Wind-light variable speed pumped storage combined capacity configuration method, system, electronic device and storage medium

Abstract

The application provides a wind-solar-fixed / variable speed pumped storage combined capacity configuration method and system, an electronic device and a storage medium. The method comprises the following steps: obtaining parameter information of a pumped storage combined operation system, constructing a pumped storage combined operation system model based on the parameter information; establishing a data-driven distributed robust optimization model; mapping the pumped storage operator and the wind-solar operator into two populations φ inv , φ dg with internal parameters and consistent information, constructing an evolutionary game model of the wind-solar-fixed / variable speed pumped storage combined operation system; coupling the evolutionary game model and the data-driven robust optimization model to obtain a capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system; and solving the capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system to obtain a capacity configuration scheme of the wind-solar-fixed / variable speed pumped storage combined operation system. The method provided by the application can maximize the interests of all parties and improve the "robustness" and "engineering practicability" of the model.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of new energy planning and configuration, and particularly relates to a wind-solar-constant / variable speed pumped storage joint capacity configuration method and system, an electronic device and a storage medium. BACKGROUND

[0002] With the increasing proportion of new energy, its randomness and volatility will quickly consume the flexible adjustment resources in the power system, reduce the adjustment and support capabilities of the system, and affect the stable operation of the power system. Pumped storage is a mature and reliable energy storage technology with large device capacity and low cost per kilowatt-hour, and has the functions of peak shaving, frequency regulation, phase modulation, etc., and is considered as an important means to solve the demand for flexible resources of new energy. The construction of pumped storage power station requires large investment and long project cycle. A reasonable and effective capacity configuration scheme can not only greatly improve the economic benefit of the pumped storage power station, enhance the construction enthusiasm of the pumped storage operator, and promote the rapid development of the pumped storage industry, but also effectively improve the support of the pumped storage to new energy, realize the rapid and effective complement of multiple energies, and fully play the advantages and roles of the pumped storage.

[0003] Pumped storage units are generally divided into constant speed pumped storage units and variable speed pumped storage units. The variable speed pumped storage unit has better power regulation characteristics and can flexibly output power, but compared with the constant speed pumped storage unit, the variable speed pumped storage unit has small capacity and is difficult to build due to technical and cost problems. Therefore, considering the complementary effect of constant speed and variable speed pumped storage, constructing a reasonable capacity ratio of constant speed and variable speed pumped storage units can better play the support role of the pumped storage unit and improve the power supply flexibility.

[0004] The current planning and configuration scheme of the power system often assumes that each operator has "complete rationality", that is, the benefit subject realizes the benefit maximization of the decision scheme under the premise of having complete information and all schemes, which has the defect of being too idealistic in engineering application. At the same time, the capacity configuration strategy of the wind-solar-constant / variable speed pumped storage joint operation system has important research value and application prospect. However, there are few studies on planning schemes considering the uncertainty of new energy such as wind and solar and the limited rationality of each operator. The economic flexibility of pumped storage to the power system is lacking, the resource optimization configuration capability is weak, and the overall benefit of the system and the engineering application are insufficient. SUMMARY

[0005] The application provides a wind-solar-constant / variable speed pumped storage joint capacity configuration method, system, electronic device and storage medium, which can effectively solve the problems of the economic flexibility of pumped storage to the power system being lacking, the resource optimization configuration capability being weak, and the overall benefit of the system and the engineering application being insufficient.

[0006] In one aspect, the application provides a wind-solar-constant / variable speed pumped storage joint capacity configuration method, comprising:

[0007] Step 1: Obtain the parameter information of the pumped storage combined operation system, including the parameter information of the pumped storage operator, the parameter information of the wind and light operator, and the parameter information of the demand response system;

[0008] Step 2: Construct a pumped storage combined operation system model based on the parameter information, including a pumped storage operator model, a wind and light operator model, and a demand response system model;

[0009] Step 3: Establish a data-driven distributed robust optimization model, wherein the deterministic part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator model and the wind and light operator model corresponding to discrete variables, and the uncertain part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator model and the wind and light operator model corresponding to continuous variables;

[0010] Step 4: Map the pumped storage operator and the wind and light operator into two populations φ inv and φ dg with consistent internal parameters and information, and construct a wind and light-pumped storage combined operation system evolutionary game model;

[0011] Step 5: Couple the evolutionary game model with the data-driven robust optimization model to obtain a capacity configuration model of the wind and light-pumped storage combined operation system;

[0012] Step 6: Solve the capacity configuration model of the wind and light-pumped storage combined operation system to obtain a capacity configuration scheme of the wind and light-pumped storage combined operation system.

[0013] In step 1, the parameter information of the pumped storage operator includes fixed-speed pumped storage unit parameter information and variable-speed pumped storage unit parameter information; the parameter information of the wind and light operator includes wind turbine parameter information and photovoltaic unit parameter information; and the parameter information of the demand response system includes load parameter information.

[0014] In step 2, the pumped storage operator model includes fixed-speed pumped storage units and variable-speed pumped storage units; the wind and light operator model includes wind turbines, photovoltaic generators, and energy storage systems; and the response system model is a load model, including interruptible loads, transferable loads, and fixed loads.

[0015] Further, the pumped storage operator model and the wind and light operator model include objective functions and constraint conditions.

[0016] The objective function of the pumped storage operator model includes pumped storage construction total cost, pumped storage construction start-stop loss cost, pumped storage operation cost, pumped storage low storage high generation income, pumped storage capacity electricity fee income, and auxiliary service income.

[0017] The constraint conditions of the pumped storage operator model include constant speed pumped storage unit constraints, variable speed pumped storage unit constraints, reservoir capacity constraints and rotating reserve constraints.

[0018] The objective function of the wind-solar operator model includes wind-solar construction cost, wind-solar maintenance cost, abandoned wind and light cost, wind-solar electricity sales income and energy storage operation income.

[0019] The constraint conditions of the wind-solar operator model include wind-solar output constraints and energy storage output constraints.

[0020] Further, the demand response system model, since the demand response system income is mainly affected by the time-of-use electricity price and the compensation strategy, is not directly affected by other interest subjects, therefore, in the present application, it is not taken as a game subject, and the demand response system model includes:

[0021] P il,min ≤P il,t ≤P il,max

[0022]

[0023] P trans,min ≤P trans,t ≤P trans,max

[0024] P pload =P trans,t +P il,t +P st,t

[0025]

[0026] In the formula, P trans,t represents the transferable load at t period; P il,t represents the interruptible load at t period; P il,max and P il,min respectively represent the upper and lower limits of the interruptible load; P trans,max and P trans,min respectively represent the upper and lower limits of the transferable load; P st,t is the fixed load; and P pload is the total load.

