Optimization dispatching strategy for an energy storage system considering its unused capacity sharing

0 Introduction

With the proposal of carbon peaking and carbon neutrality goals,increase in energy demand,and rapid depletion of traditional non-renewable fossil fuels,renewable energy power plants have been vigorously developed [1,2].However,renewable energy sources exhibit poor active power regulation capabilities.Therefore,an energy storage system with flexible charging and discharging has become a key element in the construction of new types of power systems,and new models for hybrid renewable energy and energy storage have been widely used [3].The concept of shared energy storage (SES) has been proposed and widely studied because of the limitations of traditional energy storage and the success of energy trading and sharing programs [4].Shared energy storage is an energy storage utilization model based on the concept of a sharing economy [5-7],which can provide energy storage services at a lower price and improve the profitability of the energy storage business mode.

More studies have been conducted on the operation strategies of renewable-energy-sided energy storage system RES.Reference [8] presents an optimal planning and operation architecture for multisite renewable energy generators that share an energy storage system on the generation side,and the economic and environmental benefits of the designed system were demonstrated through numerical experiments.Reference [9] proposed a sophisticated deep reinforcement learning method using a policy-based algorithm to achieve real-time optimal energy storage systems planning under curtailed renewable energy uncertainty.Reference [10] proposed a novel“energy service providers”–“distribution system operators”–“transmission system operators”coordination scheme for co-optimizing distributed renewable energy and energy storage planning at the distribution network level while simulating its coordinated operations.Reference [11] proposed a unified decision structure consisting of network partitioning and an optimal operational planning problem that optimally allocated energy storage and renewable energy sources in each partition using electrical modularity and electrical coupling strength metrics.Reference [12] proposed an energy storage system control model for a combined wind-solar storage system that was trained by interacting with a large-scale grid environment to maximize the benefits of the combined system in the electricity market.Reference [13] proposed a method that optimally deployed BESSs and determined their capacity in a twopart framework to minimize solar energy curtailment by considering network topology and tidal flow constraints to minimize solar power abandonment;the results showed that the approach proposed in this study was more effective than the conventional deployment strategy because it stored more surplus solar energy.Reference [14]established an energy storage allocation model based on policy requirements;[15] established an energy storage dispatching model based on a time-series simulation method.A study [16] established a multi-province commitment dispatching model.Another study [17] proposed a unique energy storage operation method;[18] added chance-constrained programming to wind and photovoltaic power models.Another study [19] established a reinforcement control strategy to increase the renewable energy penetration.

Previous studies [12,13,15-19] mentioned that renewable energy output power is plagued by problems of intermittency,uncertainty,and difficulty in consumption,which affect the dispatching operation of the power system.The above references mentioned that energy storage offered the advantages of compensating for renewable energy deviation,consuming renewable energy,and reducing the peak-valley difference.Therefore,it is important to study renewable-energy-side energy storage systems.

However,the above studies primarily analyzed the RES operation strategy from the energy storage planning and dispatching strategy;however,renewable energy prediction may have a small deviation,resulting in small storage energy charging and discharging power requirements.Moreover,existing storage planning and dispatching strategies are not applicable,as storage energy is not dispatched in this situation,which affects the economic interests of RES investors.Therefore,a new dispatching strategy must be developed to address this problem.

Shared energy storage is a cost-effective and efficient method of solving the problem of renewable energy consumption [20].Another study [21] proposed a peerto-peer energy trading model incorporating shared energy storage,and the simulation results showed that the proposed method could improve the benefits for each member and the overall efficiency of the alliance.Reference [22] proposed a business model based on the law of large numbers and insurance actuarial theory for sharing energy storage operators to provide deviation insurance services for renewable energy sources.Another study [23] proposed a two-layer balancing model to study the effect of point-topoint energy trading by considering shared energy storage.It determined the local marginal prices by optimizing the power flow model.Reference [24] proposed a sharing energy storage business model for DC clusters to improve economic efficiency and promote renewable energy utilization.Reference [25] proposed a joint optimization method for the capacity planning and operation of shared energy storage systems considering the power supply and load demand characteristics of large-scale 5G base stations,which reduced the planned capacity of shared energy storage systems by 40.80% compared with the planning of battery energy storage systems.Reference [26] proposed a framework for sharing energy storage within a community and optimizing the operational cost of electricity using a mixed-integer linear programming formulation.Reference [27] proposed a reactive power service pricing model for an SES to enable energy storage users to obtain reasonable benefits from the reactive power responsiveness provided.

