Distributed power supply configuration method and apparatus for energy storage system, and device

Abstract

The present application discloses a distributed power supply configuration method and apparatus for an energy storage system, and a device. The method comprises: on the basis of an optimization objective, constructing a configuration optimization model of an energy storage system; performing objective unification processing on the configuration optimization model to generate a fuzzy optimization model; and acquiring distributed power supply location information of the energy storage system, and performing calculation on the distributed power supply location information by means of the fuzzy optimization model, so as to perform distributed power supply configuration on the energy storage system. When constructing the configuration optimization model, a plurality of optimization objectives are considered, comprising a minimum grid loss value, a maximum system voltage stability margin value, and a minimum voltage deviation value, thereby helping implement comprehensive optimization of an energy storage system, and improving the operating efficiency and stability of a power grid. Objective unification processing is performed on the configuration optimization model by means of fuzzy set theory, thereby preventing conflicts among sub-objectives. Power supply configuration is performed on the basis of a calculation result, so that power flow distribution of a power grid can be optimized, thereby reducing grid loss and voltage deviation.

Description

Energy storage system distributed power supply configuration method, device and equipment

[0001] The present application claims priority to the Chinese patent application No. 202410966244.9, filed on July 18, 2024, with the Chinese Patent Office, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the technical field of power distribution network, in particular to an energy storage system distributed power supply configuration method, device and equipment. BACKGROUND

[0003] In the power supply network, if the node voltage fluctuation caused by the output power fluctuation of the distributed power supply such as wind power and photovoltaic is not controlled, it will bring certain challenges to the reliable and stable operation of the system, so appropriate measures need to be taken to suppress the power fluctuation of the distributed power supply. At present, some built or under-construction photovoltaic or wind power projects are equipped with energy storage devices, and one of the important functions of the energy storage devices is to suppress the fluctuation of the distributed power supply. The key to the use of energy storage devices to suppress the fluctuation of the distributed power supply is the control strategy, and the focus of the control strategy is how to obtain appropriate grid-connected power and energy storage power. The existing methods, such as low-pass filter algorithm, have difficulty in accurately controlling the filter time constant, and generally have poor universality, so it is imperative to study the energy storage technology and its control strategy.

[0004] At the same time, the distributed power supply configuration optimization belongs to a large-scale, nonlinear, multi-constraint, multi-objective combination optimization problem. The traditional distributed power supply configuration optimization generally models from the aspects of economy, reliability and reduction of network loss, etc. This single-objective optimization often loses one thing to gain another, and cannot take into account the overall situation. SUMMARY

[0005] The present application provides an energy storage system distributed power supply configuration method, device and equipment to solve the problem of optimal configuration of the energy storage system distributed power supply.

[0006] According to an aspect of the present application, an energy storage system distributed power supply configuration method is provided, which comprises:

[0007] Constructing a configuration optimization model of the energy storage system based on optimization objectives, wherein the optimization objectives include minimum network loss value, maximum system voltage stability margin value and minimum voltage deviation value;

[0008] Performing target singleization processing on the configuration optimization model to generate a fuzzy optimization model;

[0009] Obtaining distributed power supply location information of the energy storage system, and calculating the distributed power supply location information through the fuzzy optimization model to configure the distributed power supply of the energy storage system.

[0010] Optionally, the configuration optimization model of the energy storage system is constructed based on the optimization target, including: constructing each target function by taking the minimum network loss value, the maximum system voltage stability margin value and the minimum voltage deviation value as the optimization target respectively; determining the constraint conditions of each target function, and combining each target function and the constraint conditions to construct the configuration optimization model of the energy storage system.

[0011] Optionally, the constraint conditions include node voltage constraints, branch current constraints and distributed power access capacity constraints.

[0012] Optionally, the configuration optimization model is subjected to target unification processing to generate a fuzzy optimization model, including: establishing a fuzzy membership function of each optimization target in the configuration optimization model by using a piecewise linear function; determining a weight value corresponding to each fuzzy membership function by using a preset maximum satisfaction degree algorithm, and calculating the product of each fuzzy membership function and the corresponding weight value as a weighted value; and adding each weighted value to generate the fuzzy optimization model.

[0013] Optionally, before the location information of the distributed power is calculated by the fuzzy optimization model to determine the configuration data of the energy storage system, the method further includes: fusing a particle swarm algorithm with a Pareto sorting mechanism to form a hybrid algorithm; and improving the hybrid algorithm by using a genetic algorithm to generate an improved algorithm.

