Distributed energy storage dynamic configuration method and system

Abstract

The present invention provides a distributed energy storage dynamic configuration method and system, which includes: calculating the present value coefficient based on the number of years corresponding to each energy storage planning stage; constructing an initial objective function with the goal of minimizing the sum of energy storage operating costs and construction costs, integrating the present value coefficients of operating costs and construction costs into the initial objective function to obtain a final objective function; calculating the objective function value according to an improved genetic algorithm, obtaining individual fitness based on the objective function value, and calculating the crossover probability and mutation probability based on the individual fitness; performing a crossover operation based on the crossover probability, performing a mutation operation based on the mutation probability, obtaining the minimum objective function value under this iteration after completion, judging whether the minimum objective function value under this iteration is less than the minimum objective function value under the previous iteration; and outputting the final minimum objective function value. The embodiments of the present invention can solve the core problem that existing static models cannot meet long-term planning needs.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage configuration, and in particular to a distributed energy storage dynamic configuration method and system. Background Art

[0002] With the high proportion of new energy access and the diversification of energy supply and consumption demands on the user side, the grid structure and demand characteristics have undergone profound changes, which have put higher requirements on the configuration of distributed energy storage.

[0003] However, the current distributed energy storage configuration mostly considers the current installed capacity of new energy and load conditions to select the site and size of energy storage. It is a static configuration, which only considers the current installed capacity of new energy and load characteristics for site selection and size determination. It does not consider the impact of the evolution of the distribution network morphology at the macro level on a longer time scale on the energy storage configuration. It ignores the dynamic changes in the morphology, load characteristics and distributed power access of the distribution network during the long-term evolution process, making it difficult to match future power grid needs. Summary of the Invention

[0004] The object of the present invention is to provide a distributed energy storage dynamic configuration method and system, aiming to solve at least one problem in the background technology.

[0005] In a first aspect, the present invention provides a method for dynamically configuring distributed energy storage, the method comprising:

[0006] Obtaining the number of years corresponding to each energy storage planning stage, and calculating present value coefficients of operating costs and construction costs based on the number of years corresponding to each energy storage planning stage;

[0007] An initial objective function is constructed with the goal of minimizing the sum of the energy storage operating cost and the construction cost, and the present value coefficients of the operating cost and the construction cost are incorporated into the initial objective function to obtain a final objective function;

[0008] Calculating the objective function value of each individual with respect to the final objective function at the current iteration according to the improved genetic algorithm, obtaining the individual fitness according to the objective function value, and respectively calculating the crossover probability and the mutation probability according to the individual fitness;

[0009] Perform a crossover operation according to the crossover probability, perform a mutation operation according to the mutation probability, obtain the minimum objective function value under this iteration after completion, and determine whether the minimum objective function value under this iteration is less than the minimum objective function value under the previous iteration;

[0010] If the minimum objective function value under this iteration is less than the minimum objective function value under the historical iteration, the minimum objective function value is updated. If the minimum objective function value under this iteration is greater than or equal to the minimum objective function value under the historical iteration, the minimum objective function value under the historical iteration is retained. This process continues until the number of iterations reaches the preset threshold, and the final minimum objective function value is output.

[0011] Furthermore, the steps of obtaining the number of years corresponding to each energy storage planning stage and calculating the present value coefficients of the operating cost and the construction cost respectively according to the number of years corresponding to each energy storage planning stage include:

[0012] The present value factors for operating costs and construction costs are obtained according to the following formula:

[0013] ;

[0014] Where y represents the number of years corresponding to the energy storage planning stage, 、 They represent the present value coefficients of construction cost and operation cost respectively, represents the discount rate for construction costs, Expresses the discount rate for operating costs.

[0015] Furthermore, the steps of constructing an initial objective function with the goal of minimizing the sum of the energy storage operating cost and the construction cost, and incorporating the present value coefficients of the operating cost and the construction cost into the initial objective function to obtain the final objective function include:

[0016] The initial objective function is constructed according to the following formula:

[0017] ;

[0018] The final objective function is constructed according to the following formula:

[0019] ;

[0020] in, represents the objective function value of the initial objective function, represents the construction cost of the energy storage system, Representation scene The corresponding time, Indicates the scene, Represents a collection of scenes, Indicates hours, represents a set of hours, represents the operating cost of the energy storage system, Represents the objective function value of the final objective function.

