Energy storage system deployment optimization method and device based on starfish optimization algorithm

Abstract

The embodiment of the invention provides an energy storage system deployment optimization method and device based on a starfish optimization algorithm. The method is applied to the energy storage system deployment field. The method comprises the steps of establishing an energy storage system multi-objective optimization function and a constraint condition according to an optimization objective; under a constraint condition, solving a multi-objective optimization function of the energy storage system by utilizing a starfish optimization algorithm, and obtaining the position of a starfish individual with the lowest fitness in all starfish individual fitness when a preset iteration termination condition is met by iterating the positions and fitness of starfish individuals; and taking the position of the starfish individual with the lowest fitness as a final deployment scheme of the energy storage system. In this way, the deployment performance and optimization efficiency of the energy storage system are remarkably improved, the service life of energy storage system equipment is prolonged, cost minimization, system reliability and energy utilization rate maximization are achieved, and the optimization process is more flexible, more practical and more applicable.

Description

Technical Field

[0001] The present disclosure relates to the field of energy storage system deployment, and particularly to an optimization method and device for energy storage system deployment based on a starfish optimization algorithm. Background Art

[0002] With the construction of a new power system, the current field of energy storage system deployment optimization faces many problems and challenges. Existing technologies mostly use traditional optimization algorithms to optimize the deployment of energy storage systems, such as particle swarm optimization or genetic algorithms. These algorithms have limitations in dealing with complex multi-objective optimization problems, are prone to premature convergence, and it is difficult to balance the economy and reliability of energy storage systems simultaneously. In addition, the investment cost of energy storage systems is high, and there is a lack of an effective cost recovery mechanism, resulting in a long project return period, high investment risks for enterprises, low energy storage utilization in some areas, and even idle phenomena, serious resource waste. In terms of technical maturity and safety, there are problems such as inaccurate capacity measurement, unstable performance, and life attenuation in energy storage systems. At the same time, there is a lack of a unified performance evaluation standard and on-site detection equipment, making operation and maintenance difficult. In terms of the market mechanism, the energy storage business model is imperfect, the profit model is single, and there is a lack of an effective incentive mechanism. The root causes of these problems lie in the deficiencies of existing optimization algorithms, the constraints of economic costs, the imperfection of the market mechanism, and the lag of safety management technology.

[0003] To solve the above problems, it is urgent to develop a new optimization algorithm to optimize the deployment of energy storage systems. Summary of the Invention

[0004] The present disclosure provides an optimization method and device, equipment, and storage medium for energy storage system deployment based on a starfish optimization algorithm.

[0005] According to a first aspect of the present disclosure, there is provided an optimization method for energy storage system deployment based on a starfish optimization algorithm. The method includes: Establishing a multi-objective optimization function and constraint conditions for the energy storage system according to the optimization objective; Under the constraint conditions, using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, and by iterating the positions and fitness values of starfish individuals, obtaining the position of the starfish individual with the lowest fitness value among all starfish individual fitness values when the preset termination iteration condition is reached; Taking the position of the starfish individual with the lowest fitness value as the final deployment plan for the energy storage system.

[0006] In some implementable ways of the first aspect, the multi-objective optimization function of the energy storage system includes: ; Wherein, represents the cost function of the energy storage system, represents the reliability function of the energy storage system, Represents the energy utilization function of the energy storage system, , , respectively represent the initial investment cost, operation and maintenance cost, and energy conversion cost of the energy storage system, , respectively represent the availability and failure rate of the energy storage system, , , respectively represent the charging efficiency, discharging efficiency and energy recovery rate of the energy storage system.

[0007] In some realizable ways of the first aspect, under the constraint conditions, the starfish optimization algorithm is used to solve the multi-objective optimization function of the energy storage system, including: Initialize the configuration parameters of the energy storage system, set the feasible solution space according to the constraint conditions, and based on the configuration parameters, initialize the starfish individual positions within the feasible solution space; Determine the optimization dimension according to the optimization objective, select the corresponding search mode according to the optimization dimension to explore the initialized starfish individual positions, and update the starfish individual positions; Perform predation and regeneration operations on the starfish individuals with updated positions to obtain the positions of each starfish individual after regeneration; According to the multi-objective optimization function of the energy storage system, calculate the fitness of each starfish individual after regeneration, and use the positions of each starfish individual after regeneration as the initialized starfish individual positions to re-explore and update the starfish individual positions; Repeat the predation and regeneration operations, calculate the fitness, re-explore, and update the starfish individual positions until the preset termination iteration condition is reached, and obtain the final solution of the multi-objective optimization function of the energy storage system.

