Energy storage installation scheme decision-making method, device and equipment

Abstract

The invention provides an energy storage installation scheme decision-making method, device and equipment, and relates to the technical field of power electronics, and the method comprises the steps: obtaining an electricity price curve of a unit time period; according to the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of the single energy storage cabinet, carrying out iterative solution on the income objective function of the single energy storage cabinet to obtain a plurality of energy storage installation schemes when the income value is maximum; wherein each energy storage installation scheme comprises the number of energy storage cabinets to be installed and the charging and discharging power of each energy storage cabinet in a unit time period; and determining a target energy storage installation scheme from the plurality of energy storage installation schemes. According to the method, the energy storage optimal installation scale and the operation strategy can be decided.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and more specifically, to a method, apparatus, and equipment for making decisions on energy storage installation schemes. Background Technology

[0002] With the reform of the electricity market and the popularization of distributed energy, user-side energy storage has become an important technology for users to reduce electricity costs and improve energy efficiency. Its core profit model lies in arbitrage using the peak-valley electricity price difference.

[0003] Current methods require manual collection of meter data scattered across various monitoring points. Due to inconsistent data formats, extensive and repetitive secondary processing is necessary. Subsequent core calculations also rely on manual work in spreadsheets, resulting in a cumbersome process, high computational load, and a high risk of errors. Furthermore, reliable methods for verifying the calculation results are lacking. Simultaneously, traditional energy storage revenue calculation methods are based on historical meter data, calculating only the average power over each electricity price period and using this average as a fixed reference value for charging and discharging power. This completely ignores the dramatic fluctuations in actual load throughout the day and between days, causing a severe disconnect between charging and discharging strategies and actual electricity consumption curves, leading to low reliability of the calculation results. Secondly, it neglects the dynamic changes in battery state of charge during charging and discharging, resulting in inflated calculated revenues and failing to guarantee the sustainability and safety of the energy storage system in actual operation.

[0004] Therefore, there is an urgent need for an automated and precise method for deciding on energy storage capacity, in order to overcome the technical problems of low efficiency of manual methods, insufficient model accuracy, and disconnect between operation and planning. Summary of the Invention

[0005] This application provides a method, apparatus, and equipment for deciding on energy storage installation schemes, which can determine the optimal installed capacity and operation strategy for energy storage.

[0006] In a first aspect, embodiments of this application provide a method for deciding on energy storage installation schemes, the method comprising: Obtain the electricity price curve for a unit time period; Based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage cabinet, the revenue objective function of the single energy storage cabinet is iteratively solved to obtain multiple energy storage installation schemes with the maximum revenue value; wherein, each energy storage installation scheme includes: the number of energy storage cabinets to be installed, and the charging and discharging power of each energy storage cabinet in the unit time period. The target energy storage installation scheme is determined from the multiple energy storage installation schemes mentioned above.

[0007] Optionally, the method further includes: Obtain the user's load curve within the specified time period; The method involves iteratively solving the objective function of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of the individual energy storage unit, to obtain multiple energy storage installation schemes with the maximum revenue value, including: Based on the load curve, the preset configuration power parameters of the individual energy storage cabinet, and the transformer capacity parameters of the user's corresponding distribution area, multiple power data sets of the individual energy storage cabinet are obtained within the unit time period. Each power data set includes power data at multiple times, and the power data at each time is either charging power or discharging power. Based on the electricity price data at each time point and the multiple power data sets, the objective function of the revenue of the single energy storage cabinet is iteratively solved to obtain multiple energy storage installation schemes with the maximum revenue value.

[0008] Optionally, the preset power parameters include: maximum discharge power; The process of obtaining multiple power data sets for a single energy storage unit within the unit time period based on the load curve, the preset configuration power parameters of the single energy storage unit, and the transformer capacity parameters of the user's corresponding distribution area includes: Based on the maximum discharge power, the charge and discharge power range of the single energy storage cabinet is obtained using charge and discharge constraint conditions. Based on the load data at each moment in the load curve and the transformer capacity parameters, the charging power range of the single energy storage cabinet at each moment is obtained using the transformer capacity constraint condition. Based on the load data at each moment in the load curve, the discharge power range of the single energy storage cabinet at each moment is obtained by using the discharge power constraint condition. The plurality of power data sets are obtained based on the charging and discharging power range, the charging power range at each time moment, and the discharging power range at each time moment.

[0009] Optionally, the method further includes: Based on the preset charging efficiency and preset discharging efficiency, and using the preset state of charge calculation rules, the state of charge data constraints at each time point are obtained. The step of obtaining the multiple power data sets based on the charge / discharge power range, the charging power range at each time moment, and the discharging power range at each time moment includes: Based on the charging and discharging power range, the charging power range at each time moment, and the discharging power range at each time moment, the multiple power data sets are obtained using the state of charge data constraints at each time moment.

[0010] Optionally, the preset configuration power parameters further include: capacity data; Before obtaining the multiple power data sets based on the charge / discharge power range, the charging power range at each time moment, and the discharging power range at each time moment, using the state of charge data constraints at each time moment, the method further includes: By employing preset start and end state of charge constraints, the state of charge data at the start time and the state of charge data at the end time are obtained from the plurality of time points, such that the state of charge data at the start time and the state of charge data at the end time are both full charge state data corresponding to the capacity data.

[0011] Optionally, the electricity price curve is the grid electricity price curve; before iteratively solving the objective function of the revenue of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit to obtain multiple energy storage installation schemes with the maximum revenue value, the method further includes: The grid electricity price data at each time point are obtained from the grid electricity price curve as the charging and discharging electricity price data at each time point; The method involves iteratively solving the objective function of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of the individual energy storage unit, to obtain multiple energy storage installation schemes with the maximum revenue value, including: The method involves iteratively solving the objective function of the revenue of a single energy storage unit based on the grid electricity price data at each time point and the preset configuration power parameters of the single energy storage unit, thereby obtaining multiple energy storage installation schemes with the maximum revenue value.

[0012] Optionally, the electricity price curve includes: a grid electricity price curve and a photovoltaic electricity price curve; Before iteratively solving the objective function of the revenue of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit to obtain multiple energy storage installation schemes with the maximum revenue value, the method further includes: The grid electricity price data at each time point are obtained from the grid electricity price curve as the discharge electricity price data at each time point; The photovoltaic electricity price data at each time point are obtained from the photovoltaic electricity price curve as the charging electricity price data at each time point; The method involves iteratively solving the objective function of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of the individual energy storage unit, to obtain multiple energy storage installation schemes with the maximum revenue value, including: The method involves iteratively solving the objective function of the revenue of a single energy storage unit based on the grid electricity price data, the photovoltaic electricity price data, and the preset configuration power parameters of the single energy storage unit at each time point, to obtain multiple energy storage installation schemes with the maximum revenue value.

[0013] Optionally, the method further includes: Obtain the user's load curve within the specified time period; Based on the load curve, determine the energy source for energy storage at each moment; Based on the energy source of the energy storage at each time point and the electricity price curve, the charging electricity price data at each time point is determined.

[0014] Secondly, embodiments of this application also provide an energy storage installation scheme decision-making device, the device comprising: The acquisition module is used to acquire the electricity price curve for a unit time period; The calculation module is used to iteratively solve the revenue objective function of a single energy storage cabinet based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage cabinet, so as to obtain multiple energy storage installation schemes with the maximum revenue value; wherein, each energy storage installation scheme includes: the number of energy storage cabinets to be installed, and the charging and discharging power of each energy storage cabinet in the unit time period. The determination module is used to determine the target energy storage installation scheme from the multiple energy storage installation schemes.

[0015] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the program instructions to perform the steps of the energy storage installation scheme decision method as described in any of the first aspects. This application provides a method, apparatus, and equipment for deciding on energy storage installation schemes. After obtaining the electricity price curve for a unit time period, it first iteratively solves the revenue objective function of a single energy storage cabinet based on the electricity price data at each moment in the curve and the preset configuration power parameters of each cabinet. This yields multiple energy storage installation schemes that achieve the maximum revenue. Each scheme includes the number of energy storage cabinets to be installed and the charging / discharging power of each cabinet within a unit time period. Then, the final target energy storage installation scheme is determined from the multiple schemes. Based on the scheme provided in this application, when the electricity price curve and the energy storage cabinet configuration power parameters are input, the revenue objective function can be iteratively solved, directly outputting multiple candidate installation schemes containing the specific number of energy storage cabinets and detailed charging / discharging power plans, ensuring that each scheme achieves the maximum revenue under the current configuration. Users can directly select and determine the final optimal target scheme from a series of optimized feasible schemes without relying on complex calculations and comparisons based on manual experience. By simultaneously solving the energy storage system capacity configuration problem and the operation strategy optimization problem in a mathematical optimization model, the entire process of automated decision-making from basic parameter input to executable solution output is realized. This not only avoids the shortcomings of traditional manual planning, such as strong subjectivity, low efficiency, and difficulty in global optimization, but also ensures the optimal economic efficiency of the solution through a quantified benefit objective function, thereby significantly improving the efficiency, objectivity, and scientific decision-making level of energy storage installation planning. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a decision-making method for an energy storage installation scheme provided in this application; Figure 2 A flowchart illustrating another energy storage installation scheme decision-making method provided in this application; Figure 3 A flowchart illustrating the process of obtaining power data sets in a decision-making method for energy storage installation schemes provided in this application; Figure 4 A flowchart illustrating the process of obtaining power data sets in another energy storage installation scheme decision-making method provided in this application; Figure 5 A flowchart illustrating another energy storage installation scheme decision-making method provided in this application; Figure 6A flowchart illustrating another energy storage installation scheme decision-making method provided in this application; Figure 7 A flowchart illustrating the process of determining charging price data at various times in a decision-making method for an energy storage installation scheme provided in this application; Figure 8 A schematic diagram of an energy storage installation scheme decision device provided in an embodiment of this application; Figure 9 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0021] Before providing a detailed explanation of this application, let's first introduce its application scenarios.