As evident the above mentioned studies,sharing energy storage is an energy storage operation mode that separates the right of use and ownership of energy storage resources and creates energy storage that serves only a single subject to serve multiple subjects through a reasonable operation strategy.By utilizing the shared energy storage operation mode,energy storage resources can be reasonably utilized.Moreover,while reducing the waste of resources,it introduces benefits to investors.

However,none of the above studies have designed an operation mode for the unused capacity of energy storage,which is caused by a small forecast deviation of renewable energy.If the unused energy storage capacity can be shared for peak-shaving ancillary services,it can increase the economic benefits of energy storage and improve its utilization efficiency.Therefore,it is of great significance to study the operational mode of sharing the unused capacity of the RES.

Based on the above problems regarding the unused capacity and the purpose of fully utilizing the storage capacity,this study investigated the optimization dispatching strategy of the power system considering the unused capacity sharing of the RES.First,we proposed an unused capacity-sharing strategy for the RES.Second,this study divided RES into a“deviation-compensating energy storage”and a“sharing energy storage.”Third,an optimization-dispatching model wherein an unused capacity-sharing strategy can be integrated was established.

The contributions of this study are as follows:

1) This study proposed an unused capacity sharing strategy to improve the RES’s utilization efficiency and enhance the auxiliary service effect.

2) The concepts of“deviation-compensating energy storage”and“sharing energy storage”were defined for clearly describing RES’s response on different operation mode.

3) An optimization dispatching model was established considering the unused capacity sharing strategy for minimizing operation cost of power system.

1 RES’s capacity sharing strategy

1.1 Storage capacity division for RES

The primary function of the RES is to enable renewable energy power output to satisfy grid connection requirements while enhancing renewable energy utilization and reducing prediction deviations.However,owing to the volatility and intermittent characteristics of renewable energy power outputs such as photovoltaics and wind power,there exists a small deviation between the renewable energy prediction output curve and the practical output curve.In this case,the RES operates according to the original dispatching plan,and the energy storage capacity is not fully utilized,causing a portion of the configured energy storage to remain unused.To improve the utilization efficiency of the RES,this study proposed a capacity-sharing mode for the RES to share the unused energy storage capacity.

Figure 1 shows the RES used to eliminate the deviation between the predicted power curve and the practical power curve.In this figure,curves a and b are the capacity change curves when the energy storage capacities are A and B,respectively;curve c is the renewable energy prediction power curve;curve d is the renewable energy practical power curve;and curve e is the deviation of the renewable energy prediction power curve from the practical power curve.When there is a small deviation in renewable energy prediction (e),both energy storage A and B can eliminate the deviation in renewable energy prediction;however,energy storage A has more capacity than energy storage B.Meanwhile,it can be found from the comparison between curves a and b that the capacity of energy storage A is not fully utilized and has a large amount of spare capacity,resulting in unused energy storage capacity and a waste of energy storage resources.

Fig.1 Schematic of deviation elimination by the RES system

This study divided RES into a“deviation-compensating energy storage (DES)”and a“sharing energy storage (SES).”DES refers to the energy storage required to track and eliminate the deviation between the predicted renewable energy power output curve and the practical power output curve,and SES refers to the unused capacity of the RES used to assist peak shaving of the power system.Thus,a RES can be divided into two parts,DES and SES,and it simultaneously optimizes the operation of the two types of storage at the same time.As illustrated in Fig.2,in the operation mode of the proposed RES,renewable energy stations provide renewable energy prediction power output curves to the dispatching agency,and they determine the DES and SES capacities based on the historical power output data of renewable energy.

Fig.2 Operation strategy of the RES system

DES is used to compensate for renewable energy power deviation,and SES makes a profit by participating in the electricity market based on the time of use,peak shavings,and valley fillings to reduce the peak-to-valley difference.

This study established a DES operation model to track and eliminate the deviation,and an SES operation model to promote the peak shaving of power systems.

1.2 Sharing operation mode for RES

Assuming that the historical power output curve is the power output curve of practical renewable energy,the prediction power curves of renewable energy are grid dispatch curves,and the deviation between the prediction power of renewable energy at time t and the historical power is (i.e.,the deviation between the renewable energy prediction power curve and the practical power curve); represents the prediction power of renewable energy at t time; represents the practical power of renewable energy at t time.The objective function for optimizing the operation of the RES is expressed as

The constraints on the above objective function are denoted as:

where p g(t ) denotes the electricity price at time t, denotes the surplus amount of renewable energy deviation after conducting the renewable energy DES at time t, denotes the surplus deviation penalty of renewable energy,and denote the charging and discharging powers of the renewable energyside DES at time t,respectively.Further, and denote the charging and discharging power of the renewable-energy-side SES at time t,respectively and ∆t is the energy storage charge-discharge time interval.