[0014] Optionally, the location information of the distributed power is calculated by the fuzzy optimization model to perform distributed power configuration on the energy storage system, including: solving the fuzzy optimization model and the location information of the distributed power based on the improved algorithm to determine configuration optimization information, wherein the configuration optimization information includes the access location and access capacity of the distributed power; and performing distributed power configuration on the energy storage system according to the configuration optimization information.

[0015] Optionally, the solving process includes: initializing the population position variable and the speed variable, calculating the target function value of each particle and putting it into the non-inferior solution set; determining the historical optimal solution of the particle and the global optimal solution of the population, and determining the difference value between each particle and the optimal particle; updating the inertia weight, speed component and position component of each particle; performing mutation and crossover operation on the particle; calculating the target function value of each particle, updating the historical optimal solution according to the dominance relationship, and forming a new non-inferior solution set; updating the non-inferior solution set; selecting the global optimal solution of the population, and outputting the Pareto solution set when the termination condition is met, wherein the termination condition is to randomly select the global optimal solution from the Pareto solution set in the first twenty percent of the dense distance.

[0016] According to another aspect of the present application, a distributed power configuration device for an energy storage system is provided, which includes:

[0017] The configuration optimization model construction module is configured to construct a configuration optimization model of the energy storage system based on optimization targets, wherein the optimization targets include a minimum network loss value, a maximum system voltage stability margin value, and a minimum voltage deviation value.

[0018] The fuzzy optimization model generation module is configured to perform target unification processing on the configuration optimization model to generate a fuzzy optimization model.

[0019] The configuration data determination module is configured to obtain distributed power source position information of the energy storage system, and perform calculation on the distributed power source position information by using the fuzzy optimization model to configure a distributed power source for the energy storage system.

[0020] According to another aspect of the present application, an electronic device is provided, which includes:

[0021] at least one processor;

[0022] and a memory connected in communication with the at least one processor;

[0023] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the energy storage system distributed power source configuration method according to any one of the embodiments of the present application.

[0024] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the energy storage system distributed power source configuration method according to any one of the embodiments of the present application when executed.

[0025] The technical solution of the embodiments of the present application considers multiple optimization targets, including a minimum network loss value, a maximum system voltage stability margin value, and a minimum voltage deviation value, when constructing a configuration optimization model, which helps to achieve comprehensive optimization of the energy storage system and improve the operation efficiency and stability of the power grid. The configuration optimization model is processed by using the fuzzy set theory to perform target unification, thereby avoiding conflicts between sub-targets and optimizing the overall multi-target. According to the calculation result, the power source is configured, which can optimize the power flow distribution of the power grid and reduce the network loss and voltage deviation.

[0026] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only some of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor under the premise of these drawings.

[0028] Fig. 1 is a flow chart of a configuration method of a distributed power supply of an energy storage system according to an embodiment of the present application;

[0029] Fig. 2 is a flow chart of another configuration method of a distributed power supply of an energy storage system according to an embodiment of the present application;

[0030] Fig. 3 is a structural schematic diagram of a configuration device of a distributed power supply of an energy storage system according to an embodiment of the present application;

[0031] Fig. 4 is a structural schematic diagram of an electronic device for implementing a configuration method of a distributed power supply of an energy storage system according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the person skilled in the art better understand the present application, the following will combine the drawings in the embodiments of the present application, and the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0033] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment one

[0035] FIG. 1 is a flowchart of a method for configuring a distributed power source of an energy storage system according to an embodiment of the present application. The embodiment can be applied to the case of configuring a distributed power source of an energy storage system. The method can be executed by a distributed power source configuration device of an energy storage system. The distributed power source configuration device of the energy storage system can be realized in the form of hardware and / or software, and can be configured in a computer controller. As shown in FIG. 1, the method comprises the following steps.

[0036] In S110, a configuration optimization model of the energy storage system is constructed based on the optimization target. The optimization target includes a minimum network loss value, a maximum system voltage stability margin value, and a minimum voltage deviation value.

[0037] The energy storage system refers to a system for storing electrical energy, which can help balance power supply and demand and improve the stability of the power system. The configuration optimization model is used to determine the optimal settings of the distributed power source in the energy storage system to achieve specific performance targets and meet various constraints. The network loss value refers to the energy loss caused by factors such as resistance and reactance during the transmission of electrical energy in the power network, usually expressed in the form of power or energy. The system voltage stability margin refers to the ability and reserve level of the power system to maintain voltage stability when subjected to various disturbances or changes. The voltage deviation refers to the degree of difference between the actual voltage and the standard or expected voltage.

