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Sci Rep
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Scientific Reports
2045-2322
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10.1038/s41598-024-72397-w
Article
Optimization clearing strategy for multi-region electricity-heat market considering shared energy storage and integrated demand response
Chen Shijia 12
Ye Ze 1
Meng Yichao 19007010060@stu.csust.edu.cn

1
1 grid.440669.9 0000 0001 0703 2206 Changsha University of Science and Technology, Changsha, 410114 Hunan China
2 https://ror.org/02czw2k81 grid.440660.0 0000 0004 1761 0083 Central South University of Forestry and Technology, Changsha, 410004 Hunan China
12 9 2024
12 9 2024
2024
14 2136828 2 2024
6 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
As a new type of energy storage, shared energy storage (SES) can help promote the consumption of renewable energy and reduce the energy cost of users. To this end, an optimization clearing strategy for a multi-region electricity-heat joint market is proposed with consideration of SES and integrated demand response (DR). Firstly, the concept of shared energy storage station (SESS) is proposed, its business operation model is analyzed and its advantages over traditional energy storage are compared. Secondly, to exploit the potential for user flexibility, an integrated DR model that includes shiftable, transferable, interruptible electricity and heat load is constructed. Then, a multi-region electricity-heat joint market clearing model is constructed considering SESS and integrated DR. Finally, a three-region electricity-heat joint market connecting the external power grid, gas grid, and the SESS is used as an example. A comparison is made between the configuration of independent energy storage in each region and the configuration of SESS, which concludes that the introduction of the SESS and integrated DR can reduce the energy cost of users and improve the utilization rate of the energy storage facilities.

Keywords

Shared energy storage
Integrated demand response
Electricity-heat joint market
Clearing strategy
Subject terms

Electrical and electronic engineering
Energy infrastructure
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Improving the efficiency of energy utilization and reducing the use of fossil fuels have attracted extensive attention from countries around the world1,2. Currently, the energy system is gradually transforming from high energy consumption to low-carbon and sustainable direction3–5. Energy storage can effectively realize the conversion, storage, and utilization of energy, which helps to enhance the flexibility of the integrated energy system operation and promote the consumption of renewable energy, and it has been developed rapidly in recent years and gained wide application6. From 2010 to 2018, the average cost of energy storage was reduced by 85% globally7, which shows that energy storage has considerable application prospects under the background of the energy revolution and decarbonization transformation8. In 2017, "Guiding Opinions on Promoting the Development of Energy Storage Technology and Industry" (Development and Reform Energy [2017] 1701) was issued, which proposes to establish and improve market mechanisms for energy storage participation. At present, many provinces and regions have allowed energy storage to participate in the auxiliary service market9.

Currently, there have been extensive studies on the participation of energy storage in the operation, decision-making of the electricity market, and the investment of energy storage in the market environment. Literature10 proposed an optimal allocation method for energy storage in integrated energy systems by considering customer-side electricity substitution. Literature11 constructed an optimal allocation model of the energy storage system in an independent mode, and the optimization variables include the rated power and capacity of the energy storage system. Literature12 established a multi-objective optimization model of energy storage in microgrids with photovoltaic (PV) by introducing the price-type demand response. Literature13 drew on the concept of financial transmission rights (FTRs) to propose financial energy storage rights, which can be purchased by conventional units and loads to hedge the risk of electricity price fluctuations. Literature14 considered the lower limit constraint of expected benefits of energy storage and discussed the siting and capacity allocation of energy storage under the joint clearing of the spot market and auxiliary service market.

In recent years, with the development of the sharing economy, shared energy storage (SES) has become a hot field for the development and application of energy storage technology. On one hand, users can obtain the right to use energy storage for an agreed period through leasing and only need to pay a certain amount of energy storage service fees without huge investment costs. On the other hand, SES providers can utilize the scale benefits of SES and the diversity of users’ electricity consumption to obtain lower investment cost and the most economical investment capacity of energy storage. Driven by this concept, some scholars have conducted explorations. Literature15 proposed the concept of "energy storage capacity rental", where the renter stores the surplus electricity to the provider, and the provider charges the renter according to the rented storage capacity and time. Literature16 proposed a new type of SES model called cloud energy storage, which considers the interests of the cloud energy storage operator and users to design the operation strategy. Literature17 described the principle of the proposed cloud energy storage model, control, and communication technologies, and used an example of the Irish power system to verify the effectiveness and economy of cloud energy storage. Literature18 proposed a two-layer optimal configuration method of electricity/heat cloud energy storage for integrated energy systems and solves the optimal electricity/heat energy storage configuration parameters. Literature19 proposed an optimal scheduling model for a combined cooling, heating, and power (CCHP) microgrid considering shared energy storage station (SESS) services.

