Parameter optimization method and device of energy storage system, computer equipment and storage medium

Abstract

The invention relates to a parameter optimization method and device of an energy storage system, computer equipment and a storage medium. The method comprises the steps of obtaining a target function used for reflecting a relationship between a to-be-optimized configuration parameter in the energy storage system and a net income of the energy storage system; at least two substations in the energy storage system can perform electric energy coordination based on a power fusion system in the energy storage system; and determining target configuration parameters according to the target function by taking the power constraint condition and the charge state constraint condition of the energy storage system as constraints and taking the maximum net income of the energy storage system as a target. According to the scheme, the at least two substations in the energy storage system can carry out electric energy coordination based on the power fusion system in the energy storage system, cooperative regulation and control of the multiple substations are achieved, the target configuration parameters of the energy storage system are obtained by solving the target function, the net income of the energy storage system can be maximized, and the energy storage efficiency is improved. And the benefit of the energy storage system is improved.

Description

Technical Field

[0001] This application relates to the field of railway technology, and in particular to a method, apparatus, computer equipment, and storage medium for optimizing parameters of an energy storage system. Background Technology

[0002] With the rapid increase in railway mileage, it is crucial to reduce the energy consumption of railway traction power supply systems and improve their efficiency.

[0003] In traditional technologies, the parameters of the traction power supply system of a single substation are usually adjusted to improve the efficiency of the traction power supply system. However, the control capability of the traction power supply system of a single substation is limited, resulting in low efficiency of the traction power supply system. Summary of the Invention

[0004] Therefore, it is necessary to provide a parameter optimization method, device, computer equipment, and storage medium for an energy storage system to address the aforementioned technical problems, thereby improving the efficiency of the traction power supply system.

[0005] Firstly, this application provides a parameter optimization method for an energy storage system, wherein at least two substations in the energy storage system are capable of power coordination based on a power sharing system within the energy storage system; including:

[0006] Obtain the objective function; wherein the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power facilitation system and the rated power of the converter in the power facilitation system, and the power threshold for adjusting the maximum power demand of the substation;

[0007] Using the power constraints and state of charge constraints of the energy storage system as constraints, and taking the maximization of the net benefit of the energy storage system as the objective, the target configuration parameters are determined according to the objective function.

[0008] In one embodiment, the step of determining the target configuration parameters based on the objective function, with the power constraints and state of charge constraints of the energy storage system as constraints and the goal of maximizing the net benefit of the energy storage system, includes:

[0009] Using the power constraints and state of charge constraints of the energy storage system as constraints, an initial solution set of the configuration parameters to be optimized is randomly generated;

[0010] For each initial solution in the initial solution set, determine the unit cost and unit revenue of the energy storage system under the initial solution, and based on the objective function, determine the objective function value corresponding to the initial solution according to the unit cost and the unit revenue;

[0011] With the goal of maximizing the net benefit of the energy storage system, the initial solution set is iteratively optimized based on the objective function value corresponding to each initial solution to obtain the target configuration parameters.

[0012] In one embodiment, the step of iteratively optimizing the initial solution set based on the objective function values ​​corresponding to each initial solution to obtain the target configuration parameters includes:

[0013] For each round of optimization, obtain the current iteration number;

[0014] If the current iteration number is less than the maximum iteration number, cross-mutation and cross-recombination are performed on each initial solution in the initial solution set corresponding to this round to obtain a candidate solution set;

[0015] Based on the objective function, the objective function value corresponding to each candidate solution is determined according to the unit cost and the unit revenue corresponding to each candidate solution in the candidate solution set;

[0016] Based on the relationship between the objective function value of each initial solution in the current round and the objective function value of each candidate solution in the current round, a candidate solution set is selected from the current round's initial solution set or candidate solution set, and the candidate solution set is used as the initial solution set for the next round of iterative optimization.

[0017] If the current iteration number is equal to the maximum iteration number, the initial solution corresponding to the maximum objective function value in the initial solution set of this round is taken as the target configuration parameter;

[0018] The initial solution set for the first round is a randomly generated initial solution set.

[0019] In one embodiment, the step of selecting a candidate solution set from the initial solution set or candidate solution set corresponding to the current round based on the relationship between the objective function value corresponding to each initial solution in the current round and the objective function value corresponding to each candidate solution includes:

[0020] Determine the comparison combination; wherein, each comparison combination includes a candidate solution and an initial solution from the initial solution set corresponding to the current round; the initial solutions in each comparison combination are different, and the candidate solutions in each comparison combination are different;

[0021] The initial solution or candidate solution with the larger objective function value in each comparison combination is taken as the alternative solution;

[0022] The combination of each alternative solution is taken as the alternative solution set.