[0008] In some realizable ways of the first aspect, the starfish individual positions are initialized within the feasible solution space through the following formula: ; where, represents the position of the th starfish individual in the jth optimization dimension, represents a random number between (0, 1), and are respectively the upper and lower bounds of the starfish individual positions in the th optimization dimension.

[0009] In some realizable ways of the first aspect, performing predation and regeneration operations on the starfish individuals with updated positions to obtain the positions of each starfish individual after regeneration includes: Perform predation operations on the starfish individuals with updated positions and update the starfish individual positions again; Use the starfish individuals after the position is updated again as the regenerated individuals, perform the regeneration operation, and obtain the positions of each starfish individual after regeneration.

[0010] In some realizable ways of the first aspect, calculate the fitness of each starfish individual after regeneration through the following formula: ; Wherein, , , are the weight coefficients of the cost function, the weight coefficient of the reliability function, and the weight coefficient of the energy utilization rate function respectively, is the fitness of the starfish individual.

[0011] In some realizable ways of the first aspect, the method further includes: When calculating the fitness of each starfish individual after regeneration for the first time, set the weight coefficients of the cost function, the weight coefficient of the reliability function, and the weight coefficient of the energy utilization rate function according to the optimization objective.

[0012] In some realizable ways of the first aspect, the method further includes: During the process of using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, after each update of the starfish individual position and obtaining the positions of each starfish individual after regeneration, adjust the positions of the starfish individuals that do not meet the constraint conditions back to the feasible solution space.

[0013] According to the second aspect of the present disclosure, there is provided an optimization device for energy storage system deployment based on the starfish optimization algorithm. The device includes: An energy storage system deployment module for establishing a multi-objective optimization function and constraint conditions of the energy storage system according to the optimization objective; A deployment optimization module for solving the multi-objective optimization function of the energy storage system using the starfish optimization algorithm under the constraint conditions, and obtaining the position of the starfish individual with the lowest fitness among all the starfish individual fitnesses when reaching the preset termination iteration condition by iterating the position and fitness of the starfish individuals; A deployment plan acquisition module for using the position of the starfish individual with the lowest fitness as the final deployment plan of the energy storage system.

[0014] According to the third aspect of the present disclosure, there is provided an electronic device. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method as described above.

[0015] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method as described above.

[0016] In the present disclosure, an optimization method and device for energy storage system deployment based on a starfish optimization algorithm are proposed. The method includes establishing a multi-objective optimization function and constraint conditions for the energy storage system according to the optimization objective; under the constraint conditions, using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, and obtaining the position of the starfish individual with the lowest fitness among all starfish individual fitnesses when the preset termination iteration condition is reached by iterating the position and fitness of the starfish individuals; taking the position of the starfish individual with the lowest fitness as the final deployment plan for the energy storage system. In this way, the deployment performance and optimization efficiency of the energy storage system are significantly improved, the service life of the energy storage system equipment is extended, cost minimization, system reliability, and energy utilization rate maximization are achieved, and the optimization process is more flexible, more practical, and more applicable.

[0017] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where: Figure 1 shows a flowchart of an optimization method for energy storage system deployment based on a starfish optimization algorithm provided by an embodiment of the present disclosure; Figure 2 shows a structural diagram of an optimization device for energy storage system deployment based on a starfish optimization algorithm provided by an embodiment of the present disclosure; Figure 3 shows a structural diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0020] In addition, the term "and / or" in this text merely describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this text generally represents an "or" relationship between the associated objects before and after.

[0021] In view of the problems in the background art, the embodiments of the present disclosure propose an optimization method and device for energy storage system deployment based on the starfish optimization algorithm. The method includes establishing a multi-objective optimization function and constraint conditions for the energy storage system according to the optimization objective; under the constraint conditions, using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, and obtaining the position of the starfish individual with the lowest fitness among all starfish individuals' fitnesses when the preset termination iteration condition is reached by iterating the positions and fitnesses of the starfish individuals; taking the position of the starfish individual with the lowest fitness as the final deployment plan of the energy storage system. In this way, the deployment performance and optimization efficiency of the energy storage system are significantly improved, the service life of the energy storage system equipment is extended, the cost is minimized, the system reliability and energy utilization rate are maximized, and the optimization process is more flexible, more practical and applicable.

