Energy storage capacity configuration optimization method and system

Abstract

The embodiment of the invention provides an energy storage capacity configuration optimization method and system, and the method comprises the steps: obtaining first target data which comprises wind power historical output data, photovoltaic historical output data, day-ahead spot electricity price data, real-time spot electricity price data and energy storage technical parameters; performing structured processing on the first target data to form a first data set in a standard format; based on the first data set, performing optimization calculation by using a collaborative optimization configuration model to obtain optimized energy storage capacity configuration parameters, the energy storage capacity configuration parameters including energy storage power capacity and energy storage electric quantity capacity; and based on the optimized energy storage capacity configuration parameters, generating a day-ahead market charging and discharging strategy curve and a real-time market charging and discharging strategy curve by using a double-market collaborative optimization model. The core problem of disjunction of planning and operation is solved; by means of the energy storage double-component model, accurate response and arbitrage space mining of double market electricity price signals are achieved, and the energy storage utilization rate and the system income are improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of power energy storage, in particular to a method and system for optimizing energy storage capacity configuration. BACKGROUND

[0002] With the increasing proportion of new energy power generation, configuring energy storage has become a key technical means to improve the grid performance and economic benefits of intermittent new energy stations such as wind and solar. At present, under the background of power market reform, new energy stations need to participate in both day-ahead and real-time spot markets for trading, which brings new challenges to the capacity configuration and operation strategy of energy storage. The existing energy storage optimization configuration method focuses on optimizing in a single market (such as only day-ahead or only real-time market) or with a single physical target (such as smoothing output) or economic target (such as static income indicator), lacking of coordination between market environment and multi-dimensional target.

[0003] However, in the market dimension, the existing scheme fails to fully consider the coupling characteristics and price linkage mechanism of day-ahead and real-time double spot markets, and applies energy storage strategy to a single market, resulting in that energy storage equipment cannot maximize arbitrage as a medium connecting double markets, and its utilization rate and peak potential cannot be fully utilized. Secondly, in the optimization dimension, the existing method often considers physical or economic targets in isolation, considering only physical targets can easily lead to over-configuration of energy storage systems, causing investment waste, and considering only static economic targets ignores market dynamic characteristics, resulting in poor economic robustness of configuration results. Furthermore, in the evaluation dimension, the existing economic evaluation model has low data resolution and short time scale, which cannot realize high-precision, hourly economic calculation combined with production simulation at the micro level, and also cannot support accurate investment decisions throughout the life cycle at the macro level. These defects collectively lead to the actual disconnection between the capacity configuration strategy and market operation of the current new energy station energy storage, and the investment efficiency and operating income are difficult to achieve optimality. SUMMARY

[0004] Therefore, embodiments of the present application provide a method for optimizing energy storage capacity configuration. One or more embodiments of the present application also relate to a system for optimizing energy storage capacity configuration, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects in the prior art.

[0005] In a first aspect, embodiments of the present application provide a method for optimizing energy storage capacity configuration, comprising: obtaining first target data, wherein the first target data includes wind power historical output data, photovoltaic historical output data, day-ahead spot price data, real-time spot price data, and energy storage technology parameters, wherein the energy storage technology parameters include depth of discharge, charge and discharge efficiency, and cycle life; performing structural processing on the first target data to form a first data set in a standard format; performing optimization calculation based on the first data set by using a collaborative optimization configuration model to obtain optimized energy storage capacity configuration parameters, wherein the collaborative optimization configuration model decomposes energy storage output into a day-ahead market output component and a real-time market output component, sets at least one optimization objective function and multiple constraint conditions, and the energy storage capacity configuration parameters include energy storage power capacity and energy storage energy capacity; generating a day-ahead market charging and discharging strategy curve and a real-time market charging and discharging strategy curve based on the optimized energy storage capacity configuration parameters by using a double-market collaborative optimization model.

[0006] In a possible implementation, the optimization objective function includes at least one of a physical objective function, an economic objective function or a fuzzy multi-objective function.

[0007] In a possible implementation, the physical objective function is to minimize the variance of wind-solar-storage joint output, and the variance is calculated by the following formula:

[0008]

[0009] wherein, denotes the variance of wind-solar-storage joint output, n denotes the total number of time nodes in the optimization range, denotes the wind-solar-storage joint output of the d th time period, denotes the average value of wind-solar-storage joint output of all time periods, denotes the wind power output of the d th time period, denotes the photovoltaic output of the d th time period, denotes the energy storage output of the d th time period.

[0010] In a possible implementation, the economic objective function is to maximize the wind-solar-storage project revenue, and the revenue is calculated by the following formula:

[0011] wherein, denotes the wind-solar-storage project revenue, denotes the total number of time nodes in the optimization range, denotes the medium and long-term energy of the i th time period, denotes the medium and long-term electricity price of the i th time period, denotes the base energy of the i th time period, denotes the base electricity price of the i th time period, denotes the day-ahead settlement energy of the i th time period, denotes the day-ahead settlement electricity price of the i th time period, This represents the real-time electricity consumption during the i-th time period. This represents the real-time settlement price for the i-th time period.

[0012] In one possible implementation, the fuzzy multi-objective function is constructed based on the membership function of a descending half-trapezoidal distribution, and the expression of the fuzzy multi-objective function is:

[0013] in, Represents the value of a fuzzy multi-objective function. The fuzzy membership degree represents the physical objective function. The fuzzy membership degree represents the economic objective function. express The weighting coefficients, express The weighting coefficients, and .

[0014] In one possible implementation, the fuzzy membership degree of the physical objective function is calculated using the following descending semi-trapezoidal membership degree function:

[0015] in, This represents the actual value of the physical objective function. This represents the minimum expected value of the physical objective function. This represents the maximum allowable value of the physical objective function.