[0027] Further, after each interest subject obtains the objective function, the objective function is constrained to maximize its own income.

[0028] Wherein, in step 3, the robust optimization is a kind of mathematical programming problem considering parameter uncertainty, including basic robust optimization, multi-stage robust optimization, distributed robust optimization and the like.Distributed robust optimization problem is a special case of multi-stage robust optimization.In distributed robust optimization problem, generally, two different levels of decision variables are included, also called first-stage decision and second-stage decision.Wherein, the distributed robust optimization problem is described as follows:

[0029] (1) the parameter related to the first-stage decision is determined, and the first-stage decision needs to be made first;

[0030] (2) the parameter related to the second-stage decision is uncertain to some extent.The second-stage decision needs to be made after the first-stage decision is determined, and the corresponding second-stage decision is made after the second-stage parameter is revealed.

[0031] (3) the objective of distributed robust optimization is to jointly optimize the two-stage decision while considering the uncertainty of the second-stage parameter, that is, to optimize the total objective value corresponding to the two-stage decision under the worst case of the second-stage parameter.

[0032] Further, the mathematical model of the distributed robust optimization problem is shown as follows:

[0033]

[0034] In the formula, y is the first-stage decision variable, x is the second-stage decision variable, c is the parameter related to the first-stage decision variable and is determined, u is the parameter related to the second-stage decision variable and is uncertain, and the value range of the uncertain parameter is described by an uncertainty set; in the objective function, the first term is the objective function corresponding to the first-stage decision, which needs to be determined first;in the objective function, the second term is the objective function corresponding to the second-stage decision, which needs to be made after the first-stage decision is determined;the objective function aims to find the worst case, that is, the objective value of the second stage under the worst case;the whole objective function is to optimize the total objective function under the worst case, so that the worst case is the best, and the solution has very good robustness.

[0035] Further, in order to improve the accuracy and solving speed of the model, the application constructs a distributed robust optimization model based on data driving on the basis of the distributed robust model.

[0036] Furthermore, the deterministic part of the data-driven distributed robust optimization model is composed of the objective function of the pumped storage operator model and the objective function of the wind-solar operator model corresponding to discrete variables, including: discrete variables such as the construction quantity and capacity of pumped storage and wind-solar units in the model, and the operating status of the pumped storage units and their corresponding objective functions; the uncertain part of the data-driven distributed robust optimization model is composed of the objective function of the pumped storage operator model and the objective function of the wind-solar operator model corresponding to continuous variables, including: continuous variables such as the output size of the pumped storage and wind-solar units in the model, and the reservoir capacity and their corresponding objective functions.

[0037] Furthermore, the data-driven distributed robust optimization model can be expressed as follows:

[0038]

[0039] Where: p s is the probability of scenario s occurring; C cer 、C unc represent deterministic and uncertain objective functions respectively.

[0040] The essence of the data-driven distributed robust optimization model is to convert the value of the uncertain part into the corresponding expectation, thus achieving the decoupling of the max-min problem. s Closer to the actual value, the probability distribution p is generated based on 1-norm and ∞-norm s The confidence constraint set Θ is constructed by p , where the norm is defined as:

[0041]

[0042] Therefore, the confidence constraint set Θ based on the norm p The following is shown below:

[0043]

[0044] Where p0 is the initial probability distribution; the discrete scenario s comes from the observation value S, where S is obtained by the scenario generation method, and each discrete scenario includes N s Initial scene, p0 is expressed as: p0 = N k / M. And according to the definition, we get q1, q ￥ The calculation formula is as follows:

[0045]

[0046]

[0047] Where: α1, α∞ is the confidence level.

[0048] This model uses historical data to derive output scenarios for wind and solar turbines through scenario clustering. Generally speaking, the greater the number of clusters, the more accurate the resulting probability distribution. However, too many scenarios can reduce the model's computational efficiency and create a "curse of dimensionality." The elbow method uses the ratio of the average intra-cluster distance to the average inter-cluster distance as the clustering error, effectively balancing the conflict between cluster accuracy and the number of clusters with a streamlined algorithm. The model is as follows:

[0049]

[0050] Where: SE is the clustering error; nSE is the average distance within the class; ωSE is the average distance between classes; δ i is the i-th type sample; ks is δ i Sample m in i is the mean of the samples in class i; kn is the number of samples in class i. As the number of clusters increases, the magnitude of the decrease decreases significantly, while the change is minimal, leading to a corresponding decrease. As the number of clusters approaches the optimal number of clusters, the magnitude of the decrease decreases, causing the corresponding decrease to decrease. Therefore, the relationship diagram produced by this method is elbow-shaped, and the optimal number of clusters is the value corresponding to the elbow.

[0051] Furthermore, in order to quickly solve the distributed robust optimization model, the C&CG algorithm is used to divide the data-driven distributed robust optimization model into the main problem (MP) and subproblems (SP) for iterative solution. The compact form of the MP problem is as follows:

[0052]

[0053] Where: η is the slack variable, is the probability distribution of each scenario after the kth iteration.

[0054] Obtain the optimal value x of the first-stage variable from the MP problem * , substitute it into the SP problem and update the worst probability distribution of each scenario. The compact form of the SP problem is as follows:

[0055]

[0056] After solving the MP and SP problems, the upper and lower bounds of the distributed robust optimization model are updated based on the results and the algorithm is iterated. Finally, when the convergence condition is met, the iteration stops and the results are output.

[0057] The specific solution process is as follows:

[0058] Step1: initialize parameters, let the upper bound UB = +∞, the lower bound LB = -∞, let the probability distribution obtained by clustering be the initial probability distribution of each scene, and let the iteration number k = 1;

[0059] Step2: solve the MP problem to obtain the optimal value x * of the first stage variable, and update the lower bound LB k = max{LB k-1 ,a T x * + η};

[0060] Step3: substitute x * into the SP problem to obtain the worst probability distribution , and update the upper bound

[0061] Step4: if UB k -LB k < ε, stop iteration and output the result. Otherwise, let k = k + 1 and repeat Step2.

[0062] In step 4, the evolutionary game model is a model constructed based on evolutionary game theory, including its game strategy set, payoff function and replicator dynamic equation.

[0063] Further, the strategy set refers to in evolutionary game, the pumped storage operator and the wind-solar operator need to formulate a limited strategy set according to their own situation, based on which the evolutionary process is analyzed to find the potential optimal solution.

