Distributed energy storage dynamic configuration method and system

Abstract

The invention provides a distributed energy storage dynamic configuration method and system. The method comprises the steps that a present value coefficient is obtained through calculation according to the number of years corresponding to each energy storage planning stage; constructing an initial target function by taking minimization of the sum of the energy storage operation cost and the building cost as a target, and integrating present value coefficients of the operation cost and the building cost into the initial target function to obtain a final target function; calculating an objective function value according to the improved genetic algorithm, obtaining individual fitness according to the objective function value, and respectively calculating a crossover probability and a mutation probability according to the individual fitness; performing crossover operation according to the crossover probability, performing mutation operation according to the mutation probability, obtaining a minimum objective function value under the current iteration after completion, and judging whether the minimum objective function value under the current iteration is smaller than the minimum objective function value under the last iteration; and outputting a final minimum objective function value. According to the embodiment of the invention, the core problem that the existing static model cannot match the long-term planning demand can be solved.

Description

Technical Field

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

[0002] With the high proportion of new energy access and the diversification of energy supply and demand on the user side, the grid form and demand characteristics have undergone profound changes, posing higher requirements for the configuration of distributed energy storage.

[0003] However, the current configuration of distributed energy storage mostly considers the current installed capacity of new energy and the load situation for site selection and capacity determination of energy storage, which is a static configuration. It only considers the current installed capacity of new energy and load characteristics for site selection and capacity determination, without considering the impact of the evolution of the distribution network form on energy storage configuration at the macroscopic level in a relatively long time scale, ignoring the dynamic changes in the form, load characteristics, and distributed power access of the distribution network during the long-term evolution process, resulting in difficulty in matching future grid demands. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for dynamically configuring distributed energy storage, aiming to solve at least one problem in the background art.

[0005] In a first aspect, the present invention provides a method for dynamically configuring distributed energy storage, the method comprising: Obtaining the number of years corresponding to each energy storage planning stage, and respectively calculating present value coefficients for operating costs and construction costs according to the number of years corresponding to each energy storage planning stage; Constructing an initial objective function with the minimum of the sum of energy storage operating costs and construction costs as the goal, and integrating the present value coefficients of the operating costs and construction costs into the initial objective function to obtain a final objective function; Calculating the objective function value of each individual for 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 respectively calculating the crossover probability and mutation probability according to the individual fitness; Performing a crossover operation according to the crossover probability and a mutation operation according to the mutation probability. After completion, obtaining the minimum objective function value in the current iteration, and determining whether the minimum objective function value in the current iteration is less than the minimum objective function value in the previous iteration; If the minimum objective function value in the current iteration is less than the minimum objective function value in the historical iteration, then update the minimum objective function value. If the minimum objective function value in the current iteration is greater than or equal to the minimum objective function value in the historical iteration, then retain the minimum objective function value in the historical iteration. Thus, until the number of iterations reaches a preset number threshold, output the final minimum objective function value.

[0006] Further, 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: Obtain the present value coefficients of the operating cost and the construction cost according to the following formula: ; where y represents the number of years corresponding to the energy storage planning stage, , represent the present value coefficient of the construction cost and the present value coefficient of the operating cost respectively, represents the discount rate of the construction cost, represents the discount rate of the operating cost.

[0007] Further, the steps of constructing an initial objective function with the minimization of the sum of the energy storage operating cost and the construction cost as the goal and integrating the present value coefficients of the operating cost and the construction cost into the initial objective function to obtain the final objective function include: Construct an initial objective function according to the following formula: ; Construct a final objective function according to the following formula: ; where, represents the objective function value of the initial objective function, represents the construction cost of the energy storage system, represents the time corresponding to the scenario , represents the scenario, represents the set of scenarios, represents hours, represents the set of hours, represents the operating cost of the energy storage system, represents the objective function value of the final objective function.