[0022] The following will combine the accompanying drawings and elaborate on the optimization method and device for energy storage system deployment based on the starfish optimization algorithm provided by the embodiments of the present disclosure through specific embodiments.

[0023] Figure 1 The flowchart of an optimization method for energy storage system deployment based on the starfish optimization algorithm provided by the embodiments of the present disclosure is shown. Method 100 includes the following steps: S110, establish a multi-objective optimization function and constraint conditions for the energy storage system according to the optimization objective.

[0024] In some embodiments, the multi-objective optimization function of the energy storage system includes: ; Wherein, represents the cost function of the energy storage system, represents the reliability function of the energy storage system, represents the energy utilization rate function of the energy storage system, , , respectively represent the initial investment cost, operation and maintenance cost, and energy conversion cost of the energy storage system, , respectively represent the availability and failure rate of the energy storage system, , , respectively represent the charging efficiency, discharging efficiency, and energy recovery rate of the energy storage system.

[0025] In some embodiments, , , The calculation formula of ; Wherein, , respectively represent the capacity and unit capacity cost of the th energy storage system, is the number of energy storage systems, , respectively represent the charging power and discharging power of all energy storage systems at time , respectively represent the unit costs of charging and discharging of all energy storage systems, is the charge-discharge time period, , respectively are the input energy and output energy of all energy storage systems at time , respectively represent the unit costs of the input energy and output energy of all energy storage systems.

[0026] In some embodiments, 、 The calculation formula of ; Wherein, , respectively represent the system power outage probability and system failure probability at time Furthermore, the system power outage probability includes the probability of at least one energy storage system having a power outage; The system failure probability includes the probability of at least one energy storage system failing.

[0027] In some embodiments, , , The calculation formula of ; Wherein, is the energy stored in all energy storage systems at time is the energy recovered by all energy storage systems at time is the energy wasted by all energy storage systems at time

[0028] In some embodiments, the constraint conditions include: Capacity constraint, charge and discharge power constraint, charge and discharge efficiency constraint, and system stability constraint; Further, the capacity constraint is that the capacity of each energy storage system should be greater than or equal to the minimum value of the capacity and less than or equal to the maximum value of the capacity; The charge and discharge power constraint is that the charge and discharge power of each energy storage system should be greater than or equal to the minimum value of the charge and discharge power and less than or equal to the maximum value of the charge and discharge power; The charge and discharge efficiency constraint is that the charge and discharge efficiency of each energy storage system should be greater than or equal to the minimum value of the charge and discharge efficiency and less than or equal to the maximum value of the charge and discharge efficiency; The system stability constraint is that the stability of each energy storage system should be greater than or equal to the preset stability threshold.

[0029] S120. Under the constraint conditions, use the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system. By iterating the positions and fitness values of the starfish individuals, obtain the position of the starfish individual with the lowest fitness value among all the fitness values of the starfish individuals when the preset termination iteration condition is reached.

[0030] In some embodiments, under the constraint conditions, using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system includes: Initialize the configuration parameters of the energy storage system, set the feasible solution space according to the constraint conditions, and based on the configuration parameters, initialize the positions of the starfish individuals within the feasible solution space; Determine the optimization dimension according to the optimization objective, select the corresponding search mode according to the optimization dimension to explore the initialized positions of the starfish individuals, and update the positions of the starfish individuals; Perform the predation and regeneration operations on the starfish individuals with updated positions to obtain the positions of each starfish individual after regeneration; According to the multi-objective optimization function of the energy storage system, calculate the fitness values of each starfish individual after regeneration, and use the positions of each starfish individual after regeneration as the initialized positions of the starfish individuals to re-explore and update the positions of the starfish individuals; Repeat the predation and regeneration operations, calculate the fitness values, re-explore, and update the positions of the starfish individuals until the preset termination iteration condition is reached, and obtain the final solution of the multi-objective optimization function of the energy storage system; Furthermore, when initializing the configuration parameters of the energy storage system, the parameters of the starfish optimization algorithm are also initialized. For example, the maximum number of iterations is set, and the boundary values of the starfish individual positions are determined. Among them, the boundary values of the starfish individual positions are determined according to the constraint conditions. After determining the boundary values of the starfish individual positions, the feasible solution space can be set according to the boundary values and the multi-objective optimization function of the energy storage system. The configuration parameters include capacity configuration, charge and discharge power configuration, and charge and discharge efficiency configuration. Initializing the configuration parameters of the energy storage system is equivalent to giving the starting point for the deployment optimization of each energy storage system. For example, the capacity of energy storage system A is initialized to 100 kWh, the charge and discharge power is initialized to 20 KW, and the charge and discharge efficiency is initialized to 90%. The capacity of energy storage system B is initialized to 90 kWh, the charge and discharge power is initialized to 25 KW, and the charge and discharge efficiency is initialized to 96%. Based on the initialized configuration parameters of energy storage systems A and B, the starfish individual positions can be initialized within the feasible solution space. One starfish individual position represents a deployment plan, that is, one starfish individual position represents a deployment plan for energy storage system A and energy storage system B.