[0064] Specifically, the fixed / variable speed pumped storage operator constructs a strategy set S inv with the number of fixed / variable speed pumped storage units N s , N v , the corresponding maximum power P i,s,max , P i,v,max as decision variables, and the wind-solar operator constructs a strategy set S dg with the number of photovoltaic and wind turbine units N pv , N wt , the corresponding rated power P pv,i,N , P wt,i,N as decision variables, as follows:

[0065]

[0066] Further, the payoff function is the objective function pursued by the pumped storage operator and the wind-solar operator in the evolutionary game process.

[0067] Specifically, it includes the payoff function U​inv , the revenue function U of wind and solar operators dg As shown below:

[0068]

[0069] Furthermore, the replicator dynamic equation is based on the game strategies and revenue functions of the pumped storage operator and the wind and solar operator. By constructing a reasonable replicator dynamic equation, the strategy selection mechanism of the game process can be effectively simulated.

[0070] Specifically include: They represent the proportion of individuals in various populations that choose the i-th strategy set in the evolutionary game at time t, satisfying the following relationship:

[0071]

[0072] At this time, the fitness functions corresponding to the strategies in the fixed / variable speed pumped storage operator population and the wind / solar operator population are:

[0073]

[0074] Considering that in the process of evolutionary game, each population will adjust its strategy selection by comparing its own benefits with those of other populations. Therefore, when constructing the present invention, the dynamic equations of the mutual influence between populations are considered, and the strategy conversion coefficient ρ is introduced to quantify the influence between populations. The dynamic equations corresponding to fixed / variable speed pumped storage operators and wind and solar operators are as follows:

[0075]

[0076] Furthermore, in the process of evolutionary game, the Logit protocol is used to dynamically simulate the mutual influence between pumped storage operators and wind and solar operators. The specific expression is:

[0077]

[0078] Where: η is the external influencing factor.

[0079] In step 6, the specific process of solving the capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system and obtaining the capacity configuration scheme of the wind-solar-fixed / variable speed pumped storage combined operation system includes:

[0080] Step 1: Initialize data and randomly generate n policy sets S inv and S dg ;

[0081] Step 2: Each group randomly selects a strategy from the strategy set until all strategies are selected;

[0082] Step3: Calculate the revenue function of each strategy through random combination by constructing a data-driven distributed robust optimization model;

[0083] Step4: Calculate the fitness function and strategy conversion coefficient and iterate through the dynamic equation;

[0084] Step5: Repeat Step2-Step4 until the evolutionary stable state is reached;

[0085] Step6: Output the capacity configuration scheme as the optimal capacity configuration scheme of the pumped storage joint operation system.

[0086] In one aspect, the application provides a wind-solar-pumped storage joint system, comprising: a parameter information acquisition module, a pumped storage joint operation module, a robust optimization model construction module, an evolutionary game model construction module, a coupling module, a solving module, an information interaction module and an energy interaction module;

[0087] The parameter information acquisition module is used to acquire the parameter information of the pumped storage operator, the wind-solar operator and the demand response system; the parameter information includes the parameter information of the pumped storage operator, the parameter information of the wind-solar operator and the parameter information of the demand response system;

[0088] The pumped storage joint operation module is used to construct a pumped storage joint operation system model according to the parameter information; the pumped storage joint operation system model includes constructing a pumped storage operator model, a wind-solar operator model and a demand response system model;

[0089] The robust optimization model construction module is used to improve the accuracy and solving speed of each model in the pumped storage joint operation module; wherein the deterministic part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator model and the wind-solar operator model corresponding to discrete variables, and the uncertain part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator model and the wind-solar operator model corresponding to continuous variables;

[0090] The evolutionary game model construction module maps the pumped storage operator and the wind-solar operator into two populations φ inv 、φ dg with the same internal parameters and information, and constructs an evolutionary game model of the wind-solar-pumped storage joint operation system;

[0091] The coupling module is used to couple the evolutionary game model with the data-driven robust optimization model to obtain a capacity configuration model of the wind-solar-pumped storage joint operation system;

[0092] The solving module is configured to solve the capacity configuration model of the wind-solar-constant / variable pumped storage combined operation system to obtain a capacity configuration scheme of the wind-solar-constant / variable pumped storage combined operation system.

[0093] Further, the parameter information acquisition module, the pumped storage combined operation module, the robust optimization model construction module, the evolutionary game model construction module, the coupling module and the solving module are connected through the information interaction module to realize information transmission, and are connected through the energy interaction module to realize energy transmission.

[0094] In one aspect, the application provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the wind-solar-constant / variable pumped storage combined capacity configuration method.

[0095] In one aspect, an electronic readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the wind-solar-constant / variable pumped storage combined capacity configuration method.

[0096] Through the above technical solutions conceived by the application, the application has the following advantages:

[0097] 1. Through the coordinated planning of constant and variable pumped storage units, the support capability of pumped storage to the power system is improved, the economic flexibility of the power system is improved, and the capability of resource optimization configuration is improved.

[0098] 2. The limited rationality of each operator is considered, and the overall income of the system is improved while maximizing the self-interest of each operator through the evolutionary game, thereby enhancing the engineering application of the model.

[0099] Other features and advantages of the application will be set forth in the following description of the application, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the application can be realized and obtained by the structure indicated in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0100] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0101] Figure 1 A wind-solar-constant / variable pumped storage combined capacity configuration method flowchart is shown.

[0102] Figure 2 A wind-solar-constant / variable pumped storage combined system structure schematic diagram is shown.

[0103] Figure 3 A wind-solar-constant / variable pumped storage combined electronic device structure schematic diagram is shown. DETAILED DESCRIPTION

[0104] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0105] In an embodiment, referring to Figure 1 , a wind-solar-constant / variable pumped storage combined capacity configuration method is provided, comprising:

[0106] Step 1: obtaining parameter information of a pumped storage combined operation system, including parameter information of a pumped storage operator, parameter information of a wind-solar operator, and parameter information of a demand response system;

[0107] Step 2: constructing a pumped storage combined operation system model based on the parameter information, including a pumped storage operator model, a wind-solar operator model, and a demand response system model;

[0108] Step 3: establishing a data-driven distributed robust optimization model, a deterministic part of the data-driven distributed robust optimization model being composed of an objective function of the pumped storage operator model and an objective function of the wind-solar operator model corresponding to discrete variables, and an uncertain part of the data-driven distributed robust optimization model being composed of an objective function of the pumped storage operator model and an objective function of the wind-solar operator model corresponding to continuous variables;

[0109] Step 4: mapping the pumped storage operator and the wind-solar operator into two populations φ inv and φ dg respectively with internal parameters and consistent information, and constructing an evolutionary game model of a wind-solar-constant / variable pumped storage combined operation system;

[0110] Step 5: coupling the evolutionary game model and the data-driven robust optimization model to obtain a capacity configuration model of the wind-solar-constant / variable pumped storage combined operation system;

[0111] Step 6: solving the capacity configuration model of the wind-solar-constant / variable pumped storage combined operation system to obtain a capacity configuration scheme of the wind-solar-constant / variable pumped storage combined operation system.

