A Capacity Optimization Configuration and Operation Method for a Regional Hybrid Energy Storage System

Abstract

The present invention discloses a capacity optimization configuration and operation method for a regional hybrid energy storage system. The method includes: establishing a slow response energy storage operation model for slow response energy storage, inputting the historical net load power and electricity cost parameters of the regional hybrid energy storage system into the model for processing to obtain the monthly charge and discharge time of the slow response energy storage, and then establishing optimization configuration and operation boundary conditions; establishing a capacity optimization configuration and operation model considering the optimization configuration and operation boundary conditions, inputting the annual net load power and electricity cost parameters into the model for processing, and outputting the energy storage capacity and output power as control instructions to achieve the optimization configuration and operation control of the capacity of the regional hybrid energy storage system. The present invention can achieve the optimal configuration of the capacity of the regional hybrid energy storage system and the formulation of operation strategies on the premise of comprehensively considering the synergistic effect of slow and fast response energy storage.

Description

Technical Field

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

[0002] In recent years, with the large-scale access of renewable energy such as distributed photovoltaic power, its intermittency and volatility have brought challenges to the supply-demand balance on the user side and the stability of the power system. Improving the flexible regulation ability on the user side has become the key to promoting the consumption of new energy and ensuring power supply reliability. As an important means to suppress power fluctuations, the energy storage system has broad prospects in the new power system. However, the high output cost and low utilization rate limit the application of single-user energy storage, and introducing regional energy storage serving 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, and optimizing the energy storage capacity configuration and operation strategy has become the key to improving the input cost.

[0003] Traditional energy storage planning methods usually only consider the new energy and load fluctuation characteristics under typical scenarios, while ignoring the power fluctuations on a long time scale and the impact of extreme weather, and it is difficult to comprehensively reflect the annual dynamic characteristics, resulting in insufficient reliability of the results. At the same time, the regional energy storage mode is still unstable, restricting large-scale development. Therefore, it is urgent to model the ability of different types of energy storage systems in the region to regulate power fluctuations, and formulate a suitable capacity configuration and operation optimization method for the hybrid energy storage system according to the respective 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 in the background art, the present invention provides a method for capacity optimization configuration and operation of a regional hybrid energy storage system. The method of the present invention aims to solve the technical problems of capacity optimization configuration and operation of a hybrid energy storage system that suppresses the impact of regional net load fluctuations on the power system through the synergistic effect between slow response energy storage and fast response energy storage under the condition of high proportion of new energy grid connection, 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 as follows:

[0006] The method for capacity optimization configuration and operation of the regional hybrid energy storage system of the present invention includes:

[0007] S1: Establish a regional hybrid energy storage system including slow-response energy storage and fast-response energy storage. Based on the density clustering algorithm, establish a slow-response energy storage operation model for 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 after processing, output the monthly charge and discharge time of the slow-response energy storage.

[0008] S2: Based on the monthly charge and discharge time of the slow-response energy storage, establish the optimal configuration and operation boundary conditions for the slow-response energy storage and the fast-response energy storage; the boundary conditions consider the operation constraints and capacity constraints of the slow-response energy storage and the fast-response energy storage.

[0009] S3: Establish a capacity optimal configuration and operation model for the regional hybrid energy storage system considering the optimal 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 optimal 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 the planning target and control instruction to realize the optimal configuration and operation control of the capacity of the regional hybrid energy storage system.

[0010] Based on the boundary conditions for the capacity optimal configuration and operation of the regional hybrid energy storage system, construct a bi-objective function for evaluating the operation input cost and net load fluctuation of the regional hybrid energy storage, and transform it into a single-objective function through the correlation coefficient to obtain the capacity optimal configuration and operation model for the regional hybrid energy storage system.

[0011] In step S1 described above, 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.

[0012] In step S1 described above, the slow-response energy storage operation model for the slow-response energy storage is as follows:

[0013] min ∑ t λ´ d,t t ∈ t d , t d+m

[0014] s.t. t d+m - t d = m

[0015] λ´ d,t =∑ d λ d,t ∈ λ dmax (​λ d,t ) / | λ dmax |

[0016] P load ´ d,t =∑ d P load d,t ∈ P load dmax ( P load d,t ) / | P load dmax |

[0017] m = n × η c × η d

[0018] n = t c+n - t c

[0019] Among them, λ´ d,t and λ d,t respectively represent the typical predicted electricity cost parameter and the historical electricity cost parameter at the d th t moment of the regional hybrid energy storage system, P load ´ d,t and P load d,t respectively represent the typical predicted net load power and the historical net load power at the d th t moment of the regional hybrid energy storage system; t d and t d+m respectively represent the start and end times of the discharge of the slow-response energy storage, m represents the discharge duration of the slow-response energy storage; λ dmax and P load dmax respectively represent thed The electricity cost parameter for the day and the upper limit of the net load power; n Indicates the charging duration of the slow-response energy storage; η c and η d respectively represent the charging efficiency and discharging efficiency of the slow-response energy storage, t c and t c+n respectively represent the start and end times of the charging of the slow-response energy storage.

[0020] Determine the monthly charging and discharging time of the slow-response energy storage according to the start and end times of the charging and discharging of the slow-response energy storage.

