An energy storage capacity configuration method, system, device and storage medium

Abstract

This disclosure relates to the field of energy storage system optimization configuration technology, and in particular to an energy storage capacity configuration method, system, device, and storage medium. The method includes: acquiring user-side load data and electricity price data; constructing a revenue objective function with energy storage power and energy storage energy as optimization variables based on the user-side load data and electricity price data; constructing charge / discharge power constraints and state of charge constraints based on the user-side load data; randomly generating multiple candidate capacity configuration information within a preset capacity range based on the charge / discharge power constraints and state of charge constraints, and constructing a dynamic penalty fitness function through the revenue objective function to calculate the fitness value of each candidate capacity configuration information; updating each candidate capacity configuration information based on the fitness value to obtain updated capacity configuration information including energy storage power and energy storage energy, thereby achieving fast and precise energy storage configuration.

Description

Technical Field

[0001] This disclosure relates to the field of energy storage system optimization configuration technology, and in particular to an energy storage capacity configuration method, system, device and storage medium. Background Technology

[0002] With the promotion of time-of-use pricing and electricity market trading mechanisms, the economic value of energy storage systems in peak shaving, valley filling, and electricity price arbitrage is becoming increasingly prominent. The power and energy configuration of energy storage systems directly affect the investment cost and return on investment of projects.

[0003] As the power system develops towards a high proportion of renewable energy integration and marketization, significant changes are occurring in power system operation modes and user-side energy consumption patterns. In recent years, various market-based trading mechanisms, such as time-of-use pricing, the electricity spot market, and ancillary services markets, have been gradually promoted, resulting in significant price differences across different time periods. Particularly with the widening peak-valley price gap, the economic value of users participating in the electricity market by adjusting their consumption behavior is continuously increasing. Against this backdrop, energy storage systems, with their flexible charging and discharging capabilities, can charge during periods of lower electricity prices and discharge during periods of higher prices, thereby achieving peak shaving, valley filling, price arbitrage, and load shifting. This not only improves user-side energy utilization efficiency but also alleviates peak-valley load differences in the power grid to some extent, enhancing the stability of grid operation.

[0004] However, in practical applications, energy storage capacity allocation problems are typically characterized by multiple constraints, multiple variables, and nonlinearity. The need to consider various factors simultaneously during capacity allocation makes the problem highly complex. Furthermore, in user-side energy storage applications, user load curves often exhibit significant uncertainty and volatility, and electricity pricing mechanisms may be continuously adjusted with electricity market reforms, making it difficult to achieve rapid and precise capacity allocation under multiple constraints and nonlinear conditions. Summary of the Invention

[0005] To address the aforementioned technical issues, this disclosure provides a method, system, device, and storage medium for configuring energy storage capacity.

[0006] This disclosure provides a method for configuring energy storage capacity, including: Obtain user-side load data and electricity price data; Based on the user-side load data and electricity price data, a revenue objective function is constructed with energy storage power and energy storage energy as optimization variables; Charge and discharge power constraints and state of charge constraints are constructed based on user-side load data; Based on the charging and discharging power constraints and the state of charge constraints, multiple candidate capacity configuration information are randomly generated within a preset capacity range, and a dynamic penalty fitness function is constructed through the profit objective function. The fitness value of each candidate capacity configuration information is calculated based on the dynamic penalty fitness function. The candidate capacity configuration information is updated based on the fitness value to obtain the updated capacity configuration information, which includes energy storage power information and energy storage energy information.

[0007] Further, the step of randomly generating multiple candidate capacity configuration information within a preset capacity range based on the charging / discharging power constraint and the state of charge constraint, constructing a dynamic penalty fitness function through the profit objective function, and calculating the fitness value of each candidate capacity configuration information based on the dynamic penalty fitness function includes: Construct constraint penalty terms; The penalty value is calculated using the aforementioned penalty item; The penalty value is added to the candidate capacity configuration information that violates the charge / discharge power constraint or the state of charge constraint; The dynamic penalty fitness function is constructed based on the objective function of the profit and the penalty term; The fitness value of each candidate capacity configuration information is calculated using the dynamic penalty fitness function.

[0008] Furthermore, the step of constructing a revenue objective function based on the user-side load data and electricity price data, with energy storage power and energy storage energy as optimization variables, includes: A revenue function is constructed using the electricity price data; An investment cost function is constructed using the user-side load data. The target return function is constructed using the operating return function and the investment cost function.

[0009] Furthermore, the construction of the operating revenue function using the electricity price data includes: An operating revenue function is constructed based on the electricity price data and the discharge power, charging power, discharge efficiency, and charging efficiency within the target time period from the user-side load data.

