Optimal configuration method and device of energy storage power station, computer equipment, storage medium and program product

Abstract

The invention relates to an optimal configuration method and device for an energy storage power station, computer equipment, a storage medium and a program product. The method comprises the following steps: constructing a target function by taking power distribution network loss minimization of a target city power distribution network and economic benefit maximization of an energy storage power station as targets, the constraint conditions of the target function comprise the power constraint of the energy storage power station, the capacity constraint of the energy storage power station, the charging and discharging efficiency constraint of the energy storage power station, the voltage constraint of the target urban power distribution network and the power balance constraint of the target urban power distribution network; and solving the target function to obtain a globally optimal solution, evaluating the globally optimal solution to obtain an evaluation result, and taking the globally optimal solution as the optimal configuration of the energy storage power station under the condition that the evaluation result is passing. By adopting the method, the optimal configuration of the energy storage power station comprising the network construction type energy storage converter can be realized.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to an optimization configuration method, apparatus, computer equipment, storage medium and program product for an energy storage power station. Background Technology

[0002] With the large-scale integration of new energy power generation into urban power grids and the continuous growth and increased fluctuations in electricity load, the stable operation, power supply reliability, and economic operation of urban power distribution networks are facing unprecedented pressure. Grid-based energy storage converters, as an advanced technology, can significantly improve the active grid connection capability of energy storage power stations and their support strength to the power grid.

[0003] However, how to rationally configure energy storage power stations with grid-type energy storage converters for the unique environment of urban power distribution networks has become an urgent problem to be solved. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, storage medium, and program product for optimizing the configuration of an energy storage power station with a grid-type energy storage converter, which can address the above-mentioned technical problems.

[0005] Firstly, this application provides an optimal configuration method for energy storage power stations. The method includes: constructing an objective function with the objectives of minimizing distribution network losses in a target city's distribution network and maximizing the economic benefits of the energy storage power station; the constraints of the objective function include power constraints, capacity constraints, charging and discharging efficiency constraints, voltage constraints of the target city's distribution network, and power balance constraints of the target city's distribution network; solving the objective function to obtain a global optimal solution; the global optimal solution includes multiple dimensions, including the capacity dimension of the energy storage power station, the installation location dimension of the energy storage power station, and the charging and discharging parameter information dimension of the energy storage power station; evaluating the global optimal solution to obtain an evaluation result; and if the evaluation result is satisfactory, using the global optimal solution as the optimal configuration of the energy storage power station.

[0006] In one embodiment, solving the objective function to obtain the global optimal solution includes: generating position information of multiple particles using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the particle's location; iteratively updating the position information of the multiple particles based on the objective function; and when the iteration termination condition is met, taking the currently obtained global optimal candidate solution as the global optimal solution.

[0007] In one embodiment, the position information of multiple particles is iteratively updated based on the objective function, including: in the i-th iteration update, for each particle, based on the objective function, selecting the individual optimal solution of the particle in the i-th iteration from the position information of the particle obtained after the (i-1)-th iteration and the individual optimal solution of the particle in the (i-1)-th iteration; where i is a positive integer greater than or equal to 2; selecting the global optimal candidate solution in the i-th iteration from the individual optimal solutions of multiple particles in the i-th iteration and the global optimal candidate solution in the (i-1)-th iteration based on the objective function; updating the position information of multiple particles obtained after the (i-1)-th iteration based on the global optimal candidate solution in the i-th iteration and the individual optimal solutions of multiple particles in the i-th iteration, to obtain the position information of multiple particles obtained after the i-th iteration.

[0008] In one embodiment, solving the objective function to obtain the global optimal solution includes: generating location information of multiple cats in the cat group, using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the location of individual cats in the cat group; dividing the cat group into a search cat group and a tracking cat group, and iteratively updating the location information of each cat in the search cat group and the location information of each cat in the tracking cat group based on the objective function; and when the iteration termination condition is met, taking the currently obtained candidate solution as the global optimal solution.

[0009] In one embodiment, the location information of each cat in the search cat group is iteratively updated based on the objective function, including: in the i-th iteration update, for each cat in the search cat group, the location information of the cat individual obtained after the (i-1)-th iteration is copied multiple times to obtain multiple first candidate location information; the first candidate location information of the cat individual is updated based on the number of changes and the domain of changes to obtain multiple updated first candidate location information; the fitness of each updated first candidate location information is calculated based on the objective function to obtain the fitness corresponding to each updated first candidate location information; the location information of the cat individual obtained after the (i-1)-th iteration is updated based on the location information with the highest fitness among the multiple updated first candidate location information to obtain the location information of the cat individual obtained after the i-th iteration.