[0016] In one possible implementation, the constraints include at least one of the following: upper limit constraint on energy storage capacity, upper and lower limit constraint on energy storage output, and upper and lower limit constraint on energy storage state of charge, wherein the upper and lower limit constraint on energy storage output includes a lower limit on charging power and an upper limit on discharging power. The upper limit constraint on the energy storage capacity is expressed as follows: ,in The stored energy is the amount of energy stored in time period d. This is the upper limit of the energy storage capacity; The lower limit of the charging power is expressed as follows: The upper limit of the discharge power is expressed as: ,in, The energy storage charging power for time period d. Let d be the energy storage discharge power during the d-th time period. This represents the maximum power output at the energy storage converter terminal. For the overall conversion efficiency of the energy storage system; The upper and lower limits of the energy storage state of charge constraints represent: ,in, and respectively represent the lower limit and the upper limit of the energy storage state of charge, is the energy storage state of charge for the d-th period.

[0017] In a possible implementation, the method further includes a double-strategy coupling step, specifically including: According to the coupling relationship between the day-ahead market electricity price and the real-time market electricity price, the energy storage output strategy is divided into a combination of day-ahead market components and real-time market components, the combination including charging, no instruction or discharging states, and the day-ahead market charging and discharging strategy curve and the real-time market charging and discharging strategy curve are generated based on the combination.

[0018] In a possible implementation, the method is implemented through a software system, and the software system includes a data entry module, an optimization calculation module and a strategy output module, wherein the optimization calculation module is configured to call a third-party optimization platform to perform calculation of the collaborative optimization configuration model and the spot arbitrage model.

[0019] In a second aspect, the embodiments of the present application provide a storage capacity configuration optimization system, including: The data acquisition module is configured to acquire first target data, wherein the first target data includes wind power historical output data, photovoltaic historical output data, day-ahead spot electricity price data, real-time spot electricity price data and storage technology parameters, wherein the storage technology parameters include deep discharge depth, charging and discharging efficiency and cycle life; The data processing module is configured to perform structured processing on the first target data to form a first data set in a standard format; The optimization calculation module is configured to perform optimization calculation based on the first data set by using a collaborative optimization configuration model to obtain optimized storage capacity configuration parameters, wherein the collaborative optimization configuration model decomposes energy storage output into day-ahead market output components and real-time market output components, and sets at least one optimization objective function and multiple constraint conditions, and the storage capacity configuration parameters include storage power capacity and storage energy capacity; The strategy generation module is configured to generate a day-ahead market charging and discharging strategy curve and a real-time market charging and discharging strategy curve based on the optimized storage capacity configuration parameters by using a double-market collaborative optimization model.

[0020] In a third aspect, the embodiments of the present application provide a computing device, including: a memory and a processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the above-mentioned storage capacity configuration optimization method.

[0021] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, which stores computer executable instructions, and the instructions, when executed by a processor, implement the steps of the energy storage capacity configuration optimization method.

[0022] In a fifth aspect, the embodiments of the present application provide a computer program, which, when executed in a computer, causes the computer to perform the steps of the energy storage capacity configuration optimization method.

[0023] The technical scheme provided by the embodiments of the present application first acquires wind power / photovoltaic historical output, double-market electricity price and energy storage technical parameters to form multi-source data, and inputs the coordinated optimization configuration model after structured processing; the model decomposes the energy storage output into day-ahead and real-time market components, and jointly solves under multi-objective functions and physical constraints to synchronously output optimal energy storage power / electricity capacity parameters; then, based on the capacity results, the coordinated charging and discharging strategy curve of the day-ahead and real-time markets is generated through the double-market coordinated optimization model. The scheme solves the core problem of disconnection between planning and operation in the traditional method through coordinated optimization of capacity configuration and operation strategy; with the help of the double-component model of energy storage, the precise response to the double-market electricity price signal and arbitrage space mining are realized, and the energy storage utilization rate and system revenue are significantly improved; at the same time, relying on the multi-objective optimization framework and the physical constraint system, the global optimality of the configuration scheme under the multi-dimensional targets of smoothness, economy and engineering feasibility is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a scene schematic diagram of an energy storage capacity configuration optimization method provided by an embodiment of the present application; Figure 2 is a flowchart of an energy storage capacity configuration optimization method provided by an embodiment of the present application; Figure 3 is a structural schematic diagram of an energy storage capacity configuration optimization system provided by an embodiment of the present application; Figure 4 is a structural block diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0025] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in a variety of ways beyond the specific embodiments described herein without departing from the scope of the present application. Accordingly, the present application is not limited to the specific embodiments described in the following description.

[0026] The terminology used in this disclosure, in one or more embodiments, is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the present disclosure. As used in this disclosure and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0027] It will be understood that, although the terms first, second, etc. can be used herein to describe various information, these terms are not intended to denote a temporal sequence. Rather, these terms are used only as distinguish one from another. For example, without departing from the scope of one or more embodiments, first can be termed second, and, similarly, second can be termed first. The word "if' as used herein means "when" or "upon" or "in response to a determination" depending on the context.

[0028] First, the noun terms related to one or more embodiments of the present application are explained.

[0029] 1, Output: Specifically refers to the active power actually generated by power generation equipment (such as wind turbines, photovoltaic panels, thermal power generating units) in an electric power system at a certain moment, usually in kilowatts (kW) or megawatts (MW). It describes the "ability" or "rate" of power generation.

[0030] 2, Historical output data: Refers to the active power data recorded in time sequence by power generation equipment in the past period of time (such as one year, one month or one day). These data are usually collected and recorded at fixed time intervals (such as 15 minutes, 1 hour), forming a power curve that changes with time.

[0031] 3, Day-ahead spot price data: Refers to the electricity trading price for each period (usually 96 15-minute points or 24-hour points) in the next 24 hours determined by market bidding and centralized clearing before the actual operation day.

[0032] 4, Real-time spot price data: Market clearing price calculated and released in a very short time interval (usually 15 minutes or 5 minutes) based on the actual and constantly changing operation state of the power grid (such as load fluctuation, new energy output fluctuation, network congestion, etc.) on the actual operation day.

[0033] In the present application, a method for optimizing the configuration of energy storage capacity is provided, and the present application also relates to a system for optimizing the configuration of energy storage capacity, a computing device, and a computer-readable storage medium, which are described in detail one by one in the following embodiments.

