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Optimal Price-Energy Demand Bids for Aggregate Price-Responsive Loads


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In this paper we seek to optimally operate a retailer that, on one side, aggregates a group of price-responsive loads and on the other, submits block-wise demand bids to the day-ahead and real-time markets. Such a retailer/aggregator needs to tackle uncertainty both in customer behavior and wholesale electricity markets. The goal in our design is to maximize the profit for the retailer/aggregator. We derive closed-form solutions for the risk-neutral case and also provide a stochastic optimization framework to efficiently analyze the risk-averse case. In the latter, the price-responsiveness of the load is modeled by means of a non-parametric analysis of experimental random scenarios, allowing for the response model to be non-linear. The price-responsive load models are derived based on the Olympic Peninsula experiment load elasticity data. We benchmark the proposed method using data from the California ISO wholesale electricity market.
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Optimal price-energy demand bids for aggregate
price-responsive loads
Javier Saez-Gallego, Mahdi Kohansal, Student Member, IEEE, Ashkan Sadeghi-Mobarakeh, Student
Member, IEEE, and Juan M. Morales, Senior Member, IEEE
Abstract—In this paper we seek to optimally operate a retailer
that, on one side, aggregates a group of price-responsive loads and
on the other, submits block-wise demand bids to the day-ahead
and real-time markets. Such a retailer/aggregator needs to tackle
uncertainty both in customer behavior and wholesale electricity
markets. The goal in our design is to maximize the profit
for the retailer/aggregator. We derive closed-form solutions for
the risk-neutral case and also provide a stochastic optimization
framework to efficiently analyze the risk-averse case. In the latter,
the price-responsiveness of the load is modeled by means of a non-
parametric analysis of experimental random scenarios, allowing
for the response model to be non-linear. The price-responsive load
models are derived based on the Olympic Peninsula experiment
load elasticity data. We benchmark the proposed method using
data from the California ISO wholesale electricity market.
Index Terms—Price-energy bidding, demand response, elec-
tricity market, smart grid, data-driven.
The main notation used throughout the paper is stated below
for quick reference. Other symbols are defined as required.
A. Indexes and sets
tTime period t∈ {1,2, . . . 24}.
bBidding block b∈ {1,2,...B}.
wRealization of the stochastic variables, represented as
scenarios w={1,2,...N}.
B. Input stochastic processes
ΛDDay-ahead price.
ΛRReal-time price.
ΠRetail price.
C. Decision variables
XDStochastic process representing scheduled energy in
the day-ahead market.
t,w Scheduled energy in the day-ahead market for time
tand scenario w.
J. Saez-Gallego, J. M. Morales (corresponding author) are with the
Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark (email
addresses: {jsga, jmmgo}, and their work is partly funded by
DSF (Det Strategiske Forskningsr˚
ad) through the CITIES research center
(no. 1035-00027B) and the iPower platform project (no. 10-095378). M.
Kohansal and A. Sadeghi are with the Department of Electrical and Computer
Engineering, University of California, Riverside, USA. E-mail: {mkohansal,
asadeh004} Their work is supported in part by the United States
National Science Foundation grants 1253516, 1307756, and 1319798.
ut,b Price bid for time tand block b.
Remark: a subscript tunder the stochastic processes indicate
the associated random variable for time t.
D. Parameters
φwProbability of each scenario w.
xt,w Load at time tand scenario w.
t,w Day-ahead price at time tand scenario w.
t,w Real-time price at time tand scenario w.
πt,w Retail price at time tand scenario w.
CbWidth of energy block b.
LFraction of the load that must be purchased in the
day-ahead market.
βProbability of occurrence of chance constraint.
With the increasing deployment of smart grid technologies
and demand response programs, more markets around the
world are fostering demand bids that reflect the response of
the consumers to changing electricity prices [1], [2]. In this
paper, we consider the case of a retailer who procures energy
to a pool of consumers in a typical two-settlement electricity
market, as for example, the California wholesale electricity
market CAISO [3]. The retailer submits price-energy demand
bids to the day-ahead market, and only energy quantity bids to
the real-time market in order to counterbalance the deviations
from the scheduled day-ahead energy market to the actual load.
The possibility of arbitrage is indirectly allowed depending on
the submitted bid to the day-ahead market and the realization
of the stochastic processes affecting the problem.
We assume that the load is price-responsive, in the sense
that it may change depending on the price of electricity
during the considered period. The retailer passes the retail
price onto her consumers, who react accordingly. We do not
make any assumption about the means that the consumers
use to adjust their consumption based on the retail price,
because the proposed methodology relies on historical data
of aggregate load and retail price to estimate the relationship
between them. Also because of this, we do not need to make
any assumption on the nature of the load that the retailer
aggregates. Furthermore, we consider that the retail price is
directly linked to the market price and prefixed beforehand, for
example, as a prearranged percentage of the day-ahead price.
Therefore, the retail price is out of the retailer’s control (in the
short term at least). Finally, note that the communication flow
between the retailer and her consumers is one-directional: the
price is communicated to the consumers by the retailer, who,
in turn, observes the aggregate load.
The contributions of the paper are summarized as follows:
An analytic solution to the problem of finding opti-
mal block-wise price-energy demand bids in the day-
ahead market when risk is not considered. Moreover,
we propose a mixed-integer linear programming solution
approach to the risk-averse case.
The dynamic price-responsive behavior of consumers is
modeled based on scenarios. The conditional probability
of the load given a certain retail price trajectory is
estimated using a non-parametric approach.
We assess the practicality of the proposed methodology
by using data from a real-world experiment.
The estimation of demand bids has been extensively studied
in the past years [4, ch. 7]. Several papers share the common
goal of estimating price-energy bids relative to specific types
of load, for example, time-shiftable loads [5], electric vehicles
[6] and thermostatically-controlled loads [7]. Our methodology
differs with those in the fact that we do not make any
assumption on the nature of the load. Methodologies based on
forecasting tools [8], [9] generally do not make assumptions
on the type of price-responsive load either, but, on the other
hand, do not tackle the bidding problem.
