Wind and light storage capacity optimal configuration method and device based on multi-objective optimization, medium and product

Abstract

The invention provides a wind and light storage capacity optimal configuration method and device based on multi-objective optimization, a medium and a product, and the method comprises the steps: obtaining the data content corresponding to each data item of a project configuration planning parameter; constructing a mathematical model according to the data content corresponding to each data item of the project configuration planning parameter; solving the mathematical model according to a set method to obtain an optimal decision variable value, wherein the set method is a multi-objective optimization algorithm; wherein the objective function of the mathematical model comprises total investment cost minimization, wind and light absorption rate maximization and power supply reliability maximization, and the decision variables comprise wind power installed capacity, photovoltaic installed capacity, energy storage rated power and energy storage rated energy. The constraint conditions comprise wind / light curtailment constraint, power shortage constraint, wind / light-electricity installation constraint and self-generation and self-use proportion lower limit / lower limit constraint. According to the method, the optimal configuration of the economical efficiency, the absorption rate and the reliability of the wind and light storage capacity is realized, and reliable theoretical support and practical guidance can be provided for practical engineering application.

Description

Methods, devices, media, and products for optimizing wind, solar, and energy storage capacity based on multi-objective optimization. Technical Field

[0001] This invention relates to the field of integrated power generation, grid, load and storage technology, specifically to a method, device, medium and product for optimizing the configuration of wind, solar and storage capacity based on multi-objective optimization. Background Technology

[0002] The integration of power generation, grid, load, and energy storage aims to break the relatively independent and fragmented operation mode of power sources, grids, loads, and energy storage in traditional power systems. Through advanced information and communication technologies, control technologies, and market mechanisms, it achieves deep collaboration, interaction, and integration among multiple links, thereby improving the efficiency, security, and sustainability of the entire energy system.

[0003] Optimizing wind, solar, and energy storage capacity is a core approach and key step in the planning and design phases of integrated power generation, grid, load, and storage systems. It lays the foundation for the safe, stable, and economical operation of the integrated system by accurately calculating the optimal ratio of wind, solar, and energy storage. How to allocate resources such as photovoltaics, wind power, and energy storage to meet the autonomous peak-shaving and self-balancing needs of the new local consumption model, while ensuring relatively low planning costs, is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] The first objective of this invention is to provide a method for optimizing the configuration of wind, solar and energy storage capacity based on multi-objective optimization, which can achieve the optimal configuration of wind, solar and energy storage capacity in terms of economy, absorption rate and reliability.

[0005] A second objective of this invention is to provide a computer device for implementing the above-described method for optimizing the allocation of wind, solar and energy storage capacity based on multi-objective optimization.

[0006] A third objective of this invention is to provide a computer-readable storage medium for implementing the above-described method for optimizing wind, solar, and energy storage capacity configuration based on multi-objective optimization.

[0007] The fourth objective of this invention is to provide a computer program product that implements the above-mentioned method for optimizing the allocation of wind, solar and energy storage capacity based on multi-objective optimization.

[0008] To achieve the aforementioned first objective, this invention provides a method for optimizing the allocation of wind, solar, and energy storage capacity based on multi-objective optimization, comprising the following steps: obtaining the data content corresponding to each data item of the project configuration planning parameters; constructing a mathematical model based on the data content corresponding to each data item of the project configuration planning parameters; and solving the mathematical model according to a set method to obtain the optimal decision variable values, wherein the set method is a multi-objective optimization algorithm; wherein the objective function of the mathematical model includes minimizing total investment cost, maximizing wind and solar power absorption rate, and maximizing power supply reliability; the decision variables of the mathematical model include wind power installed capacity, photovoltaic installed capacity, rated energy storage power, and rated energy storage; and the constraints of the mathematical model include wind curtailment constraints, solar curtailment constraints, power shortage constraints, wind power installed capacity constraints, photovoltaic installed capacity constraints, lower limit constraints on self-consumption ratio, upper limit constraints on self-consumption ratio, upper limit constraints on grid connection ratio, and energy storage capacity constraints.