[0010] Furthermore, the step of constructing the investment cost function using the user-side load data includes: The cost is based on the user-side unit power cost, user-side unit energy cost, energy storage system power, and energy storage system energy construction investment cost function.

[0011] Furthermore, the construction of charge / discharge power constraints and state of charge constraints based on user-side load data includes: A charge / discharge power constraint is constructed by setting a first threshold and a second threshold, wherein the charge / discharge power is greater than the first threshold and less than the second threshold. The state of charge constraint is constructed by the energy storage system's charging efficiency, energy storage system's discharging efficiency, charging power within the target time period, and charging power within the target time period.

[0012] This disclosure also provides an energy storage capacity configuration system, including: The acquisition module is used to acquire user-side load data and electricity price data; The module is used to construct a revenue objective function with energy storage power and energy storage energy as optimization variables based on user-side load data and electricity price data; The constraint module is used to construct charging and discharging power constraints and state of charge constraints based on user-side load data; The calculation module is used to randomly generate multiple candidate capacity configuration information within a preset capacity range based on the charging and discharging power constraint and the state of charge constraint, and to construct a dynamic penalty fitness function through the benefit objective function, and to calculate the fitness value of each candidate capacity configuration information based on the dynamic penalty fitness function. The update module is used to update each candidate capacity configuration information based on the fitness value to obtain the updated capacity configuration information, which includes energy storage power information and energy storage energy information.

[0013] Furthermore, the computing module is specifically used for: Construct constraint penalty terms; The penalty value is calculated using the aforementioned penalty item; The penalty value is added to the candidate capacity configuration information that violates the charge / discharge power constraint or the state of charge constraint; The dynamic penalty fitness function is constructed based on the objective function of the profit and the penalty term; The fitness value of each candidate capacity configuration information is calculated using the dynamic penalty fitness function.

[0014] This disclosure also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the energy storage capacity configuration method.

[0015] This disclosure also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the energy storage capacity configuration method.

[0016] The technical solution provided in this disclosure has the following advantages compared with the prior art: The system acquires user-side load and electricity price data; based on these data, it constructs a revenue objective function with energy storage power and energy storage capacity as optimization variables; it then constructs charge / discharge power constraints and state of charge constraints based on the user-side load data; based on these constraints, it randomly generates multiple candidate capacity configurations within a preset capacity range, and constructs a dynamic penalty fitness function using the revenue objective function to calculate the fitness value of each candidate capacity configuration; finally, it updates each candidate capacity configuration based on the fitness value, obtaining updated capacity configuration information including energy storage power and energy storage capacity, thus achieving rapid and precise energy storage configuration. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0018] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic diagram of an energy storage capacity configuration method provided in an embodiment of this disclosure; Figure 2 A schematic diagram of a method for calculating fitness values ​​provided in an embodiment of this disclosure; Figure 3 This is a schematic diagram of an energy storage capacity configuration system provided in an embodiment of this disclosure. Detailed Implementation

[0020] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0021] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0022] Figure 1 This is a schematic diagram of the energy storage capacity configuration method provided in the embodiments of this disclosure; as shown Figure 1 As shown, an energy storage capacity configuration method includes: Step S1: Obtain user-side load data and electricity price data; In this embodiment, load data and corresponding electricity price data for the user side within a preset time period are acquired through a data acquisition system, energy management system, or power information platform. User-side load data typically includes power consumption or electricity usage information at different time steps, such as load curves sampled at 15-minute, 30-minute, or 1-hour intervals, reflecting changes in user electricity demand over different time periods. Electricity price data includes time-of-use pricing information corresponding to the time step, such as peak, flat, and off-peak prices. By acquiring this data, a basic dataset reflecting actual user electricity consumption characteristics and electricity price variation patterns can be formed, providing a reliable data foundation for subsequent capacity configuration optimization. Historical load data can be automatically acquired through smart meters or power data interfaces, while corresponding time-of-use pricing data can be obtained from the electricity market or grid platform. Data synchronization and preprocessing are performed according to a unified time scale to form a unified input data sequence. This approach accurately reflects the characteristics of user-side electricity demand changes over time and the temporal differences in electricity prices. This allows for optimized capacity configuration of energy storage systems based on actual operating environments, avoiding biases introduced by empirical values ​​or static assumptions, and improving the accuracy and practicality of capacity configuration results. Furthermore, by incorporating real load and electricity price data, a reliable data source can be provided for constructing the subsequent revenue objective function. This makes the economic analysis of energy storage systems in application scenarios such as peak shaving, load shifting, and electricity price arbitrage more accurate, thereby enhancing the applicability and feasibility of the entire energy storage capacity configuration method in practical engineering applications.