[0010] In one embodiment, the position information of each cat in the tracking cat group is iteratively updated based on the objective function, including: in the i-th iteration update, based on the objective function, candidate solutions are selected from the position information of all cats in the search cat group obtained after the (i-1)-th iteration and the position information of all cats in the tracking cat group obtained after the (i-1)-th iteration; for each cat in the tracking cat group, the position information of the cats obtained after the (i-1)-th iteration is updated based on the candidate solutions in the i-th iteration to obtain the position information of the cats obtained after the i-th iteration.

[0011] Secondly, this application also provides an optimized configuration device for an energy storage power station, the device comprising:

[0012] The module is used to construct an objective function with the goal of minimizing the distribution network loss of the target city's distribution network and maximizing the economic benefits of the energy storage power station. The constraints of the objective function include the power constraints of the energy storage power station, the capacity constraints of the energy storage power station, the charging and discharging efficiency constraints of the energy storage power station, the voltage constraints of the target city's distribution network, and the power balance constraints of the target city's distribution network.

[0013] The calculation module is used to solve the objective function and obtain the global optimal solution. The global optimal solution includes multiple dimensions, including the capacity dimension of the energy storage power station, the installation location dimension of the energy storage power station, and the charging and discharging parameter information dimension of the energy storage power station.

[0014] The evaluation module is used to evaluate the global optimal solution, obtain the evaluation result, and if the evaluation result is satisfactory, the global optimal solution is taken as the optimal configuration of the energy storage power station.

[0015] In one embodiment, the calculation module is specifically used to generate position information of multiple particles using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the particle's location; iteratively update the position information of the multiple particles based on the objective function; and when the iteration termination condition is met, take the currently obtained global optimal candidate solution as the global optimal solution.

[0016] In one embodiment, the calculation module is specifically used to, in the i-th iteration update, for each particle, based on the objective function, select the individual optimal solution of the particle in the i-th iteration from the position information of the particle obtained after the (i-1)-th iteration and the individual optimal solution of the particle in the (i-1)-th iteration; where i is a positive integer greater than or equal to 2; based on the objective function, select the global optimal candidate solution in the i-th iteration from the individual optimal solutions of multiple particles in the i-th iteration and the global optimal candidate solution in the (i-1)-th iteration; based on the global optimal candidate solution in the i-th iteration and the individual optimal solutions of multiple particles in the i-th iteration, update the position information of multiple particles obtained after the (i-1)-th iteration to obtain the position information of multiple particles obtained after the i-th iteration.

[0017] In one embodiment, the calculation module is specifically used to generate location information of multiple cats in the cat group, using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the location of individual cats in the cat group; divide the cat group into a search cat group and a tracking cat group, and iteratively update the location information of each cat in the search cat group and the location information of each cat in the tracking cat group based on the objective function; when the iteration termination condition is met, the currently obtained candidate solution is taken as the global optimal solution.

[0018] In one embodiment, the calculation module is specifically used to, in the i-th iteration update, for each individual cat in the search cat group, copy the position information of the individual cat obtained after the (i-1)-th iteration multiple times to obtain multiple first candidate position information; update each first candidate position information of the individual cat based on the number of changes and the domain of changes to obtain multiple updated first candidate position information; calculate the fitness of each updated first candidate position information based on the objective function to obtain the fitness corresponding to each updated first candidate position information; and update the position information of the individual cat obtained after the (i-1)-th iteration based on the position information with the highest fitness among the multiple updated first candidate position information to obtain the position information of the individual cat obtained after the i-th iteration.

[0019] In one embodiment, the calculation module is specifically used to, in the i-th iteration update, based on the objective function, filter the position information of all cats in the search cat group obtained after the (i-1)-th iteration and the position information of all cats in the tracking cat group obtained after the (i-1)-th iteration to obtain the candidate solution in the i-th iteration; for each cat in the tracking cat group, update the position information of the cats obtained after the (i-1)-th iteration based on the candidate solution in the i-th iteration to obtain the position information of the cats obtained after the i-th iteration.

[0020] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any one of the first aspects above.

[0021] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0022] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0023] The aforementioned optimization configuration method, device, computer equipment, storage medium, and program product for energy storage power stations construct an objective function with the goal of minimizing the distribution network loss of the target city's distribution network and maximizing the economic benefits of the energy storage power station. The constraints of the objective function include the power constraints, capacity constraints, charging and discharging efficiency constraints, voltage constraints, and power balance constraints of the target city's distribution network. Then, the objective function is solved to obtain a global optimal solution with multiple dimensions, including the capacity dimension of the energy storage power station, the installation location dimension of the energy storage power station, and the charging and discharging parameter information dimension of the energy storage power station. The global optimal solution is evaluated to obtain the evaluation result. If the evaluation result is satisfactory, the global optimal solution is taken as the optimal configuration of the energy storage power station. Thus, since the constraints of the objective function include the power constraint, capacity constraint, charging and discharging efficiency constraint of the energy storage power station, voltage constraint of the target city distribution network, and power balance constraint of the target city distribution network, and the objective function is constructed with the goal of minimizing the distribution network loss of the target city distribution network and maximizing the economic benefits of the energy storage power station, it also involves the installation location dimension of the energy storage power station, that is, it considers the unique environment of the city distribution network. Therefore, the global optimal solution obtained based on this objective function is the optimal configuration of the energy storage power station. When the energy storage power station is a grid-type energy storage converter, the optimal configuration of the energy storage power station with a grid-type energy storage converter is achieved. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is an application environment diagram of an energy storage power station optimization configuration method in one embodiment;

[0026] Figure 2 This is a flowchart illustrating an optimized configuration method for an energy storage power station in one embodiment.