[0034] Referring to Figure 1 , Figure 1 A scene schematic diagram of a method for optimizing energy storage capacity configuration is shown according to an embodiment of the application.

[0035] In Figure 1 application scenarios, a typical optimization operation and configuration analysis framework of a wind-solar-storage integrated station in an electricity spot market environment is depicted. The application scenario structure can be divided into three core levels: 1. Scene input layer The system takes historical and predicted output data of wind power and photovoltaic power as new energy side input, combines day-ahead and real-time electricity price data in the electricity market environment as economic driving signals, and simultaneously incorporates key technical parameters (such as charging and discharging efficiency, cycle life, etc.) of the energy storage system to form a basic data source for optimization analysis.

[0036] 2. Model optimization layer This is the core of the scheme, and the implementation process is as follows: first, based on the input data, a collaborative optimization configuration model is constructed. The model sets physical targets (such as smoothing wind and light fluctuations), economic targets (such as maximizing market revenue), or multi-objective fusion functions, and strategically decomposes the actual output of the energy storage into two virtual components acting on the day-ahead market and the real-time market. Subsequently, the model is solved under strict physical constraints (such as energy storage SOC, power upper and lower limits) and market rule constraints, and simultaneously outputs the optimal energy storage power and capacity configuration parameters.

[0037] 3. Strategy output layer After determining the energy storage capacity configuration, the scheme enters the strategy generation stage. Through the double-market collaborative optimization model, the day-ahead market quantity curve and the real-time market adjustment curve guiding the actual operation of the station are generated. Finally, the system can output comprehensive optimization results including the energy storage configuration scheme, market revenue analysis, and output smoothing effect, and can perform sensitivity analysis on different wind speeds, light intensities, and market scenarios, providing quantitative basis for investment decisions and operation management.

[0038] The scheme realizes the best balance between physical operation and market profitability through the closed-loop process of "data-driven-model optimization-strategy generation", and provides a complete technical solution for the efficient and economic operation of new energy stations in a market environment.

[0039] Referring to Figure 2 , Figure 2 A flowchart of a method for optimizing energy storage capacity configuration is shown according to an embodiment of the application, specifically comprising the following steps.

[0040] Step 201: obtaining first target data, wherein the first target data comprises wind power historical output data, photovoltaic historical output data, day-ahead spot electricity price data, real-time spot electricity price data, and energy storage technology parameters, wherein the energy storage technology parameters comprise depth of discharge, charge-discharge efficiency, and cycle life.

[0041] In some embodiments, the system collects wind power and photovoltaic historical output time series data from a new energy station monitoring system, obtains day-ahead and real-time two-time dimension spot electricity price curves from a power trading platform, and extracts key performance parameters including depth of discharge, charge-discharge efficiency, and cycle life from an energy storage device technical manual, to form a multi-source heterogeneous original data set. Through this data acquisition method, first, the wind and light output data provides a physical basis for reflecting the fluctuation characteristics of new energy for subsequent optimization, enabling the configuration scheme to effectively cope with uncertainties in actual operation; second, the double-market electricity price data as the driving signal for economic optimization enables the model to accurately capture arbitrage opportunities brought by price differences in different markets; finally, the introduction of energy storage technology parameters ensures that the optimization calculation strictly follows the physical constraints of the device, fundamentally ensuring the engineering feasibility and device operation safety of the obtained capacity configuration scheme and strategy, laying an accurate and comprehensive data foundation for subsequent collaborative optimization calculation.

[0042] Step 202: structuring the first target data to form a first data set in a standard format.

[0043] In some embodiments, first, the multi-source heterogeneous original data collected is cleaned to handle missing values and outliers; then, the timestamps are aligned and resampled to unify different time resolutions to the same optimization time step; then, each data type is normalized to eliminate dimension differences; finally, the processed data is organized according to a unified two-dimensional table structure, where each row represents an optimization period and each column corresponds to a specific variable, forming a standardized data set that can be directly called by the optimization model. Through this data processing method, the original disorganized data can be converted into regular data with strict spatiotemporal consistency, effectively eliminating the interference of data quality problems on optimization calculation, providing accurate and reliable input for subsequent optimization algorithms, significantly improving the convergence speed and stability of optimization calculation, and at the same time, the standardized data structure enhances the compatibility and scalability of the system for different sources of data.

[0044] Step 203: based on the first data set, using a collaborative optimization configuration model to perform optimization calculation to obtain optimized energy storage capacity configuration parameters, wherein the collaborative optimization configuration model decomposes energy storage output into day-ahead market output component and real-time market output component, and sets at least one optimization objective function and multiple constraints, and the energy storage capacity configuration parameters include energy storage power capacity and energy storage capacity.

[0045] In some embodiments, first, the model mathematically decomposes the total output of the energy storage system into two virtual components with explicit market orientation, namely, a day-ahead market output component and a real-time market output component, wherein the day-ahead component is mainly used to optimize the bidding strategy in the day-ahead market to obtain the planned income, and the real-time component is used to capture arbitrage opportunities in the real-time market due to deviation power. On this basis, the model builds a mathematical framework containing at least one optimization objective function, which can be flexibly configured according to actual needs to pursue physical performance optimization (such as minimizing the variance of wind-solar-storage joint output to improve grid friendliness), maximum economic benefit (such as maximizing total market revenue over the life cycle), or a fuzzy multi-objective form that takes both into account. At the same time, the model introduces a multi-constraint condition system that comprehensively reflects the physical characteristics and market rules of the system, including: energy storage power capacity constraints, charge and discharge power upper and lower limit constraints based on the maximum power and comprehensive conversion efficiency of the energy storage converter, energy storage state of charge (SOC) operating range constraints, and excess profit deviation constraints to ensure market compliance. The solution process of this optimization model is: taking the energy storage power capacity, energy storage power capacity, and double-market output components of each period as joint decision variables, through mathematical optimization algorithms (such as linear programming, mixed integer programming, or intelligent optimization algorithms), the decision variable combination that can make the objective function optimal is searched under the premise of meeting all constraint conditions. By decomposing and associating the energy storage output to different markets, the function of energy storage system as a flexible medium connecting the day-ahead and real-time markets is realized, which technically solves the problem of the fragmentation of double-market strategies in traditional methods, significantly improving the adaptability and profitability of energy storage in complex market environments. Secondly, through the simultaneous optimization of capacity parameters and operating strategies, the technical limitations of the traditional sequential decision-making mode of "first configuration and then operation" are broken through, ensuring that the finally determined energy storage power and capacity configuration scheme can support its optimal operating strategy in a physical sense and maximize the life cycle revenue in an economic sense, fundamentally avoiding the waste of investment or insufficient performance caused by the disconnection between planning and operation. Finally, by integrating energy storage technology parameters (such as DOD, efficiency) as hard constraints into the model, it is ensured that the optimization results strictly follow the physical limits of the equipment, greatly enhancing the feasibility and reliability of the scheme in real engineering environments, and providing precise and reliable technical decision support for the scientific planning of new energy station energy storage.