[0009] As can be seen from the above scheme, the present invention can obtain the data content corresponding to each data item of the project configuration planning parameters of the newly set wind-solar-storage capacity optimization configuration project, thereby determining the specific mathematical model based on the data content. After solving the mathematical model through a multi-objective optimization algorithm, the optimal decision variable values ​​corresponding to minimizing the total investment cost, maximizing the wind-solar absorption rate, and maximizing the power supply reliability are obtained. This enables the acquisition of a better configuration scheme in a shorter time, improves the diversity of configuration schemes, and provides reliable theoretical support and practical guidance for actual engineering applications.

[0010] A further approach is to include project planning parameters such as project planning condition data and project planning curve data, with the project planning curve data being historical or forecast data.

[0011] This shows that mathematical models can be built based on historical or predictive data, making model determination more flexible.

[0012] A further option is to include the project planning conditions data, which includes the project planning type, either off-grid or grid-connected.

[0013] Therefore, it is evident that specific data models can be built for different types of projects to meet the diverse needs of different project types, and the optimized results can be more suitable for specific project types.

[0014] A further proposed approach is to use the NSGA-II algorithm.

[0015] This shows that it can improve the solution efficiency and the distribution of the solution set.

[0016] A further approach is to express the total investment cost in the mathematical model as: Initial Investment = C wind ·W cap +C pv ·S cap+ C bat,p ·P bat + C conv ·(W cap +S cap + P bat Annual maintenance cost = Initial investment · r om Present value = Initial investment + Annual maintenance costs , where C wind This represents the unit cost of wind power (yuan / kW), W cap C represents the installed capacity of wind power. pv S represents the unit cost of photovoltaic power (yuan / kW). cap P represents the installed capacity of photovoltaic power. bat Indicates the rated power of energy storage C bat,p C represents the unit cost of energy storage power (yuan / kW). conv The converter cost factor (yuan / kW) is represented by r. om This indicates the annual maintenance cost rate. Indicates the project's lifespan (in years). The discount rate is represented by the absorption rate, which is expressed as: theoretical wind power generation = Theoretical photovoltaic power generation = Total abandoned electricity = The grid connection rate = 1 – (total abandoned power / (theoretical wind power generation + theoretical photovoltaic power generation)), where, This represents the theoretical wind power output (kW) at time t. This represents the theoretical photovoltaic output (kW) at time t. This represents the actual wind power output (kW) utilized. The actual photovoltaic output (kW) utilized is represented; power supply reliability is expressed as the power shortage rate obtained from the total power shortage and total load: Total power shortage = Total load = Power shortage rate = total power shortage / total load, where, This represents the load demand (kW) at time t. This represents the energy storage discharge power (kW) at time t. This represents the electricity consumed online at time t (kW).

[0017] A further approach is to include energy storage state of charge (SOC) updates as constraints in the mathematical model, whereby the SOC update is expressed as... ,in, This represents the state of charge of the stored energy at time t. This represents the charging power (kW) at time t. This represents the discharge power (kW) at time t. Indicates charging efficiency. Indicates discharge efficiency. (Hourly time step). Constraints are: , The value is 0.1. The value is 0.9.

[0018] A further proposed solution is to express the wind curtailment constraint as: G1 = (curtailed wind power / theoretical wind power generation) - Y w,max ≤0, Y w,max This represents the maximum allowable wind curtailment rate; the solar curtailment constraint is expressed as: G2 = (curtailed solar power / theoretical photovoltaic power generation) - Y s,max ≤0, Y s,max This represents the maximum permissible light rejection rate; the power shortage constraint is expressed as: G3 = LPSP - LPSP max ≤0, LPSP max This represents the maximum allowable power shortage rate; the wind power installed capacity constraint is expressed as: G4 = W cap - W max ≤0, W max This represents the maximum installed capacity of wind power; the constraint on photovoltaic installation is expressed as: G5 = S cap - S max ≤0, S max The maximum installed capacity of photovoltaic power is represented by: G6 = ZXZFZYZB – (Self-consumption electricity / Total power generation) ≤ 0, where ZXZFZYZB represents the minimum self-consumption ratio; G7 = (Self-consumption electricity / Total power generation) - 1 ≤ 0; G8 = (Grid-connected electricity / Total power generation) - ZDSWZB ≤ 0, where ZDSWZB represents the maximum grid-connected ratio; and G9 = min_ration – (E bat / P bat )≤0, P bat E represents the rated power of energy storage. bat This represents the rated energy of the energy storage, and min_ratio represents the minimum ratio of energy storage to power.