[0023] In one embodiment, the power constraint includes:

[0024] The discharge power and charging power of the energy storage unit are within the rated power range of the energy storage unit;

[0025] The instantaneous transmission power of the converter is less than or equal to the rated power of the converter.

[0026] In one embodiment, the state of charge constraint includes:

[0027] The state of charge of the energy storage unit is between the lower limit of the state of charge of the energy storage unit and the upper limit of the state of charge of the energy storage unit.

[0028] Secondly, this application also provides a parameter optimization device for an energy storage system, wherein at least two substations in the energy storage system are capable of power coordination based on a power sharing system within the energy storage system; including:

[0029] An acquisition module is used to acquire an objective function; wherein the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power facilitation system and the rated power of the converter in the power facilitation system, and the power threshold for adjusting the maximum power demand of the substation;

[0030] The solution module is used to determine the target configuration parameters based on the objective function, taking the power constraints and state of charge constraints of the energy storage system as constraints and the goal of maximizing the net benefit of the energy storage system.

[0031] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0032] Obtain the objective function; wherein the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power facilitation system and the rated power of the converter in the power facilitation system, and the power threshold for adjusting the maximum power demand of the substation;

[0033] Using the power constraints and state of charge constraints of the energy storage system as constraints, and taking the maximization of the net benefit of the energy storage system as the objective, the target configuration parameters are determined according to the objective function.

[0034] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0035] Obtain the objective function; wherein the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power facilitation system and the rated power of the converter in the power facilitation system, and the power threshold for adjusting the maximum power demand of the substation;

[0036] Using the power constraints and state of charge constraints of the energy storage system as constraints, and taking the maximization of the net benefit of the energy storage system as the objective, the target configuration parameters are determined according to the objective function.

[0037] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0038] Obtain the objective function; wherein the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power facilitation system and the rated power of the converter in the power facilitation system, and the power threshold for adjusting the maximum power demand of the substation;

[0039] Using the power constraints and state of charge constraints of the energy storage system as constraints, and taking the maximization of the net benefit of the energy storage system as the objective, the target configuration parameters are determined according to the objective function.

[0040] The aforementioned energy storage system parameter optimization method, device, computer equipment, and storage medium obtain an objective function. The objective function reflects the relationship between the configuration parameters to be optimized and the net benefit of the energy storage system. At least two substations in the energy storage system can coordinate power based on the power sharing system within the system. The configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage units, the start-up threshold of the power sharing system, the rated power of the converter in the power sharing system, and the power threshold for adjusting the maximum power demand of the substations. Using the power constraints and state-of-charge constraints of the energy storage system as constraints, and aiming to maximize the net benefit of the energy storage system, the target configuration parameters are determined according to the objective function. In this scheme, at least two substations in the energy storage system can coordinate power based on the power sharing system, achieving coordinated control of multiple substations. Furthermore, by solving the objective function to obtain the target configuration parameters of the energy storage system, the net benefit of the energy storage system can be maximized, thereby improving the efficiency of the energy storage system. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating a parameter optimization method for an energy storage system in one embodiment;

[0043] Figure 2 This is a flowchart illustrating the process of determining target configuration parameters in one embodiment;

[0044] Figure 3 This is a schematic diagram of the optimization solution framework of the difference optimization algorithm in one embodiment;

[0045] Figure 4 This is a flowchart illustrating the process of determining target configuration parameters in another embodiment;

[0046] Figure 5 This is a flowchart illustrating a parameter optimization method for an energy storage system in another embodiment;

[0047] Figure 6 This is a structural block diagram of a parameter optimization device for an energy storage system in one embodiment;

[0048] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] The parameter optimization method for energy storage systems provided in this application can be applied to the parameter optimization of energy storage systems that support railway train operation.

[0051] This method can be executed by a server or a terminal. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses.

[0052] In one exemplary embodiment, such as Figure 1 As shown, a parameter optimization method for an energy storage system is provided. Taking the application of this method to a server as an example, at least two substations in the energy storage system can coordinate power based on the power sharing system within the energy storage system. The method includes the following steps:

[0053] S101, Obtain the target function.

[0054] Among them, the energy storage system can be understood as a multi-substation collaborative energy storage system, which includes at least two substations, energy storage units, a power conditioner (RPC) system, and a traction network, and is used to realize the cross-substation recovery and utilization of regenerative braking energy and the smoothing of peak traction loads.

