Energy storage capacity and power configuration method, equipment, medium and product

Abstract

The invention discloses an energy storage capacity and power configuration method and device, a medium and a product, and relates to the field of energy storage data processing, and the method comprises the steps: building an investment operation and maintenance cost model of a system in a co-reservoir operation mode, so as to obtain an outer layer optimization model; constructing an outer-layer optimization constraint condition, an inner-layer optimization model and an inner-layer optimization constraint condition; under an outer layer optimization constraint condition, optimizing the outer layer optimization model by using a particle swarm intelligent optimization algorithm to obtain an outer layer optimal instruction; under the inner-layer optimization constraint condition, the whale intelligent optimization algorithm is used for optimizing the inner-layer optimization model to obtain an inner-layer optimal instruction; returning the inner-layer optimal instruction to the outer-layer optimization model to obtain economic characteristics of the system; and on the basis of system economic characteristics, when it is determined that the outer-layer optimization model meets set conditions, scheduling is completed based on the inner-layer optimal instruction and the optimized outer-layer optimization model. According to the invention, on the basis of improving the flexibility and reliability of the energy storage system, the operation cost of the energy storage system is reduced to the greatest extent.

Description

Technical Field

[0001] The present application relates to the field of energy storage data processing, and in particular to an energy storage capacity and power configuration method, device, medium and product. Background Art

[0002] The volatility of wind and solar energy will affect the stability and reliability of the power system. Energy storage can alleviate this problem by shifting peak loads to fill valleys. Among them, energy storage batteries have the advantages of small footprint, high energy density, smaller space requirements, and more flexible deployment locations. Pumped storage power stations have the advantages of high energy storage capacity, relatively long cycle life, and better economy.

[0003] In actual projects, it is determined by the deviation between the local installed capacity of new energy and the load capacity, as well as the carrying capacity of the local power grid. The existing capacity configuration method is to configure the energy storage capacity and power through a double-layer optimization structure, that is, the outer layer optimizes the energy storage capacity and power, and the internal memory optimizes the scheduling model. However, the energy interaction link of the existing capacity configuration method does not pass through the power grid and is not restricted by the capacity of the power grid line. As a result, the energy transmission of different substations is significantly restricted by the capacity of the power grid line, and it cannot reduce the operating cost of the pumped energy storage system to the greatest extent on the basis of improving the flexibility and reliability of the energy storage system. Summary of the invention

[0004] The purpose of this application is to provide a method, device, medium and product for configuring energy storage capacity and power, which can reduce the operating cost of the pumped energy storage system to the greatest extent while improving the flexibility and reliability of the energy storage system.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for configuring energy storage capacity and power, comprising:

[0007] Obtaining system economic information; the system economic information includes one or more of the following: investment cost unit price of wind turbines in each area, investment cost unit price of photovoltaic cells, investment cost unit price of reversible pump-turbines, investment cost unit price of energy storage batteries, investment cost unit price of reservoir capacity, operation and maintenance cost unit price of wind turbines in each area, operation and maintenance cost unit price of photovoltaic cells, operation and maintenance cost unit price of reversible pump-turbines, operation and maintenance cost unit price of energy storage batteries, operation and maintenance cost unit price of energy storage battery charging and discharging, operation and maintenance cost unit price of reservoir capacity, discount rate, and project period;

[0008] Based on the system economic information, establish the investment cost model and operation and maintenance cost model of the system under the shared reservoir operation mode;

[0009] Constructing an outer optimization model based on the investment cost model and the operation and maintenance cost model;

[0010] According to the shared reservoir operation mode of pumped storage power station, the outer optimization constraints are constructed;

[0011] Under the outer optimization constraint conditions, the outer optimization model is optimized using a particle swarm intelligent optimization algorithm to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area;

[0012] Acquire historical data of the distribution network, and establish a prediction model based on the historical data of the distribution network; the historical data of the distribution network includes historical photovoltaic power, historical wind power and historical load power;

[0013] Acquire current data of the distribution network, and input the current data of the distribution network into the prediction model to obtain distribution network prediction data; the current data of the distribution network includes photovoltaic power, wind power and load power; the distribution network prediction data includes photovoltaic prediction power, wind power prediction power and load prediction power;

[0014] In the shared reservoir operation mode of the pumped storage power station, an inner optimization model and inner optimization constraints are established based on the distribution network prediction data;

[0015] Under the inner optimization constraint conditions, the inner optimization model is optimized using the whale intelligent optimization algorithm to obtain the inner optimal instructions for the energy storage capacity and power configuration of the distribution station area;

[0016] Returning the inner optimal instructions of the energy storage capacity and power configuration of the distribution station area to the outer optimization model to obtain the economic characteristics of the system; the economic characteristics include: investment cost and operation and maintenance cost;

[0017] Determining whether the outer optimization model meets set conditions based on the economic characteristics of the system;

[0018] When the outer optimization model satisfies the set conditions, the energy storage capacity and power configuration are completed based on the inner optimal instructions of the energy storage capacity and power configuration of the distribution station area at this time and the optimized outer optimization model;

[0019] When the outer optimization model meets the set conditions, the particle speed and particle position of the particle swarm intelligent optimization algorithm are adjusted, and the process returns to execute "under the outer optimization constraints, the outer optimization model is optimized using the particle swarm intelligent optimization algorithm to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area".

[0020] Optionally, the outer optimization constraints include: constraints on the configuration capacity of the upstream reservoirs of each substation, constraints on the configuration capacity of the downstream reservoirs of each substation, constraints on the configuration power of the water pumps of each substation, constraints on the configuration power of the turbines of each substation, and one or more constraints on the energy storage capacity of a single group of energy storage batteries in each substation.