[0046] In some embodiments, the optimization objective function includes at least one of a physical objective function, an economic objective function, or a fuzzy multi-objective function.

[0047] The physical objective function is to minimize the variance of the wind-solar-storage joint output, which is calculated by the following formula: Formula One Equation Two wherein, denotes the variance of the wind-solar-storage joint output, n denotes the total number of time nodes within the optimization range, denotes the wind-solar-storage joint output of the dth time period, denotes the average value of the wind-solar-storage joint output of all time periods, denotes the wind power output of the dth time period, denotes the photovoltaic output of the dth time period, denotes the storage output of the dth time period.

[0048] The system first calculates the wind-solar-storage joint output value of each time period continuously based on the structured data set at a preset time interval , which is composed of the algebraic superposition of wind power output, photovoltaic output and storage output; then calculates the average value of all within the optimization period ; finally, the variance value representing the degree of output fluctuation is accurately calculated through the above-mentioned Equation One. In this calculation process, the storage output as a key adjusting variable, dynamically adjusts its charging and discharging state through the optimization algorithm, so that the joint output curve tends to be smooth. Through the above calculation method, first, from the perspective of grid safety, this method significantly reduces the inherent randomness and volatility of wind-solar power generation by minimizing the output variance, making the output characteristics of new energy stations closer to traditional power sources, greatly improving the grid's ability to absorb new energy and operational stability; second, from the perspective of equipment protection, the smooth output curve effectively avoids the impact of power fluctuations on storage converters and grid equipment, prolonging the service life of key equipment; finally, from the perspective of optimization mechanism, the physical smoothness is converted into a quantifiable mathematical index, providing an accurate evaluation basis for multi-objective optimization, enabling the system to achieve a scientific trade-off between smoothness and other objectives (such as economy). This optimization method with the goal of minimizing variance provides key technical support for the safe and stable operation of the grid under high-proportion new energy access conditions.

[0049] In some embodiments, the economic objective function is to maximize the wind-solar-storage project revenue, which is calculated by the following formula: Equation Three wherein, denotes the wind-solar-storage project revenue, denotes the total number of time nodes within the optimization range, denotes the medium and long-term electricity of the ith time period, denotes the medium and long-term electricity price of the ith time period, denotes the base electricity of the ith time period, denotes the base electricity price of the ith time period, denotes the day-ahead settlement electricity of the ith time period, denotes the day-ahead settlement price of the i-th time period, denotes the real-time settlement quantity of the i-th time period, denotes the real-time settlement price of the i-th time period.

[0050] The system multiplies the medium and long-term electricity quantity, the base quantity, the day-ahead settlement electricity quantity and the real-time settlement electricity quantity of each settlement time period i by the corresponding electricity price based on the structured data set, forms four income components of the time period, and then adds and sums all the income of each time period m to finally obtain the accurate quantified total income value . The calculation process builds a complete income calculation system by uniformly modeling the income sources of different markets and different time scales. Through this calculation method, first, the function realizes full market income coverage of the medium and long-term market, the base quantity market, the day-ahead spot market and the real-time spot market in the energy storage optimization configuration for the first time, overcoming the technical limitations of traditional methods that only consider single market income; second, by modeling the settlement mechanism of each market in detail, the optimization result can accurately reflect the real profitability of energy storage in a complex market environment, providing a reliable economic evaluation basis for investment decisions; finally, the synergistic effect of the income calculation model and the physical constraint conditions ensures that the optimized energy storage configuration scheme meets the economic optimum and meets the safety boundary of device operation, realizing the unity of technical feasibility and economic rationality, and providing key technical support for scientific investment of new energy station energy storage.

[0051] In some embodiments, the fuzzy multi-objective function is constructed based on a membership function of a descending half trapezoidal distribution, and the fuzzy multi-objective function expression is:

[0052] wherein, denotes the fuzzy multi-objective function value, denotes the fuzzy membership of the physical objective function, denotes the fuzzy membership of the economic objective function, denotes the weight coefficient of , denotes the weight coefficient of , and . .

[0053] The fuzzy membership of the physical objective function is calculated by the following descending half trapezoidal membership function:

[0054] wherein, denotes the actual value of the physical objective function, denotes the minimum expected value of the physical objective function, denotes the maximum allowable value of the physical objective function.

[0055] The system first constructs a membership function based on a descending half trapezoidal distribution, and respectively standardizes the physical target function value and the economic target function value into satisfaction indexes μ1 and μ2 in the interval [0, 1]; wherein for the physical target, the satisfaction is 1 when , the satisfaction is 0 when , and in the intermediate region, it decreases linearly; subsequently, the two satisfaction indexes are linearly combined through weighting coefficients w1, w2 to construct a new optimization function with the goal of maximizing the weighted sum F = w1·μ1 + w2·μ2. Through the above manner, firstly, the technical difficulty of the non-uniform dimension and large order of magnitude between the physical target and the economic target is successfully solved through the membership function, making the multi-objective optimization mathematically possible; secondly, by adopting the specific function form of the descending half trapezoidal distribution, the physical meaning of the target function value is retained, and the explicit boundary constraint is provided for optimization by setting the minimum expected value and the maximum allowed value , thereby enhancing the guidance of optimization; finally, through the flexible configuration of the weight coefficient, the system can dynamically adjust the priority of the physical performance and the economic benefit according to the actual demand, realize the technical leap from "single target optimization" to "multi-target satisfaction", and provide a more scientific and flexible decision basis for the comprehensive evaluation of new energy station energy storage.