[0022] In the investment planning and preliminary feasibility study phases of energy storage systems, the core need is to accurately quantify the economic value of the system and determine the optimal configuration. For example, what capacity and power rating of the equipment is most cost-effective for installing an energy storage system? After installation, how should the equipment be charged and discharged daily to maximize returns? Currently, solving these problems requires a significant amount of time to compile long-term electricity bills and load data, repeatedly calculating using averages in spreadsheets. This process is not only time-consuming and labor-intensive, but the calculation results also heavily rely on the engineer's personal experience and fixed charging / discharging logic and averaged power data. It fails to dynamically optimize strategies based on real-time electricity price fluctuations and equipment performance, resulting in installation plans that theoretically cannot maximize returns. Ultimately, this leads to inaccurate energy storage installation decisions, increasing investment risk and hindering the large-scale application of energy storage technology.

[0023] Based on this, this application provides a method, apparatus, and equipment for energy storage installation scheme decision-making. After obtaining the electricity price curve for a unit time period, the method first iteratively solves the objective function of the revenue of a single energy storage cabinet based on the electricity price data at each moment in the curve and the preset configuration power parameters of each cabinet. This yields multiple energy storage installation schemes that maximize revenue. Each scheme includes the number of energy storage cabinets to be installed and the charging and discharging power of each cabinet within a unit time period. Then, the final target scheme is determined from the multiple schemes. By simultaneously solving the energy storage system capacity configuration problem and the operation strategy optimization problem in a mathematical optimization model, the entire process of automated decision-making from basic parameter input to executable scheme output is achieved. This not only avoids the shortcomings of traditional manual planning, such as strong subjectivity, low efficiency, and difficulty in global optimization, but also ensures optimal economic efficiency through a quantified objective function, thereby significantly improving the efficiency, objectivity, and scientific decision-making level of energy storage installation planning.

[0024] The following explanation, in conjunction with the accompanying drawings, uses several embodiments to illustrate the concepts. Figure 1 A flowchart illustrating a decision-making method for energy storage installation schemes provided in this application is shown below. Figure 1 As shown, the decision-making method for this energy storage installation scheme includes: S101, obtain the electricity price curve for a unit time period.

[0025] In this embodiment, the first step is to obtain the electricity price curve for a unit time period. The electricity price curve is a pre-collected data sequence reflecting the electricity purchase price at different times within a complete unit time period (e.g., a day, a month, etc.). It is important to emphasize that the electricity price curve obtained in this application has a fine-grained time division, and its data points are typically sampled and defined at intervals of 15 minutes, 30 minutes, or 1 hour, thereby accurately depicting the fluctuation characteristics of electricity prices over short time scales.

[0026] Electricity price curves are typically divided into multiple sub-periods with different electricity price levels, based on the operating rules of the electricity market or electricity sales contracts. These sub-periods include peak periods with higher prices, normal periods with moderate prices, and valley periods with lower prices. Each sub-period has a clear start and end time and a corresponding fixed or floating price, which together form a segmented and continuous electricity price curve. By acquiring electricity price curves in real time, charging and discharging strategies can be flexibly adjusted according to different electricity price periods, thereby maximizing the economic benefits of energy storage installation solutions.

[0027] Specifically, the electricity price curve can be obtained in various ways, such as by directly parsing the official time-of-use price table published by the power company or electricity sales company, or by receiving a structured file (such as an Excel file) uploaded by the user through a terminal device (such as a personal computer or tablet) that already contains time-of-use division and price data.

[0028] After acquisition, the electricity price curve will be standardized, including timestamp alignment, missing value imputation and validity verification, and the boundaries and prices of each sub-period (peak, flat and valley) will be clearly marked, providing an accurate and structured input basis for subsequent optimization calculations based on fine time granularity.

[0029] S102, based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage cabinet, iteratively solve the objective function of the revenue of a single energy storage cabinet to obtain multiple energy storage installation schemes with the maximum revenue value.

[0030] Each energy storage installation plan includes: the number of energy storage cabinets to be installed, and the charging and discharging power of each energy storage cabinet in a unit time period.

[0031] After obtaining the electricity price curve, the revenue objective function of a single energy storage unit can be iteratively solved based on the electricity price data at each moment in the curve and the preset configuration power parameters of the single energy storage unit. During this process, the charging and discharging power of the energy storage unit will be dynamically adjusted at each moment, with the goal of maximizing the revenue of the energy storage system.

[0032] The revenue objective function calculates revenue based on electricity price data at various times in the electricity price curve and the charging and discharging power of the energy storage cabinet. It optimizes the charging and discharging behavior at multiple times to maximize revenue. This revenue objective function is expressed as formula (1): Formula (1) in, Represents a specific moment. ; for The discharge power of the energy storage cabinet at any given time. for The charging power of the energy storage cabinet at any time. for Electricity price at any time The total number of moments divided within a unit of time period. This represents a fixed time interval between two adjacent moments (e.g., 15 minutes, 30 minutes, or 1 hour).

[0033] It needs further clarification that the unit time period in this application and the individual moments it is divided into constitute a clear whole-part relationship. Specifically, the unit time period (e.g., one day) is the complete cycle for conducting revenue assessments and developing charging and discharging power plans. And the individual moments... This means dividing the unit of time period into fixed time intervals. After segmentation, a series of consecutive time points are obtained. Therefore, the electricity price curve and the proposed charging and discharging power plan are both based on this. Defined and calculated over consecutive moments.

[0034] It is important to emphasize that the profit objective function proposed in this application is fundamentally different from the traditional calculation method based on fixed average power and simple low-charge-high-discharge rules. The charge and discharge power in this application... and It is a decision variable that dynamically changes with the electricity price at each moment in the electricity price curve.

[0035] In the calculation process, in order to maximize the final benefit, it is also necessary to provide charging and discharging power constraints for the iterative solution of the benefit objective function based on the preset configuration power parameters of the energy storage cabinet, so as to ensure that no scheme that does not meet the capabilities of the energy storage cabinet equipment is generated when calculating the maximum benefit value.

[0036] Specifically, preset configuration power parameters are a set of technical parameters used to define the basic performance, operating boundaries, and long-term operating characteristics of a single energy storage cabinet. For example, preset configuration power parameters may typically include parameters characterizing the power output capability of the energy storage cabinet (such as maximum charge and discharge power), parameters characterizing the energy storage capacity of the energy storage cabinet (such as rated capacity), and parameters characterizing the energy conversion efficiency of the energy storage cabinet (such as charge and discharge efficiency).

[0037] In the iterative solution process, the preset power parameters of a single energy storage cabinet can be used as a basis, initializing the number of energy storage cabinets to be installed to one. Then, for the current installed capacity, a mathematical model is constructed with a revenue objective function as the optimization objective, constrained by the preset power parameters and the constraints defined by the actual operating environment. These constraints include ensuring that the charging and discharging power does not exceed the limits of the energy storage cabinet equipment, that the energy storage state is maintained within a reasonable range, and that grid connection specifications are met. In practical technical implementation, mature mathematical programming tools can be used to define and solve this model. For example, the PuLP linear programming optimization library in the Python programming environment can be used. This library provides a concise interface that can efficiently express the revenue objective function and constraints as a standard linear programming problem and call the underlying solver for calculation. The mathematical programming solver (such as a linear programming solver) is used to solve the model, thereby obtaining the optimization result that achieves the theoretical maximum revenue under the current installed capacity.

[0038] Each energy storage installation plan comprises two core components: firstly, the number of energy storage units to be installed to achieve the maximum revenue at the current installed capacity; and secondly, the number of units to be installed from... arrive The planned sequence of charging and discharging power of energy storage cabinets at all times, i.e., the charging and discharging power of each energy storage cabinet in a unit of time period. It should be explained that the installed capacity represents the number of energy storage cabinets in the energy storage system and the overall capacity limit of the energy storage cabinets, including the total power and total storage capacity of the energy storage system.