[0021] Optionally, under the outer optimization constraint conditions, the outer optimization model is optimized using a particle swarm intelligent optimization algorithm to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area, specifically including:

[0022] Determine the optimization variables of the outer optimization model; the optimization variables include the installed capacity of water pumps in each substation, the installed capacity of turbines, the storage capacity of upstream reservoirs and the storage capacity of downstream reservoirs;

[0023] forming a variable space based on the optimization variables and placing particles in the variable control;

[0024] The maximum number of iterations is set, and the number of iterations as well as the initial position and initial velocity of each particle in the particle swarm are initialized. The outer optimization model is iteratively solved until the maximum number of iterations is reached, and the position information of each particle is obtained. The position information of each particle is used as the outer optimal instruction for the energy storage capacity and power configuration of the distribution station area; the position information includes the particle position and velocity.

[0025] Optionally, the shared reservoir operation mode of the pumped storage power station includes: a shared upstream reservoir operation mode and a shared downstream reservoir operation mode;

[0026] In the common upstream reservoir operation mode, the downstream reservoirs of each substation share the same upstream reservoir;

[0027] In the shared downstream reservoir operation mode, the upstream reservoirs of each substation share the same downstream reservoir.

[0028] Optionally, the prediction model is an extreme learning machine, a support vector machine or an autoregressive model.

[0029] Optionally, the inner optimization constraints include one or more of the power constraints of wind power plants and photovoltaic power plants, the power constraints of pumped storage units, the charging and discharging power constraints of energy storage batteries, the system power balance constraints, the water storage capacity constraints of reservoirs, the capacity constraints of energy storage batteries and the change value constraints of the power injection into the transmission network.

[0030] Optionally, in a shared reservoir operation mode of a pumped storage power station, an inner optimization model is established based on the distribution network prediction data, including:

[0031] In the shared reservoir operation mode of the pumped storage power station, based on the distribution network prediction data, a function of abandoned wind and photovoltaic power generation, a function of transmission line occupancy, and a function of load shedding are established;

[0032] The inner optimization model is obtained by integrating the abandoned wind power and photovoltaic power generation function, the transmission line occupancy function, and the load shedding function, and allocating weights of each function.

[0033] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the energy storage capacity and power configuration method provided above.

[0034] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the energy storage capacity and power configuration method provided above.

[0035] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the energy storage capacity and power configuration method provided above.

[0036] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0037] The present application provides a method, device, medium and product for configuring energy storage capacity and power. According to the different energy states of the new energy and energy storage systems in each substation, different energy storage capacities, different bearing capacities of the transmission lines, different power supply conditions of the new energy and pumped storage systems, and different power consumption conditions of the loads, a multi-mode energy scheduling model (investment cost model and operation and maintenance cost model) and an optimization control structure (i.e., an outer optimization model and an inner optimization model) between the new energy and pumped storage systems are formed. The system capacity and power configuration are optimized according to the optimized control structure of the system under this operating mode, which can reduce the operating cost of the pumped storage system to the greatest extent on the basis of improving the flexibility and reliability of the energy storage system. In addition, swarm intelligence optimization algorithm (i.e., particle swarm intelligence optimization algorithm and whale intelligence optimization algorithm) is used to consider the control optimization process between different substations from the perspective of the local power grid, and the optimal coordinated control results and operation modes between different substations are calculated, effectively changing the energy supply relationship of multiple substations from a single energy mutual assistance mode to a mode of mutual assistance of electricity and water energy, forming the optimal control of the overall system. While considering the inner optimal control, the swarm intelligence optimization algorithm is used to consider the capacity and power configuration issues of the outer system, which will be of great significance to the stability and economy of the local power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 This is an application environment diagram of an energy storage capacity and power configuration method in one embodiment of the present application;

[0040] Figure 2 A schematic diagram of a flow chart of a method for configuring energy storage capacity and power provided in one embodiment of the present application;

[0041] Figure 3 A schematic diagram of a pumped storage power station and a downstream reservoir operation mode provided in an embodiment of the present application;

[0042] Figure 4 The pumped storage power station shared upstream reservoir operation mode intention provided by another embodiment of the present application;

[0043] Figure 5 A schematic diagram of a process for obtaining inner-layer optimal control instructions using a whale intelligent optimization algorithm provided in an embodiment of the present application;

[0044] Figure 6 A schematic diagram of an implementation flow of a method for configuring energy storage capacity and power provided in one embodiment of the present application;

[0045] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0048] The energy storage capacity and power configuration method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the system economic information and the current data of the distribution network to the server 104. After the server 104 receives the system economic information and the current data of the distribution network, for the system economic information and the current data of the distribution network, the server 104 establishes an investment and operation and maintenance cost model of the system under the common reservoir operation mode to obtain an outer optimization model; constructs outer optimization constraints, inner optimization models and inner optimization constraints; under the outer optimization constraints, the particle swarm intelligent optimization algorithm is used to optimize the outer optimization model to obtain the outer optimal instruction; under the inner optimization constraints, the whale intelligent optimization algorithm is used to optimize the inner optimization model to obtain the inner optimal instruction; the inner optimal instruction is returned to the outer optimization model to obtain the system economic characteristics; based on the system economic characteristics, it is determined that when the outer optimization model meets the set conditions, the energy storage capacity and power configuration are completed based on the inner optimal instruction and the optimized outer optimization model. The server 104 may feed back the obtained energy storage capacity and power configuration scheme to the terminal 102. In addition, in some embodiments, the energy storage capacity and power configuration method may also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 may directly configure the energy storage capacity and power for the system economic information and the current data of the distribution network, or the server 104 may obtain the system economic information and the current data of the distribution network from the data storage system, and configure the energy storage capacity and power for the system economic information and the current data of the distribution network.