[0023] Step S2: Based on user-side load data and electricity price data, construct a revenue objective function with energy storage power and energy storage energy as optimization variables; In this embodiment, a revenue objective function for energy storage system capacity configuration is constructed based on the acquired user-side load data and electricity price data. The rated power and rated energy of the energy storage system are introduced into the objective function as optimization variables. The revenue objective function typically consists of two parts: the operating revenue of the energy storage system and the investment cost. The operating revenue mainly comes from the electricity price difference generated by charging and discharging the energy storage system during different electricity price periods, such as charging during periods of lower electricity prices and discharging during periods of higher electricity prices, thereby achieving electricity price arbitrage. The investment cost includes the total investment cost formed by the unit power cost and unit energy cost of the energy storage system. By combining these two parts, a revenue objective function reflecting the overall economic efficiency of the energy storage system can be formed, and the optimal capacity configuration scheme is determined by maximizing this revenue objective function. Using energy storage power and energy storage as optimization variables allows the optimization process to simultaneously consider the impact of the energy storage system's power and energy scale on economic benefits, thus avoiding the limitations of optimizing only a single parameter. This approach integrates various factors, such as user-side electricity consumption characteristics, electricity price fluctuations, and energy storage investment costs, into a single mathematical model, thereby forming a complete economic evaluation index and providing a clear optimization objective for subsequent capacity optimization calculations. By constructing this revenue objective function, the energy storage capacity allocation problem can be transformed into a clear mathematical optimization problem, allowing subsequent swarm intelligence search algorithms to use this objective function as an evaluation criterion for iterative optimization, thus improving the economic rationality of the energy storage capacity allocation results.

[0024] Step S3: Construct charging and discharging power constraints and state of charge constraints based on user-side load data; In this embodiment, operational constraints that need to be met during capacity configuration are constructed based on user-side load data and the operating characteristics of the energy storage system. These constraints mainly include charge / discharge power constraints and state of charge (SOC) constraints. The charge / discharge power constraints limit the charging and discharging power of the energy storage system at any given time step to within the system's rated power range, ensuring that the system operates within the limits allowed by the physical equipment. The SOC constraints describe the energy changes of the energy storage system's batteries at different time steps and ensure that the battery's SOC remains within a safe operating range, for example, varying between minimum and maximum SOC. Furthermore, the SOC constraints can also be described using an energy balance relationship, where the SOC of the energy storage system at a given moment equals the SOC of the previous moment plus charging energy minus discharging energy, taking into account charging and discharging efficiency. By constructing these constraints, it can be ensured that the capacity configuration scheme obtained during capacity optimization calculations is not only economically advantageous but also feasible in actual operation. =It can incorporate the physical operating characteristics and safety requirements of energy storage systems into the optimization model, thereby avoiding capacity configuration schemes that violate equipment operating limits during the optimization process and improving the engineering feasibility of the optimization results. At the same time, by introducing these constraints, the energy storage system can also maintain stable operation when participating in peak shaving and valley filling or electricity price arbitrage, thereby improving the safety and reliability of the entire energy storage system operation.

[0025] Step S4: Based on the charging and discharging power constraints and the state of charge constraints, multiple candidate capacity configuration information are randomly generated within a preset capacity range. A dynamic penalty fitness function is constructed through the profit objective function, and the fitness value of each candidate capacity configuration information is calculated based on the dynamic penalty fitness function. In this embodiment, multiple candidate capacity configurations are first randomly generated within a preset energy storage capacity range. Each candidate capacity configuration typically consists of two parameters: energy storage power and energy storage capacity, forming a candidate solution set as the initial population for the swarm intelligence search algorithm. Subsequently, each candidate capacity configuration scheme is substituted into the profit objective function to calculate its corresponding economic profit value, and a dynamic penalty fitness function is constructed based on the constraints. Specifically, when a candidate capacity configuration scheme satisfies all constraints, its fitness value equals its corresponding profit value; when certain constraints are violated, the profit value is corrected through a penalty term, thereby reducing the fitness value of the scheme. Simultaneously, by introducing a dynamic penalty factor, the penalty intensity gradually increases with the number of algorithm iterations, maintaining strong search capability in the early stages of the algorithm while focusing more on the feasibility of the solution in the later stages. Through this method, constraints can be effectively integrated into the fitness evaluation process, enabling the fitness function to reflect both the economic efficiency and operational feasibility of the capacity configuration scheme. It can achieve a comprehensive evaluation of different capacity configuration schemes, enabling the swarm intelligence search algorithm to prioritize the retention of candidate solutions with higher returns and that meet the constraints in subsequent iterations, thereby improving the efficiency and stability of the optimization search.

