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Citation: Yang, M.; Zhang, Y.; Liu, J.;
Yin, S.; Chen, X.; She, L.; Fu, Z.; Liu,
H. Distributed Shared Energy Storage
Double-Layer Optimal Configuration
for Source-Grid Co-Optimization.
Processes 2023,11, 2194. https://
doi.org/10.3390/pr11072194
Academic Editors: Chenyu Wu,
Zhongkai Yi and Chenhui Lin
Received: 17 June 2023
Revised: 14 July 2023
Accepted: 19 July 2023
Published: 21 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
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Attribution (CC BY) license (https://
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4.0/).
processes
Article
Distributed Shared Energy Storage Double-Layer Optimal
Configuration for Source-Grid Co-Optimization
Meng Yang 1, Yihan Zhang 1, Junhui Liu 1, Shuo Yin 1, Xing Chen 1, Lihui She 2,*, Zhixin Fu 2and Haoming Liu 2
1
State Grid Henan Economic Research Institute, Zhengzhou 450052, China; yangmeng7@ha.sgcc.com.cn (M.Y.);
zhangyihan2@ha.sgcc.com.cn (Y.Z.); liujunhui4@ha.sgcc.com.cn (J.L.); yinshuo@ha.sgcc.com.cn (S.Y.);
chenxing13@ha.sgcc.com.cn (X.C.)
2College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China;
zhixinfu@hhu.edu.cn (Z.F.); liuhaom@hhu.edu.cn (H.L.)
*Correspondence: 211606010138@hhu.edu.cn
Abstract:
Shared energy storage is an energy storage business application model that integrates
traditional energy storage technology with the sharing economy model. Under the moderate scale of
investment in energy storage, every effort should be made to maximize the benefits of each main
body. In this regard, this paper proposes a distributed shared energy storage double-layer optimal
allocation method oriented to source-grid cooperative optimization. First, considering the regulation
needs of the power side and the grid side, a distributed shared energy storage operation model is
proposed. Second, a distributed shared energy storage double-layer planning model is constructed,
with the lowest cost of the distributed shared energy storage system as the upper-layer objective, and
the lowest daily integrated operation cost of the distribution grid-distributed new energy stations as
the lower-layer objective. Third, a double-layer iterative particle swarm algorithm combined with
tide calculation is used to solve the distributed shared energy storage configuration and distribution
grid-distributed new energy stations’ economic operation problem. Finally, a comparative analysis
of four scenarios verifies that configuring distributed shared energy storage can increase the new
energy consumption rate to 100% and reduce the net load peak-valley difference by 61%. Meanwhile,
distributed shared energy storage operators have realized positive returns.
Keywords:
distributed shared energy storage; double-layer optimal; new energy consumption;
net load peak-to-valley difference; particle swarm algorithm
1. Introduction
In order to cope with the environmental problems caused by global warming, new
energy power generation is attracting great attention from all over the world [
1
]. However,
with the increasing scale of new energy access, the problem of imbalance between the
intermittent power output and the spatial and temporal matching of the load has become
more and more prominent, resulting in the phenomenon of wind and light curtailment
and peak-to-valley differences increasing year by year [
2
,
3
]. As a flexible power regulation
resource, energy storage can achieve energy leveling at the spatial and temporal levels,
promote local consumption of new energy, and reduce peak-to-valley differences [4,5].
Reasonable selection of the location and capacity of energy storage is important to
improve the safety and economy of power system operation [
6
,
7
]. There has been a lot of
research on the optimal configuration of distributed energy storage. Ding et al. [
8
] estab-
lished a double-layer coordinated siting and capacity optimization model for distributed
PV and energy storage, where the upper layer optimizes the capacity and power of energy
storage to minimize the annual integrated system cost, and the lower layer optimizes the
grid connection location of energy storage with the objective of minimizing the system
network loss. Gong et al. [
9
] used the dynamic planning method to solve for the distributed
energy storage capacity and location to meet the operational needs of the active distribution
Processes 2023,11, 2194. https://doi.org/10.3390/pr11072194 https://www.mdpi.com/journal/processes
Processes 2023,11, 2194 2 of 17
network, with the whole life cycle cost of energy storage as the optimization objective.
Li et al. [10]
proposed a two-stage robust optimization model for the capacity configura-
tion of integrated biogas–solar–wind energy systems applicable to rural areas, solving the
configuration problem of energy storage in the first stage and the optimal operation of the
system in the second stage. Guo et al. [
11
] first constructed a multi-attribute integrated
index assessment model to determine the location of energy storage, and then a two-layer
planning model to determine the storage capacity.
However, the current distributed energy storage investment costs are high [
12
,
13
], and
the utilization efficiency is low [
14
]. To address this issue, some scholars have conducted re-
search on shared energy storage models. Shuai et al. [
15
] constructed an optimal allocation
model for shared energy storage under multi-regional integrated energy system intercon-
nection. Xie et al. [
16
] constructed a multi-micro grid shared energy storage two-layer
planning model that takes into account the economic consumption of new energy sources.
Yang et al. [
17
] selected three types of industrial users with different peak types as research
objects and established a shared energy storage optimal allocation model to maximize
the overall net benefit of multiple users. Liu et al. [
18
] proposed a producer–consumer
energy-sharing mechanism and verified that the new energy consumption rate can be
improved by sharing energy storage.
In summary, research on shared energy storage configurations is still in its infancy.
Existing research mainly focuses on centralized shared energy storage, a single type of
shared energy storage user, with less analysis on the cost settlement between shared
energy storage users and shared energy storage. Based on the above problems, this paper
proposes a distributed shared energy storage double-layer optimal allocation method for
source-grid co-optimization. First, a distributed shared energy storage operation model for
source-grid co-optimization is proposed. Secondly, a distributed shared energy storage two-
layer planning model is constructed, and a two-layer iterative particle swarm algorithm
combined with tide calculation is used to solve the distributed shared energy storage
configuration and distribution grid-distributed new energy stations’ economic operation
problem. Finally, the effectiveness and economy of the proposed configuration method are
verified by simulation analysis of arithmetic cases.
In this paper, the main innovations of this paper are as follows:
1.
There are limitations on storage power ratings, line transmission capacity, etc., so
centralized shared storage no longer meets demand in actual use. Therefore, this
paper investigates the optimal allocation of distributed shared energy storage;
2.
This paper proposes a distributed shared energy storage operation model oriented
to source-network co-optimization, and analyzes the operation mode of each subject
and the profit mechanism of the shared energy storage operator;
3.
Most of the existing literature considers energy storage sharing in the context of
multiple single subjects, e.g., multi-industrial users, multi-microgrids, etc. In this
paper, we consider the energy storage contribution of distributed new energy stations
and distribution grids. In addition, this paper constructs a double-layer planning
model for distributed shared energy storage, which comprehensively considers the
operating costs of distributed shared energy storage operator and distribution grid-
distributed new energy stations and realizes the maximization of the interests of
each subject.
2. Distributed Shared Energy Storage Operation Model for Source-Grid
Co-Optimization
Shared energy storage is an energy storage business application model that integrates
traditional energy storage technology with the sharing economy model, which is an energy
storage power plant invested by a third party to provide charging and discharging services
for multiple subjects. The distributed shared energy storage studied in this paper takes into
account the regulation needs of both the power side and the grid side, and the schematic
Processes 2023,11, 2194 3 of 17
diagram of the distributed shared energy storage operation model for source-grid co-
optimization is shown in Figure 1.
Processes 2023, 11, x FOR PEER REVIEW 3 of 18
services for multiple subjects. The distributed shared energy storage studied in this paper
takes into account the regulation needs of both the power side and the grid side, and the
schematic diagram of the distributed shared energy storage operation model for source-
grid co-optimization is shown in Figure 1.
Figure 1. The schematic diagram of the distributed shared energy storage operation model for
source-grid co-optimization.