[0031] In some embodiments, the starfish individual positions are initialized within the feasible solution space by the following formula: ; Where represents the position of the -th starfish individual in the j-th optimization dimension, represents a random number between (0, 1), and are the upper and lower bounds of the starfish individual position in the -th optimization dimension respectively; Furthermore, if the optimization objectives are system capacity, charge and discharge efficiency, charge and discharge power, and system stability, the optimization dimension is 4D, and these 4 dimensions are system capacity, charge and discharge efficiency, charge and discharge power, and system stability respectively.

[0032] In some embodiments, according to the optimization dimension, the corresponding search mode is selected to explore the initialized starfish individual positions and update the starfish individual positions, including: If the optimization dimension is less than or equal to 5, a one-dimensional search mode is adopted. If the optimization dimension is greater than 5, a five-dimensional search mode is adopted to explore the initialized starfish individual positions and update the starfish individual positions; Furthermore, in the one-dimensional search mode, the starfish individual positions are updated according to the following formula: ; Where , are the current position and the updated position of the starfish individual respectively, , They are the positions of two randomly selected starfish individuals in the p - dimension, , are two random numbers between (−1, 1) respectively, and p is the dimension randomly selected in the optimization dimension; In the five - dimensional search mode, the position of the starfish individual is updated according to the following formula: ; where, represents the best position of the starfish individual in the p - dimension, is a random number between (0, 1), , are the current iteration number and the maximum iteration number respectively, represents the random direction angle when the position of the starfish individual is updated, and it is an angle within the range of [0, π / 2].

[0033] In some embodiments, predation and regeneration operations are performed on the starfish individuals after position update to obtain the positions of each starfish individual after regeneration, including: Perform predation operations on the starfish individuals after position update and update the positions of the starfish individuals again; Take the starfish individuals after the positions are updated again as the regenerated individuals and perform regeneration operations to obtain the positions of each starfish individual after regeneration; Furthermore, when performing predation operations, a parallel two - way search strategy is adopted to update the positions of the starfish individuals, and the formula is as follows: ; where, represents the position of the starfish individual after being updated again, , are random numbers between (0, 1) respectively, represents the position of a randomly selected starfish individual in the p - dimension; This two - way search strategy can make the starfish individual approach the current best solution while avoiding falling into local optimal solutions; When performing regeneration operations, a special movement strategy is adopted to ensure the convergence of the algorithm, and the positions of each starfish individual after regeneration are calculated by the following formula: ; where, represents the position of the starfish individual after regeneration; As the number of iterations increases, the movement step of the starfish individual towards the current best solution gradually decreases, so as to ensure that the algorithm can search the solution space more finely in subsequent iterations and improve the convergence accuracy of the algorithm; By performing predation and regeneration operations, the balance between global exploration and local exploitation can be achieved, and the optimization efficiency and result quality can be significantly improved.

[0034] In some embodiments, the fitness of each starfish individual after regeneration is calculated by the following formula: ; wherein, , , are the weight coefficients of the cost function, the weight coefficient of the reliability function, and the weight coefficient of the energy utilization rate function respectively, is the fitness of the starfish individual; Furthermore, the fitness of the starfish individual is used to measure the comprehensive performance of the starfish individual under the current configuration scheme. The position of the starfish individual with the lowest fitness obtained through multiple iterations is the optimal configuration scheme.