[0056] In some embodiments, the constraint conditions include at least one of a storage energy amount upper limit constraint, a storage output upper and lower limit constraint, and a storage state of charge upper and lower limit constraint, wherein the storage output upper and lower limit constraint includes a charging power lower limit and a discharging power upper limit; The storage energy amount upper limit constraint is expressed as: , wherein is the storage energy amount of the dth time period, is the storage energy amount upper limit; The charging power lower limit is expressed as: , and the discharging power upper limit is expressed as: , wherein is the storage charging power of the dth time period, is the storage discharging power of the dth time period, is the maximum power at the storage converter end, is the comprehensive conversion efficiency of the storage system; The storage state of charge upper and lower limit constraint is expressed as: , wherein and respectively represent the lower limit and the upper limit of the storage state of charge, is the storage state of charge of the dth time period.

[0057] The constraint system realizes the physical feasibility and operational safety of the optimization model by establishing multi-dimensional technical boundaries: the upper limit constraint of the energy storage power limits the maximum energy storage capacity of the energy storage system, ensuring that the configuration scheme does not exceed the inherent capacity of the device; the upper and lower limit constraints of the energy storage output and respectively regulate the extreme values of the charging and discharging power, wherein represents the energy loss during charging and discharging, which not only guarantees the safety of the converter device, but also accurately reflects the actual physical process of energy conversion; the energy storage state of charge constraint sets a reasonable operating interval to effectively prevent overcharging and discharging of the battery, significantly extending the service life of the energy storage system. These constraint conditions collectively form a complete technical boundary system, which has technical effects in three aspects: first, by converting the technical parameters of the energy storage device into hard constraints in the mathematical model, it ensures that the optimization results strictly follow the physical laws and safety requirements of the device; second, the synergistic effect of multiple constraints ensures that the optimization scheme always operates within a safe and stable operating range while pursuing economic targets; finally, this constraint system provides an accurate search space definition for optimization calculation, avoiding the generation of invalid solutions and significantly improving the convergence speed and computational efficiency of the optimization algorithm, ultimately ensuring the high feasibility of the configuration scheme in engineering practice.

[0058] Step 204: Based on the optimized energy storage capacity configuration parameters, generate the day-ahead market charging and discharging strategy curve and the real-time market charging and discharging strategy curve using the dual-market collaborative optimization model.

[0059] In some embodiments, the system first inputs the optimal energy storage power capacity and energy capacity calculated by the collaborative optimization configuration model as fixed parameters into the dual-market collaborative optimization model; then, based on the time series data of day-ahead and real-time electricity prices, it generates a planned charging and discharging strategy curve for the day-ahead market and an adjusted charging and discharging strategy curve for the real-time market by solving an optimization problem considering the actual operation constraints of the energy storage, with the goal of maximizing market revenue; the day-ahead strategy curve is mainly used for bidding in the day-ahead market to form a basic revenue framework, while the real-time strategy curve is dynamically adjusted according to the actual price fluctuations during operation to capture market deviation revenue. In this way, the determined energy storage capacity parameters can be combined with dynamic market price signals, ensuring the engineering feasibility of the generated strategy curves and fully utilizing the arbitrage ability of energy storage in the dual-market environment; at the same time, the synergistic cooperation of the dual-strategy curves effectively reduces the risk of single-market decision-making, significantly improving the overall revenue level and operational stability of the new energy power plant in a complex market environment.

[0060] In one embodiment, the day-ahead strategy and real-time strategy are as follows: Day-ahead strategy: more than 60% of the time of the energy storage is used for day-ahead charging. The day-ahead market charging can store the power generated by the wind farm in the energy storage. Mainly because the day-ahead spot price is low during these periods, the stored power can be discharged during other periods to obtain high price benefits.

[0061] Real-time strategy: more than 50% of the time of the energy storage is discharged in the real-time market. Mainly concentrated in 15:30~19:15, the real-time price is relatively high during this period. It is worth mentioning that when the energy storage is charged in the day-ahead strategy, even if there is no instruction in the real-time strategy, the real-time electricity fee benefit can be improved. Mainly because the actual power and the predicted power deviation increases, the power settled by the real-time spot price increases, thereby improving the real-time spot electricity fee benefit.

[0062] In some embodiments, the method further comprises a double-strategy coupling step, specifically comprising: dividing the energy storage output strategy into a combination of day-ahead market components and real-time market components according to the coupling relationship between the day-ahead market price and the real-time market price, the combination including charging, no instruction or discharging state, and generating the day-ahead market charging and discharging strategy curve and the real-time market charging and discharging strategy curve based on the combination.

[0063] The double-strategy coupling mechanism realizes the coordinated decision-making framework of the day-ahead and real-time markets: the system first constructs a decision matrix containing nine strategy quadrants, where the vertical axis represents the three states of the energy storage in the day-ahead market (charging, no instruction, discharging), and the horizontal axis represents the three corresponding states in the real-time market, forming a complete strategy combination space; then based on the real-time comparison relationship between the day-ahead price and the real-time price, the optimal strategy quadrant is dynamically selected, which is specifically manifested as when the real-time price is significantly higher than the day-ahead price, the system preferentially adopts the combined strategy of "day-ahead charging-real-time discharging", realizing arbitrage maximization through day-ahead market low-price storage and real-time market high-price sale; when the price relationship appears reverse characteristics, then start the reverse arbitrage mode of "day-ahead discharging-real-time charging"; for periods with insignificant price differences, single-market strategy or silence is adopted. This coupling mechanism first converts the complex market decision-making process into a computable and optimizable technical solution through the formal description of the strategy quadrant table; secondly, it realizes the decoupling control of the physical output of the energy storage and the virtual output of the market, so that the energy storage system can participate in the optimization operation of the two markets simultaneously without increasing the frequency of device action; finally, through the coordinated mode of day-ahead strategy correction of bid amount curve and real-time strategy optimization of actual output, it not only guarantees the compliance of market bidding, but also maximizes the arbitrage potential of the energy storage, significantly improving the comprehensive benefits of the new energy farm in the complex market environment.