[0027] Figure 3 This is a schematic diagram of the target city's power distribution network in one embodiment;

[0028] Figure 4 This is a flowchart illustrating a method for solving the objective function in one embodiment;

[0029] Figure 5 This is a trend graph of the objective function value in one embodiment;

[0030] Figure 6 This is a flowchart illustrating the method for solving the objective function in another embodiment;

[0031] Figure 7 This is a flowchart illustrating a method for iteratively updating the location information of each cat in a search cat group, as an example.

[0032] Figure 8 This is a flowchart illustrating a method for iteratively updating the location information of each individual cat in a cat tracking group, as an example.

[0033] Figure 9 This is a schematic diagram illustrating the optimized configuration principle of an energy storage power station in one embodiment;

[0034] Figure 10 This is a structural block diagram of an optimized configuration device for an energy storage power station in one embodiment;

[0035] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0037] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0038] The optimized configuration method for energy storage power stations provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. Server 104 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0039] In one exemplary embodiment, such as Figure 2 As shown, an optimized configuration method for an energy storage power station is provided, which can be applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps 201 to 203. Wherein:

[0040] Step 201: Construct an objective function with the goal of minimizing the distribution network loss of the target city's distribution network and maximizing the economic benefits of the energy storage power station. The constraints of the objective function include the power constraints of the energy storage power station, the capacity constraints of the energy storage power station, the charging and discharging efficiency constraints of the energy storage power station, the voltage constraints of the target city's distribution network, and the power balance constraints of the target city's distribution network.

[0041] It should be noted that this application applies to medium-voltage urban power distribution networks with 50-200 load nodes and a renewable energy access ratio of 20%-50%.

[0042] In this study, distribution network losses are assessed through power flow calculations, and economic benefits are considered in light of the construction and operating costs of the energy storage power station, as well as the revenue from participating in grid peak shaving and frequency regulation services. The energy storage power station can be an energy storage power station incorporating a grid-type energy storage converter.

[0043] In one example, the objective function is:

[0044]

[0045] in, It is the objective function; It is the distribution network loss; It is the total cost; It is the benefit-cost ratio; , , These are weighting coefficients, determined based on the actual conditions and planning objectives of the target city's power distribution network. For example, for areas with high power supply reliability requirements, the weighting coefficient can be appropriately increased. For situations where economic costs are more sensitive, the amount can be increased. ; It is the first Power loss of the line; It is the construction cost of the energy storage power station. It is the operating cost; It is the first Service revenue, It is the first Service costs.

[0046] In solving the objective function, the control characteristics of the grid-type energy storage converter, such as the impact of virtual synchronous machine control and droop control, on the operation of the energy storage power station are considered. Taking virtual synchronous machine control as an example, its inertia... and damping coefficient The frequency response characteristics of energy storage power stations are affected, and their operational performance in urban power distribution networks can be optimized by adjusting these parameters. Considering the response speed and power regulation capabilities of grid-connected energy storage converters under different operating scenarios, the configuration scheme of energy storage power stations can be optimized to better adapt to the dynamic changes of urban power distribution networks.

[0047] The power constraint of the energy storage power station is:

[0048]

[0049] in, This is the maximum charging and discharging power of the energy storage power station. This is the actual charging and discharging power.

[0050] The capacity constraint of energy storage power stations is:

[0051]

[0052] in, and These are the minimum and maximum values ​​of the state of charge of the energy storage power station, respectively. It is the actual state of charge of the energy storage power station.

[0053] The charging and discharging efficiency constraints for energy storage power stations are:

[0054]

[0055] in, , These are charging efficiency and discharging efficiency, respectively. For charging power, To input power, This is the amount of discharge capacity. To store electricity.

[0056] The voltage constraints for the target city's power distribution network are:

[0057]

[0058] in, , For the allowable voltage range, For load nodes The voltage, that is, the voltage of the target city. Voltage in each region.

[0059] The power balance constraints of the target city's power distribution network are:

[0060]

[0061] in, , , , These are respectively the sets of power generation equipment, energy storage power stations, load nodes, and line losses. , , , These represent the power of power generation equipment, energy storage power stations, load nodes, and line losses, respectively.

[0062] The parameters in the constraints can be obtained by collecting relevant data on the target city's power distribution network and analyzing that data.