[0049] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0050] In an exemplary embodiment, Figure 2 As shown, a method for configuring energy storage capacity and power is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 200 to 212. Among them:

[0051] Step 200: Obtaining system economic information. The system economic information includes one or more of the investment cost unit price of wind turbines in each area, the investment cost unit price of photovoltaic cells, the investment cost unit price of reversible pump turbines, the investment cost unit price of energy storage batteries, the investment cost unit price of reservoir capacity, the operation and maintenance cost unit price of wind turbines in each area, the operation and maintenance cost unit price of photovoltaic cells, the operation and maintenance cost unit price of reversible pump turbines, the operation and maintenance cost unit price of energy storage batteries, the operation and maintenance cost unit price of energy storage battery charging and discharging, the operation and maintenance cost unit price of reservoir capacity, the discount rate, and the project period.

[0052] Step 201: Based on the system economic information, establish an investment cost model and an operation and maintenance cost model of the system in the shared reservoir operation mode.

[0053] Step 202: construct an outer optimization model based on the investment cost model and the operation and maintenance cost model.

[0054] Step 203: Construct outer optimization constraints according to the shared reservoir operation mode of the pumped storage power station.

[0055] Step 204: Under the outer optimization constraint conditions, the outer optimization model is optimized using a particle swarm intelligent optimization algorithm to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area.

[0056] Step 205: Obtain historical data of the distribution network, and establish a prediction model based on the historical data of the distribution network. The historical data of the distribution network includes historical photovoltaic power, historical wind power, and historical load power.

[0057] Step 206: Obtain current data of the distribution network, and input the current data of the distribution network into the prediction model to obtain distribution network prediction data. Current data of the distribution network photovoltaic power, wind power and load power. Distribution network prediction data includes photovoltaic prediction power, wind power prediction power and load prediction power.

[0058] Step 207: Under the shared reservoir operation mode of the pumped storage power station, an inner optimization model and inner optimization constraints are established based on the distribution network prediction data.

[0059] Step 208: Under the inner optimization constraint conditions, the inner optimization model is optimized using the Whale Intelligent Optimization Algorithm to obtain the inner optimal instructions for the energy storage capacity and power configuration of the distribution station area.

[0060] Step 209: Return the inner optimal instructions of the energy storage capacity and power configuration of the distribution station area to the outer optimization model to obtain the economic characteristics of the system. The economic characteristics include: investment cost and operation and maintenance cost.

[0061] Step 210: Determine whether the outer optimization model meets the set conditions based on the system economic characteristics.

[0062] Step 211: When the outer optimization model meets the set conditions, the energy storage capacity and power configuration is completed based on the inner optimal instructions of the energy storage capacity and power configuration of the distribution station area at this time and the optimized outer optimization model.

[0063] Step 212: When the outer optimization model meets the set conditions, the particle speed and particle position of the particle swarm intelligent optimization algorithm are adjusted, and the process returns to step 204.

[0064] Implementing the above steps 201 to 212 can reduce the operating cost of the pumped energy storage system to the greatest extent on the basis of improving the flexibility and reliability of the energy storage system. In addition, this application uses a swarm intelligence optimization algorithm (i.e., a particle swarm intelligence optimization algorithm and a whale intelligence optimization algorithm) to consider the control optimization process between different stations from the local power grid, and calculates the optimal collaborative control results and operation modes between different stations, effectively changing the energy supply relationship of multiple stations from a single energy mutual assistance mode to a mode of mutual assistance of electricity and water energy, forming the optimal control of the overall system, and using a swarm intelligence optimization algorithm to consider the capacity and power configuration issues of the outer system while considering the inner optimal control, which will be of great significance to the stability and economy of the local power grid operation.

[0065] In another exemplary embodiment of the present application, in terms of system economics, relevant information of the new energy station and the energy storage system is obtained to form the basic economic information of the distribution area under the micro pumped storage power station shared reservoir operation mode (i.e., system economic information). For example, taking the two-area micro pumped storage shared reservoir power station, the new energy station and the load access to the grid under a local power grid structure as an example, the basic economic information of the area includes the investment cost unit price of the wind turbine, photovoltaic cell, reversible pump turbine, energy storage battery and reservoir capacity of the ith area, which are recorded as and The unit prices of operation and maintenance costs of wind turbines, photovoltaic cells, reversible pump turbines, energy storage batteries, energy storage battery charging and discharging, and reservoir capacity in the ith area are respectively recorded as The discount rate r is 5%. Project period T a .

[0066] In another exemplary embodiment of the present application, the investment cost model and the operation and maintenance cost model in the above step 201 constitute the economic cost model of the system in the shared reservoir operation mode. Taking the two-station micro pumped storage shared reservoir power station, new energy station and load access to the grid under a local power grid structure as an example, the initial investment cost of the i-th station is

[0067]

[0068] In the formula, is the number of wind turbines in the i-th wind farm, is the number of photovoltaic cells in the photovoltaic power station, is the total installed capacity of the pump and turbine, is the number of energy storage batteries, is the total storage capacity of the reservoir. is the total installed capacity of the pumps and turbines in the ith area, is the installed capacity of the water pump, is the installed capacity of the turbine. is the total reservoir capacity of the ith station, which is composed of 50% of the shared reservoir capacity and the non-shared reservoir capacity. In the common upstream reservoir model, is the upstream reservoir capacity, It is the storage capacity of the downstream reservoir.

[0069] The operation and maintenance cost of the ith station is

[0070]

[0071] In the formula, is the wind power generation power, is the photovoltaic power generation power, is the total operating power of the pump and turbine, It is the charging and discharging installed power of the energy storage battery.