[0064] In some embodiments, the energy storage capacity configuration optimization method provided by the embodiments of the present application can be implemented through a software system, which includes a data entry module, an optimization calculation module and a strategy output module, wherein the optimization calculation module is configured to call a third-party optimization platform to perform calculation of a collaborative optimization configuration model and a spot arbitrage model.

[0065] In an implementation, the optimization model of the third-party platform is an original model. The software can realize two-way data interaction with the third-party optimization platform at the software level. The user-entered data is transmitted to the third-party platform through the software, and the optimization calculation module is called to perform calculation. Finally, the optimization result is transmitted back to the platform. After optimization is completed, the third-party platform transmits the result back to the software, and the optimization result is displayed through the display module.

[0066] The software system realizes its technical functions through the collaborative operation of the three core modules: the data entry module receives the numerical parameters input by the user and the array data imported through the specification template, and uses visual indicator lights to feedback the data entry state in real time; the optimization calculation module establishes a two-way data channel with the third-party optimization platform through a software-level interface, transmits the preprocessed data to the external platform for model solving, and displays the calculation state in real time through a dynamic progress bar; the strategy output module is responsible for receiving the optimization result, synchronously displaying the energy storage configuration parameters and the double-market strategy curve through a graphical interface, and is equipped with a sensitivity analysis knob and a result export function. The technical effects of the system are reflected in three aspects: first, the modular design realizes the separation of data processing, model calculation and result display, greatly improving the maintainability and expandability of the system; second, using third-party professional optimization software for core calculation not only ensures the accuracy and efficiency of model solving, but also reduces the system development complexity through standardized interfaces; finally, the visual interactive design converts the complex optimization process into an intuitive operation process, so that non-professional users can also successfully complete the energy storage configuration optimization, significantly improving the engineering practicability and decision support capability of the system.

[0067] Corresponding to the above method embodiments, the present application also provides energy storage capacity configuration optimization system embodiments, Figure 3 Fig. 1 shows a structural schematic diagram of an energy storage capacity configuration optimization system according to an embodiment of the present application. As shown in the figure, Figure 3 The device comprises: The data acquisition module 301 is configured to acquire first target data, wherein the first target data includes wind power historical output data, photovoltaic historical output data, day-ahead spot electricity price data, real-time spot electricity price data and energy storage technology parameters, wherein the energy storage technology parameters include depth of discharge, charge-discharge efficiency and cycle life; The data processing module 302 is configured to perform structured processing on the first target data to form a first data set in a standard format; The optimization calculation module 303 is configured to perform optimization calculation based on the first data set by using a co-optimization configuration model to obtain optimized energy storage capacity configuration parameters, wherein the co-optimization configuration model decomposes the energy storage output into a day-ahead market output component and a real-time market output component, sets at least one optimization objective function and multiple constraint conditions, and the energy storage capacity configuration parameters include energy storage power capacity and energy storage energy capacity; The strategy generation module 304 is configured to generate a day-ahead market charging and discharging strategy curve and a real-time market charging and discharging strategy curve based on the optimized energy storage capacity configuration parameters by using a double-market co-optimization model.

[0068] In a possible implementation, the optimization objective function includes at least one of a physical objective function, an economic objective function, or a fuzzy multi-objective function.

[0069] In a possible implementation, the physical objective function is to minimize the variance of the wind-solar-storage combined output, and the variance is calculated by the following formula:

[0070]

[0071] wherein, represents the variance of the wind-solar-storage combined output, n represents the total number of time nodes in the optimization range, represents the wind-solar-storage combined output of the dth time period, represents the average value of the wind-solar-storage combined output of all time periods, represents the wind power output of the dth time period, represents the photovoltaic output of the dth time period, represents the energy storage output of the dth time period.

[0072] In a possible implementation, the economic objective function is to maximize the wind-solar-storage project revenue, and the revenue is calculated by the following formula:

[0073] wherein, represents the wind-solar-storage project revenue, represents the total number of time nodes in the optimization range, represents the medium and long-term energy of the ith time period, represents the medium and long-term electricity price of the ith time period, represents the base energy of the ith time period, represents the base electricity price of the ith time period, represents the day-ahead settlement energy of the ith time period, represents the day-ahead settlement electricity price of the ith time period, represents the real-time settlement energy of the ith time period, a real-time settlement price of the i-th time interval.

[0074] In a possible implementation, the fuzzy multi-objective function is constructed based on a membership function of a descending half-trapezoidal distribution, and an expression of the fuzzy multi-objective function is:

[0075] wherein, denotes a fuzzy multi-objective function value, denotes a fuzzy membership of a physical objective function, denotes a fuzzy membership of an economic objective function, denotes a weight coefficient of the fuzzy membership of the physical objective function, denotes a weight coefficient of the fuzzy membership of the economic objective function, and .

[0076] In a possible implementation, the fuzzy membership of the physical objective function is calculated by the following descending half-trapezoidal membership function:

[0077] wherein, denotes an actual value of the physical objective function, denotes a minimum expected value of the physical objective function, denotes a maximum allowed value of the physical objective function.