[0008] Further, obtain the operating cost according to the following formula: ; Obtain the construction cost according to the following formula: ; where, represents the cost per unit power of the energy storage, represents the power purchase of the distribution network, represents the active power loss 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 discharging power of the energy storage system, Represents the active load of the distribution network load, Represents the active power generated by the distributed power source, rs represents the line, Represents the set of lines, Represents the network loss cost, Represents the resistance per unit length of the line, Represents the length of the line, Represents the square of the line current, Represents the construction cost per unit capacity of the energy storage system, Represents the installed capacity of the energy storage system, Represents the construction cost per unit power of the energy storage system, Represents the installed power of the energy storage system, Represents the cost coefficient of the energy storage system, Represents the building cost in the objective function.

[0009] Further, the step of 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 and obtaining the individual fitness according to the objective function value includes: Screen out the minimum objective function value from the objective function values of all individuals at the current iteration; Calculate the individual fitness according to the following formula: ; Wherein, Represents the individual fitness of the i-th individual at the k-th iteration, Represents the objective function value of the i-th individual at the k-th iteration, Represents the minimum objective function value at the k-th iteration.

[0010] Further, the step of calculating the crossover probability and the mutation probability respectively according to the individual fitness includes: Calculate the crossover probability according to the following formula: ; Wherein, Represents the crossover probability of the i-th individual at the k-th iteration, Represents the maximum individual fitness at the k-th iteration, Represents the average individual fitness at the k-th iteration; Calculate the mutation probability according to the following formula: ; Wherein, Represents the mutation probability of the i-th individual at the k-th iteration, , Both represent custom parameters.

[0011] In a second aspect, the present invention provides a distributed energy storage dynamic configuration system, and the system includes: A present value coefficient acquisition module, configured to obtain the number of years corresponding to each energy storage planning stage, and calculate the present value coefficients for the operating cost and the construction cost respectively according to the number of years corresponding to each energy storage planning stage; A target function construction module, configured to construct an initial target function with the minimization of the sum of the energy storage operating cost and the construction cost as the target, and incorporate the present value coefficients of the operating cost and the construction cost into the initial target function to obtain a final target function; A fitness calculation module, configured to calculate the objective function value of each individual for the final target function under 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 the mutation probability respectively according to the individual fitness; An objective function value detection module, configured to 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; An iterative solution module, configured to update the minimum objective function value if the minimum objective function value under this iteration is less than the minimum objective function value under the historical iteration, and retain the minimum objective function value under the historical iteration if the minimum objective function value under this iteration is greater than or equal to the minimum objective function value under the historical iteration. In this way, until the number of iterations reaches a preset number threshold, the final minimum objective function value is output.

[0012] In a third aspect, the present invention provides a storage medium, and the storage medium stores one or more programs, and when the program is executed by a processor, the above-mentioned distributed energy storage dynamic configuration method is implemented.

[0013] In a fourth aspect, the present invention provides an electronic device, and the electronic device includes a memory and a processor, wherein: The memory is used to store a computer program; The processor is configured to implement the above-mentioned distributed energy storage dynamic configuration method when executing the computer program stored on the memory.

[0014] Compared with the prior art, the present invention has the following advantages: 1. For the first time, the evolution process of the distribution network is incorporated into the energy storage configuration framework, and the decoupling between the planning and operation levels is achieved through hierarchical modeling: the upper-layer model takes energy storage nodes and capacity as variables to dynamically optimize the multi-stage construction cost; the lower-layer model calculates the minimum operation cost based on the upper-layer scheme, and reduces the costs of each stage to the initial year through the present value coefficient to realize the economic evaluation of the whole life cycle. This model not only supports the prediction scenarios of future power grid forms (such as the expansion of distributed power sources and the evolution of load demand), but also can dynamically adjust the energy storage configuration scheme to adapt to the power grid constraints at different stages, solving the core problem that the existing static models cannot meet the long-term planning requirements. In addition, by integrating the mixed integer programming and multi-stage dynamic reduction mechanism, the model significantly improves the technical feasibility and economic balance ability in complex scenarios.