Besides the works on forecasting, another group of papers
focus on finding the optimal bid for generic loads. The
work in [10] elaborates on a robust bidding strategy against
procurement costs higher than the expected one, considering
uncertainty in the prices only. Uncertain prices and demand
are taken into account in [11] but minimizing imbalances and
disregarding the economic side of the bidding. Our approach
resembles that of [12] with the main differences being that
we use data to estimate the price-response of the dynamic
load, and that we consider energy-block bidding in a one-
price balancing market as the US CAISO [3]. Authors of
[13] consider, from the theoretical point of view, the problem
of allocating a deterministic load by deciding which fraction
should be purchased in the day-ahead market and which in
the real-time market. Finally, authors of [14] study demand
curves in an arbitrage- and risk-free situation by using a game
Regarding the generation of scenarios of the stochastic
processes, our methodology is inspired from [15]–[17]. From
the application point of view, our approach differs in the
final goal, as they deal with wind energy production. To our
knowledge, there is no previous work that characterizes the
dynamic price-responsive load with a set of scenarios. From
the methodological point of view, our approach differs with
the existing literature in the estimation of the conditional
distribution of the price-responsive load, taking into account
the full trajectory of the day-ahead price. This enables us to
capture the full dynamics of the load across the hours of the
next operational day. The real-time price is modeled in an
analogous manner. In both cases, we model their distributions
using a non-parametric approach that allows for non-linear
responses to a given day-ahead price trajectory.
The paper is structured as follows. In Section II we intro-
duce the retailer’s bidding problem. Section III provides the
analytic solution to the risk-neutral case. In Section IV we
formulate the stochastic optimization model for solving the
bidding problem with risk constraints. Section V elaborates
on the scenario-generation technique. Next, in Section VI we
analyze results from the bidding problem under the generated
scenarios. Finally, in Section VII we draw conclusions and
Consider a utility retailer/aggregator that seeks to maximize
its profit based on the revenue that it collects from its loads, the
payments it makes to the day-ahead market, and the payments
it makes or receives in the real-time market. Mathematically
speaking, we need to solve the following optimization pro-
t=1 ΠtXtΛD
subject to
CbI(ut,b ΛD
t)t, b (1b)
ut,b+1 ut,b t, b = 1 . . . B 1(1c)
t[(1 L)Xt,(1 + L)Xt]βt(1d)
λut,b λt, b (1e)
where I(·)is the 0-1 indicator function.
The objective function (1a) is the expected total daily profit,
composed of three terms. The first term represents the revenue
that the retailer makes form selling energy to the consumers
at the retail price. The second term represents the cost of
purchasing energy from the day-ahead market. The third term
accounts for the cost/revenue of purchasing/selling energy
from/to the real-time market. The energy purchased or sold
in the real-time market is equal to the difference between the
purchased quantity at the day-ahead market and the realized
load, i.e., XtXD
Constraint (1b) defines the scheduled energy in the day-
ahead market to be equal to the sum of the width of the blocks
of energy which have a price-bid higher than the market price.
In other words, blocks of energy will be purchased if their
price-bid is higher or equal to the day-ahead price. Note that
ut,b is the decision variable which determines the shape of the
submitted bidding curve to the day-ahead market.
Constraint (1d) models the risk-aversion of the retailer
through two parameters. Parameter Lrepresents the maximum
fraction of the load that can be procured in the real-time
market. This parameter could be defined by the retailer, but
could also be constrained by the ISO as a way to avoid
putting too much pressure on the real-time market, this way
safeguarding and prioritizing the security and stability of the
power system. Values of Lclose to 1 indicate that the full
amount of the load can potentially be bought in the real-time
market. On the other hand, as Ldecreases, we give priority
to purchasing energy in the day-ahead market. Parameter β
indicates the minimum probability with which the constraint
(1d) must be fulfilled. Values of βclose to 1 indicate a hard
constraint, while lower values of βindicate that the constraint
is loose. The parameter βcan be interpreted as the aversion of
the retailer towards purchasing a certain fraction of the load in
the day-ahead market. Low values of βcan be interpreted as a
sign that the retailer seeks to profit from arbitrage rather than
from serving the load. As we show in the case study, higher
values of βyield lower expected profit but also lower risk.
Note that, for large Land small β, constraint (1d) becomes
irrelevant, indicating the neutrality of the retailer towards risk.
Constraint (1c) ensures that the estimated bidding curve is
monotonically decreasing which is a typical requirement in
electricity markets. Finally, constraint (1e) set lower and upper
bounds to the price bids, which are given by the market rules
[18]. All in all, the expected profit depends on the decision
variable “price-bid” and also on the realization of the input
stochastic variables.
The maximum number of blocks that is allowed depend on
the market rules [18] as well. The width of each block Cb
must be set by the retailer depending on the magnitude of the
As in practice, here we assume that the retail price is given
exogenously, in other words, it is not a decision variable of the
retailer. The main driver for this consideration is the fact that
the retail price must, to a certain extent, represent the true cost
of electricity. This might not always be the case if the retail
price is subject to the will of the retailer. As a consequence, the
retailer’s bidding strategy does not directly affect the behavior
of the load, since the behavior of the load depends on the retail
price and other factors such as the weather conditions. Another
implication is that only the profit of the retailer is affected by
her bidding strategy and the realized market prices.
In this subsection we elaborate on the closed-form analytic
solution to problem (1), when the risk constraint (1d) is
disregarded, or equivalently, when L→ ∞ and/or β= 0.
The retailer’s bidding problem (1) can be decomposed by
time period, so that 24 smaller optimizations problems can be
solved instead, one for each time t.
