Energy storage optimization configuration method and device, equipment, storage medium and program product

Abstract

This invention relates to the field of energy storage configuration technology, and discloses an energy storage optimization configuration method, device, equipment, storage medium, and program product. The energy storage optimization configuration method includes: acquiring basic energy storage data; determining the application scenario of the energy storage system based on the basic energy storage data; matching the application scenario with a preset scenario demand database to obtain corresponding energy storage demand information; performing power supply and demand characteristic analysis on the energy storage system to generate multiple energy storage configuration candidate schemes; performing levelized cost analysis on each energy storage configuration candidate scheme to obtain a target energy storage configuration candidate scheme based on the levelized cost analysis results; performing multiple optimizations with the net present value as the objective function and various technical constraints as constraints to obtain multiple candidate energy storage optimization configuration schemes; and selecting the target energy storage optimization configuration scheme based on the evaluation results. This invention selects candidate schemes by determining the application scenario and then determines the parameters of the candidate schemes to achieve energy storage configuration for different scenarios.

Description

Technical Field

[0001] This invention relates to the field of energy storage configuration technology, specifically to energy storage optimization configuration methods, devices, equipment, storage media, and program products. Background Technology

[0002] With the continuous growth of new energy installed capacity, the demand for flexible resources is also increasing. As an important flexible resource, energy storage plays a vital supporting role in the construction of new power systems. Therefore, it is necessary to optimize the configuration of energy storage.

[0003] In related technologies, the energy storage optimization configuration method is to design and determine different methods for different energy storage scenarios (such as new energy supporting, grid-side peak shaving, user-side arbitrage, etc.), without a unified and universal process. Moreover, this method is based on a simplified model of a typical day, which leads to energy storage configuration that does not conform to the actual situation. Summary of the Invention

[0004] This invention provides an energy storage optimization configuration method, apparatus, equipment, storage medium, and program product to solve the problem that energy storage configurations in related technologies do not conform to actual conditions.

[0005] In a first aspect, the present invention provides an energy storage optimization configuration method, comprising: acquiring user-inputted basic energy storage data; determining the application scenario of the energy storage system based on the basic energy storage data; matching the application scenario with a preset scenario demand database to obtain corresponding energy storage demand information; performing power supply and demand characteristic analysis on the energy storage system based on the energy storage demand information to generate multiple energy storage configuration alternatives; performing levelized cost analysis on each energy storage configuration alternative based on the basic energy storage data; and selecting the target energy storage configuration alternative with the lowest levelized cost among the multiple energy storage configuration alternatives and the target energy storage configuration based on the levelized cost analysis results. The system sets the corresponding operation mode information for the candidate schemes. The operation mode information includes the operation logic, operation mode boundary conditions, and multiple optimizable energy storage configuration parameters. Using the maximum net present value as the objective function and various technical constraints as constraints, the system optimizes multiple energy storage configuration parameters based on the operation mode information to obtain candidate energy storage optimization configuration schemes under a single set of parameters. The optimization parameters are then switched, and the system returns to the steps of optimizing multiple energy storage configuration parameters based on the operation mode information to obtain multiple candidate energy storage optimization configuration schemes. Each candidate energy storage optimization configuration scheme is evaluated, and the target energy storage optimization configuration scheme is selected based on the evaluation results.

[0006] This invention acquires user-input basic energy storage data, determines the application scenarios of the energy storage system based on this data, matches the application scenarios with a preset scenario demand database to obtain corresponding energy storage demand information, and adapts to multiple application scenarios based on the basic energy storage data. This effectively covers different types of energy storage needs, thereby improving the accuracy and practicality of energy storage configuration. This invention analyzes the power supply and demand characteristics of the energy storage system based on the energy storage demand information, generating multiple energy storage configuration alternatives, effectively covering different types of energy storage needs, thereby improving the accuracy and practicality of energy storage configuration. This invention performs levelized cost of electricity (LCOE) analysis on each energy storage configuration alternative based on the basic energy storage data. Based on the LCOE analysis results, it selects the target energy storage configuration alternative with the lowest LCOE and its corresponding operating mode information from among multiple alternatives. Through detailed evaluation of each energy storage configuration alternative, it selects the target energy storage configuration alternative with the optimal cost and its operating mode information, further optimizing resource allocation efficiency. This invention uses maximizing net present value as the objective function and various technical constraints as conditions. Based on operational mode information, it optimizes multiple energy storage configuration parameters to obtain candidate energy storage optimization configuration schemes under a single set of parameters. It then switches optimization parameters and returns to the step of optimizing multiple energy storage configuration parameters based on operational mode information, resulting in multiple candidate energy storage optimization configuration schemes. Optimization is then performed based on different optimization parameters to obtain candidate energy storage optimization configuration schemes with different optimization key points. This not only improves the technical feasibility of the candidate energy storage optimization configuration schemes but also enhances their economic value. This invention evaluates each candidate energy storage optimization configuration scheme and selects the target energy storage optimization configuration scheme based on the evaluation results, thus achieving optimized energy storage configuration of the energy storage system. Compared with related technologies, this invention realizes a universal energy storage optimization configuration process applicable to multiple scenarios. Through scenario matching and a unified algorithm framework, it solves the problems of scenario fragmentation and poor algorithm reusability, significantly improving engineering application efficiency. By analyzing the levelized cost of electricity (LCOE), it obtains target energy storage configuration alternatives suitable for the scenario, as well as the corresponding operating mode information of the target energy storage configuration alternatives, ensuring that the operating strategy matches the scenario requirements. By switching optimization parameters, it generates multiple sets of candidate energy storage optimization configuration schemes, diversifying the candidate energy storage optimization configuration schemes to adapt to more user needs. This invention is adaptable to multiple application scenarios, and for each application scenario, a corresponding energy storage configuration scheme can be selected. This invention first selects multiple energy storage configuration alternatives based on the application scenario, then optimizes the parameters in each energy storage configuration alternative, and finally selects the optimal target energy storage optimization configuration scheme based on the evaluation results, greatly improving the quality and efficiency of energy storage configuration.

[0007] In one optional implementation, the application scenario of the energy storage system is determined based on the basic energy storage data, and the corresponding energy storage demand information is obtained by matching the application scenario with a preset scenario demand database. This includes: determining the application scenario of the energy storage system based on the application data in the basic energy storage data; and inputting the application scenario into the preset scenario demand database for matching to obtain the energy storage demand information corresponding to the application scenario.

[0008] In one optional implementation, the power supply and demand characteristics of the energy storage system are analyzed based on the energy storage demand information to generate multiple alternative energy storage configuration schemes. This includes: performing load-generation correlation analysis based on the energy storage demand information to obtain multiple charging and discharging demand periods of the energy storage system; performing stochastic analysis based on the energy storage demand information to obtain multiple regulation capacity requirements of the energy storage system; performing sensitivity analysis based on the energy storage demand information to obtain multiple key parameters of the energy storage system; and randomly combining the multiple charging and discharging demand periods, multiple regulation capacity requirements, and multiple key parameters to obtain multiple alternative energy storage configuration schemes.

