Regional hybrid energy storage system-oriented capacity optimization configuration and operation method

Abstract

The invention discloses a capacity optimization configuration and operation method for a regional hybrid energy storage system. The method comprises the following steps: establishing a low-speed response energy storage operation model of low-speed response energy storage, inputting historical net load power and electric quantity cost parameters of a regional hybrid energy storage system into the model for processing to obtain monthly charging and discharging time of low-speed response energy storage, and then establishing optimal configuration and operation boundary conditions; and establishing a capacity optimization configuration and operation model considering optimization configuration and operation boundary conditions, inputting the annual net load power and the electric quantity cost parameters into the model, processing the annual net load power and the electric quantity cost parameters, and outputting energy storage capacity and output power as control instructions to realize capacity optimization configuration and operation control of the regional hybrid energy storage system. According to the method, the optimal capacity configuration and operation strategy formulation of the regional hybrid energy storage system can be realized on the premise of comprehensively considering the slow and quick response energy storage synergistic effect.

Description

Technical Field

[0001] The present invention relates to a capacity configuration and operation method of an energy storage system, and to the technical field of stable operation of an electric power system, and in particular to a capacity optimization configuration and operation method for a regional hybrid energy storage system. Background Art

[0002] In recent years, with the large-scale access of renewable energy such as distributed photovoltaics, its intermittency and volatility have brought challenges to the supply and demand balance on the user side and the stability of the power system. Improving the flexible adjustment capability on the user side has become the key to promoting the consumption of new energy and ensuring the reliability of power supply. As an important means to smooth power fluctuations, energy storage systems have broad prospects in new power systems. However, high output costs and low utilization rates limit the application of single-user energy storage. Introducing regional energy storage that serves multiple users has become an effective way to optimize resource utilization and promote the large-scale development of energy storage. However, the high cost of energy storage equipment has hindered its large-scale promotion. Optimizing energy storage capacity configuration and operation strategies have become the key to improving input costs.

[0003] Traditional energy storage planning methods usually only consider the characteristics of new energy and load fluctuations in typical scenarios, while ignoring the power fluctuations and extreme weather effects on a long time scale, making it difficult to fully reflect the dynamic characteristics throughout the year, resulting in insufficient reliability of the results. At the same time, the regional energy storage model is still unstable, which limits its large-scale development. Therefore, it is urgent to model the ability of different types of energy storage systems in the region to adjust power fluctuations, and to develop appropriate hybrid energy storage system capacity configuration and operation optimization methods based on the response characteristics of slow and fast response energy storage, so as to provide technical support for promoting the large-scale grid connection of energy storage systems and improving the stability of power systems. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the present invention provides a capacity optimization configuration and operation method for a regional hybrid energy storage system. The method of the present invention solves the capacity optimization configuration and operation technical problems of a hybrid energy storage system under the condition of high proportion of new energy grid connection, through the synergy between slow response energy storage and fast response energy storage to smooth the impact of regional net load fluctuations on the power system, and provides technical guarantee for the efficient utilization and large-scale grid connection of user-side energy storage systems.

[0005] The technical solution adopted by the present invention is: The capacity optimization configuration and operation method for a regional hybrid energy storage system of the present invention comprises: S1: Establish a regional hybrid energy storage system including slow response energy storage and fast response energy storage, establish a slow response energy storage operation model of the slow response energy storage based on the density clustering algorithm, input the historical net load power and electricity cost parameters of the regional hybrid energy storage system into the slow response energy storage operation model, and output the monthly charging and discharging time of the slow response energy storage after processing.

[0006] S2: Based on the monthly charging and discharging time of the slow response energy storage, the optimal configuration and operating boundary conditions of the slow response energy storage and the fast response energy storage are established; the boundary conditions take into account the operating constraints and capacity constraints of the slow response energy storage and the fast response energy storage.

[0007] S3: Establish a capacity optimization configuration and operation model for the regional hybrid energy storage system that takes into account the optimization configuration and operation boundary conditions, input the annual net load power and electricity cost parameters of the regional hybrid energy storage system into the capacity optimization configuration and operation model, and after processing, output the capacity and output power of the slow response energy storage and fast response energy storage as planning targets and control instructions to achieve optimal configuration and operation control of the capacity of the regional hybrid energy storage system.

[0008] Based on the boundary conditions for optimal capacity configuration and operation of regional hybrid energy storage systems, a dual-objective function for evaluating the input cost and net load fluctuation of regional hybrid energy storage operation is constructed. It is converted into a single-objective function through the correlation coefficient to obtain the capacity optimal configuration and operation model for regional hybrid energy storage systems.

[0009] In the step S1, the slow response energy storage has a larger response capacity and a slower response speed, while the fast response energy storage has a smaller response capacity and a faster response speed.

[0010] In the step S1, the slow response energy storage operation model of the slow response energy storage is as follows: min ∑ t l´ d,t t ∈[ t d , t d+m ] st t d+m - t d = m l´ d,t =∑ d l d,t ∈ l dmax ( l d,t ) / | l dmax | P load ´ d,t =∑ d Pload d,t ∈ P load dmax ( P load d,t ) / | P load dmax | m = n × or c × or d n = t c+n - t c in, l´ d,t and l d,t They represent the first d sky t Typical predicted power cost parameters and historical power cost parameters at the time, P load ´ d,t and P load d,t They represent the first d sky t Typical forecasted net load power and historical net load power at the time; t d and t d+m Respectively represent the start and end time of the discharge of the slow response energy storage, m Indicates the discharge duration of the slow response energy storage; l dmax and P load dmax They represent the first d Daily electricity cost parameters and upper limit of net load power; n Indicates the charging time of slow response energy storage; or c and or d They represent the charging efficiency and discharging efficiency of slow response energy storage, t c and t c+n They respectively represent the start and end time of charging of the slow response energy storage.

[0011] The monthly charging and discharging time of the slow response energy storage in the current month is determined according to the starting and ending time of the charging and discharging of the slow response energy storage.

[0012] In step S2, the optimized configuration and operating boundary conditions of the slow response energy storage are as follows: P ess,slow d,tc = P ess,slow d,tc+1 =···= P ess,slow d,t =···= P ess,slow d,tc+n P ess,slow d,td = P ess,slow d,td+1 =···= P ess,slow d,t =···= P ess,slow d,td+m ∫ tc+n tc P ess,slow d,t or c tdt +∫ td+m td P ess,slow d,td / or d tdt =0 – P ess,slow,cap < P ess,slow d,t <P ess,slow,cap E slow =max{0.8∫ tc+n tc P ess,slow,cap or c tdt , 0.8∫ td+m td Pess,slow,cap / or d tdt} 0.1 E slow ≤ E slow d,t ≤0.9 E slow E slow d,t = E slow d,t-1 + P ess,slow d,t or c △t , when t ∈( t c , t c+n ) E slow d,t = E slow d,t-1 + P ess,slow d,t / or d △t , when t ∈( t d , t d+m ) E slow d,t = E slow d,t-1 , other in, P ess,slow d,tc , P ess,slow d,tc+1 、···、 P ess,slow d,t 、···、 P ess,slow d,tc+n Respectively represent d sky t c , t c+1, ···, t 、···、 t c + n The output power of the slow response energy storage at all times, t c and t c+n Respectively represent the start and end time of charging of slow response energy storage, n Indicates the charging time of slow response energy storage; P ess,slow d,td , P ess ,slow d,td+1 、···、 P ess,slow d,td+m Respectively represent d sky t d , t d +1, ···, t d + m The output power of the slow response energy storage at all times, t d and t d+m Respectively represent the start and end time of the discharge of the slow response energy storage, m Indicates the discharge duration of the slow response energy storage; or c and or d They represent the charging efficiency and discharging efficiency of slow response energy storage respectively; P ess,slow,cap Indicates the maximum charge and discharge power of slow response energy storage; E slow represents the capacity of slow response energy storage, E slow d,t and E slow d,t-1 They represent the slow response energy storage in the d sky t and t -1 The energy stored at a moment; △t Indicates the optimization time interval.