Distributed shared energy storage operators are responsible for the operation and
management of multiple energy storage plants. Each energy storage is connected to the
distribution grid. A distributed new energy station is responsible for the operation and
management of multiple new energy power stations. Each new energy station is con-
nected to the distribution grid. The operational objectives of each of the distributed shared
energy storage operators, distributed new energy stations, and distribution grid are de-
scribed below:
Shared energy storage operators aim to provide charging and discharging services
for distributed new energy sites and distribution grids to achieve the lowest capacity al-
location and operating costs for distributed shared energy storage systems. At the optimal
allocation level, energy storage operators will aggregate the charging and discharging
needs of distributed new energy sites and active distribution grids to centralize and opti-
mize the allocation of distributed shared energy storage system capacity. At the optimized
operation level, shared energy storage operators provide charging and discharging ser-
vices, and charge service fees while trading power through “low storage and high dis-
charge” to achieve price arbitrage.
Distributed new energy stations aim to maximize the utilization of distributed new
energy power generation and reduce the rate of wind and light curtailment by utilizing
the charging and discharging services of distributed shared energy storage plants. The
distributed new energy stations will give priority to the distribution grid to support its
load, and if the new energy output exceeds the demand of the distribution grid, the excess
new energy output will be charged to the distributed shared energy storage system in the
form of electricity sales.
The distribution grid aims to reduce the net load peak-to-valley differential by utiliz-
ing the charging and discharging services of distributed shared energy storage plants. In
the distribution grid, priority will be given to the consumption of new energy output to
meet the load demand, and the power imbalance will be the net load of the distribution
grid. During peak load periods, the distribution grid will shave peaks by discharging
power from distributed shared energy storage systems or purchasing power from the
main grid. During low load periods, the distribution grid will be filled by charging from
a distributed shared energy storage system to further reduce the net load peak-to-valley
difference.
Figure 1.
The schematic diagram of the distributed shared energy storage operation model for
source-grid co-optimization.
Distributed shared energy storage operators are responsible for the operation and
management of multiple energy storage plants. Each energy storage is connected to
the distribution grid. A distributed new energy station is responsible for the operation
and management of multiple new energy power stations. Each new energy station is
connected to the distribution grid. The operational objectives of each of the distributed
shared energy storage operators, distributed new energy stations, and distribution grid are
described below:
Shared energy storage operators aim to provide charging and discharging services
for distributed new energy sites and distribution grids to achieve the lowest capacity
allocation and operating costs for distributed shared energy storage systems. At the optimal
allocation level, energy storage operators will aggregate the charging and discharging needs
of distributed new energy sites and active distribution grids to centralize and optimize the
allocation of distributed shared energy storage system capacity. At the optimized operation
level, shared energy storage operators provide charging and discharging services, and
charge service fees while trading power through “low storage and high discharge” to
achieve price arbitrage.
Distributed new energy stations aim to maximize the utilization of distributed new
energy power generation and reduce the rate of wind and light curtailment by utilizing
the charging and discharging services of distributed shared energy storage plants. The
distributed new energy stations will give priority to the distribution grid to support its
load, and if the new energy output exceeds the demand of the distribution grid, the excess
new energy output will be charged to the distributed shared energy storage system in the
form of electricity sales.
The distribution grid aims to reduce the net load peak-to-valley differential by utilizing
the charging and discharging services of distributed shared energy storage plants. In the
distribution grid, priority will be given to the consumption of new energy output to meet
the load demand, and the power imbalance will be the net load of the distribution grid.
During peak load periods, the distribution grid will shave peaks by discharging power
from distributed shared energy storage systems or purchasing power from the main grid.
During low load periods, the distribution grid will be filled by charging from a distributed
shared energy storage system to further reduce the net load peak-to-valley difference.
3. Double-Layer Planning Model for Optimal Allocation of Distributed Shared
Energy Storage
Double-layer planning divides the problem into two layers: upper-layer optimization
and lower-layer optimization [
19
]. The upper- and lower-layer optimization models have
their own optimization objectives, constraints, and decision variables. A schematic diagram
Processes 2023,11, 2194 4 of 17
of the distributed shared energy storage double-layer planning model in this paper is
shown in Figure 2. From the figure, it can be seen that the upper and lower optimization
problems are coupled with each other through the parameter transfer between layers. The
upper-layer model passes the decision variables, i.e., the rated capacity and rated power
of the distributed shared energy storage, to the lower-layer model as the constraints of
the lower-layer model. The lower-layer model seeks the optimization of the charging
and discharging power and position of each energy storage on this basis and feeds the
optimization results of the power exchange between each body to the upper layer. The
optimal values of the upper and lower layers are obtained through continuous iteration.
Double-layer planning is used to find the lower layer optimum under the condition of the
upper layer optimum, thus maximizing the interests of each subject.
Processes 2023, 11, x FOR PEER REVIEW 4 of 18
3. Double-Layer Planning Model for Optimal Allocation of Distributed Shared
Energy Storage
Double-layer planning divides the problem into two layers: upper-layer optimization
and lower-layer optimization [19]. The upper- and lower-layer optimization models have
their own optimization objectives, constraints, and decision variables. A schematic dia-
gram of the distributed shared energy storage double-layer planning model in this paper
is shown in Figure 2. From the figure, it can be seen that the upper and lower optimization
problems are coupled with each other through the parameter transfer between layers. The
upper-layer model passes the decision variables, i.e., the rated capacity and rated power
of the distributed shared energy storage, to the lower-layer model as the constraints of the
lower-layer model. The lower-layer model seeks the optimization of the charging and dis-
charging power and position of each energy storage on this basis and feeds the optimiza-
tion results of the power exchange between each body to the upper layer. The optimal
values of the upper and lower layers are obtained through continuous iteration. Double-
layer planning is used to find the lower layer optimum under the condition of the upper
layer optimum, thus maximizing the interests of each subject.
Upper layer opt imizatio n: energy storage po wer rat ing and
rated ca pacity optimi zation
Optimizat
ion Goal
Distri buted sha red energy stora ge system s have
the l owest cost
Decision va riables: en ergy stora ge rated capacity and rated
power
Const rain ts: energy multiplier constraints, etc.
Solution al gorithm: Pa rticle swarm algorithm
Lower l ayer opt imizat ion: en ergy st orage lo cation and
operat ion opti mizat ion
Optimizat
ion Goal
Lowest combin ed daily op erat ing costs fo r
distribution grid - distribute d new energy stations
Decis ion varia bles: en ergy stora ge locatio n, energy s torage
chargi ng and disc harging
Const raints : Distri buted new energy stati ons output
constr aints, etc.
Soluti on alg orithm : Particle swa rm algori thm c omb ined
with tide calculation
Each
energy
storage
rated
power,
rated
capacity
Optimization
result s of
power
exchange
between
subjects
Figure 2. The schematic diagram of the distributed shared energy storage double-layer optimization
model.
3.1. Upper-Layer Model
The upper-layer model is used to solve the distributed shared energy storage plant-
rated capacity problem. The lowest cost of the distributed shared energy storage system
is used as the objective function to plan the rated capacity and rated power of distributed
shared energy storage.
3.1.1. Objective Function
The upper-layer optimization objective is the lowest cost of a distributed shared en-
ergy storage system, which can be expressed as
1
min
s
to ser adn new
CC C C C=−+ − (1)
where 1
C is the cost of a distributed shared energy storage system;
s
to
C is the average
daily investment and maintenance cost of distributed shared energy storage; new
C is the
cost of trading electricity between distributed shared energy storage and distributed new
energy stations; adn
C is the electricity transaction cost between distributed shared energy
storage and the distribution grid; and
s
er
C is the distributed shared energy storage ca-
pacity lease service fee.
The average daily investment and maintenance cost of distributed shared energy
storage is expressed as
Figure 2.