In the risk-neutral case, each of these smaller optimization
problems writes as follows:
t,ut,b EXtΠtΛR
subject to (1b), (1c) and (1e). The advantage of reformulation
(2) is that we can perform simpler optimization problems in
parallel. Note that the first term of (2) is constant with respect
to the decision variables ut,b and XD
t, whereas the last term
is not. Hence, both the stochastic load Xtand the retail price
Πtcan be dropped out of the optimization problem (1) in this
case. Interestingly, this implies that, in the risk-neutral case,
the retailer’s optimal bidding strategy is not affected by the
price-responsive nature of the load.
Next we analyze the case when ΛDand ΛRare statistically
independent. Results are presented in Theorem 1. For ease of
reading, and given that the maximization problem (2) can be
decomposed per time period, we drop the time index tin the
remaining of this section.
Theorem 1: The optimal price bid u
bin problem (2), when
the day-ahead and real-time prices are independent, is equal
to the expected value of the real-time price.
The proof of Theorem 1 is given in Appendix A. Theorem 1
also shows that, given the risk-neutral setup and independent
prices, we do not obtain extra benefit from bidding a curve
instead of a single price-quantity bid.
The assumption of statistically independent prices is not
necessarily fulfilled in practice (see, for example, [19, Fig. 1]).
For this reason, in Theorem 2 below, we provide the analytic
solution to problem (2) when ΛDand ΛRare statistically
Theorem 2: A global optimum solution to problem (2)
satisfies that the price bids for all blocks is equal to u.
Moreover, uis equal to either λ,λ, or EΛR|ΛD=u
with d
du EΛR|ΛD=u<1in the latter case.
The proof of Theorem 2 is given in Appendix B. One could
interpret the result of Theorem 2 in the following way: the
optimal price bid will be the one for which price consistency
holds, namely, for which the expected real-time price is equal
to the day-ahead price. A second conclusion drawn from
Theorem 2 is that the maximum profit is achieved with the
same price-bid for each block. If there is more than one price
bid that maximizes the expected profit (i.e., several global
maxima), then the price bid for each block can be chosen
indistinctly between them. Similarly as with Theorem 1, we
do not obtain extra benefit from bidding a curve when prices
are dependent.
From a practical point of view, Theorem 1 and 2 allow us
to simplify the demand curve to a simple price-quantity bid.
By taking into account this implication, we can obtain the
optimal price bid in the case when the distributions of prices
are discrete, which allow us to compute the optimal price bid
when the uncertainty is modeled by scenarios. The optimal
price bid can be chosen by evaluating the profit in the local
maxima, which are characterized according to the following
Remark 2: Given a discrete set of scenarios for the
day-ahead and real-time prices, let us consider the re-
ordered pair of terms {λD
w}such that
w+1. Local maxima1are achieved at the stationary
points u=λD
wsuch that λD
w+1 >EΛR|ΛD=λD
Note that, due to market rules, the price bid have a maxi-
mum and minimum allowed values. In practice, one needs to
check also if the maximum profit is achieved when the price
bid is equal to one of its bounds. Using Remark 2 one can
find the optimal price-bid by just performing a finite set of
simple calculations.
As a final remark, it is noteworthy to say that the results
from Theorem 1 and 2 show that the solution to (2) does not
depend on the retail price, neither on the load. From a practical
point of view this means that the risk-neutral retailer acts as
1The proof is available upon request.
a financial trader, making profit by selling and buying energy
in both markets.
In this section we present a solution to problem (1) using
a scenario-based approach. The input for every time tis a set
of Nscenarios, each one characterized by a realization of the
retail price πt,w, the day-ahead price λD
t,w, the real-time price
t,w, and the load xt,w . Each scenario has a probability of
occurrence of φw.
We reformulate constraint (1b) by adding a binary variable
yt,w,b. Then, constraint (1b) is replaced by:
t,w =X
yt,w,bCbt, w
ut,b λDA
t,w Myt,w,b t, w, b
ut,b +λDA
t,w M(1 yt,w,b)t, w , b
yt,w,b ∈ {0,1} ∀t, w, b
where M is a large enough constant. The equations above
imply that yt,w,b = 1 if ut,b λD
t,w and 0 otherwise.
Next, we reformulate constraint (1d) by adding two extra
binary variables. We first define zt,w = 1 if xD
t,w (1
L)xt,w, and zt,w = 0 otherwise. Secondly, we define zt,w = 1
if xD
t,w (1 + L)xt,w, and zt,w = 0 otherwise. Consequently,
the chance constraint (1d) can be replaced by the following
set of equations:
t,w (1 L)xt,w M(1 zt,w)w
t,w + (1 L)xt,w Mzt,w w
t,w (1 + L)xt,w Mzt,w w
t,w + (1 + L)xt,w M(1 zt,w)w
wzt,w +zt,w1β .
All in all, taking into consideration the reformulations
presented above, the optimal price-bid is found by maximizing,
for every time t,
φwπt,wxt,w λD
t,w(xt,w xD
subject to (1c), (1e), (3), and (4).
In this section we elaborate on the modeling of the stochas-
tic variables by scenarios. The proposed approach to generate
scenarios has several advantages. First, we do not need to make
any assumption on the type of price-responsive load we model.
The response of the load to the price is directly observed in
the data and modeled by a non-parametric distribution. For
this very same reason, the response of the load to the price is
allowed to be non-linear. Second, it is a fast approach, hence,
big datasets can be quickly processed. Finally, the proposed
approach is adequate for bidding purposes, since forecasting
the load is not the main goal of the paper but rather account
for its uncertainty in order to make an informed decision.