[0009] In one optional implementation, a levelized cost of electricity (LCOE) analysis is performed on each energy storage configuration candidate based on basic energy storage data. Based on the LCOE analysis results, a target energy storage configuration candidate with the lowest LCOE and its corresponding operating mode information are selected from multiple energy storage configuration candidate options. This includes: determining the LCOE corresponding to each energy storage configuration candidate option based on cost and revenue data in the basic energy storage data; LCOE is used to characterize the cost per unit of electricity; sorting the LCOE corresponding to multiple energy storage configuration candidate options to obtain an LCOE sequence; selecting the target energy storage configuration candidate option with the lowest LCOE from the LCOE sequence; and obtaining the operating mode information corresponding to the target energy storage configuration candidate option.

[0010] In one optional implementation, the process involves optimizing multiple energy storage configuration parameters based on operating mode information, using maximum net present value as the objective function and various technical constraints as constraints, to obtain candidate energy storage optimization configuration schemes under a single set of parameters. The optimization parameters are then switched, and the process returns to the step of optimizing multiple energy storage configuration parameters based on operating mode information to obtain multiple candidate energy storage optimization configuration schemes. This includes: using maximum net present value as the objective function, inputting the objective function, constraints, and operating mode information into a preset optimization algorithm to obtain candidate energy storage optimization configuration schemes under a single set of parameters; using maximum net present value as the objective function, switching the energy storage type, or adjusting at least one of the key economic parameters, constraints, and operating mode information input parameters, and returning to the step of optimizing multiple energy storage configuration parameters based on operating mode information to obtain multiple candidate energy storage optimization configuration schemes.

[0011] In one optional implementation, each candidate energy storage optimization configuration scheme is evaluated, and a target energy storage optimization configuration scheme is selected based on the evaluation results. This includes: acquiring multiple preset evaluation indicators and performing hierarchical analysis on the multiple preset evaluation indicators to obtain the indicator weights corresponding to each preset evaluation indicator; determining the target indicator matrix corresponding to each candidate energy storage optimization configuration scheme based on the indicator parameters and indicator weights corresponding to each candidate energy storage optimization configuration scheme; standardizing the target indicator matrix corresponding to each candidate energy storage optimization configuration scheme and extracting the positive and negative ideal solutions from the standardized matrix; determining the relative proximity of each candidate energy storage optimization configuration scheme based on the Euclidean distance between each candidate energy storage optimization configuration scheme and the positive and negative ideal solutions, respectively; the relative proximity is used to characterize the comprehensive similarity between the candidate energy storage optimization configuration scheme and the ideal optimal scheme; and selecting the candidate energy storage optimization configuration scheme with the highest relative proximity as the target energy storage optimization configuration scheme.

[0012] Secondly, the present invention provides an energy storage optimization configuration device, comprising: an application scenario determination module, used to acquire user-input energy storage basic data, determine the application scenario of the energy storage system based on the energy storage basic data, and match the application scenario with a preset scenario demand database to obtain corresponding energy storage demand information; a characteristic analysis module, used to perform power supply and demand characteristic analysis on the energy storage system based on the energy storage demand information, and generate multiple energy storage configuration alternatives; and an operation mode determination module, used to perform levelized cost analysis on each energy storage configuration alternative based on the energy storage basic data, and select the target energy storage configuration alternative with the lowest levelized cost from multiple energy storage configuration alternatives based on the levelized cost analysis results. The system includes: an operating mode information module for the selected energy storage configuration alternatives; the operating mode information includes operating logic, operating mode boundary conditions, and multiple optimizable energy storage configuration parameters; an alternative configuration determination module, which optimizes multiple energy storage configuration parameters based on the operating mode information with the net present value as the objective function and various technical constraints as constraints, to obtain candidate optimized energy storage configuration schemes under a single set of parameters, switches optimization parameters, and returns to the steps of optimizing multiple energy storage configuration parameters based on the operating mode information to obtain multiple candidate optimized energy storage configuration schemes; and a target configuration determination module, which evaluates each candidate optimized energy storage configuration scheme and selects the target optimized energy storage configuration scheme based on the evaluation results.

[0013] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the energy storage optimization configuration method of the first aspect or any corresponding embodiment described above.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the energy storage optimization configuration method of the first aspect or any corresponding embodiment described above.

[0015] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the energy storage optimization configuration method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the energy storage optimization configuration method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a second process for an energy storage optimization configuration method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the third process of the energy storage optimization configuration method according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the fourth process of the energy storage optimization configuration method according to an embodiment of the present invention; Figure 6 This is a structural block diagram of an energy storage optimization configuration device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0020] As an optional application scenario of this invention, such as Figure 1 As shown, the energy storage optimization configuration system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0021] The terminal device can specifically be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0022] Among related technologies, energy storage optimization configuration methods suffer from two major flaws: First, the scenarios are severely fragmented, with independent algorithms designed for different scenarios such as new energy support, grid-side peak shaving, and user-side arbitrage, lacking a unified and universal process. In actual production, researchers find it difficult to quickly adapt to the needs of different scenarios. Second, the algorithms are oversimplified, focusing on typical daily models without fully considering the dynamic changes throughout the entire life cycle. Furthermore, they fail to systematically consider the matching relationship between energy storage rated power and demand, the impact of operating modes on economics, and the transmission relationships of various implicit constraints such as demand constraints, technical characteristic constraints, and external scenario constraints. This results in a significant deviation between the final energy storage configuration results and actual engineering needs, making it impossible to directly guide engineering applications.

[0023] This invention provides an energy storage optimization configuration method, which selects alternative schemes by determining the application scenario, and then determines the parameters of the alternative schemes to achieve the effect of energy storage configuration in different scenarios.

[0024] According to an embodiment of the present invention, an embodiment of an energy storage optimization configuration method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] This embodiment provides an energy storage optimization configuration method that can be used in computer equipment. Figure 2 This is a first flowchart of an energy storage optimization configuration method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the basic energy storage data input by the user, determine the application scenario of the energy storage system based on the basic energy storage data, match the preset scenario demand database according to the application scenario, and obtain the corresponding energy storage demand information.

[0026] The basic energy storage data provides users with various types of raw data, covering load data, energy generation data, economic parameters, equipment parameters, application data, and policy data. Load data includes historical hourly load curves, load fluctuation ranges, and peak-valley load distribution. Energy generation data includes hourly output data for photovoltaic / wind power, output volatility statistics, and curtailment rate data. Economic parameters include peak-valley time-of-use electricity prices, initial investment costs of energy storage equipment, operation and maintenance costs, discount rates, and equipment lifespan. Equipment parameters include the charge / discharge efficiency, rated power, capacity limit, cycle life, and SOC (State of Charge) operating range of candidate energy storage equipment. Application data includes application scenarios. Policy data includes energy storage subsidy policies, grid-connected power assessment standards, and carbon trading-related revenue parameters. The pre-set scenario demand database is a pre-configured database used to store multiple application scenarios and the corresponding energy storage demand information for each scenario.

[0027] In some optional implementations, the basic energy storage data is preprocessed, including outlier removal, missing value completion, and data standardization transformation, to ensure the integrity and validity of the data and provide high-quality data support for subsequent analysis. The basic energy storage data used in subsequent applications are all preprocessed basic energy storage data.

[0028] In some optional implementations, the application scenarios are the core uses / business models of energy storage systems. For example, the application scenarios include, but are not limited to, new energy supporting energy storage, grid-side peak-shaving energy storage, and user-side peak-valley arbitrage energy storage.

