A method and device for optimizing capacity configuration of a virtual power plant including wind, solar, and water storage

Abstract

The present invention relates to the field of distributed energy dispatching and operation of intelligent power plants, and in particular to a method and device for optimizing capacity configuration of a virtual power plant including wind, solar, and water storage. The method comprises: obtaining wind power data, photovoltaic data, and load forecast values ​​in the area where the virtual power plant is located; using Latin hypercube sampling to generate scenarios, and reducing the scenarios by using a fast previous generation elimination method based on probability distance to eliminate uncertainty problems; establishing an optimization model for the capacity configuration of the virtual power plant inside and outside the two layers; using a gurobi solver to optimize the internal resources of the inner layer of the aggregated resources of the virtual power plant, and obtaining the output of each power station in the virtual power plant and the current capacity configuration plan; according to the output of each power station in the virtual power plant and the current capacity configuration plan, using a particle swarm algorithm optimized by a genetic algorithm to solve the outer layer of the virtual power plant, and obtaining the capacity optimization result of the aggregated resources of the virtual power plant. The present invention can reduce the cost of capacity configuration of energy storage units.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy dispatching and operation of smart power plants, and in particular to a method and device for optimizing capacity configuration of a virtual power plant including wind, solar, and water storage. Background Art

[0002] With the increasing installed capacity of wind and solar power generation, the reliable grid connection and sufficient absorption of renewable energy generation have become urgent issues to be solved. Since wind power generation and photovoltaic power generation are limited by the local natural environment and climatic conditions, they have strong intermittent and volatile characteristics, and their regulation and transient support capabilities are weak, making it impossible for them to maintain stable power generation, which poses a new challenge to the safe and stable operation of the power grid. Energy storage technology can not only improve the reliability of the power grid, but also improve the quality of electricity, which is crucial to the development of renewable energy.

[0003] For the capacity configuration of energy storage units in virtual power plants with wind, solar, and hydropower, the currently published patents have proposed a two-layer optimization model to accurately evaluate the long-term economic benefits of the system, or an optimization model that takes time-of-use electricity prices into account and takes the net profit of the microgrid group as the target to optimize the capacity configuration of the energy storage system. However, most of the existing studies consider economic factors, and have not yet proposed a suitable strategy and algorithm to solve the capacity configuration of energy storage units in virtual power plants with multiple objective factors, ignoring the reliability of power supply, environmental benefits, and the consumption of renewable energy. Summary of the invention

[0004] In order to solve the existing problems that no suitable strategy and algorithm have been proposed to solve the capacity configuration of virtual power plant energy storage units with multiple objective factors, and the reliability of power supply, environmental benefits and the absorption of renewable energy have been ignored, the embodiment of the present invention provides a method and device for optimizing the capacity configuration of a virtual power plant including wind, solar and water storage. The technical solution is as follows:

[0005] On the one hand, a method for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage is provided. The method is implemented by a device for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage. The method comprises:

[0006] S1. Obtain wind power data, photovoltaic data and load forecast values ​​in the area where the virtual power plant is located;

[0007] S2. Calculate the output forecast result of renewable energy in the area where the virtual power plant is located based on the wind power data, photovoltaic data and load forecast value;

[0008] S3. Based on the output prediction result of the renewable energy, Latin hypercube sampling is used to simulate and generate output and load data of multiple uncertain renewable energy sources; multiple uncertain renewable energy output and load data are reduced by a fast predecessor elimination method based on probability distance to obtain renewable energy output and load data after simulating uncertainty;

[0009] S4. Establishing an internal and external double-layer capacity configuration optimization model for a virtual power plant; the internal and external double-layer capacity configuration optimization model for a virtual power plant includes: an internal optimization model for aggregating resources of a virtual power plant and an external optimization model for finding the optimal capacity;

[0010] S5. Input the renewable energy output and load data after simulating uncertainty into the inner capacity optimization model of the aggregated resources of the virtual power plant, and use the gurobi solver to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station in the virtual power plant and the current capacity configuration plan;

[0011] S6. Input the output of each power station in the virtual power plant and the current capacity configuration plan into the outer optimization model for finding the optimal capacity, and use the particle swarm algorithm based on genetic algorithm optimization to solve the outer optimization model for finding the optimal capacity to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

[0012] Optionally, the specific implementation process of S2 includes S21-S22:

[0013] S21. Calculate the output power of distributed photovoltaic power and the output power of distributed wind power in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load forecast value;

[0014] S22. Use the output power of distributed photovoltaic power and the output power of distributed wind power as the output prediction result of renewable energy in the area where the virtual power plant is located.

[0015] Optionally, the process of establishing the inner capacity configuration optimization model of the virtual power plant of S3 includes:

[0016] The objective function of the inner optimization model of the aggregated resources of the virtual power plant is established, and the constraints of the inner optimization model of the aggregated resources of the virtual power plant are determined.

[0017] Optionally, the objective function of establishing the inner optimization model of the virtual power plant aggregated resources includes:

[0018] According to the reliability of the complementary power generation system, the first objective function is constructed; according to the volatility of the complementary power generation system, the second objective function is constructed; according to the pumped storage cost of the complementary power generation system, the third objective function is constructed; according to the abandoned wind, solar and hydropower output of the complementary power generation system, the fourth objective function is constructed;

[0019] The judgment matrix method is used to merge the first objective function, the second objective function, the third objective function and the fourth objective function into a single objective function as the objective function of the inner optimization model of the virtual power plant aggregated resources.