[0026] Step S5: Update the capacity configuration information for each candidate based on the fitness value to obtain the updated capacity configuration information, which includes energy storage power information and energy storage energy information.

[0027] In this embodiment, the candidate capacity configuration information is iteratively updated based on the calculated fitness value to gradually search for a better energy storage capacity configuration scheme. A swarm intelligence search strategy, such as an artificial bee colony search mechanism, can be used to update candidate solutions through stages of hired bees, observation bees, and scout bees. During the update process, the energy storage power and energy in the candidate capacity configuration are adjusted through random perturbation, individual difference search, and reward guidance, thereby generating new candidate capacity configuration schemes. Subsequently, the updated candidate solutions are again substituted into the reward objective function and dynamic penalty fitness function for evaluation, and the fitness value determines whether to replace the original candidate solutions. By continuously repeating the above update and evaluation process, the algorithm can gradually eliminate capacity configuration schemes with low fitness and retain schemes with high fitness, thereby enabling the entire population to gradually converge towards the optimal solution region. When the preset number of iterations is reached or the convergence condition is met, the final capacity configuration result is output, which includes the optimal energy storage power and the optimal energy storage. It can continuously improve the economic benefits of capacity configuration schemes through iterative optimization, and ensure that the obtained capacity configuration schemes meet the system operation constraints, thereby achieving efficient configuration of energy storage system capacity and improving the economy and operating efficiency of energy storage systems in actual operation.

[0028] Figure 2 This is a schematic diagram of the method for calculating fitness values ​​provided in the embodiments of this disclosure; as shown. Figure 2 As shown, in some possible implementations, step S4, based on charge / discharge power constraints and state of charge constraints, randomly generates multiple candidate capacity configuration information within a preset capacity range, and constructs a dynamic penalty fitness function through a benefit objective function, and calculates the fitness value of each candidate capacity configuration information based on the dynamic penalty fitness function, including: step S41, constructing a constraint penalty term; step S42, calculating a penalty value through the penalty term; step S43, adding the penalty value to the candidate capacity configuration information that violates the charge / discharge power constraints or state of charge constraints; step S44, constructing a dynamic penalty fitness function based on the benefit objective function and the penalty term; step S45, calculating the fitness value of each candidate capacity configuration information through the dynamic penalty fitness function.

[0029] In this embodiment, after randomly generating multiple candidate capacity configuration information within a preset capacity range, a comprehensive evaluation of the feasibility and economy of each candidate capacity configuration scheme is required. Therefore, a constraint penalty term is first constructed to quantify the degree to which the candidate capacity configuration scheme violates the system operation constraints. Corresponding constraint functions can be established for charging / discharging power constraints and state of charge constraints. When a candidate capacity configuration scheme experiences situations such as charging or discharging power exceeding the allowable range or the battery state of charge exceeding the safe range during energy storage scheduling calculations, the degree of violation is calculated through the constraint function, and a corresponding penalty value is obtained accordingly. Subsequently, the penalty value is calculated through the penalty term and added to the candidate capacity configuration information that violates the charging / discharging power constraints or state of charge constraints, thereby penalizing the candidate solutions that violate the constraints, making their evaluation results lower than those of the candidate solutions that meet the constraints.

[0030] Furthermore, a dynamic penalty fitness function is constructed based on the objective function and the penalty term. This fitness function reflects both the economic benefits of the energy storage capacity configuration scheme and whether the scheme meets the system's operational constraints in actual operation. In this process, the dynamic penalty factor gradually increases with the number of optimization algorithm iterations, giving the algorithm strong global search capabilities in the early stages and prioritizing solution feasibility in later stages, thus preventing the algorithm from getting trapped in infeasible solution regions. Finally, the fitness value of each candidate capacity configuration is calculated using the dynamic penalty fitness function, and this fitness value serves as an important basis for subsequent swarm search algorithms to update and select solutions. This effectively integrates system operational constraints into the optimization evaluation system, ensuring that the final capacity configuration scheme not only has high economic benefits but also meets the physical limitations of the energy storage system's actual operation, thereby improving the engineering feasibility and operational safety of the optimization results.

[0031] In some possible implementations, step S2 involves constructing a revenue objective function with energy storage power and energy storage energy as optimization variables based on user-side load data and electricity price data. This includes: constructing an operational revenue function using electricity price data; constructing an investment cost function using user-side load data; and constructing the revenue objective function using the operational revenue function and the investment cost function.