[0013] In step S2, the optimized configuration and operating boundary conditions of the fast response energy storage are as follows: ∫ T 1 P ess,fast d,t tdt =0 – P ess,fast,cap < P ess,fast d,t <P ess,fast,cap E fast =0.8∫ T / 2 1 P ess,fast,cap tdt 0.1 E fast ≤ E fast d,t ≤0.9 E fast E fast d,t = E fast d,t-1 + P ess,fast d,t △t in, T Indicates the number of optimizations in a day; P ess,fast d,t Indicates d sky t The output power of the energy storage can be quickly responded at any time; P ess,fast,cap Indicates the maximum charge and discharge power of fast response energy storage; E fast represents the capacity of fast response energy storage, E fast d,t and E fast d,t-1 They represent the fast response energy storage in the d sky t and t -1 The energy stored at a moment; △t Optimize the time interval.

[0014] In step S3, the capacity optimization configuration and operation model are as follows: min - OF 1+ β × OF 2 OF 1≥0 OF 2≤ OF w / o 2=∑ D d=1 [∑ T t=1 ( P loadD d,t - P´ w / o d ) 2 / T ] 1 / 2 P´ w / o d =∑ T t=1 P loadD d,t / T in, OF 1 and OF 2 represent the standard deviation of the annual operation input cost and annual net load power of the regional hybrid energy storage system, β represents the annual operating input cost of the regional hybrid energy storage system OF 1 and the standard deviation of the annual net load power OF The correlation coefficient between 2; OF w / o 2 represents the standard deviation of the annual net load power when the region is not equipped with a regional hybrid energy storage system; D Indicates the number of days in a year; T Indicates the number of optimizations in a day; P loadD d,t The regional hybrid energy storage system d sky t Net load power at the moment; P´ w / o d It represents the daily average net load power before the regional hybrid energy storage system is configured in the region.

[0015] The net load power and electricity cost parameters of the regional hybrid energy storage system at each moment of each day are determined according to the annual net load power and electricity cost parameters of the regional hybrid energy storage system. The solver is called to solve the model during the solution.

[0016] The standard deviation of the annual operating input cost and annual net load power of the regional hybrid energy storage system is as follows: OF 1= C w / o - C w C w / o =∑ D d=1 ∑ T t=1 ( P loadD d,t l D d,t △t ) C w =∑ D d=1 ∑ T t=1 [( P loadD d,t +P ess,slow d,t +P ess,fast d,t ) l D d,t D t ] +C ess C ess = C ess,slow + C ess,fast C ess,slow = l slow CRF m slow E slow + x slow m slow E slow C ess,fast = l fast CRF m fast E fast + x fast m fast Efast l slow CRF = r (1+ r ) Tslow / [(1+ r ) Tslow -1] l fast CRF = r (1+ r ) Tfast / [(1+ r ) Tfast -1] P´ w d =∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t ) / T OF 2=∑ D d=1 ||∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t - P´ w d )|| 2 2 / T 1 / 2 in, C w / o and C w They represent the operation costs before and after the regional hybrid energy storage system is configured in the region; l D d,t The regional hybrid energy storage system d sky t The power cost parameter at the time; △t Indicates the optimization time interval; P ess,slowd,t and P ess ,fast d,t Respectively represent d sky t The output power of slow response energy storage and fast response energy storage at the moment; C ess represents the output cost of the hybrid energy storage system, C ess,slow and C ess,fast They represent the output costs of slow response energy storage and fast response energy storage respectively; l slow CRF and l fast CRF They represent the capacity decay rates of slow response energy storage and fast response energy storage respectively; m slow and m fast They represent the unit capacity output cost of slow response energy storage and fast response energy storage respectively; E slow and E fas Respectively represent the capacity of slow response energy storage and fast response energy storage; x slow and x fast They represent the ratio of the annual operation and maintenance cost to the initial output cost of slow response energy storage and fast response energy storage respectively; r represents the discount rate; T slow and T fast Respectively represent the operating cycles of slow response energy storage and fast response energy storage; || || 2 2 represents the two-norm; P´ w d It represents the daily average net load power after the regional hybrid energy storage system is configured in the region.

[0017] The capacity optimization configuration and operation device for a regional hybrid energy storage system of the present invention comprises: The data acquisition unit is used to obtain the historical net load power and electricity cost parameters of the regional hybrid energy storage system and the annual net load power and electricity cost parameters.

[0018] The model building unit is used to establish a slow response energy storage operation model of the slow response energy storage, and to construct an optimal configuration and operation boundary conditions based on the historical net load power and electricity cost parameters of the regional hybrid energy storage system and the slow response energy storage operation model, thereby establishing a capacity optimization configuration and operation model that takes into account the optimal configuration and operation boundary conditions.

[0019] The capacity optimization configuration and operation unit is used to input the annual net load power and electricity cost parameters of the regional hybrid energy storage system into the capacity optimization configuration and operation model, and after processing, output the capacity and output power of the slow response energy storage and the fast response energy storage as control instructions to achieve the optimal configuration and operation control of the capacity of the regional hybrid energy storage system.

[0020] The electronic device of the present invention comprises: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method as described above.

[0021] The computer-readable storage medium of the present invention stores program data thereon, and when the program data is executed by a processor, the method described above is implemented.

[0022] The method of the present invention first generates a typical regional net load power curve and electricity price curve through a density clustering algorithm, and then formulates a monthly charging and discharging plan for slow response energy storage. On this basis, a multi-objective optimization model that comprehensively considers the synergy of slow response energy storage and fast response energy storage is constructed, with the goal of maximizing the regional operation input cost and minimizing the regional net load power fluctuation. By introducing the correlation coefficient, it is transformed into a single-objective optimization problem. By solving the optimization problem, the capacity optimization configuration plan and 8760-hour operation strategy of the regional hybrid energy storage system can be finally obtained. This optimization method has significant advantages in improving regional energy management efficiency, reducing operating costs and promoting renewable energy self-consumption, and can provide more flexible and efficient solutions for regional energy management.