[0019] To achieve the second objective mentioned above, the present invention provides a computer device comprising a processor and a memory, wherein: the memory stores a computer program, and when the computer program is executed by the processor, it implements the above-mentioned method for optimizing the configuration of wind, solar and storage capacity based on multi-objective optimization.

[0020] To achieve the third objective mentioned above, the present invention provides a computer-readable storage medium storing a computer program thereon, characterized in that: when the computer program is executed by a processor, it implements the above-mentioned method for optimizing the configuration of wind, solar and storage capacity based on multi-objective optimization.

[0021] To achieve the fourth objective mentioned above, the present invention provides a computer program product, including computer instructions, wherein: when the computer instructions are executed by a processor, they implement the above-mentioned wind, solar and storage capacity optimization configuration method based on multi-objective optimization. Attached Figure Description

[0022] Figure 1 is a flowchart of the wind, solar and storage capacity optimization configuration method based on multi-objective optimization according to the present invention.

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0024] The wind, solar and energy storage capacity optimization configuration method based on multi-objective optimization of the present invention determines specific optimization objectives according to the requirements of wind, solar and energy storage synergistic optimization, namely, comprehensive optimization in terms of economy, reliability, environmental protection and other aspects, and constructs a corresponding mathematical model on this basis. The mathematical model includes decision variables, objective function and constraints. By solving the mathematical model, a set of optimal decision variable values ​​are determined, thereby guiding the planning and operation of wind power, photovoltaic and energy storage.

[0025] An embodiment of a multi-objective optimization method for wind, solar and energy storage capacity optimization: Specifically, as shown in Figure 1, it includes the following steps: S1: Obtain the data content corresponding to each data item of the project configuration planning parameters.

[0026] S2: Determine the mathematical model based on the data content corresponding to each data item in the project configuration planning parameters.

[0027] S3: Solve the mathematical model according to the set multi-objective optimization method to obtain the optimal decision variable values.

[0028] The following details the specific content of step S1 above.

[0029] The data items for project configuration planning parameters are determined according to a preset joint optimization system, including project planning condition data and project planning curve data, which are used to guide the specific setting of objective functions, constraints and decision variables.

[0030] The project specification data includes the following optional data items: project planning type, minimum grid connection rate, minimum self-consumption ratio, minimum grid-connected electricity volume, whether wind power capacity needs optimization, wind power installed capacity, whether photovoltaic capacity needs optimization, photovoltaic installed capacity, wind power unit cost, photovoltaic unit cost, energy storage unit cost, energy storage rated power, and energy storage rated energy. The project planning type can be selected from off-grid, grid-connected (surplus electricity fed to the grid, electricity purchased when there is a shortage), and grid-connected (valley electricity purchase, surplus electricity fed to the grid). If the data for "whether wind power capacity needs optimization" is "yes," then "wind power installed capacity" must be entered; otherwise, it is not required. If the data for "whether photovoltaic capacity needs optimization" is "yes," then "photovoltaic installed capacity" must be entered; otherwise, it is not required.

[0031] The project planning curve data includes the following optional long-term series data: time-of-use electricity price, time-of-use electricity purchase price, user load curve, wind power generation curve, and photovoltaic power generation curve. The project planning curve data can be historical data (e.g., the past year, i.e., 8760 hours of data) or future data predicted based on historical data (e.g., the next year, i.e., 8760 hours of data).

[0032] Therefore, after a user establishes a wind, solar and energy storage capacity optimization configuration project (hereinafter referred to as "the current project"), they can fill in the data content corresponding to each data item of the project configuration planning parameters corresponding to the current project. In the subsequent step S2, the mathematical model corresponding to the current project is determined. In the subsequent step S23, the mathematical model is solved to obtain the decision variable values ​​of the mathematical model corresponding to the current project.

[0033] The joint optimization system includes an indicator system and a constraint system.

