ArticlePDF Available

Game-Theory Modeling for Social Welfare Maximization in Smart Grids

Authors:

Abstract and Figures

In this paper, we study the Stackelberg game-based evolutionary game with two players, generators and energy users (EUs), for monetary profit maximization in real-time price (RTP) demand response (DR) systems. We propose two energy strategies, generator’s best-pricing and power-generation strategy and demand’s best electricity-usage strategy, which maximize the profit of generators and EUs, respectively, rather than maximizing the conventional unified profit of the generator and EUs. As a win–win strategy to reach the social-welfare maximization, the generators acquire the optimal power consumption calculated by the EUs, and the EUs obtain the optimal electricity price calculated by the generators to update their own energy parameters to achieve profit maximization over time, whenever the generators and the EUs execute their energy strategy in the proposed Stackelberg game structure. In the problem formulation, we newly formulate a generator profit function containing the additional parameter of the electricity usage of EUs to reflect the influence by the parameter. The simulation results show that the proposed energy strategies can effectively improve the profit of the generators to 45% compared to the beseline scheme, and reduce the electricity charge of the EUs by 15.6% on average. Furthermore, we confirmed the proposed algorithm can contribute to stabilization of power generation and peak-to-average ratio (PAR) reduction, which is one of the goals of DR.
Content may be subject to copyright.
energies
Article
Game-Theory Modeling for Social Welfare
Maximization in Smart Grids
Yu Min Hwang 1ID , Issac Sim 1, Young Ghyu Sun 1ID , Heung-Jae Lee 2and Jin Young Kim 1 ,*
1Department of Wireless Communications Engineering, Kwangwoon University, Seoul 01897, Korea;
yumin@kw.ac.kr (Y.M.H.); dltkr34@kw.ac.kr (I.S.); yakrkr@kw.ac.kr (Y.G.S.)
2Department of Electrical Engineering, Kwangwoon University, Seoul 01897, Korea; hjlee@kw.ac.kr
*Correspondence: jinyoung@kw.ac.kr; Tel.: +82-02-940-5567
Received: 30 June 2018; Accepted: 14 August 2018; Published: 3 September 2018


Abstract:
In this paper, we study the Stackelberg game-based evolutionary game with two players,
generators and energy users (EUs), for monetary profit maximization in real-time price (RTP)
demand response (DR) systems. We propose two energy strategies, generator’s best-pricing and
power-generation strategy and demand’s best electricity-usage strategy, which maximize the profit
of generators and EUs, respectively, rather than maximizing the conventional unified profit of the
generator and EUs. As a win–win strategy to reach the social-welfare maximization, the generators
acquire the optimal power consumption calculated by the EUs, and the EUs obtain the optimal
electricity price calculated by the generators to update their own energy parameters to achieve
profit maximization over time, whenever the generators and the EUs execute their energy strategy
in the proposed Stackelberg game structure. In the problem formulation, we newly formulate
a generator profit function containing the additional parameter of the electricity usage of EUs
to reflect the influence by the parameter. The simulation results show that the proposed energy
strategies can effectively improve the profit of the generators to 45% compared to the beseline scheme,
and reduce the electricity charge of the EUs by 15.6% on average. Furthermore, we confirmed the
proposed algorithm can contribute to stabilization of power generation and peak-to-average ratio
(PAR) reduction, which is one of the goals of DR.
Keywords:
smart grid; game-theoretic modeling; social welfare maximization; Stackelberg game;
pricing and power-generation strategy
1. Introduction
With advanced communication networks and intelligent controllable electrical devices or energy
users (EUs), the smart grid makes it possible to achieve the distributed control and the distributed
energy management (DEM) [
1
]. DEM plays a key role in the distributed monitoring, controlling,
scheduling, and optimization of the profit of both generators and demands for the implementation of
demand response (DR) programs [
2
]. DR, defined as the energy-usage changes of users in response to
varying electricity prices or to incentive payments [
3
], induces EUs to consume less energy during
periods of high wholesale market prices or at the peak power consumption. This price-based DR
program can be optimally implemented through a continuous interaction between the users and
the service provider. The user needs to adjust the electricity usage in consideration of the varying
electricity price over time, while the service provider also needs to properly adjust the electricity price
with the amount of power generation to motivate users to evenly use electricity over time [
4
]. The most
efficient use of the smart grid, for example, is the increasing of the energy efficiency through measures
such as the decrease of the peak-to-average ratio (PAR) of the energy demand; however, in reality,
this will determine the extent of the monetary profits in the generator and demand sides.
Energies 2018,11, 2315; doi:10.3390/en11092315 www.mdpi.com/journal/energies
Energies 2018,11, 2315 2 of 23
To maximize the monetary profit (or social welfare), various studies on smart-home scheduling
have been completed, as follows: the formulation of a linear programming problem for smart-home
scheduling in consideration of the uncertainty of energy consumption [
5
], a Markov-chain model of the
scheduling problem and the development of the backtrack algorithm based on a decision threshold [
6
],
a dynamic-programming algorithm to schedule home appliances in consideration of multiple power
levels [
7
], the consideration of the distributed-load-management problem as a congestion game
with a dynamic pricing strategy to discourage the energy consumption at peak hours [
8
], and the
deployment of pricing strategies by local aggregators to control the energy load [
9
,
10
]. In [
11
],
an optimal management system of battery energy storage to enhance the resilience of the microgrid
is proposed while maintaining its operational cost at a minimum level. In [
12
], a two stage energy
management strategy for the contribution of plug-in electrical vehicles (PEVs) in demand response
programs of commercial building microgrids is addressed for energy management optimization.
In [
13
], by forming coalitions for gaining competitiveness in the energy market, a smart transactive
energy (TE) framework in which home microgrids (H-MGs) can collaborate with each other in a
multiple H-MG system is presented and analyzed. In [
14
], an optimization-based algorithm in which
an objective function premised on economic strategies, distribution limitations and the overall demand
in the market structure is proposed with emphasizing optimum use of electrical/thermal energy
distribution resources, while maximizing profit for the owners of the H-MGs.
Over the past few years, related studies that are based on game-theory modeling for the energy
management in a real-time price (RTP)-based DR have also been conducted. In [
15
], a cake-cutting game
(CCG) for the selection of discriminate prices for different users was investigated. In [
4
], an RTP-based
DR algorithm for the achievement of an optimal load control regarding the devices in a facility that is
obtained through the forming of a virtual electricity-trading process is proposed. In [
16
], an aggregate
game is adopted for the modeling and analysis of the energy-consumption control in the smart grid
and Nash seeking strategies are developed. In [
17
,
18
], the Stackelberg game was leveraged to model
the interaction between the demand-response aggregators and generators. In [
19
], the authors propose
a light-weight DR scheme for managing energy consumption based on a non-iterative Stackelberg
model and historical real-time pricing without iterations for the massive smart manufacturing systems.
In [
20
], a multiagent-based energy market for multi-microgrid systems using game-theoretic and
hierarchical optimization approaches is proposed to achieve the optimal operation of smart microgrids
in distribution systems. In [
21
], an advanced retail electricity market based on game theory for the
optimal operation of H-MGs and their interoperability within active distribution networks is proposed
and the optimal solution is achieved using the Nikaido-Isoda Relaxation Algorithm (NIRA) in a
non-cooperative gaming structure. In [
22
] and [
23
], a consensus-based distributed-energy-management
algorithm for both sides of an indirectly connected network is proposed. However, the following
shortcomings relative to the game structure and formulas for profit maximization can be identified in
the previous research, which have been addressed in this paper:
1.
It is necessary to study an optimization methodology that maximizes the monetary profit of the
generators and the demands, respectively, rather than the optimization methodology based on
the unified profit of the generators and the demands [24,25].
2. As an revolutionary game based on the Stackelberg game structure, it is necessary to study that
the players (demands and generators) obtain the each other’s energy parameters, such as the
time-varying electricity price and an amount of electricity consumption with spying on each
other’s energy strategy to effectively and adaptively maximize their profit.
In view of these needs for research, in this paper, the Stackelberg game model where the generators
and the demands that are the players of the stackelberg game alternatively maximize their respective
profits using their own energy strategy over time while watching each other’s energy strategies is
studied. The contributions of this paper are as follows:
Energies 2018,11, 2315 3 of 23
1.
We propose two energy strategies for profit maximization for both the generators and
EUs (Generator’s Best-Pricing and Power-Generation Strategy in Section 3.1, Demand’s Best
Electricity-Usage Strategy in Section 3.2) based on the Stackelberg game as an evolutionary game
where the players (demands and generators) alternately perform their energy strategy with
spying on each other’s energy strategy to update their own energy parameters to achieve profit
maximization over time in smart grid demand response. Whenever the generators and the EUs
execute their energy strategy in the proposed Stackelberg game structure, the generators acquire
the optimal power consumption calculated by the EU, and the EUs obtain the optimal electricity
price calculated by the generator. To the best knowledge of the authors, this game structure based
on the aforementioned parameter exchange (optimal power consumption and optimal electricity
price) for profit maximization in the smart grid demand response is first studied.
2.
We newly formulate a generator profit function including the additional parameter,
the electricity-consumption of EUs, compared with that of the conventional profit function [
26
]
since the profit of generators can be influenced by the electricity-consumption of EUs.
3.
We greatly improve the monetary profit of the generators and EUs using the proposed two energy
strategies by optimizing the amount of the power generation and the electricity price in the
generator side, and electricity consumption in the EU side.
4.
As one of the simple and powerful electricity-usage control strategy of the demand that
is applicable to the time-varying electricity market, we newly propose a market-adaptive
electricity-usage scheduling algorithm which maximizes the demand’s profit by calculating
the amount of power that should be consumed according to the time-varying electricity price.
5.
The proposed profit maximization algorithm can solve the existing PAR reduction problem
because the energy usage immediately increases as the electricity price goes down, and the
energy usage goes down as the price goes up according to the proposed game structure. It is
also possible to alleviate the problem that the price greatly fluctuates because if the price changes
greatly, the power plant will lose its benefits and will not be able to withstand it. The problem of
making power generation stable is consistent with reducing PAR, which is one of DR’s ultimate
goals. Stable power generation can reduce useless power generation, which makes our energy
resources the most efficient to use, and can contribute to addressing issues such as global warming
in the energy industry.
The Stackelberg game model is used to realistically model the profit maximization scenarios
wherein two players, the generators and the demands, repeatedly modify their energy strategies
by checking on each other’s energy strategy in the order of time. It is confirmed here that the
monetary profit of the both is optimized when the two players compete strategically over time. Further,
a modeling of the demand profit and the optimization of the EU energy usage is performed based on a
time-varying electricity price. Since the electricity price at different times can be significantly different
even in a single day, it is necessary to optimally schedule the electricity usage for all of the demands,
while the electricity-price changes are monitored. It is assumed here that the comprehensive judgment
and control of the EU electricity usage are optimally performed by the smart-home scheduler. In this
paper, we provide the profit maximization methodology based on the profit of the generators and EUs,
respectively, not the unified profit of the generators and EUs.
The rest of this paper is organized as follows: in Section 2, we formulate the new profit function of
the generators and the profit maximization problem for the generators and EUs, respectively, based on
the Stackelberg game. In Section 3, we propose two profit maximization algorithms as a solution of the
problem, and present the schematic overview and application of the proposed algorithms. In Section 4,
simulation results are presented and the practical benefits and advantages of the proposed algorithm
are described. Finally, we conclude the paper in Section 5.
Energies 2018,11, 2315 4 of 23
2. System Model and Problem Formulation
For the problem formulation, a smart grid with a network of
K
-distributed EUs and
I
generators
was considered. It was assumed that each EU
k∈ K ={1, . . . , K}
and each generator
i∈ I ={1, . . . , I}
are directly connected as the communication topology of the network (i.e., any two nodes are connected
by a directed path). In this paper, it should be noted that the assumption of the strongly connected
communication network renders the distributed-energy-management problem to be more general
and relaxes the undirected-connection assumption [
22
]. A block-processing model was adopted to
schedule the load demands according to periodic time blocks. We divide the total scheduling time
into ttime periods
t∈ T ={1, . . . , T}
with a constant length
tl
. We consider that scheduling horizon
for an EU is 24 h (a single day), that is
tl
and
T
are set to 1 h and 24, respectively, in this paper.