[0021] Furthermore, the operating cost is obtained according to the following formula:

[0022] ;

[0023] The construction cost is obtained according to the following formula:

[0024] ;

[0025] in, represents the cost of energy storage per unit power, represents the purchased power of the distribution network, Indicates the active power lost on the line, represents a 0-1 variable, r represents a node, represents the set of all nodes, represents the charging power of the energy storage system, represents the discharge power of the energy storage system, Indicates the active load of the distribution network. Indicates the active power generated by the distributed power source, rs indicates the line, Represents a collection of lines, represents the network loss cost, Indicates the resistance per unit length of the line, Indicates the length of the line, represents the square of the line current, represents the unit capacity construction cost of the energy storage system, represents the installed capacity of the energy storage system, represents the unit power construction cost of the energy storage system, represents the installed power of the energy storage system, represents the energy storage system cost coefficient, represents the construction cost in the objective function.

[0026] Furthermore, the step of calculating the objective function value of each individual with respect to the final objective function in the current iteration according to the improved genetic algorithm and obtaining the individual fitness according to the objective function value includes:

[0027] Filter out the minimum objective function value from the objective function values ​​of all individuals in the current iteration;

[0028] The individual fitness is calculated according to the following formula:

[0029] ;

[0030] in, represents the individual fitness of the i-th individual under the k-th iteration, represents the objective function value of the i-th individual under the k-th iteration, Represents the minimum objective function value under the k-th iteration.

[0031] Furthermore, the step of calculating the crossover probability and the mutation probability according to the individual fitness comprises:

[0032] The crossover probability is calculated according to the following formula:

[0033] ;

[0034] in, represents the crossover probability of the i-th individual under the k-th iteration, represents the maximum individual fitness under the k-th iteration, represents the average individual fitness under the k-th iteration;

[0035] The mutation probability is calculated according to the following formula:

[0036] ;

[0037] in, represents the mutation probability of the i-th individual under the k-th iteration, 、 Both represent custom parameters.

[0038] In a second aspect, the present invention provides a distributed energy storage dynamic configuration system, the system comprising:

[0039] a present value coefficient acquisition module, configured to obtain the number of years corresponding to each energy storage planning stage, and to calculate the present value coefficients of the operating cost and the construction cost respectively based on the number of years corresponding to each energy storage planning stage;

[0040] An objective function construction module is used to construct an initial objective function with the goal of minimizing the sum of energy storage operating costs and construction costs, and incorporate the present value coefficients of the operating costs and construction costs into the initial objective function to obtain a final objective function;

[0041] a fitness calculation module, configured to calculate the objective function value of each individual with respect to the final objective function in the current iteration according to the improved genetic algorithm, obtain the individual fitness according to the objective function value, and calculate the crossover probability and mutation probability respectively according to the individual fitness;

[0042] An objective function value detection module is used to perform a crossover operation according to the crossover probability and a mutation operation according to the mutation probability, obtain the minimum objective function value of the current iteration after completion, and determine whether the minimum objective function value of the current iteration is less than the minimum objective function value of the previous iteration;

[0043] The iterative solution module is used to update the minimum objective function value if the minimum objective function value under the current iteration is less than the minimum objective function value under the historical iteration. If the minimum objective function value under the current iteration is greater than or equal to the minimum objective function value under the historical iteration, the minimum objective function value under the historical iteration is retained. This continues until the number of iterations reaches the preset threshold and the final minimum objective function value is output.

[0044] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned distributed energy storage dynamic configuration method.

[0045] In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein:

[0046] The memory is used to store computer programs;

[0047] When the processor is used to execute the computer program stored in the memory, the above-mentioned distributed energy storage dynamic configuration method is implemented.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] 1. For the first time, the evolution of the distribution network is incorporated into the energy storage configuration framework, achieving decoupling between planning and operation through hierarchical modeling: the upper-level model uses energy storage nodes and capacity as variables to dynamically optimize multi-stage construction costs; the lower-level model calculates the minimum operating cost based on the upper-level solution and uses present value coefficients to return the costs of each stage to the initial year, achieving a full lifecycle economic evaluation. This model not only supports predictive scenarios of future grid structures (such as distributed power generation capacity expansion and load demand evolution), but also dynamically adjusts energy storage configuration plans to adapt to grid constraints at different stages, solving the core problem that existing static models cannot meet long-term planning needs. In addition, by integrating mixed integer programming with a multi-stage dynamic reduction mechanism, the model significantly improves the ability to balance technical feasibility and economic efficiency in complex scenarios.