Demand response (DR) guides users to change their traditional energy use behaviors through price or incentive signals to realize load shifting or curtailment. Integrated DR is an extension and expansion of the traditional electricity DR in multi-energy systems, where users can adjust the demand by changing their own energy use patterns. Literature20 constructed a multi-objective optimal operation model considering economic, energy, and environmental factors by considering the shifting of cold, heat, and power loads. Literature21 proposed an optimal scheduling strategy based on integrated DR and master–slave game for the multi-microgrid integrated energy system containing power interactions. Literature22 proposed an integrated DR model that considers power, heat and gas loads by exploring their flexible characteristics and dispatchable values.

In summary, although some studies involve the optimal configuration of energy storage or clearing strategy of power market including energy storage, it fails to consider the potential of integrated DR (power and heat DR) resources and the participation of SES in a multi-region market. In fact, the SES has greater potential in the multi-region system, and the application of SES and integrated DR can be promoted more easily in the multi-region system. To this end, the concept of SES and integrated DR are effectively combined and applied to the clearing strategy of a multi-region electricity-heat joint market in this paper. Main contributions are summarized as follows:The combination of SES and integrated DR in a multi-region electricity-heat joint market is first achieved, which can improve the utilization rate of SES and exploit the DR potential of different users in different regions.

A multi-region electricity-heat joint market clearing model considering SES and integrated DR is constructed, which considers the primary energy expenditure and demand response compensation of different users in different regions, thus enhancing the applicability of this model.

The influence of introducing SESS and integrated DR on the facility utilization hour has been analyzed quantitatively. The SESS can enhance the utilization hour of energy storage significantly compared with configuring independent ES in each region.

The rest of this paper is as follows. The concept and operation model of SESS are presented in "Concept and operation model of SESS" section. "Integrated DR modeling" section provides the mathematical model of integrated DR. "Multi-region electricity-heat joint market clearing model considering SES and integrated DR" section constructs the multi-region electricity-heat joint market clearing model considering SES and integrated DR. "Case study" section demonstrates the effectiveness of our proposed strategy via an actual three-region electricity-heat system. "Discussion" section summarizes the primary efforts of this paper, and "Conclusion" section discusses several directions in the future.

Concept and operation model of SESS

The SESS is a new type of grid-side energy storage business model, which usually refers to the energy storage station located at key nodes of the power grid and serving all power market participants in the region. It has both “shared” and “independent” attributes. The form means that the energy storage is not limited to serving a single entity in the power system, but is open for multiple entities. The latter means that the energy storage is invested, constructed, and operated by an independent third party, and participates in the power market trading independently.

Compared with the traditional energy storage that serves a single subject, SESS has the following advantages:The SESS can greatly improve the utilization rate, and improve the benefits by serving multiple entities.

The SESS makes energy storage facilities no longer tied to a single subject or for a single purpose. While participating in the consumption of renewable energy, SESS can also provide services such as peak shaving and frequency regulation, further broadening revenue channels and enriching the value of energy storage.

The SESS can participate in the power market trading as an independent identity. Specifically, SESS can trade with the power grid and other customers independently without relying on third-party entities. This will greatly simplify the trading process, and reduce settlement risk and cash flow pressure.

The SESS in this paper is shown in Fig. 1. The SESS provider invests to establish a large-scale SESS among user groups to provide SESS service for different users in different regions. Users pay service fees to the SESS provider in exchange for SESS service to meet their demands without time and capacity limitations.Fig. 1 Schematic diagram of SESS.