[0020] Optionally, the constraints of the inner optimization model of the virtual power plant aggregated resources include:

[0021] Power balance constraints, pumped storage power station equipment constraints, chemical energy storage power station equipment constraints, and hydropower station equipment constraints.

[0022] On the other hand, a device for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage is provided, and the device is applied to a method for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage. The device comprises:

[0023] The acquisition unit is used to obtain wind power data, photovoltaic data and load forecast values ​​in the area where the virtual power plant is located.

[0024] A calculation unit, used to calculate the output prediction result of renewable energy in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load prediction value;

[0025] A simulation unit is used to simulate and generate a plurality of uncertain renewable energy output and load data by using Latin hypercube sampling according to the output prediction result of the renewable energy; and to reduce the plurality of uncertain renewable energy output and load data by using a fast predecessor elimination method based on probability distance to obtain the renewable energy output and load data after simulating uncertainty;

[0026] A construction unit is used to establish an internal and external double-layer capacity configuration optimization model for a virtual power plant; the model includes: an internal optimization model for aggregating resources of the virtual power plant and an external optimization model for finding the optimal capacity;

[0027] The first solving unit is used to input the renewable energy output and load data after simulating uncertainty into the inner capacity optimization model of the aggregated resources of the virtual power plant, and use the gurobi solver to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station of the virtual power plant and the current capacity configuration plan;

[0028] The second solving unit is used to input the output of each power station of the virtual power plant and the current capacity configuration plan into the outer optimization model for finding the optimal capacity, and use the particle swarm algorithm optimized based on the genetic algorithm to solve the outer optimization model for finding the optimal capacity to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

[0029] Optionally, the computing unit is used to:

[0030] Calculate the output power of distributed photovoltaic power and distributed wind power in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load forecast value;

[0031] The output power of distributed photovoltaic power and the output power of distributed wind power are used as the output prediction results of renewable energy in the area where the virtual power plant is located.

[0032] Optionally, the process of establishing the virtual power plant inner capacity configuration optimization model includes:

[0033] The objective function of the inner optimization model of the aggregated resources of the virtual power plant is established, and the constraints of the inner optimization model of the aggregated resources of the virtual power plant are determined.

[0034] Optionally, the objective function of establishing the inner optimization model of the virtual power plant aggregated resources includes:

[0035] According to the reliability of the complementary power generation system, the first objective function is constructed; according to the volatility of the complementary power generation system, the second objective function is constructed; according to the pumped storage cost of the complementary power generation system, the third objective function is constructed; according to the abandoned wind, solar and hydropower output of the complementary power generation system, the fourth objective function is constructed;

[0036] The judgment matrix method is used to merge the first objective function, the second objective function, the third objective function and the fourth objective function into a single objective function as the objective function of the inner optimization model of the virtual power plant aggregated resources.

[0037] Optionally, the constraints of the inner optimization model of the virtual power plant aggregated resources include:

[0038] Power balance constraints, pumped storage power station equipment constraints, chemical energy storage power station equipment constraints, and hydropower station equipment constraints.

[0039] On the other hand, a device for optimizing the capacity configuration of a virtual power plant including wind, solar, water and storage is provided, and the device for optimizing the capacity configuration of a virtual power plant including wind, solar, water and storage comprises: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned methods for optimizing the capacity configuration of a virtual power plant including wind, solar, water and storage is implemented.

[0040] On the other hand, a computer-readable storage medium is provided, in which at least one instruction is stored. The at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for optimizing capacity configuration of a virtual power plant including wind, solar, and hydropower storage.

[0041] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0042] The embodiment of the present invention first obtains wind power data, photovoltaic data and load forecast values ​​of the area where the virtual power plant is located; calculates the output forecast results of renewable energy in the area where the virtual power plant is located based on the wind power data, photovoltaic data and load forecast values; uses Latin hypercube sampling to simulate and generate multiple uncertain renewable energy output and load data based on the output forecast results of renewable energy; reduces multiple uncertain renewable energy output and load data through a fast predecessor elimination method based on probability distance, and obtains renewable energy output and load data after simulating uncertainty; secondly, establishes a double-layer capacity configuration optimization model for the virtual power plant; virtual The double-layer capacity configuration optimization model of the power plant includes: an inner optimization model of the aggregated resources of the virtual power plant and an outer optimization model for finding the optimal capacity; the renewable energy output and load data after simulating uncertainty are input into the inner capacity optimization model of the aggregated resources of the virtual power plant, and the gurobi solver is used to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station in the virtual power plant and the current capacity configuration plan; finally, according to the output of each power station in the virtual power plant and the current capacity configuration plan, the particle swarm algorithm based on genetic algorithm optimization is used to solve the outer layer of the virtual power plant to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

[0043] The embodiment of the present invention first obtains the predicted values ​​of wind power, photovoltaic power and load demand through regional power consumption and energy resource condition analysis; Latin hypercube sampling is used to generate scenarios, and the fast previous generation elimination method based on probability distance is used to reduce scenarios to eliminate uncertainty problems; secondly, a two-layer optimization model of virtual power plant capacity including wind power, photovoltaic power, hydropower station, pumped storage power station and chemical energy storage power station is established; wherein, the inner layer uses the gurobi solver to solve the multi-objective problem considering reliability, environmental benefits, renewable energy consumption and economy, and obtains the output of each power station in the virtual power plant and the current capacity configuration plan; according to the output of each power station in the virtual power plant and the current capacity configuration plan, the particle swarm algorithm based on genetic algorithm optimization is used to solve the outer optimization model of the virtual power plant to obtain the optimal capacity of the pumped storage power station and the chemical energy storage power station. The embodiment of the present invention can reduce the cost of energy storage unit capacity configuration, effectively improve the reliability of the power grid, improve the power quality, effectively improve the economy of the virtual power plant, and reduce the amount of renewable energy discarded. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a schematic diagram of the structure of a virtual power plant including wind, solar, and water storage provided by an embodiment of the present invention;