[0050] 2. By formulating an algorithm to adjust fitness, the probability of selecting high-quality individuals is differentiated and improved, avoiding the problem of insufficient fitness differentiation caused by the traditional "taking the inverse" method. In addition, an adaptive crossover and mutation mechanism is set up, and the crossover probability is dynamically adjusted according to the individual fitness. The retention probability of individuals with high individual fitness increases, and individuals with low individual fitness gain improvement opportunities through crossover. The mutation probability uses dynamic parameters, taking into account both global search and convergence speed. The improved genetic algorithm shows stronger robustness in complex distribution network scenarios (such as those with a large number of nodes and strong constraint coupling), can quickly converge to the global optimal solution, and has flexible parameter adjustment, which is suitable for the hierarchical optimization needs of different evolution stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a distributed energy storage dynamic configuration method proposed in one embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the structure of an energy storage configuration model according to an embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of fitness calculation results before and after improvement proposed in one embodiment of the present invention;

[0054] Figure 4 This is a structural diagram of a distributed energy storage dynamic configuration system proposed in one embodiment of the present invention.

[0055] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamically configuring distributed energy storage, the method comprising steps S101 to S105, wherein:

[0058] Step S101: obtaining the number of years corresponding to each energy storage planning stage, and calculating the present value coefficients of the operating cost and the construction cost respectively according to the number of years corresponding to each energy storage planning stage;

[0059] It should be noted that, considering the uncertainty of energy storage nodes and energy storage capacity, and the fact that different access nodes and access capacities will affect the operation of the distribution network, this embodiment separates the planning level from the operation level, first providing a planning scheme, and then checking whether the planning scheme can meet the operation constraints.

[0060] If the planning layer is considered the upper layer and the operation layer the lower layer, a two-layer model for energy storage system site selection and sizing can be obtained. The upper layer model uses the connected nodes and connected capacity as variables, where the connected nodes are discrete variables (0-1) and the connected power is a continuous variable. The lower layer model solves for the minimum operating cost corresponding to the connected power and connected nodes set by the upper layer model and returns it to the upper layer.

[0061] To determine the energy storage configuration plan for the initial phase, the objective function should be to minimize the present value cost of energy storage within one year. The present value cost is still composed of construction cost and operating cost. Because construction cost is significantly higher than operating cost, it is necessary to attribute the construction cost equivalent to each year of operation. This means multiplying the construction cost by the capital recovery factor.

[0062] Specifically, for each phase, the energy storage construction cost and operating cost expressions for each year in each energy storage phase can be considered to be consistent with those in the initial phase. However, since each phase involves the expansion of energy storage capacity and the connection of new energy storage nodes, the objective function of each phase should be calculated back to the beginning of each phase, that is, the construction cost and operating cost amortized to each year are converted to the first year of each phase. In some embodiments, the present value coefficients of operating cost and construction cost are obtained according to the following formula:

[0063] ;

[0064] Where y represents the number of years corresponding to the energy storage planning stage, 、 They represent the present value coefficients of construction cost and operation cost respectively, represents the discount rate for construction costs, Expresses the discount rate for operating costs.

[0065] Step S102: constructing an initial objective function with the goal of minimizing the sum of the energy storage operating cost and the construction cost, and incorporating the present value coefficients of the operating cost and the construction cost into the initial objective function to obtain a final objective function;

[0066] In this step, the initial objective function is constructed according to the following formula:

[0067] ;

[0068] The final objective function is constructed according to the following formula:

[0069] ;

[0070] in, represents the objective function value of the initial objective function, represents the construction cost of the energy storage system, Representation scene The corresponding time, Indicates the scene, Represents a collection of scenes, Indicates hours, represents a set of hours, represents the operating cost of the energy storage system, Represents the objective function value of the final objective function.