[0112] In step 1, the parameter information of the pumped storage operator includes parameter information of fixed-speed pumped storage unit and parameter information of variable-speed pumped storage unit; the parameter information of the wind-solar operator includes parameter information of wind turbine unit and parameter information of photovoltaic unit; and the parameter information of the demand response system includes load parameter information.

[0113] In step 2, the pumped storage operator model includes fixed-speed pumped storage unit and variable-speed pumped storage unit; the wind-solar operator model includes wind turbine unit, photovoltaic unit and energy storage system; and the response system model is a load model including interruptible load, transferable load and fixed load.

[0114] Further, the pumped storage operator model and the wind-solar operator model include an objective function and a constraint condition.

[0115] The objective function of the pumped storage operator model includes total construction cost of pumped storage, construction start-stop loss cost of pumped storage, operation cost of pumped storage, low-storage-high-generation income of pumped storage, capacity electricity charge income of pumped storage and auxiliary service income; and the constraint condition of the pumped storage operator model includes constraint of fixed-speed pumped storage unit, constraint of variable-speed pumped storage unit, reservoir capacity constraint and spinning reserve constraint.

[0116] The objective function of the wind-solar operator model includes wind-solar construction cost, wind-solar maintenance cost, abandoned wind and abandoned light cost, wind-solar electricity sale income and energy storage operation income; and the constraint condition of the wind-solar operator model includes wind-solar output constraint and energy storage output constraint.

[0117] Further, in the objective function and the constraint condition of the pumped storage operator model,

[0118] The total construction cost of pumped storage is specifically expressed as:

[0119] C inv,con =C s,con +C v,con

[0120]

[0121] E i,s,max =P i,s,max T s η s,c

[0122]

[0123] E i,v,max =P i,v,max T v η v,c

[0124] In the formula, C inv,conrepresents the total construction cost of pumped storage operators; C s,con v,con represents the construction cost of fixed-speed pumped storage units and variable-speed pumped storage units, respectively; λ sc vc represents the unit price of fixed-speed pumped storage capacity and variable-speed pumped storage capacity, respectively; E i,s,max i,v,max represents the maximum capacity of the i-th fixed-speed pumped storage unit and variable-speed pumped storage unit, respectively; r is the discount rate; Y s v represents the life cycle of fixed-speed pumped storage units and variable-speed pumped storage units, respectively; P i,s,max i,v,max represents the maximum power of the i-th fixed-speed pumped storage unit and variable-speed pumped storage unit, respectively; T s v represents the full-load pumping time of fixed-speed pumped storage units and variable-speed pumped storage units, respectively; η s,c v,c represents the pumping efficiency of fixed-speed pumped storage units and variable-speed pumped storage units, respectively; Θ s v represents the set of fixed-speed pumped storage units and variable-speed pumped storage units, respectively.

[0125] The specific expression of the pumped storage construction start-stop loss cost is:

[0126] C inv,q =C qsp +C qsg +C qvp +C qvg

[0127]

[0128] In the formula, C inv,q represents the total start-stop loss cost of the pumped storage system; C qsp qsg qvp qvg represents the pumping and power generation condition conversion cost of fixed-speed pumped storage units and the pumping and power generation condition conversion cost of variable-speed pumped storage units, respectively; λ qsp qsg qvp qvg represents the unit conversion cost of pumping and power generation conditions of fixed-speed pumped storage units and the unit conversion cost of pumping and power generation conditions of variable-speed pumped storage units, respectively; x sp,i,t sg,i,t vp,i,t vg,i,t represents the state variable (taking Boolean value 0 or 1) of the i-th fixed-speed pumped storage unit and variable-speed pumped storage unit in the t period of pumping and power generation conditions, which is 1 when the pumped storage unit is in the state; Θ T represents the operation time of the combined system.​​​​​​​​​​​​​​​​​

[0129] The specific expression of the pumped storage operation cost is:

[0130] C inv,run =C s,run +C v,run

[0131]

[0132] Where: C inv,run represents the total operating cost of the pumped storage system; C s,run 、C v,run Respectively represent the operating costs of fixed-speed and variable-speed pumped storage units; λ s,run ,λ v,run are the unit operating costs of fixed-speed and variable-speed pumped storage units respectively; P sg,i,t 、P sp,i,t 、P vg,i,t 、P vp,i,t are the output powers of the i-th fixed-speed and variable-speed pumped storage unit in pumping and generating conditions during period t, respectively.

[0133] The specific expression of the pumped storage low storage and high generation income is:

[0134] C inv,se =C s,se +C v,se

[0135]

[0136] Where: C inv,se represents the total low-storage and high-generation income of the pumped storage system; C s,se 、C v,se Respectively represent the electricity sales revenue of fixed-speed and variable-speed pumped storage units; λ buy ,λ se They represent the unit price of purchasing and selling electricity of the pumped storage unit respectively.

[0137] The pumped storage capacity electricity fee income expression is:

[0138] C inv,cap =C s,cap +C v,cap

[0139]

[0140] Where: C inv,cap represents the total capacity electricity revenue of the pumped storage system; C s,cap 、C v,cap Represent the capacity electricity fee income of fixed-speed and variable-speed pumped storage units respectively; λ cap Indicates the unit capacity price of the pumped storage unit.

[0141] The auxiliary service income expression is:

[0142] C inv,as = C s,as + C v,as

[0143]

[0144] In the formula, C inv,as represents the total auxiliary service income of the pumped storage system; C s,as , C v,as respectively represent the auxiliary service income of the constant-speed pumped storage unit and the variable-speed pumped storage unit; λ as represents the auxiliary service transaction unit price; R sgu,i,t , R sgd,i,t , R vgu,i,t , R vgd,i,t respectively represent the upper and lower rotating reserve capacity of the i-th constant-speed pumped storage unit and the variable-speed pumped storage unit at the t time period.

