New energy station energy storage capacity optimization method and system

Abstract

The invention discloses a new energy station energy storage capacity optimization method and system, and relates to the technical field of novel power system energy storage planning, and the method comprises the steps: building a fuzzy uncertainty set of new energy output based on a Wasserstein distance method, forming an uncertainty set of a distribution robust optimization model, and enabling the uncertainty set to be used for describing the possible distribution range of the new energy output; establishing an energy storage configuration model under multiple time scales, wherein the energy storage configuration model comprises an investment decision-making layer, a day-ahead operation scheduling strategy decision-making layer and an intra-day operation simulation layer; a dual theorem and a CVaR theorem are adopted to convert distribution robust opportunity constraints, an original problem is converted into a cone linear programming problem which can be solved directly, and a particle swarm algorithm is adopted to solve an energy storage investment decision-making layer; and in the day-ahead scheduling operation stage, modeling is carried out based on a YALMIP toolbox, a CPLEX solver is called to realize solution, and finally, an optimal energy storage capacity configuration result is output. The potential of each flexible resource on the power generation side of the power system is fully played.

Description

Technical Field

[0001] The present invention relates to the technical field of new power system energy storage planning, and in particular to a method and system for optimizing energy storage capacity of a new energy station. Background Art

[0002] With the large-scale access of new energy to the power grid, the inherent uncertainty and volatility of new energy have brought new challenges to the safe and stable operation of the power system. As a major flexibility resource, energy storage is of great significance to promoting the consumption of new energy and ensuring the safe and stable operation of the power system.

[0003] The planning of energy storage capacity is a complex optimization problem involving the economic efficiency of energy storage devices, system operation flexibility, and uncertainty in the output of new energy. At present, the investment cost of energy storage is still relatively high. From a technical and economic perspective, it is difficult to rely solely on large-scale energy storage to meet the requirements of system flexibility and smooth the fluctuations of new energy. Therefore, in the process of energy storage planning, the impact of existing adjustable traditional generators on flexibility should be taken into account, and appropriate wind and solar power abandonment or load shedding should be taken into account to reduce the system's demand for flexibility.

[0004] In view of the uncertainty of renewable energy output, research methods can be divided into three categories: stochastic optimization (SP), robust optimization (RO) and distributed robust optimization (DRO). Stochastic optimization must assume that random variables obey a certain deterministic distribution, but for wind and solar prediction errors, it is difficult to obtain their probability distribution in practice. Robust optimization uses data sets to describe random variables and solves the worst scenarios, and its results are often too conservative. Distributed robust optimization overcomes the shortcomings of stochastic optimization and robust optimization. There is no need to set the type and parameters of probability distribution. It establishes a fuzzy set of probability distribution based on data and makes decisions based on the worst probability distribution in the fuzzy set. Distributed robust optimization has been applied in the fields of unit combination, multi-energy complementarity, reactive power optimization, and optimal power flow of power systems.

[0005] Based on the distributed robust optimization method, Yang Libin et al. considered the uncertainty of wind and solar output in “Storage capacity configuration method for wind farms considering wind power uncertainty and wind curtailment rate constraints” in Automation of Electric Power Systems, 2020, 44(16):45-52. Taking the wind curtailment rate as a constraint, a storage configuration method for wind farms was proposed. GUO Zhongjie et al. considered line power loss and voltage distribution in “Sizing energy storage to reduce renewable power curtailment considering network power flows: adistributionally robust optimisation approach” in IET Renewable Power Generation, 2020, 14(16):3273-3280, and introduced AC power flow into the energy storage planning model. Si Yang et al. proposed a method for optimizing the hydrogen storage capacity of a wind-hydrogen hybrid system considering the uncertainty of thermal balance in “Optimal configuration of hydrogen storage capacity for a wind-hydrogen hybrid system based on distributed robust optimization” in Electric Power Automation Equipment, 2021, 41(10):3-10. Li Xiaozhu et al. proposed a Bayesian-based distributed robust optimization method in "Application of distributed robust optimization in energy storage configuration" in Power System Technology, 2021, 1-11, to solve the problem of energy storage power station configuration with stable support requirements. However, the flexibility requirements of the system are not considered in the above energy storage planning.

[0006] Based on the above problems, this paper comprehensively considers the cost of energy storage devices, the flexibility of system operation and the uncertainty of renewable energy output, and proposes a method for energy storage capacity planning of power systems based on distributed robust optimization. First, a fuzzy uncertain set of renewable energy output is constructed based on the Wasserstein distance method; secondly, a multi-stage energy storage capacity configuration model is established. The first stage considers the investment cost of energy storage, the second stage considers the uncertainty of renewable energy output, and the third stage considers the flexibility of system operation; then, the duality theorem and CVaR theorem are applied to approximate the transformation of the objective function and constraints, and different algorithms or solvers are selected to solve the optimization problems according to the specific manifestations of the optimization problems at different stages; finally, an example analysis is carried out based on the IEEE-RTS24 node system, and the energy storage configuration results of the proposed planning method are compared with those of random optimization and robust optimization, and the influence of the number of model training samples and the radius of the Wasserstein ball on the solution time and planning results is analyzed. Summary of the invention

[0007] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0008] In view of the above existing problems, the present invention is proposed.

[0009] Therefore, the present invention provides a method and system for optimizing energy storage capacity of a new energy station, which can solve the problems mentioned in the background technology.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0011] In a first aspect, the present invention provides a method for optimizing energy storage capacity of a new energy station, which comprises constructing a fuzzy uncertainty set of new energy output based on a Wasserstein distance method to form an uncertainty set of a distributed robust optimization model, wherein the uncertainty set is used to describe a possible distribution range of new energy output;

[0012] Establishing a multi-time scale energy storage configuration model, the energy storage configuration model includes an investment decision layer, a day-ahead operation scheduling strategy decision layer and an intraday operation simulation layer;

[0013] The duality theorem and CVaR theorem are used to transform the distributed robust chance constraints, transforming the original problem into a cone linear programming problem that can be directly solved, and the particle swarm algorithm is used to solve the energy storage investment decision layer;

[0014] In the day-ahead scheduling operation phase, the model is built based on the YALMIP toolbox, and the CPLEX solver is called to solve the problem, ultimately outputting the optimal energy storage capacity configuration result.