[0039] Next, the number of energy storage cabinets is increased incrementally according to a preset step size (e.g., one cabinet per increment), and the above process is repeated. Each solution outputs a complete energy storage installation plan, including the number of energy storage cabinets and the complete energy storage installation plan sequence of charge and discharge power at all times. When, after several consecutive iterations (i.e., increasing the number of energy storage cabinets), the maximum revenue value obtained no longer increases or increases below a preset threshold, it is considered that the revenue growth boundary has been explored, and the iteration terminates. At this point, multiple energy storage installation plans corresponding to different assumed installation numbers, all reaching the revenue upper limit at their respective scales, are obtained. Each plan includes the number of energy storage cabinets to be installed when the maximum revenue value at that scale is reached, as well as matching, all-encompassing energy storage cabinets. The charging and discharging power plan sequence at each moment, that is, the charging and discharging power of each energy storage cabinet in a unit time period.

[0040] Through iterative solution, the number of energy storage cabinets installed and the charging and discharging power plan can be comprehensively optimized under actual constraints, ensuring that profits are maximized for each installed capacity.

[0041] S103, determine the target energy storage installation scheme from multiple energy storage installation schemes.

[0042] Through iterative solutions, a series of energy storage installation schemes achieving theoretical maximum returns under different installation scales were obtained. Each scheme includes the number of energy storage cabinets to be installed and the charging and discharging power of each cabinet at various times within a unit time period. Next, a target energy storage installation scheme will be selected from these schemes. This process needs to consider the investment costs, operation and maintenance costs corresponding to different installation scales. Therefore, it is necessary to perform actuarial calculations and comparisons of all energy storage installation schemes based on their full life-cycle financial indicators to select the target energy storage installation scheme with the best overall economic performance.

[0043] Specifically, the Internal Rate of Return (IRR) is introduced as the core evaluation indicator. The higher the IRR, the better the investment return of the corresponding project, and therefore the higher its economic value.

[0044] For each energy storage installation plan, its long-term return on investment (ROI) will be calculated based on its corresponding charging and discharging strategy (i.e., charging and discharging power at each moment within a unit time period), the number of energy storage cabinets, and the electricity price curve. Then, all energy storage installation plans are ranked from highest to lowest according to their calculated IRR. The plan with the highest IRR indicates the highest return on investment and the best capital utilization efficiency, and is therefore determined as the final target energy storage installation plan. The target energy storage installation plan not only provides the optimal installed capacity (the number of energy storage cabinets to be installed) but also includes a specific charging and discharging power plan down to the moment.

[0045] The implementation process of this step will be explained below using a specific application scenario.

[0046] For example, through the iterative solution of S102, three candidate schemes were obtained: Scheme A (2 units installed, theoretical daily profit of 500 yuan), Scheme B (3 units installed, theoretical daily profit of 650 yuan), and Scheme C (4 units installed, theoretical daily profit of 700 yuan). If only theoretical profit is considered, Scheme C has the highest. However, after calculating the IRR in this step, it is found that the IRR of Scheme A is 18%, the IRR of Scheme B is 15%, and the IRR of Scheme C is 12%. Although Scheme C has a higher absolute profit, its investment lowers the rate of return. Therefore, Scheme A, with the highest IRR, was determined as the target energy storage installation scheme. The final output to the user is the installation configuration of Scheme A (2 units) and its corresponding charging and discharging strategy, along with its expected internal rate of return of up to 18%.

[0047] In practice, after obtaining multiple theoretically optimal solutions, IRR is introduced for financial evaluation to ensure that the target energy storage installation plan recommended to users is not only technically feasible, but also has a considerable return on investment. This improves the accuracy of energy storage planning and the scientific nature of decision-making, and provides reliable quantitative decision-making for the large-scale application of energy storage technology.

[0048] In this embodiment, after inputting the electricity price curve and the power parameters of the energy storage cabinet configuration, the revenue objective function can be iteratively solved, directly outputting multiple candidate installation schemes containing specific energy storage cabinet quantities and detailed charging and discharging power plans, ensuring that each scheme achieves the maximum revenue state under the current configuration. Users do not need to rely on manual experience for complex calculations and comparisons; they can directly select and determine the final optimal target scheme from a series of optimized feasible schemes. By simultaneously solving the energy storage system capacity configuration problem and the operation strategy optimization problem in a mathematical optimization model, the entire process of automated decision-making from basic parameter input to executable scheme output is realized. This not only avoids the shortcomings of traditional manual planning, such as strong subjectivity, low efficiency, and difficulty in global optimization, but also ensures the optimal economic efficiency of the scheme through a quantified revenue objective function, thereby significantly improving the efficiency, objectivity, and scientific decision-making level of energy storage installation planning.

[0049] In the above Figure 1 Based on the corresponding embodiments, in order to more clearly demonstrate the decision-making process for energy storage installation schemes, this application also provides a possible implementation of the energy storage installation scheme decision-making method. Figure 2 A flowchart illustrating another energy storage installation scheme decision-making method provided in this application. (See attached diagram.) Figure 2 As shown, based on the above S101-S103, the method further includes: S201 obtains the user's load curve over a unit time period.

[0050] In this embodiment, when making decisions on energy storage installation schemes, in addition to obtaining the electricity price curve, it is also necessary to obtain the user's load curve within a unit time period. The load curve refers to a data sequence reflecting the changes in the actual power consumption of users at various times within the same unit time period (such as a day). It should be emphasized that the time division of the load curve obtained in this application is consistent with that of the electricity price curve obtained in S101, usually with intervals of 15 minutes, 30 minutes, or 1 hour, thereby ensuring the alignment of electricity price data and load data on the time axis and laying the foundation for building an accurate optimization model in the future.

[0051] Specifically, load data can be obtained in various ways. For example, historical electricity consumption data can be directly exported from users' smart meters, or load data can be directly collected from users through electricity metering devices, or load data files in standard formats (such as Excel) can be received from users. After obtaining the raw load data, necessary preprocessing is required, including data cleaning (removing outliers) and timestamp alignment (ensuring a one-to-one correspondence with the time points on the electricity price curve). Ultimately, a load curve that is completely synchronized with the electricity price curve in time and reflects the actual electricity consumption behavior of users is formed, serving as an important basis for constructing constraints in the subsequent optimization model.

[0052] In step S102 above, based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit, the objective function for the revenue of a single energy storage unit is iteratively solved to obtain multiple energy storage installation schemes with the maximum revenue value, including: S202, based on the load curve, the preset configuration power parameters of a single energy storage cabinet, and the transformer capacity parameters of the user's corresponding distribution area, obtains multiple power data sets for a single energy storage cabinet within a unit time period. Each power data set includes power data at multiple times, and the power data at each time is either charging power or discharging power.

[0053] This step is preparation before iteratively solving the objective function. Iteratively solving the profit objective function requires at each time step... Determining a charging or discharging power requires that this power be physically feasible, meaning it must satisfy the limitations of the load, transformer, and energy storage cabinet itself. Simultaneously, it must strictly adhere to the basic operating logic of the energy storage cabinet, namely the mutual exclusion constraint between charging and discharging: at any given moment... The energy storage cabinet cannot be in a charging and discharging state at the same time.

[0054] In this embodiment, after obtaining both the electricity price curve and the load curve, the mutually exclusive charging or discharging power allowed for a single energy storage cabinet at each moment within a unit time period can be determined based on the load curve, preset power parameters, and transformer capacity parameters, and superimposed with charging and discharging mutual exclusion constraints. In other words, all feasible charging and discharging power sequences that conform to the mutual exclusion rules can be found in advance, thus obtaining multiple different and complete charging and discharging power sequences. A complete charging and discharging power sequence, which is an independent plan, is called a power data set.

[0055] It should be clarified that in this application, the power data at each moment consists of two parts: first, the operating state (charging, discharging, or idle) that the energy storage cabinet should perform at that moment; and second, the non-negative power data corresponding to that state at that moment. A power data set is a complete sequence of multiple such (operating state, power data) pairs arranged in chronological order. The selection of the operating state strictly follows the principle of mutual exclusion between charging and discharging.

[0056] Specifically, to transform the charge-discharge mutual exclusion constraint into a computable form, a state flag is introduced: Let Indicates time Is the energy storage cabinet in charging operation mode (1 for yes, 0 for no)? Indicates time Is the energy storage cabinet in a discharge operation state (1 for yes, 0 for no)? The charging and discharging mutual exclusion constraint is then manifested as: The physical meaning of this inequality is that at any given moment... The two status flags cannot be 1 at the same time, thus mathematically guaranteeing the mutual exclusion of charging and discharging operations.