The energy storage service charge is a fee per unit of electricity that users are required to pay to the SESS when the SESS provides charging and discharging services. The energy storage service fee uses a day as the settlement period. When users have surplus power, the remaining power is stored in the SESS. When users are short of power, the required power is preferentially retrieved from the SESS. The total service fee is calculated based on the power stored and retrieved by the SESS in a dispatching cycle. According to the planned charging and discharging power required by all users in each period of a settlement cycle, the required capacity, maximum charging and discharging power of the SESS are configured.

The SESS provider can make use of the complementary features of users' power consumption behaviors so that they can make the investment of energy storage most economical on the premise of meeting the energy storage needs of users. Meanwhile, the SESS provider can make full use of the scale benefits of SES to obtain a lower investment cost, reducing the investment recovery period of energy storage equipment.

Integrated DR modeling

According to the mode of participation in DR, the load can be classified into the following four categories21:Fixed load: the load is uncontrollable, and the system cannot change the mode and time of energy use.

Shiftable load: the time of energy use spans over multiple periods, and can be adjusted according to the plan, but needs to be shifted holistically.

Transferable load: the demand of energy use can be adjusted flexibly, but needs to satisfy the need for the total load to remain unchanged before and after dispatching.

Interruptible load: the load can be allowed to be interrupted or reduced partly, and the amount of the reduction is decided according to the supply and demand situation.

It should be noted that both the shiftable load and transferable load have the characteristic that the time of energy use can be changed according to demand. The difference between the two is as follows. The shiftable load needs to be shifted as a whole, the energy consumption time can not be interrupted and the duration is fixed. The required power can not be changed as well, such as washing machine, sterilization cabinet.

However, the transferable load is more flexible, and the amount of energy consumption in each period can be adjusted flexibly. The energy consumption time is allowed to be interrupted and the duration can be varied so that it is only necessary to satisfy that the total demand of load before and after dispatching is unchanged. A typical transferable load is the electric vehicle. The charging time and power of electric vehicle can be adjusted in an ordered charging mode, but the total charging quantity required remains unchanged.

Shiftable load

The shiftable load can be used continuously with fixed working hours. This type of load needs to be shifted as a whole, not in segments. It is assumed that the acceptable shifting period of the shiftable load is [tshift−,tshift+]. When the load is shifted to the interval with t0 as the starting period, to ensure continuous running time, we have1 ∑t=t0t0+td-1αt=td

where td is the duration of the shiftable load, αt is the 0–1 state variable to judge whether the load has shifted, αt = 1 indicates that the load has shifted to the period t. The power of shiftable load during period t after dispatching Pshift is2 Pshift,t=αtLshift

where Lshift is the rated power of the shiftable load.

According to the relevant agreement signed, users can obtain the corresponding compensation cost Cshift after shifting, which can be expressed as3 Cshift=ρshift∑t=tshift -tshift +-td+1αtPshift,t

where ρshift is the compensation price of shiftable load.

Transferable load

The transferable load is not constrained by time continuity, and the working hours can be adjusted flexibly, but the total amount of energy use in a scheduling cycle must be guaranteed to remain unchanged. Set the acceptable transferable interval is [ttran−,ttran+], the constraint of keeping the total amount of energy unchanged before and after dispatching can be denoted by4 ∑t=1T(Ltran,t-Ptran,t)=0

where Ltran,t and Ptran,t is the rated power of transferable load during period t before and after dispatching, respectively; T is the number of periods in a scheduling cycle.

The compensation cost given to users after transferring Ctran is5 Ctran=ρtran∑t=ttran -ttran +Ptran,t

where ρtran is the compensation price of transferable load.

The transferable load must meet the following constraints:Transferred power constraint6 βtPtran,min≤Ptran,t≤βtPtran,max

where Ptran,max, Ptran,min are the upper and lower power of transferable load, respectively; βt is the 0–1 state variable that determines whether the load has transferred.

Minimum duration constraint7 ∑tt+ttran,min-1βt≥ttran,min(βt-βt-1)

where ttran,min is minimum continuous running time.