[0072] In another exemplary embodiment of the present application, taking a two-station micro-pumped storage shared reservoir power station, a new energy station, and load access to the grid under a local power grid structure as an example, in order to make the system more economical, the investment cost and operation and maintenance cost of the system are considered, and an objective function related to the investment cost and operation and maintenance cost of the system is established to obtain an outer optimization model. For example, the outer optimization model is expressed as:

[0073]

[0074] In another exemplary embodiment of the present application, the outer optimization constraints constructed by the present application include one or more of the constraints on the configuration capacity of the upstream reservoir of each substation, the configuration capacity of the downstream reservoir of each substation, the configuration power of the pump of each substation, the configuration power of the turbine of each substation, and the energy storage capacity of a single group of energy storage batteries in each substation. For example, the configuration capacity of the upstream reservoir of the i-th substation is Configuration capacity of the downstream reservoir of the i-th station Configuration power of the water pump in the i-th area Configuration power of turbine in the ith section Energy storage capacity of a single group of energy storage batteries in the i-th station

[0075] In another exemplary embodiment of the present application, in step 204, a particle swarm intelligent optimization algorithm is used to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area in the shared reservoir operation mode of the micro pumped storage power station, taking into account the system investment cost and operation and maintenance cost, and the configuration instructions are transmitted to the inner computing system operation strategy, including:

[0076] Step 1: Determine the optimization variables of the outer optimization model. The optimization variables include the number of wind turbines in the i-th wind farm: The number of photovoltaic cells in a photovoltaic power plant Number of energy storage battery packs Energy storage battery charging and discharging installed power Installed capacity of water pump Installed capacity of turbine Upstream reservoir capacity Downstream reservoir capacity and the total storage capacity of upstream and downstream reservoirs

[0077] Step 2: Based on the optimization variables, a variable space of dimension B1 is formed, and N1 particles are placed in the variable control. The position of the particle represents the installed capacity of the pump, the installed capacity of the turbine, the upstream reservoir, and the downstream reservoir. For example, if there is 1 pump, 1 turbine, 1 upper reservoir, and 1 lower reservoir, then there are 4 optimization variables, assuming they are represented as x1, x2, x3, and x4. Then the position of a particle is the variable composed of these 4 variables, recorded as [x1, x2, x3, x4].

[0078] Step 3: Set the maximum number of iterations to S, initialize the number of iterations s = 0 and the initial position and initial velocity of each particle in the particle swarm, iterate and solve the outer optimization model until the maximum number of iterations is reached, and obtain the position information of each particle. The position information of each particle is used as the outer optimal instruction for the energy storage capacity and power configuration of the distribution station area. The position information includes the particle position and velocity. For example, after the sth iteration, the position of the jth particle is Speed Output the current position and pass it to the inner layer, which can be used as the constraint condition of the inner layer.

[0079] In another exemplary embodiment of the present application, according to the position information of each particle in the outer layer of the system and the information of the power network structure, new energy stations, energy storage system and load in the region, the shared reservoir operation mode of the pumped storage power station is set to include the shared upstream reservoir operation mode and the shared downstream reservoir operation mode. Among them, the information of the power network structure, new energy stations, energy storage system and load in the region includes but is not limited to the maximum change value δ of the injected power that the power grid can withstand, the configuration power of the water pump and turbine in the ith substation Pumped storage efficiency and turbine power generation efficiency of the i-th station Configuration storage capacity of upstream and downstream reservoirs in the i-th station area The hydraulic head h between the upstream and downstream reservoirs of the i-th station (i) , the configuration charging and discharging power of the energy storage battery in the i-th station Charging efficiency of the energy storage battery in the i-th station And discharge efficiency The maximum capacity of a single group of energy storage batteries in the ith area is The number of energy storage batteries is Selected time interval Δt, DOD (i) It is represented as the discharge depth of the energy storage battery in the i-th station.

[0080] Different from traditional pumped storage power stations, in the shared reservoir operation mode of pumped storage power stations, one upstream reservoir can correspond to multiple downstream reservoirs, or one downstream reservoir can correspond to multiple upstream reservoirs. At the same time, energy storage batteries are used to realize the conversion of energy forms and energy integration and scheduling between multiple stations. Among them:

[0081] (1) Establish a pumped storage power station with a shared downstream reservoir operation mode, whereby the upstream reservoirs of each substation share the same downstream reservoir, and the water volume of different substations is coordinated and controlled through the shared downstream reservoir. Figure 3 shown.

[0082] For example, to make the system's daytime scheduling continuous, set the daily final time T N The water volume in the upper and lower reservoirs is a fixed value. represents the final water storage capacity of the upstream reservoir at the end of a typical day in the i-th station under the shared reservoir mode, V L,end V represents the final water storage capacity of the downstream reservoir at the end of a typical day under the common downstream reservoir mode. L (T N )=V L,end .

[0083] For example, in the common downstream reservoir operation mode, the water volume V of the downstream reservoir at time (k+1) is L(k+1) and the reservoir water volume V at time k L The relevant expression for (k) is:

[0084]

[0085] The water volume of the upstream reservoir of the i-th station at time (k+1) and the amount of water at time k The relevant expression is:

[0086]

[0087] Where ρ is the density of water, g is the local gravitational acceleration, and h is (i) is the water head of the ith station, is the water pump energy storage efficiency of the ith station, is the turbine power generation efficiency of the ith station, Δt is the given time interval, is the operating power of the water pump in the ith area, is the operating power of the turbine in the ith section. The total operating power of the pump and turbine is The total storage capacity of the reservoir in the ith station in the common downstream reservoir operation mode is

[0088] (2) Establish a pumped storage power station with a shared upstream reservoir operation mode, where the downstream reservoirs of each substation share the same upstream reservoir, and the coordinated control of water volume in different substations is achieved through the shared downstream reservoir. Figure 4 shown.