[0063] In one example, relevant data for the target city's power distribution network includes load data, grid structure data, and renewable energy access data. Smart meters, sensors, and other devices can be used to collect at least one year's worth of historical load data for the target city's power distribution network, including active and reactive power demand in different areas and time periods, with a data collection frequency of no less than 15 minutes per instance. Grid structure data, such as line parameters, transformer capacity, and node information, should also be collected. Renewable energy access data, such as the installed capacity and generation characteristics of distributed photovoltaic and wind power, should also be collected. Figure 3 As shown, a schematic diagram of a target city's power distribution network is provided, illustrating the load distribution, power access points, and possible installation locations of energy storage power stations in different areas.

[0064] After collecting relevant data on the target city's power distribution network, it is necessary to clean and preprocess the data to ensure its accuracy and completeness. For example, outliers can be removed by filtering and missing values ​​can be added by data interpolation.

[0065] Taking load data analysis as an example, time series analysis models, such as ARIMA (Autoregressive Integrated Moving Average) models, are used to predict the load demand of different time periods and regions in the target city, providing a basis for the capacity configuration of energy storage power stations.

[0066] The ARIMA model is:

[0067]

[0068] in, It is time series data (such as load data); It is a backward difference operator; yes Autoregressive polynomial of order 1; yes Order moving average polynomial; It is the difference order; It is a white noise sequence.

[0069] It's understandable that the constraints of the objective function can be embodied in the form of a penalty function. In other words, the objective function also includes a penalty function, the value of which is determined by the aforementioned constraints. For example, the greater the charging / discharging power exceeds the maximum charging / discharging power, the larger the value of the penalty function, which in turn leads to a larger value of the objective function. The penalty function includes multiple penalty terms, each corresponding to a constraint, and each penalty term has a weight coefficient. In different scenarios, the weight coefficients of different penalty terms are different. For example, for urban power distribution networks with a higher proportion of renewable energy integration, the weight coefficient of the penalty term corresponding to the voltage constraint is larger.

[0070] Step 202: Solve the objective function to obtain the global optimal solution. The global optimal solution includes multiple dimensions, including the capacity dimension of the energy storage power station, the installation location dimension of the energy storage power station, and the charging and discharging parameter information dimension of the energy storage power station.

[0071] In one example, the global optimum is obtained by solving the objective function using the particle swarm optimization algorithm. In another example, the global optimum is obtained by solving the objective function using the cat swarm optimization algorithm.

[0072] Step 203: Evaluate the global optimal solution, obtain the evaluation result, and if the evaluation result is satisfactory, use the global optimal solution as the optimal configuration of the energy storage power station.

[0073] In one possible implementation, the global optimal solution is applied to a simulation model of the target city's power distribution network to perform power flow calculations and stability analysis. The effectiveness of the global optimal solution in improving the power supply reliability, network losses, and economic benefits of the city's power distribution network is evaluated. If the evaluation results meet the expected requirements (i.e., the evaluation is passed), the global optimal solution is used as the optimal configuration for the energy storage power station. If the evaluation results do not meet the expected requirements (i.e., the evaluation is failed), the parameters in the objective function (such as weighting coefficients, energy storage power station parameters, etc.) or the parameters of the optimization algorithm (such as inertia weights, learning factors, etc.) are recalculated and the scheme is evaluated until a satisfactory configuration scheme is obtained. The power flow calculation can employ the Newton-Raphson method or the fast decoupling method.

[0074] Based on the optimal configuration, the construction and equipment installation of the energy storage power station will proceed. After the energy storage power station is put into operation, a real-time monitoring system will be established to continuously monitor parameters such as the operating status of the urban power distribution network, the charging and discharging status of the energy storage power station, and the state of charge. Based on actual operating data, the optimal configuration will be evaluated and adjusted periodically to ensure that the energy storage power station is always in optimal operating condition, providing reliable support for the target urban power distribution network.

[0075] The above-mentioned optimization configuration method for energy storage power stations constructs an objective function with the goal of minimizing the distribution network loss of the target city's distribution network and maximizing the economic benefits of the energy storage power station. The constraints of the objective function include the power constraints, capacity constraints, charging and discharging efficiency constraints, voltage constraints, and power balance constraints of the target city's distribution network. Then, the objective function is solved to obtain a global optimal solution with multiple dimensions, including the capacity dimension of the energy storage power station, the installation location dimension of the energy storage power station, and the charging and discharging parameter information dimension of the energy storage power station. The global optimal solution is evaluated to obtain the evaluation result. If the evaluation result is satisfactory, the global optimal solution is taken as the optimal configuration of the energy storage power station. Thus, since the constraints of the objective function include the power constraint, capacity constraint, charging and discharging efficiency constraint of the energy storage power station, voltage constraint of the target city distribution network, and power balance constraint of the target city distribution network, and the objective function is constructed with the goal of minimizing the distribution network loss of the target city distribution network and maximizing the economic benefits of the energy storage power station, it also involves the installation location dimension of the energy storage power station, that is, it considers the unique environment of the city distribution network. Therefore, the global optimal solution obtained based on this objective function is the optimal configuration of the energy storage power station. When the energy storage power station is a grid-type energy storage converter, the optimal configuration of the energy storage power station with a grid-type energy storage converter is achieved. Furthermore, by comprehensively considering various factors of the target city's power distribution network, the optimal configuration of energy storage power stations was achieved, thereby improving the stability, reliability, and economy of the target city's power distribution network.