Interruptible load

The interruptible load can be partially reduced to meet users’ demand. The power during period t after load reduction Pcut,t is8 Pcut,t=(1-γtθt)Lcut,t

where Lcut,t is the power of interruptible load during period t before load reduction, θt is load reduction coefficient during period t, 0 ≤ θt ≤ 1; γt is a 0–1 state variable that determines whether the load has reduced; γt = 1 indicates that load reduction has occurred.

The compensation cost given to users after reduction is9 Ccut=ρcut∑t=1TγtθtLcut,t

where ρcut is compensation price of interruptible load.

The interruptible load can be reduced with meeting the following constraints:Minimum continuous reduction time constraint10 ∑tt+tcut,min-1γt≥tcut,min(γt-γt-1)

where tcut,min is minimum continuous load reduction time.

Reduction frequency constraint11 ∑t=1Tγt≤Nmax

where Nmax is maximum reduction times.

In this paper, we consider the power user and heat user. The former is divided into the fixed power load, shiftable power load, transferable power load, and interruptible power load. Considering the sensitivity of heat load, the heat user can be divided into the fixed heat load, shiftable heat load, and interruptible heat load.

Multi-region electricity-heat joint market clearing model considering SES and integrated DR

Objective function

The optimization objective is to minimize the daily total cost of all users of different regions in the multi-region electricity-heat joint market with SESS. For brevity, the game between different subjects has been neglected. Therefore, the objective function of clearing model is set as follows:12 minC=C1+C2-C3-C4+C5

The cost of purchasing electricity from the grid C113 C1=∑i=1N∑t=1T[cgrid(t)Pgrid,i(t)]

where cgrid(t) is the price of electricity purchased from the grid during period t; Pgrid,i(t) is power of region i purchased from the grid during period t; N is the number of region.

The cost of purchasing heat from the heat network C214 C2=∑i=1N∑t=1T[cheat(t)Pheat,i(t)]

where cheat(t) is the price of heat purchased from the heat network during period t; Pheat,i(t) is the power of region i purchased from the heat network during period t.

The compensation cost given to power users C315 C3=∑i=1N(Cshift,i,E+Ctran,i,E+Ccut,i,E)

where Cshift,i,E, Ctran,i,E, Ccut,i,E are the compensation cost of the shiftable power load, transferable power load, and interruptible power load of region i, respectively.

The compensation cost given to heat users C416 C4=∑i=1N(Cshift,i,H+Ccut,i,H)

where Cshift,i,H, Ccut,i,H are the compensation cost for shiftable heat load, interruptible heat load of region i respectively.

Total service fees paid to SESS C517 C5=∑i=1N∑t=1Tcserve(t)[Pbuy,i(t)+Psell,i(t)]

where cserve(t) is the service fee price paid by users to SESS during period t; Pbuy,i(t) is the power of region i purchased from SESS during period t; Psell,i(t) is the power of region i sold to SESS during period t.

Constraints

Charging and discharging power of SESS18 0≤PEss,C(t)≤UC(t)PEss,max0≤PEss,D(t)≤UD(t)PEss,maxUC(t)+UD(t)≤1

where PEss,max is the maximum power of SESS; PEss,C(t), PEss,D(t) are the charging and discharging power of SESS during period t respectively; UC(t), UD(t) are the charging and discharging state of SESS during period t, which are 0–1 state variables.

Storage state of SESS19 EEss(t)=EEss(t-1)+[ηCPEss,C(t)-PEss,D(t)PEss,D(t)ηDηD)]EEss,min≤EEss(t)≤EEss,max

where EEss(t) is the energy storage of SESS during period t; EEss,max, EEss,min are the maximum and minimum storage capacity of SESS respectively; ηC, ηD is charging and discharging efficiency of SESS respectively.

Power balance of SESS

The charging and discharging power of SESS is equal to the sum of the interaction power between each region and SESS.20 ∑i=1N[Pbuy,i(t)-Psell,i(t)]=PEss,D(t)-PEss,C(t)

Power balance constraints21 PEB,i,E(t)+Pw,i(t)+Ppv,i(t)+Pbuy,i(t)=Pi,E(t)+Psell,i(t)Pi,E(t)=Pbase,i,E(t)+Pshift,i,E(t)+Ptran,i,E(t)+Pcut,i,E(t)

where PEB,i,E(t) is the power consumed by the electric boiler of region i during period t; Pw,i(t) is the wind power of region i during period t; Ppv,i(t) is the PV output of region i during period t; Pbase,i,E(t), Pshift,i,E(t), Ptran,i,E(t), Pcut,i,E(t) are the fixed power load, shiftable power load, transferable power load, and interruptible power load of region i during period t ,respectively.