[0046] Figure 2 It is a flow chart of a method for optimizing capacity configuration of a virtual power plant including wind, solar, and water storage provided by an embodiment of the present invention;

[0047] Figure 3 It is a flow chart of solving the optimization result of capacity configuration of a virtual power plant including wind, solar, water and storage provided by an embodiment of the present invention;

[0048] Figure 4 It is a block diagram of a capacity configuration optimization device for a virtual power plant including wind, solar, and water storage provided by an embodiment of the present invention;

[0049] Figure 5 It is a structural schematic diagram of a capacity configuration optimization device for a virtual power plant including wind, solar, and water storage provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0052] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0053] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0054] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0055] The embodiment of the present invention provides a method for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage. The method can be implemented by a device for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage. The device for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage can be a terminal or a server. Figure 1 Shown is a schematic diagram of the structure of a virtual power plant according to an embodiment of the present invention; wherein the power sources of the power generation system include wind turbines, photovoltaics, conventional hydropower stations and hydropower stations with pumped storage function; the energy storage units are battery energy storage and pumped storage.

[0056] like Figure 2 The flowchart of the method for optimizing capacity configuration of a virtual power plant including wind, solar, and water storage is shown. The processing flow of the method may include the following steps:

[0057] S1. Obtain wind power data, photovoltaic data and load forecast values ​​in the area where the virtual power plant is located.

[0058] S2. Calculate the output forecast result of renewable energy in the area where the virtual power plant is located based on wind power data, photovoltaic data and load forecast values.

[0059] Optionally, the specific implementation process of S2 includes S21-S22:

[0060] S21. Calculate the output power of distributed photovoltaic power and the output power of distributed wind power in the area where the virtual power plant is located based on wind power data, photovoltaic data and load forecast values;

[0061] S22. Use the output power of distributed photovoltaic power and the output power of distributed wind power as the output prediction result of renewable energy in the area where the virtual power plant is located.

[0062] S3. Based on the output prediction results of renewable energy, Latin hypercube sampling is used to simulate and generate multiple uncertain renewable energy output and load data; through the fast predecessor elimination method based on probability distance, multiple uncertain renewable energy output and load data are reduced to obtain renewable energy output and load data after simulating uncertainty.

[0063] S4. Establish an internal and external double-layer capacity configuration optimization model for the virtual power plant; the internal and external double-layer capacity configuration optimization model for the virtual power plant includes: an inner optimization model for aggregating resources of the virtual power plant and an outer optimization model for finding the optimal capacity.

[0064] Optionally, the process of establishing the inner capacity configuration optimization model of the virtual power plant of S3 includes: establishing the objective function of the inner optimization model of the aggregated resources of the virtual power plant and determining the constraint conditions of the inner optimization model of the aggregated resources of the virtual power plant.

[0065] Optionally, an objective function of an inner optimization model of the virtual power plant aggregated resources is established, including:

[0066] According to the reliability of the complementary power generation system, a first objective function is constructed;

[0067] Among them, 1 hour is used as the optimization time length, T=24, and the objective function of the inner optimization model of the virtual power plant aggregated resources is established.

[0068] The first objective function is constructed by using the load power failure rate. The first objective function can be expressed by the following formula (1):

[0069]

[0070] Among them, P t wind Indicates wind power; P t pv Represents photovoltaic power generation; P t h Indicates pumped power generation power; P t ba,d Power generation capacity of chemical energy storage power station; P t H Indicates the power of the hydropower station; P t Load represents load demand; f LPSP Indicates the load power failure rate.

[0071] According to the volatility of the complementary power generation system, a second objective function is constructed;

[0072] The second objective function can be expressed by the following formula (2):

[0073] minf2=μ wind P t wind +μ pv P t pv +μ pump (P t h -P t p )+μ ba (P t ba,d +P t ba,c )+μ H P tH (2)

[0074] Among them, μ wind Represents a wind power station; P t wind Indicates wind power; μ pv Represents photovoltaic power station; P t pv Represents photovoltaic power generation; μ pump Represents a pumped storage power station; P t h Indicates pumped power generation power; P t p represents pumped storage power; μ ba Represents chemical energy storage power station; P t ba,d Represents the power generation capacity of the chemical energy storage power station; P t ba,c Represents the charging power of the chemical energy storage power station; μ H It represents the unit equivalent carbon emission during the operation of the hydropower station; P t H Indicates the power of the hydropower station.

[0075] According to the pumped storage cost of the complementary power generation system, the third objective function is constructed;

[0076] The third objective function can be expressed by the following formula (3):

[0077] minf3=Cost1+Cost2 (3)

[0078] Among them, Cost1 represents the investment and operation and maintenance cost of the pumped storage power station; Cost2 represents the investment and operation and maintenance cost of the chemical energy storage power station.

[0079] According to the abandoned wind, solar and hydro output of the complementary power generation system, a fourth objective function is constructed;

[0080] The fourth objective function can be expressed by the following formula (4):

[0081] minf4=P wind,PV,water (4)

[0082] Among them, P wind,PV，water Indicates the amount of new energy waste.

[0083] In a feasible implementation, since the value ranges of the dimensions are different and cannot be compared, normalization is adopted to normalize different variables to the range of [0, 1]. The normalization formula can be expressed by the following formula (5):

[0084]

[0085] Among them, X' represents the normalized variable; X represents the initial variable; X min Indicates the minimum value of the variable; X max Indicates the maximum value of a variable.