[0015] As a preferred solution of the method for optimizing energy storage capacity of new energy stations of the present invention, the fuzzy uncertain set for constructing new energy output includes:

[0016] Collect historical data on renewable energy output, generate a renewable energy output data set, and divide it into a training set and a test set;

[0017] A fuzzy uncertainty set of renewable energy output is defined, wherein the uncertainty set is based on Wasserstein distance metric and includes a cluster of probability distribution functions close to the empirical reference distribution distance;

[0018] According to the fuzzy uncertainty set of the new energy output, the objective function of the distributed robust optimization model is constructed.

[0019] As a preferred solution of the method for optimizing energy storage capacity of new energy stations of the present invention, the energy storage configuration model includes:

[0020] At the investment decision-making level, the energy storage capacity to be planned is used as the decision variable to minimize the equal annual investment cost of the energy storage investment capacity and meet the upper and lower limit constraints of the energy storage system capacity;

[0021] In the day-ahead scheduling strategy decision layer, a two-layer optimization model is established, wherein the two-layer optimization model includes an outer layer optimization and an inner layer optimization;

[0022] The outer optimization uses the pre-dispatched output of the thermal power unit and the energy storage system as decision variables, and the objective function includes the start-up and shutdown costs of the thermal power unit, the fuel cost, the cost of the unit providing ramp spare capacity, and the operating cost of the energy storage;

[0023] The inner optimization adopts the real-time adjustment amount of the thermal power unit in response to the wind and solar power forecast error as the decision variable, and the objective function includes the expected value of the output adjustment cost of the thermal power unit under a given probability distribution;

[0024] In the intraday operation simulation layer, based on the unit arrangement obtained in the day-ahead operation scheduling stage, the penalty for insufficient intraday flexibility is taken into account, and the calculation method is based on the scheduling results of the intraday operation simulation layer and the new energy output test set.

[0025] As a preferred solution of the method for optimizing energy storage capacity of new energy stations of the present invention, the process of outputting the optimal energy storage capacity configuration result includes:

[0026] The original joint opportunity constraint is decomposed into multiple opportunity constraints by using Bonferroni inequality, and the opportunity constraint is transformed into a linear constraint by using CVaR theorem;

[0027] The transformed linear constraint problem is transformed into a cone linear programming problem which can be solved directly;

[0028] A particle swarm algorithm is used to solve the investment decision layer, and the fitness function input into the particle swarm algorithm is the objective function of the energy storage configuration model;

[0029] Based on the YALMIP toolbox modeling, the CPLEX solver is called to solve the day-ahead scheduling operation phase. The input data to the solver include the day-ahead arrangement of energy storage, the start-up and shutdown plan of the unit, and the expected output of the unit.

[0030] According to the day-ahead dispatch results and the new energy output test set, the penalty cost of insufficient flexibility in the intraday operation simulation layer is calculated;

[0031] The particle positions and velocities in the particle swarm algorithm are updated according to the calculation results, and the solution process is repeated until the convergence conditions are met, and the optimal energy storage capacity configuration result is output.

[0032] As a preferred solution of the method for optimizing energy storage capacity of new energy stations described in the present invention, the constraints of the outer optimization include:

[0033] The time constraints and cost constraints for unit startup and shutdown are as follows:

[0034]

[0035] Among them, T S ,T O They are the minimum shutdown time and the minimum startup time of the unit, u g,i is the start and stop state of unit g in period i, and the start state u g,i =1, shutdown state u g,i =0,H g ,J g are the single startup cost and shutdown cost of unit g respectively;

[0036] The ramp constraints of the unit and the output constraints considering the ramp reserve of the unit are as follows:

[0037]

[0038] in, is the maximum ramp rate of unit g per unit time, P g are the maximum and minimum outputs of unit g respectively;

[0039] The state of charge constraints of the energy storage system are as follows:

[0040]

[0041] Among them, SOC k,t-1 , SOC k,t , SOC k,min , SOC k,max are the state of charge and its upper and lower limits of the kth energy storage system at time t-1 and t, respectively. k,T , SOC k,0 are the charge states of the kth energy storage system in the T period and the starting period, respectively; η is the charging and discharging efficiency of the energy storage system, is the charging and discharging power of the energy storage system k at time t, discharging is positive and charging is negative, is the maximum charging and discharging power of energy storage system k, ΔT is the time interval;

[0042] The energy state of the energy storage system must be equal at the beginning and end of the scheduling cycle, subject to the following constraints:

[0043] SOC k,T=SOC k,0

[0044] The maximum charging and discharging power constraints of energy storage are as follows:

[0045]

[0046] In a scheduling cycle, the constraints on the number of charge and discharge times of the energy storage system are as follows:

[0047]

[0048] Among them, X k,t , Y k,t The energy storage system k is in the charging state and discharging state at time t, respectively. The charging state X k,t =1,Y k,t =0, discharge state X k,t =0, Y k,t =1, N1, N2 are the limits on the number of times the energy storage system can be charged and discharged.