[0038] Specifically, the controller will clearly define the optimization target based on the requirements and expectations of the performance of the energy storage system. After the optimization target is defined, a large amount of relevant data needs to be collected, such as the topology of the power grid, line parameters, load characteristics, power distribution, and other basic information. At the same time, the technical parameters of the energy storage system itself need to be obtained, such as energy storage capacity, charging and discharging efficiency, response time, etc. Then, a mathematical model is established based on the obtained optimization target and collected data. For the minimum network loss value, methods such as power flow calculation can be used to evaluate the network loss under different configuration schemes. For the maximum system voltage stability margin value, the calculation and analysis of voltage stability indicators can be performed. For the minimum voltage deviation value, the voltage deviation of each node can be quantified and evaluated. In the process of establishing the mathematical model, various constraints need to be considered. The constraints can include power balance constraints, voltage amplitude and phase limitations, energy storage system operation constraints (such as charging and discharging power limitations, energy storage capacity limitations, etc.), and safe operation constraints of the power grid. By integrating the objective function and the constraint conditions, the configuration optimization model can be constructed.

[0039] Optionally, the configuration optimization model of the energy storage system is constructed based on the optimization target, which includes: constructing each objective function based on the minimum network loss value, the maximum system voltage stability margin value, and the minimum voltage deviation value as the optimization target; determining the constraint conditions of each objective function, and combining each objective function and the constraint conditions to construct the configuration optimization model of the energy storage system.

[0040] The goal of minimizing network losses aims to reduce energy loss during power transmission. This is achieved through the rational configuration of energy storage systems, reducing unnecessary energy consumption in the grid, improving energy efficiency, and lowering operating costs. The goal of maximizing system voltage stability margin focuses on ensuring the voltage stability of the power system. A larger voltage stability margin means the system can better maintain voltage stability in the face of various disturbances and changes, avoiding excessive voltage fluctuations that could lead to equipment failure or affect power quality. The goal of minimizing voltage deviation aims to reduce voltage deviation, ensuring that the voltage at each node is as close as possible to the standard value, thereby providing users with a stable and reliable power supply.

[0041] Specifically, the controller will construct objective functions with the minimum network loss, the maximum system voltage stability margin, and the minimum voltage deviation as optimization objectives, and determine the constraints for each objective function. These constraints may include operational constraints of the energy storage system, such as charging and discharging power limits and energy storage capacity limits; grid operational constraints, such as power balance constraints and voltage amplitude and phase limits; and possibly also investment cost limits and environmental factors related to the energy storage system.

[0042] In a specific implementation, the objective function for minimizing network loss is expressed by the following formula (1):

[0043] Among them, P loss Let b be the network loss, g be the total loss of the branches, and g be the total loss of the branches. k U represents the conductance of the k-th branch, where i and j are the start and end node numbers of the k-th branch. i U j θ represents the voltage magnitude at nodes i and j. ij This represents the voltage phase difference between nodes i and j. When the system load increases, if capacity is not expanded in time, even a single peak load could cause the system to experience voltage collapse. Integrating distributed generation into the distribution network can effectively improve the system's voltage stability. By establishing an objective function that maximizes the voltage stability margin, it can effectively address the adverse effects of increased load.

[0044] Specifically, since the static voltage stability index L is a value between 0 and 1.0, and the magnitude of L is inversely proportional to the system voltage stability, when the value of L exceeds 1.0, the system voltage collapses. The voltage stability margin indicates how close the system is to voltage collapse from its current state. Therefore, the system voltage stability margin M can be defined as M = 1 - L (2).

[0045] The above equation shows that M can only be maximized when L is minimized. Therefore, the objective function for maximizing M is the objective function for maximizing the system voltage stability margin, expressed by the following formula (2): f2=min(L) (3)

[0046] Among them, L is the static voltage stability index. Distributed power source optimization configuration usually takes node voltage as a constraint condition, but the optimization result may make the voltage of some nodes extremely close to the limit, on the verge of exceeding the limit. Therefore, the objective function is set as the deviation between the node voltage and the specified voltage. This way, the optimized node voltage can be near its specified voltage, rather than close to the limit, so that the system voltage is stabilized at a reasonable level. The objective function for minimizing the voltage deviation is expressed by the following formula (3):

[0047] in, These represent the upper and lower voltage limits specified for the i-th node, respectively. Specify the voltage value for the i-th node.