[0089] For example, to make the system's daytime scheduling continuous, set the daily final time T N The water volume of the upper and lower reservoirs is a fixed value. H,end V represents the final water storage capacity of the upstream reservoir at the end of a typical day under the common upstream reservoir mode. H (T N )=V H,end . represents the final water storage capacity of the downstream reservoir at the end of a typical day in the ith station under the common upstream reservoir mode, In the common upstream reservoir operation mode, the water volume V of the upstream reservoir at time (k+1) is H (k+1) and the water volume V of the upstream reservoir at time k H The relevant expression for (k) is:

[0090]

[0091] The correlation expression between the water volume of the downstream reservoir of the i-th station at time (k+1) and the water volume of the reservoir at time k is:

[0092]

[0093] The total storage capacity of the reservoir in the i-th station in the common upstream reservoir operation mode is expressed as

[0094] In another exemplary embodiment of the present application, in step 205 and step 206, a prediction model such as an extreme learning machine, a support vector machine or an autoregressive model can be established based on the historical data of the distribution network, such as the historical power of photovoltaic, wind power and load, and the historical power of photovoltaic, wind power and load is input to perform a day-ahead prediction to obtain the predicted power of photovoltaic, wind power and load on the second day. On the day of the twelfth day, the maximum power generated by the wind power plant and photovoltaic power plant in the ith area at time k in a day is expressed as The line load power of the ith substation at time k in a day is expressed as

[0095] In another exemplary embodiment of the present application, the inner optimization constraint conditions include but are not limited to the power constraint conditions of the wind power plant and the photovoltaic power plant, the power constraint conditions of the pumped storage unit, the charging and discharging power constraint conditions of the energy storage battery, the system power balance constraint conditions, the reservoir water storage capacity constraint conditions, the energy storage battery capacity constraint conditions and the power injection value of the transmission network. Based on this, in step 207, the process of establishing the inner optimization model and the inner optimization constraint conditions based on the distribution network prediction data in the shared reservoir operation mode of the pumped storage power station can be described as follows:

[0096] Step 1: The output power of the wind power plants in each area at any time during a typical day is less than the maximum power generation at that time, and the output power of the photovoltaic power plants in each area at any time during a typical day is less than the maximum power generation at that time.

[0097] For example, the output power of the wind power plant in the i-th station at time k in a typical day is Less than the maximum power generated at this moment Right now The output power of the photovoltaic power plant in the i-th station at time k in a typical day Less than the maximum power generated at this moment Right now Select the wind power generation capacity at time k in a typical day in the ith station Photovoltaic power generation Optimize variables for the inner layer.

[0098] Step 2: According to the data of each particle in the outer layer, obtain the configuration information corresponding to each particle in each substation. The configuration information includes but is not limited to the installed capacity information of the water pump and the installed capacity information of the turbine corresponding to each particle in each substation. The storage power of the water pump in each substation at any time in a typical day is less than its installed capacity, and the power generation power of the water turbine in each substation at any time in a typical day is less than its installed capacity. The pumped storage power and the power generation power of the water turbine at a certain time in a typical day of each substation are selected as the optimized inner variables.

[0099] For example, according to the data of N1 particles in the outer layer, the configuration information corresponding to each particle in the ith area is obtained, where the installed capacity of the water pump corresponding to the jth particle in the ith area is The installed capacity of the turbine is The energy storage power of the pump in the i-th station at time k in a typical day is less than its installed capacity Right now The power generation of the turbine in the i-th station at time k in a typical day Smaller than its installed capacity Right now Select the pumped storage power of the i-th station at time k in a typical day And turbine power generation Optimize variables for the inner layer.

[0100] Step 3: According to the data of each particle in each outer layer, the configured storage capacity of the upstream reservoir and the configured storage capacity of the downstream reservoir are obtained. The water storage capacity of the upstream reservoir of each area at a certain time in a typical day is less than its configured capacity. The water storage capacity (i.e., capacity) of the downstream reservoir of each area at a certain time in a typical day is less than its configured capacity.

[0101] For example, according to the jth particle data of the ith station in the outer layer, the configuration storage capacity of the upstream reservoir is obtained Downstream reservoir configuration capacity The water storage capacity of the upstream reservoir of the i-th station at time k in a typical day Less than its configured capacity Right now The water storage capacity of the downstream reservoir of the ith station at time k in a typical day Less than its configured capacity Right now

[0102] Step 4: According to the data of each particle in each outer layer area, the number of energy storage battery groups is obtained, and the constraint conditions of the energy range of the energy storage batteries in each area at a certain time in a typical day are established.

[0103] For example, according to the jth particle data of the i-th station in the outer layer, the number of energy storage batteries is obtained as The energy range of the energy storage battery in the i-th station at time k in a typical day for Among them, the maximum capacity of a single group of energy storage batteries in the i-th station is DOD (i) represents the discharge depth of the energy storage battery in the ith area, Represents the configuration energy of the energy storage battery corresponding to the jth particle in the i-th station.

[0104] Step 5: According to the data of each particle in each outer layer area, the configured charging and discharging power of the energy storage battery in each area is obtained, and the constraint range of the charging and discharging power of the energy storage battery group in each area at time k in a typical day is established.

[0105] For example, according to the jth particle data of the i-th outer layer, the configuration charging and discharging power of the energy storage battery in the i-th outer layer is obtained as The constraint range of the charging and discharging power of the energy storage battery group in the i-th station at time k in a typical day is

[0106] Step 6: Establish the power balance equation of the ith substation. The sum of the photovoltaic power generation power, wind power generation power, transmission line transmission power, total pumped storage power, total energy storage battery power, and line load power consumed by each substation at a certain time on a typical day is zero.

[0107] For example, the power balance equation of the ith area is established. The sum of the photovoltaic power generation, wind power generation, transmission line transmission power, total pumped storage power, total energy storage battery power, and line load power consumed by the ith area at time k in a typical day is zero.