[0015] 2. By formulating an algorithm to adjust the fitness, the selection probability of high-quality individuals is improved differentially, avoiding the problem of insufficient fitness discrimination caused by the traditional "taking the reciprocal" method; in addition, an adaptive crossover and mutation mechanism is set, the crossover probability is dynamically adjusted according to the individual fitness, the retention probability of individuals with high fitness increases, and individuals with low fitness obtain improvement opportunities through crossover; the mutation probability adopts 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 a large number of nodes and strong coupling of constraints), can quickly converge to the global optimal solution, and has flexible parameter adjustment, which is suitable for the hierarchical optimization requirements at different evolution stages. Brief Description of the Drawings

[0016] Figure 1 It is a flowchart of the distributed energy storage dynamic configuration method proposed in an embodiment of the present invention; Figure 2 It is a schematic structural diagram of the energy storage configuration model in an embodiment of the present invention; Figure 3 It is a schematic diagram of the fitness calculation results before and after improvement proposed in an embodiment of the present invention; Figure 4 It is a schematic structural diagram of the distributed energy storage dynamic configuration system proposed in an embodiment of the present invention.

[0017] The following specific embodiments will further illustrate the present invention in conjunction with the above drawings. Specific Embodiments

[0018] To make the objectives, 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. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the art to which the present invention pertains. The words such as "including" used herein mean that the elements or items appearing before this word cover the elements or items listed after this word and their equivalents, without excluding other elements or items.

[0019] As Figure 1 shown, an embodiment of the present invention provides a method for dynamic configuration of distributed energy storage. The method includes steps S101 to S105, where: Step S101: Obtain the number of years corresponding to each energy storage planning stage, and calculate the present value coefficients for operation cost and construction cost respectively according to the number of years corresponding to each energy storage planning stage; It should be noted that considering the uncertainty of energy storage nodes and the uncertainty of energy storage capacity, and at the same time, different access nodes and access capacities will affect the operation of the distribution network. Therefore, in this embodiment, the planning level and the operation level are separated. First, a planning scheme is given, and then it is checked whether the planning scheme can meet the operation constraints.

[0020] If the planning level is regarded as the upper layer and the operation level is regarded as the lower layer, a two-layer model for energy storage system site selection and capacity determination can be obtained. The upper layer model takes the access nodes and access capacity as variables, where the access nodes are 0-1 discrete variables and the access power is a continuous variable; the lower layer model solves the minimum operation cost corresponding to the access power and access nodes set by the upper layer model and returns it to the upper layer.

[0021] To obtain the energy storage configuration scheme in the initial stage, the objective function should aim to minimize the present value cost of energy storage within one year. Among them, the components of the present value cost are still the construction cost and the operation cost. Since the construction cost is much larger than the operation cost, it is necessary to equivalently allocate the construction cost to each year of the operation period, that is, multiply the construction cost by the capital recovery factor.

[0022] Specifically, for different stages, it can be considered that for each year in each energy storage stage, the expressions of the energy storage construction cost and the operation cost are the same as those in the initial stage. However, due to the expansion of the energy storage capacity and the access of new energy storage nodes in each stage, the objective function of each stage should be attributed to the beginning of each stage, that is, the construction cost evenly distributed to each year and the operation cost of each year are discounted to the first year of each stage. In some embodiments, the present value coefficients of the operation cost and the construction cost are obtained according to the following formula: ; where y represents the number of years corresponding to the energy storage planning stage, , respectively represent the present value coefficient of the construction cost and the present value coefficient of the operation cost, represents the discount rate of the construction cost, represents the discount rate of the operation cost.

[0023] Step S102: Construct an initial objective function with the minimization of the sum of the energy storage operation cost and the construction cost as the goal, and incorporate the present value coefficients of the operation cost and the construction cost into the initial objective function to obtain the final objective function; In this step, the initial objective function is specifically constructed according to the following formula: ; The final objective function is constructed according to the following formula: ; where, represents the objective function value of the initial objective function, represents the construction cost of the energy storage system, represents the scenario corresponding time, represents the scenario, represents the set of scenarios, represents the hour, represents the set of hours, represents the operation cost of the energy storage system, represents the objective function value of the final objective function.

