Distributed energy storage stationing and volume fixing method, system and equipment, medium and product

Abstract

The invention relates to the technical field of electric power systems, and discloses a distributed energy storage stationing and sizing method, a system, equipment, a medium and a product. According to the method, quantitative indexes of node volatility, economic capacity of energy storage equipment and load power shortage rate in an optimization strategy of distributed energy storage stationing and sizing are considered; the energy storage construction and operation cost minimization, the load power shortage rate minimization and the network branch fluctuation rate minimization are taken as optimization objectives, system power balance state constraints in an actual scene are integrated, an energy storage stationing and constant volume optimization model is established, the energy storage stationing and constant volume optimization model is optimized and solved, and the optimal energy storage stationing and constant volume optimization model is obtained. And according to the optimal solution, determining an energy storage stationing and constant volume optimization scheme, thereby reasonably determining the energy storage quantity, improving the operation reliability, reducing the power fluctuation of branches in the low-voltage power supply area, and easily meeting the increasing load power demand.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, equipment, medium and product for distributing and sizing distributed energy storage. Background Art

[0002] With the influx of distributed photovoltaic power generation into low-voltage distribution substations, power and voltage limits in these substations have been exceeded. The current main technical solution is to deploy energy storage devices to limit the overall substation voltage from exceeding limits and severely impact the power transmission of distribution transformers when photovoltaic power generation units generate large amounts of power. However, deploying large amounts of energy storage for energy consumption reduces operational reliability and is economically expensive. Therefore, the use of AC / DC interconnection technology, combined with distributed energy storage, allows for connections between adjacent substations, or between different branches within the same substation, via a DC network. This not only facilitates active power mutual assistance, but also facilitates local voltage regulation.

[0003] The integration of large-scale renewable energy sources into DC microgrids, particularly under grid-friendly interaction constraints (replacing islanded operation), is more likely to lead to increased power fluctuations within branches within low-voltage power supply areas. This can lead to unmet demand for increasing loads and make standardization of distributed energy storage capacity difficult. In this context, the orderly integration and optimization of distributed energy storage capacity and location within flexible, interconnected low-voltage power supply areas are pressing challenges for their efficient operation.

[0004] At present, the fixed-capacity deployment of distributed energy storage uses a large amount of energy storage for consumption, resulting in low operational reliability. Moreover, under the constraints of grid-friendly interaction (replacing island operation), it is more likely to lead to increased power fluctuations in branches within the low-voltage power supply area, and the increasing load power demand cannot be met. Summary of the Invention

[0005] In view of this, the present invention provides a method, system, equipment, medium and product for the location and capacity determination of distributed energy storage, which solves the technical problem that the location and capacity determination method of distributed energy storage is equipped with a large amount of energy storage for absorption, which results in low operational reliability. Moreover, under the constraints of grid-friendly interaction (replacing island operation), it is more likely to lead to increased power fluctuations in branches within the low-voltage power supply area, and the increasing load power demand cannot be met.

[0006] A first aspect of the present invention provides a method for locating and sizing distributed energy storage, comprising:

[0007] The objective function is constructed with the optimization goals of minimizing energy storage construction and operation costs, minimizing load power shortage rate, and minimizing network branch fluctuation rate;

[0008] Determining the constraints of the objective function according to the power balance state of the power system, and constructing an energy storage point and capacity optimization model based on the objective function and the constraints;

[0009] The energy storage location and capacity optimization model is optimized and solved, and an energy storage location and capacity optimization plan is determined based on the optimal solution.