Let the maximum DES capacity of RES be ,the maximum SES capacity of RES be ,the utilized capacity of the SES in t be ,and the utilized capacity of the DES in t be .Then,the sharing capacity and deviation-compensating capacity of the RES should follow the principle that the sum of the maximum renewable energy-side DES capacity and the maximum SES capacity equals the maximum RES capacity .This is expressed as

The changes in the deviation-compensating capacity and sharing capacity during the charging/discharging process are expressed as

where ηch and ηdch denote the charging/discharging efficiencies of the RES.

The DES and SES capacities are both derived from RES.Thus,the sum of the DES and SES charging/discharging powers cannot exceed the system’s maximum charging/discharging power of RES.The charging/discharging relationship and constraints between the renewable-energy-side SES and DES are expressed as follows:

According to (4)– (8) and as shown in Fig.3,the energy storage capacity-sharing strategy in this study divided renewable energy-side energy storage into two parts and shared one set of energy storage facilities.As shown in (4),the capacity of both the DES and the SES was the capacity of the RES,and the sum of the DES and SES capacities was equal to the capacity of the RES.As expressed in (7) and (8),the powers of both the DES and SES are the powers of the RES,and the charging and discharging states of the DES and SES are the same and cannot be simultaneously charged and discharged.

Fig.3 Relationship of RES to SES and DES

2 Optimization dispatching model considering RES capacity sharing

Owing to the uncertainty of the power output of the renewable energy system,when there is a small deviation in the renewable energy prediction,the RES becomes underutilized.To improve energy storage utilization efficiency,this study established an optimization dispatching model that considered the deviation in renewable energy prediction and RES capacity sharing.

2.1 Operation dispatching model based on renewable energy prediction power curve

The operation dispatching model uses traditional generators and grid-side energy storage (GES) as study objects for scheduling and considers traditional generator constraints (including technical output constraints,ramping constraints,start-stop constraints,etc.),energy storage constraints (including charging/discharging power constraints,capacity constraints,etc.),system power balance constraints,and other constraints.Meanwhile,it minimizes the total operation cost as an objective function to establish a dispatching model that considers the abandonment penalty for renewable energy and the cost of energy storage charging and discharging losses.The model dispatches grid-side energy storage to participate in peak shaving and renewable energy consumption,thereby mitigating the impact of renewable energy power output uncertainty on the power system operation.The established optimization dispatching model is represented as follows:

where Fg denotes the operation cost of the traditional unit,Fst denotes the unit’s start-stop cost,Fren_ab denotes the curtailing wind/photovoltaic cost of the renewable energy station,and Fbat denotes the charging/discharging cost of the grid-side storage system:

where si(t) represents the start-stop state variable of traditional unit i (values at 1 and 0 represent the start and stop states,respectively),Ai,Bi,and Ci are the power generation cost coefficients of traditional unit i,Bi Pg ,i(t ) represents the power output of traditional unit i, represents the start-up cost of the traditional unit,Kren_ab is the penalty coefficient for curtailing wind and photovoltaic of renewable energy station, represents the renewable energy dispatching power based on renewable energy prediction power curve,pbat represents the gridside energy storage charging/discharging price, and represent the charging and discharging power of grid-side energy storage,respectively,and µch and µdch represent the charging and discharging efficiencies of the grid-side energy storage system,respectively.

The optimized dispatching model established in this study must consider the constraints of traditional generators,renewable energy dispatching power,grid-side energy storage operation,and system operation.

1) Traditional unit constraints

The traditional generators’ power constraints:

where and denote the maximum and minimum generating power of unit i,respectively.

The traditional generators’ ramping constraints can be obtained by:

where represent the maximum values of the downward and upward ramping rates of the unit i,respectively.

The unit start-stop constraints:

where represent the minimum duration of continuous startup and shutdown of the unit i,respectively.

2) Renewable energy dispatching power constraints:

3) Grid-side energy storage operation constraints

The energy storage charging/discharging constraints can be obtained by:

where denote the maximum charging/discharging power.