[0029] In some alternative implementations, energy storage demand information is a quantitative demand derived from the application scenario, such as the target peak shaving ratio, expected investment payback period, NPV (Net Present Value), and Internal Rate of Return.

[0030] Step S202: Analyze the power supply and demand characteristics of the energy storage system based on the energy storage demand information, and generate multiple alternative energy storage configuration schemes.

[0031] Among them, the analysis of power supply and demand characteristics involves statistical analysis of the time distribution of power supply and demand on the user side, peak-valley difference, supply-demand gap, renewable energy output fluctuations, and electricity price fluctuation patterns. For example, the analysis of power supply and demand characteristics includes load-generation correlation analysis, load volatility and generation randomness analysis, and sensitivity analysis.

[0032] In some alternative implementations, the alternative energy storage configurations are randomly composed of different charging and discharging periods, power, energy storage capacity, etc.

[0033] Step S203: Perform a levelized cost of electricity (LCOE) analysis on each energy storage configuration candidate based on the basic energy storage data. Based on the LCOE analysis results, select the target energy storage configuration candidate with the lowest LCOE from among multiple energy storage configuration candidate candidates, along with the corresponding operation mode information for the target energy storage configuration candidate. The operation mode information includes the operation logic, operation mode boundary conditions, and multiple optimizable energy storage configuration parameters.

[0034] The cost per kilowatt-hour analysis involves calculating the full lifecycle cost and power generation / revenue for each energy storage configuration candidate to determine the average cost per unit of electricity. The target energy storage configuration candidate is the scheme that meets the cost per kilowatt-hour requirement and has room for optimization. The operation mode information is a set of rules describing the operation mode of the energy storage system. For example, lithium battery energy storage is preferred in user-side scenarios, while pumped hydro storage or compressed air energy storage can be selected in grid-side large-capacity peak-shaving scenarios. The energy storage configuration parameters are the core variables that can be optimized, such as rated charge and discharge power, rated capacity, charge and discharge efficiency, cycle life, initial investment unit price, unit operation and maintenance cost, SOC upper and lower limits, charge and discharge rate constraints, etc.

[0035] Step S204: Using the maximum net present value as the objective function and various technical constraints as constraints, optimize multiple energy storage configuration parameters based on the operation mode information to obtain candidate energy storage optimization configuration schemes under a single set of parameters. Switch the optimization parameters and return to the step of optimizing multiple energy storage configuration parameters based on the operation mode information to obtain multiple candidate energy storage optimization configuration schemes.

[0036] Net present value (NPV) is the present value of net cash flows over the entire lifecycle of an energy storage project, reflecting its long-term investment value. Technical constraints are the technical boundary conditions that an energy storage system must meet to operate, such as power constraints, capacity constraints, lifespan constraints, and grid connection constraints.

[0037] In some optional implementations, the objective function, constraints, and operating mode information are input into a preset optimization algorithm to obtain candidate energy storage optimization configuration schemes under a single set of parameters. The optimization parameters are then switched, and the process of optimizing multiple energy storage configuration parameters based on the operating mode information is returned to obtain multiple candidate energy storage optimization configuration schemes. For example, the preset optimization algorithm can be a single-factor or two-factor analysis algorithm.

[0038] Step S205: Evaluate each candidate energy storage optimization configuration scheme and select the target energy storage optimization configuration scheme based on the evaluation results.

[0039] Among them, a multi-dimensional evaluation system was constructed, and a comprehensive evaluation model combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was adopted to quantitatively score and rank the above candidate energy storage optimization configuration schemes, and to select the target energy storage optimization configuration scheme that takes into account economy, reliability and advanced technology.

[0040] In some optional implementations, candidate energy storage optimization configuration schemes and multiple candidate energy storage optimization configuration schemes under a single set of parameters are evaluated, and the target energy storage optimization configuration scheme is selected based on the evaluation results.

[0041] The energy storage optimization configuration method provided in this embodiment acquires user-inputted basic energy storage data, determines the application scenarios of the energy storage system based on the basic energy storage data, matches the application scenarios with a preset scenario demand database to obtain corresponding energy storage demand information, and determines application scenarios based on the basic energy storage data. This adapts to multiple application scenarios, effectively covering different types of energy storage demands, thereby improving the accuracy and practicality of energy storage configuration. This embodiment of the invention analyzes the power supply and demand characteristics of the energy storage system based on energy storage demand information, generating multiple energy storage configuration alternatives, effectively covering different types of energy storage demands, thereby improving the accuracy and practicality of energy storage configuration. This embodiment of the invention performs levelized cost analysis on each energy storage configuration alternative based on the basic energy storage data, selects the target energy storage configuration alternative with the lowest levelized cost and its corresponding operating mode information from among multiple alternatives based on the levelized cost analysis results, and further optimizes resource allocation efficiency by conducting a detailed evaluation of each energy storage configuration alternative and filtering out the target energy storage configuration alternative with the optimal cost and its operating mode information. This invention uses maximizing net present value as the objective function and various technical constraints as conditions. Based on operational mode information, it optimizes multiple energy storage configuration parameters to obtain candidate energy storage optimization configuration schemes under a single set of parameters. It then switches optimization parameters and returns to the step of optimizing multiple energy storage configuration parameters based on operational mode information, resulting in multiple candidate energy storage optimization configuration schemes. Optimization is performed based on different optimization parameters to obtain candidate energy storage optimization configuration schemes with different optimization key points. This not only improves the technical feasibility of the candidate energy storage optimization configuration schemes but also enhances their economic value. This invention evaluates each candidate energy storage optimization configuration scheme and selects the target energy storage optimization configuration scheme based on the evaluation results, thus achieving optimized energy storage configuration of the energy storage system. Compared with related technologies, this invention provides a universal energy storage optimization configuration process applicable to multiple scenarios. Through scenario matching and a unified algorithm framework, it solves the problems of scenario fragmentation and poor algorithm reusability, significantly improving engineering application efficiency. By analyzing the levelized cost of electricity (LCOE), it obtains target energy storage configuration alternatives suitable for the scenario, along with the corresponding operating mode information, ensuring that the operating strategy matches the scenario requirements. Multiple sets of candidate energy storage optimization configuration schemes are generated by switching optimization parameters, resulting in diversified candidate schemes that meet more user needs. This invention is adaptable to multiple application scenarios, allowing for the selection of corresponding energy storage configuration schemes for each scenario. The invention first selects multiple energy storage configuration alternatives based on the application scenario, then optimizes the parameters in each alternative scheme, and finally selects the optimal target energy storage optimization configuration scheme based on the evaluation results, greatly improving the quality and efficiency of energy storage configuration.

[0042] This embodiment provides an energy storage optimization configuration method that can be used in computer equipment. Figure 3 This is a second flowchart of the energy storage optimization configuration method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Obtain the basic energy storage data input by the user, determine the application scenario of the energy storage system based on the basic energy storage data, match the preset scenario demand database according to the application scenario, and obtain the corresponding energy storage demand information.

[0043] Specifically, step S301 includes: Step S3011: Determine the application scenarios of the energy storage system based on the application data in the energy storage basic data.

[0044] The application data includes application scenarios. For example, the application data includes new energy supporting energy storage, grid-side peak-shaving energy storage, and user-side peak-valley arbitrage energy storage.