[0071] The running cost is obtained according to the following formula:

[0072] ;

[0073] The construction cost is obtained according to the following formula:

[0074] ;

[0075] in, represents the cost of energy storage per unit power, represents the purchased power of the distribution network, Indicates the active power lost on the line, represents a 0-1 variable, r represents a node, represents the set of all nodes, represents the charging power of the energy storage system, represents the discharge power of the energy storage system, Indicates the active load of the distribution network. Indicates the active power generated by the distributed power source, rs indicates the line, Represents a collection of lines, represents the network loss cost, Indicates the resistance per unit length of the line, Indicates the length of the line, represents the square of the line current, represents the unit capacity construction cost of the energy storage system, represents the installed capacity of the energy storage system, represents the unit power construction cost of the energy storage system, represents the installed power of the energy storage system, represents the energy storage system cost coefficient, represents the construction cost in the objective function.

[0076] Different from traditional operating costs, in this step, since the load and distributed power capacity in each stage of the distribution network evolution are predetermined and do not change with the change of energy storage access scenario, the calculation formula of the operating cost is redesigned. In addition, the cost of each stage is converted back to the initial year through the present value coefficient to achieve full life cycle economic evaluation. This model not only supports the prediction scenarios of future grid forms (such as distributed power expansion and load demand evolution), but also dynamically adjusts the energy storage configuration plan to adapt to the grid constraints at different stages, solving the core problem that the existing static model cannot match the long-term planning needs. Then, according to the final objective function, the energy storage configuration model is constructed, such as Figure 2 shown. Figure 2 The decision variables and constraints indicated in the above are not adjusted and belong to the prior art, so they will not be described in detail in this embodiment.

[0077] Furthermore, in some embodiments, in order to obtain an energy storage configuration plan in the initial stage, the objective function should be to minimize the present value cost of energy storage within one year. The present value cost is still composed of construction cost and operating cost. Since the construction cost is much larger than the operating cost, it is necessary to convert the construction cost into each year of operation, that is, to multiply the construction cost by the energy storage system cost coefficient, which is expressed as follows:

[0078] ;

[0079] in, represents the inflation rate, Indicates the operating life of the energy storage system.

[0080] Step S103: calculating the objective function value of each individual with respect to the final objective function in the current iteration according to the improved genetic algorithm, obtaining the individual fitness according to the objective function value, and calculating the crossover probability and mutation probability according to the individual fitness;

[0081] It should be noted that since the energy storage configuration model established in the above steps cannot actively solve and give the optimal solution for energy storage configuration, it is considered to introduce an optimization algorithm to solve the optimal configuration solution. That is, first try to give a set of feasible solutions, then change the original solution set according to certain methods, iterate, and after a certain number of iterations, determine the optimal solution as the approximate result of the optimal energy storage configuration solution.

[0082] Distribution networks contain numerous nodes, yet it's impossible to determine in advance whether every node can serve as an energy storage node. Therefore, the algorithm used to solve the energy storage planning problem in this paper must have strong global search capabilities. Furthermore, because the energy storage configuration model involves solving mixed-integer problems, the selected algorithm must also have fast convergence speed. Finally, the selected algorithm must also be easy to adjust parameters to adapt to different scenarios at different evolutionary stages.

[0083] Genetic algorithm, which aims to minimize the final objective function, often uses the inverse of the objective function value of each individual in the population as the fitness. However, since the objective function values ​​corresponding to each individual in the model are close, it is difficult to distinguish high-quality individuals. Therefore, the fitness calculation formula is set as follows:

[0084] ;

[0085] in, represents the individual fitness of the i-th individual under the k-th iteration, represents the objective function value of the i-th individual under the k-th iteration, Represents the minimum objective function value under the k-th iteration.

[0086] like Figure 3 As shown, compared to the traditional method of directly taking the inverse of the calculation result, calculating the individual fitness according to the formula set in this embodiment can improve the fitness of high-quality individuals and increase the probability of high-quality individuals being selected. At the same time, the fitness of high-quality individuals will not be too large, thus avoiding falling into a local optimal solution too early.