[0086] The judgment matrix method is used to combine the first objective function, the second objective function, the third objective function and the fourth objective function into a single objective function as the objective function of the inner optimization model of the virtual power plant aggregation resources. The single objective function can be expressed by the following formula (6):

[0087] F1=min(ω1f LPSP +ω2γ L +ω3Cost PS +ω4P wind,PV ) (6)

[0088] Among them, ω1 represents the weight corresponding to the first objective function; ω2 represents the weight corresponding to the second objective function; ω3 represents the weight corresponding to the third objective function; ω4 represents the weight corresponding to the fourth objective function.

[0089] In a feasible implementation, a judgment matrix is ​​determined according to the ranking relationship between each target; in the embodiment of the present invention, for the optimization planning problem, each target can be classified according to importance; among them, reliability reflects the safe operation state of the system as the first-level target; carbon emissions reflect the environmental benefits of pumped storage, wind power and photovoltaics as the second-level indicator; the total equipment cost reflects the important evaluation indicator of the economic feasibility of the current project as the third-level target; the output of abandoned wind power and photovoltaic power generation are used as the fourth-level indicators.

[0090] Among them, according to the above-mentioned grades and grade formation criteria, the scale is determined and the judgment matrix is ​​generated; the grade formation criteria are shown in Table 1.

[0091] Table 1

[0092]

[0093]

[0094] Among them, the generated judgment matrix can be expressed as:

[0095]

[0096] In a feasible implementation, according to the judgment matrix, the eigenvalues ​​of the judgment matrix and the eigenvectors of the judgment matrix are calculated, and according to the eigenvalues ​​of the judgment matrix and the eigenvectors of the judgment matrix, the objective weight vectors ω1 are obtained as 57.14%, ω2 is 16.8115%, ω3 is 14.799%, and ω4 is 11.245%.

[0097] Among them, setting objective weights is more objective than directly using experience to design weights. Setting objective weights reflects the relative importance of each goal.

[0098] Optionally, the constraints of the inner optimization model of the virtual power plant aggregated resources include: power balance constraints, pumped storage power station equipment constraints, chemical energy storage power station equipment constraints and hydropower station equipment constraints.

[0099] In a feasible implementation, the functional balance constraint can be expressed by the following formula (7):

[0100] P t wind +P t pv +P t h +P t H -P t p +P t ba,d -P t ba,c =P t Load (7)

[0101] Among them, P t wind Indicates wind power; P t pv Represents photovoltaic power generation; P t h Indicates pumped power generation power; P t ba,d Power generation capacity of chemical energy storage power station; P t H Indicates the power of the hydropower station; P t p Represents pumped storage power; P t ba,c Represents the charging power of the chemical energy storage power station; P t Load Indicates load demand.

[0102] In a feasible implementation, the equipment constraints of the pumped storage power station can be expressed by the following formula (8) to formula (14):

[0103] 0≤P t p ≤U p (t)P phsN (8)

[0104] Among them, U p(t) represents the start-up and shutdown working variable of the pumped storage power station pumping condition, which takes the value of 1 when working and 0 when not working; P phsN Represents; t represents the time variable.

[0105] 0≤P t h ≤U h (t)P phsN (9)

[0106] Among them, U h (t) represents the start-up and shutdown working variables of the pumped storage power station.

[0107] U p (t)+U h (t)≤1 (10)

[0108] 0.1Q phsN ≤Q phs (t)≤0.9Q phsN (11)

[0109] Among them, Q phsN Indicates the rated capacity of the power station; Q phs (t) represents the power station capacity at time t.

[0110] Q phs (0) = Q phs (24) = 0.35Q phsN (12)

[0111]

[0112] Among them, P t h Represents pumped power generation power; R c1 Indicates the ramp limit value of the pumped storage power station; Indicates the power generation of the pumped storage power station at the last moment; Indicates the pumping power of the pumped-storage power station at the previous moment.

[0113]

[0114] In a feasible implementation, the chemical energy storage power station equipment constraints can be expressed by the following formula (15) to formula (19):

[0115] S OC,min ≤S OC (t)≤S OC,max (15)

[0116] Among them, S OC,min Indicates the minimum value of the state of charge; S OC,max Indicates the maximum value of the state of charge; SOC (t) indicates the current state of charge.

[0117]

[0118] in, Indicates the maximum charging power.

[0119]

[0120] in, Indicates the maximum discharge power.

[0121] P t ba,d ·P t ba,c =0 (18)

[0122]

[0123] Among them, R c2 Indicates the electronic climbing limit value of chemical energy storage.

[0124] In a feasible implementation, the hydropower station equipment constraints can be expressed by the following formula (20)-formula (24):

[0125] V t+1 =V t +(I t -Q e,t -Q d,t )Δt (20)

[0126]

[0127] Among them, Z u,t Indicates the water level above the dam of the hydropower station; Q t Indicates the discharge flow of the hydropower station; Q e,t Indicates the power generation flow; Q d,t Indicates the abandoned water flow; Z d,t represents the tailwater level of the hydropower station; A represents the hydropower conversion constant; γ represents the comprehensive output coefficient of the hydropower station; V t Represents the reservoir capacity of the hydropower station.

[0128] Z min ≤Z u,t ≤Z max (twenty two)

[0129] Among them, Z max Indicates the maximum water level required; Z min Indicates the minimum water level required.

[0130] Q min ≤Q t ≤Qmax (twenty three)

[0131] Among them, Q min Indicates the minimum value of the downstream flow; Q max Indicates the maximum value of the downstream flow rate.