[0032] In this embodiment, to achieve an economic evaluation of the energy storage system capacity configuration scheme, a revenue objective function needs to be constructed based on user-side load data and electricity price data, with the energy storage system's power capacity and energy capacity introduced as optimization variables into this function. First, an operational revenue function is constructed using electricity price data to describe the economic benefits obtained by the energy storage system from charging and discharging operations during different electricity price periods. Then, an investment cost function is constructed using user-side load data to reflect the power investment cost and energy investment cost incurred during the construction of the energy storage system. The operational revenue function and the investment cost function quantitatively evaluate the capacity configuration scheme from two aspects: the operational revenue and the construction cost of the energy storage system. The operational revenue mainly comes from the electricity price difference generated by charging the energy storage system during low-price periods and discharging during high-price periods, while the investment cost reflects the increased equipment investment resulting from the expansion of the energy storage system's capacity. Finally, by combining the operational revenue function and the investment cost function, a complete revenue objective function can be constructed, and maximizing this revenue objective function is used as the goal of capacity configuration optimization. In this revenue objective function, energy storage power capacity and energy storage capacity are the core optimization variables. Their values ​​directly affect the profitability and investment scale of the energy storage system in operation modes such as peak shaving and valley filling, electricity price arbitrage, and load shifting. By adopting this approach, user-side electricity consumption characteristics, electricity price fluctuation patterns, and energy storage investment costs can be uniformly incorporated into the same optimization model. This transforms the energy storage capacity allocation problem into a clear mathematical optimization problem, providing a clear optimization objective for subsequent swarm intelligence search algorithms to optimize capacity. Simultaneously, this revenue objective function comprehensively reflects the economics of the energy storage system throughout its entire lifecycle, ensuring that the final capacity allocation scheme guarantees high operational returns while avoiding investment waste caused by excessive capacity, thereby improving the overall economic efficiency of energy storage system construction and operation.

[0033] In some possible implementation methods, an operating revenue function is constructed using electricity price data, including: constructing an operating revenue function based on electricity price data within a target time period and the discharge power, charging power, discharge efficiency, and charging efficiency within the target time period from user-side load data.

[0034] In this embodiment, an operational revenue function for the energy storage system is constructed using electricity price data and user-side load data to quantify the economic benefits gained by the energy storage system through charge and discharge scheduling within a target time period. First, the electricity price levels for different time periods are determined based on the electricity price data within the target time period, such as peak-hour, normal-hour, and off-peak prices. Then, the charging and discharging power of the energy storage system in each time period is determined by combining user-side load data. Subsequently, based on the charging and discharging behavior of the energy storage system in each time period and the corresponding electricity price differences, an operational revenue function can be constructed. The energy storage system charges during periods of lower electricity prices and discharges during periods of higher electricity prices, thereby achieving electricity price arbitrage profits. During the revenue calculation process, the charging and discharging efficiency of the energy storage system also needs to be considered, because there is a certain energy loss during charging and discharging. Therefore, the actual obtainable revenue needs to be adjusted based on efficiency parameters. For example, when calculating the discharge revenue, the impact of discharge efficiency on the actual output power needs to be considered, while when calculating the charging cost, the impact of charging efficiency on the actual input power needs to be considered. By integrating parameters such as charging power, discharging power, electricity price data, and charging / discharging efficiency within a target time period, a complete operational revenue function can be established to evaluate the economic benefits of different capacity configuration schemes in actual operation. This approach more realistically reflects the operational revenue of energy storage systems in actual electricity market environments, enabling the optimization model to fully consider the impact of electricity price fluctuations and load changes on energy storage revenue, thereby improving the accuracy and practical applicability of capacity configuration optimization results. Furthermore, by accurately describing the charging and discharging revenue of the energy storage system in different time periods, the optimization algorithm can more accurately determine the merits of different capacity configuration schemes, improving the economic efficiency of the capacity configuration results.

[0035] In some possible implementations, an investment cost function is constructed using user-side load data, including: constructing an investment cost function based on user-side unit power cost, user-side unit energy cost, the power of the energy storage system, and the energy of the energy storage system.