[0057] For each moment within a unit of time period The user load at that moment is obtained from the load curve. Simultaneously, based on the transformer capacity parameters of the user's corresponding distribution area, the maximum apparent power limit for the transformer's long-term safe operation is obtained and recorded as the upper limit of transformer capacity. The preset power parameters of the energy storage cabinet are used to calculate the maximum mutually exclusive charging power and the maximum allowable discharging power of the energy storage cabinet at that moment, based on the constraints.

[0058] Among them, the constraints are used to ensure that the charging and discharging operations of the energy storage cabinet comply with the safety carrying capacity of the on-site power facilities (such as transformers) and the grid operation specifications.

[0059] Subsequently, based on all The allowable charging and discharging power range that meets the constraints, calculated at each time point, is the value of each time point. Construct a feasible set of (operating states, power data) including all possible charging options (operating state is charging, power data is within the allowable charging power range), all possible discharging options (operating state is discharging, power data is within the allowable discharging power range), and an idle option (operating state is idle, power data is 0). By combining and selecting from the mutually exclusive feasible sets at all times, multiple complete (operating states, power data) sequences are generated. Each such complete sequence constitutes a power data set, representing an operational plan.

[0060] Specifically, each power data group covers the entire The operation plan table for each moment records the operation of the energy storage cabinet at each moment. The specific operation to be performed and its corresponding power value; for example, a power data set can be represented as follows: (Charging, 50kW); (Idle, 0); (Discharge, 80kW); ...; (Charging, 30kW). Each row indicates the operating state as charging, discharging, or idle, followed by a value indicating the power output at that moment and in that state. Each such complete schedule constitutes a power data set.

[0061] In this way, based on the load curve and transformer capacity limitations, and under the core rule of mutual exclusion between charging and discharging, power data that meets the actual power demand and equipment capacity at each moment can be provided, ensuring that the charging and discharging strategy of the energy storage cabinet is both technically and economically feasible.

[0062] Ultimately, multiple different power data sets will be obtained, each representing a theoretically feasible charging and discharging operation of a single energy storage cabinet within a unit of time, provided that all given rules are met.

[0063] S203, based on the electricity price data at each time point and multiple power data sets, iteratively solves the objective function of the revenue of a single energy storage cabinet to obtain multiple energy storage installation schemes with the maximum revenue value.

[0064] After acquiring multiple power data sets representing various feasible charging and discharging operations of a single energy storage unit, the power data sets are combined with the electricity price data at each time point. By iteratively solving the revenue objective function, the power data set that achieves economic optimization is selected and extended to different installed capacity scales, thereby obtaining multiple energy storage installation schemes with the maximum revenue value.

[0065] Specifically, each power data set generated in the above steps is substituted into the profit objective function formula defined in S102. For each power data set, since it contains data from each time step... The (operating status, power data) data pair allows us to determine the charging power at each moment based on this data pair (if the status is charging, then...). (Equal to the power data, otherwise 0) and discharge power (if the state is discharge, then...) (Equal to the power data, otherwise 0), so that all and Substituting the values ​​into the revenue objective function, the revenue per unit corresponding to the power data set can be directly calculated. Then, among the revenue values ​​corresponding to all power data sets, the power data set with the highest revenue is selected as the optimal power data set for a single energy storage unit under the current constraints. The optimal power data set for a single energy storage unit defines the charging and discharging operation arrangement that an energy storage unit should follow to obtain maximum revenue per unit time; that is, it clarifies whether charging, discharging, or remaining idle should be performed at each moment, and the corresponding optimal power value under that operation.

[0066] Next, using the aforementioned optimal power data set for a single energy storage unit as a benchmark, the objective function for revenue under different installed capacities is iteratively solved. The core of this process is that, driven by the same electricity price structure, for an energy storage system composed of multiple energy storage units to achieve maximum revenue, its operating state (charging, discharging, or idle) at each moment should be consistent with the state determined by the optimal data set for a single unit.

[0067] First, based on the upper limit of transformer capacity The rated power of the energy storage unit in the preset configuration power parameters of a single energy storage unit determines the maximum number of energy storage units required. The number of installations to be evaluated All need Next, within a defined range, iterations are performed for each number of installations to be evaluated. At every moment Assuming that the charging and discharging operations are consistent with the above-mentioned single-unit optimal data set, solve for the state at each time step. The optimal total power output of all energy storage units is determined to maximize the overall benefit of the energy storage system. The solution process must ensure that the total power output of the energy storage system meets the safety constraints imposed by user load, transformer capacity, and the capacity of the energy storage units themselves. Each step considers the number of installed units... Solving the objective function for revenue yields an output that includes the current number of installed units. This involves a set of total charging and discharging power arrangements that are consistent with the optimal operating state of a single unit, maximize benefits under constraints, and cover all time points. Under the actual distributed control strategy, this total power arrangement can be directly converted into a sequence of plans (operating state, power data) that each energy storage unit should execute. Therefore, by iterating through all possible installed capacities... The above-mentioned solution optimization was completed, and finally, multiple energy storage installation schemes with the maximum benefit value were obtained. All these schemes were derived by optimizing multiple energy storage cabinets as a whole. The final charging and discharging power of each energy storage cabinet in a unit time period is limited by multiple power data sets generated by S202, and the benefit is maximized under different scales through overall optimization.

[0068] Therefore, through the iterative solution described above, this step ultimately outputs multiple energy storage installation schemes. Each scheme includes the number of energy storage cabinets to be installed, the charging and discharging power of each cabinet within a unit time period, and a corresponding maximum theoretical return value. Based on this, for example, by calculating the internal rate of return (IRR) of each scheme and conducting a comprehensive economic comparison, the final recommended target energy storage installation scheme can be selected from the multiple schemes.

[0069] The implementation process of this embodiment will be explained below with reference to a specific application scenario.

[0070] For example, a factory's electricity load is higher during daytime production and lower at night. The load curve is obtained through step S201, and combined with the electricity price curve, step S202 generates multiple feasible power data sets for a single energy storage unit. After calculation and comparison, the power data set requiring "charging during nighttime off-peak hours and the lowest electricity price, and discharging during daytime peak hours and the highest electricity price" is determined to be the optimal single-unit power data set. Subsequently, based on the charging and discharging timing defined by this optimal power data set, and strictly considering the factory's transformer capacity limitations, multi-unit scale optimization is performed. After iterative solution, three candidate schemes are obtained: Scheme A (1 unit installed, operating entirely according to the optimal group, daily revenue of 300 yuan), Scheme B (2 units installed, under the same charging and discharging timing as the optimal group, the power of each unit needs to be reduced due to transformer capacity limitations, overall daily revenue of 520 yuan), and Scheme C (3 units installed, under stronger constraints at the same timing, the power of each unit is further reduced, overall daily revenue of 690 yuan). All schemes have the same charging and discharging timing, but the power values ​​differ due to constraints. Ultimately, the most economical target energy storage installation scheme can be selected from these options based on investment costs.

[0071] In this embodiment, by introducing load curves and transformer capacity parameters, the generation process of energy storage installation schemes not only considers economic objectives, but also ensures the safety (not exceeding transformer capacity) and feasibility (charging and discharging power matching the load) of the schemes in the actual power consumption scenarios of users. This effectively avoids the generation of invalid schemes that are theoretically profitable but cannot be connected or operated in practice, and greatly improves the engineering practical value and implementation feasibility of the output schemes.

[0072] In the above Figure 2 Based on the corresponding embodiments, in order to more clearly demonstrate the process of acquiring power data sets, this application also provides a possible implementation method for acquiring power data sets in the energy storage installation scheme decision-making method. Figure 3 This is a schematic diagram illustrating the process of obtaining power data sets in a decision-making method for energy storage installation schemes provided in this application. Figure 3 As shown, in S202 above, based on the load curve, the preset configuration power parameters of a single energy storage cabinet, and the transformer capacity parameters of the user's corresponding distribution area, multiple power data sets for a single energy storage cabinet within a unit time period are obtained, including: The preset power parameters include: maximum discharge power.

[0073] S301, based on the maximum discharge power, uses charge and discharge constraints to obtain the charge and discharge power range of a single energy storage cabinet.

[0074] In this step, the preset power parameters specifically include the maximum discharge power characterizing the power output capability of the energy storage cabinet, denoted as... The charge and discharge constraints are fundamental limitations set based on the physical limits of the energy storage cabinet's power conversion system. Their core principle is to stipulate that at any given moment... The charging power performed by the energy storage cabinet or discharge power Their size must not exceed .

[0075] Specifically, the charge / discharge constraints include two aspects: charging power constraints: Ensure charging power does not exceed the device's maximum limit; discharge power constraints: This ensures that the discharge power does not exceed the equipment's upper limit. Therefore, the theoretical boundary range of the allowable charging and discharging power of a single energy storage unit can be directly determined without considering external power grid and load conditions; that is, the charging and discharging power range of a single energy storage unit is... This provides an initial domain of definition based on the device's own capabilities for all subsequent further screening.