[0010] Preferably, the objective function is:

[0011]

[0012] Where, 、 and are weight coefficients, is the total objective function value, For energy storage construction and operation costs, is the load power failure rate, is the network branch volatility;

[0013] in,

[0014] Where, is the total number of distributed energy storage units; and are the start and end times of maximum charge / discharge of the energy storage unit, respectively; is the charge / discharge power of the kth energy storage unit at time t; The optimal single-unit capacity for distributed energy storage; is the penalty factor;

[0015]

[0016] Where, is the power demand of the DC island microgrid at time t; 、 and are the power of load i, the output power of photovoltaic j and the output power of energy storage k at time t respectively; 、 are the number of loads and photovoltaics respectively; is the operation cycle;

[0017]

[0018] in,

[0019] Where, represents the branch at time t m is the total number of branches, b is the decision variable for whether the branch corresponding to the candidate energy storage access point is connected to the energy storage, and B is the set of candidate energy storage access points.

[0020] Preferably, the constraints include system energy balance constraints, energy storage energy balance constraints, energy storage power constraints and branch power constraints.

[0021] Preferably, the step of optimizing and solving the energy storage location and capacity optimization model and determining the energy storage location and capacity optimization plan according to the optimal solution includes:

[0022] The energy storage point location and capacity optimization model is optimized and solved based on the improved goat algorithm, and the energy storage point location and capacity optimization scheme is determined according to the optimal solution.

[0023] Preferably, the step of optimizing and solving the energy storage location and capacity optimization model based on the improved goat algorithm and determining the energy storage location and capacity optimization scheme according to the optimal solution includes:

[0024] Initializing a goat population, wherein individual goats in the goat population are obtained by encoding candidate energy storage point placement and capacity solutions;

[0025] Calculating the fitness of each goat in the goat population according to the energy storage point distribution and constant capacity optimization model to obtain the fitness of each goat individual;

[0026] Sorting the goat individuals according to their fitness, and determining the goat individual corresponding to the best fitness as the current optimal solution for the current number of iterations;

[0027] Determine whether the current optimal solution or the current number of iterations reaches a preset iteration stop condition;

[0028] If it is determined that the current optimal solution or the current number of iterations does not reach the preset iteration stop condition, the state of each goat individual is updated by exploration, optimization, and jumping, and the states are sorted from best to worst according to the states, and all goat individuals are avoided according to the state sorting to generate a new goat population;

[0029] Using the new goat population as the current goat population, updating the number of iterations plus 1, and switching to the step of performing fitness calculation on each individual goat in the goat population according to the energy storage point distribution and constant capacity optimization model to obtain the fitness of each individual goat;

[0030] Until it is determined that the current optimal solution or the current number of iterations reaches the preset iteration stop condition, the iteration stops and the final optimal solution is output as the energy storage point layout and capacity optimization plan.

[0031] Preferably, the process of updating the status of each goat individual by exploring, following the best, and jumping, sorting the status from best to worst, and performing avoidance operations on all goat individuals according to the status sorting to generate a new goat population includes:

[0032] The exploration phase for the optimal state is performed by random movement of each goat individual; wherein the exploration phase is expressed as:

[0033]

[0034] Where S is the goat's step length, x is a random number that obeys Gaussian distribution, is the state of the i-th goat at time t, is the state of the i-th goat at time t+1, is a set of random numbers, 、 are the upper and lower bounds of the established population respectively;

[0035] Each goat individual adopts a strategy close to the current optimal solution to enter the optimization phase; wherein the optimization phase is represented as:

[0036]

[0037] Where, To follow Uber's long is the optimal solution among all populations in time period t;

[0038] Each goat individual jumps out of the current group and goes to other parts to enter the jumping stage; wherein the jumping stage is expressed as:

[0039]

[0040] Where, is the jump step length, For any one of the goat individuals;

[0041] The goat individuals are sorted according to their fitness, and a preset number of goat individuals with low rankings are selected to return to the initial positions to generate the new goat population.

