Electric quantity and electricity price optimization method and system based on combination of wind, light and water storage

Abstract

The invention relates to the technical field of operation scheduling of a'wind-light-water-storage 'joint participation market, and particularly discloses a wind-light-water-storage joint-based electric quantity and electricity price optimization method and system, and the method comprises the steps: building a Nash negotiation cooperative game model with the maximization of an income improvement value of a wind-light-water-storage multi-energy complementary system as a target; according to a Nash negotiation cooperative game model, constructing a transaction electric quantity optimization model of the complex, wherein the transaction electric quantity optimization model comprises a target function with maximum benefits of the wind-light-water-storage alliance, a wind power operation constraint, a photovoltaic operation constraint, a hydropower operation constraint and a pumped storage operation constraint; according to a Nash negotiation cooperative game model, constructing a transaction electricity price optimization model of the complex, wherein the transaction electricity price optimization model comprises a target function and related operation constraints between electric energy transactions; and according to an alternating direction multiplier algorithm, respectively determining optimal solutions of the transaction electric quantity optimization model and the transaction electricity price optimization model.

Description

Technical Field

[0001] The present invention relates to the field of operation and scheduling technology of a "wind-solar-hydro-storage" joint market, and specifically to a method and system for optimizing the electricity quantity and electricity price based on the combination of wind, solar, hydro and storage. Background Art

[0002] The wind, solar, and hydropower integrated power generation project has the advantages of strong regulation ability, great transformation potential, and low cost, and will be one of the important directions for the construction of large-scale energy bases in the future. At the same time, the power markets in various regions are also in the process of construction, and the multi-energy complementary system will gradually participate in market transactions. Carrying out research on the operation strategy of the wind, solar, and hydropower multi-energy complementary system participating in market transactions is beneficial to give full play to the complementary benefits of renewable energy units, maximize the comprehensive benefits and resource utilization of wind, solar, and water resources, and has important practical significance and theoretical value for the development of large-scale energy bases and renewable energy consumption in the future.

[0003] In order to improve the economy of wind, solar, and hydropower systems and the absorption rate of renewable energy, the present invention proposes an optimization scheduling model for the joint participation of "wind, solar, and hydropower" in the market based on the consideration of the economic benefits, power supply reliability, and greenness of the multi-energy system, thereby ensuring the economic, social, and environmental benefits of the "wind, solar, and hydropower" multi-energy system and promoting the sustainable development of the multi-energy base. Summary of the invention

[0004] To achieve the purpose of the present invention, the present application provides a method for optimizing the electricity price based on the combination of wind, solar, and hydropower, including:

[0005] Step S1: Taking the maximization of the revenue enhancement value of the wind, solar, and hydropower multi-energy complementary system as the goal, a Nash negotiation cooperation game model is constructed;

[0006] Step S2: constructing a consortium's transaction power optimization model based on the Nash negotiation cooperation game model, including: an objective function for maximizing the wind-solar-hydropower alliance's revenue, wind power operation constraints, photovoltaic operation constraints, hydropower operation constraints, and pumped storage operation constraints;

[0007] Step S3: constructing a transaction electricity price optimization model of the consortium according to the Nash negotiation cooperative game model, including: an objective function and relevant operating constraints between electricity transactions;

[0008] Step S4: determining the optimal solutions of the transaction power optimization model and the transaction power price optimization model respectively according to the alternating direction multiplier algorithm.

[0009] In some specific embodiments, the step S1 includes:

[0010] Taking the maximization of the benefit enhancement value of the wind, solar, and hydropower multi-energy complementary system as the decision-making goal, the Nash negotiation game theory is used to determine the optimization objective function of the cooperation subject:

[0011]

[0012] In the formula, represents the revenue of the pumped storage power station under the joint state, represents the maximum revenue of the pumped storage power station in an independent state, represents the revenue of wind power in the joint state, Indicates the maximum benefit of wind power in an independent state, represents the revenue of photovoltaic under the joint state, Indicates the maximum benefit of photovoltaic in independent state, represents the revenue of hydropower in the joint state, Represents the maximum benefit of hydropower in an independent state.

[0013] In some specific embodiments, step S2 includes:

[0014] Step S21: Establish the objective function of the consortium's electricity trading optimization model, taking the maximum benefit of the wind, solar and hydropower alliance as the objective function, as shown below:

[0015]

[0016] In the formula, represents the joint venture's profit, w WD 、w PV 、w HY 、w PS Respectively represent the income value of each unit of the electricity transaction between wind turbines, photovoltaic units, hydropower units and pumped storage units;

[0017] Step S22: constructing the operation constraints of the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines according to the objective function of the consortium's electricity trading optimization model.

[0018] In some specific embodiments, step S3 includes:

[0019] The objective function for solving the transaction electricity price optimization model is established:

[0020]

[0021] In the formula, They represent the optimal solutions for wind turbines, photovoltaic units, hydropower units and pumped storage units in the consortium’s electricity trading optimization model. They respectively represent the electricity transaction amount between wind, solar and water units and pumped storage units.