[0021] In the step S2 described above, the optimal configuration and operation boundary conditions of the slow-response energy storage are as follows:

[0022] P ess,slow d,tc = P ess,slow d,tc+1 =···= P ess,slow d,t =···= P ess,slow d,tc+n

[0023] P ess,slow d,td = P ess,slow d,td+1 =···= P ess,slow d,t =···= P ess,slow d,td+m

[0024] ∫ tc+n tc P ess,slow d,t η c tdt +∫ td+m td P ess,slow d,td / η d tdt =0

[0025] – Pess,slow,cap < P ess,slow d,t <P ess,slow,cap

[0026] 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}

[0027] 0.1 E slow ≤ E slow d,t ≤ 0.9 E slow

[0028] E slow d,t = E slow d,t-1 + P ess,slow d,t η c △t , when t ∈( t c , t c+n )

[0029] E slow d,t = E slow d,t-1 + P ess,slow d,t / η d △t , when t ∈( t d , t d+m )

[0030] Eslow d,t = E slow d,t-1 , and others

[0031] Among them, P ess,slow d,tc 、 P ess,slow d,tc+1 、···、 P ess,slow d,t 、···、 P ess,slow d,tc+n respectively represent the output power of the slow-response energy storage at the d th day t c 、 t c +1, ···, t 、···、 t c + n moment, t c and t c+n respectively represent the start and end times of the charging of the slow-response energy storage, n represents the charging duration of the slow-response energy storage; P ess,slow d,td 、 P ess ,slow d,td+1 、···、 P ess,slow d,td+m respectively represent the output power of the slow-response energy storage at the d th day t d 、 t d +1, ···, t d + m moment, t d and t d+m respectively represent the start and end times of the discharging of the slow-response energy storage, m represents the discharging duration of the slow-response energy storage; η c and η d respectively represent the charging efficiency and discharging efficiency of the slow-response energy storage; P ess,slow,capRepresents 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 respectively represent the energy stored in the slow-response energy storage at the d day t and t -1 moment; △t Represents the optimization time interval.

[0032] In the step S2 described above, the optimization configuration and operation boundary conditions of the fast-response energy storage are as follows:

[0033] ∫ T 1 P ess,fast d,t tdt = 0

[0034] – P ess,fast,cap < P ess,fast d,t <P ess,fast,cap

[0035] E fast = 0.8∫ T / 2 1 P ess,fast,cap tdt

[0036] 0.1 E fast ≤ E fast d,t ≤ 0.9 E fast

[0037] E fast d,t = E fast d,t-1 + P ess,fast d,t △t

[0038] Among them, T Represents the number of optimizations in a day; P ess,fast d,tIndicates the output power of the fast-response energy storage at the d th day t P ess,fast,cap fast Indicates the maximum charge-discharge power of the fast-response energy storage; E fast d,t E fast d,t-1 and E fast d,t-1 respectively indicate the energy stored in the fast-response energy storage at the d th day t and t -1 moment; △t Optimization time interval.

[0039] In the step S3 described above, the capacity optimization configuration and operation model are as follows:

[0040] min - OF 1 + β × OF 2

[0041] OF 1 ≥0

[0042] OF 2 ≤ OF w / o 2 =∑ D d=1 [∑ T t=1 ( P loadD d,t - P´ w / o d ) 2 / T 1 / 2

[0043] P´ w / o d =∑ T t=1 P loadD d,t / T

[0044] Among them, OF 1 and OF 2 ​respectively represent the annual operating input cost of the regional hybrid energy storage system and the standard deviation of the annual net load power 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 2 the correlation coefficient between them; OF w / o 2 represents the standard deviation of the annual net load power when the regional hybrid energy storage system is not configured in the region; D represents the number of days in a year; T represents the number of optimization times in a day; P loadD d,t represents the d day t net load power at the moment of the regional hybrid energy storage system; P´ w / o d represents the daily average net load power before the regional hybrid energy storage system is configured in the region.

[0045] Determine the net load power and electricity cost parameters of the regional hybrid energy storage system at each moment of each day according to the annual net load power and electricity cost parameters of the regional hybrid energy storage system. When solving, call the solver to solve the model.

[0046] The annual operating input cost and the standard deviation of the annual net load power of the described regional hybrid energy storage system are as follows:

[0047] OF 1 = C w / o - C w

[0048] C w / o =∑ D d=1 ∑ T t=1 ( P loadD d,t λ D d,t △t )

[0049] C w =∑ D d=1 ∑ T t=1 [( PloadD d,t +P ess,slow d,t +P ess,fast d,t ) λ D d,t Δ t +C ess

[0050] C ess = C ess,slow + C ess,fast

[0051] C ess,slow = λ slow CRF μ slow E slow + ξ slow μ slow E slow

[0052] C ess,fast = λ fast CRF μ fast E fast + ξ fast μ fast E fast

[0053] λ slow CRF = r (1 + r ) Tslow / [(1 + r ) Tslow - 1]

[0054] λ fast CRF = r (1 + r ) Tfast / [(1 +​r ) Tfast -1]

[0055] P´ w d =∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t ) / T

[0056] 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

[0057] Among them, C w / o and C w represent the operating costs before and after configuring the regional hybrid energy storage system, respectively; λ D d,t represents the electricity cost parameter at the d th t moment of the regional hybrid energy storage system; △t represents the optimization time interval; P ess,slow d,t and P ess ,fast d,t respectively represent the output powers of the slow-response energy storage and the fast-response energy storage at the d th t moment; C ess represents the output cost of the hybrid energy storage system, Cess,slow and C ess,fast respectively represent the output costs of slow-response energy storage and fast-response energy storage; λ slow CRF and λ fast CRF respectively represent the capacity decay rates of slow-response energy storage and fast-response energy storage; μ slow and μ fast respectively represent the output costs per unit capacity of slow-response energy storage and fast-response energy storage; E slow and E fas respectively represent the capacities of slow-response energy storage and fast-response energy storage; ξ slow and ξ fast respectively represent the ratios of the annual operation and maintenance costs to the initial output costs of slow-response energy storage and fast-response energy storage; r represents the discount rate; T slow and T fast respectively represent the operation cycles of slow-response energy storage and fast-response energy storage; || || 2 2 represents the two-norm; P´ w d represents the daily average net load power after configuring the regional hybrid energy storage system.