All parameters and functions used in this paper are listed in Table 1. Furthermore, the systemic model
of the electricity buying and selling is shown in Figure 1. The notion used is listed in Table 1. The users
are equipped with an advanced metering infrastructure (AMI) and an energy-management controller
(EMC) [
22
]. Load information for each EU is exchanged between these two modules. The AMI is
used to schedule, control and optimize the electricity usage for each EU and enables bidirectional
communication between the EUs and the DEM which is connected to the generators. DEM plays a role
in optimizing the benefits of both the EU network (demand) and the generator network (energy supply)
based on the DR programs and real-time pricing to improve the energy-usage efficiency. Further,
the following three key optimization variables (the electricity price, the generation power, and the
demand power) are employed to realize the coordination of the generators and the EUs.
In the proposed evolutionary game, we assume that the players have perfect rationality.
They always act in a way that maximizes their profit, and are capable of obtaining the energy
information to calculate the best response to other players’ energy strategies. The tractability of the
perfect rationality game can be realized by using the infrastructure, such as smart meter, EMC, DEM
and wired/wireless communications to obtain maximum profit of the cooperative and win-win players.
Energies 2017, 10, x FOR PEER REVIEW 4 of 22
nodes are connected by a directed path). In this paper, it should be noted that the assumption of the
strongly connected communication network renders the distributed-energy-management problem to
be more general and relaxes the undirected-connection assumption [22]. A block-processing model
was adopted to schedule the load demands according to periodic time blocks. We divide the total
scheduling time into t time periods ={1, … , } with a constant length . We consider that
scheduling horizon for an EU is 24 h (a single day), that is and are set to 1 h and 24, respectively,
in this paper. All parameters and functions used in this paper are listed in Table 1. Furthermore, the
systemic model of the electricity buying and selling is shown in Figure 1. The notion used is listed in
Table 1. The users are equipped with an advanced metering infrastructure (AMI) and an energy-
management controller (EMC) [22]. Load information for each EU is exchanged between these two
modules. The AMI is used to schedule, control and optimize the electricity usage for each EU and
enables bidirectional communication between the EUs and the DEM which is connected to the
generators. DEM plays a role in optimizing the benefits of both the EU network (demand) and the
generator network (energy supply) based on the DR programs and real-time pricing to improve the
energy-usage efficiency. Further, the following three key optimization variables (the electricity price,
the generation power, and the demand power) are employed to realize the coordination of the
generators and the EUs.
In the proposed evolutionary game, we assume that the players have perfect rationality. They
always act in a way that maximizes their profit, and are capable of obtaining the energy information
to calculate the best response to other players’ energy strategies. The tractability of the perfect
rationality game can be realized by using the infrastructure, such as smart meter, EMC, DEM and
wired/wireless communications to obtain maximum profit of the cooperative and win-win players.
Figure 1. Systemic model of social-welfare maximization in a smart grid with demand response (DR)
systems.
Figure 1.
Systemic model of social-welfare maximization in a smart grid with demand response
(DR) systems.
Energies 2018,11, 2315 5 of 23
Table 1. Notation list.
Notations Meanings of Notations
K={1, . . . , K},k∈ K The set of energy users (EUs)
I={1, . . . , I},i∈ I The set of generators
T={1, . . . , T},t∈ T Total scheduling time set
tlThe constant time length
pt(cents/kWh) The electricity price per unit energy
Pi,t(kWh) The amount of the actual providable power
PL
i,t(kWh) The transmission losses
PT
i,t(kWh) The total amount of generated power
Ci,tPT
i,t(cents) The cost to generate power for generator i
aicents/kWh2,bi(cents/kWh), and ci(cents)The fitting parameters of the cost function Ci,tPT
i,t
diThe coefficient for power loss
Pm
iand PM
iThe minimum and maximum bound of PT
i,t
UG
i,tThe profit function for generator i
UG
tThe total profit for all generators
RtReal profit of generators
Mi,tMaximum achievable profit of the generator i
Pk,tThe amount of the electricity usage of UE k
ˆ
Pk,tThe amount of electricity to be used in the future
divided by the remaining time
Pk,tThe additional or abandoned electricity usage
UEU
kThe electricity charge for UE k
UEU The total electricity charge for all UEs
G=(N,A,U)
The Stackelberg game where Nis a player set that is
composed of generators and EUs, Ais the constraint
set, and Uis the profit set
pmand pMThe minimum and maximum prices of the electricity
Pm
kand PM
kThe minimum and maximum electricity-usage
ϕkThe minimum required electricity consumption
η?The optimum solution of P1
P?
i,t,p?
tand P?
k,tThe optimized value of Pi,t,ptand Pk,t
FA set of feasible solutions
LGThe Lagrange dual function of P3
βi,t,γi,t,δt,εt, and θtThe Lagrange multipliers
NG
Mand NG
S
The number of iterations for the master-loop and
slave-loop in Algorithm 1
ω1,ω2,ω3,ω4, and ω5The iteration steps for optimization
ptThe base price as the historical average of pt
twThe window size of the time slots
UEU The total electricity charge accumulated for T
2.1. Generator Profit
As the player of the Stackelberg game for each time t, each generator iaims to maximize its own
profit by adjusting and optimizing the electricity price per unit energy
pt
(cents/kWh) and the amount
of the actual providable power
Pi,t
(kWh), excluding the transmission losses
PL
i,t
(kWh), in the total
amount of generated power
PT
i,t=Pi,t+PL
i,t
(kWh). To design a practical electric-power-transmission
system, the parameters of the modeling of the transmission-loss factors were considered since these
are inevitable in a power grid [
22
]. According to the micro-incremental transmission losses of each
generator [
24
], the transmission-loss amount
PL
i,t
that is induced by the ican be represented using the
following simple quadratic function:
PL
i,t=diPT
i,t2(1)
Energies 2018,11, 2315 6 of 23
where
di
is the loss coefficient. The total amount of generated power
PT
i,t
is derived by jointly solving
the Equation (1) and PT
i,t=Pi,t+PL
i,tas follows:
PT
i,t=Pi,t+PL
i,t=Pi,t+12Pi,tdip14Pi,tdi
2di
(2)
In reference to [
25
27
], the cost
Ci,tPT
i,t
can be represented by the quadratic function of
PT
i,t
,
as follows:
Ci,tPT
i,t=aiPT
i,t2+biPT
i,t+ci(3)
where
aicents/kWh2
,
bi(cents/kWh)
, and
ci(cents)
are the fitting parameters of the cost function,
and the minimum (maximum) bound of
PT
i,t
is denoted by
Pm
iPM
i
. Therefore, a new profit
function
UG
i,t(Pi,t,ptPk,t)
and the real-profit-to-maximum-achievable-profit ratio (RMR), with the
latter composed of the two profit functions for the ifor each time t, was defined, as follows:
UG
i,t(Pi,t,ptPk,t)=Real profit
Maximum achievable profit =Rt(pt|Pk,t)
Mi,t(Pi,t,pt)
=ptK
k=1Pk,t
ptPi,tCi,t(Pi,t+PL
i,t)
(4)
where
Pk,t
is the amount of the electricity usage of each EU kfor each t. It becomes evident that
the newly defined profit function of (4) is more reasonable and practical compared with that of [
26
],
since the additional parameter
Pk,t
is considered in (4) to reflect the relationship between the real profit
and the maximum achievable profit. The generator total profit for each tis represented as the sum of
the profits of the i, as follows:
UG
t(Pi,t,ptPk,t)=Rt(ptPk,t)
Mt(Pi,t,pt)=Rt(ptPk,t)
I
i=1Mi,t(Pi,t,pt)(5)
2.2. EU Profit
It was supposed that EUs schedule their energy consumption in consideration of the time-varying
electricity price to minimize their electricity charge without reducing the total amount of electricity
that should be used. The electricity charge accumulated until the
t
for the
Pk,t
of the kis written as
UEU
k(Pk,tpt), as follows:
UEU
k(Pk,tpt)=
t
t=1
UEU
k,t(Pk,tpt)=
t
t=1
ptPk,t=
t
t=1
ptˆ
Pk,t+Pk,t(6)
where
ˆ
Pk,t
is the amount of electricity to be used in the future divided by the remaining time, and
Pk,t
is the additional or abandoned electricity usage for the current tin consideration of the current
electricity price pt.
Remark 1.
Reducing the total electricity usage itself will obviously reduce the electricity charge, but its usage
is not intended. In this paper,
Pk,t
was modeled to reduce the electricity charge through the adjusting of the
Pk,t
while the total electricity consumption was retained. For example, the
Pk,t
increased at the low
pt
and
decreased at the high
pt
in consideration of the setting of different prices for different periods by the generators.
Then, the total electricity charge accumulated until the
t
, and then
UEU
was introduced as the sum of each k
electricity charge, as follows:
UEU (Pk,tpt)=
K
k=1
UEU
k(Pk,tpt)(7)
Energies 2018,11, 2315 7 of 23
2.3. Optimization of the Problem Formulation Based on the Stackelberg Game Model
In the game model of the present study, the generator acts as a follower to observe the electricity
usage of the EUs for each tand the maximization of its profit; then, the EU acts as a leader to observe
the generator electricity price of each tand the minimization of its electricity charge. Based on the
game model, the Stackelberg game was formulated as
G=(N,A,U)
, where
N
is a player set that
is composed of generators and EUs,
A
is the constraint set, and
U
is the profit set. Regarding P1,
the generator profit,
UG
t
is defined as the extent to which the generator can increase the profit given the
EU electricity usage. In this paper, the constraints of the power generation unit were not considered.
In other words, we assume that the power generators are able to flexibly adjust the amount of the
power generation to maximize their profit by the proposed dynamic algorithm in Section 3. To solve
P1, it was assumed that the generator can observe the EU electricity usage
Pk
. In P2,
UEU
t
is the EU
electricity charge that is for the maximization of its profit in consideration of the current electricity
price. The optimization problem for the two players is formulated as follows:
(P1)max
{Pi,t,pt∈AG}η=UG
t(Pi,t,ptPk,t)(8)
subject to:
AG=
(Pi,t,pt|Pk)
Pm
iPi,tPM
i
pmptpM
K
k=1
Pk,tI
i=1
Pi,t
(9)
(P2)min
{Pk,t∈AEU }UEU(Pk,tpt)(10)
subject to:
AEU =
(Pk,tpt)
Pm
kPk,tPM
k
T
t=1
Pk,tϕk
(11)
Here,
pm
and
pM
are the minimum and maximum prices of the electricity, and
Pm
k
and
PM
k
are the
minimum and maximum electricity-usage values of each k, respectively. For each responsive demand,
a certain adjustable range of the electricity usage maximizes its own profit through the adjusting of the
Pk,t
for the t. Furthermore, the minimum required electricity consumption
ϕk
of each EU is available
for the entire time and an electricity amount should be efficiently used. This enables the EUs to shift
heavy consumption loads from the peak-price time slots to the nonpeak-price time slots [28].
3. Profit Maximization
In this section, the two profit-optimization algorithms which can maximize the social welfare,
which are for the generators and the EUs, are proposed based on the time-hierarchy structure of the
Stackelberg game.