[0078] In a possible implementation, the constraint condition includes at least one of a storage energy amount upper limit constraint, a storage output upper and lower limit constraint, and a storage state of charge upper and lower limit constraint, wherein the storage output upper and lower limit constraint includes a charging power lower limit and a discharging power upper limit; The storage energy amount upper limit constraint is expressed as: wherein is a storage energy amount of the d-th time interval, is a storage energy amount upper limit; The charging power lower limit is expressed as: The discharging power upper limit is expressed as: wherein, is a storage charging power of the d-th time interval, is a storage discharging power of the d-th time interval, is a maximum power at a storage converter end, is a comprehensive conversion efficiency of a storage system; The storage state of charge upper and lower limit constraint is expressed as: wherein, and denote a lower limit and an upper limit of a storage state of charge, respectively, is a storage state of charge of the d-th time interval.

[0079] In a possible implementation, the method further includes a dual-strategy coupling step, specifically including: According to the coupling relationship between the day-ahead market electricity price and the real-time market electricity price, the energy storage output strategy is divided into a combination of day-ahead market components and real-time market components, the combination including charging, no instruction or discharging states, and a day-ahead market charging and discharging strategy curve and a real-time market charging and discharging strategy curve are generated based on the combination.

[0080] In a possible implementation, the method is implemented through a software system, the software system including a data entry module, an optimization calculation module and a strategy output module, wherein the optimization calculation module is configured to call a third-party optimization platform to perform calculation of the collaborative optimization configuration model and the spot arbitrage model.

[0081] The technical scheme of the present application obtains wind power / photovoltaic historical output, dual-market electricity price sequence and energy storage technology parameters through a data acquisition module, forms a structured data set after data cleaning, time alignment and standardization processing; then uses a collaborative optimization configuration model to decompose the energy storage output into two virtual components of day-ahead and real-time markets, takes smoothness, economy or fuzzy multi-objective as the optimization direction, and synchronously solves the optimal energy storage power capacity and energy capacity under the premise of meeting the energy storage physical constraints and market rules; finally, based on the given capacity parameters, a day-ahead planning curve and a real-time adjustment strategy are generated through a dual-market collaborative optimization model. For example, after a certain wind-solar-storage station applies the present scheme, the model calculates the best configuration as a 10MW / 20MWh energy storage system according to the wind-solar output data of 8,760 hours in a year and the dual-market electricity price, and generates a specific operation strategy: charging during the day-ahead market low-valley electricity price period (such as 2:00-5:00 in the morning), and discharging during the real-time market electricity price surge period (such as 18:30-19:45 in the evening), to realize the maximization of benefits through this dual-strategy coupling mechanism. The present scheme solves the problem of disconnection between planning and operation through the collaborative optimization of capacity configuration and operation strategy; makes full use of the dual-market arbitrage space through the dual-component model of energy storage, to improve the utilization rate of energy storage; and ensures the comprehensive optimization in terms of smoothing fluctuations, improving benefits and ensuring equipment safety through the multi-objective optimization framework and strict physical constraint system.

[0082] The above is a schematic scheme of the energy storage capacity configuration optimization system of the present embodiment. It should be noted that the technical scheme of the energy storage capacity configuration optimization system belongs to the same concept as the technical scheme of the energy storage capacity configuration optimization method described above, and the details of the technical scheme of the energy storage capacity configuration optimization system that are not described in detail can be referred to the description of the technical scheme of the energy storage capacity configuration optimization method.

[0083] Figure 4A structural block diagram of a computing device 400 is shown, according to one embodiment of the present application. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 through a bus 430, and a database 450 is used to store data.

[0084] The computing device 400 also includes an access device 440 that enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of networks such as the Internet. The access device 440 can include one or more of any type of network interface (for example, a network interface card (NIC)), wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC).

[0085] In one embodiment of the present application, the above-mentioned components of the computing device 400, as well as other components not shown in FIG. 4, can be connected to each other, for example, through a bus. It should be understood that the computing device structure block diagram shown is for the purpose of example only, and is not a limitation on the scope of the present application. Other components can be added or replaced as needed by those skilled in the art. Figure 4 Figure 4 It should be understood that the computing device structure block diagram shown is for the purpose of example only, and is not a limitation on the scope of the present application. Other components can be added or replaced as needed by those skilled in the art.

[0086] The computing device 400 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (for example, a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, and the like), a mobile phone (for example, a smartphone), a wearable computing device (for example, a smart watch, smart glasses, and the like), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 400 can also be a mobile or stationary server.​

[0087] The processor 420 is configured to execute the computer-executable instructions as follows: when the computer-executable instructions are executed by the processor, the steps of the energy storage capacity configuration optimization method described above are implemented. The above describes a schematic solution of the computing device of the embodiment. It should be noted that the technical solution of the computing device and the technical solution of the energy storage capacity configuration optimization method described above belong to the same concept, and the details of the technical solution of the computing device that are not described in detail can be referred to the description of the technical solution of the energy storage capacity configuration optimization method.

[0088] An embodiment of the present application further provides a computer readable storage medium, which stores computer executable instructions. When the computer executable instructions are executed by a processor, the steps of the energy storage capacity configuration optimization method described above are implemented.

[0089] The above describes a schematic solution of the computer readable storage medium of the embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the energy storage capacity configuration optimization method described above belong to the same concept, and the details of the technical solution of the storage medium that are not described in detail can be referred to the description of the technical solution of the energy storage capacity configuration optimization method.

[0090] An embodiment of the present application further provides a computer program. When the computer program is executed in a computer, the computer is enabled to execute the steps of the energy storage capacity configuration optimization method described above.

[0091] The above describes a schematic solution of the computer program of the embodiment. It should be noted that the technical solution of the computer program and the technical solution of the energy storage capacity configuration optimization method described above belong to the same concept, and the details of the technical solution of the computer program that are not described in detail can be referred to the description of the technical solution of the energy storage capacity configuration optimization method.

[0092] The specific embodiments of the present application are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order and still achieve desirable results. In addition, the process depicted in the figures does not necessarily require the particular order shown or sequential order to achieve desirable results. In certain implementations, multitasking and parallel processing can be advantageous.

[0093] The computer readable medium can include any entity or apparatus capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium can include appropriate additions or subtractions according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0094] It should be noted that for the foregoing method embodiments, in order to facilitate description, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the embodiments of the present application are not limited by the order of the described actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the embodiments of the present application.