Each scenario is characterized by a 24-long sequence of
day-ahead prices, real-time prices, retail prices and observed
load. The proposed method to approximate their joint distri-
bution is summarized as follows. First of all, we model the
marginal distribution of the day-ahead price. Note that the day-
ahead price is not dependent on the real-time price, neither on
the bid of a small price-taker consumer. Second, we model the
distribution of the load conditioned on the retail price using
a non-parametric approach. Lastly, we model the distribution
of the real-time price conditioned on the day-ahead price. The
real-time price depends on the day-ahead price, but not on the
load of a price-taker retailer.
The rest of this section is organized as follows. First, in
Section V-A, we briefly elaborate on the technique to generate
scenarios of day-ahead price. Then, for each scenario of day-
ahead price, we generate conditional scenarios of real-time
price and load in Section V-B.
A. Day-ahead Price Scenarios
The first step in the scenario generation procedure is to
model the day-ahead price using an Autoregressive Integrated
Moving Average model (ARIMA). We choose the most ad-
equate model according to the AICc criteria [22]. Using the
estimated model, we draw scenarios using the methodology
explained in [15]. Because the scenarios are used in day-
ahead trading, they are generated in a rolling horizon manner
everyday at 12:00 with a lead time of 13 to 36 hours.
B. Load and Real-time Price Scenarios
In this section, we elaborate on the proposed methodology
to draw scenarios from the distribution of load conditioned
on the retail price. The methodology to generate conditional
real-time price scenarios is analogous, hence, we omit it for
For this subsection, we consider a scenario of day-ahead
prices ˜
1, . . . , λD
24}that is generated using the metho-
dology explained in Section V-A. Under the considered setup,
as explained in the sections above, the retail price is given
exogenously. In the case study, we assume the retail price to
be proportional to the day-ahead price, that is, Π = D.
Therefore a scenario of retail price is directly specified from
a scenario of day-ahead price.
The procedure outlined next allows us to weigh the his-
torical trajectories, such that trajectories with a retail price
“closer” to the given retail price ˜πweigh more. These weights
are used later in this section to compute the conditional density
function of the load, given ˜π. To begin with, we define π(j)as
the 24-long vectors of retail price, with each element referring
to an hour of the day, and with jreferring to the index of the
historical day considered. Then, we compute the Euclidean
distance d(j)=||π(j)˜π||. In this way, we “summarize”
each historical price trajectory π(j)with a single value, so that
trajectories “closer” to the given retail price ˜πhave a lower
distance. Next, we use a Gaussian kernel to weight trajectories,
90 100 110 120 130 140 150
0.0 0.1 0.2 0.3 0.4
Distance (kWh)
K = 0.5
K = 1
K = 10
Fig. 1. The weights of the historical retail price trajectories are shown against
their distance to the price reference.
such that the weights are equal to w(j)0=f(d(j)), where fis
the probability density function of a normal distribution with
mean 0 and standard deviation σf. For the case study, we used
σf=d, meaning that the standard deviation for fis equal
to the standard deviation of the distances σd, multiplied by
abandwidth parameter K. Finally, we normalize the weight
Pw(j)0so that their sum is equal to 1.
The effect of the bandwidth parameter Kover the weights
can be seen in Fig. 1. On its x-axis, we represent d(j)and on
the y-axis the weights w(j). A smaller bandwidth penalizes
price references further away. This is the reason why, when
K= 0.5, there are few scenarios with a weight significantly
greater than zero. On the other hand, when K= 10, all
scenarios weigh similarly.
The procedure to generate each scenario is inspired from
[15] and [16]. In short, we first transform the load data to
a normal distribution using a non-parametric transformation.
Then, we compute its covariance, and finally, generate random
correlated Gaussian errors that are transformed back to the
original distribution. The procedure consists of the following
seven steps:
1) For each hour of the day, we compute a non-parametric
estimation of the density of the price-responsive load [23]
conditional on a retail price trajectory ˜π. We do this by
computing the kernel density estimator at hour twith the
weights w(j)in the following way:
ft(x|˜π) = 1
where Gh(x)is a kernel (non-negative function that
integrates to one and has zero mean), his its bandwidth,
and x(j)
tis the observed load at time tand day j. An
example of a estimated density using a Gaussian kernel
is shown in Fig. 2, for different values of Kand same
h. For Kclose to zero (K= 0.5in the case study),
the weighting gives relatively high importance to few
observations, therefore, the estimated density is more
localized around them.
2) Using ˆ
ft(x|˜π)from Step 1, we compute the cumulative
density function, called ˆ
80 100 120 140 160
0.00 0.02 0.04 0.06
Load (kWh)
● ● ●● ● ●
K = 0.5
K = 1
K = 10
Fig. 2. The estimated conditional distribution of the load given the retail
price is shown for different values of the bandwidth parameter K.
3) The transformed load values y(j)
every hour tfollow a uniform distribution U(0,1). Then,
we normalize the load data through the transformation
t= Φ1(y(j)
t), where Φ1(Y)is the probit function.
Consequently, (z(1)
t, . . . , z(J)
4) We estimate the variance-covariance matrix Σof the
transformed load Z, relative to the 24 hours of the day.
One could do it recursively as in [15].
5) Using a multivariate Gaussian random number generator,
we generate a realization of the Gaussian distribution
6) We use the inverse probit function to transform ˜
Zto a
uniform distribution, that is, ˜
Y= Φ( ˜
7) Finally, we obtain a scenario of load by transforming back
Yusing the inverse cumulative density function from step
2, that is, ˜xt=ˆ
Yt|˜π),t. Numerically, we use a
smoothing spline to interpolate ˆ
The procedure outline above generates a scenario of load
conditioned on the retail price. Steps 5 to 7 are repeated as
many times as needed if more scenarios of load per retail price
are desired.
In this section we first introduce the datasets and the
generated scenarios using the methodology from Section V.
Then, in Section VI-B, we analyze in detail the solution
of the bidding model with and without considering risk.