The schematic diagram of the distributed shared energy storage double-layer
optimization model.
3.1. Upper-Layer Model
The upper-layer model is used to solve the distributed shared energy storage plant-
rated capacity problem. The lowest cost of the distributed shared energy storage system is
used as the objective function to plan the rated capacity and rated power of distributed
shared energy storage.
3.1.1. Objective Function
The upper-layer optimization objective is the lowest cost of a distributed shared energy
storage system, which can be expressed as
minC1=Csto −Cser +Cadn −Cnew (1)
where
C1
is the cost of a distributed shared energy storage system;
Csto
is the average daily
investment and maintenance cost of distributed shared energy storage;
Cnew
is the cost of
trading electricity between distributed shared energy storage and distributed new energy
stations;
Cadn
is the electricity transaction cost between distributed shared energy storage
and the distribution grid; and
Cser
is the distributed shared energy storage capacity lease
service fee.
The average daily investment and maintenance cost of distributed shared energy
storage is expressed as
Csto =
n
∑
i=1r(1+r)y
365[(1+r)y−1](δpPsto,i+δeEsto,i) + δmPsto,i(2)
where
n
is the number of energy storage units;
r
is the discount rate;
y
is the life cycle of
energy storage equipment;
δp
and
δe
are the investment cost per unit power and capacity
of energy storage, respectively;
Psto,i
and
Esto,i
are the rated power and capacity of energy
storage, respectively; and δmis the maintenance cost per unit power.
Processes 2023,11, 2194 5 of 17
The cost of trading electricity between distributed shared energy storage and dis-
tributed new energy stations is expressed as
Cnew =
T
∑
t=1
N
∑
j=1
δt
newPt
sto,new,j(3)
where
T
is 24;
N
is the number of distributed new energy stations;
δt
new
is the selling
electricity price per unit electricity of distributed new energy stations at time
t
; and
Pt
sto,new,j
is the power selling from new energy station
j
to distributed shared energy storage system
at time t.
The electricity transaction cost between distributed shared energy storage and the
distribution grid is expressed as
Cadn =
T
∑
t=1
(δt
stoPt
sto,adn,d−δt
adnPt
sto,adn,c)(4)
where
δt
sto
is the selling electricity price per unit electricity of distributed shared energy
storage at time
t
;
δt
adn
is the selling electricity price per unit electricity of the distribution
grid at time
t
;
Pt
sto,adn,d
is the electricity sold by the distributed shared energy storage system
to the distribution grid at time
t
; and
Pt
sto,adn,c
is the electricity sold by the distribution grid
to the distributed shared energy storage system at time t.
The distributed shared energy storage capacity lease service fee is expressed as
Cser =δs
T
∑
t=1
(Pt
sto,adn,c+Pt
sto,adn,d) + δs
T
∑
t=1
N
∑
j=1
Pt
sto,new,j(5)
where
δs
is a unit power service fee paid by the distribution grid and distributed new
energy stations to the distributed shared energy storage system.
3.1.2. Constraint Condition
The energy multiplier constraint can be expressed as
Esto,i=βPsto,i(6)
where
β
is the energy storage battery rate, which refers to the energy ratio constraint
between the capacity of the energy storage battery and the rated power.
The distributed shared energy storage power constraint can be expressed as
Psto,i,min ≤Psto,i≤Psto,i,max (7)
where
Psto,i,min
and
Psto,i,max
are the minimum and maximum power of distributed shared
energy storage installed at each node, respectively.
The distributed shared energy storage charging and discharging power constraint can
be expressed as
n
∑
i=1
(Pt
sto,i,d−Pt
sto,i,c) = Pt
sto,sdn,d−Pt
sto,sdn,c−
N
∑
j=1
Pt
sto,new,j
0≤Pt
sto,i,c≤At
sto,i,cPsto,i
0≤Pt
sto,i,d≤At
sto,i,dPsto,i
At
sto,c,iAt
sto,d,i=0
(8)
Processes 2023,11, 2194 6 of 17
where
Pt
sto,i,c
and
Pt
sto,i,d
are the charging and discharging power of energy storage
i
at time
t
, respectively, and
At
sto,i,c
and
At
sto,i,d
are the charge and discharge flags of energy storage
i
at time t, respectively.
The distributed shared energy storage charge constraint can be expressed as
Et
sto,i=Et−1
sto,i+ (ηsto,cPt
sto,c,i−Pt
sto,d,i.ηsto,d)∆t
0.1Esto,i≤Et
sto,i≤0.9Esto,i
E0
sto,i=ET
sto,i=0.2Esto,i
(9)
where
Et
sto,i
is the charge of energy storage
i
at time
t
and
ηsto,c
and
ηsto,d
are the charging
and discharging efficiency of energy storage, respectively.
3.2. Lower Layer Model
The lower-layer model is used for solving distributed shared energy storage siting
and distribution grid-distributed new energy stations
0
economic operation problems. The
objective is to optimize each energy storage’s location and charging and discharging
power, achieving the lowest comprehensive daily operating cost of the distribution of
grid-distributed new energy stations.
3.2.1. Objective Function
The lower-layer optimization objective is to achieve the lowest integrated daily operating
cost of the distribution grid-distributed new energy stations, which can be expressed as
minC2=Cgrid +Cser +Cadn −Cnew +Cpeak−val ley (10)
where
Cgrid
is the cost of electricity purchased from the main grid by the distribution grid
and Cpeak−val ley is the penalty cost of the net load peak-to-valley difference.
The cost of electricity purchased from the main grid by the distribution grid is ex-
pressed as
Cgrid =
T
∑
t=1
δt
pPt
grid (11)
where
δt
p
is the price of electricity sold by the main grid at time
t
and
Pt
grid
is the power sold
by the main grid to the distribution grid at time t.
The penalty cost of the net load peak-to-valley difference is expressed as
Cpeak−val ley =δpeak−valley Lmax
load −Lmin
load
Lt
load =M
∑
k=1
Pt
load,k+Pt
sto,adn,c−Pt
sto,adn,d−
N
∑
j=1
Pt
adn,new,j
(12)
where
δpeak−val ley
is the net load peak–valley difference unit power penalty cost of
0.65 Yuan/kW [
20
];
Lmax
load
and
Lmin
load
are the net load maximum and minimum values,
respectively;
Lt
load
is the net distribution grid load at time
t
;
Pt
load,k
is the load at the node
k
at time
t
; and
Pt
adn,new,j
is the power sold by the new energy station
j
to the distribution
grid at time t.
3.2.2. Constraint Condition
The capacity constraint of a distributed new energy site can be expressed as
0≤Pt
new,j≤Pt
new_0,j(13)
where
Pt
new,j
is the actual output of the new energy station
j
at time
t
and
Pt
new_0,j
is the ideal
output of the new energy station j.
Processes 2023,11, 2194 7 of 17
The distributed new energy stations and distributed shared energy storage purchase
and sale constraint can be expressed as
0≤Pt
sto,new,j≤Pmax
sto,new (14)
where
Pmax
sto,new
is the maximum interactive power between the new energy station and the
distributed shared energy storage.
The power balance constraint of distributed new energy stations can be
expressed as
N
∑
j=1
Pt
new,j=N
∑
j=1
(Pt
adn,new,j+Pt
sto,new,j)
N
∑
j=1
Pt
adn,new,j=min(M
∑
k=1
Pt
load,k,N
∑
j=1
Pt
new_0,j)
(15)
The distribution grid and distributed shared energy storage purchase and sale con-
straint can be expressed as
0≤Pt
sto,adn,d≤Bt
sto,adn,dPmax
sto,adn
0≤Pt
sto,adn,c≤Bt
sto,adn,cPmax
sto,adn
(16)
where
Bt
sto,adn,d
and
Bt
sto,adn,c
are the flag bits of the power interaction between the distribu-
tion grid and distributed shared energy storage and
Pmax
sto,adn
is the maximum interaction
power between the distribution grid and distributed shared energy storage.