[0087] In addition, the crossover operation relies on the crossover probability. In order to ensure that high-quality individuals are retained as much as possible after each crossover, this embodiment ensures that the crossover probability of individuals with high fitness is low, while the crossover probability of individuals with low fitness is high, thereby ensuring that high-quality individuals are retained as much as possible. The crossover probability is calculated according to the following formula:

[0088] ;

[0089] in, represents the crossover probability of the i-th individual under the k-th iteration, represents the maximum individual fitness under the k-th iteration, represents the average individual fitness at the kth iteration.

[0090] It's also worth noting that by recalculating fitness after each selection operation and dynamically adjusting the crossover probability of individuals based on the calculated results, we can effectively ensure that high-quality individuals are retained while also allowing other individuals to improve. Furthermore, compared to performing a crossover operation on each individual, the adaptive algorithm can effectively reduce the number of operations and speed up iterations.

[0091] Furthermore, mutation operations are responsible for the genetic algorithm's strong global search capabilities. A mutation probability as high as possible ensures that the boundaries of feasible solutions in the population are expanded as much as possible during each iteration. However, an excessively high mutation probability can affect the algorithm's convergence. Therefore, a dynamic mutation probability calculation formula is used, as follows:

[0092] ;

[0093] in, represents the mutation probability of the i-th individual under the k-th iteration, 、 Both represent custom parameters.

[0094] The effect is similar to the crossover probability expression. By calculating the mutation probability of each iteration, each individual is dynamically assigned a value, retaining high-quality individuals as much as possible and expanding the search range.

[0095] Step S104: performing a crossover operation according to the crossover probability and performing a mutation operation according to the mutation probability, obtaining the minimum objective function value of this iteration after completion, and determining whether the minimum objective function value of this iteration is less than the minimum objective function value of the previous iteration;

[0096] It should be noted that the minimum objective function value is screened out from the objective function values ​​of all individuals in the current iteration, and the maximum individual fitness is screened out from all individual fitnesses in the current iteration.

[0097] Step S105: If the minimum objective function value under this iteration is less than the minimum objective function value under the historical iteration, the minimum objective function value is updated; if the minimum objective function value under this iteration is greater than or equal to the minimum objective function value under the historical iteration, the minimum objective function value under the historical iteration is retained, and so on, until the number of iterations reaches the preset threshold, and the final minimum objective function value is output.

[0098] It should be pointed out that the overall solution process of the improved genetic algorithm is as follows:

[0099] (1) Set the number of iterations and individuals.

[0100] (2) Call the energy storage configuration model, generate the initial individual and the objective function value corresponding to the individual, and calculate the individual fitness.

[0101] (3) Select individuals according to the roulette rule, generate a new population and calculate the fitness.

[0102] (4) Calculate the crossover probability of individuals in the population, perform crossover operations, generate a new population and calculate the fitness.

[0103] (5) Calculate the mutation probability of individuals in the population, perform mutation operations, generate a new population and calculate the fitness.

[0104] (6) Record the optimal individual value of this iteration, compare it with the historical results, and update the optimal solution.

[0105] (7) Determine whether the number of iterations has been reached. If so, end the iteration; otherwise, return to step (2).

[0106] (8) Output the optimal individual.

[0107] In summary, the embodiments of the present invention have the following advantages:

[0108] 1. For the first time, the evolution of the distribution network is incorporated into the energy storage configuration framework, achieving decoupling between planning and operation through hierarchical modeling: the upper-level model uses energy storage nodes and capacity as variables to dynamically optimize multi-stage construction costs; the lower-level model calculates the minimum operating cost based on the upper-level solution and uses present value coefficients to return the costs of each stage to the initial year, achieving a full lifecycle economic evaluation. This model not only supports predictive scenarios of future grid structures (such as distributed power generation capacity expansion and load demand evolution), but also dynamically adjusts energy storage configuration plans to adapt to grid constraints at different stages, solving the core problem that existing static models cannot meet long-term planning needs. In addition, by integrating mixed integer programming with a multi-stage dynamic reduction mechanism, the model significantly improves the ability to balance technical feasibility and economic efficiency in complex scenarios.