[0145] The constant-speed pumped storage unit constraint expression is:

[0146] x sp,i,t + x sg,i,t ≤ 1

[0147] P sp,i,t = x sp,i,t p0

[0148] P sg,min x sg,i,t ≤ P sg,i,t ≤ P sg,max x sg,i,t ;

[0149] In the formula, p0 is the rated pumping power of the constant-speed pumped storage unit; P sg,min , P sg,max respectively represent the minimum and maximum power generation of the constant-speed pumped storage unit.

[0150] The variable-speed pumped storage unit constraint expression is:

[0151] x vp,i,t + x vg,i,t ≤ 1

[0152]

[0153] In the formula, P vp,min , P vp,max , P vg,min , P vg,max respectively represent the minimum pumping power, the maximum pumping power, the minimum power generation, and the maximum power generation of the variable-speed pumped storage unit.

[0154] The reservoir capacity constraint expression is:

[0155]

[0156] V UR,min ≤V UR,t ≤V UR,max

[0157] V LR,min ≤V LR,t ≤V LR,max

[0158] Where: V UR,t 、V LR,t Respectively represent the water storage capacity of the upper reservoir and the lower reservoir in period t; V UR,max 、V UR,min 、V LR,max 、V LR,min They represent the maximum and minimum water storage capacity of the upper reservoir and the maximum and minimum water storage capacity of the lower reservoir respectively; Δt is the unit time period; c g 、c p They represent the power-flow conversion coefficient, which is a constant representing the ability of the pumped storage unit to convert power into flow.

[0159] The spinning reserve constraint expression is:

[0160] (P sg,i,t +R sgu,i,t )x sg,i,t ≤P sg,max x sg,i,t

[0161] (P sg,i,t -R sgd,i,t )x sg,i,t ≥P sg,min x sg,i,t

[0162] (P vg,i,t +R vgu,i,t )x vg,i,t ≤P vg,max x vg,i,t ;

[0163] (P vg,i,t -R vgd,i,t )x vg,i,t ≥P vg,min x vg,i,t

[0164]

[0165] Where: R sgu,i,t 、R sgd,i,t 、R vgu,i,t 、R vgd,i,tP represents the upper and lower spinning reserve capacity of the i-th fixed-speed and variable-speed pumped storage unit in period t respectively; pv,i,t 、P wt,i,t Respectively represent the output of the i-th photovoltaic and wind turbine groups in period t; P pv,i,max 、P pv,i,min 、P wt,i,max 、P wt,i,min They represent the upper and lower limits of the output of the i-th photovoltaic and wind turbine groups respectively.

[0166] Furthermore, in the objective function and constraints of the wind and solar operator model;

[0167] The objective function expression of the wind and solar construction cost is:

[0168] C dg,con =C pv,con +C wt,con +C des,con

[0169]

[0170] Where: C dg,con represents the total construction cost of wind and solar operators; C pv,con 、C wt,con 、C des,con Represent the construction costs of photovoltaic, wind power and energy storage units respectively; P pv,i,N 、P wt,i,N 、P des,i,N Respectively represent the rated power of the i-th photovoltaic, wind power and energy storage unit; λ pvc ,λ wtc ,λ desc Represents the capacity unit price of photovoltaic, wind power and energy storage units respectively; Y pv 、Y wt 、Y des Represent the life cycles of photovoltaic, wind power and energy storage units respectively; Θ pv 、Θ wt 、Θ des Represents the collection of photovoltaic, wind power and energy storage units respectively.

[0171] The objective function expression of the wind and solar maintenance cost is:

[0172] C dg,ope =C pv,ope +C wt,ope

[0173]

[0174] Where: C dg,ope represents the total maintenance cost of wind and solar operators; C pv,ope 、C wt,operespectively represent the maintenance cost of photovoltaic and wind turbine; λ pv,ope wt,ope respectively represent the unit maintenance cost of photovoltaic and wind turbine; P pv,i,pro wt,i,pro respectively represent the predicted output of the ith photovoltaic and wind turbine.

[0175] The objective function expression of the abandoned wind and light cost is:

[0176] C dg,gu =C pv,gu +C wt,gu

[0177]

[0178] In the formula, C dg,gu represents the total abandoned light and wind cost of the wind and light operator; C pv,gu , C wt,gu respectively represent the abandoned light and wind cost of the wind and light operator; λ pv,gu , λ wt,gu respectively represent the abandoned light and wind penalty unit price of the wind and light operator.

[0179] The objective function expression of the wind and light electricity selling income is:

[0180] C dg,se =C pv,se +C wt,se

[0181]

[0182] In the formula, C dg,se represents the total electricity selling income of the wind and light operator; C pv,se , C wt,se respectively represent the electricity selling income of photovoltaic and wind turbine; λ pv,se , λ wt,se respectively represent the electricity selling unit price of photovoltaic and wind.

[0183] The objective function expression of the energy storage operation income is:

[0184] C des,run =C des,se -C des,q

[0185]

[0186] In the formula, C des,run represents the total operation income of the energy storage system; C des,q , C des,se respectively represent the electricity consumption cost and electricity selling income of the energy storage system; λ des,q , λ​​des,buy , λ des,se respectively represent the unit electricity consumption cost of the energy storage system and the electricity purchase and sale price; P ch,i,t , P dis,i,t respectively represent the charging and discharging power of the i th energy storage at the t th period.

[0187] The wind and light output constraint expression is:

[0188]

[0189] In the formula: P pv, i ,max , P pv, i, m in , P wt,i,max , P wt,i,min respectively represent the upper and lower limits of the output of the i th photovoltaic and wind turbine.

[0190] The energy storage output constraint expression is:

[0191]

[0192] In the formula: SOC i,t represent the state of charge of the i th energy storage device at the t th period; SOC max , SOC min respectively represent the upper and lower limits of the state of charge; E des,i represent the rated capacity of the energy storage; η ch , η dis respectively represent the charging and discharging efficiency of the energy storage; P ch,max , P dis,max respectively represent the upper limits of the charging and discharging of the energy storage.

[0193] Further, the demand response system model, since the demand response system benefit is mainly affected by the time-of-use electricity price and the compensation strategy, is not directly affected by other interest subjects, therefore, in the present application, it is not taken as a game subject, and the demand response system model comprises:

[0194]

[0195] In the formula: P trans,t represent the transferable load at the t th period; P il,t represent the interruptible load at the t th period; P il,max , P il,min respectively represent the upper and lower limits of the interruptible load; P trans,max , P trans,min respectively represent the upper and lower limits of the transferable load; P st,t is a fixed load; P pload is a total load.

[0196] Further, after each interest subject gets the objective function, the objective function is constrained to maximize the self-benefit.