[0034] Based on load curves and electricity consumption characteristics, combined with data such as wind and solar scale, energy storage configuration information, and electricity price parameters, an indicator system is formed to support the optimized configuration of wind, solar, and energy storage capacity.

[0035] The indicator system includes fundamental indicators applicable to the optimized configuration of wind, solar, and energy storage capacity, specifically including load information, power supply information, electricity price information, and energy storage information. Load information includes 8760 hours of data annually. Electricity price information includes time-of-use pricing. Power supply information includes photovoltaic scale, photovoltaic unit cost, wind power scale, wind power unit cost, annual 8760-hour wind and solar output curves, and average annual usage hours for wind and solar power. Energy storage information includes energy storage scale, unit cost, project type (off-grid, grid-connected), and energy storage performance indicators (efficiency, cell replacement period).

[0036] By tracking policy documents on power generation, grid, load and storage, we analyze core requirements such as a grid integration rate of ≥50%, peak-valley arbitrage, and energy storage duration of ≥2 hours. Combining the differences between off-grid and grid-connected projects and technical constraints such as peak shaving and valley filling, we extract three-dimensional linkage goals of policy compliance, economic optimization, and grid friendliness, forming a constraint system of grid integration capacity, energy storage configuration, and cost-effectiveness.

[0037] The indicator system includes hard constraint indicators and soft constraint indicators. Hard constraint indicators are used to meet policy compliance requirements, while soft constraint indicators are used to respond to load demand and economic rationality. Specific hard constraint indicators may include minimum grid connection rate, minimum green electricity consumption, the proportion of renewable energy self-consumption available electricity, the proportion of renewable energy self-consumption to total electricity consumption, the proportion of grid-connected electricity, continuous charging and discharging duration of energy storage, and renewable energy utilization rate. Soft constraint indicators may include continuous charging and discharging duration of energy storage, energy storage charging and discharging power, maximizing peak-valley arbitrage, and minimizing investment costs.

[0038] Depending on the project type, specific indicators can be selected within the indicator system. For example, hard constraint indicators are applicable to all project types, while maximizing peak-valley arbitrage and minimizing investment costs are not applicable to off-grid projects. Specific quantifiable indicators include: minimum grid connection rate ≥ 50%, minimum green electricity consumption ≥ 200 million kWh, self-consumption of renewable energy ≥ 60%, total self-consumption of renewable energy ≥ 30%, grid-connected electricity < 20%, continuous charging / discharging time of energy storage ≥ 2 hours, and renewable energy utilization rate higher than the provincial public grid renewable energy utilization rate during the same period. Examples include continuous charging / discharging time of energy storage ≥ 2 hours and energy storage charging / discharging power ≥ 5 MW.

[0039] The constraint system includes categories of equipment operation constraints, policy and market constraints, and time-control coupling constraints. Different constraint items are set under different constraint categories, and different constraint items have different impacts on the optimization configuration objectives.

[0040] Equipment operation constraints include power output constraints and energy storage charge / discharge constraints. Power output constraints are used to analyze the output of wind and solar renewable energy. Their impact on optimization goals lies in limiting the power range of equipment, affecting power supply, and requiring other resources to balance renewable energy fluctuations, increasing costs and complexity. The greater the power output fluctuation, the higher the requirements for energy storage capacity and regulation capabilities, directly increasing initial investment costs and operational complexity. Energy storage charge / discharge constraints are used to analyze power constraints, capacity constraints, charge / discharge efficiency, self-discharge rate, and cycle life. Their impact on optimization goals lies in determining the peak-shaving and frequency regulation capabilities and duration of energy storage through power and capacity; affecting the operating costs and economic returns of energy storage through efficiency and lifespan, which are key to optimization; and coupling operation across different time periods through SOC (State of Charge) constraints, which are spatiotemporal coupling constraints. Energy storage configuration is determined by power and capacity constraints, which determine its instantaneous response and continuous power supply capabilities. Efficiency and lifespan directly affect economic costs, while the SOC time-chain constraint ensures the safety and logical feasibility of cross-time period operation.