Afterwards, in Section VI-C, we benchmark the performance
of the proposed models and present the final conclusions.
A. The Data and Practical Considerations
The scenarios of day-ahead and real-time prices are gen-
erated using historical hourly values from CAISO [3]. We
use three months of training data, from August to October
2014. The test period spans over November 2014. For the retail
price and price-responsive load, we use data from the Olympic
Peninsula experiment [21]. In this experiment, the electricity
price was sent out every fifteen minutes to 27 households that
participated in the experiment. The price-sensitive controllers
and thermostats installed in each house decided when to turn
30 40 50 60 70
Day−ahead price ($)
0 4 8 12 16 20
Fig. 3. Actual price, point forecast and generated scenarios for the day-ahead
on and off the appliances, based on the price and on the house
owner’s preferences. The training and test months are the same
as for the CAISO data, but relative to year 2006.
Some practical considerations need to be addressed. Firstly,
that the day-ahead price and the retail price come from two
different datasets. For this reason, prices are normalized. The
second practical consideration is that we assume Π = kΛD
with k= 1 even though it is not fulfilled in practice. However,
this does not affect the comparison of the proposed models,
due to the fact that we use the same set of scenarios for the
benchmark and for all the models. This issue could be solved
in future work when data from new experiments becomes
Throughout the case study, we set a total of 20 blocks, where
the width is equality distributed between a maximum and a
minimum bidding quantities, set to be equal to the historical
range of the scenarios at every hour. They are represented by
the dotted lines in Fig. 6(b).
For the case study we use a total of 150 scenarios. For
the estimation of the densities, we use a Gaussian kernel
with a bandwidth hgiven by Silverman’s rule of thumb [23].
Also, the bandwidth parameter is set to K= 0.5. For the
model of the day-ahead price we use an ARIMA(3,1,2)(1,1,1)
with a seasonal period of 24 hours. The Root Mean Square
Error (RMSE) for the model of the day-ahead price (13 to
36 lead hours) is, on average, $3.22, which is in line with
the forecasting performance that other authors have achieved
using similar methods [24].
A subset of the generated scenarios of day-ahead price is
given in Fig. 3. By graphical inspection we conclude the
scenarios of day-ahead price are a plausible representation of
the actual day-ahead price and its uncertainty.
B. Model Analysis
To begin with, we discuss the results from the risk-neutral
model (2). The solution to this model, for a given set of
scenarios, is calculated either using Remark 2 or by solving
(5) with β= 0. In Fig. 4, we show the scenarios of retail
price and load in dots for hour 20 of November 1st, and hour
2 of November 2nd. The estimated bidding curve is displayed
as a dashed green line. In accordance with Theorem 2, the
resulting bidding curve is flat.
16 18 20 22 24
25 35 45
Hour 2
Price ($)
35 40 45 50 55
30 50 70 90
Hour 20
Price ($)
LRisk UncRisk Scenarios
Fig. 4. The left figure is relative to the 2nd of November, while the right
figure is relative to the 1st of November.
100 200 300 400
Exected profit ($)
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4
Exp. profit
Frontier line
Fig. 5. On the left axis, in dashed red, the expected profit for every β, with L
being in the feasibility frontier line. On the right axis, the feasibility frontier
line is shown for combinations of βand L.
Next, we discuss the results from the risk-averse model (5).
We start by analyzing the effect of the risk parameters Land
βon the expected profit and the feasibility of the problem. In
Fig. 5 we show, on the right axis, the feasibility frontier plot
for Land βand its standard deviation in a shadowed area.
We calculated it empirically, using data relative to the 1st of
November. The combinations of Land βshown below the
displayed dark line result, on average, in an infeasible solution.
The frontier line is dependent on the scenarios of load: higher
variability in the scenarios of load will require a greater value
of Lfor the problem to be feasible. On the left axis of Fig.
5, we show the expected profit for the risk-averse problem,
with the combination of βand Lthat lay on the frontier line.
Naturally, the highest profits are achieved for low values of
β, that is, when the retailer is less risk averse. From now on,
we set the risk parameter βto 0.8. The value of Lis chosen
from the frontier plot, to be as small as possible.
Fig. 6(a) shows the scenarios of day-ahead price (continuous
lines), together with the estimated optimal price bids (horizon-
tal segments), for each hour. We observe that the magnitude
of the price bid depends on the scenarios of day-ahead price.
In Fig. 6(b), we show the amount of energy bought in the day-
ahead market for each scenario and the span of the bidding
blocks in dashed lines. In Fig. 6(c), we show the scenarios of
load. On average, in day-ahead market we buy approximately
the expected value of the load.
20 40 60 80
Price ($)
10 30 50 70
Hour of the day
Load (Kwh)
10 30 50 70
Hour of the day
Load (Kwh)
0 4 8 12 16 20
Fig. 6. In (a), the day-ahead scenarios (lines) are shown together with the
estimated price-bids (horizontal segments). The day-ahead purchase for each
scenario, and the load in each scenario, are shown in (b) and (c), respectively.
The estimated price bid by the risk-averse model is repre-
sented by the continuous red line in Fig. 4. Note that, at hour 2,
the estimated price-responsiveness is much smaller than during
hour 20. The reason is that, according to the scenarios of load,
the load shows a lower variation during the early morning than
during the early night.
C. Benchmark: Results in November and December
In this subsection, we benchmark the following models:
ExpBid: Single block model, where E1is equal to the
expected value of the load, and the price-bid of the single
block is equal to infinity. In other words, we always buy the
expected load in the day-ahead market. No optimization is
needed as the solution is trivial.
LRisk: Risk-averse model (5) with 20 bid blocks. The
price-bid for each block is optimized.
UncRisk: Unconstrained risk model (2). The solution can
be obtained by using Remark 2 or by solving (5) with β= 0.