[0036] In this embodiment, an investment cost function is constructed using user-side load data and cost parameters of the energy storage system to describe the equipment costs required for the construction phase of the energy storage system. Specifically, firstly, the electricity consumption scale and characteristics of users are analyzed based on user-side load data to determine the power capacity and energy capacity range that the energy storage system may need to configure. Subsequently, an investment cost function is constructed based on the unit power cost and unit energy cost of the energy storage system equipment. The unit power cost typically represents the investment cost of related equipment such as power converters and power control devices in the energy storage system, while the unit energy cost represents the investment cost of energy storage equipment such as battery energy storage units. By multiplying the energy storage system's power capacity by the unit power cost and the energy storage system's energy capacity by the unit energy cost, the total investment cost of the energy storage system can be obtained. This investment cost function can reflect the cost increase trend brought about by the expansion of the energy storage system's capacity, thus effectively avoiding the problem of excessive investment costs due to over-configuration of energy storage capacity during the optimization process. By combining this investment cost function with the operating benefit function, the optimization model can simultaneously consider the balance between the construction cost and operating benefit of the energy storage system, thereby achieving the most economically optimal capacity configuration scheme. This enables capacity configuration optimization to better meet actual engineering construction needs, allowing energy storage systems to achieve reasonable control of investment costs while meeting operational requirements, thereby improving the overall return on investment for energy storage projects and enhancing the economic feasibility of energy storage systems in user-side energy management.

[0037] In some possible implementations, step S3, constructing charging and discharging power constraints and state of charge constraints based on user-side load data, includes: constructing charging and discharging power constraints by using a preset first threshold and a preset second threshold, wherein the charging and discharging power is greater than the preset first threshold and less than the preset second threshold; and constructing state of charge constraints by using the energy storage system charging efficiency, energy storage system discharging efficiency, charging power within a target time period, and charging power within a target time period.

[0038] In this embodiment, to ensure that the scheme obtained during the capacity configuration optimization of the energy storage system meets the actual operational requirements, it is necessary to construct charging and discharging power constraints and state of charge constraints based on user-side load data. First, charging and discharging power constraints are constructed by presetting a first threshold and a second threshold. The first threshold represents the minimum allowable charging and discharging power value of the energy storage system, while the second threshold represents the maximum allowable charging and discharging power value. This ensures that the charging or discharging power of the energy storage system remains within a safe operating range at any given time. This constraint effectively prevents overcharging or over-discharging during operation, thereby protecting the energy storage device and extending its service life. Second, state of charge constraints are constructed using the charging efficiency, discharging efficiency, and charging and discharging power within a target time period to describe the energy change process of the energy storage system in different time periods. The state of charge of the energy storage system in a certain time period is equal to the state of charge in the previous time period plus the charging energy minus the discharging energy, while also considering energy losses due to charging and discharging efficiency, and ensuring that the state of charge is always between the minimum and maximum state of charge. By introducing this state-of-charge constraint, the energy storage system can maintain a reasonable energy level throughout its entire operating cycle, avoiding unsafe situations such as over-discharge or over-charge of the battery. This approach integrates the physical operating characteristics and safety requirements of the energy storage system into the capacity configuration optimization model, resulting in optimization outcomes that not only offer high economic benefits but also meet actual operating conditions, thereby improving the reliability and feasibility of the capacity configuration scheme in engineering applications.

[0039] In some possible implementations, the individual encoding method represents each individual bee in the colony as a set of energy storage capacity parameters: ; Economic benefit function of energy storage system: ; ; in, For the annual economic benefits of energy storage systems, For the daily economic benefits of the energy storage system, This represents the number of days the energy storage system operates annually.

[0040] Investment cost function: ; Optimize the objective function: ; Constraints and penalty functions: SOC update constraints: ; ; Power constraints: ; ; Penalty definition: ; Where K is the total number of constraints, and gk(Xi) is the Kth constraint function.

[0041] To ensure that the energy storage system capacity configuration meets operational constraints, a constraint violation penalty function is constructed to quantify candidate solutions that violate the constraints. When a candidate solution meets the constraints, its penalty value is zero; when a candidate solution violates the constraints, its penalty value increases with the degree of violation, thereby suppressing the selection of infeasible solutions during the optimization process.

[0042] In practical engineering applications, in addition to the SOC and charge / discharge power constraints mentioned above, additional constraints such as cycle life and grid-connected capacity can be introduced based on the energy storage system's access location, operating scenario, and management requirements. These additional constraints can be incorporated into the constraint system in the form of penalty functions.

[0043] Dynamic penalty fitness function ; ; in, This represents the economic benefit of the i-th capacity configuration scheme without considering constraint penalties; It is a dynamic penalty factor that monotonically increases with the number of algorithm iterations t.

[0044] Benefit guidance during the bee-hiring phase: ; Let j be the j-th component of the current candidate solution. For the updated candidate solution components, For the randomly selected comparison individuals, Substitute for random disturbance coefficients. Weighting coefficients for returns This indicates the trend of the objective function with respect to the capacity variable.