Heat balance constraints22 Pheat,i(t)+PEB,i,H(t)=Pi,H(t)Pi,H(t)=Pbase,i,H(t)+Pshift,i,H(t)+Pcut,i,H(t)

where PEB,i,H(t) is the heat output of the electric boiler of region i during period t; PEB,i,H(t) = ηEBPEB,i,E(t), ηEB is the heat production efficiency of electric boiler; Pbase,i,H(t), Pshift,i,H(t), Pcut,i,H(t) are the fixed heat load, shiftable heat load, and interruptible load of region i during period t, respectively.

Equipment output constraints23 0≤Pw,i(t)≤Pw,max0≤Ppv,i(t)≤Ppv,max0≤PEB,i,H(t)≤PEB,max

where Pw,max, Ppv,max, PEB,max are the maximum power of wind output, PV output, electric boiler heat output, respectively.

Model solving

The decision variables of clearing model include the maximum capacity, maximum power of SESS, charging and discharging power, and state during each period of SESS, the interaction power between users and the SESS during each period, purchased power of each region from the grid during each period, purchased heat power of each region from the heat network during each period, the power consumption and heat production of electric boiler, wind output, PV output and the power of flexible load.

It is clear that constraint (18) contains two nonlinear constraints. We use the Big-M method19 to transform it into (24), where M is a sufficiently big number.24 0≤PEss,C(t)≤PEss,max0≤PEss,C(t)≤UC(t)M0≤PEss,D(t)≤PEss,max0≤PEss,D(t)≤UD(t)MUC(t)+UD(t)≤1

With the above transformation, the multi-region electricity-heat joint market clearing model including SESS and integrated DR can be converted into a mixed-integer linear programming problem. We use the toolbox YALMIP and solver CPLEX to solve this mixed-integer linear programming problem in Matlab 2018a.

Case study

Description

The case selects a multi-region system consisting of three regions in Jiangsu Province, which contains office area (region A), living area (region B), and commercial area (region C). Each region is directly connected to the SESS, and there is no transmission line between the three regions. The structure is shown in Fig. 2a. The electricity and heat demand, wind and PV output of the three regions are given in Fig. 2b–d.Fig. 2 Electricity and heat demand, wind and PV output of region A, B and C.

The number of periods in a dispatching cycle is 24, and the price of electricity purchased from the power grid in each region adopts the time-of-use (TOU) price from23. The parameters of flexible load are from24. The gas price is 2.2 CNY/m3. The service price charged by the SESS is 0.1 CNY/kWh. For SESS, the charging and discharging efficiency is 0.95, the life is 6 years, the capacity price and power price are 1897 CNY/kWh, 1000 CNY/kW respectively, and the operation and maintenance price is 72 CNY/ (year·kW).

Result and analysis

The optimal configuration of SESS results in a maximum power of 3000 kW and a maximum capacity of 22,049 kWh. The clearing results of charging power, discharging power, and state of SESS are shown in Fig. 3.Fig. 3 Clearing results of charging, discharging power, and storage capacity of SESS.

As can be seen from Fig. 3, the SESS is in the charging state from 1:00–8:00, and the power storage rises from 6545 to 19,845 kWh, reaching the upper limit of 0.9EEss,max, and the maximum charging power is 3000 kW. Besides, the SESS is discharging during 9:00–24:00, and the power storage falls from 19,845 to 4410 kWh.

During 20:00–21:00, the SESS reaches the maximum discharging power of 1699 kW. This is mainly because there is surplus power in each region from 1:00 to 8:00, and it is possible to obtain a certain economic benefit by paying a lower price of service charge for depositing the surplus power into the SESS.

In other periods, the electricity and heat demand in each region is relatively larger, only their wind power, PV output, purchased electricity and gas can not meet their own needs. The discharging service of SESS is required at this moment.