[0055] The Power Coordination System (RPC) consists of at least two converters and a DC bus / AC tie line. It is the core equipment for breaking down the electrical boundaries between substations and realizing cross-substation energy allocation.

[0056] The rated capacity of the energy storage unit measures the total amount of energy that the energy storage device can store; the charge / discharge start-up power of the energy storage unit measures the power threshold at which the energy storage unit begins charging or discharging. The start-up threshold of the power synergy system is the power condition for the power synergy system to coordinate power supply to at least two substations; the rated power of the converter in the power synergy system is the maximum instantaneous transmission power that the converter can withstand; the power threshold for adjusting the maximum power demand of a substation is used to limit the maximum power that a substation can purchase from the grid.

[0057] For example, an objective function can be constructed based on the total cost and total revenue of the energy storage system over its entire life cycle.

[0058] The total cost over the entire life cycle includes construction costs, ongoing costs, replacement costs, and residual value costs.

[0059] The construction cost includes the expenses for equipment purchase, installation, and infrastructure construction required to build the energy storage system. The construction cost primarily depends on the power rating of the energy storage system and can be expressed as:

[0060]

[0061] In the formula, C CAP For the construction cost of energy storage systems, and These represent the construction costs of the RPC and the energy storage unit, respectively. and These represent the unit power price of the RPC and the energy storage unit, respectively. This indicates the price per unit of energy in the energy storage medium. For the design capacity of RPC, The rated power of the energy storage unit. This refers to the rated capacity of the energy storage unit.

[0062] System equipment purchase cost This includes the purchase cost of the DC / DC converters required for energy storage, as well as the system integration cost of the entire energy storage system. The calculation method is as follows:

[0063]

[0064] In the formula, Cost of purchasing equipment per unit power.

[0065] Continuous costs encompass the energy loss and maintenance costs incurred during the operation of the energy storage system. Since these costs occur at different stages of the energy storage system's entire lifecycle, a discount rate is needed to discount its future cash flows to their present value, hence they can be expressed as:

[0066]

[0067] In the formula, For the ongoing costs of energy storage systems, This indicates the total daily operating losses of the energy storage system; and These represent the unit prices of energy loss cost and operation and maintenance cost, respectively; T represents the total life cycle time of the energy storage system, in years; i represents the service life of the energy storage system. This indicates the annual growth rate of operation and maintenance costs; This represents the discount rate.

[0068] The traction power supply system frequently switches between traction and regenerative braking loads, causing the energy storage medium to continuously undergo charge-discharge state transitions. The traction power supply system includes substations, the traction network, and electric locomotives. Given that converters typically have a design life exceeding 20 years, while the energy storage medium has a limited lifespan, the replacement cost of the energy storage medium must be included in the life-cycle cost analysis. The replacement cost C of the energy storage medium is... REP It can be represented as:

[0069]

[0070]

[0071] In the formula, and These represent the number of charge-discharge cycles per day and throughout the entire lifespan of the energy storage medium, respectively. This represents the total number of permutations; j is the j-th permutation. This indicates the cycle life of the energy storage medium.

[0072] Residual value cost refers to the remaining value of an energy storage system at the end of its entire life cycle. Residual value cost mainly depends on the remaining lifespan of the energy storage medium, and can therefore be expressed as:

[0073]

[0074]

[0075] In the formula, The residual value cost of the energy storage system, This indicates the remaining lifespan of the energy storage medium.

[0076] The total cost of an energy storage system over its entire lifecycle is expressed as C. RBESS :

[0077]

[0078] The total benefits of an energy storage system throughout its entire life cycle include energy savings and peak shaving benefits.

[0079] The energy-saving benefits stem from the recovery and utilization of regenerative braking energy. This measure effectively reduces the amount of electricity purchased from the grid by the energy storage system, thereby reducing electricity costs and additional electricity charges. The calculation formula is as follows:

[0080]

[0081] In the formula, Indicates the unit price of energy-saving benefits; This indicates the sampling time interval for energy calculation; for example, it can be set to 1 second. This represents the optimized power on the grid side at time t. This represents the unoptimized power on the grid side at time t.

[0082] The discharge of energy storage units can effectively smooth peak loads in the traction power supply system, thereby reducing maximum demand and lowering basic electricity charges. Given that most substations use the maximum demand method for billing, peak shaving revenue B... DC Its calculation model is as follows:

[0083]

[0084] In the formula, This indicates the unit price of peak shaving revenue; and These represent the start time of the maximum demand of the traction power supply system.