[0108]

[0109] The total power generated by the reservoir at time k in the i-th station on a typical day is

[0110]

[0111] or 1, or 1 and is an integer. represents the activation of the pumped storage unit at time k in a typical day in the i-th station area, The pumped storage unit stops running. When the pumped storage unit starts running, It represents the activation of the turbine generator set at time k in a typical day in the i-th station area, The turbine generator set stops running. At time , the turbine generator set starts running. The total power of the energy storage battery charged and discharged at time k in the i-th station in a typical day is

[0112]

[0113] or 1, or 1 and is an integer. represents the charging status of the energy storage battery at time k in a typical day in the i-th station area, The energy storage battery stops charging. When the energy storage battery starts charging, represents the discharge of the energy storage battery at time k in a typical day in the i-th station area, The energy storage battery stops discharging. The energy storage battery starts to discharge.

[0114] Step 7: To reduce the impact of each area on the power transmission network during the energy transmission process with the power transmission network, establish the constraint conditions for the change value of the power injected into the power transmission network of each area at a certain time. For example, control the change value of the power injected into the power transmission network of the two areas at time k satisfy

[0115] In another exemplary embodiment of the present application, in order to enable the system to meet the requirements of minimum abandoned wind and photovoltaic power generation, minimum transmission line occupancy, and minimum load shedding, in step 207, the process of establishing an inner optimization model based on distribution network prediction data can be described as follows:

[0116] Step 1: Under the shared reservoir operation mode of the pumped storage power station, establish the abandoned wind and photovoltaic power generation power function, the transmission line occupancy function and the load shedding function based on the distribution network forecast data.

[0117] (1) In order to make the system meet the requirements of minimum abandoned wind and photovoltaic power generation, minimum transmission line occupation, and minimum load shedding, consider the abandoned photovoltaic power generation and wind power generation of two stations in a typical day. Set the function related to the abandoned photovoltaic and wind power generation, that is, the abandoned wind and photovoltaic power generation function, which is expressed as:

[0118]

[0119] (2) In order to reduce the occupancy of the transmission line between the two stations, ensure the stability of the transmission line, and maximize the advantage of the shared reservoir operation mode of the pumped storage power station in transmitting energy between the two stations, a function of the local power grid injection power is established. Considering the power injection of the two stations in a typical day, a function related to the line injection power is set, that is, the transmission line occupancy function, which is expressed as:

[0120]

[0121] (3) In order to maximize the energy supply for the line load and reduce the load shedding amount, a function related to the system load shedding amount is established, namely, the load shedding amount function, which is expressed as

[0122]

[0123] Step 2: Integrate the abandoned wind and photovoltaic power generation functions, the transmission line occupancy function, and the load shedding function, and assign weights to each function to obtain the inner optimization model. The inner optimization model is set as the objective function of the system, expressed as:

[0124]

[0125] In the formula, ε is the weight of the function considering the minimum power of wind power and photovoltaic power generation, σ is the weight of the function considering the reduction of the occupancy of the transmission line between the two intervals, (1-ε-σ) is the weight of the load shedding in the function, and J represents the objective function.

[0126] In another exemplary embodiment of the present application, in step 208, the whale intelligent optimization algorithm is used to obtain the real-time optimal control instructions of the wind, light, energy storage battery and pumped storage in the distribution area of ​​the inner micro pumped storage power station under the shared reservoir operation mode under the conditions corresponding to each outer particle, so as to achieve the purpose of minimizing the abandoned wind and photovoltaic power generation power, minimizing the occupancy of the transmission line, and minimizing the load shedding. The specific implementation process of this process is as follows Figure 5 As shown, including:

[0127] Step 1: The optimization variables of the model include the photovoltaic power generation at time k in a typical day in the i-th station area Wind power generation Pumped storage power Turbine power generation Pump working state variables Turbine operating state variables Total pumped storage power Water storage capacity of upstream reservoir Water storage capacity of downstream reservoirs Energy storage battery charging power Energy storage battery discharge power Energy storage battery charge state variables Energy storage battery discharge state variables Total power of energy storage battery Energy storage battery capacity Grid injection power Changes in grid injected power The total power supplied by the system to the load in, The water pump stops running. The water pump starts running. When the turbine generator set stops running, When the turbine generator set starts running. Non-continuous variables, when Time to take when Pick The same reason Time to take when Pick The energy storage battery stops charging. The energy storage battery starts charging. When the energy storage battery stops discharging, The energy storage battery begins to discharge. Non-continuous variables, when Time to take when Pick The same reason Time to take when Pick Δt represents the time interval selected within a typical day. Place N2 whales in the B2-dimensional space composed of the above variables.

[0128] Step 2: Set the number of whales N2 and the maximum number of inner layer iterations V of the algorithm, and initialize the location information X(s) of each whale. Where v represents the number of inner layer optimization iterations.

[0129] Step 3: Calculate the fitness of each whale and find the current optimal whale position X * (s) and retain. And calculate the coefficient vectors A and C.

[0130] A=2a·r1-a (15)

[0131] C=2r2 (16)

[0132] Where r1 and r2 are random vectors in [0,1]. p represents the probability of the predation mechanism, which is a random number in the range of [0,1]. a decreases linearly from 2 to 0 during the entire iteration process. If the probability p is less than 50% (i.e. 0.5), proceed to step 4 below, otherwise the bubble net predation mechanism is used. The bubble net predation mechanism uses the following formula for position update:

[0133] X(v+1)=D'·e bl ·cos(2πl)+X * (v) (17)

[0134] D'=|X * (v)-X(v)| (18)

[0135] Where D' represents the distance between the current search individual and the current optimal solution. b represents the spiral shape parameter. l represents a random number with a uniform distribution in the range [-1,1]. * (v) is the position of the current optimal solution, and X(v) is the position of the current search individual.

[0136] Step 4: Determine whether the absolute value of the coefficient vector A is less than 1. If so, surround the prey and update the position according to the following formula.