[0076] Meanwhile, to ensure that the mutual exclusion of charging and discharging operations is achieved at the power level, the values ​​of charging power and discharging power need to be determined in relation to the state flag variables that characterize their operating states. and Combined, charging power and discharge power Must meet: , These two inequalities mean that the power variable is allowed to be greater than zero only when the state flag is 1, and its maximum value is affected by the maximum discharge power. Constraints: When the state flag is 0, the corresponding power variable is forced to zero. This constitutes the basic rule that must be satisfied at every moment when constructing a power data set.

[0077] S302, based on the load data at each moment in the load curve and the transformer capacity parameters, uses the transformer capacity constraint condition to obtain the charging power range of a single energy storage cabinet at each moment.

[0078] In this step, the load curve can be used to obtain the values ​​at each moment. User load data Based on the transformer capacity parameters, the upper limit of the transformer capacity can be obtained. .

[0079] The transformer capacity constraint is designed to prevent overload of the power distribution system, and its requirement is that at any given time... User load data With the charging power of the energy storage cabinet The sum must not exceed the upper limit of transformer capacity. That is, satisfying .

[0080] Based on this inequality, it can be derived that at time t, the maximum allowable charging power of the energy storage cabinet is: This upper limit must be related to the device's maximum discharge power. The smaller value is chosen to simultaneously satisfy equipment capacity limitations. Therefore, considering both grid security and equipment capacity, at any given time... Energy storage cabinet charging power The actual allowable range is the closed interval. This range is dynamic, and its upper limit varies with user load. The increase and decrease directly reflect the constraint of the transformer's remaining capacity on the charging power.

[0081] S303: Based on the load data at each moment in the load curve, the discharge power constraint condition is used to obtain the discharge power range of a single energy storage cabinet at each moment.

[0082] In this step, constraints determined by load data are imposed on the discharge behavior. The discharge power constraint is designed to prevent reverse power feed into the grid, and its requirement is that at any given time... The discharge power of the energy storage cabinet The load data at that moment must not exceed the limit, i.e., it must meet the requirements. .

[0083] The discharge power constraint must also be considered in conjunction with the equipment's capabilities. The discharge power must not exceed [a certain limit]. Therefore, at any given moment Energy storage cabinet discharge power The actual allowable range is the closed interval. This range is also dynamic, with its upper limit determined by the maximum discharge power. Load data at the current time The smaller value in the formula determines the discharge power, thus ensuring that the discharge power is always within the real-time load range of the user and meets the requirements for safe operation of the equipment.

[0084] It should be noted that steps S302 and S303 are logically parallel, but there is no strict requirement for their order of execution. The charging power range and the discharging power range at each moment can be calculated in parallel or in any order.

[0085] S304 acquires multiple power data sets based on the charging and discharging power range, the charging power range at each time, and the discharging power range at each time.

[0086] After obtaining the charging and discharging power range, the charging power range at each time moment, and the discharging power range at each time moment through the above steps, multiple power data sets can be generated.

[0087] Specifically, to generate a power data set, that is, a complete sequence of multiple (operating state, power data) data pairs arranged in chronological order, it is necessary to perform a power data set for each time step. Define a specific data pair (operating status, power data).

[0088] Every moment The set of all possible (operating state, power data) data pairs consists of the following three elements: 1. Charging option: operating state is charging, power data is satisfied. any 2. Discharge option: Operation status is discharge, power data is satisfied. any 3. Idle option: The operation status is idle, and the power data is 0.

[0089] The process of acquiring multiple power data sets is to acquire data for all moments within the entire unit time period. The process of selecting one item from a set of feasible (operating state, power data) data pairs and combining these selections in chronological order to form a complete sequence is called a power data set. Since there are usually multiple possible (operating state, power value) data pairs at each moment (e.g., choosing to charge at 50kW, discharge at 30kW, or idle), combining the options at different moments can generate a large number of different complete sequences, thus obtaining multiple power data sets.

[0090] Ultimately, these power data sets will serve as the core input for step S203. In S203, each power data set represents a specific and feasible operating strategy. Its economic benefits will be calculated based on electricity price data, and the optimal revenue scheme for different installed capacity scales will be determined through iterative solutions, thus outputting multiple complete energy storage installation schemes. Furthermore, in step S103, these schemes will be comprehensively evaluated and compared using financial indicators such as the internal rate of return (IRR), ultimately selecting the target energy storage installation scheme that combines technical feasibility and investment economics, completing the full closed loop from feasibility analysis to optimal investment decision-making.

[0091] The implementation process of this embodiment will be explained below with reference to a specific application scenario.

[0092] Suppose a user is at a certain location, at a specific moment... Its load data upper limit of transformer capacity Maximum discharge power of a single energy storage cabinet First, based on the charging and discharging constraints, the charging power... and discharge power All shall not exceed Therefore, without considering external limitations, the theoretical boundary of the charging and discharging power of a single energy storage cabinet is: , Then, based on the transformer capacity constraint, it can be deduced that... By combining the charging and discharging power range of a single energy storage cabinet, and taking the smaller value, the allowable range of charging power at that moment is obtained. Based on the discharge power constraint, it can be derived that... By combining the charging and discharging power range of a single energy storage unit, the smaller value is taken to obtain the allowable range of discharge power at that moment. Based on the above range, at this moment The option for "(Operation Status, Power Data)" is: Operation status is charging. Available Values ​​can be selected within a range (e.g., 10kW, 50kW, 200kW, etc.); the operating state is discharge. exist The values ​​are taken within the range (e.g., 80kW, 150kW, 200kW, etc.); the operating state is idle, and the power data is 0. The set of feasible power data pairs at this moment consists of all the charging options, all discharging options, and the idle option. By performing the above calculations for each moment within a unit time period, the set of feasible options for each moment can be obtained. By systematically combining the option sets of all moments (e.g., selecting "charging, 150kW" at moment 1, selecting "discharging, 80kW" at moment 2, selecting "idle, 0" at moment 3, etc.), multiple complete operating sequences can be generated, which are the multiple power data sets required by S202.

[0093] In this embodiment, the theoretical power boundary for charging and discharging of the energy storage cabinet is defined based on the maximum discharge power. Then, the maximum allowable charging power and maximum discharge power at each moment are calculated in combination with the user load and transformer capacity. This ensures that charging does not exceed the transformer capacity and discharging does not exceed the load absorption capacity. Finally, a set of power data covering all feasible operations is generated, ensuring that the global optimal solution is not missed in subsequent economic optimization and laying a solid physical foundation for economic solution. This allows the optimization process to focus entirely on benefit optimization without repeated safety verification, thereby significantly improving the efficiency of the overall decision-making process and the credibility of the results.

[0094] In the above Figure 3 Based on the corresponding embodiments, in order to more clearly demonstrate the process of acquiring power data sets, this application also provides a possible implementation method for acquiring power data sets in the energy storage installation scheme decision-making method. Figure 4 This is a schematic diagram of the process for obtaining power data sets in another energy storage installation scheme decision-making method provided in this application. (See attached diagram.) Figure 4As shown, based on the above S301-S304, the method further includes: S401, based on the preset charging efficiency and preset discharging efficiency, adopts the preset state of charge calculation rules to obtain the state of charge data constraints at each time point.

[0095] In this step, to further ensure the continuity, safety, and cyclic feasibility of the energy storage cabinet's operation at the energy level, a preset charging efficiency, which characterizes the energy conversion efficiency of the energy storage cabinet, is introduced. With preset discharge efficiency The system employs a preset State of Charge (SOC) calculation rule to obtain the SOC data of the energy storage cabinet at each moment within a unit time period. The constraints.

[0096] The preset state of charge (SOC) calculation rule refers to a predefined mathematical method used to calculate the change in the battery's SOC based on the charging and discharging behavior of the energy storage unit. The core of this rule is establishing a recursive relationship between SOC and time. The constraints on the SOC data at each moment refer to the mathematical relationships that must be satisfied when applying the above SOC calculation rule, constituting the basic requirements for any SOC data.

[0097] The pre-defined rules for calculating the state of charge are specifically reflected in the establishment of The recursive relationship of dynamic changes in charging and discharging power is defined by formula (2): Formula (2) in, and They are time points and the next moment The state of charge; and They are time points Charging power and discharging power; It represents the fixed time interval between two adjacent moments.

[0098] The physical meaning of this formula is: at time... State of charge Equal to time State of charge Add the actual amount of electricity stored during that period after charging efficiency conversion, and subtract the amount of electricity actually withdrawn after discharge efficiency conversion to meet the discharge power requirements.

[0099] Therefore, by adopting the above calculation rules, the final state of charge data constraints at each moment are the mathematical relationship expressed by formula (2). This constraint clearly stipulates that the state of charge data sequence at any adjacent moment must strictly satisfy this recursive relationship. This is the fundamental mathematical principle that must be followed when performing specific SOC data calculations and energy dimension feasibility verification of power data groups.