[0022] In some specific embodiments, step S4 includes:

[0023] The optimal solution of the trading electricity quantity optimization model is imported as a parameter into the trading electricity price optimization model to iteratively determine the optimal solution of the trading electricity price optimization model, and then the profits of the wind-solar-hydropower alliance and the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines are calculated according to the optimal solution of the trading electricity price optimization model.

[0024] To achieve the same invention purpose, the present application also provides a system for optimizing electricity quantity and electricity price based on wind, solar, and hydropower storage, including:

[0025] Game model construction module: used to build a Nash negotiation cooperation game model with the goal of maximizing the value of the wind, solar, and hydropower multi-energy complementary system;

[0026] The electricity model construction module is used to construct the transaction electricity optimization model of the consortium according to the Nash negotiation cooperation game model, including: the objective function of maximizing the benefits of the wind-solar-hydropower alliance, wind power operation constraints, photovoltaic operation constraints, hydropower operation constraints, and pumped storage operation constraints;

[0027] The electricity price model construction module is used to construct a transaction electricity price optimization model of the consortium according to the Nash negotiation cooperative game model, including: an objective function and relevant operating constraints between electricity transactions;

[0028] Model solving module: used to determine the optimal solutions of the transaction power optimization model and the transaction power price optimization model respectively according to the alternating direction multiplier algorithm.

[0029] In some specific embodiments, the game model building module is used to:

[0030] Taking the maximization of the benefit enhancement value of the wind, solar, and hydropower multi-energy complementary system as the decision-making goal, the Nash negotiation game theory is used to determine the optimization objective function of the cooperation subject:

[0031]

[0032] In the formula, represents the revenue of the pumped storage power station under the joint state, represents the maximum revenue of the pumped storage power station in an independent state, represents the revenue of wind power in the joint state, Indicates the maximum benefit of wind power in an independent state, represents the revenue of photovoltaic under the joint state, Indicates the maximum benefit of photovoltaic in independent state, represents the revenue of hydropower in the joint state, Represents the maximum benefit of hydropower in an independent state.

[0033] In some specific embodiments, the power model building module is used to perform the following steps:

[0034] Step S21: Establish the objective function of the consortium's electricity trading optimization model, taking the maximum benefit of the wind, solar and hydropower alliance as the objective function, as shown below:

[0035]

[0036] In the formula, represents the joint venture's profit, w WD 、w PV 、w HY 、w PS Respectively represent the income value of each unit of the electricity transaction between wind turbines, photovoltaic units, hydropower units and pumped storage units;

[0037] Step S22: constructing the operation constraints of the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines according to the objective function of the consortium's electricity trading optimization model.

[0038] In some specific embodiments, the electricity price model building module is used to:

[0039] The objective function for solving the transaction electricity price optimization model is established:

[0040]

[0041] In the formula, They represent the optimal solutions for wind turbines, photovoltaic units, hydropower units and pumped storage units in the consortium’s electricity trading optimization model. They respectively represent the electricity transaction amount between wind, solar and water units and pumped storage units.

[0042] In some specific embodiments, the model solving module is used to:

[0043] The optimal solution of the trading electricity quantity optimization model is imported as a parameter into the trading electricity price optimization model to iteratively determine the optimal solution of the trading electricity price optimization model, and then the profits of the wind-solar-hydropower alliance and the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines are calculated according to the optimal solution of the trading electricity price optimization model.

[0044] Beneficial effects of the above technical solution:

[0045] This application provides a method for optimizing the operation of a "wind-solar-hydro-storage" multi-energy system that jointly participates in the electricity market, thereby ensuring that while economic benefits grow, social responsibilities can be fulfilled, the greenness and reliability of electricity supply can be improved, and a solution can be provided for improving the comprehensive benefits of the "wind-solar-hydro-storage" multi-energy complementary system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 A flow chart of a method for optimizing electricity quantity and electricity price based on wind, solar, and hydropower storage combined is provided for one embodiment of the present invention;

[0048] Figure 2 A schematic diagram of the structure of a system for optimizing electricity quantity and electricity price based on wind, solar, and hydropower storage combined is provided for one embodiment of the present invention;

[0049] Figure 3 A schematic diagram of a solution idea of ​​a Nash negotiation cooperation game model for a method for optimizing the electricity quantity and electricity price based on the combination of wind, solar, and hydropower storage provided by an embodiment of the present invention;

[0050] Figure 4 A schematic diagram of wind turbine output according to an optimization method for electricity quantity and electricity price based on wind, solar, and hydropower storage combined is provided in accordance with an embodiment of the present invention;

[0051] Figure 5 A schematic diagram of photovoltaic unit output based on a method for optimizing electricity quantity and electricity price based on wind, solar, and hydropower storage combined is provided as an embodiment of the present invention;