The interaction power of SESS with regions A, B, and C is shown in Fig. 4. As can be seen in Fig. 4, the clearing results of interaction power between SESS and three regions are consistent with those of Fig. 3. The discharging periods of SESS mainly include 9:00–24:00, and its charging periods are from 1:00 to 8:00, which is closely related to the load and output of renewable energy in each region.Fig. 4 The interaction power of SESS and different regions.

The distribution of power and heat flexible load on the demand side before and after optimization is shown in Fig. 5.Fig. 5 Electricity and heat demand before and after optimization.

Comparing (a) with (c) in Fig. 5, it can be seen that for flexible power load, the shiftable load is shifted from 19:00–22:00 (high-price periods) to 6:00–9:00 (low-price periods), which suggests that the shiftable load do not have any change during the shifting process, and the power consumption in the duration periods is also equal to the original. Besides, the transferable load is transferred from 12:00–17:00 to 3:00–8:00, and the total amount of energy consumption remains unchanged before and after dispatching.

In addition, the optimal reduction periods of power load are 9:00–12:00 and 13:00–20:00, which are the peak of the electricity price. This is because by reducing part of the demand, the power and cost of purchasing electricity during the peak periods can be reduced significantly, thus improving the economy of electricity consumption.

For the flexible heat load, it can be seen from Fig. 5b and d that, similar to the power load, the reduction of heat load mainly occurs in the peak hours of electricity price and the peak hours of heat load. Reducing a certain proportion of heat load can significantly reduce the demand for purchased electricity and the peak regulation pressure of power grid, playing a role in peak shaving and valley filling.

Further, to analyze the economy of introducing the SESS and DR, the following four cases are set for comparative analysis. The optimal clearing results of four cases are shown in Table 1.Case 1: Configuration of independent energy storage in each region without integrated DR.

Case 2: Configuration of independent energy storage in each region considering integrated DR.

Case 3: Each region accesses an SESS without integrated DR.

Case 4: Each region accesses an SESS considering integrated DR. (case in this paper).

Table 1 Optimal clearing results of four cases.

Items	Case 1	Case 2	Case 3	Case 4	
Cost of purchasing electricity C1/CNY	18,575.27	13,767.04	12,102.03	10,064.43	
Cost of purchasing gas C2/CNY	5099.80	4920.20	2780.86	2601.26	
Compensation cost for power DR C3/CNY	–	1022.40	–	1044.00	
Compensation cost for heat DR C4/CNY	–	437.40	–	437.40	
Service fees paid to the SESS C5/CNY	–	–	4259.90	3143.33	
Average daily investment, operation and maintenance cost of independent energy storage C6/CNY	4032.93	3208.70	–	–	
Total daily cost/CNY	27,708.00	20,436.14	19,142.79	14,327.62	

Among these cases, Cases 1 and 2 are optimized with the following objective function:25 minC=C1+C2+C6

26 C6=∑i=1NcPPEss,max,i+cEEEss,max,iTd+Mi

where C6 is the total of average daily investment, operation and maintenance cost of energy storage, cP, cE are the power price and capacity price of energy storage respectively, PEss,max,i, EEss,max,i are the maximum power and capacity of energy storage in region i respectively, Td is the expected use days of energy storage; Mi indicates is daily operation and maintenance cost of energy storage in region i.

As can be seen from Table 1, Case 4 that considers SESS with integrated DR is able to obtain the highest benefits. Compared with Case 1, Case 2 can reduce the demand for purchased electricity significantly by considering integrated DR, thus reducing the cost of purchased electricity by 7271.86 CNY with a reduction of 26.25%.

Meanwhile, the integrated DR can also reduce the demand for energy storage. The energy storage capacities of regions A, B, and C under Case 1 are 2114.96 kWh, 1247.92 kWh, and 663.53 kWh, with the maximum power of 430.20 kW, 262.90 kW, and 140.71 kW, respectively. The independent energy storage capacities of regions A, B, and C under Case 2 are 1691.57 kWh, 1153.05 kWh, and 328.01 kWh, and the maximum power is 345.30 kW, 259.74 kW, and 99.28 kW. The decrease in maximum power and capacity demand makes the average daily investment, operation, and maintenance cost of independent energy storage in each region decrease by 824.23 CNY.