[0132] P H,min ≤P t H ≤P H,max (twenty four)

[0133] Among them, P H,min Indicates the minimum output of the hydropower station; P H,max Indicates the maximum output of the hydropower station.

[0134] In a feasible implementation, based on the nonlinear relationship between the water level on the dam and the storage capacity and the tailwater level and the discharge flow, the nonlinear relationship between the water level on the dam and the storage capacity and the tailwater level and the discharge flow is transformed into a linear relationship in sections to shorten the model solution time and improve the solution efficiency. min , V max ], the reservoir capacity is discretized into L intervals, where the water level-reservoir capacity relationship is:

[0135]

[0136] Among them, V min Indicates; V max Indicates; V c,l represents the storage capacity of the lth storage capacity interval of the hydropower station, which is a constant; Z u,c,l It represents the water level above the dam in the lth storage capacity interval of the hydropower station, which is a constant; Represents a 0-1 indicator variable, used to determine the interval in which the storage capacity is located; Used to ensure that the storage capacity falls within a certain range; V t,l In which, the original dam water level-reservoir capacity relationship can be transformed into the following formula (26):

[0137]

[0138] In a feasible implementation, the reservoir outflow variation interval [Q min ,Q max ] is discretized into M intervals, where the relationship between the tailwater level and the downstream flow can be expressed by the following formula (27):

[0139]

[0140] Among them, Q c,m represents the discharge flow rate of the mth discharge flow interval; Zd,c,m represents the tailwater level of the mth discharge flow interval; ψ t,m It is a 0-1 indicator variable, used to determine the interval of storage capacity; It is used to ensure that the reservoir capacity falls within a certain range. The original tailwater level-discharge flow relationship can be converted into the following formula (28):

[0141]

[0142] in, Figure 3 It is a flow chart for solving the capacity configuration optimization result of a virtual power plant including wind, solar and water storage provided by an embodiment of the present invention; in a feasible implementation manner, the wind power data, photovoltaic data and load forecast value of the area where the virtual power plant is located are obtained, and input into the inner and outer double-layer capacity configuration optimization model of the virtual power plant, and the input data is processed to eliminate uncertainty, so as to obtain the input data after uncertainty processing; the unit state is initialized; the inner layer objective function and constraint conditions are established; the gurobi solver is used to optimize the internal resources of the inner layer optimization model of the virtual power plant's aggregated resources, and the output of each power station in the virtual power plant and the current capacity configuration plan are obtained; the output of each power station in the virtual power plant and the current capacity configuration plan are input into the outer model of the virtual power plant, and the particle swarm algorithm based on genetic algorithm optimization is used to solve the outer layer of the virtual power plant to obtain the capacity optimization result of the aggregated resources of the virtual power plant. Among them, the implementation process of solving the outer layer of the virtual power plant by using a particle swarm algorithm based on genetic algorithm optimization includes: initializing particles, giving initial speed and position to each particle of the decision variables of the pumped storage power station capacity and the chemical energy storage power station capacity in the outer model of the virtual power plant; evaluating each particle to obtain the global optimum; updating the speed and position of each particle according to the global optimum and the set constraints; introducing a crossover mutation link; wherein the introduction of crossover mutation can prevent local optimum; taking the objective function in the inner optimization model of the aggregated resources of the virtual power plant as the fitness function, evaluating the function fitness value of each particle, and selecting the optimal capacity according to the output of each power station in the virtual power plant and the current capacity configuration plan; updating the historical optimal position of each particle, evaluating the historical optimal position, and obtaining the optimal solution of the rated capacity of the energy storage unit power station of the virtual power plant, wherein the optimal solution of the rated capacity of the energy storage unit power station of the virtual power plant is the final capacity optimization result of the aggregated resources of the virtual power plant.

[0143] S5. Input the renewable energy output and load data after simulating uncertainty into the inner capacity optimization model of the virtual power plant's aggregated resources, and use the gurobi solver to optimize the internal resources of the inner optimization model of the virtual power plant's aggregated resources to obtain the output of each power station in the virtual power plant and the current capacity configuration plan.

[0144] In a feasible implementation, according to the objective function and constraints of the inner optimization model of the aggregated resources of the virtual power plant, the gurobi solver is used to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station in the virtual power plant and the current capacity configuration plan.

[0145] S6. Input the output of each power station in the virtual power plant and the current capacity configuration plan into the outer optimization model for finding the optimal capacity, and use the particle swarm algorithm based on genetic algorithm optimization to solve the outer optimization model for finding the optimal capacity, so as to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

[0146] In a feasible implementation manner, the specific implementation process of S6 may include:

[0147] S61, initializing particles, and giving initial speed and position to each particle of the capacity decision variable of the pumped storage power station and each particle of the capacity decision variable of the chemical energy storage power station in the outer model of the virtual power plant;

[0148] S62, evaluate each particle to obtain the global optimum;

[0149] S63, updating the speed and position of each particle according to the global optimum and the outer constraints;

[0150] S64, introducing a crossover mutation link; wherein the crossover mutation is introduced to prevent local optimality;

[0151] S65, taking the objective function in the inner optimization model of the virtual power plant aggregation resources as the fitness function, evaluating the function fitness value of each particle, and selecting the optimal capacity according to the output of each power station of the virtual power plant and the current capacity configuration plan;

[0152] S66, updating the historical optimal position of each particle, and evaluating the historical optimal position;

[0153] S67. Repeat steps S63-S66 until the optimal solution for the rated capacity of the virtual power plant energy storage unit power station is obtained, wherein the optimal solution for the rated capacity of the virtual power plant energy storage unit power station is the final capacity optimization result of the virtual power plant aggregated resources.