[0045] Step S3012: Input the application scenario into the preset scenario demand database for matching to obtain the energy storage demand information corresponding to the application scenario.

[0046] By searching the preset scenario demand database according to the application scenario, the energy storage demand information corresponding to the application scenario can be obtained.

[0047] Step S302: Analyze the power supply and demand characteristics of the energy storage system based on the energy storage demand information, and generate multiple alternative energy storage configuration schemes.

[0048] Specifically, step S302 includes: Step S3021: Perform load-generation correlation analysis based on energy storage demand information to obtain multiple charging and discharging demand periods for the energy storage system.

[0049] Among them, load-generation correlation analysis involves quantitatively analyzing the time matching relationship between load changes and power generation to identify the supply-demand gap periods in the time matching relationship between load changes and power generation. For example, periods when power generation exceeds load are periods of charging demand, and periods when load exceeds power generation are periods of discharging demand.

[0050] Step S3022: Perform a stochastic analysis based on the energy storage demand information to obtain multiple regulation capacity requirements of the energy storage system.

[0051] Among them, the stochastic analysis includes load fluctuation and generation stochastic analysis. The energy storage regulation capacity is determined by load fluctuation and generation stochastic analysis. By quantifying the fluctuation amplitude / frequency of the load and the degree of random fluctuation of the generation, the extreme value and rate of change of the total power gap / surplus after the superposition of the two are calculated, thereby determining the power regulation range, response speed and capacity regulation capacity that the energy storage needs to have.

[0052] Step S3023: Perform sensitivity analysis based on energy storage demand information to obtain multiple key parameters of the energy storage system.

[0053] Sensitivity analysis involves fixing other parameters and adjusting the value of the target parameter to quantify the impact of parameter changes on energy storage revenue, and then selecting multiple key parameters by ranking them according to their degree of impact.

[0054] Step S3024: Randomly combine multiple charging and discharging demand periods, multiple regulation capacity requirements, and multiple key parameters to obtain multiple alternative energy storage configuration schemes.

[0055] Step S303: Perform a levelized cost of electricity (LCOE) analysis on each energy storage configuration candidate based on the basic energy storage data. Based on the LCOE analysis results, select the target energy storage configuration candidate with the lowest LCOE from among multiple energy storage configuration candidate candidates, along with the corresponding operation mode information for the target energy storage configuration candidate. The operation mode information includes the operation logic, operation mode boundary conditions, and multiple optimizable energy storage configuration parameters.

[0056] Specifically, step S303 includes: Step S3031: Based on the cost data and revenue data in the energy storage basic data, determine the levelized cost of electricity (LCOE) for each energy storage configuration alternative; LCOE is used to characterize the cost per unit of electricity.

[0057] The process involves constructing a levelized cost of electricity (LCOE) model. Cost and revenue data from the energy storage infrastructure are input into the LCOE model to obtain the LCOE corresponding to each energy storage configuration option. Specifically, the LCOE model construction process involves: obtaining a first summation result based on the sum of the initial investment cost and the total lifecycle operation and maintenance cost; obtaining a second summation result based on the sum of the residual value and various revenues; obtaining a target difference based on the difference between the first and second summations; and determining the LCOE corresponding to each energy storage configuration option based on the quotient of the target difference and the total lifecycle effective discharge capacity. Various revenues include peak-valley arbitrage revenue, ancillary service revenue, subsidy revenue, and reduced curtailment revenue. The initial investment cost is calculated based on the equipment manufacturer's quotation, civil engineering / installation costs, or reference to the industry average cost. The total lifecycle operation and maintenance cost is calculated based on the manufacturer's annual operation and maintenance fee or reference to the industry's conventional ratio. The total lifecycle effective discharge capacity is calculated based on the energy storage technical parameters (rated capacity, cycle life, etc.) provided by the manufacturer, combined with operational strategy simulation, and adjusted for factors such as maintenance and losses.

[0058] For example, the cost per kilowatt-hour model can be expressed as:

[0059] in, For the cost per kilowatt-hour, For initial investment costs, For total lifecycle maintenance costs, For equipment residual value, For all types of benefits throughout the entire life cycle, This refers to the effective discharge capacity throughout the entire lifespan.

[0060] In some alternative implementations, the cost per kilowatt-hour is the levelized cost of storage (LCOS).

[0061] Step S3032: Sort the levelized electricity cost corresponding to multiple energy storage configuration alternatives to obtain a levelized electricity cost sequence. Select the target energy storage configuration alternative corresponding to the target levelized electricity cost with the lowest levelized electricity cost from the levelized electricity cost sequence, and obtain the operation mode information corresponding to the target energy storage configuration alternative.

[0062] The operational mode information includes preliminary operational strategies, business model boundaries, and key parameters, specifically including defining core revenue streams (peak-valley arbitrage / ancillary services / reduction of power curtailment, etc.), operational boundaries, and key profitability parameters.

[0063] Step S304: Using the maximum net present value as the objective function and various technical constraints as conditions, optimize multiple energy storage configuration parameters based on the operation mode information to obtain candidate optimized energy storage configuration schemes under a single set of parameters. Switch the optimization parameters and return to the step of optimizing multiple energy storage configuration parameters based on the operation mode information to obtain multiple candidate optimized energy storage configuration schemes. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0064] Step S305: Evaluate each candidate energy storage optimization configuration scheme and select the target energy storage optimization configuration scheme based on the evaluation results. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0065] The energy storage optimization configuration method provided in this embodiment can accurately identify the matching relationship between peak load and off-peak generation (corresponding to discharge demand periods) and off-peak load and peak generation (corresponding to charging demand periods) by analyzing the correlation between load and power generation data. This clarifies the charging and discharging behavior windows of the energy storage system in different scenarios. Through stochastic analysis, it can quantify the power or capacity adjustment margin required by the energy storage system when facing uncertain factors, and obtain the adjustment capacity requirements under different confidence levels. Through sensitivity analysis, it can identify the key parameters that have the most significant impact on the economics and technical performance of energy storage configuration. By randomly combining charging and discharging periods, adjustment capacity requirements and key parameters, it can generate a multi-dimensional set of alternative solutions covering different supply and demand sequences, different adjustment capacities and different combinations of key parameters.

[0066] This embodiment provides an energy storage optimization configuration method that can be used in computer equipment. Figure 4 This is a third flowchart of the energy storage optimization configuration method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps: Step S401: Obtain the user-inputted basic energy storage data; determine the application scenario of the energy storage system based on the basic energy storage data; match the application scenario with a preset scenario demand database to obtain the corresponding energy storage demand information. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0067] Step S402: Based on the energy storage demand information, perform a power supply and demand characteristic analysis on the energy storage system to generate multiple alternative energy storage configuration schemes. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0068] Step S403: Based on the basic energy storage data, perform a levelized cost of electricity (LCOE) analysis on each energy storage configuration candidate. Based on the LCOE analysis results, select the target energy storage configuration candidate with the lowest LCOE from among multiple candidate options, along with the corresponding operation mode information. The operation mode information includes the operation logic, operation mode boundary conditions, and multiple optimizable energy storage configuration parameters. For details, please refer to [link to relevant documentation]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0069] Step S404: Using the maximum net present value as the objective function and various technical constraints as constraints, optimize multiple energy storage configuration parameters based on the operation mode information to obtain candidate energy storage optimization configuration schemes under a single set of parameters. Switch the optimization parameters and return to the step of optimizing multiple energy storage configuration parameters based on the operation mode information to obtain multiple candidate energy storage optimization configuration schemes.