[0042] In a second aspect, the present invention further provides a distributed energy storage location and capacity determination system, comprising:

[0043] The optimization target determination module is used to construct an objective function with the optimization objectives of minimizing energy storage construction and operation costs, minimizing load power shortage rate, and minimizing network branch fluctuation rate;

[0044] An optimization model determination module is used to determine the constraints of the objective function according to the power balance state of the power system, and to construct an energy storage point and capacity optimization model based on the objective function and the constraints;

[0045] The energy storage point distribution and capacity optimization module is used to find the optimal solution for the energy storage point distribution and capacity optimization model and determine the energy storage point distribution and capacity optimization plan based on the optimal solution.

[0046] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for locating and sizing distributed energy storage as described in the first aspect.

[0047] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for locating and sizing distributed energy storage as described in the first aspect.

[0048] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the steps of the method for locating and sizing distributed energy storage as described in the first aspect.

[0049] It can be seen from the above technical solutions that the present invention considers the node volatility, the economic capacity of the energy storage equipment, and the quantitative indicators of the load power shortage rate in the optimization strategy of distributed energy storage point placement and sizing, takes minimizing the energy storage construction and operation costs, minimizing the load power shortage rate, and minimizing the network branch fluctuation rate as the optimization goals, and comprehensively considers the system power balance state constraints in actual scenarios to establish an energy storage point placement and sizing optimization model, and seeks an optimal solution for the energy storage point placement and sizing optimization model. The energy storage point placement and sizing optimization scheme is determined based on the optimal solution, thereby reasonably determining the amount of energy storage, improving operational reliability, and reducing the power fluctuation of the branches within the low-voltage power supply area, making it easy to meet the increasing load power demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1A diagram illustrating an application environment for a method for locating and sizing distributed energy storage provided by an embodiment of the present invention;

[0052] Figure 2 A flow chart of a method for locating and sizing distributed energy storage provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of a distributed energy storage system with fixed capacity and location provided by an embodiment of the present invention;

[0054] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0056] The distributed energy storage location and capacity determination method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 101 communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The terminal 101 or the server 102 constructs an objective function with the optimization objectives of minimizing the energy storage construction and operation costs, minimizing the load power shortage rate, and minimizing the network branch fluctuation rate; according to the power balance state of the power system, the constraints of the objective function are determined, and according to the objective function and the constraints, an energy storage point sizing optimization model is constructed; the energy storage point sizing optimization model is optimized and solved, and the energy storage point sizing optimization plan is determined according to the optimal solution.

[0057] The terminal 101 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and the like.

[0058] The server 102 may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0059] like Figure 2 As shown, the embodiment of the present application provides a method for arranging and sizing distributed energy storage, which is applied to Figure 1The terminal 101 or the server 102 in the embodiment is used as an example to illustrate the method, which includes the following steps S1 to S3.

[0060] Step S1: construct an objective function with the optimization objectives of minimizing energy storage construction and operation costs, minimizing load power shortage rate, and minimizing network branch fluctuation rate.

[0061] In the embodiment of the present application, when studying the problem of fixed capacity and location of distributed energy storage in a DC island microgrid, the three factors of energy storage construction and operation cost, DC island microgrid load satisfaction rate and network power fluctuation are mainly considered as the final control targets for optimization during the planning process.

[0062] To minimize the construction and operation costs of distributed energy storage within a DC microgrid—in other words, to optimize construction economics—it's necessary to configure the minimum possible energy storage capacity. Furthermore, since the capacity of energy storage units and their construction and operation costs increase, it's important to consider both energy storage construction and operating costs.

[0063] The load power shortage rate reflects the probability of a power shortage in the power supply system within a given period. This paper assumes that total load consumption during the daily operating cycle exceeds photovoltaic power generation, i.e., a power-consuming microgrid. In islanded operation, diesel generators will eventually be required for power replenishment. This is not applicable to power-generating microgrids, i.e., DC microgrids that primarily rely on photovoltaic and wind power generation or require curtailment of wind and solar power.

[0064] The network branch fluctuation rate reflects the power flow on the branch within a certain period of time. A smaller power fluctuation rate means that the local load / photovoltaic and local energy storage have achieved better coordination.