The energy storage state of charge constraint is expressed as:

The charging/discharging balance constraint can be obtained using:

The charging/discharging constraints can be obtained using:

where denotes the maximum grid-side energy storage capacity.

4) System operation constraints

Power balance constraint:

Spinning reserve constraint:

wher e P load(t) denotes the load prediction power at moment t and P sp(t) denotes the backup power of the system at time t.

By solving the optimization dispatching model,the start and stop states of the unit and the power dispatch situation of renewable energy at each moment can be obtained.The dispatch values obtained above are substituted into the economic dispatching model that considers the renewable energy prediction power output and practical power output deviation for further solutions.

2.2 Operation dispatching model considering renewable energy prediction power and practical power deviation

The operation dispatching model considers the deviation of the prediction power and practical power of renewable energy and involves traditional generators,grid-side energy storage,renewable energy-side SES,and renewable energyside DES.Thus,the dispatching model must consider the traditional generator operation cost,start-stop cost,curtailment penalty of renewable energy,grid-side energy storage charging/discharging cost,deviation penalty cost,and income from RES sharing.In addition,the dispatching model must minimize the system’s total operational cost as the objective function and establish an operational dispatching model that considers the deviation between the renewable energy prediction power output and renewable energy practical power output.

The objective function of the dispatching model is:

where pbat_ren denotes the RES charging/discharging price,which is the renewable energy grid-connected electricity price, denotes the renewable energy side SES peak shaving income,Fbat_ren denotes the RES charging/discharging cost,which is the cost of electricity lost when energy storage is in operation,and denotes the practical dispatch power for renewable energy.

The operation dispatching model of renewable energy prediction power and practical power deviation must consider traditional generator,energy storage operation,and system operation constraints.Compared with the operational dispatching model based on the renewable energy prediction power curve,this model yields the following new constraints:

1) Power balance constraint:

2) Eliminating renewable energy prediction power output and practical power output deviation constraint:

3) Renewable energy dispatch constraint:

By solving an economic dispatching model that considers the deviation between the renewable energy prediction power and practical power,the output of the traditional generators and the output of the renewable energy system within the dispatch period were adjusted,and the grid-side energy storage power output,renewable energy-side DES power output,and renewable energy-side SES power output are determined.

2.3 System optimization dispatching strategy considering capacity sharing of RES

The system optimization dispatching strategy considering the capacity sharing of the RES process is as follows:

1) The renewable energy station provides a predictive power curve.Subsequently,the traditional generators and grid-side energy storage are dispatched according to the load and renewable energy prediction power curves,and the dispatching plan of the unit is obtained by following the power balance principle and renewable energy prediction power curve.The proportions of renewable energy-side SES and DES capacities are determined based on the historical power output curve and deviation.

2) The renewable energy station provides a historical power output curve and compares it with the renewable energy prediction power curve to determine the prediction deviation.Following the deviation in the renewable energy prediction was eliminated by the renewable-energy-side DES,the deviation-compensating renewable energy power output curve was obtained.

3) The traditional generators,grid-side energy storage,and RES sharing capacity are dispatched according to the load power curve and the deviation-compensating renewable energy power output curve and follow the power balance principle.In this approach,an operational dispatching plan that considers the deviation of renewable energy prediction power and historical power output is obtained.Herein,the unit is dispatched according to dispatching planning (startstop planning) based on the renewable energy prediction power curve.

Subsequently,considering the system optimization dispatching strategy,a flowchart of RES capacity sharing is shown in Fig.4.

Fig.4 Flow chart of the energy storage system optimization dispatching strategy

3 Case study

The case analysis system used in this study included five traditional generators (the parameters are listed in Table 1).A renewable energy station equipped with an energy storage power station was used as the research object,with an installed RES capacity of 40 MWh,maximum charging/discharging power of 20 MW,charging/discharging efficiency of 95%,and SES capacity of 40%.In addition,the deviation in the renewable energy prediction penalty was 300 yuan/MWh,installed capacity of the grid-side energy storage was 100 MWh,maximum charging/discharging power was 50 MW,and charging/discharging efficiency was 95%.The grid-side energy storage and renewable energy-side SES peak shaving compensation were 300 yuan/MWh.The power output of the renewable energy system included photovoltaic and wind power outputs,and the total installed capacity was 230 MW.The power-output curves are shown in Fig.5.