[0045] During the bee-hiring phase, the update of candidate solutions consists of a random difference search term and a benefit-guiding term. The random difference search term is used to maintain the global search capability of the swarm intelligence algorithm, while the benefit-guiding term is used to guide the candidate solutions to be adjusted in the direction of improving the economic benefits of the energy storage system, thereby improving the convergence of the algorithm while ensuring search diversity.

[0046] Specifically, the energy storage system capacity configuration method for Factory A based on time-of-use electricity pricing: Application Scenarios and Basic Data: Off-peak hours: 00:00-6:00, 12:00-14:00; Flat hours: 6:00-12:00, 14:00-16:00; Peak hours: 16:00-24:00; Peak periods: 20:00-24:00 in July and August, and 18:00-22:00 in January and December. Electricity prices are increased by 20% based on peak electricity prices, as shown in Table 1, Peak-valley electricity prices for 10kV industrial and commercial users.

[0047] Table 1 Factory A has an average daily electricity consumption of 7186 kWh. The load from 0:00 to 8:00 is approximately 260 kW, the load from 8:00 to 18:00 is approximately 330 kW, and the load from 18:00 to 24:00 is approximately 290 kW.

[0048] The target discharge period is determined based on the time-of-use pricing policy and user load characteristics. The main discharge period for the energy storage system is determined to be 16:00~24:00, with a discharge duration of 8 hours.

[0049] Recommended energy range calculation: The amount of electricity required for discharge is: ; The operating state of charge (SOC) range of the energy storage system is set as follows: ; The required energy storage is: ; Based on engineering margins, a recommended energy range is formed: ; The recommended power range is calculated as follows: the peak power during the discharge period is 330kW, and the average power is (330×2+290×6) / 8≈300kW. Considering peak load support, fast response, power margin, and remaining grid-connected capacity, the recommended power range is formed as follows: ; Fine-tuning of capacity: Determine the fitness function; Initialize the bee colony individuals (generation 0), and randomly generate several initial capacity configuration schemes within the search space; Hired Bees Phase: Revenue-Driven Update (Generations 1-3); Observation peak stage: Superior individuals are reinforced (generations 4-6). As iterations proceed, the observation bees select more high-yield individuals based on fitness probability. The bee colony gradually gathers towards the high-energy boundary region, forming a candidate solution concentration area.

[0050] The reconnaissance peak stage and dynamic penalty mechanism: For candidate solutions with relatively high power but insufficient energy, which trigger the SOC constraint, the penalty term is positive, and the dynamic penalty factor λ(t) increases with iteration, significantly reducing their fitness. Related individuals are eliminated and randomly regenerated. This mechanism effectively suppresses infeasible solutions and accelerates the convergence of the bee colony towards a feasible optimal solution.

[0051] Figure 3 This is a schematic diagram of an energy storage capacity configuration system provided in an embodiment of this disclosure; as shown below. Figure 3 As shown, this disclosure also provides an energy storage capacity configuration system, including: an acquisition module 301 for acquiring user-side load data and electricity price data; a construction module 302 for constructing a revenue objective function with energy storage power and energy storage energy as optimization variables based on user-side load data and electricity price data; a constraint module 303 for constructing charge / discharge power constraints and state of charge constraints based on user-side load data; a calculation module 304 for randomly generating multiple candidate capacity configuration information within a preset capacity range based on charge / discharge power constraints and state of charge constraints, constructing a dynamic penalty fitness function through the revenue objective function, and calculating the fitness value of each candidate capacity configuration information based on the dynamic penalty fitness function; and an update module 305 for updating each candidate capacity configuration information based on the fitness value to obtain updated capacity configuration information, wherein the updated capacity configuration information includes energy storage power information and energy storage energy information.

[0052] In some possible implementations, the calculation module is specifically used for: constructing constraint penalty terms; calculating penalty values ​​using the penalty terms; adding the penalty values ​​to candidate capacity configuration information that violates the charge / discharge power constraint or the state of charge constraint; constructing the dynamic penalty fitness function based on the benefit objective function and the penalty terms; and calculating the fitness value of each candidate capacity configuration information using the dynamic penalty fitness function.

[0053] In some possible implementations, the construction module 302 includes: a first construction submodule for constructing an operating revenue function using the electricity price data; a second construction submodule for constructing an investment cost function using the user-side load data; and a third construction submodule for constructing the revenue objective function using the operating revenue function and the investment cost function.