[0154] In the embodiment of the present invention, wind power data, photovoltaic data and load forecast values ​​of the area where the virtual power plant is located are firstly obtained; the output forecast results of renewable energy in the area where the virtual power plant is located are calculated according to the wind power data, photovoltaic data and load forecast values; Latin hypercube sampling is used to simulate and generate multiple uncertain renewable energy output and load data according to the output forecast results of renewable energy; multiple uncertain renewable energy output and load data are reduced by the fast predecessor elimination method based on probability distance to obtain the renewable energy output and load data after simulating uncertainty; secondly, a double-layer capacity configuration optimization model for the virtual power plant is established; the virtual power plant The plant-inside and outside double-layer capacity configuration optimization model includes: an inner optimization model of the aggregated resources of the virtual power plant and an outer optimization model for finding the optimal capacity; the renewable energy output and load data after simulating uncertainty are input into the inner capacity optimization model of the aggregated resources of the virtual power plant, and the gurobi solver is used to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station in the virtual power plant and the current capacity configuration plan; finally, according to the output of each power station in the virtual power plant and the current capacity configuration plan, the particle swarm algorithm based on genetic algorithm optimization is used to solve the outer model of the virtual power plant to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

[0155] The embodiment of the present invention first obtains the predicted values ​​of wind power, photovoltaic power and load demand through regional power consumption and energy resource condition analysis; Latin hypercube sampling is used to generate scenarios, and the fast previous generation elimination method based on probability distance is used to reduce scenarios to eliminate uncertainty problems; secondly, a two-layer optimization model of virtual power plant capacity including wind power, photovoltaic power, hydropower station, pumped storage power station and chemical energy storage power station is established; wherein, the inner layer uses the gurobi solver to solve the multi-objective problem considering reliability, environmental benefits, renewable energy consumption and economy, and obtains the output of each power station in the virtual power plant and the current capacity configuration plan; according to the output of each power station in the virtual power plant and the current capacity configuration plan, the particle swarm algorithm based on genetic algorithm optimization is used to solve the outer optimization model of the virtual power plant to obtain the optimal capacity of the pumped storage power station and the chemical energy storage power station. The embodiment of the present invention can reduce the cost of energy storage unit capacity configuration, effectively improve the reliability of the power grid, improve the power quality, effectively improve the economy of the virtual power plant, and reduce the amount of renewable energy discarded.

[0156] Figure 4 The block diagram of a device for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage is shown according to an exemplary embodiment. The device is used in a method for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage. Figure 4 The device includes an acquisition unit 310, a calculation unit 320, a simulation unit 330, a construction unit 340, a first solution unit 350 and a second solution unit 360. Wherein:

[0157] The acquisition unit 310 is used to acquire wind power data, photovoltaic data and load forecast values ​​in the area where the virtual power plant is located.

[0158] A calculation unit 320, configured to calculate an output prediction result of renewable energy in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load prediction value;

[0159] The simulation unit 330 is used to simulate and generate a plurality of uncertain renewable energy output and load data by using Latin hypercube sampling according to the output prediction result of the renewable energy; reduce the plurality of uncertain renewable energy output and load data by using a fast predecessor elimination method based on probability distance to obtain the renewable energy output and load data after simulating uncertainty;

[0160] A construction unit 340 is used to establish an internal and external double-layer capacity configuration optimization model of a virtual power plant; the model includes: an internal optimization model for aggregating resources of the virtual power plant and an external optimization model for finding the optimal capacity;

[0161] The first solving unit 350 is used to input the renewable energy output and load data after simulating uncertainty into the inner capacity optimization model of the aggregated resources of the virtual power plant, and optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant by using the gurobi solver to obtain the output of each power station of the virtual power plant and the current capacity configuration plan;

[0162] The second solving unit 360 is used to input the output of each power station of the virtual power plant and the current capacity configuration plan into the outer optimization model for finding the optimal capacity, and use the particle swarm algorithm based on genetic algorithm optimization to solve the outer optimization model for finding the optimal capacity to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

[0163] Optionally, the computing unit 320 is configured to:

[0164] Calculate the output power of distributed photovoltaic power and distributed wind power in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load forecast value;

[0165] The output power of distributed photovoltaic power and the output power of distributed wind power are used as the output prediction results of renewable energy in the area where the virtual power plant is located.

[0166] Optionally, the process of establishing the virtual power plant inner capacity configuration optimization model includes:

[0167] The objective function of the inner optimization model of the aggregated resources of the virtual power plant is established, and the constraints of the inner optimization model of the aggregated resources of the virtual power plant are determined.

[0168] Optionally, the objective function of establishing the inner optimization model of the virtual power plant aggregated resources includes:

[0169] According to the reliability of the complementary power generation system, the first objective function is constructed; according to the volatility of the complementary power generation system, the second objective function is constructed; according to the pumped storage cost of the complementary power generation system, the third objective function is constructed; according to the abandoned wind, solar and hydropower output of the complementary power generation system, the fourth objective function is constructed;

[0170] The judgment matrix method is used to merge the first objective function, the second objective function, the third objective function and the fourth objective function into a single objective function as the objective function of the inner optimization model of the virtual power plant aggregated resources.

[0171] Optionally, the constraints of the inner optimization model of the virtual power plant aggregated resources include:

[0172] Power balance constraints, pumped storage power station equipment constraints, chemical energy storage power station equipment constraints, and hydropower station equipment constraints.