[0109] 2. By formulating an algorithm to adjust fitness, the probability of selecting high-quality individuals is differentiated and improved, avoiding the problem of insufficient fitness differentiation caused by the traditional "taking the inverse" method. In addition, an adaptive crossover and mutation mechanism is set up, and the crossover probability is dynamically adjusted according to the individual fitness. The retention probability of individuals with high individual fitness increases, and individuals with low individual fitness gain improvement opportunities through crossover. The mutation probability uses dynamic parameters, taking into account both global search and convergence speed. The improved genetic algorithm shows stronger robustness in complex distribution network scenarios (such as those with a large number of nodes and strong constraint coupling), can quickly converge to the global optimal solution, and has flexible parameter adjustment, which is suitable for the hierarchical optimization needs of different evolution stages.

[0110] like Figure 4 As shown, an embodiment of the present invention further provides a distributed energy storage dynamic configuration system, the system comprising:

[0111] The present value coefficient acquisition module 10 is used to obtain the number of years corresponding to each energy storage planning stage, and calculate the present value coefficients of the operating cost and the construction cost respectively according to the number of years corresponding to each energy storage planning stage;

[0112] An objective function construction module 20 is configured to construct an initial objective function with the goal of minimizing the sum of the energy storage operating cost and the construction cost, and incorporate the present value coefficients of the operating cost and the construction cost into the initial objective function to obtain a final objective function;

[0113] a fitness calculation module 30 for calculating the objective function value of each individual with respect to the final objective function at the current iteration according to the improved genetic algorithm, obtaining the individual fitness according to the objective function value, and calculating the crossover probability and mutation probability according to the individual fitness;

[0114] an objective function value detection module 40 for performing a crossover operation according to the crossover probability and a mutation operation according to the mutation probability, obtaining a minimum objective function value of the current iteration after completion, and determining whether the minimum objective function value of the current iteration is less than the minimum objective function value of the previous iteration;

[0115] The iterative solution module 50 is used to update the minimum objective function value if the minimum objective function value under the current iteration is less than the minimum objective function value under the historical iteration; if the minimum objective function value under the current iteration is greater than or equal to the minimum objective function value under the historical iteration, the minimum objective function value under the historical iteration is retained, and so on, until the number of iterations reaches a preset threshold, and the final minimum objective function value is output.

[0116] On the other hand, the present invention further proposes a storage medium having one or more programs stored thereon, which implement the above-mentioned distributed energy storage dynamic configuration method when executed by a processor.

[0117] On the other hand, the present invention further proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned distributed energy storage dynamic configuration method.

[0118] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0119] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0120] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0121] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A distributed energy storage dynamic configuration method, characterized in that: The method comprises: Obtaining the number of years corresponding to each energy storage planning stage, and calculating present value coefficients of operating costs and construction costs based on the number of years corresponding to each energy storage planning stage; The present value factors for operating costs and construction costs are obtained according to the following formula: ； Where y represents the number of years corresponding to the energy storage planning stage, 、 They represent the present value coefficients of construction cost and operation cost respectively, represents the discount rate for construction costs, represents the discount rate for operating costs; An initial objective function is constructed with the goal of minimizing the sum of the energy storage operating cost and the construction cost, and the present value coefficients of the operating cost and the construction cost are incorporated into the initial objective function to obtain a final objective function; The initial objective function is constructed according to the following formula: ； The final objective function is constructed according to the following formula: ； in, represents the objective function value of the initial objective function, represents the construction cost of the energy storage system, Representation scene The corresponding time, Indicates the scene, Represents a collection of scenes, Indicates hours, represents a set of hours, represents the operating cost of the energy storage system, represents the objective function value of the final objective function; Calculating the objective function value of each individual with respect to the final objective function at the current iteration according to the improved genetic algorithm, obtaining the individual fitness according to the objective function value, and respectively calculating the crossover probability and the mutation probability according to the individual fitness; Perform a crossover operation according to the crossover probability, perform a mutation operation according to the mutation probability, obtain the minimum objective function value under this iteration after completion, and determine whether the minimum objective function value under this iteration is less than the minimum objective function value under the previous iteration; If the minimum objective function value under this iteration is less than the minimum objective function value under the historical iteration, the minimum objective function value is updated. If the minimum objective function value under this iteration is greater than or equal to the minimum objective function value under the historical iteration, the minimum objective function value under the historical iteration is retained. This process continues until the number of iterations reaches the preset threshold, and the final minimum objective function value is output.