3.1. Generator’s Best-Pricing and Power-Generation Strategy
In this subsection, to maximally increase the generator profit with the knowledge of the UE
energy consumption, the generator profit is maximized as a part of the Stackelberg game. Firstly,
to successfully acquire the P1 maximum profit, the nonconvex function of P1 was transformed into
the convex function using nonlinear fractional programming [
29
], since the solving of the nonconvex
function of P1 is extremely complex, and the optimum values can only be found using a brute-force
approach. Then, the Lagrangian dual decomposition was applied as a greedy-type iterative solution to
the transformed convex function to estimate the optimum argument set
{Pi,t,pt}
, where the constraint
Energies 2018,11, 2315 8 of 23
set
AG
is guaranteed. By exploiting the properties of nonlinear fractional programming [
29
], P1 is
equivalent to P3, as follows:
(P3)max
{Pi,t,pt∈AG}R(ptPk,t)ηM(Pi,t,pt)(12)
subject to
AG
, where
η?
is the P1 optimum solution when the
{Pi,t,pt}
is equal to the optimal argument
set
nP?
i,t,p?
to
. To mathematically prove that P3 is the convex and equivalent function, the theorem for
the transformation was given with the defining
F
as a set of feasible solutions and the maximum profit
as η?in the maximization problem P1, as follows [29]:
Theorem 1. η?=max
{Pi,t,pt∈AG}UG
t(Pi,t,ptPk,t){Pi,t,pt}F=R(p?
t|Pk,t)
M(P?
i,t,p?
t)
if and only if,
max
{Pi,t,pt∈AG}R(ptPk,t)η?M(Pi,t,pt)=R(p?
tPk,t)η?MP?
i,t,p?
t=
0, for
R(ptPk,t)
0and
M(Pi,t,pt)>0.
Theorem 1 represents P1 in the fractional form that can equivalently be transformed into the
subtractive form of P3. To prove Theorem 1, the transformed function F(η)was defined as follows:
F(η)=max
{Pi,t,pt∈AG}R(ptPk,t)ηM(Pi,t,pt)(13)
By following the approaches of [29], it was possible to prove Theorem 1.
Proof of Theorem 1. Convexity and equivalence.
Lemma 1. F(η)is exactly monotonically decreasing for η(i.e., F(η0)>F(η00 )if η0<η00 .
Proof
. Let
η00
maximize
F(η00 )
, then
F(η00 ) = max
{P
i,t,pt∈AG}R(ptP
k,t)η00 M(P
i,t,pt)=R(p00
tP
k,t)
η00 M(P00
i,t,p00
t)<R(p00
tP
k,t)η0M(P00
i,t,p00
t)R(p0
tP
k,t)η0M(P0
i,t,p0
t) = max
{P
i,t,pt∈AG}R(ptP
k,t)η0M(P
i,t,pt)
=F(η0)
, where
R(p00
tPk,t)η0M(P00
i,t,p00
t)Ri,t(p0
tPk,t)η0M(P0
i,t,p0
t)
is reasonable,
because
R(p0
tPk,t)η0M(P0
i,t,p0
t)
is the maximized value when the input value
η0
is given in
the function F(·).
Lemma 2. Let any set be {P0
i,t,p0
t}and the set satisfies η0=R(p0
t|Pk,t)
M(P0
i,t,p0
t),then F(η0)0.
Proof.F(η0) = max
{Pi,t,pt∈AG}R(ptPk,t)η0M(Pi,t,pt)R(p0
tPk,t)η0M(P0
i,t,p0
t) = 0.
As shown in Lemma 1 and Lemma 2, it is natural that the transformed function
F(η)
is convex,
since
F(η)
is monotonically decreasing and converges to zero. Further, the convergence of
F(η)
to zero
is representative of the generator profit,
η
, reaching the maximum value. Thus, it became possible to
see P3 as equivalent to P1, and P3 is used as an equivalent objective function of P1 in the rest of this
paper. Furthermore, to illustrate the solving of P1 through P3 as pseudocode, we construct the iterative
algorithm, Algorithm 1: Generator’s Profit Maximization, in page 10. In Algorithm 1, the lines 3–8
are performed by the generators with the operation of the master-loop algorithm that is based on the
slave-loop algorithm.
Remark 2.
When the number of master-loop iterations,
NG
M
, continues, the
η
increases and converges to
η?
if
F(η)<τ
, as shown in the lines 3–8 in Algorithm 1. Note that, for the convergence of the function
F(η)
,
the threshold parameter
τ
is set to approximately positive zero, as represented by
τ+
0. Furthermore,
Energies 2018,11, 2315 9 of 23
we have introduced nonlinear fractional programming [
29
] to ensure that our proposed solution has stability
in reaching equilibrium. From Proof of Theorem 1, we confirmed that the P3 is not only equivalent to the P1,
but also a function which is monotonically decreasing and converges to zero (Lemma 1 and Lemma 2). The P3
is monotonically decreasing converges to zero when the iteration number
NG
M
of Master-loop algorithm in
Algorithm 1 increases, which means the equilibrium of P1 can be obtained with stability.
The slave-loop algorithm of Algorithm 1 line 5, can be considered as the solution of P3. By using
the Lagrange dual method [
30
], it was then possible to solve the convex optimization problem of P3 to
propose the slave-loop algorithm. To estimate the optimal arguments of
P?
i,t
and
p?
t
, the Lagrange dual
function of P3 was derived as follows:
LG(Pi,t,pt,βi,t,γi,t,δt,εt,θt)=Rt(ptPk,t)ηMt(Pi,t,pt)βi,tI
i=1Pm
iPi,t
γi,tI
i=1Pi,tPM
iδt(pmpt)εtptpMθtK
k=1Pk,tI
i=1Pi,t(14)
where
βi,t
,
γi,t
,
δt
,
εt
, and
θt
are the Lagrange multipliers. The Lagrange dual problem of P3 can be
formulated as follows:
min
{βi,t,γi,t,δt,εt,θt}max
{Pi,t,pt∈AG}LG(Pi,t,pt,βi,t,γi,t,δt,εt,θt)(15)
By solving the following formulas of (16) and (17), which were derived using the
Karush–Kuhn–Tucker (KKT) conditions [
30
], it is possible to simply derive the optimal values,
P?
i,t
and
p?
t, as follows:
LG
Pi,t
=η(ai+bidi)
dip14diPi,t
η+βi,tγi,t+θtηpt(=0, Pi,t>0.
<0, otherwise. (16)
LG
pt
=ηPi,t+δtεt+K
k=1Pk,t
I(=0, pt>0.
<0, otherwise. (17)
P?
i,t="(Aηbi)(2ηai+ηbidi+diA)
(2ηai+2diA)2#+
(18)
p?
t="ai+aiB1
2+bidiB1
2
di
+βi,tγi,t+θt
η#+
(19)
where
A=ηptβi,t+γi,tθ
, and
B=
1
4
di(δi,tεi,t+K
k=1
Pk,t)/Iη
,
[X]+=max{X, 0}
. Moreover,
the Lagrange multiplier
βi,t
,
γi,t
,
δt
,
εt
and
θt
can be updated by using gradient methods in a distributed
manner, as follows:
βi,tNG
S+1="βi,tNG
Sω1
I
i=1Pm
iP?
i,t#+
(20)
γi,tNG
S+1="γi,tNG
Sω2
I
i=1P?
i,tPM
i#+
(21)
δtNG
S+1="δtNG
Sω3
I
i=1
(pmp?
t)#+
(22)
εtNG
S+1="εtNG
Sω4
I
i=1p?
tpM#+
(23)
Energies 2018,11, 2315 10 of 23
θtNG
S+1="θtNG
Sω5 K
k=1
Pk,t
I
i=1
P?
i,t!#+
(24)
where the iteration steps
ω1
,
ω2
,
ω3
,
ω4
, and
ω5
are positive values, which are like a learning rate,
for a more rapid convergence of the algorithm, and the parameter
NG
S
is the number of iterations
for the slave loop in the line 5 of Algorithm 1: Generator ’s Profit Maximization. The pseudocode of
the iterative slave-loop algorithm is proposed in Algorithm 3. This is the operating structure of the
master- and slave-loop algorithms, where the result of the slave-loop algorithm is the input of the
master-loop algorithm.
In this paper, we assumed that there are multiple power generators and multiple EUs as game
players in the proposed game algorithm. Each profit of the power generators and EUs, respectively,
was defined in Sections 2.1 and 2.2. However, we maximize the sum of the profits of the power
generators and the EUs, so there exist two profit sums for the power generators and the EUs, given by
Equations (5) and (7), respectively. If the profit sums are successfully maximized by the proposed
algorithm, the profit of each player can be distributed according to the pre-defined profit function
given by Equations (4) and (6) in Section 2.
The detailed description of the operation of Algorithm of Social-Welfare Maximization is as
follows. The Algorithm of Social-Welfare Maximization is the hierarchical bi-level iterative algorithm
with the Stackelberg-loop iteration number
t∈ T ={1, . . . , T}
, and is composed of the two
pseudocode tables, Algorithms 1 and 2. We define the generators as the “leader”, and the energy users
as the “follower” as a game player in the proposed game structure. Originally
t
denotes time set, but it
is recognized as an iteration number. If the
T
is set to be 24 and the time interval is 1 h, the Algorithm of
Social-Welfare Maximization repeats 24 times until the Stackelberg-loop iteration number
t
becomes 24.
Whenever the iteration for
t
is performed, the proposed Algorithms 1 and 2 are performed in succession.
They play their algorithm in the Stackelberg game and do not play the game at the same time but
play alternately over time to “interact” with each other and maximize their profit. Algorithm 1 is for
generator’s profit maximization, and iterates with the master-loop iteration number
NG
M
until the line
6,
R(p?
tPk,t)η?M(P?
i,t,p?
t)<τ
, is satisfied. Note that the threshold parameter
τ
for the convergence
of the function
R(p?
tPk,t)η?M(P?
i,t,p?
t)
should be set to around positive zero,
τ+
0, by the proof
of Lemmas 1 and 2. Furthermore, before the line 6 is performed, the optimal value P?
i,tand p?
tshould
be calculated in the line 5 as an outcome of the Slave-loop Algorithm of Algorithm 1, Algorithm 3,
with the slave-loop iteration number
NG
S
. The Slave-loop Algorithm is performed to solve the Lagrange
dual problem P3 based on Equations (18)–(24). When the line 6 is satisfied, the maximized profit for
the generators is obtained with the optimal electricity price
p?
t
and the optimal power generation
P?
i,t
.
To interact with each other and effectively maximize the profit, if Algorithm 1 ends, the optimal price
p?
t
calculated from Algorithm 1 is passed to the input of Algorithm 2, and Algorithm 2 is performed.
Algorithm 2 is for energy user’s profit maximization, and the specific methodology is described in
next Section 3.2. If Algorithm 2 is successfully performed and the maximum EU’s profit is calculated
with the optimal electricity usage of the EUs
P?
k,t
, the optimal electricity usage
P?
k,t
is also passed to the
input of Algorithm 1 (which means the “interact”) and
t
is incremented by one. We have described this
passing of values as “spy on” in Introduction. By running the algorithm repeatedly and alternatively
over time and spying on each other’s energy parameters, they can effectively maximize their own
profit. The Algorithm of Social-Welfare Maximization (combination of Algorithms 1 and 2, lines 1–18)
is as follows:
Energies 2018,11, 2315 11 of 23
Algorithm 1.
Generator’s Profit Maximization (lines 3–8, performed by the generator, master-loop algorithm).
1: Input: time set t∈ T ={1, . . . , T}.
2: While tT,do
3:
Input
: active generator set
i∈ I ={1, . . . , I}
,
Pi,t
,
pt
,
Pk,t=ˆ
Pk,t
,
η=
0,
τ+
0 (positive zero),
NG
M=
1.
4: Initialize η=UG
tPi,t,ptPk,t=R(pt|Pk,t)
M(Pi,t,pt)=0.
5: Update P?
i,tand p?
tfrom Slave-loop algorithm (“Algorithm 3”).
6: If Rp?
tPk,tη?M(P?
i,t,p?
t)<τ
7: Return optimal parameters {P?
i,t,p?
t}and optimal profit of generators, η?=R(p?
t|Pk,t)
M(P?
i,t,p?
t).
8: else Update η=R(p?
t|Pk,t)
M(P?
i,t,p?
t)and NG
M=NG
M+1, then Go to line 5.
Algorithm 2. Energy-User’s Profit Maximization (Market-adaptive Electricity-Usage Scheduling Algorithm,
lines 9–17, performed by the EU).
9: Input:p?
t, active EU set k∈ K ={1, . . . , K},Pk,t,ϕk,t,T,tw,Pk,t.
10: For k∈ K do
11: Calculate ptwith p?
taccording to (26).
12: Calculate Pk,twith ptaccording to (25).
13: Calculate P?
k,twith Pk,taccording to (27).
14: Update ˆ
Pk,t+1with ˆ
Pk,tand Pk,taccording to (28).