[0058] The capacity optimization configuration and operation device for the regional hybrid energy storage system of the present invention includes:

[0059] A data acquisition unit for acquiring 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.

[0060] A model establishment unit for establishing an operation model of slow-response energy storage of slow-response energy storage, and constructing optimization 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 operation model of slow-response energy storage, so as to establish a capacity optimization configuration and operation model considering the optimization configuration and operation boundary conditions.

[0061] 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. After processing, it outputs 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.

[0062] The electronic device of the present invention includes: a memory and a processor coupled to each other. Among them, the memory stores program data, and the processor calls the program data to execute the method as described above.

[0063] The computer-readable storage medium of the present invention stores program data thereon, and when the program data is executed by a processor, it implements the method as described above.

[0064] The method of the present invention first generates the regional typical net load power curve and electricity price curve through the density clustering algorithm, and then formulates the monthly charge and discharge plan of the slow-response energy storage. On this basis, a multi-objective optimization model that comprehensively considers the synergistic effect of the slow-response energy storage and the 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 this optimization problem, the capacity optimization configuration scheme and the 8760-hour operation strategy of the regional hybrid energy storage system can be finally obtained. This optimization method has significant advantages in improving the regional energy management efficiency, reducing the operation cost, and promoting the self-consumption of renewable energy, and can provide a more flexible and efficient solution for the energy management at the regional level.

[0065] The beneficial effects of the present invention are:

[0066] 1) The present invention uses the density-based clustering algorithm to extract the regional historical net load power data and electricity cost parameter data, generates the typical net load power curve and electricity cost parameter curve, and then formulates the monthly charge and discharge plan of the slow-response energy storage, effectively realizing the efficient utilization of the slow-response energy storage on the basis of predicting the net load fluctuation.

[0067] 2) The present invention constructs a multi-objective optimization model with the goal of maximizing the regional operation input cost and minimizing the regional net load power fluctuation by integrating the slow-response energy storage and the fast-response energy storage. This model not only improves the benefit of the hybrid energy storage system, but also significantly enhances the willingness to deploy the energy storage system in the region, thus promoting the popularization and application of energy storage technology.

[0068] 3) On the basis of accurately simulating 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 the net load power fluctuation on the power system, providing a more accurate and efficient solution for regional energy management.

[0069] 4) The present invention fully considers the synergistic effect of slow-response energy storage and fast-response energy storage in capacity configuration and operation characteristics, realizes the optimal configuration of the regional hybrid energy storage system, and proposes an optimization framework for the hybrid energy storage system with practical operability, providing an important reference for the optimal decision-making of energy management at the regional level and contributing to the realization of the low-carbon and sustainable development goals of the energy system.

[0070] On the premise of comprehensively considering the synergistic effect between slow-response energy storage and fast-response energy storage, the present invention realizes the optimal capacity configuration and operation strategy formulation of the regional hybrid energy storage system. The method of the present invention can not only smooth the net load fluctuation during the peak energy consumption period, reduce the grid pressure, but also maximize the regional benefits by using the difference in peak-valley electricity cost parameters, thus promoting the efficient utilization and large-scale grid connection of the user-side energy storage system. Description of the Drawings

[0071] Figure 1 is the flowchart of the method of the present invention;

[0072] Figure 2 is the Pareto optimal solution set diagram under different correlation coefficient values shown according to an exemplary embodiment;

[0073] Figure 3 is the diagram of the net load power and charge-discharge power of Scheme A shown according to an exemplary embodiment, wherein, Figure 3 in (a) is the diagram of the net load power when the hybrid energy storage system is configured in Scheme A, Figure 3 in (b) is the diagram of the net load power when the hybrid energy storage system is not configured in Scheme A, Figure 3 in (c) is the diagram of the charge-discharge power of the slow-response energy storage in Scheme A, Figure 3 in (d) is the diagram of the charge-discharge power of the fast-response energy storage in Scheme A;

[0074] Figure 4 is the diagram of the net load power and charge-discharge power of Scheme C shown according to an exemplary embodiment, wherein, Figure 4 in (a) is the diagram of the net load power when the hybrid energy storage system is configured in Scheme C, Figure 4 in (b) is the diagram of the net load power when the hybrid energy storage system is not configured in Scheme C, Figure 4 in (c) is the diagram of the charge-discharge power of the slow-response energy storage in Scheme C, Figure 4 in (d) is the diagram of the charge-discharge power of the fast-response energy storage in Scheme C;

[0075] Figure 5 is the Pareto optimal solution set diagram under different conditions shown according to an exemplary embodiment;

[0076] Figure 6 is a diagram of the net load power fluctuation in different cases shown according to an exemplary embodiment, where, Figure 6 in (a) of is the diagram of the net load power fluctuation in Case 1, Figure 6 in (b) of is the diagram of the net load power fluctuation in Case 2, Figure 6 in (c) of is the diagram of the net load power fluctuation in Case 3, Figure 6 in (d) of is the diagram of the net load power fluctuation without configuring the hybrid energy storage system;

[0077] Figure 7 is a diagram of the system capacity configuration and the operating input cost of the hybrid energy storage system under different correlation coefficient values shown according to an exemplary embodiment, where, Figure 7 in (a) of is the diagram of the system capacity configuration and the maximum charge and discharge power of the hybrid energy storage system under different correlation coefficient values, Figure 7 in (b) of is the diagram of the operating input cost and the net load power fluctuation of the hybrid energy storage system under different correlation coefficient values;