[0100] In step S304 above, based on the charge / discharge power range, the charging power range at each moment, and the discharging power range at each moment, multiple power data sets are obtained, including: S402, based on the charging and discharging power range, the charging power range at each moment, and the discharging power range at each moment, uses the state of charge data constraints at each moment to obtain multiple power data sets.

[0101] This step involves further verifying the power data set generated by S304 using the acquired state-of-charge data constraints at various time points. Its purpose is to combine power-level feasibility with the dynamic continuity constraints of battery energy, thereby selecting a power data set that is realistic and feasible in both power and energy dimensions.

[0102] Specifically, the system receives the preliminary power data set obtained based on the charging and discharging power range and the charging and discharging power range at each moment, and simultaneously presets an initial state of charge. For each preliminary power data set, the state-of-charge data constraints obtained by S401 are strictly applied, i.e., the recursive relationship defined by formula (2), from... arrive Calculate the theoretically corresponding sequence of charged states time by time, and check each calculated value during this process. Is the value a physically valid numerical value (e.g., between 0 and the normalized value of 1 corresponding to the battery's maximum capacity); if all If all values ​​are valid, the power data set is considered to satisfy the state of charge data constraints; if an invalid value (such as a negative value) appears, it is considered to violate the constraints and is not feasible at the energy level.

[0103] The preliminary power data sets that pass the above verification (i.e., satisfy the constraints of the state of charge data at all times) will be retained as the final, valid power data sets output. Simultaneously, the complete calculated SOC sequence for each set that passes the verification will be recorded. Preliminary power data sets that fail the verification (i.e., contain invalid values ​​in their theoretically corresponding SOC sequence) will be discarded.

[0104] In this embodiment, each power data set output is a physically achievable charge-discharge power sequence that has passed energy rule verification. This provides a high-quality input for multiple energy storage installation schemes to obtain the maximum benefit value, with dual guarantees of power safety and energy continuity. It fundamentally avoids the problem of inflated theoretical benefits or power sequences that cannot be practically executed due to ignoring the dynamic characteristics of the battery, greatly improving the engineering rigor of energy storage installation scheme decision-making and the practical value of the results.

[0105] In the above Figure 4 Based on the corresponding embodiments, optionally, before obtaining multiple power data sets in S402 by using the state-of-charge data constraints at each moment according to the charge / discharge power range, the charging power range at each moment, and the discharging power range at each moment, the method further includes: The preset power parameters also include: capacity data.

[0106] S501, using preset start and end state of charge constraints, acquires state of charge data at the start and end times from multiple times, so that the state of charge data at the start and end times are both full charge state data corresponding to the capacity data.

[0107] In this step, to ensure that the energy storage cabinet returns to its initial fully charged state at the end of each day (or each operating cycle), thereby achieving continuous daily cycle operation and protecting battery health, preset start and end state of charge constraints are introduced. These preset start and end state of charge constraints are an important special case and enhancement of the aforementioned state of charge data constraints, specifically mandating the energy state at the start and end of the operating cycle.

[0108] The preset start and end state of charge constraints are specifically defined as: at the start time of a unit time period (e.g., a day). and the end time The state of charge of the energy storage cabinet must be equal to its rated capacity. The corresponding fully charged electrical state. This condition can be expressed as: and ;in, This is the state of charge data at the initial moment. The state of charge data at the end time. This refers to the rated capacity data of the energy storage cabinet based on preset power parameters. For ease of calculation and comparison, the capacity... and A consistent normalized reference is typically used, in which case the fully charged electrical state can be expressed as: The above constraints are then simplified to and .

[0109] Therefore, in this step, obtaining the state of charge data at the start and end of multiple time points means, based on the above constraints, explicitly setting: the state of charge data at the start time... Fixed as (i.e., fully charged state), state of charge data at the end of the charge period. The target value was also set as (That is, it requires returning to a fully charged state).

[0110] Meanwhile, these preset start and end state-of-charge constraints will directly affect and be integrated into the verification logic of the subsequent S402 step. In S402, when applying the preset state-of-charge calculation rules to perform energy continuity calculations on any preliminary power data set, the starting conditions for the calculation will strictly follow the conditions set in this step. At the same time, the power data set must be determined as finally valid, and its calculated termination state must be determined accordingly. The conditions set in this step must be met. This is the objective. Therefore, the start and end state settings output in this step are the key input conditions regarding the energy state boundary that S402 uses when performing energy feasibility screening.

[0111] In this embodiment, by applying preset start and end state-of-charge constraints, a clear boundary condition is set for the energy flow within the entire unit time period: it starts at full charge and must end at full charge. This not only defines the starting point for energy calculation but also adds a hard termination target that must be met for the generation and verification of all subsequent power data sets. This ensures that any operating scheme that is ultimately adopted has daily cycle sustainability and avoids the battery being in a non-fully charged state at the end of the cycle, which would affect the scheduling of the next day or long-term battery balancing.

[0112] In the above Figure 1 Based on the corresponding embodiments, in order to more clearly demonstrate the decision-making process for energy storage installation schemes, this application also provides a possible implementation of the energy storage installation scheme decision-making method. Figure 5 A flowchart illustrating another energy storage installation scheme decision-making method provided in this application. (See attached diagram.) Figure 5 As shown, in S102 above, before iteratively solving the objective function of a single energy storage unit's revenue based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit, and obtaining multiple energy storage installation schemes with the maximum revenue value, the method further includes: Among them, the electricity price curve is the grid electricity price curve.

[0113] S601, obtain the grid electricity price data at each time point from the grid electricity price curve as the charging and discharging electricity price data at each time point.

[0114] In this embodiment, for the typical scenario where users purchase electricity solely from the grid, the electricity price curve specifically refers to the grid electricity price curve. This curve fully reflects the sequence of electricity prices that users need to pay at different times when purchasing electricity from the public grid. In this embodiment, the price data corresponding to each moment in the grid electricity price curve is simultaneously established as a single electricity price benchmark for calculating the charging cost and discharging revenue of the energy storage cabinet at that moment, thereby simplifying the price input in the subsequent optimization model.

[0115] Specifically, after obtaining the grid electricity price curve, a unified data extraction and assignment process is performed. For any given moment within a unit of time... The corresponding grid electricity price is read from this curve and used as the charging and discharging price data for each moment. This means that at any given moment, regardless of whether the energy storage unit performs a charging operation (drawing power from the grid) or a discharging operation (supplying power to the load or feeding power back to the grid), the economic value of the electricity involved is measured against this same grid electricity price. During charging, the cost is calculated based on this price; during discharging, the resulting revenue or cost savings are also calculated using this price. This step establishes a clear, consistent, and unique price signal input for subsequent revenue calculations, corresponding to a typical business model of peak-valley arbitrage using time-of-use grid pricing.

[0116] In step S102 above, based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit, the objective function for the revenue of a single energy storage unit is iteratively solved to obtain multiple energy storage installation schemes that maximize the revenue value, including: S602, based on the grid electricity price data at each time point and the preset configuration power parameters of a single energy storage cabinet, iteratively solves the objective function of the revenue of a single energy storage cabinet to obtain multiple energy storage installation schemes with the maximum revenue value.

[0117] After establishing the grid electricity price as the charging and discharging price data at each time point, the obtained grid electricity price data at each time point is used as the price parameter in the revenue objective function (formula (1)). Furthermore, by combining the various physical and operational constraints defined by the preset power parameters of a single energy storage unit, a revenue objective function is constructed and iteratively solved to obtain multiple energy storage installation schemes that can maximize theoretical revenue under different assumed installed capacity.

[0118] Specifically, starting with one energy storage unit, an optimization solver (such as PuLP) is used to calculate the maximum revenue and charge / discharge power plan sequence under the current scale. Subsequently, the number of energy storage units is increased in fixed steps, and the solution process is repeated. The iteration stops when the revenue growth tends to level off, and finally outputs a series of optimal solutions under different installed scales. Each solution includes the number of installed units, the maximum revenue value, and the charge / discharge power plan, providing direct input for subsequent determination of the target energy storage installation scheme.

[0119] In this embodiment, the charging and discharging electricity price is unified with the grid electricity price. In a pure grid scenario, a series of energy storage installation schemes that maximize theoretical benefits can be automatically generated. This provides a clear, comparable, and direct quantitative basis for determining the target energy storage installation scheme based on the arbitrage potential of grid price differences, which significantly improves the scientific nature and accuracy of the planning.

[0120] In the above Figure 1 Based on the corresponding embodiments, in order to more clearly demonstrate the decision-making process for energy storage installation schemes, this application also provides a possible implementation of the energy storage installation scheme decision-making method. Figure 6 A flowchart illustrating another energy storage installation scheme decision-making method provided in this application. (See attached diagram.) Figure 6 As shown, in S102 above, before iteratively solving the objective function of a single energy storage unit's revenue based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit, and obtaining multiple energy storage installation schemes with the maximum revenue value, the method further includes: The electricity price curves include the grid electricity price curve and the photovoltaic electricity price curve.