[0035] In some embodiments, method 100 further includes: When calculating the fitness of each starfish individual after regeneration for the first time, set the weight coefficients of the cost function, the weight coefficient of the reliability function, and the weight coefficient of the energy utilization rate function according to the optimization objective; For example, assuming that the optimization objective is mainly to reduce costs, with improving energy utilization rate and system reliability as secondary considerations, the weight coefficient of the cost function should be larger than the weight coefficients of the reliability function and the energy utilization rate function. For example, the weight coefficient of the cost function can be 70%, the weight coefficient of the reliability function is 20%, and the weight coefficient of the energy utilization rate function is 10%. After setting the weight coefficients of the cost function, the reliability function, and the energy utilization rate function, keep the set weight coefficients unchanged during the subsequent iteration process for calculating the fitness of each starfish individual. Through multiple iterations, the starfish individual with the minimum fitness can be obtained.

[0036] In some embodiments, method 100 further includes: During the process of using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, after each update of the starfish individual position and obtaining the positions of each starfish individual after regeneration, adjust the positions of the starfish individuals that do not meet the constraint conditions back to the feasible solution space; Furthermore, in the exploration stage and the predation and regeneration stage, after each update of the starfish individual position and obtaining the positions of each starfish individual after regeneration, it is necessary to determine whether the updated starfish individual position and the positions of each starfish individual after regeneration meet the constraint conditions. For the positions of the starfish individuals that do not meet the constraint conditions, adjust them back to the feasible solution space by adjusting them to the nearest boundary value; By reasonably applying the constraint conditions, the efficiency of the optimization process and the practicality of the optimization results can be ensured.

[0037] In some embodiments, the preset termination iteration condition can be the maximum number of iterations or the change rate of the fitness of each current starfish individual is less than the preset threshold.

[0038] In some embodiments, the present disclosure comprehensively considers various factors (such as cost, system reliability, energy utilization rate, etc.), uses the starfish optimization algorithm to obtain the optimal deployment plan, and makes the optimization process more flexible and adaptable by dynamically adjusting the weight coefficients of the cost function, reliability function, and energy utilization rate function, so as to achieve the dual optimization of economy and reliability. This optimization method can significantly reduce the investment and operation costs of the energy storage system, improve the overall performance and service life of the system, and is particularly suitable for the intelligent and efficient configuration requirements of energy storage devices in modern power systems. In addition, in the starfish optimization algorithm, information sharing occurs among starfish individuals to collaboratively optimize the quality of the solution. In each iteration, the information of the current best solution is transmitted to other starfish individuals, enabling them to adjust their positions with reference to a better configuration plan, thereby accelerating the convergence speed of the algorithm and enhancing the global search ability. This efficient search mechanism can quickly handle large-scale complex problems, significantly improving the efficiency and performance of the energy storage system deployment, and providing a more efficient, more economical, and more reliable solution for the optimal configuration of the energy storage system.

[0039] S130, take the position of the starfish individual with the lowest fitness as the final deployment plan of the energy storage system.

[0040] According to an embodiment of the present disclosure, an optimization method and device for the deployment of an energy storage system based on the starfish optimization algorithm are proposed. The method includes establishing a multi-objective optimization function and constraint conditions for the energy storage system according to the optimization objective; under the constraint conditions, using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, and obtaining the position of the starfish individual with the lowest fitness among the fitness values of all starfish individuals when the preset termination iteration condition is reached by iterating the positions and fitness values of the starfish individuals; taking the position of the starfish individual with the lowest fitness as the final deployment plan of the energy storage system. In this way, the deployment performance and optimization efficiency of the energy storage system are significantly improved, the service life of the energy storage system equipment is extended, cost minimization, system reliability, and energy utilization rate maximization are achieved, and the optimization process is more flexible, practical, and applicable.

[0041] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0042] The above is the introduction of the method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0043] Figure 2The structure diagram of an optimization device for energy storage system deployment based on the starfish optimization algorithm provided by an embodiment of the present disclosure is shown. The device 200 includes: An energy storage system deployment module 210, configured to establish a multi-objective optimization function and constraint conditions of the energy storage system according to the optimization objective.