[0078] Figure 8 is a diagram of the Pareto optimal solution set considering different energy storage configuration schemes shown according to an exemplary embodiment;

[0079] Figure 9 is a diagram of the Pareto optimal solution set based on prior and posterior data shown according to an exemplary embodiment, where, Figure 9 in (a) of is the diagram of the Pareto optimal solution set based on prior data, Figure 9 in (b) of is the diagram of the Pareto optimal solution set based on posterior data. Detailed implementation manners

[0080] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0081] As Figure 1 shown, the capacity optimization configuration and operation method of the regional hybrid energy storage system of the present invention are specifically as follows:

[0082] The present invention selects the net load power data, electricity cost parameter data, and hybrid energy storage system parameters of a certain area with more than 300 users as test cases. Among them, the net load data is the sum of the power of distributed photovoltaic power generation, fixed loads represented by household appliances, and flexible loads represented by electric vehicles. The electricity cost parameter data is derived from the data of the power plant, and the parameter configuration of the regional hybrid energy storage system is shown in Table 1. To verify the method of the present invention, the test cases focus on highlighting the trade-off between maximizing the operating input cost of the regional hybrid energy storage system configuration and minimizing the regional net load power fluctuation. Because these two objectives are essentially conflicting, maximizing the regional operating input cost will increase the net load power fluctuation by changing the operating mode of the hybrid energy storage system due to the difference in peak-valley electricity cost parameters, while minimizing the regional net load power fluctuation will, to a certain extent, lead to a reduction in the regional operating input cost.

[0083] Table 1

[0084]

[0085] 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. The fast-response energy storage has a smaller response capacity and a faster response speed. The slow-response energy storage is represented by heat pump energy storage and pumped-storage energy storage, and the fast-response energy storage is represented by battery energy storage. Among them, the characteristics of the slow-response energy storage are having a large response capacity and a low operating cost, but its response speed is slow, and it is suitable for adjusting the power fluctuations that change slowly on a long time scale, such as the fluctuations of the regional load power. The characteristics of the fast-response energy storage are having a small response capacity and a high operating cost, but its response speed is fast, and it is suitable for adjusting the power fluctuations that change rapidly on a short time scale, such as the fluctuations of renewable energy power generation. The hybrid integration of the slow-response energy storage and the fast-response energy storage together constitutes the regional hybrid energy storage system, and its main application scenario is the regional microgrid. The source side of this regional microgrid includes the rooftop photovoltaic array and the public grid within the region. Among them, the rooftop photovoltaic array and the public grid are respectively connected to the AC bus through inverters and transformers, and support the grid connection of the surplus photovoltaic power generation. The load side includes household appliances, electric vehicles, etc. In addition, the slow-response energy storage and the fast-response energy storage are also connected to the AC bus through inverters to suppress the power fluctuations within the region.

[0086] Then, a slow-response energy storage operation model of the slow-response energy storage is established based on the density clustering algorithm as follows:

[0087] min ∑ t λ´ d,t t ∈ t d , td+m

[0088] s.t. t d+m - t d = m

[0089] λ´ d,t =∑ d λ d,t ∈ λ dmax ( λ d,t ) / | λ dmax | t = 1, 2, …, 48

[0090] P load ´ d,t =∑ d P load d,t ∈ P load dmax ( P load d,t ) / | P load dmax | t = 1, 2, …, 48

[0091] m = n × η c × η d

[0092] n = t c+n - t c

[0093] Among them, λ´ d,t and λ d,t respectively represent the typical predicted electricity cost parameter and the historical electricity cost parameter at the d th t moment of the regional hybrid energy storage system, P load ´ d,t and P ​load d,t respectively represent the typical predicted net load power and historical net load power at the d day t and moment of the regional hybrid energy storage system; t d and t d+m respectively represent the start and end times of the discharge of the slow - response energy storage, m representing the discharge duration of the slow - response energy storage; λ dmax and P load dmax respectively represent the electricity cost parameter and the upper limit of the net load power on the d day of the regional hybrid energy storage system; n represents the charging duration of the slow - response energy storage; η c and η d respectively represent the charging efficiency and discharge efficiency of the slow - response energy storage, t c and t c+n respectively represent the start and end times of the charging of the slow - response energy storage.

[0094] Input the historical net load power and electricity cost parameter of the regional hybrid energy storage system into the slow - response energy storage operation model. After processing, the monthly charge - discharge time of the slow - response energy storage is output. Determine the monthly charge - discharge time of the slow - response energy storage in that month according to the start and end times of the charging and discharging of the slow - response energy storage.

[0095] The response speed of the slow - response energy storage is relatively slow and has a low operation cost. The cost can be specifically measured by electricity. It mainly plays a role in consuming renewable energy and benefiting from the difference in valley - peak electricity cost parameters in the region. Therefore, the present invention comprehensively considers the changes in the regional net load power and electricity cost parameters and formulates its monthly charge - discharge plan in advance. To exclude 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 the slow - response energy storage.

[0096] First, define the sample points and construct the sample data set of the net load power curve and 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, each half - hour is a data point, and the time series can form a curve with one day as the time scale) as follows:

[0097] P loadd ={ P load d,1 、 P load d,2 、…、 P load d,t 、…、 P load d,48}

[0098] λ d ={ λ d,1 、 λ d,2 、…、 λ d,t 、…、 λ d,48}

[0099] Among them, P load d represents the net load power time series on the d th day; λ d represents the electricity cost parameter time series on the d th day.

[0100] For the data of one month (usually 30 or 31 days), the sample data sets for constructing the net load power curve and the electricity cost parameter curve are as follows:

[0101] P load ={ P load 1 、 P load 2 、…、 P load d 、…、 P load N}

[0102] λ ={ λ 1 、 λ 2 、…、 λ d 、…、 λ N}

[0103] Among them, P loadA sample data set representing the net load power curve; λ A sample data set representing the electricity cost parameter curve; N Represents the total number of days in that month.