[0070] Specifically, step S404 includes: Step S4041: Using the maximum net present value as the objective function, input the objective function, constraints, and operating mode information into the preset optimization algorithm to obtain candidate energy storage optimization configuration schemes under a single set of parameters.

[0071] The objective function considers and combines total revenue and total cost over the entire life cycle. Total revenue includes peak-valley arbitrage revenue, ancillary service revenue, and subsidy revenue, while total cost includes initial investment cost, operation and maintenance cost, equipment depreciation cost, and capital occupation cost. Constraints include power constraints, capacity constraints, lifespan constraints, and grid connection constraints.

[0072] In some optional implementations, the preset optimization algorithm can be a single-factor or two-factor analysis algorithm. In single-factor analysis, other parameters are fixed, and only the energy storage capacity is used as a variable. The range of variable values ​​is traversed to solve the NPV under different values. The NPVs are sorted, and the energy storage capacity and power configuration with the largest NPV are selected as the candidate energy storage optimization configuration scheme under this condition. In two-factor analysis, the energy storage capacity and charging / discharging power are used as core variables to construct a two-dimensional optimization space. The grid search method or genetic algorithm is used to solve the problem. The NPVs under the two-variable constraints are sorted, and the energy storage capacity and power configuration with the largest NPV are selected as the candidate energy storage optimization configuration scheme under this condition.

[0073] Step S4042: Using the maximum net present value as the objective function, switch the energy storage type, or adjust at least one of the key economic parameters, constraints, and operation mode information input parameters, and return to the step of optimizing multiple energy storage configuration parameters based on the operation mode information to obtain multiple candidate energy storage optimization configuration schemes.

[0074] To enhance the flexibility and adaptability of the configuration schemes, multiple sets of alternative optimal schemes are generated by switching core input parameters based on the above optimization process. The switchable parameters include energy storage type, key economic parameters, constraints, and operating strategies. For each set of adjusted parameters, the above optimization solution process is repeated to obtain the optimal configuration scheme under the corresponding parameter combination, ultimately forming multiple sets of candidate energy storage optimization configuration schemes with differentiated advantages.

[0075] In some alternative implementations, the constraints also include implicit constraints, specifically including demand constraints, technical characteristic constraints, external scenario constraints, initial investment cost constraints, energy storage discharge capacity-marginal electricity price constraints, and life-cycle trend constraints.

[0076] Specifically, demand constraints include the impact of energy storage application demand on operation strategies and the impact of energy storage application demand on operation modes. The relationship between the energy storage cost per kilowatt-hour and the peak-valley price difference or the average spot market price difference varies in different regions. Therefore, the energy storage operation strategies for different energy storage application scenarios may differ significantly, which in turn significantly affects their cost and revenue calculations. Thus, different application scenarios are generally matched with corresponding different energy storage optimization configuration algorithms, i.e., the impact of energy storage application demand on operation strategies. The power and electricity demand of the application scenario can initially define the optimization range of the optimization variable (power). Within this range, when the energy storage power is greater than or less than the demand power, the operation mode of energy storage is completely different. This difference in operation will affect the average daily equivalent cycle number of energy storage, which in turn will affect the average annual revenue, operating years, and average annual cost of energy storage, and thus affect the economic indicator results, i.e., the impact of energy storage application demand on operation modes.

[0077] In some optional implementations, different energy storage technologies exhibit significant differences in technical parameters such as energy conversion efficiency, charge / discharge depth, cycle life, annual degradation rate, and average annual power station downtime for maintenance, regarding technical characteristic constraints. Therefore, it is necessary to fully consider the constraints of these technical parameters in the entire energy storage optimization configuration algorithm to achieve a more reasonable energy storage optimization configuration result. Technical characteristic constraints include: energy conversion efficiency constraints, charge / discharge depth constraints, cycle life and battery swapping constraints, and average annual power station downtime for maintenance constraints. Among these, for energy conversion efficiency constraints, the rated energy of a typical energy storage power station generally refers to the rated discharge energy, which is one of the main optimization variables. Therefore, if the amount of curtailed solar power is sufficient and... When there are no charging costs (such as photovoltaic power storage on the renewable energy side), energy conversion efficiency generally does not need to be considered. However, if distributed photovoltaic power is involved, the charging capacity is limited, or the cost of purchasing electricity from the grid, such as off-peak electricity, needs to be considered to ensure reasonable calculation of key indicators such as annual operating cost, revenue, and annual equivalent number of operations for energy storage. As for the charge and discharge depth constraint, charge and discharge depth is a factor often considered in conventional energy storage configuration methods and is generally used as one of the main parameters in the algorithm calculation. However, in reality, the rated energy of an actual operational energy storage power station is the energy that can actually be released under the actual charge and discharge depth of the energy storage. Therefore, this factor does not need to be considered in the entire energy storage configuration process.Generally, the battery cell needs to deliver the target capacity at the depth of charge and discharge during energy storage. Therefore, batteries are usually over-supplied, and this cost is already included in the initial investment cost if an EPC (Engineering, Procurement, and Construction) pricing method is used. Regarding cycle life and battery swapping constraints, different energy storage methods exhibit significant differences in cycle life, and are affected by factors such as consistency. The lifespan of the battery cell, module, and energy storage system decreases in that order. For lithium-ion batteries, a single lithium-ion battery cell for energy storage has a cycle life greater than or equal to 6000 cycles with a capacity retention rate greater than or equal to 80%, while a battery pack has a cycle life greater than or equal to 5000 cycles with a capacity retention rate greater than or equal to 80%. Considering consistency factors, a lithium-ion energy storage system is generally considered to have a cycle life greater than or equal to 4000 cycles with a capacity retention rate of 80% or higher. Therefore, for battery-based energy storage methods, it is essential to first determine the specific cycle life level and then convert it to the corresponding system cycle life for energy storage optimization. Configuration calculations, based on the aforementioned operational constraints, yield the annual equivalent cycle count. Assuming the operating mode remains relatively stable throughout the energy storage's entire lifecycle, i.e., the number of cycles per year remains relatively stable, the energy storage cycle life and the annual equivalent cycle count together determine the annual degradation rate and the year for battery replacement. This, in turn, affects factors such as the total annual discharge capacity, annual revenue cash flow, battery replacement cost years and corresponding discounted amounts, and residual value throughout the energy storage's entire lifecycle. Therefore, energy storage life and battery replacement constraints are among the main constraints that all types of energy storage optimization configuration algorithms should fully consider. Regarding the constraint of the annual average power station downtime for maintenance, generally, the more mature the energy storage technology and the higher the technical level of the energy storage manufacturer, the lower the annual average power station downtime for maintenance. This factor is mainly related to the type of energy storage technology. Lithium-ion battery energy storage has the lowest annual average power station downtime for maintenance, generally 10-12 days. If other new energy storage technologies with lower maturity are used, the number of days can be appropriately increased based on experience. The average number of days of power plant outage for maintenance per year will directly affect the total annual revenue, and will also affect the availability factor of energy storage power plants in the reliability index of energy storage evaluation. It is also one of the constraints that all types of energy storage configuration algorithms should consider.