[0044] In some embodiments, the energy storage system deployment module 210 is specifically configured to: The multi-objective optimization function of the energy storage system includes: ; Wherein, represents the cost function of the energy storage system, represents the reliability function of the energy storage system, represents the energy utilization rate function of the energy storage system, , , respectively represent the initial investment cost, operation and maintenance cost, and energy conversion cost of the energy storage system, , respectively represent the availability and failure rate of the energy storage system, , , respectively represent the charging efficiency, discharging efficiency, and energy recovery rate of the energy storage system.

[0045] A deployment optimization module 220, configured to solve the multi-objective optimization function of the energy storage system by using the starfish optimization algorithm under the constraint conditions, and obtain the position of the starfish individual with the lowest fitness among all the starfish individual fitnesses when the preset termination iteration condition is reached by iterating the position and fitness of the starfish individuals.

[0046] In some embodiments, the deployment optimization module 220 is specifically configured to: Under the constraint conditions, solve the multi-objective optimization function of the energy storage system by using the starfish optimization algorithm, including: Initialize the configuration parameters of the energy storage system, set the feasible solution space according to the constraint conditions, and initialize the positions of the starfish individuals within the feasible solution space based on the configuration parameters; Determine the optimization dimension according to the optimization objective, select the corresponding search mode according to the optimization dimension to explore the initialized positions of the starfish individuals, and update the positions of the starfish individuals; Perform predation and regeneration operations on the starfish individuals with updated positions to obtain the positions of each starfish individual after regeneration; According to the multi-objective optimization function of the energy storage system, calculate the fitness of each starfish individual after regeneration, and use the positions of each starfish individual after regeneration as the initialized positions of the starfish individuals to re-explore and update the positions of the starfish individuals; Repeat the predation and regeneration operations, calculate the fitness, re - conduct exploration, and update the positions of starfish individuals until the preset termination iteration condition is reached, and obtain the final solution of the multi - objective optimization function of the energy storage system.

[0047] In some embodiments, the deployment optimization module 220 is further specifically configured to: Initialize the positions of starfish individuals in the feasible solution space through the following formula: ; Wherein, represents the position of the th starfish individual in the j - th optimization dimension, represents a random number between (0, 1), and are respectively the upper and lower bounds of the position of the starfish individual in the th optimization dimension.

[0048] In some embodiments, the deployment optimization module 220 is further specifically configured to: Perform predation and regeneration operations on the starfish individuals after position update, and obtain the positions of each starfish individual after regeneration, including: Perform predation operations on the starfish individuals after position update, and update the positions of the starfish individuals again; Use the starfish individuals after the positions are updated again as the regeneration individuals, perform regeneration operations, and obtain the positions of each starfish individual after regeneration.

[0049] In some embodiments, the deployment optimization module 220 is further specifically configured to: Calculate the fitness of each starfish individual after regeneration through the following formula: ; Wherein, , , are respectively the weight coefficients of the cost function, the weight coefficient of the reliability function, and the weight coefficient of the energy utilization rate function, is the fitness of the starfish individual.

[0050] In some embodiments, the device 200 is further specifically configured to: When calculating the fitness of each starfish individual after regeneration for the first time, set the weight coefficients of the cost function, the weight coefficient of the reliability function, and the weight coefficient of the energy utilization rate function according to the optimization objectives.

[0051] In some embodiments, the device 200 is further specifically configured to: During the process of using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, after each update of the starfish individual positions and obtaining the positions of each starfish individual after regeneration, the positions of the starfish individuals that do not meet the constraint conditions are adjusted back to the feasible solution space.

[0052] The deployment scheme acquisition module 230 is configured to use the position of the starfish individual with the lowest fitness as the final deployment scheme of the energy storage system.

[0053] It can be understood that Figure 2 Each module / unit in the device 200 shown has the functions of implementing the respective steps in the method 100 provided in the embodiments of the present disclosure and can achieve their corresponding technical effects. For the sake of brevity, they will not be described in detail herein.

[0054] Figure 3 The structure diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure is shown. The electronic device 300 is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 300 can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0055] As Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 302 or the computer program loaded from the storage unit 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The I / O interface 305 is also connected to the bus 304.

[0056] Multiple components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disc, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0057] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be executed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute method 100 in any other suitable manner (e.g., by means of firmware).

[0058] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0059] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0060] In the context of the present disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0061] It should be noted that the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute method 100 and achieve the corresponding technical effects achieved by the method of the embodiments of the present disclosure. For the sake of concise description, details are not repeated herein.

