for the
lights, and 0.45$/KW for the microwave. T
The bids for these
devices are made through the stub_bidder object of
GridLAB-D based on properly configuured scheduling.
Furthermore, we make the assumption that thhe reactive power
that is needed for the lines is supplied by synchronous
condensers which have not direct participattion in the market
but through forward contracts as it is usual inn such cases.
IV.
SIMULATION RESULTTS
Based on the above-described simulation environment, the
market characteristics and the experimennt configurations
mentioned, we presented below selectiive experimental
results. In all figures we have on the horizoontal axis the time
(24 hours) of a simulated date that starts at 00:00 and on the
vertical axis either the per unit price in $\K
KVAr or $\KW or
the power quantities in MW. We also note thhat in the Figure 1
(on the left) red line represents price for aactive power and
reactive power in $\KVA and blue corrresponds to the
additional price for reactive in $\KVAr. In F
Figure 3 grey lines
denote the price for active and reactive pow
wer, blue lines the
additional price per KVAr of reactive poower, with power
factor close to 1 and with orange or blue linees the case studies
when we change the power factor for both ddiesel engines and
wind turbines as given above.
1
0
2
0
Figure 1–Demand, supply and clearing quantitiees for experiment I
and active, reactive price (on the left).In the x axis we have the
simulation time (24 hours) and in the y axis the pprice in $/KVAr (on
the left) for the blue line and in $\KW for thhe red line and the
amount of active power in MW (on the right).
For Experiment I the active power assoociated with the
clearing quantity (blue line), seller’s (red) annd buyer’s (green)
total quantity in MW is presented in Figure 1(on the right). It
is observed that there is unsatisfied demandd of active power.
This is due to the insufficient amount off power that the
generators provide. Additionally, the generators that
participated in the market offer insufficcient amount of
reactive power and therefore the consumerrs are obliged to
pay more for reactive power. Only diesel eengines can offer
reactive power, but it is not enough to sattisfy the demand.
Therefore, the consumers have to take parrt of the reactive
power needed from the transmission networrk. The final price
that they have to pay for each unit of reacttive power, as we
can see in Figure 1 (on the left), is 0.71 $\K
KVAr, the average
of 0.678, price of diesel engine, and 0.75, price of wind
turbine or synchronous condenser off transmission network.
In Figures 2 and 3 we present the results associated with
Experiment II. In Figure 2 at Town
T
7 for example, we
observe that at 15:45 there is a big disturbance at the power
that the generators supply. This is mainly
m
on account of the
wind turbine operation and the fact that
t at that particular time
instance its supplied power is dramaatically decreased. This is
due to the low wind speed. The size of wind turbines that is
selected differs from town to town
n as it is depicted by the
different supply curve between T5
T and T4 in Figure 2.
Moreover, we note that although the supply is sufficient, the
total demand is not fully satisfied beecause of the price that the
devices may bid into the market. Th
his is due to the business
logic of controller, object of GridLA
AB-D, which is attached to
each device. Actually, the bid prrice of each device is a
function of the average price and thee standard deviation of the
clearing price from the past markets and the comfort zone the
participant is willing to use. Altho
ough, when the supply is
enough, there are consumers that bid
d to a lower price than the
most expensive producer, and thus these consumers are not
included in the market clearing.
Note that (Figure 1(on the left) and 3)
3 there are time instances
where the market operator requests more
m
reactive power from
generators that are not participating in the market. In the case
of unsatisfied reactive power those generators that offer only
its reactive power gain more profit because in that case they
are participated into the market although
a
they give only
reactive power instead of giving noth
hing.
Moreover, for experimental purposes, we change the power
factor either from diesel generators or from wind turbines in
order to observe the effect that the power factor has on the
price of reactive power. For Town 1-5 we set the power
factor of diesel engine to 0.85(blu
ue line in Figure 3) and
0.97(orange line in Figure 3). For City 1 we set the power
factor for five wind turbines to 0.97 and for the last four to
0.85 (blue line in Figure 3). For thee City 2 we do not make
any changes at the power factor of generators.