[0048] Optional constraints include node voltage constraints, branch current constraints, and distributed generation capacity constraints.

[0049] Specifically, when optimizing the configuration of distributed power sources, the values ​​of each variable should conform to relevant laws and comply with corresponding national standards. Therefore, it is necessary to constrain the power flow by applying equations or inequalities to these variables, as expressed by the following formula (5):

[0050] Among them, P i Q i U is the injected power at node i. i and U j G represents the voltage magnitudes at nodes i and j, respectively. ij and B ij θ represents the conductance and susceptance of the line between i and j, respectively. ij This is the voltage phase difference between nodes i and j. When distributed generation is connected to the distribution network, voltage, current, and distributed generation capacity need to be limited. The node voltage constraint is expressed by the following formula (6):

[0051] Among them, U i This represents the voltage magnitude at node i. These represent the upper and lower voltage limits specified for the i-th node, respectively. The branch current constraint is expressed by the following formula (7):

[0052] in, The current limit value of the branch k. The distributed power access capacity constraint is that the distributed power directly affects the system power flow, and the output of the distributed power is affected by the natural environment and its start-stop machine is not uniformly scheduled by the power grid. If a large capacity of distributed power is accessed, it will cause the node voltage to exceed the limit and have a great impact on the user. For example, if the distributed power suddenly exits operation, it will cause the voltage of the grid-connected point and the adjacent node to drop sharply. Therefore, the capacity of the distributed power must be limited to reduce the impact of the distributed power on the power grid as much as possible. Generally, 40% of the total load capacity is taken as the limit of the access capacity of the distributed power. The following formula (8) is used to represent it: ∑P DG ≤0.4∑P load (8)

[0053] In the formula, ∑P DG is the total capacity of the distributed power access, ∑P load is the total load capacity.

[0054] S120, target unification processing is performed on the configuration optimization model to generate a fuzzy optimization model.

[0055] In the formula, target unification processing refers to converting multiple different optimization targets into a relatively single target to facilitate model calculation and processing. The fuzzy optimization model refers to an optimization model generated by processing using fuzzy mathematics theory.

[0056] Specifically, in order to more effectively process the multi-objective optimization problem, the configuration optimization model is subjected to target unification processing, thereby generating a fuzzy optimization model. Through target unification processing, multiple targets can be integrated into a relatively unified target function, simplifying the complexity of calculation and analysis.

[0057] S130, the position information of the distributed power of the energy storage system is obtained, and the position information of the distributed power is calculated by the fuzzy optimization model to configure the distributed power of the energy storage system.

[0058] In the formula, the distributed power refers to small power sources scattered in the energy storage system, such as distributed solar power generation and distributed wind power generation. The position information refers to relevant data of the specific installation position of the distributed power in the energy storage system.

[0059] Optionally, before the position information of the distributed power is calculated by the fuzzy optimization model to determine the configuration data of the energy storage system, the method further comprises: fusing a Pareto sorting mechanism and a particle swarm algorithm to form a hybrid algorithm; and improving the hybrid algorithm by a genetic algorithm to generate an improved algorithm.

[0060] Specifically, the Pareto ranking mechanism and the particle swarm algorithm are fused to form a hybrid algorithm, including: (1) adaptive inertia weight, in the particle swarm algorithm, the inertia weight w is mostly linearly decreasing or the like, lacking feedback guidance after the position of the corresponding particle is updated, the difference between the position of the particle after being updated and the global optimal particle is used to guide the update of the inertia weight, which is expressed by the following formulas (9) and (10):

[0061] wherein D is the dimension of the particle, is the inertia weight of the ith particle at the kth moment, w start , w end are the initial and ending values of w respectively, x max , x min are the maximum and minimum values of the position variable of the particle respectively.(2) crossover and mutation factors, in order to improve the premature performance of the particle swarm algorithm, the crossover and mutation operations of the genetic algorithm are introduced into the particle swarm algorithm, the specific process of the crossover and mutation is expressed by the following formula (11): id min max min

[0062] wherein if the random number r id of the ith particle in the dth dimension is less than the mutation probability p m , the position of the ith particle in the dth dimension is updated, if the random number r id of the ith particle in the dth dimension is less than the crossover probability p c , the crossover is performed.(3) the non-inferior solution is updated by using the dynamic crowded distance, in order to ensure the uniformity of the distribution of the Pareto solution, the Pareto solution is selected after each iteration. The crowded distance represents the crowded degree between the particle and its surrounding particles, and reflects the uniformity of the solution to a certain extent. The crowded distance I(x i ) of the particle x i is expressed by the following formula (12):

[0063] wherein x j , x k are the two closest particles to x i , f m (x j ) is the value of the mth objective function of the particle x j , and f m max is the maximum value of the mth objective function of all particles. Further, if the number of objectives is n, the crowded distance of the particle x i is expressed by the following formula (13): ​​

[0064] Wherein, each time after calculating the dense distance, the solution with the smallest value is removed, and the N Pareto solutions are left through repeated cycles of guidance.