[0085] Total revenue R over the entire life cycle of an energy storage system RBESS It can be represented as:

[0086]

[0087] The objective function expression is as follows:

[0088]

[0089] The objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system. The configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage units in the energy storage system, the start-up threshold of the power transfer system and the rated power of the converter in the power transfer system, as well as the power threshold for adjusting the maximum power demand of the substation.

[0090] It is understandable that the configuration parameters to be optimized in an energy storage system will affect the total cost and total revenue of the energy storage system throughout its entire life cycle, and thus affect the net revenue of the energy storage system. Therefore, the expression of the objective function can be transformed into:

[0091]

[0092] This expression is illustrated using a power fusion system comprising two converters as an example, where, This refers to the rated capacity of the energy storage unit in the energy storage system. For example, the energy storage unit can be a supercapacitor. This refers to the rated capacitance of the supercapacitor. The starting power for charging and discharging of the energy storage unit, The starting threshold for the power fusion system. This is a power threshold used to regulate the maximum electrical energy demand of a substation. , The rated power is the first converter and the second converter.

[0093] S102, taking the power constraints and state of charge constraints of the energy storage system as constraints, and taking the maximization of the net benefit of the energy storage system as the objective, the target configuration parameters are determined according to the objective function.

[0094] Among them, the power constraint condition is used to limit the power output and transmission range of the energy storage unit and RPC converter to avoid equipment overload damage. The state of charge constraint condition is used to limit the state of charge range of the energy storage unit to avoid overcharging and over-discharging and extend the life of the energy storage medium; the state of charge refers to the ratio of the energy currently stored in the energy storage unit to its rated capacity.

[0095] For example, the power constraint conditions include that the discharge power and charging power of the energy storage unit are within the rated power range of the energy storage unit, and that the instantaneous transmission power of the converter is less than or equal to the rated power of the converter. Taking two RPC converters as an example, the power constraint condition expression is as follows:

[0096]

[0097] in, Let be the instantaneous power of the energy storage unit at time t. The instantaneous power of the RPC1 converter. This refers to the rated power of the RPC1 converter. The instantaneous power of the RPC1 converter. This is the rated power of the RPC1 converter.

[0098] For example, the state of charge (SOC) constraint includes: the SOC of the energy storage unit lies between the lower limit of the SOC and the upper limit of the SOC. The expression is as follows:

[0099]

[0100] in, Let be the state of charge of the energy storage unit at time t. This is the minimum value for the state of charge of the energy storage unit. This represents the upper limit of the state of charge of the energy storage unit.

[0101] For example, the power constraints and state of charge constraints of the energy storage system can be used as constraints, and the goal of maximizing the net benefit of the energy storage system can be achieved. The objective function can be solved using the differential evolution algorithm to obtain the target configuration parameters. Then, the configuration parameters of the energy storage system can be set as the target configuration parameters to maximize the net benefit of the energy storage system.

[0102] The above-mentioned parameter optimization method for energy storage systems obtains an objective function. This objective function reflects the relationship between the configuration parameters to be optimized and the net benefit of the energy storage system. At least two substations within the energy storage system can coordinate power usage based on the power sharing system within the system. The configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage units, the start-up threshold of the power sharing system, the rated power of the converters in the power sharing system, and the power threshold for adjusting the maximum power demand of the substations. Using the power constraints and state-of-charge constraints of the energy storage system as constraints, and aiming to maximize the net benefit of the energy storage system, the target configuration parameters are determined according to the objective function. In this scheme, at least two substations within the energy storage system can coordinate power usage based on the power sharing system, achieving coordinated control of multiple substations. Furthermore, by solving the objective function to obtain the target configuration parameters of the energy storage system, the net benefit of the energy storage system can be maximized, thereby improving the efficiency of the energy storage system.

[0103] In some alternative implementations, see [link to relevant documentation]. Figure 2 , Figure 2 A flowchart for determining target configuration parameters is provided, which includes the following steps:

[0104] S201, using the power constraints and state of charge constraints of the energy storage system as constraints, randomly generates an initial solution set of the configuration parameters to be optimized.

[0105] For example, core parameters such as population size, maximum number of iterations, mutation factor, and crossover probability can be preset. These parameters determine the search range and efficiency of the algorithm. For instance, a larger population results in greater diversity of the initial solution set; the maximum number of iterations determines the upper limit of iteration convergence; and the mutation factor and crossover probability control the intensity of mutation and crossover operations, affecting the speed of solution updates.