[0023] The beneficial effects of the present invention are: 1) The present invention uses a density-based clustering algorithm to extract regional historical net load power data and electricity cost parameter data, generates typical net load power curves and electricity cost parameter curves, and then formulates a monthly charging and discharging plan for slow response energy storage, effectively realizing the efficient utilization of slow response energy storage based on the prediction of net load fluctuations.

[0024] 2) By integrating slow-response energy storage and fast-response energy storage, the present invention constructs a multi-objective optimization model with the goals of maximizing the regional operating input cost and minimizing the regional net load power fluctuation. This model not only improves the benefits of the hybrid energy storage system, but also significantly enhances the willingness to deploy regional energy storage systems, thereby promoting the popularization and application of energy storage technology.

[0025] 3) Based on the accurate simulation of the annual operation mode of the hybrid energy storage system, the present invention not only improves the regional operation input cost, but also significantly reduces the impact of net load power fluctuations on the power system, providing a more accurate and efficient solution for regional energy management.

[0026] 4) The present invention fully considers the synergistic effect of slow response energy storage and fast response energy storage in capacity configuration and operating characteristics, realizes the optimal configuration of regional hybrid energy storage system, and proposes a practical and operational hybrid energy storage system optimization framework, which provides an important reference for the optimization decision of energy management at the regional level, and helps to promote the low-carbonization of energy system and the realization of sustainable development goals.

[0027] The present invention realizes the optimal capacity configuration and operation strategy formulation of the regional hybrid energy storage system under the premise of comprehensively considering the synergy between slow-response energy storage and fast-response energy storage. The method of the present invention can not only smooth the net load fluctuation during the peak energy consumption period and reduce the pressure on the power grid, but also maximize the regional benefits by utilizing the peak-valley electricity cost parameter difference, thereby promoting the efficient utilization and large-scale grid connection of the user-side energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of the method of the present invention; Figure 2 is a Pareto optimal solution set diagram under different values ​​of correlation coefficients according to an exemplary embodiment; Figure 3 is a diagram showing the net load power and charging and discharging power of scheme A according to an exemplary embodiment, wherein: Figure 3 (a) is the net load power diagram of scheme A when the hybrid energy storage system is configured. Figure 3 (b) is the net load power diagram of scheme A without hybrid energy storage system. Figure 3 (c) is the charging and discharging power diagram of the slow response energy storage of scheme A. Figure 3 (d) is the charging and discharging power diagram of the fast response energy storage of scheme A; Figure 4 is a diagram showing the net load power and charging and discharging power of scheme C according to an exemplary embodiment, wherein: Figure 4 (a) is the net load power diagram of scheme C when the hybrid energy storage system is configured. Figure 4 (b) is the net load power diagram of scheme C without hybrid energy storage system. Figure 4 (c) is the charging and discharging power diagram of the slow response energy storage of scheme C. Figure 4 (d) is the charging and discharging power diagram of the fast response energy storage of scheme C; Figure 5 is a Pareto optimal solution set diagram under different situations shown according to an exemplary embodiment; Figure 6 is a diagram showing net load power fluctuations under different conditions according to an exemplary embodiment, wherein: Figure 6 (a) is the net load power fluctuation diagram of case 1. Figure 6 (b) is the net load power fluctuation diagram of case 2. Figure 6 (c) is the net load power fluctuation diagram of case 3. Figure 6 (d) is the net load power fluctuation diagram without hybrid energy storage system; Figure 7 is a diagram showing system capacity configuration and operation input cost of a hybrid energy storage system under different values ​​of correlation coefficients according to an exemplary embodiment, wherein: Figure 7 (a) is the system capacity configuration and maximum charge and discharge power of the hybrid energy storage system under different correlation coefficient values. Figure 7 (b) is a diagram of the operating input cost and net load power fluctuation of the hybrid energy storage system under different values ​​of the correlation coefficient; Figure 8 is a Pareto optimal solution set diagram considering different energy storage configuration schemes according to an exemplary embodiment; Figure 9 is a Pareto optimal solution set diagram based on priori and posterior data according to an exemplary embodiment, wherein: Figure 9 (a) is the Pareto optimal solution set diagram based on prior data. Figure 9 (b) is the Pareto optimal solution set diagram based on the posterior data. DETAILED DESCRIPTION

[0029] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] like Figure 1 As shown, the capacity optimization configuration and operation method of the regional hybrid energy storage system of the present invention is as follows: The present invention selects the net load power data, electricity cost parameter data and hybrid energy storage system parameters of a region with more than 300 users as a test case, where the net load data is the sum of the power of distributed photovoltaic power generation, fixed load represented by household appliances and flexible load represented by electric vehicles, the electricity cost parameter data comes from the data of the power plant, and the regional hybrid energy storage system parameter configuration is shown in Table 1. In order to verify the method of the present invention, the example emphasizes the trade-off between the two goals of maximizing the operating input cost after the regional hybrid energy storage system is configured and minimizing the regional net load power fluctuation, because these two goals are essentially conflicting. Maximizing the regional operating input cost will increase the net load power fluctuation due to changing the operating mode of the hybrid energy storage system by using the peak-valley electricity cost parameter difference, while minimizing the regional net load power fluctuation will reduce the regional operating input cost to a certain extent.

[0031] Table 1

[0032] First, a regional hybrid energy storage system including slow response energy storage and fast response energy storage is established. The slow response energy storage has a larger response capacity and a slower response speed, while the fast response energy storage has a smaller response capacity and a faster response speed. Slow response energy storage is represented by heat pump energy storage and pumped storage, while fast response energy storage is represented by battery energy storage. The characteristics of slow response energy storage are large response capacity and low operating cost, but its response speed is slow, which is suitable for regulating slowly changing power fluctuations on a long time scale, such as regional load power fluctuations. The characteristics of fast response energy storage are small response capacity and high operating cost, but its response speed is fast. Fast, suitable for regulating rapidly changing power fluctuations on a short time scale, such as fluctuations in renewable energy power generation. The hybrid integration of slow-response energy storage and fast-response energy storage together constitutes a regional hybrid energy storage system, and its main application scenario is the regional microgrid. The source side of the regional microgrid includes the rooftop photovoltaic array and the public power grid in the region, where the rooftop photovoltaic array and the public power grid are connected to the AC bus through inverters and transformers respectively, and support the grid access of surplus photovoltaic power generation. The load side includes household appliances, electric vehicles, etc. In addition, slow-response energy storage and fast-response energy storage are also connected to the AC bus through inverters to smooth out power fluctuations in the region.