[0104] Then calculate the Euclidean distance between the sample data sets of the net load power curve and the electricity cost parameter curve, and cluster to obtain relevant clusters based on the density clustering algorithm. Calculate the Euclidean distance between each time series in the sample data sets of the net load power curve and the electricity cost parameter curve as follows:

[0105] d ( P load i , P load j ) = [∑ t ( P load i,t , P load j,t ) 2 1 / 2

[0106] d ( λ i , λ j ) = [∑ t ( λ i,t - λ j,t ) 2 1 / 2 t = 1, 2,..., 48

[0107] Among them, d ( ) represents the Euclidean distance.

[0108] Based on the obtained Euclidean distance, use the density clustering algorithm to cluster the sample data sets P load and λ , set two distance thresholds, and the minimum number of samples is M p and M λ , and divide the time series into several clusters according to the distance thresholds and the minimum number of samples. Among them, the cluster and the noise point set related to the sample data set P load are respectively represented as { P load d1 、 P ​​load d2 ,..., P load dk} and N P , the clusters and the set of noise points related to the sample data set λ are respectively represented as { λ d1 , λ d2 ,..., λ dk} and N λ .

[0109] Then select the cluster with the most sample points, extract the typical net load power and the electricity cost parameters, and select the cluster P load dmax ∈ { P load d1 , P load d2 ,..., P load dk} and λ dmax ∈ { λ d1 , λ d2 ,..., λ dk}, and perform point-by-point averaging on all the net load power curves and the electricity cost parameter curves within this cluster to obtain the typical predicted net load power and the electricity cost parameters.

[0110] Finally, based on the typical predicted net load power curve and the electricity cost parameter curve, determine the monthly charging plan and the monthly discharging plan for the slow-response energy storage. According to the P load ´ d,t -composed typical predicted net load power curve, identify the starting time when the net load power is negative, and define it as the starting time for the slow-response energy storage to charge t c , and the ending time when the net load power is negative, and define it as the ending time for the slow-response energy storage to charge t c+n ( t c and t c+n together constitute the monthly charging plan for the slow-response energy storage in the current month), then obtain the charging duration of the slow-response energy storage nConsidering the charge-discharge efficiency of the energy storage system, the discharge duration of the slow-response energy storage can be determined. m On this basis, according to the λ´ d,t constituted typical predicted electricity cost parameter curve, the discharge plan of the slow-response energy storage is calculated.

[0111] Then, based on the monthly charge-discharge 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 consider the operation constraints and capacity constraints of the slow-response energy storage and the fast-response energy storage.

[0112] The optimal configuration and operation boundary conditions of the slow-response energy storage are as follows:

[0113] P ess,slow d,tc = P ess,slow d,tc+1 =···= P ess,slow d,t =···= P ess,slow d,tc+n

[0114] P ess,slow d,td = P ess,slow d,td+1 =···= P ess,slow d,t =···= P ess,slow d,td+m

[0115] ∫ tc+n tc P ess,slow d,t η c tdt +∫ td+m td P ess,slow d,td / η d tdt =0

[0116] – P ess,slow,cap < P ess,slow d,t <Pess,slow,cap

[0117] 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}

[0118] 0.1 E slow ≤ E slow d,t ≤ 0.9 E slow

[0119] E slow d,t = E slow d,t-1 + P ess,slow d,t η c △t , when t ∈( t c , t c+n )

[0120] E slow d,t = E slow d,t-1 + P ess,slow d,t / η d △t , when t ∈( t d , t d+m )

[0121] E slow d,t = E slow d,t-1, Others

[0122] Among them, P ess,slow d,tc 、 P ess,slow d,tc+1 、···、 P ess,slow d,t 、···、 P ess,slow d,tc+n respectively represent the output power of the slow-response energy storage at the d th day t c 、 t c +1、···、 t 、···、 t c + n moment, t c and t c+n respectively represent the start and end times of the charging of the slow-response energy storage, n represents the charging duration of the slow-response energy storage; P ess,slow d,td 、 P ess ,slow d,td+1 、···、 P ess,slow d,td+m respectively represent the output power of the slow-response energy storage at the d th day t d 、 t d +1、···、 t d + m moment, t d and t d+m respectively represent the start and end times of the discharging of the slow-response energy storage, m represents the discharging duration of the slow-response energy storage; η c and η d respectively represent the charging efficiency and discharging efficiency of the slow-response energy storage; P ess,slow,cap represents the maximum charge-discharge power of the slow-response energy storage; E slow represents the capacity of the slow-response energy storage, Eslow d,t and E slow d,t-1 respectively represent the energy stored in the slow response energy storage at the d th day t and t -1 moment; △t represents the optimization time interval, that is, half an hour.

[0123] The optimal configuration and operation boundary conditions of the fast response energy storage are as follows:

[0124] ∫ T 1 P ess,fast d,t tdt = 0

[0125] – P ess,fast,cap < P ess,fast d,t <P ess,fast,cap

[0126] E fast = 0.8∫ T / 2 1 P ess,fast,cap tdt

[0127] 0.1 E fast ≤ E fast d,t ≤ 0.9 E fast

[0128] E fast d,t = E fast d,t-1 + P ess,fast d,t △t

[0129] Among them, T represents the number of optimizations in a day, which is set to 48 in specific implementation, that is, optimize once every half an hour; P ess,fast d,t represents the output power of the fast response energy storage at the d th day t moment;P ess,fast,cap represents the maximum charge and discharge power of the fast response energy storage; E fast represents the capacity of the fast response energy storage, E fast d,t and E fast d,t-1 respectively represent the energy stored in the fast response energy storage at the d th t and t -1 moment; △t The optimization time interval is half an hour.