[0065] The specific form of the objective function can be adjusted based on the actual power system conditions and the needs of distributed energy storage. During implementation, the construction and operating costs of energy storage can be accurately calculated based on parameters such as the total number of distributed energy storage units, the start and end times of maximum charge / discharge of the energy storage units, and the charge / discharge power of the kth energy storage unit at time t. Furthermore, by considering the impact of load power shortage rates and network branch fluctuations and assigning appropriate weight coefficients, the objective function can fully reflect the optimization objectives of the distributed energy storage location and sizing problem.

[0066] Among them, the objective function is:

[0067]

[0068] Where, 、 and are weight coefficients, is the total objective function value, For energy storage construction and operation costs, is the load power failure rate, is the network branch volatility;

[0069] in,

[0070] Where, is the total number of distributed energy storage units; and are the start and end times of maximum charge / discharge of the energy storage unit, respectively; is the charge / discharge power of the kth energy storage unit at time t; Optimal single-unit capacity for distributed energy storage (including optimal capacity for manufacturing, design, installation, operation and maintenance); is the penalty factor; the penalty factor limits the capacity of the distributed energy storage unit to exceed its optimal single unit capacity, usually , you can select , that is, the optimal single-machine capacity of the unit capacity, and its unit capacity construction and operation costs will be subject to exponential penalties.

[0071]

[0072] Where, is the power demand of the DC island microgrid at time t; 、 and are the power of load i, the output power of photovoltaic j and the output power of energy storage k at time t respectively; 、 are the number of loads and photovoltaics respectively; is the operation cycle;

[0073]

[0074] in,

[0075] Where, represents the branch at time t m is the total number of branches, b is the decision variable for whether the branch corresponding to the candidate energy storage access point is connected to the energy storage, b is a binary variable with a value of 0 or 1. When b = 0, it means that the branch corresponding to the candidate energy storage access point is not connected to the energy storage, and when b = 1, it means that the branch corresponding to the candidate energy storage access point is connected to the energy storage, and B is the set of candidate energy storage access points.

[0076] Step S2: Determine the constraints of the objective function based on the power balance state of the power system, and construct an energy storage location and capacity optimization model based on the objective function and the constraints.

[0077] Among them, the embodiment of the present application fully considers the quantitative indicators of factors such as node volatility, economic capacity of energy storage equipment and load power shortage rate in the optimization strategy of distributed energy storage deployment and sizing, and integrates the constraints in actual scenarios to establish an energy storage deployment and sizing optimization model.

[0078] Among them, the constraints include system energy balance constraints, energy storage energy balance constraints, energy storage power constraints and branch power constraints.

[0079] The system energy balance constraint is to ensure the supply and demand balance of the power system at any time, that is, the difference between the generated power and the load power is equal to the change in the energy storage power, ensuring the stable operation of the power system.

[0080] Energy storage energy balance constraints limit the charging and discharging status of energy storage equipment to avoid excessive charging and discharging that may cause equipment damage or affect its service life, while ensuring that the energy storage equipment can meet load requirements within the planning period.

[0081] Energy storage power constraint is to limit the charging and discharging power of energy storage equipment to avoid excessive impact on the power system or affecting the normal operation of other equipment.

[0082] Branch power constraint is to limit the power transmission of each branch in the power system to avoid branch overload or excessive power fluctuations that may cause equipment damage or affect the stability of the power system.

[0083] Among them, the system energy balance constraint is:

[0084]

[0085]

[0086] Where, The output power of the diesel generator, It is the maximum output power of the diesel generator.

[0087] The energy balance constraint of energy storage is:

[0088]

[0089]

[0090] Where, is the upper limit of the total energy storage power; is the upper limit of the power of the distributed energy storage unit.

[0091] The branch power constraint is:

[0092]

[0093] Where, For branch The power flowing upwards, is the upper limit of branch flow power.

[0094] Step S3: finding an optimal solution for the energy storage location and capacity optimization model, and determining an energy storage location and capacity optimization plan based on the optimal solution.