[0049] As a preferred solution of the method for optimizing energy storage capacity of new energy stations of the present invention, the constraints of the inner optimization include:

[0050] The power balance constraints of the system are as follows:

[0051]

[0052] The opportunity constraint of adjusting the output of the unit in real time in response to the wind and solar forecast error, and the probability of adjusting the output in real time to meet the constraint condition is not less than 1-α gen , α gen It indicates the significance level of the real-time output adjustment constraint of the unit, that is:

[0053]

[0054] The opportunity constraint of DC power flow security based on the generation transfer distribution factor and the probability that the line capacity does not exceed the limit is not less than 1-α grid , α grid It represents the significance level of DC power flow security constraint, namely:

[0055]

[0056] in, is the output power of the kth renewable energy station at time t, μ t is the per-unit value of the predicted output power at time t, ξ t is the per-unit value of the prediction error of the output power at time t, is the rated capacity of the new energy station, t is a random variable, For t The probability distribution function, N is the number of network nodes, p n,t is the load of node n at time t, L is the number of network lines, is the maximum transmission capacity of line l, Q g,l , Q k,l , Q n,l are the impact of the injected power of thermal power units, new energy stations and load nodes on line l, Q g,l The calculation formula is as follows:

[0057]

[0058] Among them, Δp l is the change in power flow on line l, Δp g is the output change of generator g, a and b are the nodes at both ends of line l, X ag , X bg are the elements in the ath row and gth column and the bth row and gth column of the node impedance matrix, respectively. l is the impedance of line l.

[0059] As a preferred solution of the method for optimizing energy storage capacity of new energy stations of the present invention, the objective function of the intraday operation simulation layer is as follows:

[0060]

[0061] Among them, c up 、c dn They are the penalty coefficients for insufficient flexibility increase and decrease; are the load shedding and wind and solar power abandonment caused by insufficient system flexibility at time t, respectively. The calculation formula is as follows:

[0062]

[0063] Among them, ΔP L,t is the increase or decrease of the net load within the scheduling interval ΔT, and ΔP L,t Take positive value, are the upward or downward climbing rate limits that the thermal power unit and the energy storage system can provide during the dispatching cycle at time t, and are the maximum charging and discharging power of energy storage respectively.

[0064] In a second aspect, the present invention provides a new energy station energy storage capacity optimization system, which includes: an uncertainty set construction module, a multi-time scale energy storage configuration model construction module, a distributed blue opportunity constraint conversion module and a day-ahead scheduling optimization solution module;

[0065] The uncertainty set construction module is used to construct a fuzzy uncertainty set of new energy output based on the Wasserstein distance method to form an uncertainty set of a distributed robust optimization model, and the uncertainty set is used to describe the possible distribution range of new energy output;

[0066] The multi-time scale energy storage configuration model building module is used to establish an energy storage configuration model under multiple time scales, and the energy storage configuration model includes an investment decision layer, a day-ahead operation scheduling strategy decision layer and an intraday operation simulation layer;

[0067] The distributed robust opportunity constraint conversion module is used to convert the distributed robust opportunity constraint using the duality theorem and the CVaR theorem, convert the original problem into a cone linear programming problem that can be directly solved, and use the particle swarm algorithm to solve the energy storage investment decision layer;

[0068] The day-ahead scheduling optimization solution module is used to model based on the YALMIP toolbox during the day-ahead scheduling operation phase, call the CPLEX solver to solve, and finally output the optimal energy storage capacity configuration result.

[0069] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of a method for optimizing energy storage capacity of a new energy station are implemented.

[0070] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of a method for optimizing energy storage capacity of a new energy station are implemented.

[0071] Compared with the prior art, the beneficial effect of the present invention is to give full play to the potential of various flexible resources on the power generation side of the power system, not relying solely on energy storage to solve the problem of insufficient flexibility, and making the energy storage configuration more economical and more practical; the energy storage planning scheme based on distributionally robust optimization has better robustness than random optimization, and better economy than robust optimization, so it can better balance robustness and economy; the number of samples and the radius of the Wasserstein sphere of distributionally robust optimization have a certain degree of influence on the model solution time and results, so it is necessary to select appropriate parameter values. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0073] Figure 1 A calculation flow chart of a method and system for optimizing energy storage capacity of a new energy station provided by an embodiment of the present invention;

[0074] Figure 2 An internal structure diagram of a computer device of a new energy station energy storage capacity optimization method and system provided by an embodiment of the present invention;

[0075] Figure 3 A curve diagram showing changes in the total system cost, pre-dispatch cost, penalty for insufficient flexibility and investment cost of energy storage versus the energy storage configuration capacity of a single wind farm for a method and system for optimizing energy storage capacity of a new energy station provided by one embodiment of the present invention;

[0076] Figure 4 A method for optimizing energy storage capacity of a new energy station and a curve diagram of wind abandonment (load shedding) power versus energy storage configuration capacity provided by an embodiment of the present invention;

[0077] Figure 5 A graph showing the influence of the number of training samples N on the calculation time in a multi-stage distributed robust model of a new energy station energy storage capacity optimization method and system provided by an embodiment of the present invention;

[0078] Figure 6 A diagram showing the influence of the number of training samples N and the Wasserstein sphere radius ρ on the energy storage configuration results of a new energy station energy storage capacity optimization method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0079] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0080] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0081] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0082] Example 1

[0083] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, and provides a method for optimizing energy storage capacity of a new energy station, comprising:

[0084] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to explain in detail how to implement the method for optimizing the energy storage capacity of the new energy station;

[0085] Figure 1 A method flow chart of a new energy station energy storage capacity optimization method and system is shown, including:

[0086] S1: Based on the Wasserstein distance method, a fuzzy uncertainty set of new energy output is constructed to form an uncertainty set of the distributed robust optimization model, where the uncertainty set is used to describe the possible distribution range of new energy output;

[0087] Furthermore, historical data of renewable energy output is collected to generate a renewable energy output data set, which is divided into a training set and a test set;

[0088] Define a fuzzy uncertainty set of renewable energy output. The uncertainty set is based on Wasserstein distance metric and contains a cluster of probability distribution functions close to the empirical reference distribution. The optimization results under the worst distribution function are studied.