[0130] For the charge and discharge power of the slow response energy storage, the present invention adopts a constant power mode, that is, within the t c to t c+n time period, the charging power of the slow response energy storage remains constant, and within the t d to t d+m time period, the discharge power of the slow response energy storage remains constant. 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 return to the initial state after daily cycling. Therefore, the sum of the daily charge and discharge power should be 0, where the charge and discharge efficiency of the fast response energy storage approaches 1. At the same time, the charge and discharge 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 with the energy required or released under the condition of operating at the maximum charge and discharge power, and configures the capacity of the fast response energy storage with the energy required or released under the condition of operating at the maximum charge and discharge power for half a day. At the same time, the slow response energy storage and the fast response energy storage also need to follow the capacity constraint.

[0131] Finally, a capacity optimization configuration and operation model considering the optimization configuration and operation boundary conditions of the regional hybrid energy storage system is established. Based on the boundary conditions for the capacity optimization configuration and operation of the regional hybrid energy storage system, a bi-objective function evaluating the input cost of the regional hybrid energy storage operation and the net load fluctuation is constructed, and through the correlation coefficient, it is transformed into a single-objective function to obtain the capacity optimization configuration and operation model for the regional hybrid energy storage system, specifically as follows:

[0132] min - OF 1 + β × OF 2

[0133] OF1 = C w / o - C w

[0134] C w / o =∑ D d=1 ∑ T t=1 ( P loadD d,t λ D d,t △t )

[0135] 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 Δ t +C ess

[0136] C ess = C ess,slow + C ess,fast

[0137] C ess,slow = λ slow CRF μ slow E slow + ξ slow μ slow E slow

[0138] C ess,fast = λ fast ​CRF μ fast E fast + ξ fast μ fast E fast

[0139] λ slow CRF = r (1+ r ) Tslow / [(1+ r ) Tslow -1]

[0140] λ fast CRF = r (1+ r ) Tfast / [(1+ r ) Tfast -1]

[0141] P´ w d =∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ess,fast d,t ) / T

[0142] 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

[0143] OF 1 ≥0

[0144] OF 2 ≤ OF w / o 2 =∑ D d=1 [∑ T t=1 ( P loadD d,t - P´ w / o d ) 2 / T 1 / 2

[0145] P´ w / o d =∑ T t=1 P loadD d,t / T

[0146] Among them, OF 1 and OF 2 respectively represent the annual operating input cost of the regional hybrid energy storage system and the standard deviation of the annual net load power, 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 2 the correlation coefficient between them; C w / o and C w respectively represent the operating costs before and after configuring the regional hybrid energy storage system in the region; D represents the number of days in a year; T represents the number of optimizations in a day; λ D d,t and P loadD d,t respectively represent the electricity cost parameter and the net load power at the d th t day △t represents the optimization time interval; P ess,slow d,t and​P ess,fast d,t respectively represent the output powers of the slow-response energy storage and the fast-response energy storage at the d th t day and the C ess moment; C ess,slow and C ess,fast respectively represent the output costs of the slow-response energy storage and the fast-response energy storage; λ slow CRF and λ fast CRF respectively represent the capacity decay rates of the slow-response energy storage and the fast-response energy storage; μ slow and μ fast respectively represent the output costs per unit capacity of the slow-response energy storage and the fast-response energy storage; E slow and E fas respectively represent the capacities of the slow-response energy storage and the fast-response energy storage; ξ slow and ξ fast respectively represent the ratios of the annual operation and maintenance costs to the initial output costs of the slow-response energy storage and the fast-response energy storage; r represents the discount rate; T slow and T fast respectively represent the operation cycles of the slow-response energy storage and the fast-response energy storage; || || 2 2 represents the two-norm; P´ w / o d and P´ w d respectively represent the daily average net load powers before and after configuring the regional hybrid energy storage system in the regional configuration area; OF w / o 2 represents the standard deviation of the annual net load power when the regional hybrid energy storage system is not configured in the region.

[0147] The annual net load power and electricity cost parameters for 8,760 hours of the regional hybrid energy storage system are input into the capacity optimization configuration and operation model. After processing, the capacities and output powers of the slow-response energy storage and fast-response energy storage are output as the planning objectives and control instructions. The net load power and electricity cost parameters for each moment of each day of the regional hybrid energy storage system are determined based on the annual net load power and electricity cost parameters for 8,760 hours of the regional hybrid energy storage system. When solving, a solver is called to solve the model, and finally, the optimal configuration and operation control of the capacity of the regional hybrid energy storage system are realized.

[0148] The objective function of the capacity optimization configuration and operation model for the regional hybrid energy storage system is divided into two parts. The first part is to maximize the annual operation input cost after configuring the hybrid energy storage in the region, and the second part is to minimize the impact of the net load fluctuation after configuring the hybrid energy storage in the region on the grid-connected system. This impact can be evaluated using the standard deviation of the annual net load power in the region. To balance the operation input cost after configuring energy storage in the region and the impact of net load fluctuation on the grid-connected system, the present invention introduces a correlation coefficient. β Convert the multi-objective function of the capacity optimization configuration and operation model for the regional hybrid energy storage system into a single objective function.

[0149] Among them, the standard deviation of the annual net load power in the region OF 2 The specific calculation is as follows:

[0150] 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

[0151] P´ w d =∑ T t=1 ( P loadD d,t + P ess,slow d,t +P ​ess,fast d,t ) / T

[0152] However, since OF 2 is a non-linear objective function and cannot be directly solved by a solver, it is therefore converted into a norm form for solution.