[0041] Policy and market constraints include electricity price constraints and market trading rules. Electricity price constraints are used to analyze time-of-use (TOU) pricing. TOU pricing impacts optimal allocation objectives by determining the revenue and costs of power generation, load, and storage, driving optimization strategies and connecting technology with economic benefits. Allocation plans must consider the economic viability under electricity price policies. Energy storage can charge during low prices and discharge during high prices, achieving "arbitrage" and directly affecting its economic viability and capacity allocation. Market trading rules are used to analyze electricity trading strategies. Electricity trading strategies impact optimal allocation objectives by broadening profit channels for power generation, grid, load, and storage, changing business models, and considering equipment market capabilities to maximize investment returns. Mechanisms such as green electricity trading and capacity markets in the electricity market broaden profit channels, affecting economic boundaries and thus influencing optimal capacity allocation.

[0042] Spatiotemporal coupling constraints include time-series load fluctuations. These fluctuations are used to analyze intraday / seasonal fluctuations and source-load timing matching. Load volatility requires system flexibility for real-time balancing; the mismatch between renewable energy sources and load timing is the fundamental reason for implementing measures such as energy storage. A time series model is constructed based on 8760 hours of historical load data to identify the system's surplus and shortage patterns. This ensures that energy storage capacity not only matches the load curve but also meets the complex requirements of cross-period energy coupling.

[0043] The optimization objectives for wind and solar capacity differ across various scenarios. Integrated power generation, grid, load, and storage projects are complex systems, and the operating mode chosen during the planning phase directly impacts the final configuration, economic benefits, and social and environmental impacts. This study selects grid-connected and off-grid projects as typical project types. These two modes each have their own emphasis, and project decisions require comprehensive consideration of multiple factors, including investment budget, policy guidance, grid conditions, technical feasibility, environmental factors, and risk tolerance, to achieve optimal configuration planning.

[0044] For grid-connected systems where surplus electricity is fed into the grid and electricity is purchased when there is a shortage, the core objective is to meet policy requirements and minimize energy storage costs. For grid-connected systems where electricity is purchased during off-peak hours and surplus electricity is fed into the grid, the core objective is to meet policy requirements and minimize energy storage and electricity costs. Key economic evaluation indicators for grid-connected systems include electricity price arbitrage profit, revenue from grid-connected electricity, electricity purchase cost, levelized cost of electricity (LCOE), net present value (NPV), and investment payback period. Policy requirements include a minimum grid integration rate of ≥50%, a minimum self-consumption ratio of ≥60%, a maximum grid-connected electricity ratio of <20%, and a five-tier time-of-use pricing system.

[0045] For off-grid systems, the core objective is to ensure power supply reliability and meet minimum absorption rates and self-consumption ratios. Key economic evaluation indicators for off-grid systems include system construction costs, operation and maintenance costs, and the levelized cost of electricity (LCOE).

[0046] The constraint system can specifically include constraints on wind curtailment, solar curtailment, power shortage, wind power installed capacity, photovoltaic installed capacity, lower limit constraint on self-consumption ratio, upper limit constraint on self-consumption ratio, and upper limit constraint on grid connection ratio.

[0047] The curtailment constraint is expressed as: G1 = (curtailed wind power / theoretical wind power generation) - Y w,max ≤0, Y w,max This indicates the maximum permissible wind curtailment rate.

[0048] The curtailment constraint is expressed as: G2 = (curtailed solar power / theoretical photovoltaic power generation) - Y s,max ≤0, Y s,max This indicates the maximum permissible light rejection rate.

[0049] The power shortage constraint is expressed as: G3 = LPSP - LPSP max ≤0, LPSP max This indicates the maximum permissible power shortage rate.

[0050] The wind power installation constraint is expressed as: G4 = W cap - W max ≤0, W max This indicates the maximum installed capacity of wind power.

[0051] The photovoltaic installation constraint is expressed as: G5 = S cap - S max ≤0, S max This indicates the maximum installed capacity of photovoltaic power.

[0052] The lower limit constraint for the self-consumption ratio is expressed as: G6 = ZXZFZYZB – (self-consumption electricity / total power generation) ≤ 0, where ZXZFZYZB represents the minimum self-consumption ratio.

[0053] The upper limit constraint for the proportion of self-generated electricity is expressed as: G7 = (self-generated electricity / total electricity generation) - 1 ≤ 0.