[0052] Figure 6 A schematic diagram of a hydropower unit output based on a method for optimizing the electricity quantity and electricity price of wind, solar, and hydropower storage combined is provided as an embodiment of the present invention;

[0053] Figure 7 A schematic diagram of price curves of energy market and ancillary service market based on a method for optimizing electricity price of wind, solar, and hydropower combined, provided in accordance with an embodiment of the present invention;

[0054] Figure 8 A schematic diagram of a wind power joint operation trading strategy based on a wind-solar-hydropower joint electricity quantity and price optimization method provided in one embodiment of the present invention;

[0055] Fig. 9A schematic diagram of a photovoltaic joint operation trading strategy based on a wind-solar-hydropower combined electricity quantity and price optimization method provided in one embodiment of the present invention;

[0056] Fig.10 A schematic diagram of a hydropower joint operation trading strategy based on a wind-solar-hydropower joint electricity quantity and price optimization method provided in one embodiment of the present invention;

[0057] Fig.11 A schematic diagram of a pumped storage joint operation capacity allocation strategy based on a wind-solar-hydropower joint electricity price optimization method provided by an embodiment of the present invention;

[0058] Fig.12 A schematic diagram of the convergence process of the revenue value of the electric energy transaction amount of a wind turbine generator set based on a method for optimizing the electricity quantity and electricity price of wind, solar, and hydropower storage combined according to an embodiment of the present invention;

[0059] Fig.13 A schematic diagram of the convergence process of the income value of the electricity transaction amount of a photovoltaic unit based on a wind-solar-hydropower combined electricity quantity and electricity price optimization method provided by an embodiment of the present invention;

[0060] Fig.14 A schematic diagram of the convergence process of the revenue value of the electric energy transaction amount of a hydropower unit based on a method for optimizing the electricity quantity and electricity price of wind, solar, and hydropower storage combined, provided in one embodiment of the present invention;

[0061] Fig.15 A schematic diagram of the convergence process of the revenue value of the electric energy transaction amount of a pumped storage unit based on a wind, solar, and hydropower combined electricity quantity and electricity price optimization method provided by an embodiment of the present invention;

[0062] Fig.16 A schematic diagram of the convergence process of the revenue value of the electric energy transaction amount of a wind-solar-hydro-storage alliance based on a method for optimizing the electricity quantity and electricity price of wind-solar-hydro-storage combined power provided by an embodiment of the present invention;

[0063] Fig.17 A schematic diagram of the convergence results of the electric energy transaction payment negotiation sub-problem of a method for optimizing the electricity quantity and electricity price based on the combination of wind, solar and hydropower provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0065] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0066] Embodiment 1

[0067] An embodiment of the present invention provides a method for optimizing the electricity price based on wind, solar and hydropower combined. Figure 1 As shown, including:

[0068] Step S1: With the goal of maximizing the value of the wind, solar, and hydropower multi-energy complementary system, a Nash negotiation cooperation game model is constructed.

[0069] In a specific embodiment of the present invention, the step S1 comprises:

[0070] Taking the maximization of the benefit enhancement value of the wind, solar, and hydropower multi-energy complementary system as the decision-making goal, the Nash negotiation game theory is used to determine the optimization objective function of the cooperation subject:

[0071]

[0072] In the formula, represents the revenue of the pumped storage power station under the joint state, represents the maximum revenue of the pumped storage power station in an independent state, represents the revenue of wind power in the joint state, Indicates the maximum benefit of wind power in an independent state, represents the revenue of photovoltaic under the joint state, Indicates the maximum benefit of photovoltaic in independent state, represents the revenue of hydropower in the joint state, Represents the maximum benefit of hydropower in an independent state.

[0073] Specifically, since the Nash negotiation cooperative game model is difficult to solve directly, the present invention converts the model into two sub-models that are easy to solve: a consortium transaction electricity quantity optimization model and a consortium transaction electricity price optimization model. By obtaining the optimal solutions of the two sub-models in turn, the optimal solution of the Nash negotiation cooperative game model can be obtained.

[0074] Step S2: constructing a consortium's electricity trading optimization model based on the Nash negotiation cooperation game model, including: an objective function for maximizing the wind-solar-hydropower alliance's revenue, wind power operation constraints, photovoltaic operation constraints, hydropower operation constraints, and pumped storage operation constraints.