[0121] S701, obtain the grid electricity price data at each time point from the grid electricity price curve as the discharge electricity price data at each time point.

[0122] In this embodiment, for scenarios where photovoltaic power generation systems have been configured on the user side, the electricity price curve includes the grid electricity price curve reflecting the cost of purchasing electricity from the public grid, and the photovoltaic electricity price curve reflecting the value of self-generated and self-consumed photovoltaic power.

[0123] Specifically, after obtaining the grid electricity price curve, a unified data extraction and assignment process is performed. For any given moment within a unit of time... The corresponding grid electricity price is read from the grid electricity price curve, and this value is used as the discharge electricity price data at that moment.

[0124] This step establishes a clear value benchmark for the discharge side of the energy storage system, directly linked to the external power grid market. This benchmark is a key input for subsequent discharge revenue calculations.

[0125] S702 obtains the photovoltaic electricity price data at each time point from the photovoltaic electricity price curve as the charging electricity price data at each time point.

[0126] It should be noted that the use of photovoltaic electricity price data as charging electricity price data is based on the established operating strategy of the current energy storage system using photovoltaic power generation for charging.

[0127] Specifically, after obtaining the photovoltaic electricity price curve, a unified data extraction and assignment process is performed. For any given moment within a unit of time... The corresponding photovoltaic price is extracted from the photovoltaic price curve, and this value is used as the charging price data at that moment.

[0128] Through steps S702 and S701, independent electricity price inputs are established for charging and discharging behaviors, respectively. The charging cost depends on the photovoltaic electricity price, and the discharging revenue depends on the grid electricity price, enabling the energy storage system's energy storage installation scheme to more effectively coordinate photovoltaic and grid arbitrage.

[0129] In step S102 above, based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit, the objective function for the revenue of a single energy storage unit is iteratively solved to obtain multiple energy storage installation schemes that maximize the revenue value, including: S703 uses the grid electricity price data and photovoltaic electricity price data at each time point, as well as the preset configuration power parameters of a single energy storage cabinet, to iteratively solve the objective function of the revenue of a single energy storage cabinet and obtain multiple energy storage installation schemes with the maximum revenue value.

[0130] Specifically, the discharge price data obtained by S701 and the charging price data obtained by S702 at each time are substituted into the energy storage system revenue objective function. At this point, the revenue objective function is specified as the following formula (3): Formula (3) in, That is, the electricity price from the power grid. That is, the price of photovoltaic electricity.

[0131] Based on the objective function of calculating charging costs using photovoltaic (PV) electricity prices and discharging revenue using grid electricity prices, and combined with various physical and operational constraints defined by the preset power parameters of a single energy storage unit, a revenue objective function is constructed and iteratively solved to obtain multiple energy storage installation schemes that can maximize theoretical revenue under different assumed installed capacity scales. Starting with one energy storage unit, an optimization solver is used to calculate the maximum theoretical revenue under the current installed capacity and driven by both PV and grid electricity price signals, as well as the planned sequence of charging and discharging power to achieve this revenue. Subsequently, the number of energy storage units is increased according to a preset step size, and the above solution process is repeated for each scale until the revenue growth tends to converge. Finally, a series of energy storage installation schemes under different installed capacity scales are output, each scheme including the number of units installed, the maximum revenue value achievable under the current PV power generation configuration, and the planned charging and discharging power.

[0132] In this embodiment, by linking charging costs to photovoltaic electricity prices and discharging revenue to grid electricity prices, a revenue objective function for photovoltaic-storage synergy is constructed, thereby outputting an installation scheme that combines technical feasibility and economic optimization, providing a scientific and practical quantitative basis for photovoltaic-storage integration.

[0133] In the above Figure 6 Based on the corresponding embodiments, in order to more clearly demonstrate the process of determining the charging electricity price data at each time, this application also provides a possible implementation of the energy storage installation scheme decision-making method. Figure 7 This is a flowchart illustrating the process of determining charging price data at various times in a decision-making method for energy storage installation schemes provided in this application. Figure 7 As shown, based on the above S701-S703, the method further includes: S801, Obtain the user's load curve for the specified time period.

[0134] In this embodiment, before determining the charging electricity price data, it is necessary to obtain the user's load curve within a unit time period. This curve reflects the data sequence of the user's actual power consumption changes at various times within the same unit time period (such as a day), as detailed in S201, which will not be repeated here. S802, Based on the load curve, determine the energy source for energy storage at each moment.

[0135] The core of this step is to identify the specific source of the electrical energy consumed by the energy storage system during charging, based on the real-time power balance relationship. This judgment is based on the following physical principle: at any given moment... User load data A positive value indicates that electricity is drawn from the grid, while a negative value indicates that photovoltaic power generation has a surplus and feeds electricity back to the grid. Therefore, the energy sources for energy storage at any given time include: photovoltaic power generation and the grid.

[0136] Specifically, for any moment within a unit of time period ,when This indicates that the real-time power output of local photovoltaic (PV) power generation has exceeded the total electricity demand of users at that moment, resulting in a net surplus of PV power. In this case, if the energy storage unit performs a charging operation, its charging power will be entirely provided by this surplus PV power that cannot be immediately consumed locally. Therefore, the energy source for charging the energy storage unit at this moment is determined to be PV power generation; when This indicates that local electricity demand is greater than or equal to the real-time photovoltaic power generation, and there is no surplus photovoltaic power. At this time, all of the user's electricity demand (and potential energy storage charging demand) must be supplemented by the public grid. If the energy storage unit performs a charging operation, the required electricity cannot be obtained from the photovoltaic system and must be drawn from the public grid. Therefore, the energy source for energy storage charging at this moment is determined to be the power grid.

[0137] Based on the above case-by-case analysis, for each moment... The energy storage and charging behavior clearly defines its physical energy source. This refined distinction is the logical basis for implementing differentiated and precise electricity price mapping, ensuring that the calculation of charging costs fully corresponds to the actual electricity flow and economic costs.

[0138] S803, Based on the energy source of the energy storage at each time point and the electricity price curve, determine the charging electricity price data at each time point.

[0139] Based on the determined energy source for energy storage and charging at each time point, the corresponding pricing standard is dynamically selected, thereby converting the energy source into an economic cost signal.

[0140] If the load data at that moment If the energy source for energy storage at that moment is determined to be photovoltaic power generation, then the photovoltaic electricity price data corresponding to that moment is extracted from the photovoltaic electricity price curve. This data is then directly used as the charging price data for that specific moment. The photovoltaic (PV) electricity price is used for pricing; this portion of the electricity is sold to the grid at the PV price (usually a lower feed-in tariff or subsidized tariff); if the load data for that moment... In other words, if the energy source for energy storage at that moment is determined to be the power grid, then the corresponding power grid price data for that moment is extracted from the power grid price curve. And determine it as the charging price data at that moment. In this embodiment, the energy source for energy storage charging is dynamically determined based on real-time load, and the charging price is accurately matched accordingly. This makes the revenue optimization model more closely aligned with actual operating scenarios and accurately reflects the cost advantages of utilizing photovoltaic charging. The resulting energy storage installation plan and charging / discharging strategy are more accurate in economic evaluation, significantly improving the scientific rigor and feasibility of the plan.

[0141] The following describes a decision-making device and electronic equipment for an energy storage installation scheme provided in this application. The specific implementation process and technical effects are described above and will not be repeated below.

[0142] Figure 8 A schematic diagram of an energy storage installation scheme decision device provided in this application embodiment is shown below. Figure 8 As shown, the energy storage installation scheme decision-making device includes: The acquisition module 1000 is used to acquire the electricity price curve for a unit time period; The calculation module 2000 is used to iteratively solve the objective function of the revenue of a single energy storage cabinet based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage cabinet, so as to obtain multiple energy storage installation schemes with the maximum revenue value. Each energy storage installation scheme includes: the number of energy storage cabinets to be installed, and the charging and discharging power of each energy storage cabinet in a unit time period.

[0143] The determination module 3000 is used to determine the target energy storage installation scheme from multiple energy storage installation schemes.

[0144] Optionally, the acquisition module 1000 is also used to acquire the user's load curve over a unit time period.

[0145] Optionally, the calculation module 2000 is specifically used to obtain multiple power data sets of a single energy storage cabinet within a unit time period based on the load curve, the preset configuration power parameters of a single energy storage cabinet, and the transformer capacity parameters of the user's corresponding distribution area. Each power data set includes power data at multiple times, where the power data at each time moment is either charging power or discharging power. Based on the electricity price data at each time moment and the multiple power data sets, the revenue objective function of a single energy storage cabinet is iteratively solved to obtain multiple energy storage installation schemes with the maximum revenue value.

[0146] Optionally, the preset power parameters include: maximum discharge power.