[0153] The constraint conditions of the capacity optimization configuration and operation model of the regional hybrid energy storage system are as shown in the boundary conditions. In addition, to ensure the enthusiasm for configuring the regional hybrid energy storage system, the annual operating input cost after configuring the regional hybrid energy storage should be greater than zero. At the same time, after configuring the regional hybrid energy storage system, the fluctuation of its net load power should not affect the normal operation of the grid-connected system, that is, the fluctuation of the net load power should not be more serious than when the hybrid energy storage system is not configured.

[0154] In specific implementation, the present invention analyzes the influence of the value of the correlation coefficient β on the objective function value as follows:

[0155] As Figure 2 shown, it is the objective function of the capacity optimization configuration and operation model of the regional hybrid energy storage system proposed by the present invention OF 1 and OF 2 and its Pareto optimal solution sets under different values of the correlation coefficient β (this solution set is usually used to describe the possible optimal selection range in a multi-objective optimization problem, which includes the trade-off between different objectives and multiple possible optimal solutions). Based on the model constraint conditions OF 1 ≥ 0 and OF 2 ≤ OF w / o 2 boundary restrictions, within the feasible region formed by these constraints, each solution is a feasible solution, which can meet the optimization objectives and take into account the multi-objective balance. These solutions not only provide diverse choices for the capacity configuration of the regional hybrid energy storage system, but also provide a theoretical basis for formulating operation strategies, thereby verifying the feasibility and flexibility of the proposed model in practical applications. As shown in Table 2, it gives Figure 2The objective function values of Scenarios A, B, and C shown in the figure, where the regional operation input cost of Scenario A is the largest, at 10425.6664, but at this time the net load power fluctuation is the largest; the net load power fluctuation of Scenario C is the smallest, at 24103.6469 kW, but at this time the operation input cost is the smallest. The present invention selects the relatively balanced Scenario 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 31611.0366 kW respectively. Compared with Scenario A, the regional operation input cost of Scenario B is reduced by 45.31%, and the regional net load power fluctuation is reduced by 33.78%. Compared with Scenario B, the regional input cost of Scenario C is reduced by 98.13%, and the regional load fluctuation is reduced by 23.75%. This shows that β the value of the correlation coefficient

[0156] Table 2

[0157]

[0158] The present invention also conducts an analysis of the annual charge and discharge operation strategy of the hybrid energy storage system as follows:

[0159] As Figure 3 shown, it shows the net load power and charge and discharge power of Scenario A. At this time, the system capacity configuration of the slow-response energy storage is 2577 kWh, and the maximum charge and discharge power is 401 kW. There is no intention to configure the fast-response energy storage, that is, the system capacity and the maximum charge and discharge power of the fast-response energy storage are both 0. Figure 3 The (a) of Figure 3 shows the net load power of the region after configuring the hybrid energy storage system. Comparing Figure 3 with the (b) of Figure 3 which shows the net load power of the region without configuring the hybrid energy storage system, it can be seen that the net load power fluctuation has not been improved. This is because Scenario A pays more attention to improving the operation input cost and ignores improving the net load power fluctuation.

[0160] As Figure 4 shown, it shows the net load power and charge and discharge power of Scenario C. At this time, the system capacity configuration of the slow-response energy storage is 1240 kWh, and the maximum charge and discharge power is 193 kW. The system capacity configuration of the fast-response energy storage is 369 kWh, and the maximum charge and discharge power is 25 kW. Figure 4Figure (a) shows the net load power of the area after configuring the hybrid energy storage system. Comparing with Figure 4 Figure (b) which is the net load power without the hybrid energy storage system configured, it can be clearly seen that the fluctuation of the net load power has been greatly improved. This is because compared with increasing the operation input cost, Scheme C pays more attention to improving the fluctuation of the net load power. Figure 4 Figure (c) and Figure 4 Figure (d) respectively show the charge and discharge strategies of the slow-response energy storage and the fast-response energy storage corresponding to 8760 hours under Scheme C. It can be seen that both the slow-response energy storage and the fast-response energy storage actively participate in regulating the fluctuation of the net load power of the area.

[0161] The present invention also conducts an analysis on the influence of the operation cost of the fast-response energy storage on the objective function value as follows:

[0162] With the development of energy storage technology in the future, the operation cost of the energy storage system will gradually decrease. Therefore, the present invention designs three cases for comparative analysis. In the first case, the operation cost of the fast-response energy storage is set to 200 / kWh. In the second case, the operation cost of the fast-response energy storage is set to 150 / kWh. In the third case, the operation cost of the fast-response energy storage is set to 100 / kWh. As Figure 5 shown, the Pareto optimal solution sets for different correlation coefficient values in the three cases are given. It can be seen that Case 3 has the best effect in both maximizing the operation input cost of the area and minimizing the fluctuation of the net load power. By comparing Case 1 and Case 2, it can be obtained that under the expectation of a relatively high operation input cost, the Pareto curves of Case 1 and Case 2 almost coincide. This is because when the operation cost of the fast-response energy storage is too high, its configuration willingness is low. Under the expectation of a relatively small net load fluctuation, the effect of Case 2 is better. This is because the operation cost of the fast-response energy storage is low, and it can sacrifice more profit space to reduce the net load fluctuation. Table 3 gives Figure 5 the operation input cost and the net load power fluctuation in different cases. It can be seen from Table 3 that the maximum operation input cost of Case 3 (corresponding to Scheme A 3 ) is significantly higher than the maximum operation input costs of the other two cases (corresponding to Scheme A 1 and A 2 ). Similarly, it can be obtained that Case 3 also has more advantages in regulating the net load power fluctuation (corresponding to Scheme B 3 ). As Figure 6 Figure (a), Figure 6 Figure (b), Figure 6 Figure (c) and Figure 6As shown in Fig. (d). In addition, by comparing Cases 1-3, it can be seen that under the minimum net load power scenario, as the operating cost of the fast-response energy storage decreases, the proportion of the slow-response energy storage gradually decreases. This is because the ability of the slow-response energy storage to regulate the fluctuation of the net load power is significantly inferior to that of the fast-response energy storage. However, the high operating cost of the fast-response energy storage usually restricts the capacity configuration of the fast-response energy storage.