[0076] In one exemplary embodiment, such as Figure 4 As shown, a method for solving the objective function is provided; solving the objective function yields the global optimal solution, including steps 401 and 402, wherein:

[0077] Step 401: Using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the particle's location, generate the position information of multiple particles.

[0078] The charging and discharging parameter information includes at least one of the following: charging and discharging power, charging and discharging efficiency, etc. The particle position information can be used... This indicates that the number of particles can range from 20 to 100.

[0079] Step 402: Based on the objective function, iteratively update the position information of multiple particles; when the iteration termination condition is met, the currently obtained global optimal candidate solution is taken as the global optimal solution.

[0080] In one possible implementation, during the i-th iteration update, for each particle, based on the objective function, the individual optimal solution of the particle in the i-th iteration is obtained by filtering from the particle position information obtained after the (i-1)-th iteration and the individual optimal solution of the particle in the (i-1)-th iteration; where i is a positive integer greater than or equal to 2.

[0081] Based on the objective function, the global optimal candidate solution in the i-th iteration is obtained by selecting from the individual optimal solutions of multiple particles in the i-th iteration and the global optimal candidate solution in the (i-1)-th iteration.

[0082] Based on the global optimal candidate solution in the i-th iteration and the individual optimal solutions of multiple particles in the i-th iteration, the position information of multiple particles obtained after the (i-1)-th iteration is updated to obtain the position information of multiple particles obtained after the i-th iteration.

[0083] It should be noted that in the first iteration update, based on the objective function, the global optimal candidate solution for the first iteration is obtained from the position information of the multiple particles generated in step 401, and the position information of the multiple particles generated in step 401 is used as the individual optimal solution for the multiple particles in the i-th iteration. Based on the global optimal candidate solution and the individual optimal solution of the multiple particles in the first iteration, the position information of the multiple particles generated in step 401 is updated to obtain the position information of the multiple particles after the 1i-th iteration.

[0084] Filtering based on the objective function means calculating the fitness of the particle's position information based on the objective function, and using the position information with the lowest fitness as the result of the filtering.

[0085] The speed of a particle can be used The update of particle position and velocity information can be expressed using the following data formula:

[0086]

[0087]

[0088] in, For inertial weights, , As a learning factor, , A random number between [0,1] For particles The individual optimal solution, that is, the particle The best historical location This is the globally optimal candidate solution, which is also the globally optimal position.

[0089] By continuously iterating and updating the velocity and position information of particles, the optimal solution can be found, thereby determining the optimal capacity, optimal installation location, and optimal charging and discharging parameters of the energy storage power station.

[0090] like Figure 5 As shown, a trend graph of the objective function value is provided. It can be seen that the objective function value gradually decreases as the number of iterations increases. In the early stages of iteration, the objective function value decreases rapidly because the particles quickly explore the search space and continuously approach the optimal solution. As the number of iterations increases, the particles gradually gather near the optimal solution, the rate of decrease of the objective function value slows down, and eventually tends to stabilize. Therefore, the iteration termination condition can be when the objective function value converges to a certain extent (such as meeting the preset convergence accuracy) or when the maximum number of iterations is reached. The maximum number of iterations can be 100-500.

[0091] In one exemplary embodiment, such as Figure 6 As shown, another method for solving the objective function is provided; solving the objective function yields the global optimal solution, including steps 601 and 602, wherein:

[0092] Step 601: Using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the location of individual cats in the cat group, generate the location information of multiple individual cats in the cat group.

[0093] The charging and discharging parameter information includes at least one of the following: charging and discharging power, charging and discharging efficiency, etc. The location information of an individual cat can be used... express.

[0094] Step 602: Divide the cat group into a search cat group and a tracking cat group. Based on the objective function, iteratively update the position information of each cat in the search cat group and the position information of each cat in the tracking cat group. When the iteration termination condition is met, the current candidate solution is taken as the global optimal solution.

[0095] The cat swarm optimization algorithm involves the following parameters: SMP (memory pool size), SPC (self-position determination), CDC (number of changes), and SRD (range of changes). SMP refers to copying the position information of each cat j times as a candidate solution set. SPC indicates that the current position needs to be considered; if SPC is true, this position information is retained as one of the candidate solutions, requiring only j-1 copies of the cat's position information. If SPC is false, this position information is not retained. For the optimization configuration problem of energy storage power stations, SPC is assumed to be false. CDC refers to randomly selecting the dimension to be mutated (CDC is a random value from 0 to the total number of dimensions). SRD refers to randomly perturbing each selected dimension within the range of SRD.