[0137] X(v+1)=X * (v)-A·D (19)

[0138] D=|C·X * (v)-X(v)| (20)

[0139] Otherwise, the prey is searched randomly globally and the position is updated according to the following formula:

[0140] X(v+1)=X rand (v)-A·D” (21)

[0141] D”=|C·X rand (v)-X(v)| (22)

[0142] In the formula, D” represents the distance between the current search individual and the random individual. rand (v) represents the current position of the random individual.

[0143] Step 5: After the position update is completed, the fitness of each whale is calculated and compared with the position of the previously retained optimal whale. If it is better, the new optimal solution is used to replace it.

[0144] Step 6: Determine whether the current number of iterations v reaches the maximum number of iterations V. If so, the optimal solution is obtained and the calculation ends. Otherwise, enter the next iteration and return to step 3.

[0145] In another exemplary embodiment of the present application, step 209 is to return the inner layer optimized optimal solution corresponding to each outer layer particle to the outer layer to continue calculating the economic characteristics of the system.

[0146] For example, according to the information of the optimal solution of the inner optimization corresponding to each outer particle, the value of the objective function M at the location of each particle at this moment is calculated. The information of the optimal solution of the inner optimization corresponding to each outer particle includes the power generation of the wind power station at time k in a typical day in the i-th station area. Photovoltaic power station power generation Pumped storage power Turbine power generation Water storage capacity of upstream reservoir Water storage capacity of downstream reservoirs Energy storage battery charging power Energy storage battery discharge power

[0147] Among them, the total operating power of the pump and turbine at time k in a typical day in the i-th station is The total storage capacity of the reservoir at time k in a typical day in the i-th station in the common downstream reservoir operation mode is The total reservoir capacity at time k in a typical day in the i-th station in the common upstream reservoir operation mode is

[0148] Update the optimal position searched by the particle at this time. The individual optimal solution of the jth particle after s iterations is: The objective function value corresponding to this optimal solution is recorded as That is, the objective function value corresponding to the individual optimal solution of the jth particle after s iterations. Update the optimal position searched by the entire particle swarm. The optimal solution searched by the particle swarm after s iterations is: The objective function value corresponding to this optimal solution is recorded as That is, the objective function value corresponding to the optimal solution of the particle swarm after s iterations.

[0149] In another exemplary embodiment of the present application, the implementation process of step 210 to step 212 can be described as follows:

[0150] Step 1: Determine whether the number of iterations s is less than the maximum number of iterations S of the particle swarm algorithm. If so, s=s+1 to proceed to the next step. If not, output the value of the objective function M corresponding to the optimal solution of the group at this time and the optimal solution of the group to complete the scheduling.

[0151] Step 2: Update the position and speed of the particles according to the particle swarm algorithm, obtain the new position and continue iterative optimization. Update the speed of N particles according to the speed update formula

[0152]

[0153] In the formula, It is the inertia part, which is composed of the inertia weight and the particle's own speed, and represents the particle's trust in its previous state of motion. It is the cognitive part, which represents the thinking of the particle itself, that is, the particle’s own experience part, which can be understood as the distance and direction between the particle’s current position and its own optimal historical position. The social part represents the information sharing and cooperation between particles, which comes from the experience of other excellent particles in the group. It can be understood as the distance and direction between the current position of the particle and the historical optimal position of the group. ω represents the inertia weight. c1 represents the individual learning factor. c2 represents the group learning factor. r1, r2 represent random numbers in the interval [0, 1] to increase the randomness of the search. Represents the b-th dimension velocity vector of particle j after the s-th iteration. Represents the b-th dimension position vector of particle j after the s-th iteration. It represents the historical optimal position of particle j in the bth dimension after the sth iteration, that is, the optimal solution obtained by the jth particle after the sth iteration. It represents the historical optimal position of the bth dimension of the group after the sth iteration, that is, the optimal solution in the entire particle swarm after the sth iteration.

[0154] The positions of N particles are updated according to the position update formula, which is expressed as:

[0155]

[0156] In the formula, Represents the position of the b-dimensional vector of the jth particle after the s+1th iteration. Represents the b-dimensional vector position of the j-th particle after the s-th iteration. represents the velocity of the b-dimensional vector of the jth particle after the sth iteration. The updated positions of the N1 particles are returned to step 204, and the positions at this time are output and passed to the inner layer as the constraints of the inner layer.

[0157] In summary, the implementation process of the energy storage capacity and power configuration method provided in this application can be as follows: Figure 6As shown. Compared with the research on the energy storage capacity and power configuration method of the traditional pumped storage system, the focus of the technology of this application is on the configuration method of the energy storage capacity and power of the distribution station area under the shared reservoir operation mode of the micro pumped storage power station. The energy transmission between different stations is realized with the help of the shared reservoir. The shared reservoir mode is divided into two types: shared upstream reservoir and shared downstream reservoir. The characteristics of this application are that according to the different energy states of the new energy and energy storage systems in each station area, different energy storage capacities, different bearing capacities of the transmission lines, different power supply conditions of the new energy and pumped storage systems, and different power consumption conditions of the loads, a multi-mode energy scheduling model and optimization control structure between the new energy and pumped storage systems are formed, and the system capacity and power configuration are optimized according to the control structure of the system under this operation mode. The group intelligent optimization algorithm is used to consider the control optimization process between different stations from the local power grid, and the optimal collaborative control results and operation modes between different stations are calculated, effectively changing the energy supply relationship of multiple stations. Changing from a single energy mutual assistance mode to a dual energy mutual assistance mode of electricity and water can form the optimal control of the overall system. While considering the inner optimal control, the capacity and power configuration of the outer system are considered using the swarm intelligent optimization algorithm, which will be of great significance to the stability and economy of the local power grid operation.