[0065] Optionally, the location information of the distributed power supply is calculated through the fuzzy optimization model to configure the distributed power supply for the energy storage system, including: solving the fuzzy optimization model and the location information of the distributed power supply based on an improved algorithm to determine configuration optimization information, wherein the configuration optimization information includes the access location and access capacity of the distributed power supply; and configuring the distributed power supply for the energy storage system according to the configuration optimization information.

[0066] The configuration optimization information mainly includes the access location and access capacity of the distributed power supply. The selection of the access location of the distributed power supply directly affects the efficiency and loss of energy transmission, as well as the stability and reliability of the entire power grid. The access capacity determines the amount of energy that the distributed power supply can provide for the system.

[0067] Optionally, the solving process includes: initializing the position variable and the speed variable of the population, calculating the objective function value of each particle, and putting it into the non-inferior solution set; determining the historical optimal solution of the particle and the global optimal solution of the population, determining the difference value of each particle and the optimal particle; updating the inertia weight, speed component and position component of each particle; performing mutation and crossover operation on the particle; calculating the objective function value of each particle, updating the historical optimal solution according to the dominance relationship, and forming a new non-inferior solution set; updating the non-inferior solution set; selecting the global optimal solution of the population, and when the termination condition is met, outputting the Pareto solution set, wherein the termination condition is to randomly select the global optimal solution in the Pareto solution set before the dense distance of 20%.

[0068] Specifically, in the solving process, the first step is to initialize the population position variable and the speed variable, that is, to set the initial position and movement speed for each particle participating in the solution, to provide a starting point for subsequent calculation and optimization. Then the controller will calculate the objective function value of each particle and put it into the non-inferior solution set. The objective function value reflects the adaptability or performance index of the particle at the current position. The non-inferior solution set is used to store the particle position information that has no obvious advantage or disadvantage among multiple objectives. The particle historical optimal solution is the optimal position experienced by each particle itself, and the population global optimal solution is the optimal position found by the entire population in the search process. By determining the difference, the gap between the current position of the particle and the optimal position can be determined. The controller also updates the inertia weight, speed component and position component of each particle. The inertia weight affects the tendency of the particle to maintain the original speed, the speed component determines the speed and direction of the particle movement, and the position component reflects the new position of the particle. Finally, the controller will perform mutation and crossover operations on the particles, which helps to increase the diversity of the population, avoid falling into local optimal solutions, and improve the possibility of searching for global optimal solutions.

[0069] Further, the controller will calculate the objective function value of each particle again, update the historical optimal solution according to the dominance relationship, and form a new non-inferior solution set. The dominance relationship is used to compare the advantages and disadvantages between particles, so as to update the optimal solution and the non-inferior solution set. The controller will continuously update the non-inferior solution set during the solving process to ensure that the solution set always contains the current optimal particle position information. In the solving process, selecting the population global optimal solution is a key step. When the termination condition is met, the Pareto solution set is output. The termination condition is to randomly select the global optimal solution in the first twenty percent of the Pareto solution set with dense distance. The dense distance is used to measure the distribution density of the solutions in the Pareto solution set. Randomly selecting the global optimal solution in the first twenty percent can increase randomness and flexibility while ensuring the quality of the solution set.

[0070] In summary, the technical scheme of the embodiment gradually approaches the optimal solution by continuously updating the particle state, comparing the advantages and disadvantages, and performing mutation and crossover operations, and finally outputs the required Pareto solution set.

[0071] The technical scheme of the embodiment of the application considers multiple optimization objectives, including the minimum network loss value, the maximum system voltage stability margin, and the minimum voltage deviation, when constructing the configuration optimization model, which helps to realize the comprehensive optimization of the energy storage system and improve the operation efficiency and stability of the power grid. The fuzzy set theory is used to perform single-objective processing on the configuration optimization model, thereby avoiding conflicts between sub-targets and optimizing the overall multi-objective. According to the calculation result, the power source configuration can optimize the power flow distribution of the power grid and reduce the network loss and voltage deviation.