[0054] In some possible implementations, the first construction submodule is specifically used to: construct an operating revenue function based on the electricity price data within the target time period and the discharge power, charging power, discharge efficiency, and charging efficiency within the target time period from the user-side load data.

[0055] In some possible implementations, the second construction submodule is specifically used to: construct an investment cost function based on the user-side unit power cost, the user-side unit energy cost, the power of the energy storage system, and the energy of the energy storage system.

[0056] This disclosure also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of an energy storage capacity configuration method.

[0057] This disclosure also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of an energy storage capacity configuration method.

[0058] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0059] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for configuring energy storage capacity, characterized in that, include: Obtain user-side load data and electricity price data; Based on the user-side load data and electricity price data, a revenue objective function is constructed with energy storage power and energy storage energy as optimization variables; Charge and discharge power constraints and state of charge constraints are constructed based on user-side load data; Based on the charging and discharging power constraints and the state of charge constraints, multiple candidate capacity configuration information are randomly generated within a preset capacity range, and a dynamic penalty fitness function is constructed through the profit objective function. The fitness value of each candidate capacity configuration information is calculated based on the dynamic penalty fitness function. The candidate capacity configuration information is updated based on the fitness value to obtain the updated capacity configuration information, which includes energy storage power information and energy storage energy information.

2. The energy storage capacity configuration method according to claim 1, characterized in that, Based on the charging / discharging power constraint and the state of charge constraint, multiple candidate capacity configuration information are randomly generated within a preset capacity range. A dynamic penalty fitness function is constructed using the profit objective function. The fitness value of each candidate capacity configuration information is calculated based on the dynamic penalty fitness function, including: Construct constraint penalty terms; The penalty value is calculated using the aforementioned penalty item; The penalty value is added to the candidate capacity configuration information that violates the charge / discharge power constraint or the state of charge constraint; The dynamic penalty fitness function is constructed based on the objective function of the profit and the penalty term; The fitness value of each candidate capacity configuration information is calculated using the dynamic penalty fitness function.

3. The energy storage capacity configuration method according to claim 1, characterized in that, The step of constructing a revenue objective function based on the user-side load data and electricity price data, with energy storage power and energy storage energy as optimization variables, includes: A revenue function is constructed using the electricity price data; An investment cost function is constructed using the user-side load data. The target return function is constructed using the operating return function and the investment cost function.

4. The energy storage capacity configuration method according to claim 3, characterized in that, The construction of the operating revenue function using the electricity price data includes: An operating revenue function is constructed based on the electricity price data and the discharge power, charging power, discharge efficiency, and charging efficiency within the target time period from the user-side load data.

5. The energy storage capacity configuration method according to claim 3, characterized in that, The construction of the investment cost function using the user-side load data includes: The cost is based on the user-side unit power cost, user-side unit energy cost, energy storage system power, and energy storage system energy construction investment cost function.

6. The energy storage capacity configuration method according to any one of claims 1 to 5, characterized in that, The construction of charge / discharge power constraints and state of charge constraints based on user-side load data includes: A charge / discharge power constraint is constructed by setting a first threshold and a second threshold, wherein the charge / discharge power is greater than the first threshold and less than the second threshold. The state of charge constraint is constructed by the energy storage system's charging efficiency, energy storage system's discharging efficiency, charging power within the target time period, and charging power within the target time period.

7. An energy storage capacity configuration system, characterized in that, include: The acquisition module is used to acquire user-side load data and electricity price data; The module is used to construct a revenue objective function with energy storage power and energy storage energy as optimization variables based on the user-side load data and electricity price data. The constraint module is used to construct charging and discharging power constraints and state of charge constraints based on user-side load data; The calculation module is used to randomly generate multiple candidate capacity configuration information within a preset capacity range based on the charging and discharging power constraint and the state of charge constraint, and to construct a dynamic penalty fitness function through the benefit objective function, and to calculate the fitness value of each candidate capacity configuration information based on the dynamic penalty fitness function. The update module is used to update each candidate capacity configuration information based on the fitness value to obtain the updated capacity configuration information, which includes energy storage power information and energy storage energy information.

8. The energy storage capacity configuration system according to claim 7, characterized in that, The computing module is specifically used for: Construct constraint penalty terms; The penalty value is calculated using the aforementioned penalty item; The penalty value is added to the candidate capacity configuration information that violates the charge / discharge power constraint or the state of charge constraint; The dynamic penalty fitness function is constructed based on the objective function of the profit and the penalty term; The fitness value of each candidate capacity configuration information is calculated using the dynamic penalty fitness function.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the energy storage capacity configuration method as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the energy storage capacity configuration method as described in any one of claims 1 to 6.

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