[0197] In step 3, the robust optimization is a kind of mathematical programming problem considering parameter uncertainty, including basic robust optimization, multi-stage robust optimization, distributed robust optimization, etc. The distributed robust optimization problem is a special case of multi-stage robust optimization. In the distributed robust optimization problem, generally contains two different levels of decision variables, also known as first-stage decision and second-stage decision. Among them, the distributed robust optimization problem is described as follows:

[0198] (1) The parameters related to the first-stage decision are determined, and the first-stage decision needs to be made first.

[0199] (2) The parameters related to the second-stage decision are uncertain. The second-stage decision needs to be made after the first-stage decision is determined, and the corresponding second-stage decision is made after the second-stage parameter is revealed.

[0200] (3) The goal of distributed robust optimization is to jointly optimize two-stage decisions while considering the uncertainty of second-stage parameters. That is, to optimize the total objective value of two-stage decisions corresponding to the worst case of the second-stage parameter.

[0201] Further, the mathematical model of the distributed robust optimization problem is as follows:

[0202]

[0203] In the formula, y is the first-stage decision variable; x is the second-stage decision variable; c is the parameter related to the first-stage decision variable, which is determined; u is the parameter related to the second-stage decision variable, which is uncertain, and the value range of the uncertain parameter is described by the uncertainty set; in the objective function, the first term is the objective function corresponding to the first-stage decision. This part of the decision needs to be determined first; in the objective function, the second term is the objective function corresponding to the second-stage decision. This part of the decision needs to be made after the first-stage decision is determined; this part of the objective function aims to find the worst case; that is, the second-stage objective value in the worst case; the whole objective function is to optimize the total objective function in the worst case, so that the worst case is the best, and the solution has very good robustness.

[0204] Further, in order to improve the accuracy and solving speed of the model, the application constructs a data-driven distributed robust optimization model on the basis of the distributed robust model.

[0205] Further, the deterministic part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator model and the wind and light operator model corresponding to discrete variables, including the construction quantity, capacity of pumped storage and wind and light units, and working condition state of pumped storage units, and the corresponding objective functions of the discrete variables; the uncertainty part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator model and the wind and light operator model corresponding to continuous variables, including the output size of pumped storage and wind and light units, reservoir capacity and the corresponding objective functions of the continuous variables.

[0206] Further, the data-driven distributed robust optimization model can be expressed in the following form:

[0207]

[0208] In the formula, p s is the probability of the occurrence of the scenario s; C cer , C unc respectively represent the deterministic and uncertain objective functions.

[0209] The essence of the data-driven distributed robust optimization model is to convert the value of the uncertain part into the solution of the corresponding expectation, realizing the decoupling of the max-min problem. In order to make the probability distribution p s more close to the actual value, the method of generating the probability distribution p s based on 1-norm and ∞-norm is adopted to construct the confidence constraint set Θ p , wherein the norm is defined as:

[0210]

[0211] Therefore, the confidence constraint set Θ p based on the norm is as follows:

[0212]

[0213] Wherein, p0 is the initial probability distribution; the discrete scenario s comes from the observation value S, wherein S is obtained by a scenario generation method, and each discrete scenario includes N s initial scenarios, and p0 is expressed as: p0=N k / M. And according to the definition, the calculation formula of q1 and q ￥ is as follows:

[0214]

[0215]

[0216] wherein: α1, α ∞ is the confidence level.

[0217] The model obtains the wind, light and machine group output scene according to historical data through scene clustering. Generally speaking, the more the number of clusters, the higher the accuracy of the obtained probability distribution, but too many scenes will reduce the calculation efficiency of the model and cause "dimension disaster". The elbow method effectively balances the contradiction between clustering accuracy and the number of clusters by taking the ratio of the average distance within the class to the average distance between the classes as the clustering error. The model is as follows:

[0218]

[0219]

[0220] wherein: SE is the clustering error; nSE is the average distance within the class; ωSE is the average distance between the classes; δ i is the i-th sample; ks is the average value of the i-th sample; kn is the number of the i-th sample. i i is the average value of the i-th sample; kn is the number of the i-th sample. When the number of clusters increases, the value of δ * decreases significantly but changes little, so it will decrease accordingly; when the number of clusters approaches the optimal number of clusters, the decrease amplitude decreases, so the corresponding decrease amplitude decreases. Therefore, the relationship diagram obtained by this method is elbow-shaped, and the optimal number of clusters is the value corresponding to the elbow.

[0221] Further, in order to quickly solve the distributed robust optimization model, the C&CG algorithm is adopted, and the data-driven distributed robust optimization model is divided into master problem (MP) and sub-problem (SP) for iterative solution. Among them, the compact form of the MP problem is as follows:

[0222]

[0223] wherein: η is the relaxation variable, is the probability distribution of each scene after the k-th iteration.

[0224] The optimal value x * of the first-stage variable is obtained from the MP problem,

[0225]

[0226] After solving the MP problem and the SP problem, the upper and lower bounds of the distributed robust optimization model are updated according to the results and iteration is performed. Finally, when the convergence condition is met, the iteration is stopped and the results are output.

[0227] The specific solving process is as follows:

[0228] Step 1: initialize parameters, let the upper bound UB = +∞, the lower bound LB = -∞, let the probability distribution obtained by clustering be the initial probability distribution of each scenario, and let the iteration number k = 1;

[0229] Step 2: solve the MP problem to obtain the optimal value x * of the first-stage variable, and update the lower bound LB k = max{LB k-1 ,a T x * +η};

[0230] Step 3: substitute into the SP problem to solve the worst probability distribution and update the upper bound

[0231] Step 4: if UB k -LB k <ε, stop iteration and output the results. Otherwise, let k = k + 1 and repeat Step 2.

[0232] In step 4, the evolutionary game model is a model constructed based on evolutionary game theory, including its game strategy set, payoff function and replicator dynamic equation.

[0233] Further, the strategy set refers to in evolutionary game, the pumped storage operator and the wind-solar operator need to formulate a limited strategy set according to their own situation, based on which the evolutionary process is analyzed to find the potential optimal solution.

[0234] Specifically, the fixed / variable speed pumped storage operator constructs a strategy set S inv with the number of fixed / variable speed pumped storage units N s , N v and their corresponding maximum power P i,s,max , P i,v,max as decision variables, and the wind-solar operator constructs a strategy set S dg with the number of photovoltaic and wind turbine units N pv , N wt and their corresponding rated power P pv,i,N , P wt,i,N as decision variables, as follows:

[0235] ​

[0236] Further, the benefit function is a target function pursued by the pumped storage operator and the wind-solar operator in the evolutionary game process.