[0072] Embodiment Two

[0073] Figure 2 is a flow chart of a configuration method of a distributed power supply of an energy storage system according to Embodiment Two of the present application, which is based on Embodiment One and adds steps S210 and S250. The specific contents of steps S210 and S250 are substantially the same as steps S110 and S130 in Embodiment One, and thus will not be described again in this embodiment. As shown in Figure 2, the method comprises the following steps.

[0074] S210, constructing a configuration optimization model of the energy storage system based on the optimization target, wherein the optimization target comprises a minimum network loss value, a maximum system voltage stability margin value, and a minimum voltage deviation value.

[0075] Optionally, constructing the configuration optimization model of the energy storage system based on the optimization target comprises: constructing each objective function by taking the minimum network loss value, the maximum system voltage stability margin value, and the minimum voltage deviation value as the optimization target respectively; determining the constraint conditions of each objective function, and combining each objective function and the constraint conditions to construct the configuration optimization model of the energy storage system.

[0076] Optionally, the constraint conditions comprise node voltage constraints, branch current constraints, and distributed power supply access capacity constraints.

[0077] S220, establishing a fuzzy membership function of each optimization target in the configuration optimization model by using a piecewise linear function.

[0078] Specifically, the membership functions of the three objective functions established by using the piecewise linear function reflect whether each optimization target can reach the expected value through the size of the membership; the maximum satisfaction degree method is used to fuse the three sub-targets so that each sub-target can reach the optimal value as much as possible, thereby making the overall optimal; thus, the change of the overall satisfaction degree with each sub-target membership is completed, and the multi-objective optimization problem is converted into a single-objective problem of maximizing the overall satisfaction degree. The network loss membership function is expressed by the following formula (14):

[0079] wherein, is taken as the initial value of the network loss before optimization, which represents the allowable limit value of the network loss, is taken as the minimum value of the network loss when only the network loss is optimized as a single target, which represents the expectation of the decision maker on the network loss, the dimensional objective function is converted into a dimensionless membership, and the value range of the membership is [0, 1], thereby realizing the normalization of the objective function. Similarly, the membership function expression of the voltage stability index is expressed by the following formula (15):

[0080] wherein, L M is taken as the initial value of each target before optimization, and the values of L S are the optimal values when each single target is optimized respectively. The membership function expression of the voltage deviation is expressed by the following formula (16):

[0081] wherein, ΔU M respectively take the initial values of each target before optimization, ΔU S The values of the respective single-target optimization are only the optimal values; thus, the multi-objective normalization processing is completed, and the problem of different dimensions between multi-objectives is solved.

[0082] S230, a preset maximum satisfaction degree algorithm is used to determine the weight value corresponding to each fuzzy membership function, and the product of each fuzzy membership function and the corresponding weight value is calculated as a weighted value.

[0083] Specifically, when the fuzzy multi-objective single-objective processing is performed by the maximum satisfaction degree method, the overall satisfaction S is an embodiment of the system overall performance, which is expressed by the following formula (17): S = min [μ1(P loss ), μ2(L), μ3(ΔU)] (17)

[0084] wherein, the overall satisfaction is improved with the increase of the minimum value of each sub-target membership degree, and the overall performance is improved only by increasing the minimum value of each sub-target membership degree. In this way, the multi-objective optimization problem is converted into a single-objective problem of maximizing the value of S, which is expressed by the following formula (18): max(S) = max{min[μ1(P loss ), μ2(L), μ3(ΔU)]} (18)

[0085] Further, the maximization is converted into a minimization problem, which is expressed by the following formula (19): min(-S) = min{-min[μ1(P loss ), μ2(L), μ3(ΔU)]} (19)

[0086] S240, the weighted values are added to generate a fuzzy optimization model.

[0087] S250, the distributed power source position information of the energy storage system is obtained, and the distributed power source position information is calculated by the fuzzy optimization model to configure the distributed power source of the energy storage system.

[0088] Optionally, before the distributed power source position information is calculated by the fuzzy optimization model to determine the configuration data of the energy storage system, the method further comprises: fusing a Pareto sorting mechanism and a particle swarm algorithm to form a hybrid algorithm; and improving the hybrid algorithm by a genetic algorithm to generate an improved algorithm.