[0095] It should be noted that the embodiment of the present application considers the node volatility in the optimization strategy of distributed energy storage deployment and sizing, the economic capacity of energy storage equipment, and the quantitative indicators of load power shortage rate, with the minimization of energy storage construction and operation costs, the minimization of load power shortage rate, and the minimization of network branch fluctuation rate as the optimization goals, and comprehensively considers the system power balance state constraints in actual scenarios to establish an energy storage deployment and sizing optimization model, and seeks an optimal solution for the energy storage deployment and sizing optimization model. The energy storage deployment and sizing optimization scheme is determined based on the optimal solution, thereby reasonably determining the amount of energy storage, improving operational reliability, and reducing the power fluctuation of branches within the low-voltage power supply area, making it easy to meet the increasing load power demand.

[0096] In some embodiments, optimizing and solving an energy storage location and capacity optimization model, and determining an energy storage location and capacity optimization plan based on the optimal solution, include:

[0097] The energy storage location and capacity optimization model is optimized and solved based on the improved goat algorithm, and the energy storage location and capacity optimization plan is determined according to the optimal solution.

[0098] Among them, the energy storage deployment and capacity optimization plan includes energy storage access points and capacity (output power).

[0099] The improved goat algorithm is used to find the optimal solution for the energy storage location and capacity optimization model, and the energy storage location and capacity optimization plan is determined based on the optimal solution, including:

[0100] Step S301: Initialize a goat population, wherein individual goats in the goat population are obtained by encoding candidate energy storage location and capacity solutions;

[0101] Step S302: Calculating the fitness of each individual goat in the goat population according to the energy storage point and capacity optimization model to obtain the fitness of each individual goat;

[0102] Step S303: Sort the individual goats according to their fitness, and determine the goat individual corresponding to the best fitness as the current optimal solution for the current number of iterations;

[0103] Step S304: determine whether the current optimal solution or the current number of iterations reaches a preset iteration stop condition;

[0104] Step S305: If it is determined that the current optimal solution or the current number of iterations does not reach the preset iteration stop condition, the state of each goat individual is updated by exploration, optimization, and jumping, and the states are sorted from best to worst. All goat individuals are avoided according to the state sorting to generate a new goat population;

[0105] Among them, the status of each goat individual is updated through exploration, optimization, and jumping, and the status is sorted from best to worst. All goat individuals are avoided according to the status sorting to generate a new goat population, including:

[0106] Step S3051: Perform an exploration phase for the optimal state by randomly moving each goat. The exploration phase is represented as follows:

[0107]

[0108] Where S is the goat's step length, x is a random number that obeys Gaussian distribution, is the state of the i-th goat at time t, is the state of the i-th goat at time t+1, is a set of random numbers, 、 are the upper and lower bounds of the established population respectively;

[0109] Among them, the stage of establishing the goat status is:

[0110] Each goat is represented by an n-dimensional vector in space:

[0111]

[0112] Here, i is the goat number. Assuming there are m goats in total, the initial state position of the goats is generated.

[0113]

[0114] Where rand(n) is a function used to generate a random n-dimensional vector.

[0115] Step S3052: Each goat adopts a strategy close to the current optimal solution to enter the optimization phase; wherein the optimization phase is represented as follows:

[0116]

[0117] Where, To follow Uber's long is the optimal solution among all populations in time period t;

[0118] Step S3053: Each goat individual jumps out of the current group and goes to other parts to perform a jumping phase; wherein the jumping phase is represented as:

[0119]

[0120] Where, is the jump step length, For any one of the goat individuals;

[0121] Step S3054: sort the goat individuals according to their fitness, select a preset number of goat individuals with a low ranking and return them to the initial position to generate a new goat population.

[0122] The goat population is continuously updated iteratively, gradually approaching the global optimal solution. At each iteration, the system checks whether the preset stopping condition is met based on the current optimal solution or the number of iterations. If not, the system continues updating the goat population through the exploration, optimization, and jump phases described above until the stopping condition is met.