[0089] According to the fuzzy uncertainty set of the new energy output, the objective function of the distributed robust optimization model is constructed. The objective function considers a cluster of probability distribution functions close to the empirical reference distribution distance, aiming to find the optimal solution under the worst distribution function; the mathematical model of distributed robust optimization is as follows:

[0090]

[0091] Among them, x is the decision variable of the first stage, X is the feasible domain of x, g(x) is the objective function of the first stage, ξ is the uncertain variable in the model, is the probability distribution function of ξ, is the fuzzy uncertain set containing the true distribution, y is the decision variable of the second stage, Ω(x,ξ) is the feasible domain of y, f(y) is the objective function of the second stage, (·) is the distribution The expected value of the objective function;

[0092] The fuzzy uncertain set is constructed based on the Wasserstein distance. W(1,2) is the Wasserstein distance between distributions 1 and 2. The calculation formula is as follows:

[0093]

[0094] Among them, Π is and The joint distribution of and The Wasserstein distance between Move to The minimum cost of ||ξ1-ξ2|| is the reference distance from ξ1 to ξ2, and the fuzzy uncertain set is defined as:

[0095]

[0096] in, is the set of all distributions on a polyhedron, distributed Based on empirical distribution The Wasserstein sphere with ρ as the radius and the radius ρ of the Wasserstein sphere controls the conservativeness of the distribution of the Robust problem. It is necessary to select a suitable radius so that the uncertainty set Include as much of the unknown true distribution as possible.

[0097] S2: Establish a multi-time scale energy storage configuration model, including investment decision-making layer, day-ahead operation scheduling strategy decision-making layer and intraday operation simulation layer;

[0098] Furthermore, at the investment decision layer, the energy storage capacity to be planned is used as the decision variable to minimize the equal annual investment cost of the energy storage investment capacity while satisfying the upper and lower limit constraints of the energy storage system capacity; the objective function of the energy storage planning model is established as:

[0099] minC Total =C inv +T D (C op +C risk ) (4)

[0100] Among them, C Total is the annual comprehensive cost of the system, C inv C is the annual investment cost of the energy storage system investment capacity; op is the operation and scheduling cost of the day-ahead system, C risk is the cost of inflexibility, T D is the number of days in a year;

[0101] The formula for calculating the equivalent annual investment cost of minimizing energy storage investment capacity is as follows:

[0102]

[0103] Where K is the number of energy storage to be planned, r is the discount rate, and y ESS is the actual operating life of the energy storage system, C ESS is the investment cost per unit capacity of the energy storage system, is the rated capacity of the kth energy storage system to be planned;

[0104] The constraints are the upper and lower limits of the capacity of the energy storage system to be planned as follows:

[0105]

[0106] in, They are the upper and lower limits of the required energy storage configuration capacity respectively.

[0107] Furthermore, a two-layer optimization model is established at the day-ahead scheduling strategy decision layer; the objective function of the day-ahead scheduling is as follows:

[0108]

[0109] Among them, G is the set of all thermal power units, T is the unit operation period, T = 24, p g,t is the pre-dispatched output of unit g in period t, Δp g,t In order to adjust the output in response to the uncertainty of new energy output, Provide the upward and downward climbing costs for unit g respectively, are the upward and downward climbing capacities of unit g in period t, respectively. are the startup cost and shutdown cost of unit g in period t, c ess is the operating cost of the energy storage system, is the output of energy storage system k in period t, is the probability distribution function of the output error of renewable energy, F g (·) is the coal consumption function of unit g, which can be expressed by the quadratic function of its output. The calculation formula is as follows:

[0110]

[0111] Furthermore, the outer optimization uses the pre-dispatched output of thermal power units and energy storage systems as decision variables, and the objective function includes the start-up and shutdown costs of thermal power units, fuel costs, the cost of providing ramp backup capacity for units, and the operating costs of energy storage; the constraints of the outer optimization include,

[0112] The time constraints and cost constraints for unit startup and shutdown are as follows:

[0113]

[0114] Among them, T S ,TO They are the minimum shutdown time and the minimum startup time of the unit, u g,i is the start and stop state of unit g in period i, and the start state u g,i =1, shutdown state u g,i =0,H g ,J g are the single startup cost and shutdown cost of unit g respectively;

[0115] The ramp constraints of the unit and the output constraints considering the ramp reserve of the unit are as follows:

[0116]

[0117] in, is the maximum ramp rate of unit g per unit time, P g are the maximum and minimum outputs of unit g respectively;

[0118] The state of charge constraints of the energy storage system are as follows:

[0119]

[0120] Among them, SOC k,t-1 , SOC k,t , SOC k,min , SOC k,max are the state of charge and its upper and lower limits of the kth energy storage system at time t-1 and t, respectively. k,T , SOC k,0 are the charge states of the kth energy storage system in the T period and the starting period, respectively; η is the charging and discharging efficiency of the energy storage system, is the charging and discharging power of the energy storage system k at time t, discharging is positive and charging is negative, is the maximum charging and discharging power of energy storage system k, ΔT is the time interval;

[0121] The energy state of the energy storage system must be equal at the beginning and end of the scheduling cycle, subject to the following constraints:

[0122] SOC k,T =SOC k,0 (15)

[0123] The maximum charging and discharging power constraints of energy storage are as follows:

[0124]

[0125] In a scheduling cycle, the constraints on the number of charge and discharge times of the energy storage system are as follows:

[0126]

[0127] Among them, X k,t , Y k,t The energy storage system k is in the charging state and discharging state at time t, respectively. The charging state X k,t =1,Y k,t =0, discharge state X k,t =0, Y k,t =1, N1, N2 are the limits on the number of times the energy storage system can be charged and discharged.