15: end for
16: Calculate UEU
k,twith (7).
17: Let t=t+1.
18: end while
Algorithm 3. Slave-loop Algorithm of the Generator ’s Profit Maximization.
1: Input: active generator set i{1, . . . , I},Pi,t,pt,Pk,t,ai,bi,ci,di,Pm
i,PM
i,pm,pM,βi,t,γi,t,. δt,εt,θt,. ω1,
ω2,ω3,ω4,ω5,NG
S=1.
2: While βi,t,γi,t,δt,εtand θtare not converged do
3: for i∈ I do
4: Update P?
i,t=P?
i,tNG
S+1with (18), p?
t=p?
tNG
S+1with (19), βi,tNG
S+1with (20),
γi,tNG
S+1with (21), δtNG
S+1with (22), εtNG
S+1with (23), and θtNG
S+1with (24).
5: end for
6: Let NG
S=NG
S+1.
7: end while
3.2. Demand’s Best Electricity-Usage Strategy
While the total electricity consumption was retained, the electricity charge of the EU was
minimized with the knowledge of the current electricity price
pt
, and this is another part of the
Stackelberg game along with Algorithm 2: Energy User’s Profit Maximization (Market-adaptive
electricity-usage scheduling algorithm) which is newly proposed in this subsection to solve P2.
Algorithm 2 is one of the electricity-usage controlling strategies of the demand that is applicable
to the time-varying electricity market. Firstly, it was assumed that the
Pm
k
and
PM
k
are the minimum
and maximum electricity-usage values for each tof each EU, respectively, and the total sum of the
electricity usage of the entire time for each EU should be greater than or equal to the minimum requisite
electricity consumption
ϕk
. Furthermore, the entire time of the scheduling of the electricity usage of
the EU was set to T∈ T ={1, . . . , T}.
To initialize the electricity usage of each scheduling time before the applying of the proposed
algorithm, it was assumed that each EU is supposed to consume
ϕk/T
during every scheduling time,
and the electricity of
ϕk
is used for the entire time
T
. Further,
ˆ
Pk,t
was set as the amount of electricity
that the kis expected to use during the current t, and the initial value of
Pk,t
is allocated to
ϕk/T
. Then,
Energies 2018,11, 2315 12 of 23
in consideration of the electricity price of every scheduling time, the
ˆ
Pk,t
can be increased or decreased
by the fluctuation of the electricity price. The amount of change in the electricity usage that is due to
changes in the electricity price, Pk,t, is as follows:
Pk,t=ˆ
Pk,t1pt
pt(25)
pt=11
twpt1+1
tw
pt(26)
P?
k,t=ˆ
Pk,t+Pk,t(27)
where
pt
is the base price as the historical average of
pt
based on the electricity prices of the previous
time slots,
tw
is the window size of the time slots, and
P?
k,t
is the adjusted electricity usage according to
the proposed algorithm. In Equation (25), a calculation of the amount of the change in the electricity
usage is performed using the ratio of the base price to the current price, thereby acknowledging that
the price that breaks past the average is a substantially increased price and the electricity consumption
is reduced by the rate of increase, and vice versa. Then, in Equation (27), the electricity usage is
updated for the current scheduling time.
To obtain insight regarding
pt
according to
tw
in (26), it was assumed that the
tw
values are 1,
3, and 5. When
tw
is 1,
Pk,t
is not generated in its structure, thereby meaning that the electricity
consumption is calculated regardless of the electricity-price fluctuation, and this can be used for
the control of the proposed algorithm result. Alternatively, the increasing
of tw
to 3 or 5 means the
determination of how sensitively
Pk,t
is able to react to the current market price, since it was assumed
that
pt
is the average of the prices in the previous
tw
occasions. This provides an opportunity to
adaptively reflect the market characteristics to the rapidly fluctuating or the gentle market. To retain the
total electricity consumption in the proposed algorithm, the
Pk,t
is uniformly collected or distributed
for each remaining time block. If the
Pk,t
is negative, the
Pk,t
x amount is divided by the total
remaining time
Tt
, and it is then distributed among each of the scheduling time blocks, and
ˆ
Pk,t+1
is
updated as follows:
ˆ
Pk,t+1=ˆ
Pk,tPk,t/(Tt)(28)
By performing (28), it is possible to constantly retain the electricity that is for consumption,
ϕk
.
We propose Algorithm 2: Energy User’s Profit Maximization reflecting the whole description and
formulas in Section 3.2 as a profit maximization algorithm performed by the EU.
Remark 3.
Algorithm 2 was proposed to solve the problem P2. The equilibrium of P2 is involved in the iteration
t in Algorithm of Social-Welfare Maximization, which gradually reaches the equilibrium point whenever t
increases. According to the maximization structure of the proposed algorithm, if the pre-defined total energy
consumption ϕkare all distributed according to the algorithm, the equilibrium can be stably reached.
3.3. Complexity Analysis
The computational complexity of the Algorithm of Social-Welfare Maximization based on the
optimization technique used in this paper can be evaluated as follows. First, the complexity of
the gradient method updating dual variables to obtain the optimal price
p?
t
and the optimal power
generation
P?
i,t
in Slave-loop Algorithm (Algorithm 3) of Algorithm 1 linearly increases with the
number of generators Iand the number of iterations
NG
S
, i.e.,
ONG
SI
where
O{·}
is Big O notation.
Second, as provided in Section 3.1, the dual function P3 is always convex by proof of Theorem 1,
and the gradient method was employed to update
{βi,t,γi,t,δt,εt,θt}
toward the optimal solution with
guaranteed convergence [
29
]. Thus, in Master-loop Algorithm of Algorithm 1, the complexity of the
Dinkelbach method [
29
] to update
η
is independent of I and linearly increases with the number of
Energies 2018,11, 2315 13 of 23
iterations
T2
, i.e.,
ONG
MNG
SI
. Third, when we consider the number of EUs
K
in Algorithm 2 and the
number of iteration
t
, the complexity becomes
OtNG
MNG
SIK
. Therefore, the total complexity of the
proposed algorithm is
OtNG
MNG
SIK
. For comparisons, the complexity of the exhaustive search [
31
]
is roughly O{(K+I)(K+I)}, where Kand Iare the number of generators and EUs, respectively.
3.4. Schematic Overview and Application of Proposed Algorithms
In this paper, the two game players (generators and EUs) are supposed to participate in the
Stackelberg game, and play the game in order to maximize their monetary profit in the smart grid
demand response. We proposed two algorithms (Algorithm 1: Generator’s Profit Maximization and
Algorithm 2: Energy User’s Profit Maximization) in Sections 3.1 and 3.2, respectively, as an energy
strategy to achieve profit maximization, and the generators and the EUs play Algorithms 1 and 2
alternately in time, respectively, as shown in Figure 2. The proposed game structure is “dynamic game”
from the following two reasons:
1.
The game players, generators and EUs, interact with observing the each other’s energy strategy
for profit maximization. The generators observe the electricity consumption of the EUs, and the
EUs observe the electricity price of the generators in the proposed game operation.
2.
The proposed profit maximization game was constructed as an iterative algorithm where the
energy strategies (Algorithms 1 and 2) are repeatedly performed up to the specified number of
times, for example 24 times as 24 h a day.
They play their algorithm in the Stackelberg game and do not play the game at the same time
but play alternately over time since the EU needs to know how much the generator has set the
current electricity price in order to optimally control his energy consumption
P?
k,t
, and the generator
needs to know how much power the EU is currently consuming in order to determine the electricity
price
p?
t
and the amount of power generation in each game play (the game play means the algorithm
operation). By running the algorithm repeatedly and alternatively over time and spying on each other’s
energy parameters, they can effectively maximize their own profit. The Algorithm for Social-Welfare
Maximization is detailed in Algorithms 1 and 2 as the Stackelberg-loop algorithm with the iteration
number
T
. The total architecture of the proposed social welfare maximization including Algorithms 1
and 2 is described in Figure 2as an overview of the algorithms proposed in this paper.
Energies 2017, 10, x FOR PEER REVIEW 13 of 22
2. The proposed profit maximization game was constructed as an iterative algorithm where the
energy strategies (Algorithms 1 and 2) are repeatedly performed up to the specified number of
times, for example 24 times as 24 h a day.
They play their algorithm in the Stackelberg game and do not play the game at the same time
but play alternately over time since the EU needs to know how much the generator has set the current
electricity price in order to optimally control his energy consumption ,
, and the generator needs
to know how much power the EU is currently consuming in order to determine the electricity price
and the amount of power generation in each game play (the game play means the algorithm
operation). By running the algorithm repeatedly and alternatively over time and spying on each
others energy parameters, they can effectively maximize their own profit. The Algorithm for Social-
Welfare Maximization is detailed in Algorithms 1 and 2 as the Stackelberg-loop algorithm with the
iteration number . The total architecture of the proposed social welfare maximization including
Algorithms 1 and 2 is described in Figure 2 as an overview of the algorithms proposed in this paper.
Figure 2. The diagram of the proposed iterative algorithm for social welfare maximization in smart
grid.
The proposed methodology to achieve the social-welfare maximization can provide the
advantage to maximize the monetary profit of the generators and EUs, but we also reveal the
following considering points expected in real-world implementation; 1. Algorithm 1 proposed to
maximize the profit of the generator in this paper derives a sub-optimal solution that can reduce the
computational complexity compared to the brute-force approach to be suitable for real-time
optimization in smart grid demand response. The performance of the algorithm can vary slightly
depending on the initial point and the values of the variables that make up the Algorithm; 2.
Algorithms 1 and 2 proposed in this manuscript can be implemented in a smart meter or the EMC as
a form of software to automatically control the power consumption of energy users appliances and
facilities. It is a system that can operate only in limited areas equipped with the AMI; 3. Generators
are required to have the ability to flexibly control their power generation to participate in the
proposed algorithm. We need to consider practical implementations to enable real-time operation of
the proposed cooperative and simultaneous usage of coalitional game theory methods.
1. In the EU side, the implementation method of the proposed profit maximization algorithm is as
follows: The proposed profit maximization algorithm and formulas is applicable to general
demand response applications between generators and EUs such as, residential households,
electrical appliances, new smart appliances and internet of things (IoT) devices as a real-world
Figure 2.
The diagram of the proposed iterative algorithm for social welfare maximization in smart grid.
Energies 2018,11, 2315 14 of 23
The proposed methodology to achieve the social-welfare maximization can provide the advantage
to maximize the monetary profit of the generators and EUs, but we also reveal the following considering
points expected in real-world implementation; 1. Algorithm 1 proposed to maximize the profit of the
generator in this paper derives a sub-optimal solution that can reduce the computational complexity
compared to the brute-force approach to be suitable for real-time optimization in smart grid demand
response. The performance of the algorithm can vary slightly depending on the initial point and the
values of the variables that make up the Algorithm; 2. Algorithms 1 and 2 proposed in this manuscript
can be implemented in a smart meter or the EMC as a form of software to automatically control the
power consumption of energy user’s appliances and facilities. It is a system that can operate only
in limited areas equipped with the AMI; 3. Generators are required to have the ability to flexibly
control their power generation to participate in the proposed algorithm. We need to consider practical
implementations to enable real-time operation of the proposed cooperative and simultaneous usage of
coalitional game theory methods.
1.
In the EU side, the implementation method of the proposed profit maximization algorithm is
as follows: The proposed profit maximization algorithm and formulas is applicable to general
demand response applications between generators and EUs such as, residential households,
electrical appliances, new smart appliances and internet of things (IoT) devices as a real-world
scenario. In the EU side which can be used at high priced hours, we can effectively reduce
electricity charges by adjusting the energy usage according to the electricity price with
the proposed Market-Adaptive Electricity-Usage Scheduling Algorithm (Algorithm 2 in the
manuscript). To implement the Market-Adaptive Electricity-Usage Scheduling Algorithm,
automatic electricity usage controller can be needed to be implemented and connected to EU
applications (the residential households, IoT devices and etc.). The automatic electricity usage
controller can be developed by porting a functional software which quickly and dynamically
performs the automatic electricity usage control to an AMI or an EMC. In the case of AMI and
EMC, it is possible to transmit and receive electricity price information in real time through
power line communications in smart grid. Based on this, the proposed algorithm can be fully
implemented and operate to achieve energy usage optimization and electricity charge savings.