[0095] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0096] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The alternative embodiments do not describe all the details and limit the application to the specific embodiments described. Obviously, according to the content of the embodiments of the present application, many modifications and changes can be made. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the embodiments of the present application, so that those skilled in the art can well understand and use the present application. The present application is limited by the claims and their entire scope and equivalents.

Claims

1. A method for optimizing energy storage capacity configuration, characterized in that, include: Acquire first target data, wherein the first target data includes historical wind power output data, historical photovoltaic power output data, day-ahead spot electricity price data, real-time spot electricity price data, and energy storage technology parameters, wherein the energy storage technology parameters include deep discharge depth, charge-discharge efficiency, and cycle life; The first target data is subjected to structured processing to form a first dataset in a standard format; Based on the first dataset, optimization calculations are performed using a collaborative optimization configuration model to obtain optimized energy storage capacity configuration parameters. The collaborative optimization configuration model decomposes energy storage output into day-ahead market output components and real-time market output components, and sets at least one optimization objective function and multiple constraints. The energy storage capacity configuration parameters include energy storage power capacity and energy storage capacity. Based on the optimized energy storage capacity configuration parameters, a day-ahead market charging and discharging strategy curve and a real-time market charging and discharging strategy curve are generated using a dual-market collaborative optimization model.

2. The method according to claim 1, characterized in that, The optimization objective function includes at least one of a physical objective function, an economic objective function, or a fuzzy multi-objective function.

3. The method according to claim 2, characterized in that, The physical objective function is to minimize the variance of the combined wind, solar, and energy storage output, and the variance is calculated using the following formula: in, denoted by , where represents the variance of the combined power output of wind, solar, and energy storage, and 'n' represents the total number of time points within the optimization range. This represents the combined wind, solar, and energy storage output during time period d. This represents the average combined output of wind, solar, and energy storage over all time periods. For the wind power output in time period d, For the photovoltaic power output in time period d, The energy storage output for the d-th time period.

4. The method according to claim 2, characterized in that, The economic objective function is to maximize the revenue of the wind, solar, and energy storage project, and the revenue is calculated using the following formula: in, This indicates the revenue from wind, solar, and energy storage projects. This represents the total number of time points within the optimization range. This represents the medium- to long-term electricity consumption in the i-th time period. This represents the medium- to long-term electricity price for the i-th time period. This represents the base electricity consumption in the i-th time period. This represents the base electricity price for the i-th time period. This represents the day-ahead settlement electricity volume for the i-th time period. This represents the day-ahead settlement price for the i-th time period. This represents the real-time electricity consumption during the i-th time period. This represents the real-time settlement price for the i-th time period.

5. The method according to claim 2, characterized in that, The fuzzy multi-objective function is constructed based on the membership function of the descending half-trapezoidal distribution, and the expression of the fuzzy multi-objective function is as follows: in, Represents the value of a fuzzy multi-objective function. The fuzzy membership degree represents the physical objective function. The fuzzy membership degree represents the economic objective function. express The weighting coefficients, express The weighting coefficients, and .

6. The method according to claim 5, characterized in that, The fuzzy membership degree of the physical objective function is calculated using the following descending semi-trapezoidal membership degree function: in, This represents the actual value of the physical objective function. This represents the minimum expected value of the physical objective function. This represents the maximum allowable value of the physical objective function.

7. The method according to claim 1, characterized in that, The constraints include at least one of the following: upper limit constraint on energy storage capacity, upper and lower limit constraint on energy storage output, and upper and lower limit constraint on energy storage state of charge. The upper and lower limit constraint on energy storage output includes a lower limit on charging power and an upper limit on discharging power. The upper limit constraint on the energy storage capacity is expressed as follows: ,in The stored energy is the amount of energy stored in time period d. This is the upper limit of the energy storage capacity; The lower limit of the charging power is expressed as follows: The upper limit of the discharge power is expressed as: ,in, The energy storage charging power for time period d. Let d be the energy storage discharge power during the d-th time period. This represents the maximum power output at the energy storage converter terminal. For the overall conversion efficiency of the energy storage system; The upper and lower limits of the energy storage state of charge constraints represent: ,in, and : These represent the lower and upper limits of the energy storage state of charge, respectively. This represents the state of charge of the energy storage during time period d.

8. The method according to claim 1, characterized in that, The method further includes a dual-strategy coupling step, specifically including: Based on the coupling relationship between day-ahead market electricity price and real-time market electricity price, the energy storage output strategy is divided into a combination of day-ahead market component and real-time market component. The combination includes charging, no-instruction or discharging states, and the day-ahead market charging and discharging strategy curve and the real-time market charging and discharging strategy curve are generated based on the combination.

9. The method according to claim 1, characterized in that, The method is implemented through a software system, which includes a data input module, an optimization calculation module, and a strategy output module. The optimization calculation module is configured to call a third-party optimization platform to perform calculations on the collaborative optimization configuration model and the spot arbitrage model.

10. An energy storage capacity configuration optimization system, characterized in that, The system includes: The data acquisition module is configured to acquire first target data, wherein the first target data includes historical wind power output data, historical photovoltaic output data, day-ahead spot electricity price data, real-time spot electricity price data, and energy storage technology parameters, wherein the energy storage technology parameters include deep discharge depth, charge-discharge efficiency, and cycle life. The data processing module is configured to perform structured processing on the first target data to form a first dataset in a standard format; The optimization calculation module is configured to perform optimization calculations based on the first dataset using a collaborative optimization configuration model to obtain optimized energy storage capacity configuration parameters. The collaborative optimization configuration model decomposes energy storage output into day-ahead market output components and real-time market output components, and sets at least one optimization objective function and multiple constraints. The energy storage capacity configuration parameters include energy storage power capacity and energy storage capacity. The strategy generation module is configured to generate day-ahead market charge / discharge strategy curves and real-time market charge / discharge strategy curves based on the optimized energy storage capacity configuration parameters and using a dual-market collaborative optimization model.

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