[0033] Then, the slow response energy storage operation model of slow response energy storage is established based on the density clustering algorithm as follows: min ∑ t l´ d,t t ∈[ t d , t d+m ] st t d+m - t d = m l´ d,t =∑ d l d,t ∈ l dmax ( l d,t ) / | l dmax | t =1, 2, …, 48 P load ´ d,t =∑ d P load d,t ∈ P load dmax ( P load d,t ) / | P load dmax | t =1, 2, …, 48 m = n × or c × or d n = t c+n - t c in, l´ d,t and l d,t They represent the first d sky t Typical predicted power cost parameters and historical power cost parameters at the time, P load ´ d,t and P load d,t They represent the first d sky t Typical forecasted net load power and historical net load power at the time; t dand t d+m Respectively represent the start and end time of the discharge of the slow response energy storage, m Indicates the discharge duration of the slow response energy storage; l dmax and P load dmax They represent the first d Daily electricity cost parameters and upper limit of net load power; n Indicates the charging time of slow response energy storage; or c and or d They represent the charging efficiency and discharging efficiency of slow response energy storage, t c and t c+n They respectively represent the start and end time of charging of the slow response energy storage.

[0034] The historical net load power and electricity cost parameters of the regional hybrid energy storage system are input into the slow response energy storage operation model. After processing, the monthly charging and discharging time of the slow response energy storage is output. The monthly charging and discharging time of the slow response energy storage in the current month is determined according to the start and end time of the charging and discharging of the slow response energy storage.

[0035] The response speed of slow response energy storage is relatively slow and has a low operating cost. The cost can be specifically measured in terms of electricity. It mainly plays the role of absorbing renewable energy and benefiting from the difference in peak and valley electricity cost parameters in the region. Therefore, the present invention comprehensively considers the changes in regional net load power and electricity cost parameters and formulates its monthly charging and discharging plan in advance. In order to eliminate the influence of noise, the present invention selects the regional historical net load power data and electricity cost parameter data based on the density clustering algorithm for data processing to obtain the monthly charging plan and monthly discharging plan of slow response energy storage.

[0036] First, define the sample points and construct the sample data set of the net load power curve and the electricity cost parameter curve. Define the daily net load power data and electricity cost parameter data as a time series containing 48 points (that is, one data point every half an hour, and the time series can form a curve with a time scale of one day) as follows: P load d ={ P load d,1 , P load d,2 , …, P load d,t, …, P load d,48} l d ={ l d,1 , l d,2 , …, l d,t , …, l d,48} in, P load d Indicates d Daily net load power time series; l d Indicates d Daily electricity cost parameter time series.

[0037] For a month of data (usually 30 or 31 days), a sample data set for constructing the net load power curve and the electricity cost parameter curve is as follows: P load ={ P load 1. P load 2. … P load d , …, P load N} l ={ l 1. l 2. … l d , …, l N} in, P load a sample data set representing a net load power curve; l A sample data set representing a power cost parameter curve; N Indicates the total number of days in the month.

[0038] Then, the Euclidean distance of the sample data set of the net load power curve and the electricity cost parameter curve is calculated, and the relevant clusters are obtained by clustering based on the density clustering algorithm. The Euclidean distance between each time series in the sample data set of the net load power curve and the electricity cost parameter curve is calculated as follows: d ( P load i ,P load j )=[∑ t ( P load i,t , P load j,t ) 2 ] 1 / 2 d ( l i , l j )=[∑ t ( l i,t - l j,t ) 2 ] 1 / 2 t =1, 2, …, 48 in, d ( ) represents the Euclidean distance.

[0039] Based on the Euclidean distance, a density-based clustering algorithm is used to cluster the sample data set. P load and l For clustering, set two distance thresholds and the minimum number of samples to M p and M λ , and divide the time series into several clusters according to the distance threshold and the minimum number of samples, among which the clusters with the sample data set P load The relevant clusters and noise point sets are represented as { P load d1 , P load d2 , …, P load dk}and N P , will be compared with the sample data set l The relevant clusters and noise point sets are represented as { l d1 , l d2 , …, l dk}and N λ .

[0040] Then select the cluster with the most sample points, extract the typical net load power and power cost parameters, and select the cluster with the most sample points. P load dmax ∈{ P load d1 , P load d2 , …, P load dk}and l dmax ∈{ l d1 , l d2 , …, l dk}, all net load power curves and electricity cost parameter curves in the cluster are averaged point by point to obtain typical predicted net load power and electricity cost parameters.

[0041] Finally, according to the typical predicted net load power curve and the electricity cost parameter curve, the monthly charging plan and monthly discharging plan of the slow response energy storage are determined. P load ´ d,t The typical predicted net load power curve is composed of the following: the start time of the negative net load power is identified and defined as the start time of slow response energy storage charging. t c , the end time of the negative net load power, which is defined as the end time of slow response energy storage charging t c+n ( t c and t c+n Together they constitute the monthly charging plan for the slow response energy storage in that month), then the charging time of the slow response energy storage is obtained. n Taking into account the charging and discharging efficiency of the energy storage system, the discharge time of the slow response energy storage can be determined. m On this basis, according to l´ d,t The typical predicted electricity cost parameter curve is composed and the discharge plan of slow response energy storage is calculated.

[0042] Then, based on the monthly charging and discharging time of the slow response energy storage, the optimal configuration and operation boundary conditions of the slow response energy storage and the fast response energy storage are established; the boundary conditions take into account the operation constraints and capacity constraints of the slow response energy storage and the fast response energy storage.

[0043] The optimal configuration and operating boundary conditions of slow response energy storage are as follows: P ess,slow d,tc = P ess,slow d,tc+1 =···= P ess,slow d,t =···= P ess,slow d,tc+n P ess,slow d,td = P ess,slow d,td+1 =···= P ess,slow d,t =···= P ess,slow d,td+m ∫ tc+n tc P ess,slow d,t or c tdt +∫ td+m td P ess,slow d,td / or d tdt =0 – P ess,slow,cap < P ess,slow d,t <P ess,slow,cap E slow =max{0.8∫ tc+n tc P ess,slow,cap or c tdt , 0.8∫ td+m td P ess,slow,cap / or d tdt } 0.1 E slow ≤ E slow d,t≤0.9 E slow E slow d,t = E slow d,t-1 + P ess,slow d,t or c △t , when t ∈( t c , t c+n ) E slow d,t = E slow d,t-1 + P ess,slow d,t / or d △t , when t ∈( t d , t d+m ) E slow d,t = E slow d,t-1 , other in, P ess,slow d,tc , P ess,slow d,tc+1 、···、 P ess,slow d,t 、···、 P ess,slow d,tc+n Respectively represent d sky t c , t c +1, ···, t 、···、 t c + n The output power of the slow response energy storage at all times, t c and t c+nRespectively represent the start and end time of charging of slow response energy storage, n Indicates the charging time of slow response energy storage; P ess,slow d,td , P ess ,slow d,td+1 、···、 P ess,slow d,td+m Respectively represent d sky t d , t d +1, ···, t d + m The output power of the slow response energy storage at all times, t d and t d+m Respectively represent the start and end time of the discharge of the slow response energy storage, m Indicates the discharge duration of the slow response energy storage; or c and or d They represent the charging efficiency and discharging efficiency of slow response energy storage respectively; P ess,slow,cap Indicates the maximum charge and discharge power of slow response energy storage; E slow represents the capacity of slow response energy storage, E slow d,t and E slow d,t-1 They represent the slow response energy storage in the d sky t and t -1 The energy stored at a moment; △t Indicates the optimization time interval, which is half an hour.