[0024] The operation cost is obtained according to the following formula: ; The construction cost is obtained according to the following formula: ; where, represents the cost per unit power of the energy storage, represents the power purchase of the distribution network, represents the active power loss on the line, Indicates a 0-1 variable, and r represents a node. Represents the set of all nodes. Represents the charging power of the energy storage system. Represents the discharging power of the energy storage system. Represents the active power load of the distribution network load. Represents the active power generated by the distributed power source, and rs represents the line. Represents the set of lines. Represents the network loss cost. Represents the resistance per unit length of the line. Represents 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 cost coefficient of the energy storage system. Represents the building cost in the objective function.

[0025] Different from the traditional operating cost, in this step, since the load and the distributed power source capacity in each stage of the distribution network evolution are pre-determined and do not change with the change of the energy storage access scenario, the calculation formula of the operating cost is redesigned. In addition, the costs of each stage are reduced to the initial year through the present value coefficient to achieve the economic evaluation of the whole life cycle. This model not only supports the prediction scenarios of the future grid form (such as the expansion of distributed power sources and the evolution of load demand), but also can dynamically adjust the energy storage configuration scheme to adapt to the grid constraints at different stages, solving the core problem that the existing static model cannot meet the long-term planning requirements. Then, an energy storage configuration model is constructed according to this final objective function, as Figure 2 shown. Figure 2 The decision variables and constraints etc. pointed out in

[0026] are not adjusted and belong to the prior art part, so they are not described in detail in this embodiment.

[0026] In addition, in some embodiments, in order to obtain the energy storage configuration scheme in the initial stage, the objective function should aim to minimize the present value cost of the energy storage within one year. Among them, the components of the present value cost are still the construction cost and the operating cost. Since the construction cost is much larger than the operating cost, it is necessary to equivalently reduce the construction cost to each year of the operating life, that is, it is necessary to multiply the construction cost by the energy storage system cost coefficient, and its expression is: ; Among them, represents the inflation rate, represents the operating life of the energy storage system.

[0027] Step S103: Calculate the objective function value of each individual for 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; It should be noted that since the energy storage configuration model established in the above steps cannot actively solve and give the optimal energy storage configuration solution, an optimization algorithm is considered to solve the optimal configuration solution, that is, first try to give a set of feasible solutions, and then change the original solution set according to certain methods and perform iterations. After a certain number of iterations, determine the optimal solution as an approximate result of the optimal energy storage configuration solution.

[0028] There are many nodes in the distribution network, but it is impossible to determine in advance whether each node can be used as an energy storage node. Therefore, the algorithm for solving the energy storage planning in this article should have strong global search ability. In addition, since the energy storage configuration model involves the solution of mixed integer problems, it is also required that the selected algorithm has a fast convergence speed; finally, the selected algorithm should also be easy to adjust parameters to adapt to different scenarios in different evolution stages.

[0029] For the genetic function algorithm with the minimum value of the final objective function as the goal, the reciprocal of the calculated value of the objective function of each individual in the population is mostly used 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: ; Among them, represents the individual fitness of the i-th individual in the k-th iteration, represents the objective function value of the i-th individual in the k-th iteration, represents the minimum objective function value in the k-th iteration.

[0030] As Figure 3 shown, compared with the traditional method of directly taking the reciprocal 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 premature convergence to local optimal solutions.

[0031] In addition, the crossover operation depends on the crossover probability. In order to ensure that high-quality individuals are retained as much as possible after each crossover, in this embodiment, by ensuring that the crossover probability of individuals with high fitness is small and the crossover probability of individuals with low fitness is large, high-quality individuals are retained as much as possible. The crossover probability is specifically calculated according to the following formula: ; Among them, represents the crossover probability of the $i$-th individual in the $k$-th iteration, represents the maximum individual fitness in the $k$-th iteration, represents the average individual fitness in the $k$-th iteration.

[0032] It should also be pointed out that by recalculating the fitness after each selection operation and dynamically adjusting the crossover probability of individuals according to the calculated results, it can effectively ensure the retention of high-quality individuals, while other individuals also have the possibility of improvement. At the same time, compared with performing crossover operations on each individual, the adaptive algorithm can effectively reduce the number of operations and accelerate the iteration speed.