[0106] An initial set of optimization parameters can be randomly generated using the power and state-of-charge constraints of the energy storage system as constraints. Each initial solution is a combination of values ​​for the parameters to be optimized. The initial solution set needs to cover the parameters within a reasonable range under the power and state-of-charge constraints to ensure the diversity of the solution set and lay the foundation for global optimization.

[0107] S202, for each initial solution in the initial solution set, determine the unit cost and unit revenue of the energy storage system under the initial solution, and based on the objective function, determine the objective function value corresponding to the initial solution according to the unit cost and unit revenue.

[0108] For example, unit cost and unit revenue refer to the cost and revenue per unit of time. The unit of time can be set according to the actual situation, such as per day.

[0109] For each initial solution in the initial solution set, the daily operation performance of the energy storage system can be simulated by combining the substation's measured second-level load data and energy management strategies. Specifically, the rated capacity of the energy storage unit determines the maximum energy it can store, affecting the amount of regenerative braking energy recovered and the peak load smoothing capability; the rated power of the converter in the power sharing system determines the maximum capacity for inter-substation power sharing, affecting the efficiency of energy allocation between the two substations; the rated capacity and charging / discharging start-up power of the energy storage unit determine when the energy storage begins charging / discharging, affecting the timing of regenerative energy capture and peak compensation; the start-up threshold of the power sharing system determines when inter-substation power sharing is triggered, affecting the timeliness of energy allocation; the power threshold used to regulate the substation's maximum energy demand determines when peak shaving is triggered, affecting the maximum demand smoothing effect; finally, key indicators such as energy recovery, peak load, and operating losses are output, which are the direct basis for calculating costs and benefits.

[0110] Then, the simulation results, namely key indicators such as energy recovery, peak load, and operating losses, are substituted into the objective function to calculate the daily cost and daily revenue, and then the objective function value corresponding to the initial solution is calculated, that is, the daily net revenue.

[0111] S203 aims to maximize the net benefit of the energy storage system by iteratively optimizing the initial solution set based on the objective function values ​​corresponding to each initial solution to obtain the target configuration parameters.

[0112] Furthermore, the solution set can be iteratively updated using the mutation, crossover, and selection operations of the differential evolution algorithm, with the objective function value as the criterion, until the maximum number of iterations is reached, and the optimal solution, i.e., the objective configuration parameters, is output.

[0113] For example, see Figure 3 , Figure 3 A schematic diagram of the optimization solution framework for the difference optimization algorithm is provided, which specifically includes the following steps:

[0114] Step 1: Randomly generate the initial solution set.

[0115] Generate an initial solution set containing multiple initial solutions, where each initial solution corresponds to a combination of values ​​for the parameter to be optimized. The population size is N. p .

[0116] Step 2: Calculate the fitness values ​​of individuals in the initial solution set.

[0117] For each initial solution in the initial solution set, substitute the parameters, simulate the running effect, calculate its net benefit, and use it as the fitness of the initial solution to measure the quality of the initial solution.

[0118] Step 3: Determine whether the termination criteria are met.

[0119] The termination criterion is usually that the number of iterations reaches the maximum number of iterations G. max If the condition is met, the algorithm stops and saves the optimal solution; otherwise, it continues with subsequent iterations.

[0120] Step 4: Iterative optimization.

[0121] Mutation: For an initial solution, a mutated solution is generated by linear combination of other initial solutions in the initial solution set.

[0122] Crossover: Cross the mutated solution with the initial solution along the parameter dimension to generate candidate solutions and increase the diversity of solutions.

[0123] Step 5: Calculate and select the fitness values ​​of the experimental individuals.

[0124] Competitive selection: Calculate the fitness (net benefit) of candidate solutions and compare it with the fitness of the original solution, retaining the better solution as a candidate solution.

[0125] Step 6: Traverse the initial solution set.

[0126] If l=N p This indicates that all solutions in the current round have been processed, the iteration count t is incremented by 1, and the process returns to step three; if l <N p If l is incremented by 1, then we continue processing the next solution.

[0127] Step 7: Save the optimal solution and the algorithm stops.

[0128] When the termination criterion is met, the solution with the highest fitness (net benefit) is selected from the final initial solution set as the optimal solution, i.e., the target configuration parameter, and the algorithm ends.