[0237] Specifically, the benefit function U of the fixed / variable speed pumped storage operator inv , the benefit function U of the wind-solar operator dg As follows:

[0238]

[0239] Further, the replicator dynamic equation is constructed according to the game strategy and the benefit function of the pumped storage operator and the wind-solar operator, and the game process strategy selection mechanism can be effectively simulated by constructing a reasonable replicator dynamic equation.

[0240] Specifically, let The proportion of the number of individuals selecting the i-th strategy set in each group at time t to all individuals is represented respectively, and the following relationship is satisfied:

[0241]

[0242] At this time, the fitness function corresponding to each strategy in the population of the fixed / variable speed pumped storage operator and the population of the wind-solar operator is respectively:

[0243]

[0244] Considering that in the process of evolutionary game, various groups will correct their own strategy selection by comparing the benefits of themselves and other groups, therefore, when constructing, the dynamic equation of mutual influence between groups is considered, and a strategy conversion coefficient ρ is introduced to quantify the influence between groups. The dynamic equation corresponding to the fixed / variable speed pumped storage operator and the wind-solar operator is as follows:

[0245]

[0246] Further, in the process of evolutionary game, the Logit protocol is used to dynamically simulate the mutual influence between the pumped storage operator and the wind-solar operator, and the specific expression is as follows:

[0247]

[0248] In the formula, η is an external influence factor.

[0249] In step 6, the capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system is solved, and the specific process of obtaining the capacity configuration scheme of the wind-solar-fixed / variable speed pumped storage combined operation system includes:

[0250] Step 1: initialize data, randomly generate n groups of strategy sets Sinv and S dg ;

[0251] Step2: randomly select a strategy from the strategy set until all strategies are selected;

[0252] Step3: calculate the revenue function of each strategy by constructing a data-driven distributed robust optimization model based on the random combination of strategies;

[0253] Step4: calculate the fitness function and strategy conversion coefficient and iterate through the kinetic equation;

[0254] Step5: repeat Step2-Step4 until the evolutionary stable state is reached;

[0255] Step6: output the capacity configuration scheme as the optimal capacity configuration scheme of the pumped storage joint operation system.

[0256] In an embodiment, referring to Figure 2 , a wind-solar-constant / variable speed pumped storage joint system is provided, comprising: a parameter information acquisition module, a pumped storage joint operation module, a robust optimization model construction module, an evolutionary game model construction module, a coupling module, a solving module, an information interaction module and an energy interaction module;

[0257] The parameter information acquisition module is configured to acquire parameter information of pumped storage operators, wind and solar operators and demand response systems; the parameter information includes parameter information of pumped storage operators, parameter information of wind and solar operators and parameter information of demand response systems;

[0258] The pumped storage joint operation module is configured to construct a pumped storage joint operation system model according to the parameter information; the pumped storage joint operation system model includes constructing pumped storage operator models, wind and solar operator models and demand response system models;

[0259] The robust optimization model construction module is configured to improve the accuracy and solving speed of each model in the pumped storage joint operation module; wherein the deterministic part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator models and the objective functions of the wind and solar operator models corresponding to the discrete variables, and the uncertainty part of the data-driven distributed robust optimization model is composed of the objective functions of the pumped storage operator models and the objective functions of the wind and solar operator models corresponding to the continuous variables;

[0260] The evolutionary game model construction module maps the pumped storage operators and the wind and solar operators into two populations φ inv , φ dg with the same internal parameters and information, and constructs an evolutionary game model of the wind-solar-constant / variable speed pumped storage joint operation system;

[0261] The coupling module is used to couple the evolutionary game model with the data-driven robust optimization model to obtain a capacity configuration model for the wind-solar-fixed / variable-speed pumped storage combined operation system;

[0262] The solving module is used to solve the capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system to obtain the capacity configuration plan of the wind-solar-fixed / variable speed pumped storage combined operation system.

[0263] Furthermore, information is transmitted between the parameter information acquisition module, pumping and storage joint operation module, robust optimization model construction module, evolutionary game model construction module, coupling module and solution module through the information interaction module, and energy is transmitted through the energy interaction module.

[0264] In one embodiment, see Figure 3 , provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the wind-solar-fixed / variable speed pumped storage combined capacity configuration method as described above.

[0265] In one embodiment, an electronically readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the wind-solar-fixed / variable speed pumped storage combined capacity configuration method as described above are implemented.

[0266] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for configuring the combined capacity of wind-solar and fixed / variable speed pumped storage, characterized by: Obtain parameter information of the pumped storage combined operation system, including parameter information of the pumped storage operator, parameter information of the wind and solar operator, and parameter information of the demand response system; Constructing a pumped storage combined operation system model based on the parameter information, including a pumped storage operator model, a wind and solar operator model, and a demand response system model; Establish a data-driven distributed robust optimization model. The deterministic part of the data-driven distributed robust optimization model consists of the objective functions of the pumped-storage operator model and the wind-solar operator model corresponding to discrete variables. The uncertain part of the data-driven distributed robust optimization model consists of the objective functions of the pumped-storage operator model and the wind-solar operator model corresponding to continuous variables. The pumped storage operators and wind and solar operators are mapped into two populations with consistent internal parameters and information. 、 , construct an evolutionary game model for the wind-solar-fixed / variable speed pumped storage combined operation system; By coupling the evolutionary game model with a data-driven robust optimization model, a capacity configuration model for a wind-solar-fixed / variable-speed pumped-storage combined operation system is obtained. Solve the capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system and obtain the capacity configuration scheme of the wind-solar-fixed / variable speed pumped storage combined operation system; in, The data-driven distributed robust optimization model converts the value of the uncertain part into the corresponding expectation, thus achieving the decoupling of the max-min problem. The decoupling of the max-min problem specifically uses the C&CG algorithm to divide the data-driven distributed robust optimization model into the main problem MP and the sub-problem SP for iterative solution; The compact form of the master problem MP is: , where is the slack variable, is the number of iterations, For the After iterations, the probability distribution of each scenario; The compact form of the subproblem SP is: Where, Obtain the optimal value of the deterministic variables for the main problem MP, is the number of iterations; substitute it into the subproblem SP and update the worst probability distribution of each scenario.

2. The method for configuring wind-solar-fixed / variable-speed pumped storage combined capacity according to claim 1, characterized in that: The parameter information of the pumped storage operator includes parameter information of fixed-speed pumped storage units and parameter information of variable-speed pumped storage units; The parameter information of the wind and solar operator includes wind turbine parameters and photovoltaic unit parameter information; The parameter information of the demand response system includes load parameter information.