The distribution grid power balance constraint can be expressed as
Pt
grid +
N
∑
j=1
Pt
adn,new,j+Pt
sto,adn,d=Pt
sto,adn,c+
M
∑
k=1
Pt
load,k+Ploss,t(17)
where Ploss,tis the net loss of the distribution network at time t.
The node power balance constraint can be expressed as
Pt
i=Ut
i∑
j∈i
Ut
j(Gij cos θij +Bij sin θi j )
Qt
i=Ut
i∑
j∈i
Ut
j(Gij sin θij −Bij cos θi j )(18)
where
Pt
i
and
Qt
i
are the active and reactive power injected at node
i
at time
t
, respectively;
Ut
i
and
Ut
j
are the voltage amplitudes at node
i
at time
t
, respectively;
Gij
and
Bij
are the
conductance and susceptance between nodes
i
and
j
, respectively; and
θij
is the phase angle
difference between nodes iand j.
The node voltage constraint can be expressed as
Ui,min ≤Ut
i≤Ui,max (19)
where
Ui,min
and
Ui,max
are the minimum and maximum values of the voltage amplitude
of node i, respectively.
The branch circuit capacity constraint can be expressed as
St
ij ≤Si j,max (20)
where
St
ij
is the transmitted power between nodes
i
and
j
at time
t
, and
Sij,max
is the
maximum value of the transmittable power between nodes iand j.
There is a nomenclature table in the Nomenclature section to navigate each symbol
used in the paper.
Processes 2023,11, 2194 8 of 17
3.3. Double-Layer Planning Model Solving
In the process of siting and setting the capacity of distributed shared energy storage, it
is necessary to first consider the optimal economy of the distributed shared energy storage
system, and then on this basis, consider the lowest operating costs of the distribution grid
and distributed new energy station. The double-layer planning model based on the siting
and capacity determination of distributed shared energy storage is a mutually coupled
nonlinear multi-objective problem and contains multiple variables of different types. There-
fore, this paper uses a double-layer iterative particle swarm algorithm combined with tidal
wave calculation for the solution, as shown in Figure 3.
Processes 2023, 11, x FOR PEER REVIEW 9 of 18
(a) (b)
Figure 3. The flowchart for solving a two-tier model for optimal distributed shared energy storage
allocation. (a) Flowchart for solving the upper model; (b) flowchart for solving the lower model.
4. Example Analysis
4.1. Case Setup
The algorithm uses a modified IEEE-33 node as the object of study. Among them, the
IEEE-33 node is used as a distribution grid system [21]; 1000 kW PV is connected at node
9, and 1000 kW wind power is connected at node 20 as distributed new energy stations.
For the distributed shared energy storage system, the allowed access nodes are 2–33, with
a maximum of 6 energy storage accesses; the minimum rated power of energy storage
access is 100 kW, the maximum rated power is 1000 kW, the discount rate of energy stor-
age is 0.05 [20], the service life is 15 years [8], the unit power investment cost is 1173
Yuan/kW [20], the unit capacity investment cost is 1650 Yuan/(kW·h) [8], the unit power
maintenance cost is 97 Yuan/(year·kW) [20], the energy storage unit power service fee is
0.05 Yuan/(kW·h) [20], the energy storage charging efficiency is 0.95 [20], and the energy
storage discharging efficiency is 0.9 [8]. The modified IEEE-33 node is shown in Figure 4.
The electricity sales tariffs between subjects [22] are shown in Table 1. The load power and
the output of each new energy station for a typical day are shown in Appendix A.
PV
1
2
345678910 11 12 13 14 15 16
23
17
21
22
19
20
24 25 26
27
28 29
30
31 32
33
18
WT
Figure 4. The modified IEEE-33 node.
Figure 3.
The flowchart for solving a two-tier model for optimal distributed shared energy storage
allocation. (a) Flowchart for solving the upper model; (b) flowchart for solving the lower model.
The upper model is solved by a particle swarm algorithm, where each particle consists
of two parts: the rated power of each energy storage (
Psto,i
) and the rated capacity of
each energy storage (
Esto,i
). The lower model is solved using a particle swarm algorithm
combined with a tide calculation, where each particle also includes two parts: the location
of each energy storage (
xi
) and the charging and discharging power of each energy storage
(
Pt
sto,i,c
and
Pt
sto,i,d
, respectively). The upper model passes upper-level particles to the lower
model as constraints for the lower model. The lower-layer model seeks the optimization
of the charging and discharging power and position of each energy storage on this basis,
and feeds the optimization results of the power exchange between the subjects to the
upper layer. The optimal values of the upper and lower layers are obtained through
continuous iteration.
4. Example Analysis
4.1. Case Setup
The algorithm uses a modified IEEE-33 node as the object of study. Among them,
the IEEE-33 node is used as a distribution grid system [
21
]; 1000 kW PV is connected
at node 9, and 1000 kW wind power is connected at node 20 as distributed new energy
Processes 2023,11, 2194 9 of 17
stations. For the distributed shared energy storage system, the allowed access nodes
are 2–33, with a maximum of 6 energy storage accesses; the minimum rated power of
energy storage access is 100 kW, the maximum rated power is 1000 kW, the discount rate
of energy storage is 0.05 [
20
], the service life is 15 years [
8
], the unit power investment
cost is
1173 Yuan/kW [20],
the unit capacity investment cost is 1650 Yuan/(kW
·
h) [
8
], the
unit power maintenance cost is 97 Yuan/(year
·
kW) [
20
], the energy storage unit power
service fee is 0.05 Yuan/(kW
·
h) [
20
], the energy storage charging efficiency is 0.95 [
20
],
and the energy storage discharging efficiency is 0.9 [
8
]. The modified IEEE-33 node is
shown in Figure 4. The electricity sales tariffs between subjects [
22
] are shown in Table 1.
The load power and the output of each new energy station for a typical day are shown
in Appendix A.
Processes 2023, 11, x FOR PEER REVIEW 9 of 18
(a) (b)
Figure 3. The flowchart for solving a two-tier model for optimal distributed shared energy storage
allocation. (a) Flowchart for solving the upper model; (b) flowchart for solving the lower model.
4. Example Analysis
4.1. Case Setup
The algorithm uses a modified IEEE-33 node as the object of study. Among them, the
IEEE-33 node is used as a distribution grid system [21]; 1000 kW PV is connected at node
9, and 1000 kW wind power is connected at node 20 as distributed new energy stations.
For the distributed shared energy storage system, the allowed access nodes are 2–33, with
a maximum of 6 energy storage accesses; the minimum rated power of energy storage
access is 100 kW, the maximum rated power is 1000 kW, the discount rate of energy stor-
age is 0.05 [20], the service life is 15 years [8], the unit power investment cost is 1173
Yuan/kW [20], the unit capacity investment cost is 1650 Yuan/(kW·h) [8], the unit power
maintenance cost is 97 Yuan/(year·kW) [20], the energy storage unit power service fee is
0.05 Yuan/(kW·h) [20], the energy storage charging efficiency is 0.95 [20], and the energy
storage discharging efficiency is 0.9 [8]. The modified IEEE-33 node is shown in Figure 4.
The electricity sales tariffs between subjects [22] are shown in Table 1. The load power and
the output of each new energy station for a typical day are shown in Appendix A.
PV
1
2
345678910 11 12 13 14 15 16
23
17
21
22
19
20
24 25 26
27
28 29
30
31 32
33
18
WT
Figure 4. The modified IEEE-33 node.
Figure 4. The modified IEEE-33 node.
Table 1. The electricity sales price between each subject.