[0054] The upper limit constraint for the proportion of electricity generated by the grid is expressed as: G8 = (electricity generated by the grid / total electricity generated) - ZDSWZB ≤ 0, where ZDSWZB represents the maximum proportion of electricity generated by the grid.

[0055] The energy storage capacity constraint is expressed as: G9 = min_ratio – (E bat / P bat )≤0, P bat E represents the rated power of energy storage. bat This represents the rated energy of the energy storage, and min_ratio represents the minimum ratio of energy storage to power (lower limit of discharge duration).

[0056] The following details the specific content of step S2 above.

[0057] Optimization of wind, solar, and energy storage is typically a multi-objective process, requiring a balance between conflicting goals such as economic efficiency, reliability, and energy utilization. Therefore, the following core objectives are defined: minimizing total investment cost, maximizing green energy consumption, and maximizing power supply reliability. Energy storage strategies include energy storage utilization efficiency (maximizing arbitrage profits or peak-shaving value through charging and discharging strategies) and balancing electricity purchase and sale costs (reducing operating costs in grid-connected scenarios by optimizing grid-connected and purchased electricity). The objective function needs to balance cost and reliability, as increasing energy storage capacity improves reliability but increases investment costs, and also needs to balance the conflict between utilization rate and cost, as increasing wind and solar installed capacity may increase consumption pressure and lead to higher costs.

[0058] Specifically, different objective functions, constraints, and decision variables can be set for different types of projects. According to the specific project type, corresponding collaborative optimization strategies can be adopted to optimize the setting of constraints. The collaborative optimization strategies specifically include energy storage optimization strategies and energy storage planning strategies.

[0059] For off-grid projects, the optimization objectives are to minimize investment costs, maximize wind and solar power integration, and minimize the power curtailment rate (LPSP). Constraints on the mathematical model include strictly controlling the power curtailment rate (LPSP ≤ a set threshold), limiting wind and solar curtailment rates (to avoid energy waste and improve economic efficiency), and ensuring that wind, solar, and energy storage capacities match load fluctuation characteristics (especially during extreme load periods). The initial solution generation includes: estimating the initial configuration based on load demand, with wind and solar installed capacity at 0.5 to 3 times the average load, energy storage power at 0.5 to 2 times the average load, and energy storage capacity at 1%-10% of the total load demand (to ensure the ability to cope with wind and solar power output fluctuations).

[0060] For off-grid projects, the energy storage charging and discharging strategy adopts the MPC (Model Predictive Control) type strategy. The core logic is "load priority and energy balance". The control logic is as follows: (1) Charging timing: When the actual output of wind and solar power (actual wind power = theoretical per unit value × wind power installed capacity, and the same applies to photovoltaic power) is greater than the load demand, the excess electricity is used for energy storage charging (limited by energy storage power and capacity). (2) Discharging timing: When the actual output of wind and solar power is less than the load demand, energy storage is discharged to make up the gap (energy storage is used first to avoid power shortage). (3) Constraints: Off-grid projects are prohibited from selling electricity to the grid (P_grid_sell=0), the energy storage charging and discharging power shall not exceed its rated power, and the SOC (state of charge) shall be maintained within the safe range.

[0061] For off-grid projects, the energy storage planning strategy adopts capacity and power matching, which specifically includes: (1) the energy storage power needs to cover the maximum gap between the peak and valley load difference and the fluctuation of wind and solar power output (to ensure power supply during extreme periods). (2) the energy storage capacity needs to meet the continuous power supply demand of the load during the low-output periods of wind and solar power (such as continuous rainy days) (based on an initial estimate of 1%-10% of the total energy demand). The optimization basis of the energy storage planning strategy is to select solutions that meet the power shortage rate constraint through multi-objective optimization, and to give priority to energy storage configurations with low investment costs and high wind and solar power absorption rates (in conjunction with the Euclidean distance evaluation from the ideal point).