[0173] The embodiment of the present invention first obtains wind power data, photovoltaic data and load forecast values ​​of the area where the virtual power plant is located; calculates the output forecast results of renewable energy in the area where the virtual power plant is located based on the wind power data, photovoltaic data and load forecast values; uses Latin hypercube sampling to simulate and generate multiple uncertain renewable energy output and load data based on the output forecast results of renewable energy; reduces multiple uncertain renewable energy output and load data through a fast predecessor elimination method based on probability distance, and obtains renewable energy output and load data after simulating uncertainty; secondly, establishes a double-layer capacity configuration optimization model for the virtual power plant; virtual The double-layer capacity configuration optimization model of the power plant includes: an inner optimization model of the aggregated resources of the virtual power plant and an outer optimization model for finding the optimal capacity; the renewable energy output and load data after simulating uncertainty are input into the inner capacity optimization model of the aggregated resources of the virtual power plant, and the gurobi solver is used to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station in the virtual power plant and the current capacity configuration plan; finally, according to the output of each power station in the virtual power plant and the current capacity configuration plan, the particle swarm algorithm based on genetic algorithm optimization is used to solve the outer layer of the virtual power plant to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

[0174] The embodiment of the present invention first obtains the predicted values ​​of wind power, photovoltaic power and load demand through regional power consumption and energy resource condition analysis; Latin hypercube sampling is used to generate scenarios, and the fast previous generation elimination method based on probability distance is used to reduce scenarios to eliminate uncertainty problems; secondly, a two-layer optimization model of virtual power plant capacity including wind power, photovoltaic power, hydropower station, pumped storage power station and chemical energy storage power station is established; wherein, the inner layer uses the gurobi solver to solve the multi-objective problem considering reliability, environmental benefits, renewable energy consumption and economy, and obtains the output of each power station in the virtual power plant and the current capacity configuration plan; according to the output of each power station in the virtual power plant and the current capacity configuration plan, the particle swarm algorithm based on genetic algorithm optimization is used to solve the outer optimization model of the virtual power plant to obtain the optimal capacity of the pumped storage power station and the chemical energy storage power station. The embodiment of the present invention can reduce the cost of energy storage unit capacity configuration, effectively improve the reliability of the power grid, improve the power quality, effectively improve the economy of the virtual power plant, and reduce the amount of renewable energy discarded.

[0175] Figure 5 is a structural schematic diagram of a capacity configuration optimization device for a virtual power plant including wind, solar, and water storage provided by an embodiment of the present invention, such as Figure 5 As shown, the capacity configuration optimization device of the virtual power plant including wind, solar, and water storage may include the above Figure 4 The virtual power plant capacity configuration optimization device including wind, solar, water and storage shown. Optionally, the virtual power plant capacity configuration optimization device 410 including wind, solar, water and storage may include a first processor 2001.

[0176] Optionally, the capacity configuration optimization device 410 for a virtual power plant including wind, solar, and hydropower storage may also include a memory 2002 and a transceiver 2003 .

[0177] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0178] Combine the following Figure 5 The components of the virtual power plant capacity configuration optimization device 410 including wind, solar, and water storage are specifically introduced:

[0179] The first processor 2001 is the control center of the capacity configuration optimization device 410 for a virtual power plant including wind, solar, and water storage, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0180] Optionally, the first processor 2001 can perform various functions of the virtual power plant capacity configuration optimization device 410 including wind, solar, and water storage by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002.

[0181] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 5 CPU0 and CPU1 are shown in FIG.

[0182] In a specific implementation, as an embodiment, the capacity configuration optimization device 410 for a virtual power plant including wind, solar, and water storage may also include multiple processors, such as Figure 5 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0183] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0184] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 5 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0185] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0186] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 5 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0187] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be configured through an interface circuit ( Figure 5 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0188] It should be noted that Figure 5 The structure of the virtual power plant capacity configuration optimization device 410 including wind, solar, and hydropower storage shown in the figure does not constitute a limitation on the router. The actual knowledge structure identification device may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0189] In addition, the technical effects of the virtual power plant capacity configuration optimization device 410 including wind, solar, and water storage can refer to the technical effects of the virtual power plant capacity configuration optimization method including wind, solar, and water storage described in the above method embodiment, and will not be repeated here.

[0190] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0191] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0192] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0193] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0194] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0195] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0196] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0197] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0198] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0199] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0201] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0202] 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 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 should be based on the protection scope of the claims.