Compared with Case 2, Case 4 reduces the demand for energy storage capacity and power by configuring the SESS. This improves the utilization rate of energy storage, which makes the economy of Case 4 better than that of Case 2. In addition, the total daily operating cost of Case 3 is 4518.17 CNY higher than that of Case 4, which is because Case 3 does not explore the potential of integrated DR. From the above analysis, we know that users can significantly reduce the energy cost by configuring SESS and exploiting the potential of integrated DR.

To analyze the influence of introducing SESS on the facility utilization hour, we compare the power clearing results of independent energy storage (ES) in three regions under Case 1 and those under Case 3, which are provided in Fig. 6.Fig. 6 Power clearing results of energy storage under Case 1 and Case 3.

As can be seen from Fig. 6, the utilization hours of independent ES in regions A, B, and C are 17, 16, and 16. By introducing the SESS, the utilization hour has reached 24, which is much higher than that of independent ES. This suggests that configurating the SESS can enhance the facility utilization hour compared with configurating independent ES in different regions. This is attributed to that the SESS can make full use of the complementary features of users’ behavior of using energy, which can improve the facility utilization hour and achieve the most economical allocation.

Further, to show the influence of integrated DR on the facility utilization hour, we choose Case 1 and Case 2, Case 3 and Case 4 for comparisons. The power clearing results of independent ES in three regions under Case 2 are presented in Fig. 7.Fig. 7 Power clearing results of energy storage under Case 2.

As illustrated in Fig. 7, the utilization hours of independent ES in regions A, B, and C under Case 2 are 17, 16, and 13, which is roughly equal to the utilization hours under Case 1. Similar results can be seen from the comparison between Fig. 6d (Case 3) and Fig. 3 (Case 4). Hence, we can conclude that the integrated DR mainly affects the power consumption of users and has no significant effect on the facility utilization hour.

To promote the application of SESS, the government should introduce some subsidy policies to reduce the investment cost of SESS. Besides, the TOU service fee price and ladder service fee price are effective price mechanisms to support the development of SESS. To support the application of integrated DR, enhancing the compensation price level for users who actively participate in the demand response, is the most effective way to further exploit the potential of users.

Discussion

This study does not consider the data security, privacy protection, and the lifetime loss of SESS. In the future, the three aspects will be explored in detail to establish a more comprehensive SESS model and promote the large-scale application of SESS on the demand side. Moreover, extending our method to the clearing strategy of integrated electricity-heat-gas market will be the focus of our future work as well.

Admittedly, the SESS provider and energy users are different interest subjects, and there is a game between different interest subjects. To enhance the applicability of the proposed strategy, the game between different subjects of integrated electricity-heat market will be explored in the future. For example, studying the optimal pricing of service charge based on game theory around the SESS provider and users. Specifically, a too-high service price will reduce the incentive of users to use SESS, while a too-low service price will increase the difficulty of investment cost recovery of SESS.

Conclusion

In this paper, we take the multi-region electricity-heat joint market including SESS as the research object, and propose a clearing strategy considering SESS and integrated DR. The main conclusions are as follows:Users in different regions can obtain charging and discharging services of energy storage by paying service fees to the operators of SESS, which can not only satisfy their energy demand, but also significantly reduce the cost of energy use and enhance the space for sustainable energy consumption.

Compared with the configuration of independent energy storage in each region, the configuration of SESS can significantly reduce the maximum capacity, maximum power, total cost of energy storage significantly, and improve the facility utilization rate.

With the configuration of SESS, we can further reduce the energy cost of users and improve the profitability of energy storage by exploiting the potential of integrated DR of electricity and heat consumers.

Author contributions

Shijia Chen conceived and wrote the article, Ze Ye conceived the article, Yichao Meng analyzed the results. All authors reviewed the manuscript.

Data availability

The data that support the results of this study are mainly available from the integrated energy park of Tongli in Jiangsu Province in China. If anyone needs to obtain data, please contact Shijia Chen. E-mail: t20152245@csuft.edu.cn.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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