[0078] In some alternative implementations, regarding external scenario constraints, in specific application scenarios, there are often some scenario constraints that are easy to overlook but have a significant impact, such as the grid structure and the site boundary. These external scenario constraints are strong constraints that affect the energy storage optimization configuration and can have a decisive impact on the energy storage optimization configuration results.

[0079] In some alternative implementations, the initial investment cost is often a major factor considered by energy storage investors and builders. Generally, decision-makers have a psychological expectation range for the initial investment cost, and this implicit expectation range becomes one of the main constraints on optimal energy storage allocation. It works in conjunction with constraints such as loan ratio, loan term, and loan interest rate to influence the final economic indicators. Furthermore, due to the significant differences in unit costs among different energy storage types, this implicit constraint may directly affect the optimal power output of the optimized energy storage allocation. In addition, the initial investment cost of energy storage is also related to multiple factors such as storage hours, energy storage sophistication, and technological maturity. Therefore, the coupling relationship between these factors needs to be comprehensively considered. For example, the initial investment cost of lithium-ion battery energy storage (2-8 hours) is not significantly different and can be calculated using the same value. However, for flow battery energy storage, the unit cost of 4-hour, 6-hour, and 8-hour flow battery energy storage differs significantly. Therefore, when selecting flow battery energy storage with different hours, different initial investment costs should be chosen. Similarly, if more advanced energy storage technologies are considered, their initial investment costs are generally higher than the industry average, so the advanced nature index score should be higher, and appropriate and reasonable compensation should be made in the evaluation of energy storage configuration schemes.

[0080] In some alternative implementations, regarding the constraint of energy storage discharge capacity versus marginal electricity price, for large-scale photovoltaic (PV) power plants with high curtailment rates, if energy storage can participate in the spot market alongside the PV power plants, and the output of new energy sources is part of the bidding space and affects the supply-demand ratio, thus potentially influencing the spot marginal price, then high-power energy storage discharge may affect the marginal price. In this case, using historical marginal price data for revenue calculation would overestimate the revenue from energy storage discharge. Deep learning methods can be used to train the system on market environment data and real-time spot market electricity price data to predict the impact trend of increasing energy storage discharge capacity at different levels on the marginal price, and the revenue from energy storage can be adjusted using a revenue loss factor. If energy storage participates in the market without bidding or with a bid difference, and does not affect the marginal price in the spot market, then this factor does not need to be considered.

[0081] In some alternative implementations, for the life-cycle trend constraints, in the entire life cycle of energy storage (24 years), in addition to the above constraints, there are other influencing factors, such as the long-term increasing trend of peak-valley price difference on the user side, the long-term decreasing trend of average annual spot market price, the price change trend of energy storage cells that need to be replaced, and the trend of the impact of policy changes on returns. These long-term factors will also have a significant impact on the calculation results of the economic indicators of energy storage configuration in the future, so they also need to be fully considered.

[0082] Step S405: Evaluate each candidate energy storage optimization configuration scheme and select the target energy storage optimization configuration scheme based on the evaluation results.

[0083] Specifically, step S405 includes: Step S4051: Obtain multiple preset evaluation indicators and perform hierarchical analysis on the multiple preset evaluation indicators to obtain the indicator weights corresponding to each preset evaluation indicator. Based on the indicator parameters and indicator weights corresponding to each candidate energy storage optimization configuration scheme, determine the target indicator matrix corresponding to each candidate energy storage optimization configuration scheme.

[0084] The evaluation indicators include target-level indicators, criterion-level indicators, and indicator-level indicators. The target-level indicators are comprehensive optimality evaluation indicators for energy storage configuration schemes. The criterion-level indicators include indicators in three dimensions: economy, reliability, and advancement. The indicator-level indicators are specific quantitative indicators under each criterion-level indicator. The evaluation indicator system is customized according to application scenarios and user needs.

[0085] In some optional implementations, hierarchical analysis is performed on multiple preset evaluation indicators to obtain the indicator weights corresponding to each preset evaluation indicator. This includes: obtaining the relative importance of the criteria-level indicators and indicator-level indicators as input by the user; constructing a 1-9 scale judgment matrix: constructing judgment matrices at two levels, namely, target-level indicators-criteria-level indicators and criteria-level indicators-indicator-level indicators, with elements in the matrix assigned values ​​on a 1-9 scale (1 indicates that the two indicators are equally important, 9 indicates that one indicator is much more important than the other, and intermediate numbers represent transitional importance), clarifying the importance ratio between indicators at the same level; calculating the consistency index of the judgment matrix, The random consistency ratio is used to determine the consistency of the matrix. If the random consistency ratio is less than 0.1, the matrix meets the consistency requirements and the importance of the indicators is reasonably assigned. If it does not meet the requirements, the user needs to modify the judgment matrix until it does. By calculating the eigenvalues ​​and eigenvectors of the judgment matrix, the hierarchical single ranking weights (the weights of each indicator at the same level relative to a certain indicator at the next higher level) are obtained. Then, the hierarchical total ranking weights are calculated by combining the single ranking weights of each level. Finally, the final weight coefficients of all indicators at the indicator level relative to the target level (comprehensive optimal evaluation) are obtained. This is the result of performing hierarchical analysis on multiple preset evaluation indicators to obtain the indicator weights corresponding to each preset evaluation indicator.

[0086] In some optional implementations, a target index matrix is ​​formed by the index weights corresponding to multiple preset evaluation indicators for each candidate energy storage optimization configuration scheme.

[0087] Step S4052: Standardize the target index matrix corresponding to each candidate energy storage optimization configuration scheme, and extract the positive ideal solution and negative ideal solution from the standardized matrix.

[0088] In this process, the optimal values ​​of each indicator column are extracted from the standardized matrix obtained by standardization (maximum for benefit type and minimum for cost type), and the combination is the positive ideal solution. The worst values ​​of each indicator column are extracted (minimum for benefit type and maximum for cost type), and the combination is the negative ideal solution.

[0089] Step S4053: Determine the relative proximity of each candidate energy storage optimization configuration scheme to the positive ideal solution and the negative ideal solution based on the Euclidean distance between each candidate energy storage optimization configuration scheme and the positive ideal solution and the negative ideal solution, respectively; the relative proximity is used to characterize the comprehensive similarity between the candidate energy storage optimization configuration scheme and the ideal optimal scheme.

[0090] Specifically, the Euclidean distance between each candidate energy storage optimization configuration scheme and the positive ideal solution and the negative ideal solution is calculated. The first Euclidean distance between each candidate energy storage optimization configuration scheme and the positive ideal solution and the second Euclidean distance between each candidate energy storage optimization configuration scheme and the negative ideal solution are summed to obtain the third summation result. The relative closeness of each candidate energy storage optimization configuration scheme is obtained based on the quotient of the second Euclidean distance and the third summation result.

[0091] Step S4054: Select the candidate energy storage optimization configuration scheme with the highest relative similarity as the target energy storage optimization configuration scheme.

[0092] Among them, the closer the relative proximity is to 1, the better the candidate energy storage optimization configuration scheme is. All candidate energy storage optimization configuration schemes are ranked according to the relative proximity, and the scheme ranked first is the target energy storage optimization configuration scheme.