[0123] Step S306: Using the new goat population as the current goat population, updating the number of iterations by 1, and performing fitness calculation on each individual goat in the goat population according to the energy storage point and constant capacity optimization model to obtain the fitness of each individual goat;

[0124] Step S307: until the current optimal solution is determined or the current number of iterations reaches a preset iteration stop condition, the iteration stops and the final optimal solution is output as the energy storage point and capacity optimization solution.

[0125] It is understandable that the improved goat algorithm can effectively avoid falling into local optimal solutions when solving the energy storage deployment and sizing optimization problem, thereby improving the accuracy and efficiency of the solution. By continuously iteratively updating the state of the goat population, the algorithm can gradually approach the global optimal solution, thereby obtaining the optimal energy storage deployment and sizing solution. This solution not only takes into account the minimization of energy storage construction and operating costs, but also takes into account the minimization of load power shortage rate and network branch fluctuation rate, ensuring the economy and stability of the power system. In addition, the solution also fully considers the constraints in actual scenarios, such as system energy balance constraints, energy storage energy balance constraints, energy storage power constraints, and branch power constraints, ensuring the feasibility and practicality of the solution.

[0126] Based on the same inventive concept, an embodiment of the present application further provides a distributed energy storage location and capacity determination system for implementing the above-mentioned distributed energy storage location and capacity determination method.

[0127] The implementation solution provided by the system to solve the problem is similar to the implementation solution described in the above method. Therefore, the specific limitations in the embodiments of one or more distributed energy storage point location and capacity determination systems provided below can be found in the above limitations on the distributed energy storage point location and capacity determination method, and will not be repeated here.

[0128] like Figure 3 As shown, the embodiment of the present application provides a distributed energy storage location and capacity determination system, including:

[0129] The optimization target determination module 100 is used to construct an objective function with the optimization targets of minimizing the energy storage construction and operation costs, minimizing the load power shortage rate, and minimizing the network branch fluctuation rate;

[0130] The optimization model determination module 200 is used to determine the constraints of the objective function according to the power balance state of the power system, and to construct an energy storage location and capacity optimization model based on the objective function and the constraints;

[0131] The energy storage location and capacity optimization module 300 is used to find the optimal solution for the energy storage location and capacity optimization model and determine the energy storage location and capacity optimization plan based on the optimal solution.

[0132] In some embodiments, the objective function is:

[0133]

[0134] Where, 、 and are weight coefficients, is the total objective function value, For energy storage construction and operation costs, is the load power failure rate, is the network branch volatility;

[0135] in,

[0136] Where, is the total number of distributed energy storage units; and are the start and end times of maximum charge / discharge of the energy storage unit, respectively; is the charge / discharge power of the kth energy storage unit at time t; The optimal single-unit capacity for distributed energy storage; is the penalty factor;

[0137]

[0138] Where, is the power demand of the DC island microgrid at time t; 、 and are the power of load i, the output power of photovoltaic j and the output power of energy storage k at time t respectively; 、 are the number of loads and photovoltaics respectively; is the operation cycle;

[0139]

[0140] in,

[0141] Where, represents the branch at time t The power flowing up; m is the total number of branches.

[0142] In some embodiments, the constraints include system energy balance constraints, energy storage energy balance constraints, energy storage power constraints, and branch power constraints.

[0143] In some embodiments, the site placement and capacity optimization module 300 is configured to:

[0144] The energy storage location and capacity optimization model is optimized and solved based on the improved goat algorithm, and the energy storage location and capacity optimization plan is determined according to the optimal solution.