Period
Electricity Price/(Yuan 1/(kW·h))
Main Grid Electricity
Sales Price
Electricity
Distribution Grid
Sales Price
Distributed Shared
Energy Storage
Electricity Sales Price
Distributed New
Energy Stations
Electricity Sale Price
peak 8:00–12:00
17:00–21:00 1.36 1.10 1.38 1.05
flat 12:00–17:00
21:00–24:00 0.82 0.8 0.82 0.65
valley 0:00–08:00 0.37 0.35 0.40 0.30
11 Yuan ≈0.1388 USD.
To analyze the rationality of distributed shared energy storage configuration, four
scenarios are set up in this paper for comparative analysis.
Scenario 1: no energy storage is configured, the excess power from distributed new
energy stations is directly curtailed, and the power imbalance of the distribution grid is
directly purchased from the main grid.
Scenario 2: the distribution grid, new energy station 1 (node 20 access wind power
station), and new energy station 2 (node 9 access PV station) invest in the construction of
energy storage on their own to achieve peak shaving and fill the valley and improve the
consumption rate of new energy. Parameters such as the energy storage discount rate are
the same as for distributed shared energy storage.
Scenario 3: configuration of different numbers of shared energy storage. Discusses the
economic impact of configuring shared energy storage on the system under the constraint
of the number of shared energy storage.
Scenario 4: distributed shared energy storage is configured according to the method
proposed in this paper, using distributed shared energy storage to cut peaks and fill valleys
and improve the consumption rate of new energy.
Processes 2023,11, 2194 10 of 17
4.2. Analysis of the Impact of Distributed Shared Energy Storage Systems on Peak Shaving and
New Energy Consumption
The power balance of the distribution network for scenario 1 and scenario 4 is shown
in Figure 5. In Figure 5, the positive power represents the power supplied to the distribution
grid from outside, the negative power represents the network loss within the distribution
grid and the power consumed by all electrical loads, and the difference between the
maximum and minimum values of the net load curve is the peak-to-valley difference.
Processes 2023, 11, x FOR PEER REVIEW 10 of 18
Table 1. The electricity sales price between each subject.
Period
Electricity Price/(Yuan 1/(kW·h))
Main Grid Electricity
Sales Price
Electricity Distribution
Grid Sales Price
Distributed Shared En-
ergy Storage Electricity
Sales Price
Distributed New En-
ergy Stations Electricity
Sale Price
peak 8:00–12:00
17:00–21:00 1.36 1.10 1.38 1.05
flat 12:00–17:00
21:00–24:00 0.82 0.8 0.82 0.65
valley 0:00–08:00 0.37 0.35 0.40 0.30
1 1 Yuan ≈ 0.1388 USD.
To analyze the rationality of distributed shared energy storage configuration, four
scenarios are set up in this paper for comparative analysis.
Scenario 1: no energy storage is configured, the excess power from distributed new
energy stations is directly curtailed, and the power imbalance of the distribution grid is
directly purchased from the main grid.
Scenario 2: the distribution grid, new energy station 1 (node 20 access wind power
station), and new energy station 2 (node 9 access PV station) invest in the construction of
energy storage on their own to achieve peak shaving and fill the valley and improve the
consumption rate of new energy. Parameters such as the energy storage discount rate are
the same as for distributed shared energy storage.
Scenario 3: configuration of different numbers of shared energy storage. Discusses
the economic impact of configuring shared energy storage on the system under the con-
straint of the number of shared energy storage.
Scenario 4: distributed shared energy storage is configured according to the method
proposed in this paper, using distributed shared energy storage to cut peaks and fill val-
leys and improve the consumption rate of new energy.
4.2. Analysis of the Impact of Distributed Shared Energy Storage Systems on Peak Shaving and
New Energy Consumption
The power balance of the distribution network for scenario 1 and scenario 4 is shown
in Figure 5. In Figure 5, the positive power represents the power supplied to the distribu-
tion grid from outside, the negative power represents the network loss within the distri-
bution grid and the power consumed by all electrical loads, and the difference between
the maximum and minimum values of the net load curve is the peak-to-valley difference.
(a) (b)
Figure 5. The distribution network power balance diagram. (a) Scenario 1; (b) scenario 4.
Analyzing the power balance diagram of the distribution network in scenario 1, we
can see that the distribution grid gives priority to the power provided by the distributed
new energy stations, and when the power provided by the distributed new energy stations
is insufficient, the distribution grid purchases power directly from the main grid to meet
the power demand of the load. The load has peak and valley characteristics, but the new
energy output has anti-peak characteristics. From Figure 5a, we can see that in 1–5 h and
14–16 h, the load is less but the new energy output is larger, resulting in a net load curve
close to 0. However, in 9–12 h and 18–21 h, the peak load increases but the new energy
output decreases, and the distribution grid can only purchase a large amount of power
from the main grid. Based on the net load curve, it can be seen that the peak-to-valley
difference for scenario 1 is 3040 kW.
Analyzing the power balance diagram of the distribution grid in scenario 4, we can see
that the distribution grid gives priority to consuming the power provided by distributed
energy stations; during the low-load period of 1–8 h, the distribution grid fills the valley
by selling power to distributed shared energy storage; during the peak load periods
of 9–12 h and 18–21 h, the distribution grid cuts the peak by purchasing power from
distributed shared energy storage, thus reducing the net load peak-to-valley difference of
the distribution grid. Based on the net load curve, it can be seen that the peak-to-valley
difference for scenario 4 is 1120 kW, which is 63% lower than that of scenario 1.
The power balance of distributed new energy sites for scenario 1 and scenario 4 is
shown in Figure 6. In Figure 6, the positive power represents the power output of each
new energy station, and the negative power represents the power sold by each new energy
station to the distribution grid and the distributed shared energy storage system. The
ideal power output of distributed new energy stations represents the sum of the maximum
power available from all new energy stations in that period.
Analyzing the power balance diagram of distributed new energy stations in scenario
1, we can see that the distribution grid cannot consume all the new energy output at
2–6 h and 15 h, at which time there is power curtailment in distributed new energy
stations, and the power curtailed by wind and light is 1455 kW. Scenario 4 is equipped with
Processes 2023,11, 2194 11 of 17
distributed shared energy storage. When the distribution grid cannot consume all the new
energy output, the distributed new energy stations sell the excess power to distributed
shared energy storage to improve the new energy consumption rate, and the new energy
consumption rate of scenario 4 is 100%.
Processes 2023, 11, x FOR PEER REVIEW 11 of 18
Figure 5. The distribution network power balance diagram. (a) Scenario 1; (b) scenario 4.
Analyzing the power balance diagram of the distribution network in scenario 1, we
can see that the distribution grid gives priority to the power provided by the distributed
new energy stations, and when the power provided by the distributed new energy sta-
tions is insufficient, the distribution grid purchases power directly from the main grid to
meet the power demand of the load. The load has peak and valley characteristics, but the
new energy output has anti-peak characteristics. From Figure 5a, we can see that in 1–5 h
and 14–16 h, the load is less but the new energy output is larger, resulting in a net load
curve close to 0. However, in 9–12 h and 18–21 h, the peak load increases but the new
energy output decreases, and the distribution grid can only purchase a large amount of
power from the main grid. Based on the net load curve, it can be seen that the peak-to-
valley difference for scenario 1 is 3040 kW.
Analyzing the power balance diagram of the distribution grid in scenario 4, we can
see that the distribution grid gives priority to consuming the power provided by distrib-
uted energy stations; during the low-load period of 1–8 h, the distribution grid fills the
valley by selling power to distributed shared energy storage; during the peak load periods
of 9–12 h and 18–21 h, the distribution grid cuts the peak by purchasing power from dis-
tributed shared energy storage, thus reducing the net load peak-to-valley difference of the
distribution grid. Based on the net load curve, it can be seen that the peak-to-valley differ-
ence for scenario 4 is 1120 kW, which is 63% lower than that of scenario 1.