[0093] The energy storage optimization configuration method provided in this embodiment unifies the complex energy storage configuration methods for various scenarios, proposing a universally applicable energy storage optimization configuration method. It bridges the gap between energy storage configuration algorithm research and application research, thereby enabling the rapid generation of energy storage configuration schemes. This empowers researchers in actual production energy storage configuration, greatly improving the quality and efficiency of energy storage configuration research. This embodiment considers the transmission relationships of six categories of implicit constraints, including demand constraints and technical characteristic constraints, solving the problem of large deviations between configuration results and reality caused by simplified models of related technologies, significantly improving the engineering practicality of the configuration scheme. The comprehensive evaluation method, combining the Analytic Hierarchy Process (AHP) with TOPSIS, takes into account multiple dimensions such as economy, reliability, and advancement, avoiding the one-sidedness of single-index evaluation.

[0094] This embodiment provides an energy storage optimization configuration method that can be used in computer equipment. Figure 5 This is a fourth flowchart of the energy storage optimization configuration method according to an embodiment of the present invention, such as... Figure 5 As shown, the process includes the following steps: Storage demand analysis; optimization of allocation algorithms; evaluation of allocation schemes.

[0095] Specifically, in the energy storage demand analysis stage, based on the scenario and demand analysis, data analysis and mining can be performed on the raw input data provided by users. Through levelized cost of electricity (LCOE) analysis, preliminary operation strategies and business models can be obtained, serving as important inputs for subsequent energy storage configuration algorithms. In the optimization configuration algorithm stage, the energy storage form and corresponding complete set of data parameters are selected. Single-factor or two-factor analysis can be used to obtain the configuration scheme that maximizes NPV, which is the optimal configuration scheme under that condition. The energy storage type or other input parameters can be switched according to demand to obtain multiple alternative optimal energy storage configuration schemes. In the configuration scheme evaluation stage, an evaluation system for energy storage configuration schemes based on three dimensions—economic efficiency, reliability, and advancement—is constructed. Using the Analytic Hierarchy Process (AHP) weighting method combined with TOPSIS, the optimal configuration scheme among the above schemes can be derived.

[0096] This embodiment also provides an energy storage optimization configuration device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] This embodiment provides an energy storage optimization configuration device, such as... Figure 6 As shown, it includes: The application scenario determination module 601 is used to acquire the basic energy storage data input by the user, determine the application scenario of the energy storage system based on the basic energy storage data, and match the application scenario with a preset scenario requirement database to obtain the corresponding energy storage requirement information.

[0098] The characteristic analysis module 602 is used to perform power supply and demand characteristic analysis on the energy storage system based on energy storage demand information, and generate multiple alternative energy storage configuration schemes.

[0099] The operation mode determination module 603 is used to perform levelized cost analysis on each energy storage configuration candidate scheme based on the energy storage basic data, select the target energy storage configuration candidate scheme with the lowest levelized cost from multiple energy storage configuration candidate schemes based on the levelized cost analysis results, and the operation mode information corresponding to the target energy storage configuration candidate scheme; the operation mode information includes the operation logic, operation mode boundary conditions and multiple optimizable energy storage configuration parameters.

[0100] The alternative scheme determination module 604 is used to optimize multiple energy storage configuration parameters based on the operating mode information with the maximum net present value as the objective function and multiple technical constraints as constraints, to obtain candidate energy storage optimization configuration schemes under a single set of parameters, switch optimization parameters, and return the steps of optimizing multiple energy storage configuration parameters based on the operating mode information to obtain multiple candidate energy storage optimization configuration schemes.

[0101] The target scheme determination module 605 is used to evaluate each candidate energy storage optimization configuration scheme and select the target energy storage optimization configuration scheme based on the evaluation results.

[0102] In some optional implementations, the application scenario determination module 601 includes: The application scenario determination unit is used to determine the application scenario of the energy storage system based on the application data in the energy storage basic data.

[0103] The demand information matching unit is used to input the application scenario into the preset scenario demand database for matching, and obtain the energy storage demand information corresponding to the application scenario.

[0104] In some alternative implementations, the feature analysis module 602 includes: The correlation analysis unit is used to perform load-generation correlation analysis based on energy storage demand information to obtain multiple charging and discharging demand periods of the energy storage system.

[0105] The stochastic analysis unit is used to perform stochastic analysis based on energy storage demand information to obtain multiple regulation capacity requirements of the energy storage system.

[0106] The sensitivity analysis unit is used to perform sensitivity analysis based on energy storage demand information to obtain multiple key parameters of the energy storage system.

[0107] The random combination unit is used to randomly combine multiple charging and discharging demand periods, multiple regulation capacity requirements, and multiple key parameters to obtain multiple energy storage configuration alternatives.

[0108] In some optional implementations, the operating mode determination module 603 includes: The cost per kilowatt-hour determination unit is used to determine the cost per kilowatt-hour for each energy storage configuration alternative based on the cost and revenue data in the basic energy storage data; the cost per kilowatt-hour is used to characterize the cost per unit of electricity.

[0109] The alternative scheme determination unit is used to sort the levelized cost of electricity (LCOE) corresponding to multiple energy storage configuration alternative schemes to obtain an LCOE cost sequence, select the target energy storage configuration alternative scheme with the lowest LCOE cost from the LCOE cost sequence, and obtain the operation mode information corresponding to the target energy storage configuration alternative scheme.

[0110] In some alternative implementations, the alternative determination module 604 includes: The first optimization unit is used to take the maximum net present value as the objective function, input the objective function, constraints and operating mode information into the preset optimization algorithm, and obtain the candidate energy storage optimization configuration scheme under a single set of parameters.

[0111] The second optimization unit is used to switch energy storage types or adjust at least one of the key economic parameters, constraints, and operation mode information with the objective function of maximizing net present value. It returns the steps of optimizing multiple energy storage configuration parameters based on the operation mode information to obtain multiple candidate energy storage optimization configuration schemes.

[0112] In some alternative implementations, the target solution determination module 605 includes: The indicator matrix determination unit is used to acquire multiple preset evaluation indicators and perform hierarchical analysis on the multiple preset evaluation indicators to obtain the indicator weights corresponding to each preset evaluation indicator. Based on the indicator parameters and indicator weights corresponding to each candidate energy storage optimization configuration scheme, the target indicator matrix corresponding to each candidate energy storage optimization configuration scheme is determined.

[0113] The standardization processing unit is used to standardize the target index matrix corresponding to each candidate energy storage optimization configuration scheme and extract the positive ideal solution and negative ideal solution from the standardized matrix.

[0114] The proximity determination unit is used to determine the relative proximity of each candidate energy storage optimization configuration scheme based on the Euclidean distance between each candidate energy storage optimization configuration scheme and the positive ideal solution and the negative ideal solution, respectively. The relative proximity is used to characterize the comprehensive similarity between the candidate energy storage optimization configuration scheme and the ideal optimal scheme.

[0115] The target scheme determination unit is used to select the candidate energy storage optimization configuration scheme with the highest relative similarity as the target energy storage optimization configuration scheme.

[0116] The energy storage optimization configuration device provided in this embodiment of the invention can execute the energy storage optimization configuration method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0117] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0118] The following is a detailed reference. Figure 7This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 702 or a program loaded from memory 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0119] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0120] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 708, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the energy storage optimization configuration method of the embodiments of the present invention.