Claims

1. A method for optimizing capacity configuration of a virtual power plant including wind, solar, and water storage, characterized in that: The method comprises: S1. Obtain wind power data, photovoltaic data and load forecast values ​​in the area where the virtual power plant is located; S2. Calculate the output forecast result of renewable energy in the area where the virtual power plant is located based on the wind power data, photovoltaic data and load forecast value; S3. Based on the output prediction result of the renewable energy, Latin hypercube sampling is used to simulate and generate output and load data of multiple uncertain renewable energy sources; multiple uncertain renewable energy output and load data are reduced by a fast predecessor elimination method based on probability distance to obtain renewable energy output and load data after simulating uncertainty; S4. Establishing an internal and external double-layer capacity configuration optimization model for a virtual power plant; the internal and external double-layer capacity configuration optimization model for a virtual power plant includes: an internal optimization model for aggregating resources of a virtual power plant and an external optimization model for finding the optimal capacity; S5. Input the renewable energy output and load data after simulating uncertainty into the inner capacity optimization model of the aggregated resources of the virtual power plant, and use the gurobi solver to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station in the virtual power plant and the current capacity configuration plan; The process of obtaining the output of each power plant in the virtual power plant and the current capacity configuration plan includes: The wind power data, photovoltaic data and load forecast values ​​of the area where the virtual power plant is located are obtained and input into the inner capacity configuration optimization model of the virtual power plant. The input data is processed to eliminate uncertainty and obtain the input data after uncertainty processing; the unit state is initialized; the inner objective function and constraint conditions are established; the gurobi solver is used to optimize the internal resources of the inner optimization model of the virtual power plant's aggregated resources to obtain the output of each power station in the virtual power plant and the current capacity configuration plan; S6, inputting the output of each power station of the virtual power plant and the current capacity configuration plan into the outer optimization model for finding the optimal capacity, using a particle swarm algorithm based on genetic algorithm optimization to solve the outer optimization model for finding the optimal capacity, and obtaining the capacity optimization result of the aggregated resources of the virtual power plant; The process of using a particle swarm algorithm based on genetic algorithm optimization to solve the outer optimization model for finding the optimal capacity and obtaining the capacity optimization result of the virtual power plant aggregated resources includes: S61, initializing particles, and giving initial speed and position to each particle of the capacity decision variable of the pumped storage power station and each particle of the capacity decision variable of the chemical energy storage power station in the outer model of the virtual power plant; S62, evaluate each particle to obtain the global optimum; S63, updating the speed and position of each particle according to the global optimum and the outer constraints; S64, introducing a crossover mutation link; wherein the crossover mutation is introduced to prevent local optimality; S65, taking the objective function in the inner optimization model of the virtual power plant aggregation resources as the fitness function, evaluating the function fitness value of each particle, and selecting the optimal capacity according to the output of each power station of the virtual power plant and the current capacity configuration plan; S66, updating the historical optimal position of each particle, and evaluating the historical optimal position; S67. Repeat steps S63-S66 until the optimal solution for the rated capacity of the virtual power plant energy storage unit power station is obtained, wherein the optimal solution for the rated capacity of the virtual power plant energy storage unit power station is the final capacity optimization result of the virtual power plant aggregated resources.

2. The capacity configuration optimization method of a virtual power plant including wind, solar, water and storage according to claim 1 is characterized in that: The step S2 calculates the output forecast result of renewable energy in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load forecast value, including: S21. Calculate the output power of distributed photovoltaic power and the output power of distributed wind power in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load forecast value; S22. Use the output power of distributed photovoltaic power and the output power of distributed wind power as the output prediction result of renewable energy in the area where the virtual power plant is located.

3. The capacity configuration optimization method of a virtual power plant including wind, solar, water and storage according to claim 1 is characterized in that: The process of establishing the inner capacity configuration optimization model of the S4 virtual power plant includes: The objective function of the inner optimization model of the aggregated resources of the virtual power plant is established, and the constraints of the inner optimization model of the aggregated resources of the virtual power plant are determined.

4. The capacity configuration optimization method of a virtual power plant including wind, solar, water and storage according to claim 3 is characterized in that: The objective function of establishing the inner optimization model for the virtual power plant aggregated resources includes: According to the reliability of the complementary power generation system, the first objective function is constructed; according to the volatility of the complementary power generation system, the second objective function is constructed; according to the pumped storage cost of the complementary power generation system, the third objective function is constructed; according to the abandoned wind, solar and hydropower output of the complementary power generation system, the fourth objective function is constructed; The judgment matrix method is used to merge the first objective function, the second objective function, the third objective function and the fourth objective function into a single objective function as the objective function of the inner optimization model of the virtual power plant aggregated resources.

5. The capacity configuration optimization method of a virtual power plant including wind, solar, water and storage according to claim 3 is characterized in that: The constraints of the inner optimization model of the virtual power plant aggregated resources include: Power balance constraints, pumped storage power station equipment constraints, chemical energy storage power station equipment constraints, and hydropower station equipment constraints.

6. A device for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage, the device for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage being used to implement the method for optimizing capacity configuration of a virtual power plant including wind, solar, water and storage as claimed in any one of claims 1 to 5, characterized in that: The device comprises: An acquisition unit, used to acquire wind power data, photovoltaic data and load forecast values ​​in the area where the virtual power plant is located; A calculation unit, used to calculate the output prediction result of renewable energy in the area where the virtual power plant is located according to the wind power data, photovoltaic data and load prediction value; A simulation unit is used to simulate and generate a plurality of uncertain renewable energy output and load data by using Latin hypercube sampling according to the output prediction result of the renewable energy; and to reduce the plurality of uncertain renewable energy output and load data by using a fast predecessor elimination method based on probability distance to obtain the renewable energy output and load data after simulating uncertainty; A construction unit is used to establish an internal and external double-layer capacity configuration optimization model for a virtual power plant; the model includes: an internal optimization model for aggregating resources of the virtual power plant and an external optimization model for finding the optimal capacity; The first solving unit is used to input the renewable energy output and load data after simulating uncertainty into the inner capacity optimization model of the aggregated resources of the virtual power plant, and use the gurobi solver to optimize the internal resources of the inner optimization model of the aggregated resources of the virtual power plant to obtain the output of each power station of the virtual power plant and the current capacity configuration plan; The second solving unit is used to input the output of each power station of the virtual power plant and the current capacity configuration plan into the outer optimization model for finding the optimal capacity, and use the particle swarm algorithm optimized based on the genetic algorithm to solve the outer optimization model for finding the optimal capacity to obtain the capacity optimization result of the aggregated resources of the virtual power plant.

7. A capacity configuration optimization device for a virtual power plant including wind, solar, and water storage, characterized in that: The capacity configuration optimization equipment of the virtual power plant including wind, solar, water and storage includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 5.

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