Table 1 Unit parameters

Fig.5 Renewable energy forecast-historical data

This study set SES to work according to the time of use,which was derived from a province in China,as presented in Table 2.In addition,the deviation in the renewable energy prediction penalty was 300 yuan/MWh,and the grid-side energy storage and renewable energy-side SES peaking shaving compensation were 300 yuan/MWh.

Table 2 Time of use

The experiments were implemented on a PC with a Windows 10 operating system,Intel Core i5-11400 2.60 GHz CPU,and 16 GB RAM,and solved using Gurobi.

3.1 System optimization dispatching strategy

To verify the optimization dispatching method based on the deviation in renewable energy prediction and considering RES capacity sharing,three scenarios were set up for comparative analysis:

1) Scenario 1:Only the traditional generators and gridside energy storage participated in dispatching.

2) Scenario 2:Traditional generators and grid-side energy storage participated in dispatching,and the RES only compensated for the deviation between the renewable energy predicted power output and practical power output.

3) Scenario 3:The traditional generators and gridside energy storage participated in dispatching;the RES compensated for the deviation between the renewable energy prediction output and the practical output and provided sharing capacity to assist peak shaving of the power system.

Fig.6 shows the unit’s start-stop dispatching situation,where the minimum start-stop time was set to 3 h as evident,the start and stop times of all the units complied with the settings.Specifically,unit 1 was in the entire dispatch period because of its large capacity and high economy,whereas unit 5 did not appear in the entire dispatch period because of its small capacity and poor economy.Moreover,the front unit can satisfy the load demand.

Fig.6 Unit start-stop status

As shown in Fig.7,the dispatching results of the three scenarios are compared,as shown in Fig.7(unit power),at five and six moments:the red curve of Scenario 3 was larger than the value represented by the blue and purple curves of Scenarios 2 and 1,respectively,indicating that the renewable energy-side energy storage capacity sharing operation strategy proposed in this study can assist gridside energy storage in valley filling.At the 21st moment,the red curve of Scenario 3 was smaller than the values represented by the blue and purple curves of Scenarios 2 and 1,respectively,indicating that the renewable-energyside energy storage capacity-sharing operation strategy proposed in this study can assist grid-side storage during peak shaving.

Fig.7 Comparison of three scenarios dispatching situations

As shown in Fig.7 (renewable energy power),the renewable energy curves dispatched/consumed in Scenarios 2 and 3 overlapped and exceeded those in Scenario 1.This indicates that the renewable energy-side energy storage capacity-sharing operation strategy proposed in this study can assist grid-side storage in peak shaving and valley filling without affecting the compensating deviation to obtain benefits.

Figure 8 (a) shows the deviation in the renewable energy prediction,where a positive value of the deviation indicates that the renewable energy prediction value is larger than the historical value,and vice-versa.Fig.8(b) shows the remaining deviation of Scenario 2 after deviation elimination by RES.Fig.8(c) shows the remaining deviation of Scenario 3 after deviation elimination by renewable energy.As evident,both Scenarios 2 and 3 could compensate for the deviation in renewable energy prediction,and the remaining deviations of Scenarios 2 and 3 were equal.

Fig.8 Deviation in renewable energy prediction and remaining deviation in each scenario

Figure 9 illustrates the renewable-energy-side DES charging and discharging power of Scenarios 2 and 3,where a positive value represents energy storage charging and a negative value represents energy storage discharging.It can be seen from the figure that the charging/discharging powers of Scenario 2 RES and Scenario 3 DES were the same.The above analysis shows that in the scenario set in this study,the RES does not affect the ability to eliminate deviations after sharing 40% capacity.This demonstrated the feasibility of the dispatching strategy used in this study.

Fig.9 Charging and discharging power of Scenario 2 RES and Scenario 3 DES

Table 3 presents the system operation costs and RES sharing income in different scenarios.When energy storage is not configured,renewable energy sources are subject to deviation penalties.The energy storage configuration in Scenarios 2 and 3 greatly reduced the deviation penalty,and thus,the total operation cost.The comparison of Scenarios 2 and 3 indicated that reasonable capacity sharing of RES compensated for forecast deviation and generated economic income.Although the unit operation cost of Scenario 3 was 16.4 yuan more than that of Scenario 2,the income of the renewable energy station was 10,743.7 yuan.Thus,participation in the sharing of the RES’s unused capacity compensated for the deviation in renewable energy prediction and increased the benefits of renewable energy stations.