[0062] For grid-connected projects (surplus electricity fed into the grid, electricity purchased when shortages occur), the optimization objectives are to minimize investment costs, maximize wind and solar power integration (reducing curtailment), and reduce grid purchase costs (prioritizing local wind and solar power and energy storage). The mathematical model's constraints include a wind and solar curtailment rate ≤ a set threshold (improving energy utilization), a self-consumption rate constraint (prioritizing local consumption and reducing grid dependence), and a no-arbitrage logic for grid interaction power (only surplus electricity fed into the grid, electricity purchased when shortages occur). The initial solution generation includes wind and solar installed capacity and energy storage configuration randomly generated based on load demand (population size 50), emphasizing complementarity with the grid.

[0063] For grid-connected projects (surplus electricity fed into the grid, electricity purchased when power is insufficient), the energy storage charging and discharging strategy adopts "local consumption priority, grid supplementation". The control logic includes (1) charging timing: when wind and solar power output is excessive (greater than the load), energy storage is charged first (to reduce power abandonment), and the remaining power is fed into the grid. (2) discharging timing: when wind and solar power output is insufficient (less than the load), energy storage power is released first to supplement, and if it is still insufficient, electricity is purchased from the grid. (3) grid interaction: there is no peak-valley arbitrage behavior, and the grid-connected electricity price and the purchased electricity price are priced according to fixed time periods (taken from the time-of-use electricity price table).

[0064] For grid-connected projects (surplus electricity fed into the grid, electricity purchased when there is a shortage), the energy storage planning strategy adopts capacity and power design, which specifically includes: (1) Capacity and power design: the energy storage power is mainly matched to the short-term fluctuations of wind and solar power (such as peak and valley output within the day) to reduce curtailment and electricity purchase. (2) The energy storage capacity is designed based on the curtailment of wind and solar power and the load gap on a typical day to balance the investment and grid interaction costs. The optimization basis of the energy storage planning strategy is to select the configuration with low investment cost, high self-consumption rate and low grid purchase volume through multi-objective optimization (combining the absorption rate and investment cost weights).

[0065] For grid-connected projects (purchasing electricity during off-peak hours and selling surplus electricity to the grid), the optimization objectives of the mathematical model are to minimize investment costs, maximize wind and solar power integration rates, and maximize arbitrage profits (utilizing peak-valley electricity price differences). The constraints of the mathematical model include retaining constraints on wind and solar curtailment rates and self-consumption rates, and allowing bidirectional grid interaction (purchasing electricity for charging during low-price periods and discharging and selling or self-consuming electricity during high-price periods). Initial solution generation includes: combining time-of-use electricity price curves, with wind, solar, and energy storage collaboratively participating in grid peak shaving to improve economic efficiency.

[0066] For grid-connected projects (purchasing electricity during deep valleys and feeding surplus electricity to the grid), the energy storage charging and discharging strategy adopts a composite strategy of "peak-valley arbitrage + local consumption" based on electricity price signals. The control logic is as follows: (1) Charging timing: When wind and solar power output is excessive, priority is given to charging (consuming abandoned electricity); when the electricity price is in a low period (such as at night), even if the wind and solar power output is insufficient, electricity can be purchased from the grid for charging (preparing for peak discharge arbitrage). (2) Discharging timing: When the electricity price is in a peak period, priority is given to discharging to meet the local load (reducing high-priced electricity purchase); if the local load is low, discharge to the grid during the peak period to obtain revenue. (3) Electricity price linkage: The monthly median electricity price is used to predict the electricity price trend and optimize the charging and discharging timing.

[0067] For grid-connected projects (purchasing electricity during off-peak hours and selling surplus electricity to the grid), the energy storage planning strategy adopts capacity and power design, which specifically includes: (1) the energy storage power needs to match the maximum arbitrage power during peak and off-peak hours (such as the maximum discharge power during peak hours and the maximum charging power during off-peak hours). (2) the energy storage capacity is related to the duration of peak and off-peak hours (such as covering 8-10 hours of off-peak charging to meet the discharge demand during peak hours). The optimization basis of the energy storage planning strategy is to comprehensively consider investment costs, arbitrage benefits, and wind and solar power absorption rates, and select the configuration with the highest cost-benefit ratio (screened through the "distance to ideal point" indicator in multi-objective optimization).

[0068] For the mathematical model establishment, a "divide and conquer" strategy is adopted—first, cost, absorption capacity, and reliability are quantified into three aligned objectives, which are then combined with the energy storage charging and discharging strategy as key control parameters. A multi-objective optimization algorithm is used to find an optimal balance point, achieving optimal overall performance while satisfying all constraints.