[0044] The optimal configuration and operating boundary conditions of fast response energy storage are as follows: ∫ T 1 P ess,fast d,t tdt =0 – P ess,fast,cap < P ess,fast d,t <P ess,fast,cap E fast =0.8∫ T / 2 1 P ess,fast,cap tdt 0.1 E fast ≤ E fast d,t ≤0.9 E fast E fast d,t = E fast d,t-1 + P ess,fast d,t △t in, T Indicates the number of optimizations in a day. It is set to 48 during implementation, which means optimization once every half an hour. P ess,fast d,t Indicates d sky t The output power of the energy storage can be quickly responded at any time; P ess,fast,cap Indicates the maximum charge and discharge power of fast response energy storage; E fast represents the capacity of fast response energy storage, E fast d,t and E fast d,t-1 They represent the fast response energy storage in the d sky t and t -1 The energy stored at a moment; △t Optimize the time interval, which is half an hour.

[0045] For the slow response energy storage charging and discharging power, the present invention adopts a constant power mode, that is, t c to t c+n The charging power of the slow response energy storage remains constant during the period. t d to t d+mThe discharge power of the slow response energy storage remains constant during the time period. In order to ensure the safe and stable operation of the regional power system, the slow response energy storage and the fast response energy storage need to be restored to the initial state after the daily cycle, so the sum of the daily charging and discharging power should be 0, among which the charging and discharging efficiency of the fast response energy storage approaches 1. At the same time, the charging and discharging power of the slow response energy storage and the fast response energy storage have operating constraints. Considering that the operating margin of the energy storage system is usually 10% to 90% of the total capacity, the present invention configures the system capacity of the slow response energy storage according to the energy required or released under the maximum charging and discharging power operation, and configures the capacity of the fast response energy storage according to the energy required or released under the maximum charging and discharging power operation for half a day. At the same time, the slow response energy storage and the fast response energy storage must also comply with capacity constraints.

[0046] Finally, a capacity optimization configuration and operation model of the regional hybrid energy storage system considering the optimal configuration and operation boundary conditions is established. On the basis of the boundary conditions for the capacity optimization configuration and operation of the regional hybrid energy storage system, a dual objective function for evaluating the input cost and net load fluctuation of the regional hybrid energy storage operation is constructed. The dual objective function is converted into a single objective function through the correlation coefficient to obtain the capacity optimization configuration and operation model for the regional hybrid energy storage system, as follows: min - OF 1+ β × OF 2 OF 1= C w / o - C w C w / o =∑ D d=1 ∑ T t=1 ( P loadD d,t l D d,t △t ) C w =∑ D d=1 ∑ T t=1 [( P loadD d,t +P ess,slow d,t +P ess,fast d,t ) lD d,t D t ] +C ess C ess = C ess,slow + C ess,fast C ess,slow = l slow CRF m slow E slow + x slow m slow E slow C ess,fast = l fast CRF m fast E fast + x fast m fast E fast l slow CRF = r (1+ r ) Tslow / [(1+ r ) Tslow -1] l fast CRF = r (1+ r ) Tfast / [(1+ r ) Tfast -1] P´ w d =∑ T t=1 ( P loadD d,t + P ess,slow d,t+P ess,fast d,t ) / T OF 2=∑ D d=1 ||∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t - P´ w d )|| 2 2 / T 1 / 2 OF 1≥0 OF 2≤ OF w / o 2=∑ D d=1 [∑ T t=1 ( P loadD d,t - P´ w / o d ) 2 / T ] 1 / 2 P´ w / o d =∑ T t=1 P loadD d,t / T in, OF 1 and OF 2 represent the standard deviation of the annual operation input cost and annual net load power of the regional hybrid energy storage system, β represents the annual operating input cost of the regional hybrid energy storage system OF 1 and the standard deviation of the annual net load power OF The correlation coefficient between 2; C w / o and C w They represent the operation costs before and after the regional hybrid energy storage system is configured in the region; D Indicates the number of days in a year; TIndicates the number of optimizations in a day; l D d,t and P loadD d,t They represent the first d sky t The electricity cost parameters and net load power at the time; △t Indicates the optimization time interval; P ess,slow d,t and P ess,fast d,t Respectively represent d sky t The output power of slow response energy storage and fast response energy storage at the moment; C ess represents the output cost of the hybrid energy storage system, C ess,slow and C ess,fast They represent the output costs of slow response energy storage and fast response energy storage respectively; l slow CRF and l fast CRF They represent the capacity decay rates of slow response energy storage and fast response energy storage respectively; m slow and m fast They represent the unit capacity output cost of slow response energy storage and fast response energy storage respectively; E slow and E fas Respectively represent the capacity of slow response energy storage and fast response energy storage; x slow and x fast They represent the ratio of the annual operation and maintenance cost to the initial output cost of slow response energy storage and fast response energy storage respectively; r represents the discount rate; T slow and T fast Respectively represent the operating cycles of slow response energy storage and fast response energy storage; || || 2 2 represents the two-norm; P´ w / o d and P´ w d They represent the daily average net load power before and after the regional hybrid energy storage system is configured in the region; OFw / o 2 represents the standard deviation of the annual net load power when the region is not equipped with a regional hybrid energy storage system.

[0047] The annual net load power and electricity cost parameters of the regional hybrid energy storage system for 8760 hours are input into the capacity optimization configuration and operation model. After processing, the capacity and output power of the slow response energy storage and the fast response energy storage are output as planning targets and control instructions. The net load power and electricity cost parameters of the regional hybrid energy storage system at each moment of each day are determined according to the annual net load power and electricity cost parameters of the regional hybrid energy storage system for 8760 hours. The solver is called to solve the model during the solution, and finally the optimal configuration and operation control of the capacity of the regional hybrid energy storage system are realized. The objective function of the capacity optimization configuration and operation model for regional hybrid energy storage systems is divided into two parts. The first part is to maximize the annual operation input cost after the regional hybrid energy storage is configured, and the second part is to minimize the impact of net load fluctuations on the grid-connected system after the regional hybrid energy storage is configured. This part of the impact can be evaluated using the standard deviation of the regional net load power throughout the year. In order to take into account the operation input cost after the regional energy storage is configured and the impact of net load fluctuations on the grid-connected system, the present invention introduces the correlation coefficient β The multi-objective functions of the capacity optimization configuration and operation model for the regional hybrid energy storage system are converted into a single objective function.

[0048] Among them, the standard deviation of the regional net load power throughout the year OF 2 The specific calculation is as follows: OF 2=∑ D d=1 [∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t - P´ w d ) 2 / T ] 1 / 2 P´ w d =∑ T t=1 ( P loadD d,t + P ess,slow d,t+P ess,fast d,t ) / T However, due to OF 2 is a nonlinear objective function that cannot be solved directly using the solver, so it is converted into a norm form for solution.