[0121] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0122] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the energy storage optimization configuration method shown in the above embodiments is implemented.

[0123] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0124] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for energy storage optimization configuration, characterized in that, The method includes: Obtain basic energy storage data input by the user, determine the application scenario of the energy storage system based on the basic energy storage data, match the application scenario with a preset scenario demand database, and obtain the corresponding energy storage demand information. Based on the energy storage demand information, the power supply and demand characteristics of the energy storage system are analyzed to generate multiple alternative energy storage configuration schemes. Based on the energy storage basic data, a levelized cost of electricity (LCOE) analysis is performed on each of the energy storage configuration candidate schemes. Based on the LCOE analysis results, a target energy storage configuration candidate scheme with the lowest LCOE is selected from multiple energy storage configuration candidate schemes, along with the corresponding operation mode information of the target energy storage configuration candidate scheme. The operation mode information includes operation logic, operation mode boundary conditions, and multiple optimizable energy storage configuration parameters. With the net present value as the objective function and various technical constraints as constraints, the multiple energy storage configuration parameters are optimized according to the operating mode information to obtain candidate energy storage optimization configuration schemes under a single set of parameters. The optimization parameters are switched, and the process returns to the step of optimizing multiple energy storage configuration parameters according to the operating mode information to obtain multiple candidate energy storage optimization configuration schemes. Each of the candidate energy storage optimization configuration schemes is evaluated, and the target energy storage optimization configuration scheme is selected based on the evaluation results.

2. The method of claim 1, wherein, The step of determining the application scenario of the energy storage system based on the energy storage basic data, and matching the application scenario with a preset scenario demand database to obtain the corresponding energy storage demand information includes: The application scenario of the energy storage system is determined based on the application data in the energy storage basic data; The application scenario is input into the preset scenario demand database for matching to obtain the energy storage demand information corresponding to the application scenario.

3. The method according to claim 1 or 2, characterized in that, The step involves analyzing the power supply and demand characteristics of the energy storage system based on the energy storage demand information, generating multiple alternative energy storage configuration schemes, including: Based on the energy storage demand information, load-generation correlation analysis is performed to obtain multiple charging and discharging demand periods of the energy storage system. Based on the energy storage demand information, a stochastic analysis is performed to obtain multiple regulation capacity requirements of the energy storage system; Sensitivity analysis is performed based on the energy storage demand information to obtain several key parameters of the energy storage system; By randomly combining multiple charging and discharging demand periods, multiple regulation capacity requirements, and multiple key parameters, multiple alternative energy storage configuration schemes are obtained.

4. The method according to claim 1 or 2, characterized in that, The step involves performing a levelized cost of electricity (LCOE) analysis on each of the energy storage configuration candidate schemes based on the energy storage basic data, selecting the target energy storage configuration candidate scheme with the lowest LCOE from among the multiple energy storage configuration candidate schemes based on the LCOE analysis results, and providing the corresponding operating mode information for the target energy storage configuration candidate scheme, including: Based on the cost and revenue data in the energy storage basic data, the cost per kilowatt-hour corresponding to each of the energy storage configuration alternatives is determined; the cost per kilowatt-hour is used to characterize the cost per unit of electricity. The cost per kilowatt-hour corresponding to multiple energy storage configuration alternatives is sorted to obtain a cost per kilowatt-hour sequence. The target energy storage configuration alternative corresponding to the target cost per kilowatt-hour with the lowest cost per kilowatt-hour is selected from the cost per kilowatt-hour sequence, and the operation mode information corresponding to the target energy storage configuration alternative is obtained.

5. The method according to claim 1 or 2, characterized in that, The process involves using the maximum net present value as the objective function, multiple technical constraints as conditions, and optimizing multiple energy storage configuration parameters based on the operating mode information to obtain candidate energy storage optimization configuration schemes under a single set of parameters. The optimization parameters are then switched, and the process returns to the step of optimizing multiple energy storage configuration parameters based on the operating mode information to obtain multiple candidate energy storage optimization configuration schemes. This includes: Using the maximum net present value as the objective function, the objective function, the constraints, and the operating mode information are input into a preset optimization algorithm to obtain the candidate energy storage optimization configuration scheme under a single set of parameters; Using the maximum net present value as the objective function, the energy storage type is switched, or at least one of the key economic parameters, the constraints, and the operating mode information is adjusted. The process then returns to the step of optimizing multiple energy storage configuration parameters based on the operating mode information, thereby obtaining multiple candidate energy storage optimization configuration schemes.

6. The method of claim 1 or 2, wherein, The step of evaluating each of the candidate energy storage optimization configuration schemes and selecting the target energy storage optimization configuration scheme based on the evaluation results includes: Multiple preset evaluation indicators are obtained and hierarchical analysis is performed on the multiple preset evaluation indicators to obtain the indicator weights corresponding to each preset evaluation indicator. Based on the indicator parameters and indicator weights corresponding to each candidate energy storage optimization configuration scheme, the target indicator matrix corresponding to each candidate energy storage optimization configuration scheme is determined. The target index matrix corresponding to each candidate energy storage optimization configuration scheme is standardized, and the positive ideal solution and negative ideal solution are extracted from the standardized matrix. The relative proximity of each candidate energy storage optimization configuration scheme is determined based on the Euclidean distance between each candidate energy storage optimization configuration scheme and the positive ideal solution and the negative ideal solution, respectively; the relative proximity is used to characterize the comprehensive similarity between the candidate energy storage optimization configuration scheme and the ideal optimal scheme. The candidate energy storage optimization configuration scheme with the highest relative similarity is selected as the target energy storage optimization configuration scheme.

7. An energy storage optimization configuration device, characterized by, The device includes: The application scenario determination module is used to acquire the basic energy storage data input by the user, determine the application scenario of the energy storage system based on the basic energy storage data, and match the application scenario with a preset scenario demand database to obtain the corresponding energy storage demand information. The characteristic analysis module is used to perform power supply and demand characteristic analysis on the energy storage system based on the energy storage demand information, and generate multiple energy storage configuration alternatives. The operation mode determination module is used to perform levelized cost analysis on each of the energy storage configuration candidate schemes based on the energy storage basic data, and select the target energy storage configuration candidate scheme with the lowest levelized cost from multiple energy storage configuration candidate schemes based on the levelized cost analysis results, as well as the operation mode information corresponding to the target energy storage configuration candidate scheme; the operation mode information includes operation logic, operation mode boundary conditions, and multiple optimizable energy storage configuration parameters; The alternative scheme determination module is used to optimize multiple energy storage configuration parameters based on the operation mode information with the maximum net present value as the objective function and multiple technical constraints as constraints, to obtain candidate energy storage optimization configuration schemes under a single set of parameters, switch optimization parameters, and return to the step of optimizing multiple energy storage configuration parameters based on the operation mode information to obtain multiple candidate energy storage optimization configuration schemes. The target scheme determination module is used to evaluate each of the candidate energy storage optimization configuration schemes and select the target energy storage optimization configuration scheme based on the evaluation results.

8. An electronic device, comprising: include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the energy storage optimization configuration method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the energy storage optimization configuration method according to any one of claims 1 to 6.

10. A computer program product, characterised in that, It includes computer instructions for causing a computer to execute the energy storage optimization configuration method according to any one of claims 1 to 6.

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