[0129] In some alternative implementations, see [link to relevant documentation]. Figure 4 , Figure 4 An alternative flowchart for obtaining target configuration parameters is provided, which includes the following steps:

[0130] S401: For each iteration of optimization, obtain the current iteration number.

[0131] For example, the initial iteration count is 1, and the iteration count is incremented by 1 after each mutation, crossover, or selection operation. At the start of each iteration, the current iteration count for that round can be obtained first.

[0132] S402, when the current iteration number is less than the maximum iteration number, perform cross-mutation and cross-recombination on each initial solution in the initial solution set corresponding to this round to obtain a candidate solution set.

[0133] Determine if the current iteration count equals the maximum iteration count. If the current iteration count is less than the maximum iteration count, perform cross-mutation and cross-recombination on the initial solutions in the current round's initial solution set to obtain a candidate solution set. The initial solution set for the first round is a randomly generated initial solution set.

[0134] For example, a new solution can be generated by linearly combining existing solutions in the population, such as new solution = solution 1 + F × (solution 2 - solution 3), to explore new parameter combinations. For example, the rated capacity of the energy storage unit or the rated power of the converter in the energy storage system can be changed. Here, F is the mutation factor.

[0135] Furthermore, the mutated solution is cross-operated with the original solution along its dimensions to generate candidate solutions. For example, a certain dimension inherits from the mutated solution. Another dimension inherits the original solution This increases the diversity of solutions.

[0136] The combination of candidate solutions is used as the candidate solution set.

[0137] S403, based on the objective function, determines the objective function value corresponding to each candidate solution according to the unit cost and unit revenue corresponding to each candidate solution in the candidate solution set.

[0138] Similarly, by combining the substation's measured second-level load data and energy management strategies, and based on the objective function, the unit-time operating performance of the energy storage system under each candidate solution in the candidate solution set can be simulated to obtain the unit cost and unit revenue corresponding to each candidate solution in the candidate solution set. Then, based on the unit cost and unit revenue corresponding to each candidate solution, the objective function value corresponding to each candidate solution can be determined.

[0139] S404. Based on the relationship between the objective function value of each initial solution and the objective function value of each candidate solution in the current round's initial solution set, select a candidate solution set from the current round's initial solution set or candidate solution set, and use the candidate solution set as the initial solution set for the next round of iterative optimization.

[0140] Furthermore, the kth initial solution in the initial solution set corresponding to this round can be compared with the kth candidate solution in the candidate solution set. The solution with the larger objective function value in each combination is retained as a candidate solution, and then a candidate solution set is formed as the initial solution set for the next round of iterative optimization.

[0141] For example, a competitive selection can be conducted in the following ways to generate a set of alternative solutions.

[0142] For example, the comparison combinations can be determined first; wherein each comparison combination includes a candidate solution and an initial solution in the initial solution set corresponding to the current round; the initial solutions in each comparison combination are different, and the candidate solutions in each comparison combination are different.

[0143] Furthermore, the initial solution or candidate solution with the larger objective function value in each comparison combination is selected as a candidate solution. For example, if the objective function value corresponding to the initial solution in a comparison combination is greater than the objective function value corresponding to the candidate solution, then the initial solution in that comparison combination can be selected as a candidate solution. If the objective function value corresponding to the initial solution is equal to the objective function value corresponding to the candidate solution, then either the initial solution or the candidate solution in that combination can be selected as a candidate solution. Finally, the combination of all candidate solutions is used as the candidate solution set.

[0144] S405, if the current iteration number is equal to the maximum iteration number, the initial solution corresponding to the maximum objective function value in the initial solution set of this round is used as the objective configuration parameter.

[0145] If the number of previous iterations equals the maximum number of iterations, then the initial solution corresponding to the maximum objective function value in the initial solution set of this round can be used as the objective configuration parameter.

[0146] In the above embodiments, the evolution of the solution is achieved through mutation and crossover operations, thereby enhancing the global search capability; furthermore, based on the selection mechanism of the objective function value, it is ensured that each iteration approaches a better solution; in addition, setting a maximum number of iterations can balance the optimization effect and computational efficiency.

[0147] In some alternative implementations, see [link to relevant documentation]. Figure 5 , Figure 5 A flowchart illustrating another parameter optimization method for energy storage systems is provided, which includes the following steps:

[0148] S501, obtain the target function.

[0149] S502, using the power constraints and state of charge constraints of the energy storage system as constraints, randomly generates an initial solution set of the configuration parameters to be optimized.