[0128] Furthermore, the inner optimization takes the real-time adjustment amount of the thermal power unit in response to the wind and solar forecast error as the decision variable, and the objective function is the expected value of the output adjustment cost of the thermal power unit under a given probability distribution;

[0129] The constraints for inner optimization include:

[0130] The power balance constraints of the system are as follows:

[0131]

[0132] The opportunity constraint for real-time output adjustment of the unit in response to the wind and solar forecast error describes the probability that the real-time output adjustment meets the constraint condition is not less than 1-α gen , α gen It indicates the significance level of the real-time output adjustment constraint of the unit, that is:

[0133]

[0134] The opportunity constraint of DC power flow security based on the Generation Transfer Distribution Factor (GSDF) describes the probability that the line capacity does not exceed the limit is not less than 1-α grid , α grid It represents the significance level of DC power flow security constraint, namely:

[0135]

[0136] in, is the output power of the kth renewable energy station at time t, μ t is the per-unit value of the predicted output power at time t, ξ t is the per-unit value of the prediction error of the output power at time t, is the rated capacity of the new energy station, t is a random variable, For t The probability distribution function, N is the number of network nodes, p n,t is the load of node n at time t, L is the number of network lines, is the maximum transmission capacity of line l, Q g,l , Q k,l , Qn,l are the impact of the injected power of thermal power units, new energy stations and load nodes on line l, Q g,l The calculation formula is as follows:

[0137]

[0138] Among them, Δp l is the change in power flow on line l, Δp g is the output change of generator g, a and b are the nodes at both ends of line l, X ag , X bg are the elements in the ath row and gth column and the bth row and gth column of the node impedance matrix, respectively. l is the impedance of line l, Q k,l and Q n,l The calculation method and Q g,l same;

[0139] The two-layer optimization model needs to meet the constraints of unit start and shutdown, unit operation domain constraints, energy storage system constraints, system power balance constraints, real-time output adjustment opportunity constraints and DC power flow safety opportunity constraints.

[0140] Furthermore, in the intraday operation simulation layer, based on the unit arrangement obtained in the day-ahead operation scheduling stage, the penalty for insufficient intraday flexibility is considered, and the objective function is to minimize the cost of insufficient system operation flexibility;

[0141] The flexibility deficiency penalty includes the penalty cost of insufficient flexibility upward adjustment and insufficient flexibility downward adjustment, and the calculation method is based on the dispatch results of the intraday operation simulation layer and the new energy output test set;

[0142] The objective function of the simulation layer is:

[0143]

[0144] Among them, c up 、c dn They are the penalty coefficients for insufficient flexibility increase and decrease; are the load shedding and wind and solar power abandonment caused by insufficient system flexibility at time t, respectively. The calculation formula is as follows:

[0145]

[0146]

[0147] Among them, ΔP L,t is the increase or decrease of the net load within the scheduling interval ΔT, and ΔP L,t Take positive value, are the upward or downward climbing rate limits that the thermal power unit and the energy storage system can provide during the dispatching cycle at time t, and are the maximum charging and discharging power of energy storage respectively;

[0148] When the upward reserve of thermal power units and energy storage systems cannot meet the increase in net load, the system's upward flexibility is insufficient and load shedding will occur; when the downward reserve of thermal power units and energy storage systems cannot meet the decrease in net load, the system's downward flexibility is insufficient and wind or solar power abandonment will occur.

[0149] S3: The duality theorem and CVaR theorem are used to approximate the distributed robust chance constraints, transforming the original problem into a cone linear programming problem that can be directly solved, and the particle swarm algorithm is used to solve the energy storage investment decision layer;

[0150] Furthermore, the objective function and constraint conditions of the day-ahead scheduling at time t, Equations (18)-(20), are written in the following abstract form:

[0151]

[0152] st constraints (11)-(19)

[0153] e T y1+e T E W μ=e T d, e T Y+e T E W =0 (27)

[0154]

[0155] Among them, y1,r + ,r - ,y2,u are the unit output at time t, upward and downward climbing capacity, energy storage power and the start and stop status of the unit, respectively. + ,r - ,y2 will be affected by the lower level constraints, C U and C D They are the startup and shutdown costs of the unit. For ease of explanation, we define The corresponding coefficient vector Equation (27) is obtained by matching the random variables ξ on both sides of equation (18) t The coefficients of the first-order and zero-order terms are obtained, e represents the unit column vector, and equations (26)-(29) can be transformed as follows:

[0156]

[0157] Among them, Θ is the feasible domain of the decision variables of the upper objective function, A j (Y), b j (x) is the matrix and vector determined according to equations (27)-(29), gen is the constraint of thermal power generation unit, grid is the constraint of power grid;

[0158]

[0159] However, even if a linear approximation is made to the model of the day-ahead operation scheduling phase, due to the distribution Unknown, equations (30)-(31) are still unsolvable problems. In the historical data of wind and solar power output, there are finitely many training samples of prediction errors ξ i , i≤N, so the discrete empirical distribution can be used To replace the unknown distribution The problem is transformed into a distributionally robust optimization problem based on chance constraints, namely:

[0160]

[0161] The present invention establishes an uncertain set based on Wasserstein distance When the radius of the Wasserstein ball ρ = 0, the model is a random optimization problem based on chance constraints, and it is still difficult to solve the model. The feasible domain of the lower objective function is expressed as:

[0162]

[0163] Furthermore, equation (34) is transformed into a cone linear programming (CLP) problem that can be directly solved, namely:

[0164]

[0165] Among them, λ 0 , is the auxiliary variable introduced in the dual theory, N is the number of training samples, |||| * The dual norm of the original norm |||| in the definition of Wasserstein distance.

[0166] Furthermore, the constraint condition (38) involves M = 2 (G + L) linear inequalities, which decompose the matrix A (Y) and the vector b (x) into:

[0167] A(Y)=[a1(Y)…a M (Y)] T ,b(x)=[b1(x)…b M (x)] T (38)

[0168] The Bonferroni inequality is used to divide the original joint chance constraint into m easier-to-solve chance constraints, and the feasible domain Ω CC Approximately Ω B :

[0169]

[0170] Bonferroni inequality makes the feasible domain According to the CVAR theorem, the feasible domain Ω B Written as:

[0171]

[0172] Feasible region Ω BC Contains Ω B The best convex optimization information in If for all m≤M, α m ≤N -1 , then Ω BC =Ω B , feasible domain Ω BC Can be transformed into a linear constraint:

[0173]

[0174] in, m ,γ i,m ,τ m ,s i,m is the introduced auxiliary variable.