To summarize, EUs should choose their appliances or facilities to automatically control their
energy usage to maximize monetary profit, and if they are connected to a smart meter or EMC
equipped with our proposed algorithms, the proposed profit maximization system will simply
be able to operate. We think that the proposed system can be implemented in the direction of
utilizing existing infrastructure such as the smart meter and EMC.
2.
On the generator side, the implementation method of the proposed profit maximization algorithm
is as follows: In order to realize the optimal power generation and optimal pricing based
on the proposed algorithm in the generators side, the generators should be able to integrate
and manage the total power generation and the electricity price by forming a coalitional for
profit maximization themselves. Or as a top authority for power generators that are already
integrated and managed by the government can implement the proposed coalitional game theory
methods. Or a third party such as a power retailer that runs various demand response programs
can implement the proposed game theory. If the proposed algorithm is implemented and
operated, it should be able to interact with another game player, energy user, with through power
line communication or wireless local area network (WLAN) on the smart grid for information
communication, such as real-time electricity price and electricity consumption exchange required
by this algorithm.
In view of appropriate time intervals for this real-time operation and implementation, we consider
the time interval of one hour is reasonable, and we can think about a smaller or larger interval based
on this one hour. For example, a 10-min interval that is smaller than 1 h is expected to cause confusion
because too much dynamic power generation and power consumption changes for both the generators
Energies 2018,11, 2315 15 of 23
and the energy users can be caused. On the other hand, if we set a time interval greater than one hour
to a time interval, the proposed profit maximization system can be somewhat inefficient if we run the
system once a day at a specific time because we have a fairly wide variation in power consumption
trends during a day. Therefore, it is reasonable to set the time interval appropriately between minimum
1 h and maximum 24 h in consideration of country, region and environment in which the proposed
profit maximization system operates.
4. Simulation Results
In this section, numerical results are provided to demonstrate the effectiveness of the proposed
algorithm. The system setup is as follows: six generators and 12 EUs are considered based on the
IEEE 39-BUS system [
32
]. The graph of the communication network of these generators and EUs
shows that they are strongly connected. The parameters of the EUs and the generators are given in
Tables 2and 3[
32
]. The constant time length
tl
was set to 1 h, and the total scheduling time was
set to
T=
24 (one day) with the minimum requisite electricity consumption
ϕk=TPm
k+PM
k/
2.
Further, the initial value of the
ˆ
Pk,t
was set to
ˆ
Pk,t=ϕk/T
, and it was assumed that the lower bound
of the electricity price per unit of energy is nonzero, whereas the upper bound is 50 (cents/kWh).
The window size
tw
was set to be from 1 to 10. In the following results, the maximum number of
slave-loop iterations was set to 6.
Table 2. Parameters of generators for IEEE 39-BUS [32].
Generator Parameters
Node aibicidiPm
iPM
i
1 0.0024 5.56 30 0.00021 60 339.69
2 0.0056 4.32 25 0.00031 25 479.10
3 0.0072 6.60 25 0.00011 28 290.4
4 0.0047 3.14 16 0.00022 40 306.34
5 0.0091 7.54 6 0.00041 35 593.80
6 0.0046 4.76 12
0.000121
30 443.41
Table 3. Parameters of EUs for IEEE 39-BUS [32].
Demand Parameters
Node Pm
kPM
kNode Pm
kPM
k
1 50 100.34 7 80 137.93
2 100 159.13 8 50 84.19
3 40 80.56 9 50 104.06
4 30 123.98 10 78 119.36
5 80 109.55 11 103 176.19
6 40 76.34 12 67 147.26
Figure 3shows the evolution of the generation power (kWh)
Pi,t
and the electricity price
(cents/kWh)
pt
of each generator optimized by the proposed iterative Algorithm 1. “Iterations” in
Figure 3is the number of master-loop iterations
NG
M
of Algorithm 1, and the result was averaged over
1000 independent simulations, each of which involved different scheduling time slots. From Figure 3,
it is evident that the convergence of the generation power and the price can be achieved within seven
iterations on average to maximize the
UG
t
. All of the values in each iteration change with satisfying the
AG
in (9). To maximize the generator profit, the generation power of each generator converges to a
different optimal value, which is affected by the constant parameters such as
ai
,
bi
,
ci
,
di
,
Pm
i
, and
PM
i
,
and the price converges to its optimal value in conjunction with the generation power.
Energies 2018,11, 2315 16 of 23
Energies 2017, 10, x FOR PEER REVIEW 15 of 22
Table 2. Parameters of generators for IEEE 39-BUS [32].
Generator Parameters
Node
1
0.0024
5.56
30
0.00021
60
339.69
2
0.0056
4.32
25
0.00031
25
479.10
3
0.0072
6.60
25
0.00011
28
290.4
4
0.0047
3.14
16
0.00022
40
306.34
5
0.0091
7.54
6
0.00041
35
593.80
6
0.0046
4.76
12
0.000121
30
443.41
Table 3. Parameters of EUs for IEEE 39-BUS [32].
Demand Parameters
Node
Node
1
50
100.34
7
80
137.93
2
100
159.13
8
50
84.19
3
40
80.56
9
50
104.06
4
30
123.98
10
78
119.36
5
80
109.55
11
103
176.19
6
40
76.34
12
67
147.26
Figure 3 shows the evolution of the generation power (kWh) , and the electricity price
(cents/kWh) of each generator optimized by the proposed iterative Algorithm 1. Iterations” in
Figure 3 is the number of master-loop iterations
of Algorithm 1, and the result was averaged
over 1000 independent simulations, each of which involved different scheduling time slots. From
Figure 3, it is evident that the convergence of the generation power and the price can be achieved
within seven iterations on average to maximize the . All of the values in each iteration change
with satisfying the in (9). To maximize the generator profit, the generation power of each
generator converges to a different optimal value, which is affected by the constant parameters such
as , , , , , and , and the price converges to its optimal value in conjunction with the
generation power.
Figure 3. Generation power , and electricity price versus iteration with = 6 generators.
Figure 4 demonstrates the evolution of the generator profit calculated with the employment of
the optimal values of the generation power and the electricity price from Figure 3. The optimization
Figure 3. Generation power Pi,tand electricity price ptversus iteration with I=6 generators.
Figure 4demonstrates the evolution of the generator profit calculated with the employment of the
optimal values of the generation power and the electricity price from Figure 3. The optimization process
is performed using the proposed master- and slave-loop algorithms in Algorithm 1. Even though
the generator power curves in Figure 3show fluctuations with the iterations, it is evident that all
of the curves continually increase as the iterations continue. Furthermore, a baseline scheme was
set as a profit maximization strategy which optimizes only one of the two optimization parameters
(Pi,t,pt)
compared to the proposed Algorithm 1, where the other optimization parameter was fixed.
The “fixing” means that the fixed parameter was not optimized but its value is constant, and the fixed
value of
Pi,t
was set as just one of the possible values to
Pm
i+PM
i/
2. In Figure 4, it is confirmed that
the profit of the proposed algorithm is greater than that of the baseline scheme, thereby confirming the
proper operability of the algorithm. Furthermore, the proposed optimization problem in this paper
is a non-convex optimization problem (P1 in Section 2) and the global optimum for the non-convex
optimization problem is usually only achieved by using a brute-force approach (or exhaustive search).
In this paper, to solve P1, Lagrange dual method and non-linear fractional programming were applied
to find a sub-optimal point close to the global optimum. From Figure 4, the global optimum point was
further suggested, and it was confirmed that the difference from the sub-optimal point is within 1%
when the iteration is converged to 7. Since the sub-optimal point we found may differ depending on
the initial point in the proposed profit maximization algorithm. So we propose a method to calculate
multiple sub-optimal points with multiple initial points to determine the more maximized point among
the multiple sub-optimal points. In the implementation phase, this algorithm should be devised to
generate multiple initial points and calculate the sub-optimal point corresponding to the multiple
initial points.
Energies 2018,11, 2315 17 of 23
1
Figure 4. Generator profit versus iteration.
Remark 4.
It is not possible to run the baseline scheme by fixing the
pt
. When the
pt
is fixed, it is obvious that
the Pi,twill be set to the minimum value to maximize the profit according to the structure of Equation (4).
In Figures 57, the electricity price, the electricity usage of one of the EUs according to the price,
and the accumulated electricity charge are depicted over time. All of the results were simulated for
T=
24 h (one day) with 1-h intervals, and the window size was set to be
tw=
3. Figures 57show the
changes in the electricity price as Case #1: side-crawl trend, Case #2: rising-tide trend, and Case #3:
falling-tide trend, respectively, and these are case studies where the possible trends were analyzed in
the real market to determine the effectiveness of the proposed Algorithm 2. Please note that, regarding
the totals of Algorithms 1 and 2, Algorithm of Social-Welfare Maximization, the electricity prices of
all of these cases were calculated using Algorithm 1, while the electricity usage and the electricity
charge were calculated using Algorithm 2. Also, these three cases are three of the results that were
obtained by the performance of the entire Algorithm of Social-Welfare Maximization over 1000 times.
From Figures 57, the changes of the electricity usage of EU 1 show that the proposed Algorithm 2
operates in a market-adaptive manner (EU 1 is merely a representative of the EUs, and the rest of the
EUs show the same tendency), as the expectation of this is described in Section 3.2. Based on this
market-adaptive manner, the electricity usage is reduced when the electricity is expensive, whereas
the electricity usage increases when the electricity is cheap, thereby meaning that the total electricity
consumption is the same, but the electricity charge can be considerably reduced. Please note that
this means that the proposed algorithm can alleviate the existing PAR reduction problem because
the electricity usage immediately responds to the price according to the results of Figures 57. In the
real world implementation, it is also possible to alleviate the problem that the price greatly fluctuates
because if the price changes greatly, the generators will lose its profits and will not be able to withstand
it. We can contribute to stabilization of power generation and PAR reduction, which is one of the ultimate
goals of DR through our proposed algorithm. Figures 57show that the average electricity usage is
the same in any trend according to the
AEU
, and the electricity charge can be greatly reduced from
13–18%. The comparator, which consumes the same power at all times, is “unadjusted” in the legend.
Furthermore, from Figures 57, it can be seen that the greater the variability in the market, the greater
the possibility of the adaptation to the market that facilitates the attainment of a greater benefit.
Energies 2018,11, 2315 18 of 23
Energies 2017, 10, x FOR PEER REVIEW 17 of 22
Algorithm 2 operates in a market-adaptive manner (EU 1 is merely a representative of the EUs, and
the rest of the EUs show the same tendency), as the expectation of this is described in Section 3.2.
Based on this market-adaptive manner, the electricity usage is reduced when the electricity is
expensive, whereas the electricity usage increases when the electricity is cheap, thereby meaning that
the total electricity consumption is the same, but the electricity charge can be considerably reduced.
Please note that this means that the proposed algorithm can alleviate the existing PAR reduction
problem because the electricity usage immediately responds to the price according to the results of
Figures 57. In the real world implementation, it is also possible to alleviate the problem that the price
greatly fluctuates because if the price changes greatly, the generators will lose its profits and will not
be able to withstand it. We can contribute to stabilization of power generation and PAR reduction,
which is one of the ultimate goals of DR through our proposed algorithm. Figures 57 show that the
average electricity usage is the same in any trend according to the , and the electricity charge can
be greatly reduced from 1318%. The comparator, which consumes the same power at all times, is
“unadjusted” in the legend. Furthermore, from Figures 5–7, it can be seen that the greater the
variability in the market, the greater the possibility of the adaptation to the market that facilitates the
attainment of a greater benefit.
Figure 5. Electricity price with time (Case #1: side-crawl trend) and the electricity usage adjusted
accordingly, and the confirmation of the electricity-charge saving.
Figure 5.
Electricity price with time (Case #1: side-crawl trend) and the electricity usage adjusted
accordingly, and the confirmation of the electricity-charge saving.