[0049] The constraints of the capacity optimization configuration and operation model for regional hybrid energy storage systems are shown in the boundary conditions. In addition, in order to ensure the enthusiasm of regional hybrid energy storage system configuration, the annual operation input cost after regional hybrid energy storage configuration should be greater than zero. At the same time, after the regional hybrid energy storage system is configured, its net load power fluctuation cannot affect the normal operation of the grid-connected system, that is, the net load power fluctuation should not be more serious than when the hybrid energy storage system is not configured.

[0050] During specific implementation, the present invention performs a correlation coefficient β The impact of the value of on the objective function value is analyzed as follows: like Figure 2 As shown in FIG. 1 , the objective function of the capacity optimization configuration and operation model for the regional hybrid energy storage system proposed in the present invention is OF 1 and OF 2 and its correlation coefficients in different β The Pareto optimal solution set under the value (this solution set is usually used to describe the possible optimal selection range in multi-objective optimization problems, which includes the trade-offs between different objectives and multiple possible optimal solutions). Based on model constraints OF 1≥ 0 and OF 2≤ OF w / o 2 boundary restrictions, within the feasible domain formed by these constraints, each solution is a feasible solution that can meet the optimization objectives and take into account the multi-objective balance. These solutions not only provide a variety of options for the capacity configuration of regional hybrid energy storage systems, but also provide a theoretical basis for the formulation of operation strategies, thereby verifying the feasibility and flexibility of the proposed model in practical applications. As shown in Table 2, Figure 2The objective function values ​​of Schemes A, B and C are shown in , among which Scheme A has the largest regional operation input cost of 10425.6664, but at this time the net load power fluctuation is the largest; Scheme C has the smallest regional net load power fluctuation of 24103.6469kW, but at this time the operation input cost is the smallest. The present invention selects the more balanced Scheme B as the output result of the capacity optimization configuration and operation model. At this time, the regional operation input cost and the regional net load power fluctuation are 5701.5172 and 31611.0366kW respectively. Compared with Scheme A, Scheme B reduces the regional operation input cost by 45.31%, and the regional net load power fluctuation by 33.78%. Compared with Scheme B, Scheme C reduces the regional input cost by 98.13%, and the regional load fluctuation by 23.75%, which shows that the correlation coefficient β The value of is more sensitive to the regional operation input cost than to the net load power fluctuation, and as the value of the correlation coefficient increases, the regional operation input cost decreases sharply.

[0051] Table 2

[0052] The present invention also analyzes the annual charging and discharging operation strategy of the hybrid energy storage system as follows: like Figure 3 As shown, the net load power and charging and discharging power of Scheme A are displayed. At this time, the system capacity of the slow response energy storage is configured to 2577kWh, and the maximum charging and discharging power is 401kW. There is no intention to configure fast response energy storage, that is, the system capacity and maximum charging and discharging power of the fast response energy storage are both 0. Figure 3 (a) is the net load power of the area after the hybrid energy storage system is configured. Figure 3 (b) The net load power situation without hybrid energy storage system. It can be seen that the net load power fluctuation has not been improved. This is because Scheme A focuses more on increasing the operating input cost and ignores improving the net load power fluctuation. Figure 3 (c) and Figure 3 (d) are the 8760-hour charging and discharging strategies corresponding to the slow response energy storage and the fast response energy storage, respectively. The fluctuation range of the charging and discharging power of the slow response energy storage is relatively large. Under the high expected operating input cost of plan A, the high operating cost of the fast response energy storage causes its configuration to be almost 0.

[0053] like Figure 4 As shown, the net load power and charging and discharging power of Scheme C are displayed. At this time, the system capacity of the slow response energy storage is configured to 1240kWh, the maximum charging and discharging power is 193kW, and the system capacity of the fast response energy storage is configured to 369kWh, and the maximum charging and discharging power is 25kW. Figure 4(a) is the net load power of the area after the hybrid energy storage system is configured. Figure 4 (b) The net load power situation without hybrid energy storage system. It can be clearly seen that the net load power fluctuation has been greatly improved. This is because scheme C focuses more on improving the net load power fluctuation than increasing the operating input cost. Figure 4 (c) and Figure 4 (d) gives the 8760-hour charging and discharging strategies corresponding to the slow response energy storage and the fast response energy storage under scheme C respectively. It can be seen that both the slow response energy storage and the fast response energy storage actively participate in regulating the net load power fluctuations in the area.

[0054] The present invention also analyzes the impact of the fast response energy storage operation cost on the objective function value as follows: With the development of energy storage technology in the future, the operating cost of energy storage systems will gradually decrease. For this purpose, the present invention designs three cases for comparative analysis. In the first case, the operating cost of fast response energy storage is set to 200 / kWh, in the second case, the operating cost of fast response energy storage is set to 150 / kWh, and in the third case, the operating cost of fast response energy storage is set to 100 / kWh. Figure 5 As shown, the Pareto optimal solution sets for different correlation coefficient values ​​in three cases are given. It can be seen that Case 3 is the best in terms of maximizing the regional operating input cost and minimizing the net load power fluctuation. By comparing Case 1 and Case 2, it can be obtained that under higher expected operating input costs, the Pareto curves of Case 1 and Case 2 almost overlap. This is because when the operating cost of fast response energy storage is too high, its configuration willingness is low; under smaller net load fluctuation expectations, Case 2 is better because the operating cost of fast response energy storage is low, and more profit space can be sacrificed to reduce net load fluctuations. Table 3 gives Figure 5 Operation input cost and net load power fluctuation under different conditions. As shown in Table 3, the maximum operation input cost of condition 3 (corresponding to scheme A3) is obviously higher than the maximum operation input cost of the other two conditions (corresponding to schemes A1 and A2). Similarly, condition 3 is more advantageous in regulating net load power fluctuation (corresponding to scheme B3). Figure 6 (a) Figure 6 (b) Figure 6 (c) and Figure 6 In addition, by comparing cases 1-3, it can be seen that under the scheme of minimizing net load power, as the operating cost of fast response energy storage decreases, the proportion of slow response energy storage is gradually decreasing. This is because the ability of slow response energy storage to adjust net load power fluctuations is obviously inferior to that of fast response energy storage, but the higher operating cost of fast response energy storage usually restricts the capacity configuration of fast response energy storage.

[0055] Table 3

[0056] The present invention also carries out the correlation coefficient β The impact of the value of on the capacity configuration of the hybrid energy storage system is analyzed as follows: Figure 7 (a) shows the system capacity configuration and maximum charge and discharge power of slow response energy storage and fast response energy storage under different correlation coefficient values. β As the correlation coefficient increases, the system capacity configuration and maximum charge and discharge power of slow response energy storage are gradually reduced. β This is because the main function of fast response energy storage is to participate in regulating the real-time fluctuation of net load power, and its operation cost is high. β When the correlation coefficient is small (the input cost of regional operation has a higher priority), the configuration willingness is low. β When it increases to a certain level (at this time, suppressing net load power fluctuations has a higher priority), the region will begin to configure fast response energy storage. β When it is between 0.3 and 0.7, the system capacity configuration and maximum charge and discharge power of its fast response energy storage approach 0, and then gradually increase. Figure 7 From (b), it can be seen that there is an obvious inflection point in the net load power curve. This is because there is a limit to regulating load fluctuations by configuring slow response energy storage. In order to better suppress net load power fluctuations, it is necessary to gradually increase the system configuration capacity of fast response energy storage.