[0150] S503, for each initial solution in the initial solution set, determine the unit cost and unit revenue of the energy storage system under the initial solution, and based on the objective function, determine the objective function value corresponding to the initial solution according to the unit cost and unit revenue.

[0151] S504 aims to maximize the net benefit of the energy storage system by iteratively optimizing the initial solution set based on the objective function values ​​corresponding to each initial solution to obtain the target configuration parameters.

[0152] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0153] Based on the same inventive concept, this application also provides a parameter optimization device for an energy storage system to implement the parameter optimization method of the energy storage system described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the parameter optimization device for an energy storage system provided below can be found in the limitations of the parameter optimization method for the energy storage system above, and will not be repeated here.

[0154] In one exemplary embodiment, such as Figure 6 As shown, a parameter optimization device for an energy storage system is provided, comprising:

[0155] The acquisition module 10 is used to acquire the objective function; wherein, the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charging and discharging start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power transfer system and the rated power of the converter in the power transfer system, as well as the power threshold for adjusting the maximum power demand of the substation.

[0156] The solver module 20 is used to determine the target configuration parameters based on the objective function, with the power constraints and state of charge constraints of the energy storage system as constraints and the goal of maximizing the net benefit of the energy storage system.

[0157] The aforementioned parameter optimization device for the energy storage system obtains an objective function. This objective function reflects the relationship between the configuration parameters to be optimized and the net benefit of the energy storage system. At least two substations within the energy storage system can coordinate their power based on the power sharing system within the system. The configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage units, the start-up threshold of the power sharing system, the rated power of the converters in the power sharing system, and the power threshold for adjusting the maximum power demand of the substations. Using the power constraints and state-of-charge constraints of the energy storage system as constraints, and aiming to maximize the net benefit of the energy storage system, the target configuration parameters are determined according to the objective function. This scheme enables at least two substations within the energy storage system to coordinate their power based on the power sharing system, achieving coordinated control of multiple substations. Furthermore, by solving the objective function to obtain the target configuration parameters of the energy storage system, the net benefit of the energy storage system can be maximized, thereby improving the efficiency of the energy storage system.

[0158] In one embodiment, the solving module 20 specifically includes:

[0159] The generation unit is used to randomly generate an initial set of configuration parameters to be optimized, based on the power constraints and state of charge constraints of the energy storage system.

[0160] The determination unit is used to determine the unit cost and unit revenue of the energy storage system under each initial solution in the initial solution set, and to determine the objective function value corresponding to the initial solution based on the objective function, according to the unit cost and unit revenue.

[0161] The solution unit is used to iteratively optimize the initial solution set based on the objective function value corresponding to each initial solution, with the goal of maximizing the net benefit of the energy storage system, to obtain the target configuration parameters.

[0162] In one embodiment, the solving unit is specifically used for:

[0163] For each round of iterative optimization, the current iteration number is obtained. If the current iteration number is less than the maximum iteration number, cross-mutation and cross-recombination are performed on each initial solution in the corresponding initial solution set to obtain a candidate solution set. Based on the objective function, the objective function value corresponding to each candidate solution is determined according to the unit cost and unit benefit corresponding to each candidate solution in the candidate solution set. According to the relationship between the objective function value corresponding to each initial solution in the corresponding initial solution set and the objective function value corresponding to each candidate solution, a candidate solution set is selected from the corresponding initial solution set or candidate solution set, and the candidate solution set is used as the initial solution set for the next round of iterative optimization. If the current iteration number is equal to the maximum iteration number, the initial solution corresponding to the maximum objective function value in the corresponding initial solution set is used as the target configuration parameter. The initial solution set for the first round is a randomly generated initial solution set.

[0164] In one embodiment, the solving unit is specifically used for:

[0165] Determine the comparison combination; wherein each comparison combination includes a candidate solution and an initial solution from the initial solution set corresponding to this round; the initial solutions in each comparison combination are different, and the candidate solutions in each comparison combination are different; the initial solution or candidate solution with the larger objective function value in each comparison combination is taken as the candidate solution; the combination of each candidate solution is taken as the candidate solution set.

[0166] In one embodiment, the power constraint includes:

[0167] The discharge and charging power of the energy storage unit are within the rated power range of the energy storage unit; the instantaneous transmission power of the converter is less than or equal to the rated power of the converter.

[0168] In one embodiment, the state-of-charge constraints include:

[0169] The state of charge of the energy storage unit is between the lower limit of the state of charge of the energy storage unit and the upper limit of the state of charge of the energy storage unit.