[0145] In some embodiments, the site placement and capacity optimization module 300 is used to:

[0146] Initialize a goat population, where individual goats in the goat population are encoded as candidate energy storage location and capacity solutions;

[0147] The fitness of each goat in the goat population is calculated according to the energy storage point fixed capacity optimization model to obtain the fitness of each goat;

[0148] Sort the individual goats according to their fitness, and determine the goat individual with the best fitness as the current optimal solution for the current number of iterations;

[0149] Determine whether the current optimal solution or the current number of iterations has reached the preset iteration stop condition;

[0150] If it is determined that the current optimal solution or the current number of iterations does not reach the preset iteration stop condition, the state of each goat individual is updated through exploration, optimization, and jumping, and the states are sorted from best to worst. All goat individuals are avoided according to the state sorting to generate a new goat population;

[0151] The new goat population is used as the current goat population, and the number of iterations is updated by 1. The fitness of each goat in the goat population is calculated according to the energy storage point fixed capacity optimization model to obtain the fitness of each goat individual.

[0152] The iteration stops until the current optimal solution is determined or the current number of iterations reaches the preset iteration stop condition, and the final optimal solution is output as the energy storage point and capacity optimization plan.

[0153] In some embodiments, the state of each goat individual is updated by exploration, optimization, and jumping, and the states are sorted from best to worst. All goat individuals are subjected to avoidance operations according to the state sorting to generate a new goat population, including:

[0154] The exploration phase for the optimal state is carried out by random movement of each goat individual; the exploration phase is expressed as:

[0155]

[0156] Where S is the goat's step length, x is a random number that obeys Gaussian distribution, is the state of the i-th goat at time t, is the state of the i-th goat at time t+1, is a set of random numbers, 、 are the upper and lower bounds of the established population respectively;

[0157] Each goat adopts a strategy close to the current optimal solution to enter the optimization stage; the optimization stage is expressed as:

[0158]

[0159] Where, To follow Uber's long is the optimal solution among all populations in time period t;

[0160] Each goat individual jumps out of the current group and goes to other parts to enter the jumping stage; the jumping stage is represented as:

[0161]

[0162] Where, is the jump step length, For any one of the goat individuals;

[0163] The goat individuals are sorted according to their fitness, and a preset number of goat individuals with low rankings are selected to return to the initial position to generate a new goat population.

[0164] like Figure 4As shown, an embodiment of the present application provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the distributed energy storage point layout and capacity determination method as in the above embodiment.

[0165] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the method for locating and sizing distributed energy storage as described in the above embodiment are implemented.

[0166] An embodiment of the present application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the steps of the method for locating and sizing distributed energy storage as described in the above embodiment.

[0167] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, electronic devices, and computer storage media can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0168] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or apparatuses.

[0169] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0170] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0171] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0172] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0173] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present invention via a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0174] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for locating and determining the capacity of distributed energy storage, characterized in that: include: The objective function is constructed with the optimization goals of minimizing energy storage construction and operation costs, minimizing load power shortage rate, and minimizing network branch fluctuation rate; Determining the constraints of the objective function according to the power balance state of the power system, and constructing an energy storage point and capacity optimization model based on the objective function and the constraints; The energy storage location and capacity optimization model is optimized and solved, and an energy storage location and capacity optimization plan is determined based on the optimal solution.

2. The method for distributing and sizing distributed energy storage according to claim 1, characterized in that: The objective function is: Where, 、 and are weight coefficients, is the total objective function value, For energy storage construction and operation costs, is the load power failure rate, is the network branch volatility; in, Where, is the total number of distributed energy storage units; and are the start and end times of maximum charge / discharge of the energy storage unit, respectively; is the charge / discharge power of the kth energy storage unit at time t; The optimal single-unit capacity for distributed energy storage; is the penalty factor; Where, is the power demand of the DC island microgrid at time t; 、 and are the power of load i, the output power of photovoltaic j and the output power of energy storage k at time t respectively; 、 are the number of loads and photovoltaics respectively; is the operation cycle; in, Where, represents the branch at time t m is the total number of branches, b is the decision variable for whether the branch corresponding to the candidate energy storage access point is connected to the energy storage, and B is the set of candidate energy storage access points.