[0175] S4: In the day-ahead scheduling operation phase, the YALMIP toolbox is used to build a model, and the CPLEX solver is called to solve the problem, ultimately outputting the optimal energy storage capacity configuration result.

[0176] Furthermore, the output process of outputting the optimal energy storage capacity configuration result includes:

[0177] The opportunity constraints in the distributed robust optimization model are transformed into linear constraints, specifically including decomposing the original joint opportunity constraints into multiple opportunity constraints that are easier to solve through the Bonferroni inequality, and using the CVaR theorem to transform the opportunity constraints into linear constraints;

[0178] The transformed linear constraint problem is transformed into a cone linear programming problem which can be solved directly;

[0179] A particle swarm algorithm is used to solve the investment decision layer, and the fitness function input into the particle swarm algorithm is the objective function of the energy storage configuration model;

[0180] Based on the YALMIP toolbox modeling, the CPLEX solver is called to solve the day-ahead scheduling operation phase. The input data to the solver include the day-ahead arrangement of energy storage, the start-up and shutdown plan of the unit, and the expected output of the unit.

[0181] According to the day-ahead dispatch results and the new energy output test set, the penalty cost of insufficient flexibility in the intraday operation simulation layer is calculated;

[0182] The particle positions and velocities in the particle swarm algorithm are updated according to the calculation results, and the solution process is repeated until the convergence conditions are met, and the optimal energy storage capacity configuration result is output.

[0183] Furthermore, this embodiment also provides a new energy station energy storage capacity optimization system, including:

[0184] Uncertain set construction module, multi-time scale energy storage configuration model construction module, distributed robust opportunity constraint conversion module and day-ahead dispatch optimization solution module;

[0185] The uncertainty set construction module is used to construct a fuzzy uncertainty set of new energy output based on the Wasserstein distance method to form an uncertainty set of a distributed robust optimization model, and the uncertainty set is used to describe the possible distribution range of new energy output;

[0186] The multi-time scale energy storage configuration model building module is used to establish an energy storage configuration model under multiple time scales, and the energy storage configuration model includes an investment decision layer, a day-ahead operation scheduling strategy decision layer and an intraday operation simulation layer;

[0187] The distributed robust opportunity constraint conversion module is used to convert the distributed robust opportunity constraint using the duality theorem and the CVaR theorem, convert the original problem into a cone linear programming problem that can be directly solved, and use the particle swarm algorithm to solve the energy storage investment decision layer;

[0188] The day-ahead scheduling optimization solution module is used to model based on the YALMIP toolbox during the day-ahead scheduling operation phase, call the CPLEX solver to achieve the solution, and finally output the optimal energy storage capacity configuration result.

[0189] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for optimizing the energy storage capacity of a new energy station is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0190] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented: constructing a fuzzy uncertainty set of new energy output based on the Wasserstein distance method to form an uncertainty set of a distributed robust optimization model, wherein the uncertainty set is used to describe the possible distribution range of new energy output;

[0191] Establishing a multi-time scale energy storage configuration model, the energy storage configuration model includes an investment decision layer, a day-ahead operation scheduling strategy decision layer and an intraday operation simulation layer;

[0192] The duality theorem and CVaR theorem are used to transform the distributed robust chance constraints, transforming the original problem into a cone linear programming problem that can be directly solved, and the particle swarm algorithm is used to solve the energy storage investment decision layer;

[0193] In the day-ahead scheduling operation phase, the model is built based on the YALMIP toolbox, and the CPLEX solver is called to solve the problem, ultimately outputting the optimal energy storage capacity configuration result.

[0194] Example 2

[0195] Reference Figure 3 - Figure 6 , which is the second embodiment of the present invention, and this embodiment provides a method for optimizing the energy storage capacity of a new energy station. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0196] The energy storage configuration ratio is set to be no less than 10% of the wind farm capacity and no more than 25%, that is, the capacity range of energy storage configuration for a single wind farm is [40,100]. The number of training samples N of the distributed robust optimization model is 20, the number of test samples O is 10, the radius of the Wasserstein ball is 0.005, and the confidence level 1-α=0.95.

[0197] Figure 3 The total system cost, pre-dispatch cost, insufficient flexibility penalty and energy storage investment cost proposed in the embodiment of the present invention vary with the energy storage configuration capacity of a single wind farm. Figure 3 (a)-(d) It can be seen that the total cost of the system first increases slowly with the increase of energy storage configuration capacity, then decreases significantly, and finally increases significantly. There is an optimal energy storage configuration capacity that minimizes the total cost. The pre-dispatch cost of the system decreases slightly when the energy storage capacity is small. When the energy storage capacity increases to a certain extent, it decreases significantly and then gradually stabilizes. The penalty cost for insufficient flexibility decreases with the increase of the energy storage configuration ratio. When the energy storage configuration ratio is small, although the pre-dispatch cost and the penalty for insufficient flexibility are reduced, the reduced cost is not enough to make up for the investment cost of energy storage, so the total cost increases slowly; when the energy storage configuration ratio reaches a certain value, it has a significant impact on pre-dispatch. At this time, the total cost of the system is greatly reduced and finally reaches the minimum value; when the energy storage configuration ratio continues to increase, the pre-dispatch cost and the penalty for insufficient flexibility tend to be flat, so the total cost rises significantly again.

[0198] Figure 4 The curve of wind curtailment (load shedding) power versus energy storage configuration capacity proposed in the embodiment of the present invention is as follows: Figure 4 It can be seen that the power of wind curtailment and load shedding decreases with the increase of energy storage configuration capacity, and when the energy storage configuration capacity reaches a certain value, the reduction in wind curtailment power tends to be flat.