Energies 2017, 10, x FOR PEER REVIEW 18 of 22
Figure 6. Electricity price with time (Case #2: rising-tide trend) and the electricity usage adjusted
accordingly, and the confirmation of the electricity-charge saving.
Figure 7. Electricity price with time (case #3: falling tide trend) and the electricity usage adjusted
accordingly and confirmation of electricity charge savings.
Figure 6.
Electricity price with time (Case #2: rising-tide trend) and the electricity usage adjusted
accordingly, and the confirmation of the electricity-charge saving.
Energies 2018,11, 2315 19 of 23
Energies 2017, 10, x FOR PEER REVIEW 18 of 22
Figure 6. Electricity price with time (Case #2: rising-tide trend) and the electricity usage adjusted
accordingly, and the confirmation of the electricity-charge saving.
Figure 7. Electricity price with time (case #3: falling tide trend) and the electricity usage adjusted
accordingly and confirmation of electricity charge savings.
Figure 7.
Electricity price with time (case #3: falling tide trend) and the electricity usage adjusted
accordingly and confirmation of electricity charge savings.
We can think of the proposed game methodology as a type of DR because the demand is responsive
to price as the reviewer commented, but it is not exactly a DR program. In general DR should include
the ability to convert excess power consumption to optimal power consumption (“load reduction”),
as well as to provide price elasticity to eliminate inefficiencies due to fixed prices. In the proposed
game methodology, we do not reduce the power consumption of the energy user, but optimize how
efficiently the specified power consumption will be consumed in a given period of time. In Figures 57,
to maximize the profit of the EU, we estimated the adjusted electricity usage which should be consumed
for each hour when we know an amount of energy we should use during the day. When the electricity
price changes as shown in the top figure of Figures 57, respectively, the electricity charge can be
reduced by 13%, 18% and 16% when calculating the electricity usage as shown in the middle figure of
Figures 57based on the proposed algorithm. We studied in this paper how to use the load efficiently
within the reduced load when the energy user receives an instruction to reduce the load on the DR.
Remark 5.
In the proposed algorithm, the profit function of each generator and EU is defined mathematically in
Section 2so that the profit generation and distribution can be fair. Each generator and EU does not need special
skills for fairness because it is an independent entity and can take its own profits by the defined profit function.
Figure 8represents the way the market volatility (degree of change of the electricity price) provides
the EU with benefits when the window size changes from 1 to 10. The benefit function is first given in
Figure 8, as follows:
Benefit =1UEU
UEU 100 (29)
Energies 2018,11, 2315 20 of 23
where
UEU
is the total electricity charge that is accumulated for the
T
when the electricity usage is
unadjusted and allocated in a totally flat manner. Note that the reduction percentages in Figures 57
were calculated using Equation (29). Also note that, in Figure 8, the peak-to-average ratio (PAR) is an
approximate indicator of the electricity usage as it is known, and it was assumed that the change in
the PAR reflects the volatility of the electricity price, because the electricity usage fluctuates when the
electricity price fluctuates according to Algorithm 2. It is evident that Algorithm 2 responds sensitively
to the changes in the electrical price; that is, the smaller that
tw
is, the greater the benefit that is derived,
with the exception of the case where
tw
is 1. Here,
tw
is 1, and this means that the electricity usage
has not been adaptively adjusted to the market using Equation (26), while it is also accurate that the
PAR is 1. In the meantime, it is possible to observe the trends of the increasing benefit as the PAR
is increased, and this means that, as the market volatility is increased, Algorithm 2 can increase the
benefit. The purpose of this simulation, however, is not the raising of the PAR to increase the benefit.
The purpose is the demonstration of the benefit that is attained from the proposed algorithm when the
EU has some PAR.
Energies 2017, 10, x FOR PEER REVIEW 20 of 22
Figure 8. Benefits versus window sizes with varying PAR.
Summary of Simulation Results and Insights
We showed the results of maximizing the profit of the generators through Algorithm 1 in Figures
3 and 4, and we demonstrated the results of maximizing the profit of the EUs based on Algorithm 2
in Figures 57. In Figures 3 and 4, we confirmed that the profit of the generators can be improved to
about 45% compared to existing (baseline scheme) scheme, and the electricity charge of the EUs can
be reduced by 15.6% on average compared to that of when algorithm was not applied. Please note
that the amount of power consumption of the EUs is same when the algorithm was applied and not
applied. From Figures 37, we confirmed that the proposed profit maximization algorithms
effectively improves the monetary profit of generators and EUs. We summarize the gain of monetary
profit from the proposed algorithms in Table 4.
Table 4. The gain of monetary profit from the proposed algorithm.
Energy Strategy
Gain of Profit
Algorithm 1: Generator Profit
Maximization
About 45% (compared to existing (beseline scheme) scheme)
Algorithm 2: Energy User
Profit Maximization
15.6% on average (compared to that of when algorithm was not applied)
To provide and investigate the influence of the changes of the PAR on the monetary profit
generated from the proposed algorithms, Figure 8 showed the change of the profit of the EUs. From
Figure 8, we confirmed that the EUs gain more profit as the PAR increases. What this means that
more EUs will participate in this game and algorithms when the PAR increases, and it can lead to
PAR reduction because the monetary gain that can be obtained for the current PAR has no choice but
to be limited, and it can be predicted that as many people share the profit, the PAR would decrease.
This also suggest the proposed algorithm not only can contribute to maximization of the profit of the
generators and the EUs, but also the desired goal of demand response, PAR reduction, at the same
time.
5. Conclusions
In this paper, to maximize the monetary profit in real-time price DR systems, we formulated the
Stackelberg game-based non-convex optimization problem, and proposed two energy strategies,
Algorithm 1: Generator’s Profit Maximization and Algorithm 2: Energy User’s Profit Maximization,
as an optimal solution. In the problem formulation, we newly formulated the generator profit
Figure 8. Benefits versus window sizes with varying PAR.
Summary of Simulation Results and Insights
We showed the results of maximizing the profit of the generators through Algorithm 1 in Figures 3
and 4, and we demonstrated the results of maximizing the profit of the EUs based on Algorithm 2 in
Figures 57. In Figures 3and 4, we confirmed that the profit of the generators can be improved to
about 45% compared to existing (baseline scheme) scheme, and the electricity charge of the EUs can
be reduced by 15.6% on average compared to that of when algorithm was not applied. Please note
that the amount of power consumption of the EUs is same when the algorithm was applied and not
applied. From Figures 37, we confirmed that the proposed profit maximization algorithms effectively
improves the monetary profit of generators and EUs. We summarize the gain of monetary profit from
the proposed algorithms in Table 4.
Table 4. The gain of monetary profit from the proposed algorithm.
Energy Strategy Gain of Profit
Algorithm 1: Generator Profit Maximization About 45% (compared to existing (beseline scheme) scheme)
Algorithm 2: Energy User Profit Maximization 15.6% on average (compared to that of when algorithm was not applied)
Energies 2018,11, 2315 21 of 23
To provide and investigate the influence of the changes of the PAR on the monetary profit
generated from the proposed algorithms, Figure 8showed the change of the profit of the EUs.
From Figure 8, we confirmed that the EUs gain more profit as the PAR increases. What this means that
more EUs will participate in this game and algorithms when the PAR increases, and it can lead to PAR
reduction because the monetary gain that can be obtained for the current PAR has no choice but to be
limited, and it can be predicted that as many people share the profit, the PAR would decrease. This also
suggest the proposed algorithm not only can contribute to maximization of the profit of the generators
and the EUs, but also the desired goal of demand response, PAR reduction, at the same time.
5. Conclusions
In this paper, to maximize the monetary profit in real-time price DR systems, we formulated
the Stackelberg game-based non-convex optimization problem, and proposed two energy strategies,
Algorithm 1: Generator ’s Profit Maximization and Algorithm 2: Energy User’s Profit Maximization,
as an optimal solution. In the problem formulation, we newly formulated the generator profit function
to reflect the influence of the electricity usage of EUs. To solve the non-convex optimization problem,
nonlinear fractional programming and the Lagrange-multiplier method were adopted in proposing
the energy strategy for the generators. Also, we newly proposed the energy strategy for the EUs
based on the time-window-based market-adaptive manner. We greatly improve the monetary profit of
the generators and EUs using the proposed two energy strategies by optimizing the amount of the
power generation and the electricity price in the generator side, and electricity consumption in the EU
side. In Figures 3and 4, we confirmed that the profit of the generators can be improved to about 45%
compared to the existing (baseline) scheme, and the electricity charge of the EUs can be reduced by
15.6% on average compared to that of when algorithm was not applied. Furthermore, we confirmed
that the simulation result from Figure 8suggests the proposed algorithm can contribute to not only
maximization of the profit of the generators and the EUs, but also the desired goal of demand response,
PAR reduction, at the same time.
Author Contributions:
Y.M.H. has contributed to the theoretical approaches, simulation and preparing the paper;
I.S. has contributed to the theoretical approaches and literature review; Y.G.S. has contributed to the theoretical
approaches and preparing the paper; H.-J.L. has contributed to the theoretical approaches, literature review and
paper writing; J.Y.K. has designed and supervised the paper.
Funding:
This work was, in part, supported by Basic Science Research Program through the National Research
Foundation of Korea funded by the Ministry of Education (NRF-2016R1D1A1B03933872), and in part supported
by “Human Resources Program in Energy Technology (No. 20174010201620)” of the Korea Institute of Energy
Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry &
Energy, Republic of Korea, and in part by Kwangwoon University in 2018.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Siano, P. Demand response and smart grids-A survey. Renew. Sustain. Energy Rev.
2014
,30, 461–478.
[CrossRef]
2.
Wood, A.J.; Wollenberg, B.F. Power Generation, Operation, and Control; John Wiley & Sons: Hoboken, NJ, USA, 2012.
3.
Zakariazadeh, A.; Jadid, S.; Siano, P. Smart microgrid energy and reserve scheduling with demand response
using stochastic optimization. Int. J. Elect. Power Energy Syst. 2014,63, 523–533. [CrossRef]
4.
Deng, R.; Yang, Z.; Chow, M.-Y.; Chen, J. A survey on demand response in smart grids: Mathematical models
and approaches. IEEE Access 2017,11, 570–582. [CrossRef]
5.
Chen, X.; Wei, T.; Hu, S. Uncertainty-aware household appliance scheduling considering dynamic electricity
pricing in smart home. IEEE Trans. Smart Grid 2013,4, 932–941. [CrossRef]
6. Power Smart Pricing. Available online: http://www.powersmartpricing.org/ (accessed on 12 May 2018).
7.
Kim, T.; Poor, H. Scheduling power consumption with price uncertainty. IEEE Trans. Smart Grid
2011
,
2, 519–527. [CrossRef]
Energies 2018,11, 2315 22 of 23
8.
Ibars, C.; Navarro, M.; Giupponi, L. Distributed Demand Management in Smart Grid with a Congestion
Game. In Proceedings of the First IEEE International Conference on Smart Grid Communications,
Gaithersburg, MD, USA, 4–6 October 2010; pp. 495–500. [CrossRef]
9.
Kishore, S.; Snyder, L. An Innovative RTP-based Residential Power Scheduling Scheme for Smart Grids. In
Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Prague, Czech Republic, 22–27 May 2011; pp. 5956–5959. [CrossRef]
10.
Bu, S.; Yu, F.R.; Liu, P.X. Dynamic pricing for demand-side management in the smart grid. In Proceedings
of the IEEE Online Conference on Green Communications, New York, NY, USA, 26–29 September 2011;
pp. 47–51. [CrossRef]
11.
Tavakoli, M.; Shokridehaki, F.; Akorede, M.F.; Marzband, M.; Vechiu, L.; Pouresmaeil, E. CVaR-based energy
management scheme for optimal resilience and operational cost in commercial building microgrids. Int. J.
Elec. Power Energy Syst. 2018,100, 1–9. [CrossRef]
12.
Tavakoli, M.; Shokridehaki, F.; Marzband, M.; Godina, R.; Pouresmaeil, E. A two stage hierarchical control
approach for the optimal energy management in commercial building microgrids based on local wind power
and PEVs. Sustain. Cities Soc. 2018,41, 332–340. [CrossRef]
13.