[0075] In a specific embodiment of the present invention, step S2 includes:

[0076] Step S21: Establish the objective function of the consortium's electricity trading optimization model, taking the maximum benefit of the wind, solar and hydropower alliance as the objective function, as shown below:

[0077]

[0078] In the formula, represents the joint venture profit, w WD 、w PV 、w HY 、w PS Respectively represent the income value of each unit of the electricity transaction between wind turbines, photovoltaic units, hydropower units and pumped storage units;

[0079] Step S22: constructing the operation constraints of wind turbines, photovoltaic units, hydropower units and pumped storage units according to the objective function of the consortium's electricity trading optimization model. ;

[0080] Specifically, the operation constraints of wind power are constructed as follows:

[0081]

[0082] Where N is the number of scenes, ε n is the probability of scene n, λ t represents the energy price in the electricity market at time t, represents the day-ahead market bidding power of wind turbines at time t, r WD represents the operation and maintenance cost of wind turbines, + represents the positive deviation penalty unit price, λ - represents the negative deviation penalty unit price, represents the positive deviation of the wind turbine at time t in scenario n, Indicates the negative deviation of the wind turbine at time t in scenario n, Represents the power generated by the wind turbine at time t in scenario n.

[0083] The output of wind power is closely related to wind speed and has randomness. Other related operating constraints are as follows:

[0084]

[0085] Where V n,t is the wind speed at time t in scene n, V CI is the cut-in wind speed, V CO is the cut-out wind speed, P WR is the rated power of the wind turbine, V R is the rated wind speed of the wind turbine.

[0086] S23. Construct the operation constraints of photovoltaics as follows:

[0087]

[0088] In the formula, represents the day-ahead market bidding power of the PV unit at time t, r PV It represents the operation and maintenance cost of photovoltaic units per kilowatt-hour. represents the positive deviation of the photovoltaic unit at time t in scenario n, Indicates the negative deviation of the photovoltaic unit at time t in scenario n, Represents the power generation of the photovoltaic unit at time t in scenario n.

[0089] The output of photovoltaic units is closely related to the light intensity and has randomness. Other related operating constraints are as follows:

[0090]

[0091] In the formula, I n,t is the illumination intensity of scene n at time t, K c For low irradiance, I s is the standard light intensity, P PR is the rated power of the photovoltaic unit.

[0092] S24. Construct the operation constraints of hydropower as follows:

[0093]

[0094] Where P t HY represents the day-ahead market bidding power of the hydropower unit at time t, r HY represents the operation and maintenance cost per kilowatt-hour of the hydropower unit, It represents the positive deviation of the hydropower unit at time t in scenario n, Indicates the negative deviation of the hydropower unit at time t in scenario n, Represents the power generation of the hydropower unit at time t in scenario n.

[0095] The output of a hydropower unit is related to the reservoir capacity, power generation flow, and natural water inflow. Other related operating constraints are as follows:

[0096]

[0097] In the formula, is the power generation coefficient of the hydropower unit at time t, is the power generation flow at time t in scenario n, is the reservoir capacity at time t in scenario n, is the natural water flow at time t in scenario n.

[0098]

[0099] In the formula, represents the market revenue of hydropower, P t HY，m represents the day-ahead market bidding power of the hydropower unit at time t, P t HY2PS,s Represents the amount of electricity sold to the pumped storage unit at time t.

[0100] S25. Construct the operation constraints of pumped storage. The relevant operation constraints of the energy storage unit need to consider the unit's extraction and frequency modulation power constraints, maximum charging and discharging hour constraints, charging and discharging number constraints, capacity constraints, and the quantitative relationship constraints between charging and discharging power and capacity, as shown below:

[0101]

[0102]

[0103] In the formula, Indicates the maximum discharge power, represents the maximum charging power, θ represents the upper limit of the capacity of the energy storage unit participating in the frequency regulation market, t e ,t d Indicates the continuous charge and discharge time, T e , T d Indicates the continuous charging and discharging time in each cycle. Indicates the maximum charge and discharge time, V0 ES It represents the storage capacity at the initial moment of the same charge and discharge cycle. Indicates the capacity at time T in the same charge and discharge cycle, V tt PS represents the capacity at time t, Indicates the minimum capacity, represents the maximum capacity, φ represents the coefficient of converting the discharge or charge of the energy storage unit into capacity, η1 represents the energy storage charging loss rate, and η2 represents the energy storage discharging loss rate.

[0104] When pumped storage units participate in the multi-energy complementary system, their trading partners include market users, wind power, photovoltaic power, and hydropower. By optimizing the declaration strategy, the capacity allocation strategy in the electric energy market and ancillary service market and the purchase and sale of electricity between trading partners are determined. The operation model of pumped storage units in the cooperative state is as follows:

[0105]

[0106] In the formula, It represents the net profit of the energy storage unit in the cooperative state, P t PS2WD,s , P t PS2PV,s , P tPS2HY,s They respectively represent the amount of electricity purchased by the pumped storage unit from the wind and solar water units at time t.

[0107] Step S3: constructing a transaction electricity price optimization model of the consortium according to the Nash negotiation cooperative game model, including: an objective function, and relevant operating constraints between electricity transactions.

[0108] In a specific embodiment of the present invention, step S3 includes:

[0109] The objective function for solving the transaction electricity price optimization model is established:

[0110]

[0111] In the formula, They represent the optimal solutions for wind turbines, photovoltaic units, hydropower units and pumped storage units in the consortium’s electricity trading optimization model. They respectively represent the electricity transaction amount between wind, solar and water units and pumped storage units.