[0096] like Figure 7 As shown, a method for iteratively updating the location information of each cat in a cat-hunting group is provided; based on an objective function, the method iteratively updates the location information of each cat in the cat-hunting group, including:

[0097] Step 701: In the i-th iteration update, for each individual cat in the search cat group, the position information of the individual cat obtained after the (i-1)-th iteration is copied multiple times to obtain multiple first candidate position information.

[0098] In one possible implementation, based on the SMP parameters, the location information of the individual cat obtained after the (i-1)th iteration is copied multiple times. In other words, the number of first candidate location information is equal to the set SMP parameters.

[0099] Step 702: Update the first candidate location information of each cat individual based on the number of changes and the domain of changes to obtain multiple updated first candidate location information.

[0100] In one possible implementation, the first candidate position information is updated based on the following formula:

[0101]

[0102] in, This indicates the updated first candidate location information. Indicates the selected position. A random number represented as [0,1]; Indicates the domain of change.

[0103] Step 703: Calculate the fitness of each updated first candidate position information based on the objective function to obtain the fitness corresponding to each updated first candidate position information.

[0104] Step 704: Based on the position information with the highest fitness among multiple updated first candidate position information, update the position information of the cat individual obtained after the (i-1)th iteration to obtain the position information of the cat individual obtained after the i-th iteration.

[0105] like Figure 8 As shown, a method for iteratively updating the location information of each cat in a cat tracking group is provided; based on an objective function, the method iteratively updates the location information of each cat in the cat tracking group, including:

[0106] Step 801: In the i-th iteration update, based on the objective function, candidate solutions for the i-th iteration are selected from the location information of all individual cats in the search cat group obtained after the (i-1)-th iteration and the location information of all individual cats in the tracking cat group obtained after the (i-1)-th iteration.

[0107] Step 802: For each individual cat in the tracking cat group, update the position information of the individual cat obtained after the (i-1)th iteration based on the candidate solution in the i-th iteration, and obtain the position information of the individual cat obtained after the i-th iteration.

[0108] In one possible implementation, the speed of an individual cat can be used The update of an individual cat's location and speed can be expressed using the following data formula:

[0109]

[0110]

[0111] in, A random number represented as [0,1]; For constant parameters; This is a candidate solution.

[0112] It should be noted that in the first iteration update, based on the objective function, candidate solutions are selected from the position information of all cat individuals in the cat group generated in step 601, and the position information of multiple cat individuals in the tracking cat group generated in step 601 is updated to obtain the position information of multiple cat individuals in the tracking cat group after the first iteration.

[0113] In one embodiment, such as Figure 9The diagram illustrates the principle of optimized configuration for an energy storage power station. The process involves: collecting data, including load data, grid structure data, and new energy access data; cleaning and preprocessing the collected data; constructing an objective function and setting constraints; defining the parameters in the objective function and constraints; solving the objective function using a particle swarm optimization algorithm or a cat swarm optimization algorithm to obtain the global optimal solution, i.e., the initial configuration scheme; evaluating the global optimal solution; if the global optimal solution meets preset conditions, it is used as the optimal configuration for the energy storage power station, and implementation and monitoring are based on this optimal configuration; if the global optimal solution does not meet the preset conditions, the parameters in the objective function are updated.

[0114] In summary, this application optimizes the configuration of energy storage power stations to address the complex characteristics of urban power distribution networks. It fully considers factors such as limited land resources, high electricity load density, and significant differences in power grid structure across different regions. During the configuration process, it balances the safety, economy, and optimization effect on power grid operation of energy storage power stations. Furthermore, it incorporates the control characteristics of grid-connected energy storage converters into the objective function, comprehensively considering their impact on the operation of energy storage power stations and grid support. This ensures that the constructed objective function closely matches the actual situation, resulting in a more accurate optimal configuration of energy storage power stations.

[0115] An objective function that comprehensively considers network losses and economic benefits was constructed, overcoming the limitations of single-objective optimization and more comprehensively reflecting the demand of urban distribution networks for energy storage power stations. An intelligent optimization algorithm was used to solve the model, enabling the rapid and accurate identification of the optimal configuration scheme, improving configuration efficiency and accuracy. Optimization was further performed by combining actual operating data of the urban distribution network with the characteristics of grid-type energy storage converters, making the configuration results more consistent with actual engineering needs.

[0116] From an economic perspective, this approach considers the construction and operating costs of energy storage power stations, as well as the revenue from participating in grid services. It provides investors and grid operators with a scientific basis for decision-making, improves the return on investment for energy storage power stations, and promotes the development of the urban energy storage industry. It can be flexibly adjusted according to the characteristics and needs of different urban distribution networks, making it suitable for urban distribution networks of various sizes and structures, and has broad application prospects.