Table 3 Total operation cost and the RES sharing income (yuan)

3.2 Analyzing for energy storage participated in peak shaving

In this study,the renewable-energy-side SES was exploited to assist in the peak shaving of the power system.The load power changes before and after peak shaving and the amount of renewable-energy-side SES charging and discharging are shown in Fig.10 (Only SES participated in peak shaving).Sharing the energy storage charges at moments 5 and 6 is equivalent to the grid load increment.According to the curve,moments 5 and 6 are the valley period;thus,SES plays the role of“valley filling”at these two moments.Meanwhile,SES discharged at moments 21 and 22,which is equivalent to the grid load’s reduction,and moments 21 and 22 are the peak period.Thus,SES plays the role of“peak shaving”at these two moments.To ensure the sustainable utilization of the SES,the RES was charged at 24th hour to ensure that the SOC was 0.5.

Fig.10 Results of renewable energy-side SES peak shaving

A comparison of the results of grid-side energy storage participating in peak shaving only and both grid-side energy storage and SES participating in peak shaving is shown in Fig.11 (Both SES and grid-side ES were involved in peak shaving).The red histogram shows the renewable-energyside SES charging and discharging state,the blue histogram shows the grid-side energy storage charging and discharging state,and the black curve shows the original load power curve.The red load power curve for Scenario 3 is the load power curve after the SES,and the grid-side energy storage participated in peak shaving.The green load power curve for Scenario 2 is the load power curve after only the gridside energy storage participates in peak shaving.As evident,the load power curve after the SES participated in the peak shaving was flatter than that after the grid-side energy storage only participated in peak shaving,and the load peak-valley difference is smaller.Therefore,the renewable energy-side SES can further assist peak shaving.

Fig.11 Results of energy storage participating in the peak shaving

3.3 Analyzing for RES’s sharing capacity

1) Impact of energy storage sharing capacity on RES

To analyze the impact of different DES capacity ratios,the capacity ratio was set in the range of 10%–90% and gradually increased at intervals of 10%.The results are presented in Fig.12.

Fig.12 Income curves of RES sharing

As shown in Fig.12,as the energy storage-sharing capacity increased,the income of the RES also increased.However,when the sharing capacity exceeded 70%,the capacity of the renewable-energy-side DES gradually weakened.This increased the deviation penalty cost,which reduced the net income of the RES.Overall,with the scheme designed in this study,the situation was better when SES capacity accounted for 70%.

2) Impact of energy storage sharing capacity on peak shaving

Fig.13 presents the results of the different sharing capacities of RES versus peak shaving (Only SES participated in peak shaving).In the legend,10%–90% indicate that the proportion of the RES sharing capacity ranges as 10%–90%.As the RES sharing capacity increased,the effects of peak shaving and valley filling improved,and the curve became gentler.When the sharing capacity was 90%,the peak shaving and valley filling effects were the best,and the curve was the gentlest.However,the analysis in Fig.12 indicates that when the ratio of sharing capacity exceeds 70%,the RES income decreases.Thus,the proportion of RES sharing capacity must be determined by combining its benefits.

Fig.13 Graph of grid peaking results for different sharing capacities

4 Conclusions

Aiming at the phenomenon wherein RESs are in an underutilized state owing to the small deviation in renewable energy prediction,this study proposed an energy storage system optimization dispatching strategy that considered RES capacity sharing.First,this study proposed a renewable-energy-side energy storage unused capacitysharing strategy.Second,this study divided the RES into deviation-compensating energy storage and sharing energy storage.Third,this study established an optimizationdispatching model wherein an unused capacity-sharing strategy could be integrated.The main conclusions drawn from the numerical example analysis are as follows:

1) The capacity-sharing strategy of RES proposed in this study can reduce deviations in renewable energy prediction;it also participates in peak shaving of the power system and obtains economic benefits,which can motivate the enthusiasm of RES operators for capacity sharing.This helps increase the benefits of the RES operators while reducing the operation cost of the power system.

2) The optimization dispatching model considering RES capacity sharing can reduce the deviation of renewable energy prediction and the deviation penalty cost.Moreover,it can dispatch the RES’s unused capacity to participate in grid peak shaving,reduce the pressure of power system peak shaving,and maximize the economic benefits of the RES.

3) The ratio of sharing capacity affects the deviation elimination capability of RES,and there exists an optimal ratio for RES sharing capacity.

When this study investigates the RES capacity-sharing operation strategy,the sharing capacity ratio is provided directly.A study on how to obtain the optimal sharing capacity will be undertaken in the future.

Declaration of Competing Interest

We declare no conflicts of interests.