[0112] Specifically, when solving the objective function, it is necessary to consider that the domain of the logarithmic function is greater than 0, that is, the benefits of the cooperative state of each subject should be greater than the benefits of the independent operation state. The relevant operation constraints are as follows:

[0113]

[0114] In the formula, They respectively represent the electricity transaction amount between wind, solar and water units and pumped storage units.

[0115] Step S4: determining the optimal solutions of the transaction power optimization model and the transaction power price optimization model respectively according to the alternating direction multiplier algorithm.

[0116] In a specific embodiment of the present invention, step S4 includes:

[0117] The optimal solution of the trading electricity quantity optimization model is imported as a parameter into the trading electricity price optimization model to iteratively determine the optimal solution of the trading electricity price optimization model, and then the profits of the wind-solar-hydropower alliance and the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines are calculated according to the optimal solution of the trading electricity price optimization model.

[0118] S41. Solving the optimization model of electricity transaction of the consortium. For the optimization model of electricity transaction of the wind, solar and hydropower consortium, first let:

[0119]

[0120] In the formula, They represent the amount of electricity that the wind, solar and water generators expect to sell to the pumped storage generators at time t; They respectively represent the amount of electricity that the pumped storage unit expects to purchase from the wind and solar water units at time t.

[0121] When satisfied It indicates that the wind, solar and hydropower units have reached a cooperative consensus on the amount of electricity trading through negotiations.

[0122] Take the opposite of the objective function of the consortium's electricity trading optimization problem and transform it into a minimum value problem. At the same time, introduce the Lagrange multiplier and penalty factor ρ WD , PV , HY , we can get the augmented Lagrangian function of the objective function:

[0123]

[0124] According to the ADMM algorithm solution, the equation is decomposed to obtain the quantum optimization models of wind power, photovoltaic power, hydropower, and pumped storage. The specific steps of the algorithm solution are as follows:

[0125] 1) Set the relevant parameters as follows: maximum number of iterations k max =50, convergence accuracy ε = 10 -5 , penalty factor ρ WD =ρ PV =ρ HY =10 -4 , initialization iteration number k = 0, initial wind, solar and water main power sales Lagrange multipliers

[0126] 2) For energy storage, obtain expected electricity sales data from wind power, photovoltaic power, and hydropower Solve the distributed optimization model of pumped storage and obtain the expected amount of electricity purchased

[0127] 3) For wind power, photovoltaic power and hydropower, the expected amount of electricity purchased by pumped storage Solve the respective distributed optimization models to obtain the expected electricity sales

[0128] 4) Update the Lagrange multiplier and the number of iterations;

[0129] 5) Calculate the dual residual

[0130] 6) Determine the convergence of the algorithm. If the iteration termination condition is met, the iteration is terminated. Otherwise, return to step 2) and repeat the calculation until the convergence condition or the maximum number of iterations is met.

[0131] S42. Solve the optimization model of the transaction electricity price of the consortium. By solving the optimization model of the transaction electricity, the optimal transaction electricity of wind power-pumped storage, photovoltaic-pumped storage and hydropower-pumped storage can be obtained at the same time: Substituting it into:

[0132]

[0133] According to the principle of ADMM algorithm, auxiliary variables are introduced and Decouple the transaction prices between wind, solar, and hydropower. represents the expected electricity price of wind power, photovoltaic power and hydropower, It represents the unit price that pumped storage expects to purchase electricity from wind power, photovoltaic power and hydropower.

[0134] Introducing Lagrange multipliers and penalty factor ψ WD , PV , HY , the augmented Lagrangian function of the objective function is:

[0135]

[0136] According to the solution method of the ADMM algorithm, the above formula needs to be decomposed to obtain the sub-optimization model of electricity trading prices for wind power, photovoltaic power, hydropower and pumped storage.

[0137] The specific steps of the algorithm are as follows:

[0138] 1) Set the relevant parameters as follows: maximum number of iterations k max =50, convergence accuracy ε = 10 -5 , penalty factor ψ WD =ψ PV =ψ HY =10 -4 , initialization iteration number k = 0, initial wind, solar and water main electricity selling price Lagrange multipliers

[0139] 2) For pumped storage, obtain the expected electricity price data from wind power, photovoltaic power, and hydropower Solve the optimization model of the electricity trading price of the pumped storage unit and obtain the expected electricity purchase price data

[0140] 3) For wind power, photovoltaic power and hydropower, the expected purchase price of electricity from the pumped storage entity Solve the respective electricity trading price sub-optimization models, and obtain the expected electricity selling price

[0141] 4) Update the Lagrange multiplier and the number of iterations;

[0142] 5) Calculate the dual residual

[0143] 6) Determine the convergence of the algorithm. If the iteration termination condition is met, the iteration is terminated. Otherwise, return to step 2) and repeat the calculation until the convergence condition or the maximum number of iterations is met.