g
For the rest of
the towns we make a combination
n of different values on
power factor for both wind turbines and diesel engines. At the
case where the power factor for both
h wind turbines and diesel
engine is 0.85, we observe (orange line
l
for Town 8 and 10 in
Figure 3) that the additional price is higher. This is due to the
fact that the price when the powerr factor is less than 1 is
higher because of the opportunity cost and the cost of losses
that the generator has to cover. We observe that for Town 6
(Figure 3), which is associated with four large wind turbines,
when the power factor is 0.85 for two of the wind turbines
and 0.97 for others, the price that consumers
c
have to pay is
0.576$\KVAr and when the power factor is 0.97 for all the
wind turbines the price is 0.565$\KVAr. It is worth to
mention here that in Town 6 transsmission generators offer
reactive power as well. In differeent cases, if the reactive
power from local generators is suffficient then the price will
be 0.452$\KVAr in the case where the power factor is 0.97
for the four wind turbines – the bid pricce of local wind
turbine. It is worth to mention here that if we reduce the
power factor in order to take more reactivee power does not
affect positively the additional price that thhe consumer have
to pay but the consumers have to pay more. That is also due
to the participation of diesel engines price in the calculation
of the final price.
transmission losses. We would lik
ke to note here that the
unsatisfied reactive power may leaad the consumers to shift
their demand for active power to
o off peak hours. It is
observed from the experiments thaat the unsatisfied reactive
power is occurred on on-peak hourrs. This is due to the fact
that the wind turbine has a significan
nt influence on the supply
of the reactive power. Therefore, due
d to the stochasticity of
wind turbine, the reactive power could
c
not be exclusively
provided from wind turbines. Instead distributed capacitors,
diesel generators and synchron
nous condensers from
transmission network could be used
d. However, in the case of
transmission generators the price is a crucial factor.
V.
SYNOPSIS AND FUTURE PROSPECTS
This paper presents our initial effo
forts to integrate models,
theories and technologies into a larg
ge-scale simulation engine
that combines the concepts of the active
a
and reactive power.
Specifically, we present the design
n of both our simulation
engine and our case studies and present selective
experiments, which show how the price,
p
that the consumers
have to pay, is affected by the deficient of reactive power
when the system needs more reactivee power than its supplies.
Figure 2 - Demand, supply and clearing quantitiies in the ten towns
and the two cities in Experiment II. In the x axis we have the
simulation time (24 hours) and in the y axis thhe amount of active
power in MW.
Figure 3 depicts significant variations on thhe prices in Town
9. This is due to the fact that the transmisssion generators in
that town bid on 0.38$\KVA due to the bussiness logic of the
controller. Moreover, it is observed from thee market price that
a diesel generator is participated in the marrket. The price at
which the controller bids into the markett depends on the
average price and standard deviation of the ppast market as it’s
mentioned before. So, the variations in thee price are due to
the price that the consumers actually bid into the market
which is effected by the high price of the diesel engine. At
Town 9 there are also variations on the price of reactive
power. That town is equipped only with sm
mall wind turbines
and one diesel engine and therefore the deemanded reactive
power is not satisfied from the generators thhat are included in
the market. Similar results are observed aat Town 4 where
only one wind turbine and 2 diesel engines aare installed.
For our experiments, we verify the facct that the local
compensation of reactive power costs less tto the consumers.
If we make the assumption that there are noo local generators
that could offer reactive power, the consum
mers have to pay
more for reactive power because of the loong distance that
reactive power must "travel" and the reactive power
Our experimentation shows that, as expected, there exist
realistic configurations where locall consumers need to buy
additional reactive power from local and non-local producers
at a presumably more “expenssive” price. When the
compensation of the reactive power is form the non-local
producers then the price is rising. Allthough our efforts mainly
focus on consumers, we plan to utiliize the recent studies (see
section II above) on the markets associated with reactive
power generators. This will allow
w us to study even more
realistic large-scale energy grids further elucidating the
interactions between the two markets.
Our current models and experim
mentation will be further
expanded also towards more intelligent methodologies in
order to model a bidding operation for
f reactive power as well.
The objective functions that consstruct the related multiobjective optimization problem ressponsible for the reactive
power are more complicated than thee ones associated with the
active power market. Actually theese functions are mainly
concerned about the opportunity co
ost, voltage drop and line
overloading. Transmission losses and generator reactive
power constraints must be taken und
der consideration in order
to calculate the price that maintains the system reliability and
his extension will allow
maximizes the social welfare. Th
further experimentation that will lead to deeper understanding
of the characteristics of reactive power market and its
connectivity to the active power marrket.
Other important issues should sureely be considered but are
beyond the scope of our study. Forr example, inverters may
provide real-time reactive power to compensate for large
reactive power loads. Therefore, theeir effect on the emerging
energy markets should be clearly ideentified.
ACKNOWLEDGM
MENT
The authors gratefully acknowled
dge the contributions of D.
Zimeris through our numerous technical discussions.
ES
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Figure 3 – Clearing active and reactive prices pper unit paid to the
generators for Experiment II. In the x axis we hhave the simulation
time (24 hours) and in the y axis the price in $\K
KVAr and the grey
line corresponds to the price in $\KVA
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