[0117] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0118] Based on the same inventive concept, this application also provides an energy storage power station optimization configuration device for implementing the above-described energy storage power station optimization configuration method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more energy storage power station optimization configuration device embodiments provided below can be found in the limitations of the energy storage power station optimization configuration method described above, and will not be repeated here.

[0119] In one exemplary embodiment, such as Figure 10 As shown, an optimization configuration device for an energy storage power station is provided. The optimization configuration device 1000 for the energy storage power station includes: a construction module 1001, a calculation module 1002, and an evaluation module 1003, wherein:

[0120] Module 1001 is used to construct an objective function with the goal of minimizing the distribution network loss of the target city's distribution network and maximizing the economic benefits of the energy storage power station. The constraints of the objective function include the power constraints of the energy storage power station, the capacity constraints of the energy storage power station, the charging and discharging efficiency constraints of the energy storage power station, the voltage constraints of the target city's distribution network, and the power balance constraints of the target city's distribution network.

[0121] The calculation module 1002 is used to solve the objective function to obtain the global optimal solution. The global optimal solution includes multiple dimensions, including the capacity dimension of the energy storage power station, the installation location dimension of the energy storage power station, and the charging and discharging parameter information dimension of the energy storage power station.

[0122] Evaluation module 1003 is used to evaluate the global optimal solution, obtain the evaluation result, and if the evaluation result is satisfactory, use the global optimal solution as the optimal configuration of the energy storage power station.

[0123] In one embodiment, the calculation module 1002 is specifically used to generate position information of multiple particles using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the particle's location; iteratively update the position information of multiple particles based on the objective function; and when the iteration termination condition is met, take the currently obtained global optimal candidate solution as the global optimal solution.

[0124] In one embodiment, the calculation module 1002 is specifically used to, in the i-th iteration update, for each particle, based on the objective function, select the individual optimal solution of the particle in the i-th iteration from the position information of the particle obtained after the (i-1)-th iteration and the individual optimal solution of the particle in the (i-1)-th iteration; where i is a positive integer greater than or equal to 2; based on the objective function, select the global optimal candidate solution in the i-th iteration from the individual optimal solutions of multiple particles in the i-th iteration and the global optimal candidate solution in the (i-1)-th iteration; based on the global optimal candidate solution in the i-th iteration and the individual optimal solutions of multiple particles in the i-th iteration, update the position information of multiple particles obtained after the (i-1)-th iteration to obtain the position information of multiple particles obtained after the i-th iteration.

[0125] In one embodiment, the calculation module 1002 is specifically used to generate the location information of multiple cats in the cat group, using the capacity of the energy storage power station, the installation location of the energy storage power station, and the charging and discharging parameter information of the energy storage power station as multiple dimensions of the location of individual cats in the cat group; divide the cat group into a search cat group and a tracking cat group, and iteratively update the location information of each cat in the search cat group and the location information of each cat in the tracking cat group based on the objective function; when the iteration termination condition is met, the currently obtained candidate solution is taken as the global optimal solution.

[0126] In one embodiment, the calculation module 1002 is specifically used in the i-th iteration update to copy the position information of each cat individual obtained after the (i-1)-th iteration multiple times for each cat individual in the search cat group, to obtain multiple first candidate position information; update each first candidate position information of the cat individual based on the number of changes and the domain of changes, to obtain multiple updated first candidate position information; calculate the fitness of each updated first candidate position information based on the objective function, to obtain the fitness corresponding to each updated first candidate position information; and update the position information of the cat individual obtained after the (i-1)-th iteration based on the position information with the highest fitness among the multiple updated first candidate position information, to obtain the position information of the cat individual obtained after the i-th iteration.

[0127] In one embodiment, the calculation module 1002 is specifically used to, in the i-th iteration update, based on the objective function, filter the position information of all cats in the search cat group obtained after the (i-1)-th iteration and the position information of all cats in the tracking cat group obtained after the (i-1)-th iteration to obtain the candidate solution in the i-th iteration; for each cat in the tracking cat group, update the position information of the cats obtained after the (i-1)-th iteration based on the candidate solution in the i-th iteration to obtain the position information of the cats obtained after the i-th iteration.

[0128] The various modules in the optimized configuration device of the aforementioned energy storage power station can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0129] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 11 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an optimized configuration method for an energy storage power station. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0130] Those skilled in the art will understand that Figure 11The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0131] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the above method embodiment.

[0132] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the above method embodiment.

[0133] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above method embodiments.

[0134] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory 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 can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0135] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 application.