[0089] Optionally, the location information of the distributed power supply is calculated by the fuzzy optimization model to configure the distributed power supply for the energy storage system, including: solving the fuzzy optimization model and the location information of the distributed power supply based on an improved algorithm to determine configuration optimization information, wherein the configuration optimization information includes the access location and access capacity of the distributed power supply; and configuring the distributed power supply for the energy storage system according to the configuration optimization information.

[0090] Optionally, the solving process includes: initializing the population position variable and the speed variable, calculating the objective function value of each particle and putting it into the non-inferior solution set; determining the historical optimal solution of the particle and the global optimal solution of the population, and determining the difference between each particle and the optimal particle; updating the inertia weight, speed component and position component of each particle; performing mutation and crossover operation on the particle; calculating the objective function value of each particle, updating the historical optimal solution according to the dominance relationship, and forming a new non-inferior solution set; updating the non-inferior solution set; selecting the global optimal solution of the population, and outputting the Pareto solution set when the termination condition is met, wherein the termination condition is to randomly select the global optimal solution in the Pareto solution set in the first twenty percent of the dense distance.

[0091] The technical scheme of the embodiment of the application helps to realize the comprehensive optimization of the energy storage system and improve the operation efficiency and stability of the power grid by considering multiple optimization objectives, including the minimum network loss value, the maximum system voltage stability margin and the minimum voltage deviation, when constructing the configuration optimization model. The configuration optimization model is processed by the fuzzy set theory to achieve single objective, thereby avoiding the conflict between the sub-targets and optimizing the overall multi-objective. The power supply configuration according to the calculation result can optimize the power flow distribution of the power grid and reduce the network loss and voltage deviation.

[0092] Embodiment three

[0093] Fig. 3 is a structural schematic diagram of an energy storage system distributed power supply configuration device provided by the embodiment three of the application. As shown in Fig. 3, the device includes: a configuration optimization model construction module 310 configured to construct a configuration optimization model of the energy storage system based on optimization objectives, wherein the optimization objectives include the minimum network loss value, the maximum system voltage stability margin and the minimum voltage deviation;

[0094] a fuzzy optimization model generation module 320 configured to perform single objective processing on the configuration optimization model to generate a fuzzy optimization model;

[0095] a configuration data determination module 330 configured to obtain the location information of the distributed power supply of the energy storage system, and calculate the location information of the distributed power supply by the fuzzy optimization model to configure the distributed power supply for the energy storage system.

[0096] Optionally, the configuration optimization model construction module 310 is specifically configured to: construct each target function by taking the minimum network loss value, the maximum system voltage stability margin value and the minimum voltage deviation value as optimization objectives respectively; determine constraint conditions of each target function, and combine each target function and the constraint conditions to construct the energy storage system configuration optimization model.

[0097] Optionally, the fuzzy optimization model generation module 320 is specifically configured to: establish a fuzzy membership function of each optimization objective in the configuration optimization model by using a piecewise linear function; determine a weight value corresponding to each fuzzy membership function by using a preset maximum satisfaction degree algorithm, calculate a product of each fuzzy membership function and the corresponding weight value as a weighted value; and add each weighted value to generate the fuzzy optimization model.

[0098] Optionally, the device further comprises an algorithm improvement module configured to: before the distributed power source position information is calculated by the fuzzy optimization model to determine the configuration data of the energy storage system, fuse a particle swarm algorithm and a Pareto sorting mechanism to form a hybrid algorithm; and improve the hybrid algorithm by using a genetic algorithm to generate an improved algorithm.

[0099] Optionally, the configuration data determination module 330 specifically comprises a configuration solving unit configured to: solve the fuzzy optimization model and the distributed power source position information based on the improved algorithm to determine configuration optimization information, wherein the configuration optimization information comprises a distributed power source access position and an access capacity; and configure the energy storage system according to the configuration optimization information.

[0100] The technical scheme of the embodiment of the present application, by considering multiple optimization objectives when constructing the configuration optimization model, including the minimum network loss value, the maximum system voltage stability margin value and the minimum voltage deviation value, helps to realize the comprehensive optimization of the energy storage system, and improve the operation efficiency and stability of the power grid. By using the fuzzy set theory to process the configuration optimization model to be single, the conflict between each sub-target is avoided, and the overall multi-objective is optimized. According to the calculation result, the power source is configured, which can optimize the power flow distribution of the power grid, and reduce the network loss and voltage deviation.

[0101] The energy storage system distributed power source configuration device provided in the embodiment of the present application can execute the energy storage system distributed power source configuration method provided in any embodiment of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0102] Embodiment four

[0103] FIG. 4 shows a structural diagram of an electronic device 10 that can be used to implement embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application as described and / or claimed in this document.