In order to reproduce the real-time functioning of the
markets, we validate the models using a rolling horizon
procedure. Everyday at 12:00, we generate scenarios for the
next operational day, and afterwards obtain the optimal bidding
curve for all the benchmark models. The data from the last
two months is used in the scenario-generation procedure, and
the process is repeated daily all over the months of November
and December.
In Table I we show the mean (1st column) and the standard
deviation (2nd) of the profit for the three benchmark models,
during November and December. We observe that the simple
model ExpBid under-performs the rest of the models and,
indeed, delivers a negative expected profit. The risk-optimized
Mean Std. dev.
ExpBid -1.78 34.52
LRisk 22.26 45.22
UncRisk 188.82 259.62
problem LRisk yields positive expected profit, with a variance
greater than the ExpBid model but substantially lower than for
the UncRisk problem. The risk-unconstrained model UncRisk,
as anticipated, provides the highest mean returns.
In this paper we consider the bidding problem of a retailer
that buys energy in the day-ahead market for a pool of
price-responsive consumers. Under the considered setup, the
deviations from the purchased day-ahead energy are traded
at the real-time market. We provide an analytic solution in
the case that the retailer is not risk averse. Additionally, we
formulate a stochastic programming model for optimal bidding
under risk aversion. The price-responsiveness of the consumers
is derived from a real-life dataset, and the modeling approach
is non-parametric where non-linear relationships are allowed.
The analytic results show that, in the risk-unconstrained
case, the optimal bid is a single price, meaning that there is
no extra benefit from bidding a curve. On the other hand,
the computational results from the risk-averse case show
that a block-wise bidding curve successfully mitigates the
risk in terms of profit volatility. Altogether, the proposed
methodology allows the retailer to optimally bid in the day-
ahead market, whether it is for expected-profit maximization
(by leveraging arbitrage opportunities), or for the purpose of
safely procuring energy.
Future work can follow several directions. From the view-
point of the modeling of the retailer’s trading problem, it could
be of interest to compare our chance-constraint approach for
risk control with alternative ones. For example, one could
consider, instead of the chance constraint (1d), a weighted
sum of the expected value of the retailer’s profit and its
Conditional Value at Risk (CVaR) in the objective function
of problem (1), possibly establishing a link between the risk-
related parameters that define both approaches. Likewise, it
would be interesting to extend the retailer’s trading problem
(1) to allow for complex market bids. On a data-modeling
level, one could explore how to extend the proposed method
to generate scenarios when weather variables are considered.
Finally, on a practical level, a possible avenue for future
research would be to compare the results compiled in the case
study of this paper with those that would be obtained for a
control group of price-irresponsive loads. This way, we could
properly assess how much the retailer benefits from supplying
price-sensitive loads and accounting for their price-responsive
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We start by computing the expected profit, conditional on
the day-ahead price (i.e., we treat ΛD=λDas a parameter).
We disregard the first term in (2) since it is constant with re-
spect to the decision variables uband XD, and therefore, does
not affect the solution. The expected profit (2) conditioned on
the day-ahead price λDis thus given by
Iut,b λD
Note that, since λD
tis given, XDcan be computed as
b=1 Iut,b λD
tCb. We distinguish three cases:
(a) When EΛR> λD, the second term in (8) is positive,
hence the expected profit is maximized when ub
ub+1 λD,b. This implies that uBλD.
(b) When EΛR< λD, the second term in (8) is negative,
hence the profit is maximized when ub+1 ub<
λD,b. This implies that u1λD.
(c) When EΛR=λD, any solution that satisfies ub+1
ubis optimal.
Finally, we conclude that the expected value of the real-time
price is an optimal price bid, since u
the retailer’s expected income in the three cases above.
Analogously as in Appendix A, from Equation (8), the
expected profit conditioned on ΛD
t=λDis proportional to
Iut,b λD
Next, recall that, from the basic properties of the expected
value, EX{g(X)}=R
−∞ g(x)fX(x)dx. We compute the
expected value of (9) with respect to ΛD, which is equal to:
with g(λD)equal to (9). Arranging terms, we obtain that (10)
is equal to
Cb Zub
−∞ Z
Now we relax problem (2) by dropping constraint (1c).
Then, the problem becomes decomposable by block, since (11)
is a sum of Belements. For notational purposes, let us rename
each of the Bterms in the summation in (11) by hb(ub).
Note the functions hb(ub)are continuous, since the integral
of a continuous function is continuous. Then, for each block,
the relaxed problem consists in maximizing the continuous
function hb(ub)subject to λubλ. By the intermediate
value theorem, we know that the maximum of each term in
the summation will be achieved either at u
b=λ, at u
or otherwise inside the interval λ, λ.
Considering the case when u
bis inside the interval, we
proceed to find the ubsuch that it maximizes hb(ub). In
order to achieve this, we calculate d
dubhb(ub)=0. Note that
du Ru
−∞ φ(x)dx =φ(u). With this in mind, the derivative of
hb(ub)is equal to
Assuming that fΛD(λD=ub)is different than zero, and
solving d
dubhb(ub)=0, we obtain the stationary point:
Next we calculate the second derivative2of d2
b). Its
sign depends on the value of (d
which can be interpreted as the sensitivity of the expected
real-time price to the day-ahead price at the stationary point.
Depending on the sign of the second derivative, we distinguish
three cases:
(a) When d
bis a local maximum.
From a practical point of view, it means that at day-
ahead price λD=u
b, any marginal increase of this price
will imply a comparatively lower marginal increase in the
expected real-time price, hence, it becomes not profitable
to buy energy from the day-ahead market at price levels
greater than u
(b) When d
bis a local minimum.
(c) When d
b= 1, the solution u
bis an
inflection point that delivers an expected profit equal to
After having identified the possible candidates ubthat might
maximize hb(ub), it is easy to see that at least one global
optimum to problem (2) satisfies that all ubare all equal to
each other, i.e., ub=u,b. This is so because functions
hbare all identical for all blocks, hence, the solution u
yields the highest expected profit for one block bwill also
deliver the highest expected profit for the remaining blocks.