[0144] Example analysis:

[0145] In the example simulation, the Shuibuya Hydropower Station in the Qingjiang River Basin in Central China is taken as the main body, and the Qingjiang Pumped Storage Power Station is combined to establish an integrated wind, solar and water storage base. In this multi-energy complementary base, the capacity of the hydropower station is 1820MW, the capacity of the photovoltaic power station is 720MW, the capacity of the wind power station is 600MW, and the installed capacity of the pumped storage power station is 900MW. The example uses the wind speed, light intensity, and water inflow in the Qingjiang River Basin from June 1, 2021 to May 30, 2022 as the original data, and calculates the output of the above-mentioned wind, solar and water units based on the parameters of various units. The time resolution is set to 1h, and the time window is set to 1 day, and a total of 360 sample sets are obtained. The output simulation sample sets of the above-mentioned units are deeply embedded and clustered. The optimal number of clusters is determined to be 6 according to the elbow method. The optimizer is used for optimization. The clustering results are as follows Figure 4-Figure 6 The probability of each scenario is shown in Table 1.

[0146] Table 1 Probability of each scenario

[0147]

[0148] The relevant cost parameters, operation constraint parameters and market deviation penalty parameters of wind power, photovoltaic, hydropower and pumped storage units are shown in Table 2.

[0149] Table 2 Example parameters

[0150]

[0151]

[0152]

[0153] Considering that my country is still in the early stages of power market construction and price signals have not yet been formed, we take the node electricity price curve and frequency regulation mileage and capacity price curve of a typical day in the PJM market of the United States as an example for simulation and calculation. The price curve is as follows: Figure 7 shown.

[0154] When wind power, photovoltaic, hydropower, and pumped storage units are operated jointly, the market declaration volume of each unit and the transaction volume within the unit are obtained based on the joint bidding optimization model and the alternating direction multiplier algorithm, such as Figure 8-Figure 11 shown. Figure 8 , Fig. 9 , Fig.10 The market declaration and internal trading strategies of wind, solar and water turbines under joint operation are given. Compared with the declaration strategies under independent operation, joint trading with pumped storage units reduces the impact of output uncertainty of wind, solar and water turbines, reduces market deviation assessment penalties, and thus increases the benefits of each unit. Fig.11 The trading strategy between pumped storage units and wind, solar, water and power grid is given. In the period of 1:00-5:00, the load and electricity price are at the low point, and the output of wind power and hydropower is redundant. Pumped storage can purchase electricity from wind power, hydropower and power grid, reducing the cost of electricity purchase; in the period of 11:00-15:00, the output of hydropower and photovoltaic units is high, and the output of wind power is low. Pumped storage can purchase electricity from photovoltaic, hydropower and power grid. When the pumped storage unit is in the pumping state, the frequency regulation income is high in some overlapping periods. The pumping time can be appropriately increased, and the capacity proportion participating in the energy market can be reduced, so as to obtain higher auxiliary service income. In the periods of 7:00-10:00 and 16:00-19:00, the pumped storage unit is in the discharge state. During the period of high market electricity price, the unit allocates capacity in favor of the energy market, so as to obtain higher electricity income.

[0155] The benefit functions of each subject in the wind-solar-hydropower alliance transaction power optimization sub-problem (excluding the internal transaction amount offset between subjects) and the convergence results of the alliance benefit function are as follows: Figure 12-16 As shown, it can be seen that the ADMM algorithm achieves the convergence of the total revenue of the wind, solar and hydropower alliance after about 21 iterations, and has good convergence characteristics.

[0156] Furthermore, the convergence result of the alliance transaction price subproblem is obtained through the ADMM algorithm, such as Fig.17 As shown. According to the convergence results, the electricity trading price between wind, solar, and hydropower units and pumped storage units fluctuates between 0 yuan / MWh and 302.58 yuan / MWh. Among them, the trading electricity price of hydropower units and pumped storage is lower than that of wind and solar units in most periods, which has a certain negotiation price advantage, thereby increasing the trading electricity of hydropower and pumped storage.

[0157] Based on the convergence results of the two sub-problems and the declaration and trading strategies of wind, solar and hydropower after the joint operation, the profit results after the joint operation of wind, solar and hydropower are obtained, as shown in Table 3.

[0158] Table 3 Benefits after wind, solar and hydropower combined operation

[0159]

[0160] Through the cooperation among multiple entities, the flexibility of each entity in participating in the market has been improved, the penalty cost of deviation assessment of wind, solar and hydropower units has been reduced, and the pumped storage units have effectively avoided the impact of the uncertainty of wind, solar and hydropower units. At the same time, with the cooperation of wind, solar and hydropower, the purchase cost of electricity for pumped storage units has been reduced, the phenomenon of wind, solar and hydropower abandonment has been reduced, and the renewable energy consumption rate has been increased, thereby increasing the electricity income of each unit and achieving win-win cooperation among multiple entities.