[0136] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for optimizing the configuration of an energy storage power plant, characterized in that, The method comprises: a target function is constructed with minimization of power distribution network loss of a target urban power distribution network and maximization of economic benefits of the energy storage power station as a target, and constraint conditions of the target function comprise power constraint of the energy storage power station, capacity constraint of the energy storage power station, charge-discharge efficiency constraint of the energy storage power station, voltage constraint of the target urban power distribution network and power balance constraint of the target urban power distribution network; a global optimal solution is obtained by solving the target function, and the global optimal solution comprises multiple dimensions, and the multiple dimensions comprise a capacity dimension of the energy storage power station, an installation position dimension of the energy storage power station and a charge-discharge parameter information dimension of the energy storage power station; the global optimal solution is evaluated to obtain an evaluation result, and the global optimal solution is taken as an optimal configuration of the energy storage power station when the evaluation result is passed.

2. The method of claim 1, wherein, The solving of the target function to obtain the global optimal solution comprises: position information of multiple particles is generated with the capacity of the energy storage power station, the installation position of the energy storage power station and the charge-discharge parameter information of the energy storage power station as multiple dimensions of positions of the particles; position information of multiple particles is iteratively updated based on the target function, and a global optimal candidate solution obtained at present is taken as the global optimal solution when an iteration termination condition is met.

3. The method of claim 2, wherein, The iteratively updating of the position information of multiple particles based on the target function comprises: in the i th iteration update, for each particle, the individual optimal solution of the particle in the i th iteration is selected from the position information of the particle obtained after the i-1 th iteration and the individual optimal solution of the particle in the i-1 th iteration based on the target function, wherein i is a positive integer greater than or equal to 2; a global optimal candidate solution in the i th iteration is selected from the individual optimal solutions of multiple particles in the i th iteration and the global optimal candidate solution in the i-1 th iteration based on the target function; the position information of multiple particles obtained after the i-1 th iteration is updated based on the global optimal candidate solution in the i th iteration and the individual optimal solutions of multiple particles in the i th iteration to obtain the position information of multiple particles obtained after the i th iteration.

4. The method of claim 1, wherein, The solving of the target function to obtain the global optimal solution comprises: position information of multiple cat individuals in a cat group is generated with the capacity of the energy storage power station, the installation position of the energy storage power station and the charge-discharge parameter information of the energy storage power station as multiple dimensions of positions of the cat individuals in the cat group; the cat group is divided into a search cat group and a tracking cat group, and position information of each cat individual in the search cat group and position information of each cat individual in the tracking cat group are iteratively updated based on the target function, and a candidate solution obtained at present is taken as the global optimal solution when an iteration termination condition is met.

5. The method of claim 4, wherein, The iteratively updating of the position information of each cat individual in the search cat group based on the target function comprises: In the i-th iteration update, the position information of each cat individual in the search cat group is copied multiple times to obtain multiple first candidate position information; wherein, i is a positive integer greater than or equal to 2; The first candidate position information of each cat individual is updated based on the change number and the change domain to obtain multiple updated first candidate position information; The fitness of each updated first candidate position information is calculated based on the objective function to obtain the fitness corresponding to each updated first candidate position information; The position information of each cat individual in the search cat group is updated based on the fitness of the highest position information in the multiple updated first candidate position information to obtain the position information of each cat individual after the i-th iteration.

6. The method of claim 5, wherein, The position information of each cat individual in the search cat group is updated based on the change number and the change domain to obtain multiple updated first candidate position information; In the i-th iteration update, the position information of each cat individual in the search cat group is copied multiple times to obtain multiple first candidate position information; wherein, i is a positive integer greater than or equal to 2; The first candidate position information of each cat individual is updated based on the change number and the change domain to obtain multiple updated first candidate position information; 7. An apparatus for optimizing configuration of an energy storage power plant, characterized by, The fitness of each updated first candidate position information is calculated based on the objective function to obtain the fitness corresponding to each updated first candidate position information; The position information of each cat individual in the search cat group is updated based on the fitness of the highest position information in the multiple updated first candidate position information to obtain the position information of each cat individual after the i-th iteration. The device comprises: The construction module is configured to construct an objective function with the target of minimizing the power loss of the target city power distribution network and maximizing the economic benefits of the energy storage power station, and the constraint conditions of the objective function include the power constraint of the energy storage power station, the capacity constraint of the energy storage power station, the charge and discharge efficiency constraint of the energy storage power station, the voltage constraint of the target city power distribution network, and the power balance constraint of the target city power distribution network; 8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The calculation module is configured to solve the objective function to obtain a global optimal solution; the global optimal solution includes multiple dimensions, and the multiple dimensions include the capacity dimension of the energy storage power station, the installation location dimension of the energy storage power station, and the charge and discharge parameter information dimension of the energy storage power station; 9. A computer readable storage medium having stored thereon a computer program, characterized in that, The evaluation module is configured to evaluate the global optimal solution to obtain an evaluation result, and in the case that the evaluation result is passed, the global optimal solution is taken as the optimal configuration of the energy storage power station.

10. A computer program product comprising a computer program, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1 to 6. The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 6. The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 6. The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 6.

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