[0104] As shown in FIG. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., connected to the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0105] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0106] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as a method of distributed power supply configuration for energy storage systems.

[0107] In some embodiments, a method of configuring a distributed power source for an energy storage system can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, portions or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of a method of configuring a distributed power source for an energy storage system as described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform a method of configuring a distributed power source for an energy storage system by any other suitable means, e.g., with the aid of firmware.

[0108] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0109] Computer programs used to implement the processes of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0110] In the context of this application, a computer readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium can be a machine readable signal medium. More specific examples of the machine readable storage medium will include a one or more lines of a electrical connection, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0111] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0112] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0113] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0114] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in this application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of this application can be achieved, and this application does not limit herein.

[0115] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

A distributed power supply configuration method of an energy storage system comprises the following steps: constructing a configuration optimization model of the energy storage system based on an optimization target, wherein the optimization target comprises a minimum network loss value, a maximum system voltage stability margin value and a minimum voltage deviation value; performing target singleization processing on the configuration optimization model to generate a fuzzy optimization model; obtaining distributed power supply position information of the energy storage system, and calculating the distributed power supply position information by using the fuzzy optimization model to configure the distributed power supply of the energy storage system. The method of claim 1, wherein, The step of constructing the configuration optimization model of the energy storage system based on the optimization target comprises the following steps: respectively taking the minimum network loss value, the maximum system voltage stability margin value and the minimum voltage deviation value as optimization targets to construct respective target functions; determining constraint conditions of the respective target functions, and combining the respective target functions and the constraint conditions to construct the configuration optimization model of the energy storage system. The method of claim 2, wherein, The constraint conditions comprise node voltage constraints, branch current constraints and distributed power supply access capacity constraints. The method of claim 1, wherein, The step of performing target singleization processing on the configuration optimization model to generate the fuzzy optimization model comprises the following steps: using a piecewise linear function to establish fuzzy membership functions of the respective optimization targets in the configuration optimization model; using a preset maximum satisfaction degree algorithm to determine weight values corresponding to the respective fuzzy membership functions, and calculating products of the respective fuzzy membership functions and the corresponding weight values as weighted values; adding the respective weighted values to generate the fuzzy optimization model. The method according to claim 1, before the step of calculating the distributed power supply position information by using the fuzzy optimization model to determine configuration data of the energy storage system, the method further comprises the following steps: fusing a Pareto sorting mechanism and a particle swarm algorithm to form a hybrid algorithm; improving the hybrid algorithm by using a genetic algorithm to generate an improved algorithm. The method of claim 5, wherein, The step of calculating the distributed power supply position information by using the fuzzy optimization model to configure the distributed power supply of the energy storage system comprises the following steps: solving the fuzzy optimization model and the distributed power supply position information based on the improved algorithm to determine configuration optimization information, wherein the configuration optimization information comprises distributed power supply access positions and access capacities; configuring the distributed power supply of the energy storage system according to the configuration optimization information. The method of claim 6, wherein, The solving process comprises the following steps: initializing population position variables and speed variables, calculating target function values of respective particles, and putting the target function values into a non-inferior solution set; determining particle historical optimal solutions and population global optimal solutions, and determining difference values of the respective particles and optimal particles; updating inertia weight values, speed components and position components of the respective particles; performing mutation and crossover operations on the particles; calculating target function values of the respective particles, updating historical optimal solutions according to a dominance relationship, and forming a new non-inferior solution set; updating the non-inferior solution set; selecting a population global optimal solution, and outputting a Pareto solution set when a termination condition is satisfied, wherein the termination condition is that a global optimal solution is randomly selected from Pareto solutions in a dense distance of 20 percent. A distributed power supply configuration device of an energy storage system comprises the following components: The configuration optimization model construction module is configured to construct a configuration optimization model of the energy storage system based on an optimization target, wherein the optimization target comprises a minimum network loss value, a maximum system voltage stability margin value, and a minimum voltage deviation value. The fuzzy optimization model generation module is configured to perform target unification processing on the configuration optimization model to generate a fuzzy optimization model. The configuration data determination module is configured to obtain distributed power source position information of the energy storage system, and perform calculation on the distributed power source position information by using the fuzzy optimization model to configure the distributed power source for the energy storage system. An electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores a computer program capable of being executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method of any one of claims 1-7. A computer storage medium stores computer instructions for causing a processor to execute the method of any one of claims 1-7 when executed.

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