[0161] Embodiment 2

[0162] An embodiment of the present invention provides a system for optimizing the electricity price based on wind, solar, and hydropower. Figure 2 As shown, including:

[0163] Game model construction module 10: used to construct a Nash negotiation cooperation game model with the goal of maximizing the value of the wind, solar, and hydropower multi-energy complementary system;

[0164] The electricity model construction module 20 is used to construct a transaction electricity optimization model of the consortium according to the Nash negotiation cooperation game model, including: an objective function for maximizing the benefits of the wind-solar-hydropower alliance, wind power operation constraints, photovoltaic operation constraints, hydropower operation constraints, and pumped storage operation constraints;

[0165] The electricity price model construction module 30 is used to construct a transaction electricity price optimization model of the consortium according to the Nash negotiation cooperative game model, including: an objective function and relevant operating constraints between electricity transactions;

[0166] Model solving module 40: used to determine the optimal solutions of the transaction power optimization model and the transaction power price optimization model respectively according to the alternating direction multiplier algorithm.

[0167] In some specific embodiments, the game model building module 10 is used to:

[0168] Taking the maximization of the benefit enhancement value of the wind, solar, and hydropower multi-energy complementary system as the decision-making goal, the Nash negotiation game theory is used to determine the optimization objective function of the cooperation subject:

[0169]

[0170] In the formula, represents the revenue of the pumped storage power station under the joint state, represents the maximum revenue of the pumped storage power station in an independent state, represents the revenue of wind power in the joint state, Indicates the maximum benefit of wind power in an independent state, represents the revenue of photovoltaic under the joint state, Indicates the maximum benefit of photovoltaic in independent state, represents the revenue of hydropower in the joint state, Represents the maximum benefit of hydropower in an independent state.

[0171] In some specific embodiments, the power model building module 20 is used to perform the following steps:

[0172] Step S21: Establish the objective function of the consortium's electricity trading optimization model, taking the maximum benefit of the wind, solar and hydropower alliance as the objective function, as shown below:

[0173]

[0174] In the formula, represents the joint venture's profit, w WD 、w PV 、w HY 、w PS Respectively represent the income value of each unit of the electricity transaction between wind turbines, photovoltaic units, hydropower units and pumped storage units;

[0175] Step S22: constructing the operation constraints of the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines according to the objective function of the consortium's electricity trading optimization model.

[0176] In some specific embodiments, the electricity price model building module 30 is used to:

[0177] The objective function for solving the transaction electricity price optimization model is established:

[0178]

[0179] In the formula, They represent the optimal solutions for wind turbines, photovoltaic units, hydropower units and pumped storage units in the consortium’s electricity trading optimization model. They respectively represent the electricity transaction amount between wind, solar and water units and pumped storage units.

[0180] In some specific embodiments, the model solving module 40 is used to:

[0181] The optimal solution of the trading electricity quantity optimization model is imported as a parameter into the trading electricity price optimization model to iteratively determine the optimal solution of the trading electricity price optimization model, and then the profits of the wind-solar-hydropower alliance and the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines are calculated according to the optimal solution of the trading electricity price optimization model.

[0182] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0183] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referenced to each other. The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. 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 terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention. Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0184] The method and device provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

[0185] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", "one specific embodiment" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application 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. However, 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 application.

Claims

1. A method for optimizing electricity quantity and electricity price based on wind, solar, and hydropower storage, characterized in that: include: Step S1: Taking the maximization of the revenue enhancement value of the wind, solar, and hydropower multi-energy complementary system as the goal, a Nash negotiation cooperation game model is constructed; Step S2: constructing a consortium's transaction power optimization model based on the Nash negotiation cooperation game model, including: an objective function for maximizing the wind-solar-hydropower alliance's revenue, wind power operation constraints, photovoltaic operation constraints, hydropower operation constraints, and pumped storage operation constraints; Step S3: constructing a transaction electricity price optimization model of the consortium according to the Nash negotiation cooperative game model, including: an objective function and relevant operating constraints between electricity transactions; Step S4: According to the alternating direction multiplier algorithm, the optimal solutions of the transaction power optimization model and the transaction power price optimization model are determined respectively.

2. The method for optimizing the electricity quantity and electricity price based on the combination of wind, solar, and hydropower according to claim 1 is characterized in that: The step S1 comprises: Taking the maximization of the benefit enhancement value of the wind, solar, and hydropower multi-energy complementary system as the decision-making goal, the Nash negotiation game theory is used to determine the optimization objective function of the cooperation subject: In the formula, represents the revenue of the pumped storage power station under the joint state, represents the maximum revenue of the pumped storage power station in an independent state, represents the revenue of wind power in the joint state, Indicates the maximum benefit of wind power in an independent state, represents the revenue of photovoltaic under the joint state, Indicates the maximum benefit of photovoltaic in independent state, represents the revenue of hydropower in the joint state, Represents the maximum benefit of hydropower in an independent state.