2. The distributed energy storage dynamic configuration method according to claim 1, characterized in that: The running cost is obtained according to the following formula: ； The construction cost is obtained according to the following formula: ； in, represents the cost of energy storage per unit power, represents the purchased power of the distribution network, Indicates the active power lost on the line, represents a 0-1 variable, r represents a node, represents the set of all nodes, represents the charging power of the energy storage system, represents the discharge power of the energy storage system, Indicates the active load of the distribution network. Indicates the active power generated by the distributed power source, rs indicates the line, Represents a collection of lines, represents the network loss cost, Indicates the resistance per unit length of the line, Indicates the length of the line, represents the square of the line current, represents the unit capacity construction cost of the energy storage system, represents the installed capacity of the energy storage system, represents the unit power construction cost of the energy storage system, represents the installed power of the energy storage system, represents the energy storage system cost coefficient, represents the construction cost in the objective function.

3. The distributed energy storage dynamic configuration method according to claim 2, characterized in that: The step of calculating the objective function value of each individual with respect to the final objective function in the current iteration according to the improved genetic algorithm and obtaining the individual fitness according to the objective function value comprises: Filter out the minimum objective function value from the objective function values ​​of all individuals in the current iteration; The individual fitness is calculated according to the following formula: ； in, represents the individual fitness of the i-th individual under the k-th iteration, represents the objective function value of the i-th individual under the k-th iteration, Represents the minimum objective function value under the k-th iteration.

4. The distributed energy storage dynamic configuration method according to claim 3, characterized in that: The steps of respectively calculating the crossover probability and the mutation probability according to the individual fitness include: The crossover probability is calculated according to the following formula: ； in, represents the crossover probability of the i-th individual under the k-th iteration, represents the maximum individual fitness under the k-th iteration, represents the average individual fitness under the k-th iteration; The mutation probability is calculated according to the following formula: ； in, represents the mutation probability of the i-th individual under the k-th iteration, 、 Both represent custom parameters.

5. A distributed energy storage dynamic configuration system, characterized in that: The system comprises: a present value coefficient acquisition module, configured to obtain the number of years corresponding to each energy storage planning stage, and to calculate the present value coefficients of the operating cost and the construction cost respectively based on the number of years corresponding to each energy storage planning stage; The present value factors for operating costs and construction costs are obtained according to the following formula: ； Where y represents the number of years corresponding to the energy storage planning stage, 、 They represent the present value coefficients of construction cost and operation cost respectively, represents the discount rate for construction costs, represents the discount rate for operating costs; An objective function construction module is used to construct an initial objective function with the goal of minimizing the sum of energy storage operating costs and construction costs, and incorporate the present value coefficients of the operating costs and construction costs into the initial objective function to obtain a final objective function; The initial objective function is constructed according to the following formula: ； The final objective function is constructed according to the following formula: ； in, represents the objective function value of the initial objective function, represents the construction cost of the energy storage system, Representation scene The corresponding time, Indicates the scene, Represents a collection of scenes, Indicates hours, represents a set of hours, represents the operating cost of the energy storage system, represents the objective function value of the final objective function; a fitness calculation module, configured to calculate the objective function value of each individual with respect to the final objective function in the current iteration according to the improved genetic algorithm, obtain the individual fitness according to the objective function value, and calculate the crossover probability and mutation probability respectively according to the individual fitness; An objective function value detection module is used to perform a crossover operation according to the crossover probability and a mutation operation according to the mutation probability, obtain the minimum objective function value of the current iteration after completion, and determine whether the minimum objective function value of the current iteration is less than the minimum objective function value of the previous iteration; The iterative solution module is used to update the minimum objective function value if the minimum objective function value under the current iteration is less than the minimum objective function value under the historical iteration. If the minimum objective function value under the current iteration is greater than or equal to the minimum objective function value under the historical iteration, the minimum objective function value under the historical iteration is retained. This continues until the number of iterations reaches the preset threshold and the final minimum objective function value is output.

6. A storage medium, characterized in that The storage medium stores one or more programs, which, when executed by a processor, implement the distributed energy storage dynamic configuration method according to any one of claims 1 to 4.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the distributed energy storage dynamic configuration method according to any one of claims 1 to 4.

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