3. The method for distributing and sizing distributed energy storage according to claim 1 or 2, characterized in that: The constraints include system energy balance constraints, energy storage energy balance constraints, energy storage power constraints and branch power constraints.

4. The method for distributing and sizing distributed energy storage according to claim 3, characterized in that: The step of optimizing and solving the energy storage location and capacity optimization model and determining the energy storage location and capacity optimization plan based on the optimal solution includes: The energy storage point location and capacity optimization model is optimized and solved based on the improved goat algorithm, and the energy storage point location and capacity optimization plan is determined according to the optimal solution.

5. The method for distributing and sizing distributed energy storage according to claim 4, characterized in that: The method of optimizing and solving the energy storage point distribution and capacity optimization model based on the improved goat algorithm and determining the energy storage point distribution and capacity optimization plan according to the optimal solution includes: Initializing a goat population, wherein individual goats in the goat population are obtained by encoding candidate energy storage point placement and capacity solutions; Calculating the fitness of each goat in the goat population according to the energy storage point distribution and constant capacity optimization model to obtain the fitness of each goat individual; Sorting the goat individuals according to their fitness, and determining the goat individual corresponding to the best fitness as the current optimal solution for the current number of iterations; Determine whether the current optimal solution or the current number of iterations reaches a preset iteration stop condition; If it is determined that the current optimal solution or the current number of iterations does not reach the preset iteration stop condition, the state of each goat individual is updated by exploration, optimization, and jumping, and the states are sorted from best to worst according to the states, and all goat individuals are avoided according to the state sorting to generate a new goat population; Using the new goat population as the current goat population, updating the number of iterations plus 1, and switching to the step of performing fitness calculation on each individual goat in the goat population according to the energy storage point distribution and constant capacity optimization model to obtain the fitness of each individual goat; Until it is determined that the current optimal solution or the current number of iterations reaches the preset iteration stop condition, the iteration stops and the final optimal solution is output as the energy storage point layout and capacity optimization plan.

6. The method for distributing and sizing distributed energy storage according to claim 5, characterized in that: The method updates the state of each goat individual by exploring, following the best, and jumping, and sorts the states from best to worst, performs avoidance operations on all goat individuals according to the state sorting, and generates a new goat population, including: The exploration phase for the optimal state is performed by random movement of each goat individual; wherein the exploration phase is expressed as: Where S is the goat's step length, x is a random number that obeys Gaussian distribution, is the state of the i-th goat at time t, is the state of the i-th goat at time t+1, is a set of random numbers, 、 are the upper and lower bounds of the established population respectively; Each goat individual adopts a strategy close to the current optimal solution to enter the optimization phase; wherein the optimization phase is represented as: Where, To follow Uber's long is the optimal solution among all populations in time period t; Each goat individual jumps out of the current group and goes to other parts to enter the jumping stage; wherein the jumping stage is expressed as: Where, is the jump step length, For any one of the goat individuals; The goat individuals are sorted according to their fitness, and a preset number of goat individuals with low rankings are selected to return to the initial positions to generate the new goat population.

7. A distributed energy storage system with fixed capacity and location, characterized in that: include: The optimization target determination module is used to construct an objective function with the optimization objectives of minimizing energy storage construction and operation costs, minimizing load power shortage rate, and minimizing network branch fluctuation rate; An optimization model determination module is used to determine the constraints of the objective function according to the power balance state of the power system, and to construct an energy storage point and capacity optimization model based on the objective function and the constraints; The energy storage point distribution and capacity optimization module is used to find the optimal solution for the energy storage point distribution and capacity optimization model and determine the energy storage point distribution and capacity optimization plan based on the optimal solution.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the method for locating and sizing distributed energy storage as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the method for locating and sizing distributed energy storage as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the steps of the distributed energy storage location and capacity determination method as described in any one of claims 1 to 6.

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