The power balance of distributed new energy sites for scenario 1 and scenario 4 is
shown in Figure 6. In Figure 6, the positive power represents the power output of each
new energy station, and the negative power represents the power sold by each new energy
station to the distribution grid and the distributed shared energy storage system. The ideal
power output of distributed new energy stations represents the sum of the maximum
power available from all new energy stations in that period.
(a) (b)
Figure 6. The power balance diagram of distributed new energy stations. (a) Scenario 1; (b) scenario
4.
Analyzing the power balance diagram of distributed new energy stations in scenario
1, we can see that the distribution grid cannot consume all the new energy output at 2–6
h and 15 h, at which time there is power curtailment in distributed new energy stations,
and the power curtailed by wind and light is 1455 kW. Scenario 4 is equipped with dis-
tributed shared energy storage. When the distribution grid cannot consume all the new
energy output, the distributed new energy stations sell the excess power to distributed
Figure 6.
The power balance diagram of distributed new energy stations. (
a
) Scenario 1;
(b) scenario 4.
The economic benefits of scenario 1 and scenario 4 are shown in Table 2. Scenario 1
does not configure energy storage, so the total cost of the distributed shared energy storage
system is 0. The daily integrated operating cost of the distribution grid-distributed new
energy stations is 35,873 Yuan, the net load peak-to-valley difference is 3040 kW, and the
phenomenon of wind and light curtailment exists. Scenario 4 is configured with distributed
shared energy storage; the cost of the distributed shared energy storage system is
−
84 Yuan,
the energy storage is profitable, and the distribution grid-distributed new energy stations’
daily integrated operation cost is reduced by 1786 Yuan compared to scenario 1; the net
load peak-to-valley difference is reduced by 1920 kW compared to scenario 1, and the new
energy consumption rate is 100%. The comparative analysis of scenario 1 and scenario 4
verifies that the configuration of distributed shared energy storage can effectively reduce
the peak-to-valley difference and improve the consumption rate of new energy.
Table 2. Economic benefits of scenario 1 and scenario 4.
Scenario
Distributed Shared
Energy Storage
System Cost/Yuan 1
Distribution Grid-Distributed New
Energy Stations Comprehensive Daily
Operating Cost/Yuan
Net Load
Peak-to-Valley
Difference/kW
New Energy
Consumption Rate/%
1 - 35,873 3040 93
4−84 34,087 1120 100
11 Yuan ≈0.1388 USD.
4.3. Analysis of Distributed Shared Energy Storage Optimal Allocation Results and Charging and
Discharging Behavior
The results of scenario 2 and scenario 4 energy storage optimization configurations are
shown in Table 3, where the distribution grid energy storage in scenario 2 is configured with
the peak-to-valley difference derived from scenario 4 as the constraint. It can be seen that
the total configured capacity in scenario 2 is 9580 kW
·
h and the total configured capacity of
distributed shared energy storage in scenario 4 is 6870 kW
·
h, which is 28% less than the
total configured capacity in scenario 2. It can be seen that by reasonably sharing distributed
energy storage, realizing the time, sharing multiplexing of energy storage, and improving
Processes 2023,11, 2194 12 of 17
the utilization rate of energy storage resources, the configuration of smaller power and
capacity of energy storage can meet the demand for energy storage in distributed new
energy stations and distribution grid.
Table 3. The energy storage optimization configuration results of scenario 2 and scenario 4.
Scenario Category Access Node Power Rating/kW Rated Capacity/(kW·h)
2
Energy storage for new energy station 1
20 432 2160
Energy storage for new energy station 2
9 175 875
Energy storage for distribution grid 6 1000 5000
3 309 1545
4Distributed shared energy storage 1 6 987 4935
Distributed shared energy storage 2 13 381 1935
To see the utilization of energy storage resources more intuitively, this paper will
analyze the results of scenario 4 energy storage charging and discharging behavior and
charge state optimization, as shown in Figure 7. Positive power represents energy storage
charging and negative power represents energy storage discharging.
Processes 2023, 11, x FOR PEER REVIEW 13 of 18
charge state optimization, as shown in Figure 7. Positive power represents energy storage
charging and negative power represents energy storage discharging.
(a) (b)
Figure 7. The distributed shared energy storage charge–discharge and charge state optimization
results. (a) Distributed shared energy storage 1; (b) distributed shared energy storage 2.
From Figure 7, it can be seen that both distributed shared energy storage 1 and 2
reach the maximum charging power in the low valley period and the maximum discharg-
ing power in the peak load period, i.e., both distributed shared energy storage 1 and 2
have full charging and full discharging behaviors. In addition, the distributed shared en-
ergy storage 1 reaches a maximum charge state of 0.9 at 6 h and a minimum charge state
of 0.16 at 21 h. Distributed shared energy storage 2 reaches a maximum charge state of 0.9
at 6 h and a minimum charge state of 0.1 at 21 h, indicating that all distributed shared
energy storage power reaches the upper or lower capacity limit. Distributed shared en-
ergy storage makes full use of energy storage capacity resources by aggregating the en-
ergy demand of distribution grids and distributed new energy sites and reasonably allo-
cating each energy storage charge and discharge.
The economic benefits of scenario 2 and scenario 4 are shown in Table 4. It can be
seen that the distributed shared energy storage system in scenario 4 is profitable, with a
total cost of −84 Yuan and a combined daily operating cost of 2409 Yuan less for scenario
4’s distribution grid-distributed new energy field station compared to scenario 2. Through
the comparative analysis of scenario 2 and scenario 4, it is verified that the configuration
of distributed shared energy storage can reduce the operating cost of distribution grid-
distributed new energy stations while taking into account the economics of shared energy
storage investors to achieve a win–win situation for all parties.
Table 4. Economic benefits of scenario 2 and scenario 4.
Scenario
Distributed Shared En-
ergy Storage System
Cost/Yuan 1
Distribution Grid-Dis-
tributed New Energy Sta-
tions Comprehensive
Daily Operating
Cost/Yuan
Net Load Peak-to-Valley
Difference/kW
New Energy Consump-
tion Rate/%
2 - 36,496 1120 100
4 −84 34,087 1120 100
1 1 Yuan ≈ 0.1388 USD.
Figure 7.
The distributed shared energy storage charge–discharge and charge state optimization
results. (a) Distributed shared energy storage 1; (b) distributed shared energy storage 2.
From Figure 7, it can be seen that both distributed shared energy storage 1 and 2 reach
the maximum charging power in the low valley period and the maximum discharging
power in the peak load period, i.e., both distributed shared energy storage 1 and 2 have
full charging and full discharging behaviors. In addition, the distributed shared energy
storage 1 reaches a maximum charge state of 0.9 at 6 h and a minimum charge state of 0.16
at 21 h. Distributed shared energy storage 2 reaches a maximum charge state of 0.9 at
6 h
and a minimum charge state of 0.1 at 21 h, indicating that all distributed shared energy
storage power reaches the upper or lower capacity limit. Distributed shared energy storage
makes full use of energy storage capacity resources by aggregating the energy demand of
distribution grids and distributed new energy sites and reasonably allocating each energy
storage charge and discharge.
The economic benefits of scenario 2 and scenario 4 are shown in Table 4. It can be seen
that the distributed shared energy storage system in scenario 4 is profitable, with a total
cost of
−
84 Yuan and a combined daily operating cost of 2409 Yuan less for scenario 4’s
distribution grid-distributed new energy field station compared to scenario 2. Through
Processes 2023,11, 2194 13 of 17
the comparative analysis of scenario 2 and scenario 4, it is verified that the configuration
of distributed shared energy storage can reduce the operating cost of distribution grid-
distributed new energy stations while taking into account the economics of shared energy
storage investors to achieve a win–win situation for all parties.
Table 4. Economic benefits of scenario 2 and scenario 4.