Marzband, M.; Azarinejadian, F.; Savaghebi, M.; Pouresmaeil, E.; Guerrero, J.M.; Lightbody, G. Smart
transactive energy framework in grid-connected multiple home microgrids under independent and coalition
operations. Renew. Energy 2018,126, 95–106. [CrossRef]
14.
Marzband, M.; Fouladfar, M.H.; Akorede, M.F.; Lightbody, G.; Pouresmaeil, E. Framework for smart
transactive energy in home-microgrids considering coalition formation and demand side management.
Sustain. Cities Soc. 2018,40, 136–154. [CrossRef]
15.
Tushar, W.; Yuen, C.; Smith, D.B.; Poor, H.V. Price discrimination for energy trading in smart grid: A game
theoretic approach. IEEE Trans. Smart Grid 2017,8, 1790–1801. [CrossRef]
16. Yu, M.; Hong, S.H. A real-time demand-response algorithm for smart grids: A Stackelberg game approach.
IEEE Trans. Smart Grid 2016,7, 879–888. [CrossRef]
17.
Ye, M.; Hu, G. Game design and analysis for price-based demand response: An aggregate game approach.
IEEE Trans. Cybern. 2017,47, 720–730. [CrossRef] [PubMed]
18.
Hong, S.G.; Hwang, Y.M.; Lee, S.Y.; Shin, Y.; Kim, D.I.; Kim, J.Y. Game-theoretic modeling of backscatter
wireless sensor networks under smart interference. IEEE Commun. Lett. 2018,22, 804–807. [CrossRef]
19.
Lee, C.; Park, L.; Cho, S. Light-weight Stackelberg game theoretic demand response scheme for massive
smart manufacturing systems. IEEE Access 2018,6, 23316–23324. [CrossRef]
20.
Esfahani, M.M.; Hariri, A.; Mohammed, O.A. A multiagent-based game-theoretic and optimization approach
for market operation of multi-microgrid systems. IEEE Trans. Ind. Informat. 2018,1, 1–12. [CrossRef]
21.
Marzband, M.; Javadi, M.; Pourmousavi, S.A.; Lightbody, G. An advance retail electricity market for active
distribution systems and home microgrid interoperability based on game theory. Electr. Power Syst. Res.
2018,157, 187–199. [CrossRef]
22.
Nekouei, E.; Alpcan, T.; Chattopadhyay, D. Game-theoretic frameworks for demand response in electricity
markets. IEEE Trans. Smart Grid 2015,6, 748–758. [CrossRef]
23.
Asr, N.R.; Ojha, U.; Zhang, Z.; Chow, M.Y. Incremental welfare consensus algorithm for cooperative
distributed generation/demand response in smart grid. IEEE Trans. Smart Grid 2014,5, 2836–2845.
24.
Soliman, S.A.-H.; Mantawy, A.-A.H. Modern Optimization Techniques with Applications in Electric Power Systems;
Springer: Berlin, Germany, 2011.
25.
Zhang, Z.; Chow, M.-Y. Convergence analysis of the incremental cost consensus algorithm under different
communication network topologies in a smart grid. IEEE Trans. Power Syst.
2012
,27, 1761–1768. [CrossRef]
26.
Yang, S.; Tan, S.; Xu, J.-X. Consensus based approach for economic dispatch problem in a smart grid. IEEE
Trans. Power Syst. 2013,28, 4416–4426. [CrossRef]
27.
Kar, S.; Hug, G.; Mohammadi, J.; Moura, J.M.F. Distributed state estimation and energy management in smart
grids: A consensus innovations approach. IEEE J. Sel. Top. Signal Process. 2014,8, 1022–1038. [CrossRef]
28.
Liu, Y.; Hu, S.; Huang, H.; Ranjan, R.; Zomaya, A.Y.; Wang, L. Game theoretic market driven smart home
scheduling considering energy balancing. IEEE Syst. J. 2017,11, 910–921. [CrossRef]
29. Dinkelbach, W. On nonlinear fractional programming. Manag. Sci. 1967,13, 492–498. [CrossRef]
30. Boyed, S.P.; Vandenberghe, L. Convex Optimization; Cambridge University Press: Cambridge, UK, 2004.
Energies 2018,11, 2315 23 of 23
31.
Azizi, S.; Dobakhshari, A.S.; Sarmadi, S.A.N.; Ranjbar, A.M. Optimal PMU placement by an equivalent linear
Formulation for exhaustive search. IEEE Trans. Smart Grid 2012,3, 174–182. [CrossRef]
32.
Ma, K.; Hu, G.; Spanos, C.G. Distributed energy consumption control via real-time pricing feedback in smart
grid. IEEE Trans. Control Syst. Technol. 2014,22, 1907–1914. [CrossRef]
©
2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... Its main features include the self-healing, incentivizing and protecting users, resisting attacks, providing the power quality that meets user needs, allowing the accesses of various electric power generation forms, starting the electric power market, and optimal and efficient operation of assets. The relevant research works on smart grids based on the EGT method have been launched by many researchers (Hwang et al., 2018;Noor et al., 2018;Reka and Ramesh, 2016;Zhu et al., 2016;Azar et al., 2018;Devia et al., 2021). Hwang et al. (2018) carried out a study for the benefit improvements of electric power supplier and consumer in the smart grid by using the EGM based on the Stackelberg game. ...
... The relevant research works on smart grids based on the EGT method have been launched by many researchers (Hwang et al., 2018;Noor et al., 2018;Reka and Ramesh, 2016;Zhu et al., 2016;Azar et al., 2018;Devia et al., 2021). Hwang et al. (2018) carried out a study for the benefit improvements of electric power supplier and consumer in the smart grid by using the EGM based on the Stackelberg game. Two strategies were proposed, which were the best-pricing and powergenerating strategy for the electric power supplier and best electricity-using strategy for the consumer. ...
... In addition, the peak-to-average ratio (PAR) can be decreased by using the proposed algorithm. The algorithm used in Hwang et al. (2018) is presented in Fig. 23. ...
Article
Full-text available
The evolutionary game theory method has been widely used in the research works about different kinds of energy utilization fields, especially the clean energy utilizations which can facilitate the sustainable energy development. This paper presents a research review of the evolutionary game theory (EGT)-based studies on different energy-related aspects, including the traditional energy utilizations, energy saving and carbon emission reduction, new energy utilizations, new energy vehicles, electric power market, distributed energy systems, micro-grid, smart grid and energy storage. Typical research works based on the evolutionary game theory method and relevant algorithms are introduced and summarized. To promote the sustainable developments of energy utilization technologies as well as the usage of evolutionary game theory method, typical existing problems and several general recommendations about the further EGT-based research works are also proposed. The potential further works based on the evolutionary game theory approach may include the EGT-based studies considering more complex initial conditions and influential factors, verifications of the applicabilities and feasibilities of the evolutionary game theory method based on practical examples, research works with evolutionary game models consisting of more participants, and research and development (R&D) works of new EGT-based or hybrid algorithms.
... To the best of the authors' knowledge, no other work in the literature proposes a system model or framework similar to the one adopted in this paper. Nevertheless, works concerning social welfare maximization in diverse settings or contexts, distinct from this paper, are documented in the following articles: [15,32,[34][35][36][37][38][39][40]. ...
... The pricing and the bandwidth allocation -X Heterogeneous wireless networks [38] Stackelberg game-based evolutionary game ...
Article
Full-text available
In contemporary mobile communications markets, various agents or players interact to pursue welfare. Regulatory policies enacted by governments in certain markets aim to maximize social welfare. However, some countries, both least developed and developing, often adopt successful models from developed nations without local market validation. Therefore, network economics serves as a pertinent framework for analyzing such policies. This paper introduces a novel scheme based on constrained optimization problems, where the constraints represent multilevel economic game equilibria within a system model involving three agents: the central planner, the mobile network operator, and the mobile data users. These agents strategically optimize their payoff functions by considering benefit factors and decision variables such as the subsidization factor, pricing, and data consumption. To this end, a three-stage dynamic game is proposed to model the players’ interactions, employing the backward induction method to ascertain the subgame perfect equilibrium from the Nash equilibrium. A case study is presented, demonstrating a 31.16% increase in social welfare between scenarios involving no adoption of the subsidization factor and its adoption at the optimal value when the central planner enacts it to other players in the game, even if they do not necessarily attain maximum payoff values. In countries aligning with this proposed model, social welfare is maximized through a subsidization scheme.
... The same first author considers that in a microgrid the energy flows in a transactive way and the transactions are based on bilateral contracts between peers [33]. Another concept of prosumer surplus trading based on the transactive energy concept is proposed in the literature [34][35][36][37]. Paper [34] uses a Stackelberg game-based method for solving the transactive energy problem, in which the DNO and the SSRES aggregators are participating simultaneously in the Local Electricity Market (LEM) and Wholesale Electricity Market (WEM). ...
... A comprehensive cost-benefit model for prosumer load sharing was proposed in [35], using game theory and considering non-cooperative game models of the microgrids for prosumers energy surplus. In the same context, a particular social welfare-based concept on transactive energy or demand response (DR) is applied in [36,37] using negotiated prices. ...
Article
Full-text available
In the deregulated electricity markets, trading prices are determined by the offer-demand mechanism, and retail consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are determined by the prices of transactions performed on the wholesale market. In parallel with becoming eligible for participating in the market, the consumers use increasingly local generation sources based mostly on renewable electricity generation equipment such as Photovoltaic (PV) panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. This paper proposes a two-tier local market model oriented for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as smart grids, crowdsourcing and energy poverty. Its goals are to improve the possibilities of local prosumers to sell electricity to local consumers and to increase their profitability, compared to the trading model often used in developing markets, of selling the surplus back to the grid via aggregators. The research aims to contribute to the sustainable development of the electricity sector using new and renewable sources of energy, state-of the art technologies and smart contracts, leading to prosumer proliferation and electricity cost reduction for consumers.
... However, the status of the system previous to the distribution of the goods or items defines and/or affects the concept of fairness and equity. Several examples can be found in the literature such as the emission trading [22], emission permits allocation [23], policy making for renewable energy [24], for the power generation in smart grids [25] as well as for the design of water distribution networks [26]. The analysis of the results presented in these studies show a number of weaknesses behind the metrics. ...
... = 13405 Jobs involved in current facilities (25) The first solution presented, scenario 1 in Table 3, corresponds to substituting this system unit by unit so that where a nuclear or a coal based power plant is located, a production facility based on hydrogen storage is installed with the same capacity while optimizing just the location and selection of renewable energy collection. Fig. 2 shows the location of the PV panels and the wind turbines. ...
Article
A new energy system represents investment and job creation opportunities across any region. The use of economic and/or social criteria to locate the collecting technologies and the power production plants can strengthen the economy and mitigate the effect of the substitution of fossil based power plants. Economy growth, employment and population equity indexes and a mathematical formulation are developed to evaluate the optimal social location of renewable energy collection devices and power plants. These facilities are based on the storage of wind and solar energy in the form of metal hydride that produces power in a fuel cell using the hydrogen generated in its decomposition. The case study of Spain is considered to evaluate the substitution of base electricity since coal and nuclear facilities are closing down in June 2020 and 2030 respectively. Aiming at economic growth, a more distributed location of wind farms and PV panels is selected at a cost. If job generation is the target, only PV panels are selected and the preferred location is the Southern part of the country. However, a trade-off for the lay out can be achieved considering a multiobjective solution involving costs and social background. Substituting coal and nuclear power plants can generate over 500k jobs and help develop the regions reducing the impact of depopulation.
... The present framework for Social Welfare maximisation is presented in Hwang et al. (2018) as shown below in Fig. 1. ...
Article
Full-text available
The power grids worldwide are changing both policy and infrastructure and are becoming Smart each day in order to support renewable energy sources (RES). These Smart Grids are offering interesting modern techniques like demand response (DR) for more profitable and sustainable operation of the grids in presence of RES. Traditionally demand response concentrates on electricity price which may invite many other technical challenges such as limit violation of vital system parameters like voltage, line flow, Power factor and security issues. To address this problem this work proposes an optimisation framework which tries to achieve security constrained social welfare optimisation by a novel application of DR technique, taking into account several operational issues such as intermittency of Renewable Generation, lowering of system inertia due to RES, degradation of bus PF, line stability,deviation of voltage etc. for all the Load Dispatch Centres (LDC)/consumers by the virtue of optimised curtailment, reduction of network losses, improvement of operating power factor and mitigation of line congestion. The proposed method uses Differential Evolution modified Quantum Particle Swarm Optimisation (DEQPSO) to achieve the proposed objective. When tested on modified IEEE 30 Bus system the proposed algorithm produced encouraging results.