Finally, we should point out that this global solution to
the relaxed problem (2)—without constraint (1c)— naturally
satisfies constraint (1c), hence, it must also be a global solution
to the original problem (2).
2The calculation of d2
b), where u
bis given by (13), is available
upon request.
... The bidding cost derives from the DA market cost (i.e., purchasing the electricity in the day-ahead market), the balancing cost (i.e., purchasing/selling the electricity in the RT market) and the penalty cost. It should be noticed that the DER aggregator sells electricity to customers at RT retail prices which can be assumed to be proportional to the DA or RT market prices [38]. ...
... It should be noticed that when the imbalance quantity P Bid;RT t bidding in the RT market exceeds a tolerance limit (e.g. a percentage of bidding quantity in the DA market [), penalty cost will be charged, shown as Eq. (38), otherwise, the penalty cost is 0. The penalty mechanism can avoid large energy deviation in the RT market. Eq. (39) presents the total penalty cost in the operation day. ...
This paper addresses the optimal decision problem of a distributed energy resources (DER) aggregator who manages wind turbines, solar PV systems and battery energy storage (BES) units while implementing real-time pricing (RTP) demand response program. The DER aggregator can procure electricity by bidding in the electricity market and scheduling its DER to meet the load demand of its customers. In the bidding and scheduling processes, the intrinsic uncertainties of distributed renewable generations and customer’s responsiveness to RTP program have brought economical risks to the DER aggregator, which will lower the DER aggregator’s profit. However, most of the current researches only consider the uncertainty of renewable generations while neglecting the uncertainty of customer’s responsiveness. To this end, a robust optimization-based day-ahead optimal bidding and scheduling model is proposed for DER aggregator by jointly considering these two uncertainties. The objective of the proposed model is to maximize the aggregator’s profit via optimally determining the hourly bidding quantities in the day-ahead market and the scheduled output power of distributed renewable generations and BES units. Case studies demonstrate that the proposed robust optimization model can help DER aggregator reduce the bidding and scheduling costs to obtain a higher expected profit.
The single-task deep reinforcement learning (STDRL)-based methods solve the joint bidding and pricing problem for the electricity retailer in a hierarchical electricity market by defining a bidding policy and a pricing policy separately, which may suffer from low learning efficiency, time-consuming training and local optimization. To deal with these issues, this paper proposes a novel Multi-task Deep reinforcement learning approach for Joint Bidding and Pricing (MDJBP) optimization model. MDJBP can deal with the bidding and pricing tasks concurrently through a shared long short-term memory (LSTM) representation network to distill meaningful temporal characteristics from high-dimensional environment states. Furthermore, we develop a deep neural network (DNN) structure consisting a regression branch for bidding task and a soft actor-critic (SAC) branch for pricing task with automating entropy adjustment and adaptive loss weighting to implement MDJBP. The proposed multi-task deep reinforcement learning (MTDRL)-based method is tested with the IEEE 30-bus system. Numerical results show that the proposed methodology succeeds in giving an optimal joint bidding and pricing policy by fully exploiting commonalities and differences between bidding task and pricing task, and thereby boosts the profit, improves learning efficiency, reduces training time, and enhances stability.
Electricity price forecasting is very important for market participants in a deregulated market. However, only a few literatures have investigated the impact of forecasting errors on the market participants’ behaviors and revenues. In this chapter, a general formulation of bidding in the electricity market is considered and the participant is assumed to be a price-taker which is general for most of the participants in power markets. A numerical method for quantifying the impact of forecasting errors on the bidding curves and revenues based on multiparametric linear programming (MPLP) is proposed. The forecasted prices are regarded as exogenous parameters for both deterministic and stochastic bidding models. Compared with the existing method, the proposed method can calculate how much improvement will be achieved in the cost or revenue of the bidder if he reduces the price forecasting error level, and such calculation does not require any predefined forecasting results. Numerical results and discussions based on real-market price data are conducted to show the application of the proposed method.
This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.
Massive and various bad data may be introduced to load profiles in the process of data acquisition, transmission, and storage deliberately or accidentally due to cyber attacks and equipment failures. The bad data may result in bias for load forecasting and other data analytic applications. This chapter proposes a novel bad data identification and repairing method for load profiles. In the first stage, the Singular Value Thresholding (SVT) algorithm is applied to complete the missing data and detect the anomaly spikes roughly. In the second stage, quantile regression with lag value is performed to detect local fluctuation. How the data cleaning influences the forecasting performance is also investigated. Case studies on the load data of Fujian Province, China are conducted to verify the effectiveness of the proposed method.
Transactive energy (TE) has been identified to provide better grid efficiency and reliability by market-based transactive exchanges between energy producers and energy consumers. Simulations of TE systems are crucial to evaluate the benefits and impacts of different transactive mechanisms. However, such simulations can be time consuming due to the information exchange between various participants and complex co-simulation environments. In this paper, we develop a reduced order model to speed up the simulation of transactive systems in TE simulation platform (TESP) while achieving very low error between the reduced order and full model results. Specifically, the developed reduced order model consists of an aggregate responsive load agent which utilizes two Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTMs) to enable transactive elements to collectively participate in the TE system. The proposed aggregate responsive load (ARL) agent is able to produce similar transactive behaviours to the full simulation model while achieving significant simulation time reduction. We also show that the developed model enables generalization of simulation results across different dates and across different number of loads included in the simulations.
With the grid's evolution, the end-users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies. To design efficient and precise LF, information about various factors that influence end-users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal-Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to demonstrate the influence of various factors on electrical demand. The proposed algorithm's effectiveness for LF and error formulations shows that optimal-BRA-enabled LF presents better accuracy than state-of-the-art approaches. Thus, the proposed electric demand prediction strategy could help the system operator know DR potential at different times better, leading to optimal system resources dispatching through DR actions.