[0062] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the 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 input, speech input, or tactile input).

[0063] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein ), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of the communication network include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0064] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0065] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in this disclosure can be achieved. This is not limited herein.

[0066] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. 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 substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. An optimization method for energy storage system deployment based on the starfish optimization algorithm, characterized in that Including: Establish a multi-objective optimization function and constraint conditions for the energy storage system according to the optimization objective; Under the constraint conditions, use the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system. By iterating the positions and fitness values of starfish individuals, obtain the position of the starfish individual with the lowest fitness among all starfish individual fitness values when the preset termination iteration condition is reached; Take the position of the starfish individual with the lowest fitness as the final deployment plan of the energy storage system.

2. The method according to claim 1, wherein The multi-objective optimization function of the energy storage system includes: ； Among them, represents the cost function of the energy storage system, represents the reliability function of the energy storage system, represents the energy utilization efficiency function of the energy storage system, , , respectively represent the initial investment cost, operation and maintenance cost, and energy conversion cost of the energy storage system, , respectively represent the availability and failure rate of the energy storage system, , , respectively represent the charging efficiency, discharging efficiency and energy recovery rate of the energy storage system.

3. The method according to claim 2, wherein Under the constraint conditions, using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system includes: Initialize the configuration parameters of the energy storage system, set the feasible solution space according to the constraint conditions, and based on the configuration parameters, initialize the positions of starfish individuals within the feasible solution space; Determine the optimization dimension according to the optimization objective, and select the corresponding search mode according to the optimization dimension to explore the initialized positions of starfish individuals and update the positions of starfish individuals; Perform predation and regeneration operations on the starfish individuals with updated positions to obtain the positions of each starfish individual after regeneration; According to the multi-objective optimization function of the energy storage system, calculate the fitness of each starfish individual after regeneration, and use the positions of each starfish individual after regeneration as the initialized positions of starfish individuals to re-explore and update the positions of starfish individuals; Repeat the predation and regeneration operations, calculate the fitness, re-explore, and update the positions of starfish individuals until the preset termination iteration condition is reached to obtain the final solution of the multi-objective optimization function of the energy storage system.

4. The method according to claim 3, characterized in that, Initialize the positions of starfish individuals within the feasible solution space through the following formula: ； Among them, represents the position of the th starfish individual in the j - th optimization dimension, represents a random number between (0, 1), and are respectively the upper and lower bounds of the position of the starfish individual in the th optimization dimension.

5. The method according to claim 3, characterized in that, The performing predation and regeneration operations on the starfish individuals with updated positions to obtain the positions of each starfish individual after regeneration includes: Perform predation operations on the starfish individuals with updated positions and update the positions of starfish individuals again; Take the starfish individuals with positions updated again as the regeneration individuals and perform regeneration operations to obtain the positions of each starfish individual after regeneration.

6. The method according to claim 3, characterized in that, Calculate the fitness of each starfish individual after regeneration through the following formula: ； Among them, , , are the weight coefficients of the cost function, the weight coefficient of the reliability function, and the weight coefficient of the energy utilization function respectively, is the fitness of the starfish individual.

7. The method according to claim 6, wherein The method further includes: When calculating the fitness of each starfish individual after regeneration for the first time, set the weight coefficients of the cost function, the reliability function, and the energy utilization rate function according to the optimization objective.

8. The method according to claim 3, characterized in that, The method further includes: During the process of using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system, after each update of the positions of starfish individuals and obtaining the positions of each starfish individual after regeneration, adjust the positions of starfish individuals that do not meet the constraint conditions back to the feasible solution space.

9. An energy storage system deployment optimization device based on the starfish optimization algorithm, characterized in that, Including: An energy storage system deployment module for establishing a multi-objective optimization function and constraint conditions for the energy storage system according to the optimization objective; A deployment optimization module for using the starfish optimization algorithm to solve the multi-objective optimization function of the energy storage system under the constraint conditions. By iterating the positions and fitness values of starfish individuals, obtain the position of the starfish individual with the lowest fitness among all starfish individual fitness values when the preset termination iteration condition is reached; A deployment plan acquisition module for taking the position of the starfish individual with the lowest fitness as the final deployment plan of the energy storage system.

10. An electronic device, characterized in that, Including: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-8.

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