[0057] The present invention also analyzes the impact of different energy storage system configuration schemes on regulating net load power fluctuations as follows: The present invention further compares and analyzes three energy storage configuration schemes: 1) configuring a hybrid energy storage system including slow response energy storage and fast response energy storage; 2) configuring slow response energy storage; 3) configuring fast response energy storage. Figure 8 As shown in the figure, configuring slow response energy storage can obtain a certain regional operation input cost and adjust the net load power fluctuation, but its adjustment capacity is limited, and it can only adjust the net load power fluctuation to 28900kW at most. Configuring fast response energy storage significantly improves the adjustment effect of net load power fluctuation, but the high operation cost makes it impossible to obtain the net input cost by utilizing the peak-valley power cost parameter difference. Configuring a hybrid energy storage system can combine the advantages of both, thereby achieving a higher operation input cost and the effect of adjusting the net load power fluctuation.

[0058] The present invention also conducts a sustainability analysis of the proposed capacity optimization configuration scheme as follows: In order to verify the sustainability of the method proposed in the present invention, the present invention uses the net load power data of more than 300 users in a certain two years as a priori data to determine the system capacity of the hybrid energy storage system, and then verifies it based on the net load power data of users in the area in the next two years as a posteriori data. Figure 9 As shown in (a), the Pareto optimal solution set based on prior data generated by the method proposed in the present invention is given. With the continuous deployment of photovoltaic power generation systems, the regional net load power fluctuation will become more severe. Therefore, the present invention increases the priority of regulating net load power fluctuations and selects a solution that is more suitable for future photovoltaic development (i.e. Figure 9 The five-pointed star test point in (a) of Figure 1 shows the five-pointed star test point in Figure 1). At this time, the system capacity of the slow response energy storage is configured to be 1109kWh, and the system capacity of the fast response energy storage is configured to be 47kWh. Figure 9 As shown in (b), the configuration scheme at the five-pointed star is verified in the posterior data. It can be seen that even under the maximum regional operation input cost (about 4808), the scheme can effectively improve the net load power fluctuation. Even under the minimum regional net load power fluctuation (about 28322kW), the scheme can also obtain a certain operation input cost. Therefore, the capacity optimization configuration scheme of the regional hybrid energy storage system calculated by the method proposed in the present invention has sustainable technical value.

[0059] The present invention also provides a capacity optimization configuration and operation device for a regional hybrid energy storage system, the device includes a data acquisition unit, a model building unit and a capacity optimization configuration and operation unit, the data acquisition unit is used to acquire the historical net load power and electricity cost parameters of the regional hybrid energy storage system and the annual net load power and electricity cost parameters; the model building unit is used to establish a slow response energy storage operation model of the slow response energy storage, and based on the historical net load power and electricity cost parameters of the regional hybrid energy storage system and the slow response energy storage operation model, construct the optimization configuration and operation boundary conditions, thereby establishing a capacity optimization configuration and operation model that considers the optimization configuration and operation boundary conditions; the capacity optimization configuration and operation unit is used to input the annual net load power and electricity cost parameters of the regional hybrid energy storage system into the capacity optimization configuration and operation model, and after processing, output the capacity and output power of the slow response energy storage and the fast response energy storage as control instructions, so as to realize the optimal configuration and operation control of the capacity of the regional hybrid energy storage system.

[0060] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0061] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, in which the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0062] Accordingly, the present invention also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by one or more processors, the one or more processors implement a capacity optimization configuration and operation method for a regional hybrid energy storage system as described above.

[0063] Accordingly, the present invention also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implements a capacity optimization configuration and operation method for a regional hybrid energy storage system as described above.

[0064] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the disclosure disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0065] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A capacity optimization configuration and operation method for a regional hybrid energy storage system, characterized in that: include: S1: Establish a regional hybrid energy storage system including slow response energy storage and fast response energy storage, establish a slow response energy storage operation model of the slow response energy storage, input the historical net load power and electricity cost parameters of the regional hybrid energy storage system into the slow response energy storage operation model, and output the monthly charging and discharging time of the slow response energy storage after processing; S2: Establish the optimal configuration and operation boundary conditions of slow response energy storage and fast response energy storage based on the monthly charge and discharge time of slow response energy storage; S3: Establish a capacity optimization configuration and operation model of the regional hybrid energy storage system that takes into account the optimization configuration and operation boundary conditions, input the annual net load power and electricity cost parameters of the regional hybrid energy storage system into the capacity optimization configuration and operation model, and after processing, output the capacity and output power of the slow response energy storage and the fast response energy storage as control instructions to achieve the optimal configuration and operation control of the capacity of the regional hybrid energy storage system.

2. The capacity optimization configuration and operation method for a regional hybrid energy storage system according to claim 1 is characterized in that: In the step S1, the slow response energy storage has a larger response capacity and a slower response speed, while the fast response energy storage has a smaller response capacity and a faster response speed.

3. The capacity optimization configuration and operation method for a regional hybrid energy storage system according to claim 1, characterized in that: In the step S1, the slow response energy storage operation model of the slow response energy storage is as follows: min ∑ t λ´ d,t t ∈[ t d , t d+m ] st t d+m - t d = m λ´ d,t =∑ d λ d,t ∈ λ dmax ( λ d,t ) / | λ dmax | P load ´ d,t =∑ d P load d,t ∈ P load dmax ( P load d,t ) / | P load dmax | m = n × η c × η d n = t c+n - t c in, λ´ d,t and λ d,t They represent the first d sky t The predicted power cost parameters and historical power cost parameters at the moment, P load ´ d,t and P load d,t They represent the first d sky t The predicted net load power and historical net load power at the moment; t d and t d+m Respectively represent the start and end time of the discharge of the slow response energy storage, m Indicates the discharge duration of the slow response energy storage; λ dmax and P load dmax They represent the first d Daily electricity cost parameters and upper limit of net load power; n Indicates the charging time of slow response energy storage; η c and η d They represent the charging efficiency and discharging efficiency of slow response energy storage, t c and t c+n Respectively represent the start and end time of charging of the slow response energy storage; The monthly charging and discharging time of the slow response energy storage in the current month is determined according to the starting and ending time of the charging and discharging of the slow response energy storage.