[0158] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store energy storage capacity and power configuration data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for configuring energy storage capacity and power is implemented.

[0159] Those skilled in the art will understand that Figure 7The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0160] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0161] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0163] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0164] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0165] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0166] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for configuring energy storage capacity and power, characterized in that: The energy storage capacity and power configuration method includes: Obtaining system economic information; the system economic information includes one or more of the following: investment cost unit price of wind turbines in each area, investment cost unit price of photovoltaic cells, investment cost unit price of reversible pump-turbines, investment cost unit price of energy storage batteries, investment cost unit price of reservoir capacity, operation and maintenance cost unit price of wind turbines in each area, operation and maintenance cost unit price of photovoltaic cells, operation and maintenance cost unit price of reversible pump-turbines, operation and maintenance cost unit price of energy storage batteries, operation and maintenance cost unit price of energy storage battery charging and discharging, operation and maintenance cost unit price of reservoir capacity, discount rate, and project period; Based on the system economic information, establish the investment cost model and operation and maintenance cost model of the system under the shared reservoir operation mode; Constructing an outer optimization model based on the investment cost model and the operation and maintenance cost model; According to the shared reservoir operation mode of pumped storage power station, the outer optimization constraints are constructed; Under the outer optimization constraint conditions, the outer optimization model is optimized using a particle swarm intelligent optimization algorithm to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area; Acquire historical data of the distribution network, and establish a prediction model based on the historical data of the distribution network; the historical data of the distribution network includes historical photovoltaic power, historical wind power and historical load power; Acquire current data of the distribution network, and input the current data of the distribution network into the prediction model to obtain distribution network prediction data; the current data of the distribution network includes photovoltaic power, wind power and load power; the distribution network prediction data includes photovoltaic prediction power, wind power prediction power and load prediction power; In the shared reservoir operation mode of the pumped storage power station, an inner optimization model and inner optimization constraints are established based on the distribution network prediction data; Under the inner optimization constraint conditions, the inner optimization model is optimized using the whale intelligent optimization algorithm to obtain the inner optimal instructions for the energy storage capacity and power configuration of the distribution station area; Returning the inner optimal instructions of the energy storage capacity and power configuration of the distribution station area to the outer optimization model to obtain the economic characteristics of the system; the economic characteristics include: investment cost and operation and maintenance cost; Determining whether the outer optimization model meets set conditions based on the economic characteristics of the system; When the outer optimization model satisfies the set conditions, the energy storage capacity and power configuration are completed based on the inner optimal instructions of the energy storage capacity and power configuration of the distribution station area at this time and the optimized outer optimization model; When the outer optimization model meets the set conditions, the particle speed and particle position of the particle swarm intelligent optimization algorithm are adjusted, and the process returns to execute "under the outer optimization constraints, the outer optimization model is optimized using the particle swarm intelligent optimization algorithm to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area".

2. The energy storage capacity and power configuration method according to claim 1, characterized in that: The outer optimization constraints include: constraints on the configuration capacity of the upstream reservoirs of each substation, constraints on the configuration capacity of the downstream reservoirs of each substation, constraints on the configuration power of the water pumps of each substation, constraints on the configuration power of the turbines of each substation, and one or more constraints on the energy storage capacity of a single group of energy storage batteries in each substation.

3. The energy storage capacity and power configuration method according to claim 1, characterized in that: Under the outer optimization constraint conditions, the outer optimization model is optimized using a particle swarm intelligent optimization algorithm to obtain the outer optimal instructions for the energy storage capacity and power configuration of the distribution station area, specifically including: Determine the optimization variables of the outer optimization model; the optimization variables include the installed capacity of water pumps in each substation, the installed capacity of turbines, the storage capacity of upstream reservoirs and the storage capacity of downstream reservoirs; forming a variable space based on the optimization variables and placing particles in the variable control; The maximum number of iterations is set, and the number of iterations as well as the initial position and initial velocity of each particle in the particle swarm are initialized. The outer optimization model is iteratively solved until the maximum number of iterations is reached, and the position information of each particle is obtained. The position information of each particle is used as the outer optimal instruction for the energy storage capacity and power configuration of the distribution station area; the position information includes the particle position and velocity.

4. The energy storage capacity and power configuration method according to claim 1, characterized in that: The shared reservoir operation mode of the pumped storage power station includes: a shared upstream reservoir operation mode and a shared downstream reservoir operation mode; In the common upstream reservoir operation mode, the downstream reservoirs of each substation share the same upstream reservoir; In the shared downstream reservoir operation mode, the upstream reservoirs of each substation share the same downstream reservoir.

5. The energy storage capacity and power configuration method according to claim 1, characterized in that: The prediction model is an extreme learning machine, a support vector machine or an autoregressive model.

6. The energy storage capacity and power configuration method according to claim 1, characterized in that: The inner optimization constraints include one or more of the power constraints of wind power plants and photovoltaic power plants, the power constraints of pumped storage units, the charging and discharging power constraints of energy storage batteries, the system power balance constraints, the water storage capacity constraints of reservoirs, the capacity constraints of energy storage batteries, and the power change constraints of the transmission network.

7. The energy storage capacity and power configuration method according to claim 1, characterized in that: In the shared reservoir operation mode of the pumped storage power station, an inner optimization model is established based on the distribution network prediction data, including: In the shared reservoir operation mode of the pumped storage power station, based on the distribution network prediction data, a function of abandoned wind and photovoltaic power generation, a function of transmission line occupancy, and a function of load shedding are established; The inner optimization model is obtained by integrating the abandoned wind power and photovoltaic power generation function, the transmission line occupancy function, and the load shedding function, and allocating weights of each function.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the energy storage capacity and power configuration method described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy storage capacity and power configuration method described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the energy storage capacity and power configuration method described in any one of claims 1 to 7 is implemented.

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