[0163] Table 3

[0164]

[0165] The present invention also analyzed the influence of the value of the β correlation coefficient on the capacity configuration of the hybrid energy storage system as follows:

[0166] Figure 7 Fig. (a) shows the system capacity configuration and the maximum charge-discharge power of the slow-response energy storage and the fast-response energy storage under different values of the correlation coefficient. As the correlation coefficient β increases, both the system capacity configuration and the maximum charge-discharge power of the slow-response energy storage gradually decrease. While the system capacity configuration and the maximum charge-discharge power of the fast-response energy storage increase as the correlation coefficient β increases, showing an opposite trend. This is because the main role of the fast-response energy storage is to participate in regulating the real-time fluctuation of the net load power, and its operating cost is relatively high. Therefore, when the correlation coefficient β is relatively small (at this time, the priority of the regional operation input cost is relatively high), its configuration willingness is relatively low. And when the correlation coefficient β increases to a certain extent (at this time, the priority of suppressing the fluctuation of the net load power is relatively high), the region will start to configure the fast-response energy storage. Therefore, when the correlation coefficient β is between 0.3 and 0.7, the system capacity configuration and the maximum charge-discharge power of its fast-response energy storage approach 0 and then gradually increase. As shown in Figure 7 Fig. (b), there is obviously an inflection point in the net load power curve. This is because there is a limit to regulating the load fluctuation by configuring the slow-response energy storage. In order to better suppress the fluctuation of the net load power, it is necessary to gradually increase the system configuration capacity of the fast-response energy storage.

[0167] The present invention also analyzed the influence of different energy storage system configuration schemes on regulating the fluctuation of the net load power as follows:

[0168] The present invention further comparatively analyzed three energy storage configuration schemes: 1) configuring a hybrid energy storage system including a slow-response energy storage and a fast-response energy storage; 2) configuring a slow-response energy storage; 3) configuring a fast-response energy storage. As shown in Figure 8As shown, configuring slow-response energy storage can obtain a certain regional operation input cost and regulate the net load power fluctuation. However, its regulation ability is limited, and it can only regulate the net load power fluctuation to 28,900 kW at most. While configuring fast-response energy storage can significantly improve the regulation effect on the net load power fluctuation, but the high operation cost makes it impossible to obtain a net input cost by using the peak-valley electricity cost parameter difference. Configuring a hybrid energy storage system can combine the advantages of both, and thus achieve a higher operation input cost and the effect of regulating the net load power fluctuation.

[0169] The present invention also conducts the sustainability analysis of the proposed capacity optimization configuration scheme as follows:

[0170] To verify the sustainability of the method proposed by the present invention, the present invention uses the net load power data of more than 300 users in a certain two years as prior data to determine the system capacity of the configured hybrid energy storage system, and then verifies it based on the net load power data of the users in the next two years of this region as posterior data. As Figure 9 shown in (a) of, the Pareto optimal solution set based on prior data generated by the method proposed by the present invention is given. With the continuous deployment of the photovoltaic power generation system, the regional net load power fluctuation will become more intense. Therefore, the present invention improves the priority of regulating the net load power fluctuation and selects a scheme more suitable for future photovoltaic development (i.e., Figure 9 the pentagram test point in (a) of). At this time, the system capacity configuration of the slow-response energy storage is 1,109 kWh, and the system capacity configuration of the fast-response energy storage is 47 kWh. As Figure 9 shown in (b) of, the configuration scheme at the pentagram is verified in the posterior data. It can be seen that even under the maximization of the regional operation input cost (about 4,808), this scheme can effectively improve the net load power fluctuation. Even under the minimization of the regional net load power fluctuation (about 28,322 kW), this 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 by the present invention has sustainable technical value.

[0171] 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 establishment 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, as well as the annual net load power and electricity cost parameters. The model establishment unit is used to establish a slow-response energy storage operation model for the slow-response energy storage, and construct optimization 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, so as to establish a capacity optimization configuration and operation model considering 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 output the capacity and output power of the slow-response energy storage and the fast-response energy storage as control instructions after processing, so as to realize the optimization configuration and operation control of the capacity of the regional hybrid energy storage system.

[0172] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0173] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the descriptions in the method embodiments. The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of the present invention. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0174] Correspondingly, the present invention also provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement a method for capacity optimization configuration and operation of a regional hybrid energy storage system as described above.

[0175] Correspondingly, the present invention also provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, a method for capacity optimization configuration and operation of a regional hybrid energy storage system as described above is implemented.

[0176] Other embodiments of the present application will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only illustrative, and the true scope and spirit of the present application are pointed out by the following claims.

[0177] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited 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 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 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; 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; Determine the monthly charge and discharge time of the slow response energy storage in the current month according to the start and end time of the charge and discharge of the slow response energy storage; 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.

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 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.

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 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 to 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.

5. The capacity optimization configuration and operation method for a regional hybrid energy storage system according to claim 1, 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.

6. A capacity optimization configuration and operation device for a regional hybrid energy storage system according to any one of the methods described in claims 1-5, 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.

7. 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 5.

8. 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 5 is implemented.

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