The introduction of renewables, distributed generation, microgrids, electric vehicles, and new market actors, such as aggregators, have led to a remarkable change in the power network. To address the issues that such a profound modification implies on a modern energy system, here a new hierarchical architecture is presented. Specifically, the proposed approach considers the case of an aggregator of consumers in the balancing market, in which incentives for local users (i.e., microgrids, buildings) are considered as well as flexibility assessment for demand response, and CO2 emissions. The main innovation is related to the overall architecture and to the formalization of the upper level decision problem that aims at coordinating local users in a democratic way, while, at the lower level, consumers want to track the aggregator’s reference values performing demand response programs. The approach is applied to a real case study.
Load aggregators can use demand response programs to motivate residential users toward reducing electricity demand during peak time periods. This paper proposes a demand response algorithm for residential users, while accounting for uncertainties in the load demand and electricity price, users’ privacy concerns, and power flow constraints imposed by the distribution network. To address the uncertainty issues, we develop a deep reinforcement learning (DRL) algorithm using an actor-critic method. We apply federated learning to enable users to determine the neural network parameters in a decentralized fashion without sharing private information (e.g., load demand, users’ potential discomfort due to load scheduling). To tackle the nonconvex power flow constraints, we apply convex relaxation and transform the problem of updating the neural network parameters into a sequence of semidefinite programs (SDPs). Simulations on an IEEE 33-bus test feeder with 32 households show that the proposed demand response algorithm can reduce the peak load by 33% and the expected cost of each user by 13%. Also, we demonstrate the scalability of the proposed algorithm in 330-bus and 1650-bus feeders with real-time pricing scheme. Keywords: demand response, deep reinforcement learning, federated learning, power flow, semidefinite program.
Full-text available
This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the complex market bid that best captures the price-response of the cluster. The complex market bid is defined as a series of marginal utility functions plus some constraints on demand, such as maximum pick-up and drop-off rates. The proposed modeling approach also leverages information on exogenous factors that may influence the consumption behavior of the cluster, e.g., weather conditions and calendar effects. We test the proposed methodology for a particular application: forecasting the power consumption of a small aggregation of households that took part in the Olympic Peninsula project. Results show that the price-sensitive consumption of the cluster of flexible loads can be largely captured in the form of a complex market bid, so that this could be ultimately used for the cluster to participate in the wholesale electricity market.
One year of demand bids in the California energy market are analyzed and observations are reported on type, size, shape, and other characteristics of the bids. The implications of these observations, the underlying causes, and the potentials to improve demand bids by exploiting load flexibility are discussed.
Advances in IT, control and forecasting capabilities have made demand response a viable, and potentially attractive, option to increase power system flexibility. This paper presents a critical review of the literature in the field of demand response, providing an overview of the benefits and challenges of demand response. These benefits include the ability to balance fluctuations in renewable generation and consequently facilitate higher penetrations of renewable resources on the power system, an increase in economic efficiency through the implementation of real-time pricing, and a reduction in generation capacity requirements. Nevertheless, demand response is not without its challenges. The key challenges for demand response centre around establishing reliable control strategies and market frameworks so that the demand response resource can be used optimally. One of the greatest challenges for demand response is the lack of experience, and the consequent need to employ extensive assumptions when modelling and evaluating this resource. This paper concludes with an examination of these assumptions, which range from assuming a fixed linear price-demand relationship for price responsive demand, to modelling the highly diverse, distributed and uncertain demand response resource as a single, centralised negative generator, adopting fixed characteristics and constraints.
This paper focuses on the coordination of a population of thermostatically controlled loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the device bidding and market clearing strategies to motivate self-interested users to realize efficient energy allocation subject to a peak energy constraint. This coordination problem is formulated as a mechanism design problem, and we propose a mechanism to implement the social choice function in dominant strategy equilibrium. The proposed mechanism consists of a novel bidding and clearing strategy that incorporates the internal dynamics of TCLs in the market mechanism design, and we show it can realize the team optimal solution. This paper is divided into two parts. Part I presents a mathematical formulation of the problem and develops a coordination framework using the mechanism design approach. Part II presents a learning scheme to account for the unknown load model parameters, and evaluates the proposed framework through realistic simulations.
Time-shiftable loads have recently received an increasing attention due to their role in creating load flexibility and enhancing demand response and peak-load shaving programs. In this paper, we seek to answer the following question: how can a time-shiftable load, that itself may comprise of several smaller time-shiftable subloads, submit its demand bids to the day-ahead and real-time markets so as to minimize its energy procurement cost? Answering this question is challenging because of the inter-temporal dependencies in choosing the demand bids for time-shiftable loads and due to the coupling between demand bid selection and time-shiftable load scheduling problems. Nevertheless, we answer the above question for different practical bidding scenarios and based on different statistical characteristics of practical market prices. In all cases, closed-form solutions are obtained for the optimal choices of the price and energy bids. The bidding performance is then evaluated in details by examining several case studies and analyzing actual market price data.
An optimal supply and demand bidding, scheduling, and deployment design framework is proposed for battery systems. It takes into account various design factors such as the day-ahead and real-time market prices and their statistical dependency, as well as the location, size, efficiency, lifetime, and charge and discharge rates of the batteries. Utilizing second-life/used batteries is also considered. Without loss of generality, our focus is on the California Independent System Operator (ISO) energy market and its two available bidding options, namely self-schedule bidding and economic bidding. While the formulated stochastic optimization problems are originally nonlinear and difficult to solve, we propose a methodology to decompose them into inner and outer subproblems. Accordingly, we find the global optimal solutions within a short amount of computational time. All case studies in this paper are based on real market data.