[0147] Optionally, the calculation module 2000 is specifically used to obtain the charging and discharging power range of a single energy storage cabinet based on the maximum discharge power and using charging and discharging constraints; to obtain the charging power range of a single energy storage cabinet at each moment based on the load data and transformer capacity parameters in the load curve and using transformer capacity constraints; to obtain the discharging power range of a single energy storage cabinet at each moment based on the load data and discharging power constraints in the load curve; and to obtain multiple power data sets based on the charging and discharging power range, the charging power range at each moment, and the discharging power range at each moment.

[0148] Optionally, the calculation module 2000 is also used to obtain the state of charge data constraints at each time moment according to the preset charging efficiency and preset discharging efficiency and the preset state of charge calculation rules; and to obtain multiple power data sets according to the charging and discharging power range, the charging power range at each time moment, and the discharging power range at each time moment and the state of charge data constraints at each time moment.

[0149] Optionally, the preset power parameters also include: capacity data.

[0150] Optionally, the calculation module 2000 is also used to acquire the state of charge data at the start time and the state of charge data at the end time among multiple time points by using preset start and end state of charge constraints, so that the state of charge data at the start time and the state of charge data at the end time are both full charge state data corresponding to the capacity data.

[0151] Optionally, the electricity price curve is the grid electricity price curve.

[0152] Optionally, the acquisition module 1000 is also used to acquire grid electricity price data at each time from the grid electricity price curve as charging and discharging electricity price data at each time.

[0153] Optionally, the calculation module 2000 is specifically used to iteratively solve the objective function of the revenue of a single energy storage cabinet based on the grid electricity price data at each time and the preset configuration power parameters of the single energy storage cabinet, so as to obtain multiple energy storage installation schemes with the maximum revenue value.

[0154] Optionally, the electricity price curve includes: the grid electricity price curve and the photovoltaic electricity price curve.

[0155] Optionally, the acquisition module 1000 is also used to acquire grid electricity price data at each time from the grid electricity price curve as discharge electricity price data at each time; and to acquire photovoltaic electricity price data at each time from the photovoltaic electricity price curve as charging electricity price data at each time.

[0156] Optionally, the calculation module 2000 is specifically used to iteratively solve the revenue objective function of a single energy storage cabinet based on the grid electricity price data at each time, the photovoltaic electricity price data at each time, and the preset configuration power parameters of a single energy storage cabinet, so as to obtain multiple energy storage installation schemes with the maximum revenue value.

[0157] Optionally, the acquisition module 1000 is also used to acquire the user's load curve over a unit time period.

[0158] Optionally, the determination module 3000 is also used to determine the energy source of energy storage at each time according to the load curve; and to determine the charging electricity price data at each time according to the energy source of energy storage and the electricity price curve at each time.

[0159] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0160] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0161] Figure 9 This is a schematic diagram of an electronic device provided in this application. The device may be a computing device or a server with computing processing capabilities.

[0162] The electronic device 10 includes a processor 11, a storage medium 12, and a bus 13. The storage medium 12 stores program instructions executable by the processor 11. When the electronic device 10 is executed, the processor 11 communicates with the storage medium 12 via the bus 13, and the processor 11 executes the program instructions to perform the above-described method embodiment. The specific implementation and technical effects are similar and will not be described in detail here.

[0163] Optionally, this application also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, performs the above-described method embodiments.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0167] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0168] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for deciding on energy storage installation schemes, characterized in that, The method includes: Obtain the electricity price curve for a unit time period; Based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage cabinet, the revenue objective function of the single energy storage cabinet is iteratively solved to obtain multiple energy storage installation schemes with the maximum revenue value; wherein, each energy storage installation scheme includes: the number of energy storage cabinets to be installed, and the charging and discharging power of each energy storage cabinet in the unit time period. The target energy storage installation scheme is determined from the multiple energy storage installation schemes mentioned above.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the user's load curve within the specified time period; The method involves iteratively solving the objective function of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of the individual energy storage unit, to obtain multiple energy storage installation schemes with the maximum revenue value, including: Based on the load curve, the preset configuration power parameters of the individual energy storage cabinet, and the transformer capacity parameters of the user's corresponding distribution area, multiple power data sets of the individual energy storage cabinet are obtained within the unit time period. Each power data set includes power data at multiple times, and the power data at each time is either charging power or discharging power. Based on the electricity price data at each time point and the multiple power data sets, the objective function of the revenue of the single energy storage cabinet is iteratively solved to obtain multiple energy storage installation schemes with the maximum revenue value.

3. The method according to claim 2, characterized in that, The preset power parameters include: maximum discharge power; The process of obtaining multiple power data sets for a single energy storage unit within the unit time period based on the load curve, the preset configuration power parameters of the single energy storage unit, and the transformer capacity parameters of the user's corresponding distribution area includes: Based on the maximum discharge power, the charge and discharge power range of the single energy storage cabinet is obtained using charge and discharge constraint conditions. Based on the load data at each moment in the load curve and the transformer capacity parameters, the charging power range of the single energy storage cabinet at each moment is obtained using the transformer capacity constraint condition. Based on the load data at each moment in the load curve, the discharge power range of the single energy storage cabinet at each moment is obtained by using the discharge power constraint condition. The plurality of power data sets are obtained based on the charging and discharging power range, the charging power range at each time moment, and the discharging power range at each time moment.

4. The method according to claim 3, characterized in that, The method further includes: Based on the preset charging efficiency and preset discharging efficiency, and using the preset state of charge calculation rules, the state of charge data constraints at each time point are obtained. The step of obtaining the multiple power data sets based on the charge / discharge power range, the charging power range at each time moment, and the discharging power range at each time moment includes: Based on the charging and discharging power range, the charging power range at each time moment, and the discharging power range at each time moment, the multiple power data sets are obtained using the state of charge data constraints at each time moment.

5. The method according to claim 4, characterized in that, The preset power parameters also include: capacity data; Before obtaining the multiple power data sets based on the charge / discharge power range, the charging power range at each time moment, and the discharging power range at each time moment, using the state of charge data constraints at each time moment, the method further includes: By employing preset start and end state of charge constraints, the state of charge data at the start time and the state of charge data at the end time are obtained from the plurality of time points, such that the state of charge data at the start time and the state of charge data at the end time are both full charge state data corresponding to the capacity data.

6. The method according to claim 1, characterized in that, The electricity price curve is the grid electricity price curve; before iteratively solving the objective function of the revenue of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit to obtain multiple energy storage installation schemes with the maximum revenue value, the method further includes: The grid electricity price data at each time point are obtained from the grid electricity price curve as the charging and discharging electricity price data at each time point; The method involves iteratively solving the objective function of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of the individual energy storage unit, to obtain multiple energy storage installation schemes with the maximum revenue value, including: The method involves iteratively solving the objective function of the revenue of a single energy storage unit based on the grid electricity price data at each time point and the preset configuration power parameters of the single energy storage unit, thereby obtaining multiple energy storage installation schemes with the maximum revenue value.

7. The method according to claim 1, characterized in that, The electricity price curves include: the grid electricity price curve and the photovoltaic electricity price curve; Before iteratively solving the objective function of the revenue of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage unit to obtain multiple energy storage installation schemes with the maximum revenue value, the method further includes: The grid electricity price data at each time point are obtained from the grid electricity price curve as the discharge electricity price data at each time point; The photovoltaic electricity price data at each time point are obtained from the photovoltaic electricity price curve as the charging electricity price data at each time point; The method involves iteratively solving the objective function of a single energy storage unit based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of the individual energy storage unit, to obtain multiple energy storage installation schemes with the maximum revenue value, including: The method involves iteratively solving the objective function of the revenue of a single energy storage unit based on the grid electricity price data, the photovoltaic electricity price data, and the preset configuration power parameters of the single energy storage unit at each time point, to obtain multiple energy storage installation schemes with the maximum revenue value.

8. The method according to claim 7, characterized in that, The method further includes: Obtain the user's load curve within the specified time period; Based on the load curve, determine the energy source for energy storage at each moment; Based on the energy source of the energy storage at each time point and the electricity price curve, the charging electricity price data at each time point is determined.

9. A decision-making device for energy storage installation schemes, characterized in that, The device includes: The acquisition module is used to acquire the electricity price curve for a unit time period; The calculation module is used to iteratively solve the revenue objective function of a single energy storage cabinet based on the electricity price data at each moment in the electricity price curve and the preset configuration power parameters of a single energy storage cabinet, so as to obtain multiple energy storage installation schemes with the maximum revenue value; wherein, each energy storage installation scheme includes: the number of energy storage cabinets to be installed, and the charging and discharging power of each energy storage cabinet in the unit time period. The determination module is used to determine the target energy storage installation scheme from the multiple energy storage installation schemes.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores program instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. The processor executes the program instructions to perform the steps of the energy storage installation scheme decision method as described in any one of claims 1 to 8.

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