Scenario
Distributed Shared
Energy Storage
System Cost/Yuan 1
Distribution Grid-Distributed New
Energy Stations Comprehensive
Daily Operating Cost/Yuan
Net Load
Peak-to-Valley
Difference/kW
New Energy
Consumption Rate/%
2 - 36,496 1120 100
4−84 34,087 1120 100
11 Yuan ≈0.1388 USD.
4.4. Analysis of the Impact of Different Numbers of Energy Storage on the Economics of
Distributed Shared System
To analyze the economic impact of configuring different numbers of energy storage on
the distributed shared system, computational analysis was performed for scenario 3, and
the cost of the distributed shared energy storage system with different numbers of energy
storage as the constraint was obtained, as shown in Figure 8.
Processes 2023, 11, x FOR PEER REVIEW 14 of 18
4.4. Analysis of the Impact of Different Numbers of Energy Storage on the Economics of
Distributed Shared System
To analyze the economic impact of configuring different numbers of energy storage
on the distributed shared system, computational analysis was performed for scenario 3,
and the cost of the distributed shared energy storage system with different numbers of
energy storage as the constraint was obtained, as shown in Figure 8.
Figure 8. The distributed shared energy storage cost versus the number of energy storage.
As can be seen from Figure 8, the cost of distributed shared energy storage tends to
decrease and then increase as the number of energy storage increases. Due to the con-
straints of energy storage rated power, line transmission capacity, etc., as the number and
scale of energy storage increases, the ability of distributed shared energy storage systems
to consume new energy and peak shaving is increasing (i.e., the revenue of distributed
shared energy storage is increasing), so the cost of distributed shared energy storage is on
a downward trend. However, as the number and scale of energy storage continue to in-
crease, the effect of new energy consumption and peak shaving tends to saturate, but the
investment cost of energy storage is increasing, so the cost of distributed shared energy
storage is on the rise. In this calculation example, the cost of distributed shared energy
storage is at least −84 Yuan when the number of energy storage sites is 2, and the distrib-
uted shared energy storage operator achieves profitability.
5. Conclusions
This paper proposes a distributed shared energy storage optimal allocation method
that takes into account both power-side and grid-side regulation requirements, integrates
the optimization problems at both planning and operation levels, constructs a double-
layer model for distributed shared energy storage optimal allocation, and solves it using
a double-layer iterative particle swarm algorithm combined with tide calculation, and
draws the following main conclusions:
By deploying distributed shared energy storage, distribution grid and new energy
stations receive energy storage charging and discharging services at a lower cost, increas-
ing the new energy consumption rate to 100% and reducing the peak-to-valley difference
by 61%.
Through the reasonable sharing of distributed energy storage, realize the time-shar-
ing reuse of energy storage and improve the utilization rate of energy storage resources
so that the configuration of smaller capacity energy storage can meet the demand for en-
ergy storage in distributed new energy stations and distribution grids. Distributed shared
energy storage can reduce the allocated capacity by 28% compared to the standalone dis-
tribution storage scenario.
Figure 8. The distributed shared energy storage cost versus the number of energy storage.
As can be seen from Figure 8, the cost of distributed shared energy storage tends to
decrease and then increase as the number of energy storage increases. Due to the constraints
of energy storage rated power, line transmission capacity, etc., as the number and scale
of energy storage increases, the ability of distributed shared energy storage systems to
consume new energy and peak shaving is increasing (i.e., the revenue of distributed
shared energy storage is increasing), so the cost of distributed shared energy storage is
on a downward trend. However, as the number and scale of energy storage continue
to increase, the effect of new energy consumption and peak shaving tends to saturate,
but the investment cost of energy storage is increasing, so the cost of distributed shared
energy storage is on the rise. In this calculation example, the cost of distributed shared
energy storage is at least
−
84 Yuan when the number of energy storage sites is 2, and the
distributed shared energy storage operator achieves profitability.
5. Conclusions
This paper proposes a distributed shared energy storage optimal allocation method
that takes into account both power-side and grid-side regulation requirements, integrates
the optimization problems at both planning and operation levels, constructs a double-
layer model for distributed shared energy storage optimal allocation, and solves it using a
Processes 2023,11, 2194 14 of 17
double-layer iterative particle swarm algorithm combined with tide calculation, and draws
the following main conclusions:
By deploying distributed shared energy storage, distribution grid and new energy
stations receive energy storage charging and discharging services at a lower cost, increasing
the new energy consumption rate to 100% and reducing the peak-to-valley difference
by 61%.
Through the reasonable sharing of distributed energy storage, realize the time-sharing
reuse of energy storage and improve the utilization rate of energy storage resources so
that the configuration of smaller capacity energy storage can meet the demand for energy
storage in distributed new energy stations and distribution grids. Distributed shared energy
storage can reduce the allocated capacity by 28% compared to the standalone distribution
storage scenario.
Through distributed shared energy storage system services and a reasonable number
of energy storage configurations, distribution grids and distributed new energy stations
can reduce their operating costs. At the same time, distributed shared energy storage
operators realize positive returns, and there is potential for profitable investment in building
distributed shared energy storage plants.
Author Contributions:
Conceptualization, M.Y.; methodology, M.Y. and Y.Z.; software, J.L.; for-
mal analysis, S.Y.; investigation, X.C.; resources, L.S.; writing—original draft preparation, H.L.;
writing—review
and editing, Z.F. All authors have read and agreed to the published version of
the manuscript.
Funding:
The research was supported by the R&D project “Research on the Optimized Configuration
and Operation Model of Distributed Shared Energy Storage to Promote Local Consumption of
Renewable Energy- Research on multi-objective optimization model and operational strategy for
shared energy storage (5217L0230003)” from the State Grid Henan Economic Research Institute.