[0033] In addition, the mutation operation is the reason why the genetic algorithm has strong global search ability. As large a mutation probability as possible can ensure that the boundaries of the feasible solutions of the population in each iteration are expanded as much as possible. However, too large a mutation probability will affect the convergence ability of the algorithm. For this reason, a mutation probability calculation formula with dynamic assignment is adopted, and the expression is as follows: ; where, represents the mutation probability of the $i$-th individual in the $k$-th iteration, 、 both represent user-defined parameters.

[0034] Similar to the effect of the crossover probability expression, by calculating the mutation probability of each iteration and dynamically assigning values to each individual, high-quality individuals are retained as much as possible, and the search range is expanded.

[0035] Step S104: Perform a crossover operation according to the crossover probability, perform a mutation operation according to the mutation probability, after completion, obtain the minimum objective function value in this iteration, and determine whether the minimum objective function value in this iteration is less than the minimum objective function value in the previous iteration; It should be noted that the minimum objective function value is selected from the objective function values of all individuals in the current iteration, and the maximum individual fitness is selected from the fitness of all individuals in the current iteration.

[0036] Step S105: If the minimum objective function value in this iteration is less than the minimum objective function value in the historical iteration, update the minimum objective function value; if the minimum objective function value in this iteration is greater than or equal to the minimum objective function value in the historical iteration, retain the minimum objective function value in the historical iteration. In this way, until the number of iterations reaches the preset number threshold, output the final minimum objective function value.

[0037] It should be pointed out that for this improved genetic algorithm, its overall solution process is as follows: (1) Set the number of iterations and the number of individuals.

[0038] (2) Call the energy storage configuration model to generate the initial individuals and the objective function values corresponding to the individuals, and calculate the fitness of the individuals.

[0039] (3) Select individuals according to the roulette wheel rule, generate a new population and calculate the fitness.

[0040] (4) Calculate the crossover probability of the individuals in the population, perform the crossover operation, generate a new population and calculate the fitness.

[0041] (5) Calculate the mutation probability of the individuals in the population, perform the mutation operation, generate a new population and calculate the fitness.

[0042] (6) Record the optimal individual value of this iteration, compare it with the historical results, and update the optimal solution.

[0043] (7) Determine whether the number of iterations is reached. If so, end the iteration; otherwise, return to step (2).

[0044] (8) Output the optimal individual.

[0045] In summary, the embodiments of the present invention have the following advantages: 1. For the first time, the evolution process of the distribution network is incorporated into the energy storage configuration framework, and the decoupling of the planning and operation levels is achieved through hierarchical modeling: the upper-layer model takes the energy storage nodes and capacity as variables to dynamically optimize the multi-stage construction cost; the lower-layer model calculates the minimum operation cost based on the upper-layer scheme, and reduces the costs of each stage to the initial year through the present value coefficient to realize the economic evaluation of the whole life cycle. This model not only supports the prediction scenarios of the future power grid form (such as the expansion of distributed power sources and the evolution of load demand), but also can dynamically adjust the energy storage configuration scheme to adapt to the grid constraints at different stages, solving the core problem that the existing static models cannot meet the long-term planning requirements. In addition, by integrating the mixed integer programming and multi-stage dynamic reduction mechanism, the model significantly improves the balance ability of technical feasibility and economy in complex scenarios.

[0046] 2. By formulating and adjusting the fitness algorithm to differentially increase the selection probability of high-quality individuals, the problem of insufficient fitness discrimination caused by the traditional "taking the reciprocal" method is avoided; in addition, an adaptive crossover and mutation mechanism is set, the crossover probability is dynamically adjusted according to the individual fitness, the retention probability of individuals with high fitness increases, and individuals with low fitness obtain improvement opportunities through crossover; the mutation probability adopts dynamic parameters, taking into account both the global search and the convergence speed. The improved genetic algorithm shows stronger robustness in complex distribution network scenarios (such as a large number of nodes and strong coupling of constraints), can quickly converge to the global optimal solution, and has flexible parameter adjustment, which is suitable for the hierarchical optimization requirements at different evolution stages.