3. The method for configuring wind-solar-fixed / variable-speed pumped storage combined capacity according to claim 1, characterized in that: The pumped storage operator model includes a fixed-speed pumped storage unit model and a variable-speed pumped storage unit model; The wind and solar operator model includes a wind turbine model, a photovoltaic generator model, and an energy storage system model; The demand response system model is a load model, including an interruptible load model, a transferable load model and a fixed load model.

4. The method for configuring wind-solar-fixed / variable-speed pumped storage combined capacity according to claim 1, characterized in that: The objective function of the pumped storage operator model includes the total cost of pumped storage construction, the start-up and shutdown loss cost of pumped storage construction, the pumped storage operation cost, the low-storage and high-generation revenue of pumped storage, the pumped storage capacity electricity fee revenue and the ancillary service revenue; The objective function of the wind and solar operator model includes wind and solar construction costs, wind and solar maintenance costs, wind and solar curtailment costs, wind and solar power sales revenue, and energy storage operation revenue.

5. The method for configuring wind-solar-fixed / variable-speed pumped storage combined capacity according to claim 4, characterized in that: The pumped storage operator model and the wind / solar operator model also include constraints; wherein: The constraints of the pumped storage operator model include fixed-speed pumped storage unit constraints, variable-speed pumped storage unit constraints, reservoir capacity constraints, and spinning reserve constraints. The constraints of the wind and solar operator model include wind and solar output constraints and energy storage output constraints.

6. The method for configuring wind-solar-fixed / variable speed pumped storage combined capacity according to claim 1, characterized in that: The specific process of iterative solution includes: Initialize the parameters, set the upper bound UB = +∞, the lower bound LB = -∞, set the probability distribution obtained by clustering as the initial probability distribution of each scene, and set the number of iterations ; Solve the MP problem and obtain the optimal value of the deterministic partial variables , and update the lower bound ; Will Substitute into the SP problem and find the worst probability distribution And update the upper bound ; like , stop the iteration and output the result; otherwise, let , And repeatedly solve the MP problem.

7. The method for configuring wind-solar-fixed / variable-speed pumped storage combined capacity according to claim 1, characterized in that: The evolutionary game model of the wind-solar-fixed / variable-speed pumped-storage combined operation system includes a game strategy set, a profit function and a replicator dynamic equation.

8. The method for configuring wind-solar-fixed / variable-speed pumped storage combined capacity according to claim 7, characterized in that: The game strategy set is a limited set of strategies formulated by the pumped storage operator and the wind and solar operator; The profit function is the objective function pursued by the pumped storage operator and the wind and solar operator in the evolutionary game process; The replicator dynamic equation is a strategy selection mechanism in the simulated game process constructed based on the game strategy set and the payoff function.

9. The method for configuring wind-solar-fixed / variable-speed pumped storage combined capacity according to claim 8, characterized in that: The Logit protocol is used to dynamically simulate the mutual influence between the pumped storage operator and the wind and solar operator in the evolutionary game process.

10. The method for configuring wind-solar-fixed / variable speed pumped storage combined capacity according to claim 1, characterized in that: The specific process of solving the capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system includes: Initialize data, randomly generate Group Policy Sets; Each group randomly selects strategies from the strategy set until all strategies are selected; By building a data-driven distributed robust optimization model, we can calculate the profit function of each strategy through random combination. Calculate the fitness function, strategy conversion coefficient and iterate through the dynamic equation until the evolutionary stable state is reached; The output capacity configuration scheme is used as the optimal capacity configuration scheme for the pumped storage combined operation system.

11. A wind-solar-fixed / variable speed pumped storage combined system, characterized in that: include: Parameter information acquisition module, used to obtain parameter information of pumped storage operators, wind and solar operators, and demand response systems; The parameter information includes parameter information of the pumped storage operator, parameter information of the wind and solar operator, and parameter information of the demand response system; A pumped storage combined operation module is used to construct a pumped storage combined operation system model based on the parameter information; the pumped storage combined operation system model includes constructing a pumped storage operator model, a wind and solar operator model, and a demand response system model; A robust optimization model construction module for improving the accuracy and solution speed of each model in the pumped-storage combined operation module; wherein the deterministic part of the data-driven distributed robust optimization model is composed of the objective function of the pumped-storage operator model and the objective function of the wind-solar operator model corresponding to discrete variables, and the uncertain part of the data-driven distributed robust optimization model is composed of the objective function of the pumped-storage operator model and the objective function of the wind-solar operator model corresponding to continuous variables; Evolutionary game model construction module, pumped storage operators and wind and solar operators are mapped into two populations with consistent internal parameters and information 、 , construct an evolutionary game model for the wind-solar-fixed / variable speed pumped storage combined operation system; The coupling module is used to couple the evolutionary game model with the data-driven robust optimization model to obtain the capacity configuration model of the wind-solar-fixed / variable-speed pumped storage combined operation system; A solution module is used to solve the capacity configuration model of the wind-solar-fixed / variable speed pumped storage combined operation system and obtain the capacity configuration plan of the wind-solar-fixed / variable speed pumped storage combined operation system; Among them, in the robust optimization model construction module, The data-driven distributed robust optimization model converts the value of the uncertain part into the corresponding expectation, thus achieving the decoupling of the max-min problem. The decoupling of the max-min problem specifically uses the C&CG algorithm to divide the data-driven distributed robust optimization model into the main problem MP and the sub-problem SP for iterative solution; The compact form of the master problem MP is: , where is the slack variable, is the number of iterations, For the After iterations, the probability distribution of each scenario; The compact form of the subproblem SP is: Where, Obtain the optimal value of the deterministic variables for the main problem MP, is the number of iterations; substitute it into the subproblem SP and update the worst probability distribution of each scenario.

12. The wind-solar-fixed / variable speed pumped storage combined system according to claim 11, characterized in that: The wind-solar-fixed / variable speed pumped storage combined system also includes an information interaction module and an energy interaction module; The parameter information acquisition module, the pumping and storage joint operation module, the robust optimization model construction module, the evolutionary game model construction module, the coupling module and the solution module realize information transmission through the information interaction module, and realize energy transmission through the energy interaction module.

13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by the processor, the processor executes the steps of the wind-solar-fixed / variable-speed pumped storage combined capacity configuration method according to any one of claims 1 to 10.

14. An electronically 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 wind-solar-fixed / variable-speed pumped storage combined capacity configuration method as described in any one of claims 1 to 10 are implemented.

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