Data Availability Statement: Research data have been provided in the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Nomenclature
C1
the cost of a distributed shared energy
storage system Csto
the average daily investment and maintenance cost
of distributed shared energy storage
Cnew
the cost of trading electricity between distributed
shared energy storage and distributed new
energy stations
Cadn
the electricity transaction cost between distributed
shared energy storage and the distribution grid
Cser
the distributed shared energy storage capacity lease
service fee δp
the investment cost per unit of power of
energy storage
δe
the investment cost per unit capacity of
energy storage Psto,ithe rated power of energy storage
Esto,ithe rated capacity of energy storage δmthe maintenance cost per unit of power
δt
new
the selling electricity price per unit of electricity of
distributed new energy stations at time tPt
sto,new,jthe power sold from new energy station
δt
sto
the selling electricity price per unit of electricity of
distributed shared energy storage at time tδt
adn
the selling electricity price per unit of electricity of
distribution grid at time t
Pt
sto,adn,d
the electricity sold by the distributed shared energy
storage system to the distribution grid at time tPt
sto,adn,c
the electricity sold by the distribution grid to the
distributed shared energy storage system at time
t
δs
a unit power service fee paid by the distribution
grid and distributed new energy stations to dis-
tributed shared energy storage system
βthe energy storage battery rate
Processes 2023,11, 2194 15 of 17
Psto,i,min
the minimum power of distributed shared energy
storage installed at each node Psto,i,max
the maximum power of distributed shared energy
storage installed at each node
Pt
sto,i,cthe charging power of energy storage iat time t Pt
sto,i,d
the discharging power of energy storage
i
at time
t
At
sto,i,cthe charge flags of energy storage iat time t At
sto,i,dthe discharge flags of energy storage iat time t
Et
sto,ithe charge of energy storage iat time tηsto,cthe charging efficiency of energy storage
ηsto,dthe discharging efficiency of energy storage Cgrid
the cost of electricity purchased from the main grid
by the distribution grid
Cpeak−val ley
the penalty cost of the net load peak-to-
valley difference δt
p
the price of electricity sold by the main grid at
time t
Pt
grid
the power sold by the main grid to the distribution
grid at time tδpeak −val ley
the net load peak–valley difference unit power
penalty cost
Lmax
loa d the net load maximum values Lmin
loa d the net load minimum values
Lt
loa d the net distribution grid load at time t Pt
loa d,kthe load at the node kat time t
Pt
adn,new,j
the power sold by the new energy station
j
to the
distribution grid at time tPt
new,j
the actual output of the new energy station
j
at
time t
Pt
new_0,jthe ideal output of the new energy station j Pmax
sto,new
the maximum interactive power between the
new energy station and the distributed shared
energy storage
Bt
sto,adn,d
the discharge flag bits of the power interaction be-
tween the distribution grid and distributed shared
energy storage
Bt
sto,adn,c
the charge flag bits of the power interaction be-
tween the distribution grid and distributed shared
energy storage
Pmax
sto,adn
the maximum interaction power between distribu-
tion grid and distributed shared energy storage Ploss,tthe net loss of the distribution network at time t
Pt
ithe active power injected at node iat time t Qt
ithe reactive power injected at node iat time t
Ut
ithe voltage amplitudes at node iat time t Ut
jthe voltage amplitudes at node jat time t
Gij the conductance between nodes iand j Bij the susceptance between nodes iand j
θij the phase angle difference between nodes iand j Ui,min
the minimum values of the voltage amplitude of
node i
Ui,max
the maximum values of the voltage amplitude of
node iSt
ij
the transmitted power between nodes
i
and
j
at
time t
Sij,max
the maximum value of the transmittable power
between nodes iand j
Appendix A
Processes 2023, 11, x FOR PEER REVIEW 16 of 18
peak valley
C− the penalty cost of the net load peak-to-valley
difference
t
p
δ
the price of electricity sold by the main grid
at time t
t
g
rid
P
the power sold by the main grid to the distri-
b
ution grid at time t peak valley
δ
− the net load peak–valley difference unit
power penalty cost
max
load
L
the net load maximum values min
load
L
the net load minimum values
t
load
L
the net distribution grid load at time t ,
t
load k
P the load at the node k at time t
,,
t
adn new j
P the power sold by the new energy station
j
to the distribution grid at time t ,
t
new j
P the actual output of the new energy station
j
at time t
_0,
t
new j
P the ideal output of the new energy station
j
max
,
s
to new
P
the maximum interactive power between the
new energy station and the distributed
shared energy storage
,,
t
s
to adn d
B
the discharge flag bits of the power interaction
b
etween the distribution grid and distributed
shared energy storage
,,
t
s
to adn c
B
the charge flag bits of the power interaction
b
etween the distribution grid and distributed
shared energy storage
max
,
s
to adn
P
the maximum interaction power between dis-
tribution grid and distributed shared energy
storage
,loss t
P the net loss of the distribution network at
time t
t
i
P the active power injected at node i at time t t
i
Q the reactive power injected at node i at time
t
t
i
U the voltage amplitudes at node i at time t t
j
U the voltage amplitudes at node
j
at time t
ij
G the conductance between nodes i and
j
ij
B
the susceptance between nodes i and
j
ij
θ
the phase angle difference between nodes i
and
j
,mini
U the minimum values of the voltage ampli-
tude of node i
,maxi
U the maximum values of the voltage amplitude
of node i
t
ij
S the transmitted power between nodes i and
j
at time t
,maxij
S the maximum value of the transmittable power
b
etween nodes i and
j
Appendix A
Figure A1. Load power graph for a typical day.
Figure A1. Load power graph for a typical day.
Processes 2023,11, 2194 16 of 17
Processes 2023, 11, x FOR PEER REVIEW 17 of 18
Figure A2. New energy power station output graph on a typical day.
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Figure A2. New energy power station output graph on a typical day.
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3.
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4.
Tercan, S.M.; Demirci, A.; Gokalp, E.; Cali, U. Maximizing self-consumption rates and power quality towards two-stage evaluation
for solar energy and shared energy storage empowered microgrids. J. Energy Storage 2022,51, 104561. [CrossRef]
5.
Peng, P.; Li, Y.; Li, D.; Guan, Y.; Yang, P.; Hu, Z.; Zhao, Z.; Liu, D. Optimized Economic Operation Strategy for Distributed Energy
Storage With Multi-Profit Mode. IEEE Access 2021,9, 8299–8311. [CrossRef]
6.
Chen, Y.; Shi, Y.; Zhong, H.; Wang, X.; Lei, X.; Yin, H.; Liu, X. Hydrogen-Electric Hybrid Energy Storage System Configuration
Method for Transmission Grid Containing High Percentage of Scenery Access. Electr. Power Constr. 2022,43, 85–98.
7.
Gong, Q.; Fang, J.; Qiao, H.; Liu, D.; Tan, S.; Zhang, H.; He, H. Optimal Allocation of Energy Storage System Considering
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9.
Gong, Q.; Wang, Y.; Fang, J.; Qiao, H.; Liu, D. Optimal Configuration of the Energy Storage System in ADN Considering Energy
Storage Operation Strategy and Dynamic Characteristic. IET Gener. Transm. Distrib. 2020,14, 1005–1011. [CrossRef]
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Li, X.; Zhang, L.; Wang, R.; Sun, B.; Xie, W. Two-Stage Robust Optimization Model for Capacity Configuration of Biogas-Solar-
Wind Integrated Energy System. IEEE Trans. Ind. Appl. 2023,59, 662–675. [CrossRef]
11.
Guo, W.; Xiu, X.; Li, W.; Li, J. A Method for Siting and Configuring Grid-Side Energy Storage Systems with Integrated Multi-
attribute Metrics and Economics. Electr. Power Constr. 2020,41, 53–62.
12.
Gu, C.; Wang, J.; Li, Q.; Zhang, Y. A Review of Large-Scale Centralized Energy Storage Planning Research under New Energy
Centralized Grid Integration. Electr. Power 2022,55, 2–12+83.
13.
Li, J.; Xing, Y.; Zhang, D. Planning Method and Principles of the Cloud Energy Storage Applied in the Power Grid Based on
Charging and Discharging Load Model for Distributed Energy Storage Devices. Processes 2022,10, 194. [CrossRef]
14.
Du, X.; Li, X.; Chen, L.; Hao, Y.; Mei, S. Centralized Shared Energy Storage for Robust and Optimal Configuration of Multi-Scenario
Regulation Requirements. Trans. China Electrotech. Soc. 2022,37, 5911–5921.
15.
Shuai, X.; Wang, X.; Huang, J. Optimal Allocation of Shared Energy Storage Capacity under Multi-region Integrated Energy
System Interconnection. J. Glob. Energy Interconnect. 2021,4, 382–392.
16.
Xie, Y.; Luo, Y.; Li, Z.; Xu, Z.; Li, L.; Yang, K. Optimal Allocation of Shared Energy Storage Considering Economic Consumption
of New Energy in Microgrid. High Volt. Eng. 2022,48, 4403–4413.
17.
Yang, S.; Hu, X.; Wang, H.; Ligao, J.; Meng, L.; Zhou, W.; Zhou, H. A Prosumer-Based Energy Sharing Mechanism of Active
Distribution Network Considering Household Energy Storage. IEEE Access 2022,10, 113839–113849. [CrossRef]
18.
Liu, Y.; Dai, H.; Liu, Z.; Liu, R. Decentralized Shared Energy Storage Configuration and Investment Benefit Analysis for Multiple
Types of Industrial Users. Electr. Power Autom. Equip. 2021,41, 256–264.
19.
Sun, T.; Zeng, L.; Zheng, F.; Zhang, P.; Xiang, X.; Chen, Y. Two-Layer Optimization Model for the Siting and Sizing of Energy
Storage Systems in Distribution Networks. Processes 2020,8, 559. [CrossRef]
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