[0047] Such as Figure 4As shown in the figure, an embodiment of the present invention further provides a distributed energy storage dynamic configuration system, which includes: A present value coefficient acquisition module 10, configured 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; A target function construction module 20, configured to construct an initial target function with the minimization of the sum of the energy storage operating cost and the construction cost as the target, and incorporate the present value coefficients of the operating cost and the construction cost into the initial target function to obtain a final target function; A fitness calculation module 30, configured to calculate the objective function value of each individual with respect to the final target function under 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 the mutation probability respectively according to the individual fitness; An objective function value detection module 40, configured to 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; An iterative solution module 50, configured to update the minimum objective function value if the minimum objective function value under this iteration is less than the minimum objective function value under the historical iteration, and retain the minimum objective function value under the historical iteration if the minimum objective function value under this iteration is greater than or equal to the minimum objective function value under the historical iteration. In this way, until the number of iterations reaches a preset number threshold, the final minimum objective function value is output.

[0048] On the other hand, the present invention further provides a storage medium, on which one or more programs are stored, and when the program is executed by a processor, the above-mentioned distributed energy storage dynamic configuration method is implemented.

[0049] On the other hand, the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor is used to execute the computer program stored on the memory to implement the above-mentioned distributed energy storage dynamic configuration method.

[0050] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus or device), or in conjunction with these instruction execution systems, apparatuses or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus or device.

[0051] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0052] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0053] Although 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 can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein can have other embodiments and can be implemented or realized in various 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 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; An initial objective function is constructed with the goal of minimizing the sum of the energy storage operation cost and the construction cost, and the present value coefficients of the operation cost and the construction cost are incorporated into the initial objective function to obtain a final objective function; 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 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, and so on, 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 steps of obtaining the number of years corresponding to each energy storage planning stage, and respectively calculating the present value coefficients of the operating cost and the construction cost according to the number of years corresponding to each energy storage planning stage include: 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 coefficient of construction cost and the present value coefficient of operation cost respectively, represents the discount rate of construction cost, Expresses the discount rate for operating costs.

3. The distributed energy storage dynamic configuration method according to claim 2, characterized in that: The steps of constructing an initial objective function with the goal of minimizing the sum of the energy storage operation cost and the construction cost, and integrating the present value coefficients of the operation cost and the construction cost into the initial objective function to obtain the final objective function include: 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 collection of hours, represents the operating cost of the energy storage system, Represents the objective function value of the final objective function.

4. The distributed energy storage dynamic configuration method according to claim 3 is characterized in that: The operating 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 in 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. represents the active power generated by the distributed power source, rs represents 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.

5. 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 in 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 at the kth iteration.

6. The distributed energy storage dynamic configuration method according to claim 5, characterized in that: The step of respectively calculating the crossover probability and the mutation probability according to the individual fitness comprises: 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 kth iteration, represents the average individual fitness under the kth iteration; The mutation probability is calculated according to the following formula: ； in, represents the mutation probability of the i-th individual in the k-th iteration, , Indicates custom parameters.

7. A distributed energy storage dynamic configuration system, characterized in that: The system comprises: A present value coefficient acquisition module, used to acquire 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; An objective function construction module is used to construct an initial objective function with the goal of minimizing the sum of the energy storage operation cost and the construction cost, and integrate the present value coefficients of the operation cost and the construction cost into the initial objective function to obtain a final objective function; A fitness calculation module, used 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 the mutation probability according to the individual fitness; An objective function value detection module is used to perform a crossover operation according to the crossover probability, perform a mutation operation according to the mutation probability, obtain the minimum objective function value of this iteration after completion, and determine whether the minimum objective function value of this 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, and so on, until the number of iterations reaches the preset number threshold, and the final minimum objective function value is output.

8. 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 as described in any one of claims 1 to 6.

9. 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 as described in any one of claims 1-6.

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