... The present framework for Social Welfare maximisation is presented in Hwang et al. (2018) as shown below in Fig. 1. ...
Article
Full-text available
The power grids worldwide are changing both policy and infrastructure and are becoming Smart each day in order to support renewable energy sources (RES). These Smart Grids are offering interesting modern techniques like demand response (DR) for more profitable and sustainable operation of the grids in presence of RES. Traditionally demand response concentrates on electricity price which may invite many other technical challenges such as limit violation of vital system parameters like voltage, line flow, Power factor and security issues. To address this problem this work proposes an optimisation framework which tries to achieve security constrained social welfare optimisation by a novel application of DR technique, taking into account several operational issues such as intermittency of Renewable Generation, lowering of system inertia due to RES, degradation of bus PF, line stability,deviation of voltage etc. for all the Load Dispatch Centres (LDC)/consumers by the virtue of optimised curtailment, reduction of network losses, improvement of operating power factor and mitigation of line congestion. The proposed method uses Differential Evolution modified Quantum Particle Swarm Optimisation (DEQPSO) to achieve the proposed objective. When tested on modified IEEE 30 Bus system the proposed algorithm produced encouraging results. Abbreviations CR Crossover probability DISCO Distribution Company DR Demand response GUF Generation uncertainty factor GENCO Generation Company ISO Independent system operator LUF Line utilization factor RES Renewable energy sources SCOPF Security constrained optimal power flow TRANSCO Transmission Company
... Concerning the LEM, owing to the presence of autonomous financial entities with different objective functions, and the necessity of interaction with one another at the distribution level, a Stackelberg game-based approach is selected to model the TE trading between independent players of the market. That is due to the fact that with a game-based method, the selfish behavior of all the agents can be satisfied, and a win-win situation is established among them [25]. In this market, for the sake of simplicity, the price of traded energy is assumed to be constant. ...
Article
Full-text available
Recently, with the penetration of numerous Distributed Energy Resources (DER) in Smart Distribution Networks (SDN), Local Transactive Markets have emerged. Exchanging energy between all participants of local markets results in the satisfaction of producers and consumers. Based on these issues, this study provides a novel framework for the participation of SDN-independent entities in wholesale and local electricity markets simultaneously. In this regard, the considered system’s players, namely Distribution System Operator (DSO) and DER Aggregator (AG), take part within local as well as wholesale markets in two-day ahead and real-time stages. Moreover, to deal with the inherent conflict between the existing players’ interests, a Stackelberg game-based technique is proposed. In the raised competition, the leader, DSO, attempts to minimize its operating costs, while the follower, DER AG, tends to maximize its profit. Therefore, actors’ actions choices within both markets are made non-cooperatively. On the other hand, to handle the uncertain nature of stochastic parameters in the depicted problem, Monte Carlo Simulation (MCS), together with a fast backward/forward scenario reduction approach, is exploited. Ultimately, to evaluate the efficiency of the proposed scheme, two different case studies, with and without considering the competitive environment, are implemented on a modified IEEE-33 bus SDN.
Article
Retail tariffs reform in electricity markets is attracting huge attention as policymakers seek to ensure that the marginal cost of electricity generation matches with the marginal value of demand. Notwithstanding, it is increasingly recognized that in order to appropriately reflect the marginal cost principle in electricity markets, the negative externalities of electricity generation should be fully captured in retail tariffs. Therefore, taking Fujian electricity market of China as a case, this study examines how a retail rate of time-of-use (TOU) tariffs interacts with climate policy. We calibrate a dynamic partial equilibrium model of this market and finds that interacting climate policy with TOU tariffs leads to i) increase in rate structures, ii) increase in demand response or load shifting, iii) increase in producer rent, iv) decrease in consumer rent, and v) decrease in overall welfare. Our results suggest that even though TOU tariffs and climate policy interaction in electricity markets can offer social and environmental benefits, the resulting losses to consumer rent and overall welfare may potentially undermine this approach. Therefore, in order to enhance the feasibility of transitioning towards a competitive and low-carbon electricity market globally, the cost of compliance to climate policy will have to be significantly reduced.
Article
Full-text available
As Internet-of-things (IoT) technology emerges, smart manufacturing has recently attracted a large amount of attention. Smart manufacturing leads to smart energy management because of its significant operating expenditure savings. However, it is believed that the centralized energy management of IoT devices would impose a critically large overhead since massive numbers of IoT devices are expected to be deployed. Therefore, distributed energy management or demand response is deemed to be a better solution for emerging massive smart manufacturing systems. There have been a significant number of distributed demand response algorithms, including Stackelberg game theoretic approaches. However, Stackelberg game theoretic approaches require a large number of iterations to reach Nash equilibrium, which in turn necessitates communication overheads among IoT devices. This communication overhead causes a large amount of energy consumption as well as delay. In this paper, we propose a light-weight demand response scheme based on the Stackelberg model without iterations for massive smart manufacturing systems. The proposed scheme manages energy consumption based on a non-iterative Stackelberg model and historical real time pricing. To the best of our knowledge, our approach is the first technique that considers communication overheads for the demand response technique. The performance evaluation demonstrates that the proposed scheme shifts operations to avoid peak loads, the electricity bill is significantly reduced, operations occur at preferred times, and communication energy consumption and delay are minimized.
Article
Full-text available
Pricing schemes are an important smart grid feature to affect typical energy usage behavior of energy users (EUs). However, most existing schemes use the assumption that a buyer pays the same price per unit of energy to all suppliers at any particular time when energy is bought. By contrast, here a discriminate pricing technique using game theory is studied. A cake cutting game is investigated, in which participating EUs in a smart community decide on the price per unit of energy to charge a shared facility controller (SFC) in order to sell surplus energy. The focus is to study fairness criteria to maximize sum benefits to EUs and ensure an envy-free energy trading market. A benefit function is designed that leverages generation of discriminate pricing by each EU, according to the amount of surplus energy that an EU trades with the SFC and the EU's sensitivity to price. It is shown that the game possesses a socially optimal, and hence also Pareto optimal, solution. Further, an algorithm that can be implemented by each EU in a distributed manner to reach the optimal solution is proposed. Numerical case studies are given that demonstrate beneficial properties of the scheme.
Article
This paper aims at enhancing the resilience of a photovoltaic-based microgrid equipped with battery storage, supplying a typical commercial building. When extreme weather conditions such as hurricane, tsunami and similar events occur, leading to islanding of the microgrid from the main power grid, it is not expected that the microgrid would be taken out of service for a long time. At the same time, it is not cost effective to make the electrical system to be absolutely reliable to provide service for the customers. The main contribution of this paper lies in its ability to determine the optimal point between the operational cost and grid resilience. In other words, this work proposes an optimal management system of battery energy storage in a way to enhance the resilience of the proposed microgrid while maintaining its operational cost at a minimum level. The optimization is achieved by solving a linear optimization programming problem while the Conditional Value at Risk (CVaR) is incorporated in the objective function. The CVaR is used to account for the uncertainty in the intermittent PV system generated power and that in the electricity price. Simulation analyses are carried out in MATLAB to evaluate the performance of the proposed method. Results reveal that the commercial building microgrid resilience is improved remarkably at a slight increase in the operational cost.
Article
The inclusion of plug-in electrical vehicles (PEVs) in microgrids not only could bring benefits by reducing the on-peak demand, but could also improve the economic efficiency and increase the environmental sustainability. Therefore, in this paper a two stage energy management strategy for the contribution of PEVs in demand response (DR) programs of commercial building microgrids is addressed. The main contribution of this work is the incorporation of the uncertainty of electricity prices in a model predictive control (MPC) based plan for energy management optimization. First, the optimization problem considers the operation of PEVs and wind power in order to optimize the energy management in the commercial building. Second, the total charged power reference which is computed for PEVs in this stage is sent to the PEVs control section so that it could be allocated to each PEV. Therefore, the power balance can be achieved between the power supply and the load in the proposed microgrid building while the operational cost is minimized. The predicted values for load demand, wind power, and electricity price are forecasted by a seasonal autoregressive integrated moving average (SARIMA) model. In addition, the conditional value at risk (CVaR) is used for the uncertainty in the electricity prices. In the end, the results confirm that the PEVs can effectively contribute in the DR programs for the proposed microgrid model.
Article
The concept of transactive energy (TE) been adapted in the regulation of electricity market within the context of economic planning and control for grid reliability enhancement. The objective is to improve productivity and participation of the players in the market that is composed of distributed energy resources (DER). The main goal of implementing a market structure based on TE is to secure permission for the market players so that they could attain a higher payoff. In this study, an optimization-based algorithm in which an objective function premised on economic strategies, distribution limitations and the overall demand in the market structure is proposed. The objective function is solved for near global optima using four heuristically guided optimization algorithms. The proposed algorithm which ensures that none of the independent players has priority and/or advantage over others, emphasizes optimum use of electrical/thermal energy distribution resources, while maximizing profit for the owners of the home microgrids (H-MGs). Reduction in the market clearing price (MCP) for further participation and the response of the consumers’ responsive loads are also considered in the study. The feasibility of the proposed algorithm is validated in a coalition formation scenario among the existing H-MGs. Results show an increase in the profit attained, enhanced system reliability and a reduction in the electricity cost of the consumers.
Article
This paper presents a smart Transactive energy (TE) framework in which home microgrids (H-MGs) can collaborate with each other in a multiple H-MG system by forming coalitions for gaining competitiveness in the market. Profit allocation due to coalition between H-MGs is an important issue for ensuring the optimal use of installed resources in the whole multiple H-MG system. In addition, considering demand fluctuations, energy production based on renewable resources in the multiple H-MG can be accomplished by demand-side management strategies that try to establish mechanisms to allow for a flatter demand curve. In this regard, demand shifting potential can be tapped through shifting certain amounts of energy demand from some time periods to others with lower expected demand, typically to match price values and to ensure that existing generation will be economically sufficient. It is also possible to obtain the maximum profit with the coalition formation. In essence the impact of the consumption shifting in the multiple H-MG schedule can be considered while conducting both individual and coalition operations. A comprehensive simulation study is carried out to reveal the effectiveness of the proposed method in lowering the market clearing price (MCP) for about 15% of the time intervals, increasing H-MG responsive load consumption by a factor of 30%, and promoting local generation by a factor of three. The numerical results also show the capability of the proposed algorithm to encourage market participation and improve profit for all participants.
Article
This paper proposes a multiagent-based energy market for multi-microgrid systems using game-theoretic and hierarchical optimization approaches. The proposed method is tailored to achieve the optimal operation of smart microgrids in distribution systems. Because of rapid load variations in distribution systems, it is necessary to develop fast optimization algorithms which minimize the power mismatch in and among microgrids. In this paper, a three-level market framework is proposed. The first level comprises a game-theoretic double auction mechanism for the day-ahead market while the next two levels are optimal rescheduling and inter-microgrid reverse auction model for the hour-ahead and real-time markets, respectively. Using the hierarchical optimization algorithm in a multi-agent based area, it is anticipated to not only minimize the optimization solution time but also reduce the dependency on the network in grid-connected mode or load shedding in islanded mode. Using this approach, load Demand Response (DR) capabilities along with rescheduling of Distributed Energy Storage Systems (DESSs) and Distributed Generations (DG) could be utilized in all market levels which will lead to optimal operation of multi-microgrid systems. Agents are developed in DIgSILENT PowerFactory and Dynamic Data Exchange (DDE) is activated for communication among agents communicating through a Data Distribution Service (DDS) which utilizes the Real-Time Publish-Subscribe (RTPS) communication protocol. The developed framework is applied to the modified 37-bus IEEE distribution test feeder system to validate the effectiveness of this market structure. IEEE