3. The method for optimizing the electricity quantity and electricity price based on the combination of wind, solar, and hydropower according to claim 1 is characterized in that: The step S2 comprises: Step S21: Establish the objective function of the consortium's electricity trading optimization model, taking the maximum benefit of the wind, solar and hydropower alliance as the objective function, as shown below: In the formula, represents the joint venture's profit, w WD 、w PV 、w HY 、w PS Respectively represent the income value of each unit of the electricity transaction between wind turbines, photovoltaic units, hydropower units and pumped storage units; Step S22: constructing the operation constraints of the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines according to the objective function of the consortium's electricity trading optimization model.

4. The method for optimizing the electricity quantity and electricity price based on the combination of wind, solar, and hydropower according to claim 1 is characterized in that: The step S3 comprises: The objective function for solving the transaction electricity price optimization model is established: In the formula, They represent the optimal solutions for wind turbines, photovoltaic units, hydropower units and pumped storage units in the consortium’s electricity trading optimization model. They respectively represent the electricity transaction amount between wind, solar and water units and pumped storage units.

5. The method for optimizing the electricity quantity and electricity price based on the combination of wind, solar and hydropower according to claim 1 is characterized in that: The step S4 comprises: The optimal solution of the trading electricity quantity optimization model is imported as a parameter into the trading electricity price optimization model to iteratively determine the optimal solution of the trading electricity price optimization model, and then the profits of the wind-solar-hydropower alliance and the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines are calculated according to the optimal solution of the trading electricity price optimization model.

6. A system for optimizing electricity quantity and electricity price based on wind, solar, and hydropower storage, characterized in that: include: Game model construction module: used to build a Nash negotiation cooperation game model with the goal of maximizing the value of the wind, solar, and hydropower multi-energy complementary system; The electricity model construction module is used to construct the transaction electricity optimization model of the consortium according to the Nash negotiation cooperation game model, including: the objective function of maximizing the benefits of the wind-solar-hydropower alliance, wind power operation constraints, photovoltaic operation constraints, hydropower operation constraints, and pumped storage operation constraints; The electricity price model construction module is used to construct a transaction electricity price optimization model of the consortium according to the Nash negotiation cooperative game model, including: an objective function and relevant operating constraints between electricity transactions; Model solving module: used to determine the optimal solutions of the transaction power optimization model and the transaction power price optimization model respectively according to the alternating direction multiplier algorithm.

7. The electricity quantity and price optimization system based on wind, solar, and hydropower combined with water storage according to claim 6 is characterized in that: The game model building module is used to: Taking the maximization of the benefit enhancement value of the wind, solar, and hydropower multi-energy complementary system as the decision-making goal, the Nash negotiation game theory is used to determine the optimization objective function of the cooperation subject: In the formula, represents the revenue of the pumped storage power station under the joint state, represents the maximum revenue of the pumped storage power station in an independent state, represents the revenue of wind power in the joint state, Indicates the maximum benefit of wind power in an independent state, represents the revenue of photovoltaic under the joint state, Indicates the maximum benefit of photovoltaic in independent state, represents the revenue of hydropower in the joint state, Represents the maximum benefit of hydropower in an independent state.

8. The electricity quantity and price optimization system based on wind, solar, and hydropower combined with water storage according to claim 6 is characterized in that: The power model building module is used to perform the following steps: Step S21: Establish the objective function of the consortium's electricity trading optimization model, taking the maximum benefit of the wind, solar and hydropower alliance as the objective function, as shown below: In the formula, represents the joint venture's profit, w WD 、w PV 、w HY 、w PS Respectively represent the income value of each unit of the electricity transaction between wind turbines, photovoltaic units, hydropower units and pumped storage units; Step S22: constructing the operation constraints of the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines according to the objective function of the consortium's electricity trading optimization model.

9. The electricity quantity and price optimization system based on wind, solar, and hydropower combined with water storage according to claim 6 is characterized in that: The electricity price model building module is used to: The objective function for solving the transaction electricity price optimization model is established: In the formula, They represent the optimal solutions for wind turbines, photovoltaic units, hydropower units and pumped storage units in the consortium’s electricity trading optimization model. They respectively represent the electricity transaction amount between wind, solar and water units and pumped storage units.

10. The system for optimizing the electricity quantity and electricity price based on the combination of wind, solar, and hydropower according to claim 6 is characterized in that: The model solving module is used to: The optimal solution of the trading electricity quantity optimization model is imported as a parameter into the trading electricity price optimization model to iteratively determine the optimal solution of the trading electricity price optimization model, and then the profits of the wind-solar-hydropower alliance and the wind turbines, photovoltaic turbines, hydropower turbines and pumped storage turbines are calculated according to the optimal solution of the trading electricity price optimization model.

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