[0199] The proposed energy storage configuration results based on distributed robust optimization (DRO) are compared with traditional stochastic optimization (SP) and robust optimization (RO), and the configuration results are shown in Table 1.

[0200] Table 1 Configuration results of energy storage device

[0201]

[0202] Figure 4 : is a curve showing the change of wind curtailment (load shedding) power with energy storage configuration capacity according to an embodiment of the present invention. Figure 4 It can be seen that the power of wind curtailment and load shedding decreases with the increase of energy storage configuration capacity, and when the energy storage configuration capacity reaches a certain value, the reduction in wind curtailment power tends to be flat.

[0203] Figure 5 : This is the influence of the number of training samples N on the calculation time in the multi-stage distribution robust model of the embodiment of the present invention. Figure 4 It can be seen that as the number of samples increases, the calculation time increases. When the number of samples exceeds 15, the calculation time increases significantly.

[0204] Figure 6 The influence of the number of training samples N and the radius ρ of the Wasserstein sphere on the energy storage configuration results in the embodiment of the present invention is shown in FIG. Figure 6 (a)(c) It can be seen that the total cost and the penalty cost of insufficient flexibility decrease as the number of samples N increases; Figure 6 (b) It can be seen that the pre-dispatch cost increases with the increase of the number of samples, which indicates that the results of the day-ahead dispatch phase become more conservative with the increase of the number of samples, while the inflexibility cost of the intraday dispatch phase decreases, thus reducing the total cost. For the inflexibility cost, the impact of increasing the number of samples N from 10 to 20 is much greater than that from 20 to 30, but Figure 6 The influence of different sample numbers N on the model solution time shows that the increase in the number of samples causes the model solution time to increase exponentially. Therefore, a suitable number of samples should be selected in the energy storage configuration based on distributed robust optimization. The radius ρ of the Wasserstein ball determines the size of the uncertain fuzzy set of the true distribution. It can be seen that when the number of samples N is 20 and 30, as the radius ρ increases, the cost of the intraday scheduling stage gradually increases, the penalty cost of insufficient flexibility gradually decreases, and the total cost of the system first decreases and then increases, which indicates that an appropriate amount of insufficient flexibility penalty can make the total cost of the system optimal. From the above analysis, the proposed model can control the uncertain fuzzy set by changing the number of model samples and the radius of the Wasserstein ball, and effectively balance the economy and robustness of the solution.

[0205] In summary, the embodiments of the present invention give full play to the potential of various flexible resources on the power generation side of the power system, and do not rely solely on energy storage to solve the problem of insufficient flexibility, which can make energy storage configuration more economical and more practical. In addition, the energy storage planning scheme based on distributed robust optimization has better robustness than random optimization and better economy than robust optimization, so it can better balance robustness and economy.

[0206] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0207] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0208] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0211] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0212] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing energy storage capacity of a new energy station, characterized by: Including, constructing a fuzzy uncertainty set of new energy output based on the Wasserstein distance method to form an uncertainty set of a distributed robust optimization model, wherein the uncertainty set is used to describe the possible distribution range of new energy output; Establishing a multi-time scale energy storage configuration model, the energy storage configuration model includes an investment decision layer, a day-ahead operation scheduling strategy decision layer and an intraday operation simulation layer; The duality theorem and CVaR theorem are used to transform the distributed robust chance constraints, transforming the original problem into a cone linear programming problem that can be directly solved, and the particle swarm algorithm is used to solve the energy storage investment decision layer; In the day-ahead scheduling operation phase, the model is built based on the YALMIP toolbox, and the CPLEX solver is called to solve the problem, ultimately outputting the optimal energy storage capacity configuration result.

2. The method for optimizing energy storage capacity of a new energy station according to claim 1, characterized in that: The fuzzy uncertain set for constructing new energy output includes: Collect historical data on renewable energy output, generate a renewable energy output data set, and divide it into a training set and a test set; A fuzzy uncertainty set of renewable energy output is defined, wherein the uncertainty set is based on Wasserstein distance metric and includes a cluster of probability distribution functions close to the empirical reference distribution distance; According to the fuzzy uncertainty set of the new energy output, the objective function of the distributed robust optimization model is constructed.

3. The method for optimizing energy storage capacity of a new energy station according to claim 2, characterized in that: The energy storage configuration model includes: At the investment decision-making level, the energy storage capacity to be planned is used as the decision variable to minimize the equal annual investment cost of the energy storage investment capacity and meet the upper and lower limit constraints of the energy storage system capacity; In the day-ahead scheduling strategy decision layer, a two-layer optimization model is established, wherein the two-layer optimization model includes an outer layer optimization and an inner layer optimization; The outer optimization uses the pre-dispatched output of the thermal power unit and the energy storage system as decision variables, and the objective function includes the start-up and shutdown costs of the thermal power unit, the fuel cost, the cost of the unit providing ramp spare capacity, and the operating cost of the energy storage; The inner optimization adopts the real-time adjustment amount of the thermal power unit in response to the wind and solar power forecast error as the decision variable, and the objective function includes the expected value of the output adjustment cost of the thermal power unit under a given probability distribution; In the intraday operation simulation layer, based on the unit arrangement obtained in the day-ahead operation scheduling stage, the penalty for insufficient intraday flexibility is taken into account, and the calculation method is based on the scheduling results of the intraday operation simulation layer and the new energy output test set.