4. The capacity optimization configuration and operation method for a regional hybrid energy storage system according to claim 1, characterized in that: In step S2, the optimized configuration and operating boundary conditions of the slow response energy storage are as follows: P ess,slow d,tc = P ess,slow d,tc+1 =···= P ess,slow d,t =···= P ess,slow d,tc+n P ess,slow d,td = P ess,slow d,td+1 =···= P ess,slow d,t =···= P ess,slow d,td+m ∫ tc+n tc P ess,slow d,t η c tdt +∫ td+m td P ess,slow d,td / η d tdt =0 – P ess,slow,cap < P ess,slow d,t <P ess,slow,cap E slow =max{0.8∫ tc+n tc P ess,slow,cap η c tdt , 0.8∫ td+m td P ess,slow,cap / η d tdt } 0.1 E slow ≤ E slow d,t ≤0.9 E slow E slow d,t = E slow d,t-1 + P ess,slow d,t η c △t , when t ∈( t c , t c+n ) E slow d,t = E slow d,t-1 + P ess,slow d,t / η d △t , when t ∈( t d , t d+m ) E slow d,t = E slow d,t-1 , other in, P ess,slow d,tc , P ess,slow d,tc+1 、···、 P ess,slow d,t 、···、 P ess,slow d,tc+n Respectively represent d sky t c , t c +1, ···, t 、···、 t c + n The output power of the slow response energy storage at all times, t c and t c+n Respectively represent the start and end time of charging of slow response energy storage, n Indicates the charging time of slow response energy storage; P ess,slow d,td , P ess ,slow d,td+1 、···、 P ess,slow d,td+m Respectively represent d sky t d , t d +1, ···, t d + m The output power of the slow response energy storage at all times, t d and t d+m Respectively represent the start and end time of the discharge of the slow response energy storage, m Indicates the discharge duration of the slow response energy storage; η c and η d They represent the charging efficiency and discharging efficiency of slow response energy storage respectively; P ess,slow,cap Indicates the maximum charge and discharge power of slow response energy storage; E slow represents the capacity of slow response energy storage, E slow d,t and E slow d,t-1 They represent the slow response energy storage in the d sky t and t -1 The energy stored at a moment; △t Indicates the optimization time interval.

5. The capacity optimization configuration and operation method for a regional hybrid energy storage system according to claim 1, characterized in that: In step S2, the optimized configuration and operating boundary conditions of the fast response energy storage are as follows: ∫ T 1 P ess,fast d,t tdt =0 – P ess,fast,cap < P ess,fast d,t <P ess,fast,cap E fast =0.8∫ T / 2 1 P ess,fast,cap tdt 0.1 E fast ≤ E fast d,t ≤0.9 E fast E fast d,t = E fast d,t-1 + P ess,fast d,t △t in, T Indicates the number of optimizations in a day; P ess,fast d,t Indicates d sky t The output power of the energy storage can be quickly responded at any time; P ess,fast,cap Indicates the maximum charge and discharge power of fast response energy storage; E fast represents the capacity of fast response energy storage, E fast d,t and E fast d,t-1 They represent the fast response energy storage in the d sky t and t -1 The energy stored at a moment; △t Optimize the time interval.

6. The capacity optimization configuration and operation method for a regional hybrid energy storage system according to claim 1, characterized in that: In step S3, the capacity optimization configuration and operation model are as follows: min - OF 1+ β × OF 2 OF 1≥0 OF 2≤ OF w / o 2=∑ D d=1 [∑ T t=1 ( P loadD d,t - P´ w / o d ) 2 / T ] 1 / 2 P´ w / o d =∑ T t=1 P loadD d,t / T in, OF 1 and OF 2 represent the standard deviation of the annual operation input cost and annual net load power of the regional hybrid energy storage system, β represents the annual operating input cost of the regional hybrid energy storage system OF 1 and the standard deviation of the annual net load power OF The correlation coefficient between 2; OF w / o 2 represents the standard deviation of the annual net load power when the region is not equipped with a regional hybrid energy storage system; D Indicates the number of days in a year; T Indicates the number of optimizations in a day; P loadD d,t The regional hybrid energy storage system d sky t Net load power at the moment; P´ w / o d It represents the daily average net load power before the regional hybrid energy storage system is configured in the region; The net load power and electricity cost parameters of the regional hybrid energy storage system at each moment of each day are determined according to the annual net load power and electricity cost parameters of the regional hybrid energy storage system.

7. The capacity optimization configuration and operation method for a regional hybrid energy storage system according to claim 6 is characterized in that: The standard deviation of the annual operating input cost and annual net load power of the regional hybrid energy storage system is as follows: OF 1= C w / o - C w C w / o =∑ D d=1 ∑ T t=1 ( P loadD d,t λ D d,t △t ) C w =∑ D d=1 ∑ T t=1 [( P loadD d,t +P ess,slow d,t +P ess,fast d,t ) λ D d,t D t ] +C ess C ess = C ess,slow + C ess,fast C ess,slow = λ slow CRF μ slow E slow + ξ slow μ slow E slow C ess,fast = λ fast CRF μ fast E fast + ξ fast μ fast E fast λ slow CRF = r (1+ r ) Tslow / [(1+ r ) Tslow -1] λ fast CRF = r (1+ r ) Tfast / [(1+ r ) Tfast -1] P´ w d =∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t ) / T OF 2=∑ D d=1 ||∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t - P´ w d )|| 2 2 / T 1 / 2 in, C w / o and C w They represent the operation costs before and after the regional hybrid energy storage system is configured in the region; λ D d,t The regional hybrid energy storage system d sky t The power cost parameter at the time; △t Indicates the optimization time interval; P ess,slow d,t and P ess,fast d,t Respectively represent d sky t The output power of slow response energy storage and fast response energy storage at the moment; C ess represents the output cost of the hybrid energy storage system, C ess,slow and C ess,fast They represent the output costs of slow response energy storage and fast response energy storage respectively; λ slow CRF and λ fast CRF They represent the capacity decay rates of slow response energy storage and fast response energy storage respectively; μ slow and μ fast They represent the unit capacity output cost of slow response energy storage and fast response energy storage respectively; E slow and E fas Respectively represent the capacity of slow response energy storage and fast response energy storage; ξ slow and ξ fast They represent the ratio of the annual operation and maintenance cost to the initial output cost of slow response energy storage and fast response energy storage respectively; r represents the discount rate; T slow and T fast Respectively represent the operating cycles of slow response energy storage and fast response energy storage; || || 2 2 represents the two-norm; P´ w d It represents the daily average net load power after the regional hybrid energy storage system is configured in the region.

8. A capacity optimization configuration and operation device for a regional hybrid energy storage system according to any one of the methods described in claims 1 to 7, characterized in that: include: A data acquisition unit, used to acquire historical net load power and electricity cost parameters of the regional hybrid energy storage system and annual net load power and electricity cost parameters; A model building unit is used to establish a slow response energy storage operation model of the slow response energy storage, and to construct an optimal configuration and operation boundary conditions based on the historical net load power and electricity cost parameters of the regional hybrid energy storage system and the slow response energy storage operation model, thereby establishing a capacity optimal configuration and operation model that takes into account the optimal configuration and operation boundary conditions; The capacity optimization configuration and operation unit is used to input the annual net load power and electricity cost parameters of the regional hybrid energy storage system into the capacity optimization configuration and operation model, and after processing, output the capacity and output power of the slow response energy storage and the fast response energy storage as control instructions to achieve the optimal configuration and operation control of the capacity of the regional hybrid energy storage system.

9. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having program data stored thereon, characterized in that: When the program data is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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