[0170] Each module in the parameter optimization device of the aforementioned energy storage system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0171] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores configuration parameters of the energy storage system. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a parameter optimization method for the energy storage system.

[0172] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0173] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the parameter optimization method for the energy storage system described in any of the above embodiments.

[0174] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the parameter optimization method for the energy storage system described in any of the above embodiments.

[0175] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the parameter optimization method for the energy storage system described in any of the above embodiments.

[0176] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0177] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0178] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A parameter optimization method for an energy storage system, characterized in that, At least two substations in the energy storage system are capable of power coordination based on the power sharing system within the energy storage system; the method includes: Obtain the objective function; wherein the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power facilitation system and the rated power of the converter in the power facilitation system, and the power threshold for adjusting the maximum power demand of the substation; Using the power constraints and state of charge constraints of the energy storage system as constraints, and taking the maximization of the net benefit of the energy storage system as the objective, the target configuration parameters are determined according to the objective function.

2. The method according to claim 1, characterized in that, The process involves determining target configuration parameters based on the objective function, using the power and state-of-charge constraints of the energy storage system as constraints and maximizing the net benefit of the energy storage system as the objective. This includes: Using the power constraints and state of charge constraints of the energy storage system as constraints, an initial solution set of the configuration parameters to be optimized is randomly generated; For each initial solution in the initial solution set, determine the unit cost and unit revenue of the energy storage system under the initial solution, and based on the objective function, determine the objective function value corresponding to the initial solution according to the unit cost and the unit revenue; With the goal of maximizing the net benefit of the energy storage system, the initial solution set is iteratively optimized based on the objective function value corresponding to each initial solution to obtain the target configuration parameters.

3. The method according to claim 2, characterized in that, The step of iteratively optimizing the initial solution set based on the objective function values ​​corresponding to each initial solution to obtain the target configuration parameters includes: For each round of optimization, obtain the current iteration number; If the current iteration number is less than the maximum iteration number, cross-mutation and cross-recombination are performed on each initial solution in the initial solution set corresponding to this round to obtain a candidate solution set; Based on the objective function, the objective function value corresponding to each candidate solution is determined according to the unit cost and the unit revenue corresponding to each candidate solution in the candidate solution set; Based on the relationship between the objective function value of each initial solution in the current round and the objective function value of each candidate solution in the current round, a candidate solution set is selected from the current round's initial solution set or candidate solution set, and the candidate solution set is used as the initial solution set for the next round of iterative optimization. If the current iteration number is equal to the maximum iteration number, the initial solution corresponding to the maximum objective function value in the initial solution set of this round is taken as the target configuration parameter; The initial solution set for the first round is a randomly generated initial solution set.

4. The method according to claim 3, characterized in that, The step of selecting a candidate solution set from the initial solution set or candidate solution set corresponding to the current round based on the relationship between the objective function value corresponding to each initial solution in the current round and the objective function value corresponding to each candidate solution includes: Determine the comparison combination; wherein, each comparison combination includes a candidate solution and an initial solution from the initial solution set corresponding to the current round; the initial solutions in each comparison combination are different, and the candidate solutions in each comparison combination are different; The initial solution or candidate solution with the larger objective function value in each comparison combination is taken as the alternative solution; The combination of each alternative solution is taken as the alternative solution set.

5. The method according to claim 1, characterized in that, The power constraint conditions include: The discharge power and charging power of the energy storage unit are within the rated power range of the energy storage unit; The instantaneous transmission power of the converter is less than or equal to the rated power of the converter.

6. The method according to claim 1, characterized in that, The state of charge constraints include: The state of charge of the energy storage unit is between the lower limit of the state of charge of the energy storage unit and the upper limit of the state of charge of the energy storage unit.

7. A parameter optimization device for an energy storage system, characterized in that, At least two substations in the energy storage system are capable of power coordination based on the power sharing system within the energy storage system; the device includes: An acquisition module is used to acquire an objective function; wherein the objective function is used to reflect the relationship between the configuration parameters to be optimized in the energy storage system and the net benefit of the energy storage system; the configuration parameters to be optimized include the rated capacity and charge / discharge start-up power of the energy storage unit in the energy storage system, the start-up threshold of the power facilitation system and the rated power of the converter in the power facilitation system, and the power threshold for adjusting the maximum power demand of the substation; The solution module is used to determine the target configuration parameters based on the objective function, taking the power constraints and state of charge constraints of the energy storage system as constraints and the goal of maximizing the net benefit of the energy storage system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to 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 by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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