4. The method for optimizing energy storage capacity of a new energy station according to claim 3, characterized in that: The process of outputting the optimal energy storage capacity configuration result includes: The original joint opportunity constraint is decomposed into multiple opportunity constraints by using Bonferroni inequality, and the opportunity constraint is transformed into a linear constraint by using CVaR theorem; The transformed linear constraint problem is transformed into a cone linear programming problem which can be solved directly; A particle swarm algorithm is used to solve the investment decision layer, and the fitness function input into the particle swarm algorithm is the objective function of the energy storage configuration model; Based on the YALMIP toolbox modeling, the CPLEX solver is called to solve the day-ahead scheduling operation phase. The input data to the solver include the day-ahead arrangement of energy storage, the start-up and shutdown plan of the unit, and the expected output of the unit. According to the day-ahead dispatch results and the new energy output test set, the penalty cost of insufficient flexibility in the intraday operation simulation layer is calculated; The particle positions and velocities in the particle swarm algorithm are updated according to the calculation results, and the solution process is repeated until the convergence conditions are met, and the optimal energy storage capacity configuration result is output.

5. The method for optimizing energy storage capacity of a new energy station according to claim 4, characterized in that: The constraints of the outer optimization include: The time constraints and cost constraints for unit startup and shutdown are as follows: Among them, T S ,T O They are the minimum shutdown time and the minimum startup time of the unit, u g,i is the start and stop state of unit g in period i, and the start state u g,i =1, shutdown state u g,i =0,H g ,J g are the single startup cost and shutdown cost of unit g respectively; The ramp constraints of the unit and the output constraints considering the ramp reserve of the unit are as follows: in, is the maximum ramp rate of unit g per unit time, P g are the maximum and minimum outputs of unit g respectively; The state of charge constraints of the energy storage system are as follows: Among them, SOC k,t-1 , SOC k,t , SOC k,min , SOC k,max are the state of charge and its upper and lower limits of the kth energy storage system at time t-1 and t, respectively. k,T , SOC k,0 are the charge states of the kth energy storage system in the T period and the starting period, respectively; η is the charging and discharging efficiency of the energy storage system, is the charging and discharging power of the energy storage system k at time t, where discharging is positive and charging is negative. is the maximum charging and discharging power of energy storage system k, ΔT is the time interval; The energy state of the energy storage system must be equal at the beginning and end of the scheduling cycle, subject to the following constraints: SOCIETY k,T =SOC k,0 The maximum charging and discharging power constraints of energy storage are as follows: In a scheduling cycle, the constraints on the number of charge and discharge times of the energy storage system are as follows: Among them, X k,t , Y k,t The energy storage system k is in the charging state and discharging state at time t, respectively. The charging state X k,t =1,Y k,t =0, discharge state X k,t =0, Y k,t =1, N1, N2 are the limits on the number of times the energy storage system can be charged and discharged.

6. The method for optimizing energy storage capacity of a new energy station according to claim 5, characterized in that: The constraints of the inner optimization include: The power balance constraints of the system are as follows: The opportunity constraint of adjusting the output of the unit in real time in response to the wind and solar forecast error, and the probability of adjusting the output in real time to meet the constraint condition is not less than 1-α gen , α gen It indicates the significance level of the real-time output adjustment constraint of the unit, that is: The opportunity constraint of DC power flow security based on the generation transfer distribution factor and the probability that the line capacity does not exceed the limit is not less than 1-α grid , α grid It represents the significance level of DC power flow security constraint, namely: in, is the output power of the kth renewable energy station at time t, μ t is the per-unit value of the predicted output power at time t, ξ t is the per-unit value of the prediction error of the output power at time t, is the rated capacity of the new energy station, t is a random variable, For t The probability distribution function, N is the number of network nodes, p n,t is the load of node n at time t, L is the number of network lines, is the maximum transmission capacity of line l, Q g,l , Q k,l , Q n,l are the impact of the injected power of thermal power units, new energy stations and load nodes on line l, Q g,l The calculation formula is as follows: Among them, Δp l is the change in power flow on line l, Δp g is the output change of generator g, a and b are the nodes at both ends of line l, X ag , X bg are the elements in the ath row and gth column and the bth row and gth column of the node impedance matrix, respectively. l is the impedance of line l.

7. The method for optimizing energy storage capacity of a new energy station according to claim 6, characterized in that: The objective function of the intraday simulation layer is as follows: Among them, c up 、c dn They are the penalty coefficients for insufficient flexibility increase and decrease; are the load shedding and wind and solar power abandonment caused by insufficient system flexibility at time t, respectively. The calculation formula is as follows: Among them, ΔP L,t is the increase or decrease of the net load within the scheduling interval ΔT, and ΔP L,t Take positive value, are the upward or downward climbing rate limits that the thermal power unit and the energy storage system can provide during the dispatching cycle at time t, and are the maximum charging and discharging power of energy storage respectively.

8. A new energy station energy storage capacity optimization system, based on the new energy station energy storage capacity optimization method according to any one of claims 1 to 7, characterized in that: It includes the uncertainty set construction module, the multi-time scale energy storage configuration model construction module, the distributed robust opportunity constraint transformation module and the day-ahead dispatch optimization solution module; The uncertainty set construction module is used to construct a fuzzy uncertainty set of new energy output based on the Wasserstein distance method to form an uncertainty set of a distributed robust optimization model, and the uncertainty set is used to describe the possible distribution range of new energy output; The multi-time scale energy storage configuration model building module is used to establish an energy storage configuration model under multiple time scales, and the energy storage configuration model includes an investment decision layer, a day-ahead operation scheduling strategy decision layer and an intraday operation simulation layer; The distributed robust opportunity constraint conversion module is used to convert the distributed robust opportunity constraint using the duality theorem and the CVaR theorem, convert the original problem into a cone linear programming problem that can be directly solved, and use the particle swarm algorithm to solve the energy storage investment decision layer; The day-ahead scheduling optimization solution module is used to model based on the YALMIP toolbox during the day-ahead scheduling operation phase, call the CPLEX solver to achieve the solution, and finally output the optimal energy storage capacity configuration result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for optimizing the energy storage capacity of a new energy station according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing energy storage capacity of a new energy station according to any one of claims 1 to 7 are implemented.

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