[0069] The following describes the process of constructing the mathematical model.

[0070] The objective function of the mathematical model is to minimize the total investment cost, which includes initial investment and annual operating costs. Initial investment = C wind ·W cap +C pv ·S cap + C bat,p ·P bat + C conv ·(W cap +S cap + P bat Annual maintenance cost = Initial investment · r om Present value = Initial investment + Annual maintenance costs , where C wind C represents the unit cost of wind power (yuan / kW). pv C represents the unit cost of photovoltaic power (yuan / kW). bat,p C represents the unit cost of energy storage power (yuan / kW). conv The converter cost factor (yuan / kW) is represented by r. om This indicates the annual maintenance cost rate. Indicates the project's lifespan (in years). This represents the discount rate.

[0071] The optimization objective of the mathematical model includes maximizing the wind-solar power integration rate, which is the ratio of actual wind and solar power utilization to theoretical power generation. Theoretical wind power generation = Theoretical photovoltaic power generation = Total abandoned electricity = The grid connection rate = 1 – (total abandoned power / (theoretical wind power generation + theoretical photovoltaic power generation)), where, This represents the theoretical wind power output (kW) at time t. This represents the theoretical photovoltaic output (kW) at time t. This represents the actual wind power output (kW) utilized. This indicates the actual photovoltaic output (kW) utilized.

[0072] The optimization objective of the mathematical model includes maximizing power supply reliability. Maximizing power supply reliability includes both total power shortage and total load. Total power shortage = Total load = Power Deficiency Rate (LPSP) = Total Power Deficiency / Total Load, where, This represents the load demand (kW) at time t. This represents the energy storage discharge power (kW) at time t. This represents the electricity consumed online at time t (kW).

[0073] Therefore, the objective functions of the mathematical models for each project type are: minimizing the present value, maximizing the wind and solar power absorption rate, and minimizing the power shortage rate.

[0074] In the constraints of the mathematical model, grid-connected projects include all constraints in the aforementioned constraint system, while off-grid projects include all constraints in the aforementioned constraint system except for the upper limit constraint on the proportion of grid-connected capacity. In addition to the constraints corresponding to different types of projects in the aforementioned constraint system, the constraints of the mathematical model also include energy storage charging and discharging strategies, and the energy storage state of charge update is expressed as... ,in, This represents the state of charge of the stored energy at time t. This represents the charging power (kW) at time t. This represents the discharge power (kW) at time t. Indicates charging efficiency. Indicates discharge efficiency. (Hourly time step). Constraints are: , The value is 0.1. The value is 0.9.

[0075] The decision variable in the mathematical model is the installed wind power capacity W. cap Photovoltaic installed capacity S cap Rated energy storage power P bat Energy storage rated energy E bat The unit for wind power installed capacity is kilowatt (kW), which can be fixed as an input value or optimized within a range; the unit for photovoltaic installed capacity is kilowatt, which can be fixed as an input value or optimized within a range; the unit for energy storage rated power is kilowatt, which represents the upper limit of energy storage charging and discharging power, and the value range can be, for example, [1000, 200000]; the unit for energy storage rated energy is kilowatt-hour (kWh), which is the core parameter of energy storage capacity, and the value range can be, for example, [1000, 500000].

[0076] The specific process of step S3 above is described below.

[0077] The solution method uses the NSGA-II algorithm to generate a Pareto optimal solution set, and then selects the comprehensive optimal solution using the "ideal point distance method": investment cost, utilization rate, and reliability are normalized, weighted distances are calculated, and the solution with the smallest distance is the final solution. The formula is as follows: To address the problems of slow convergence speed and uneven solution set distribution in traditional multi-objective optimization algorithms (such as NSGA-II) for wind, solar and energy storage capacity configuration, this invention proposes an improved NSGA-II algorithm that integrates an adaptive crossover mutation mechanism and a reference point guidance strategy. Specifically, it includes adaptive parameter adjustment, reference point guidance, and constraint handling mechanisms.

[0078] The adaptive parameter adjustment dynamically adjusts the crossover probability (Pc ∈ [0.7, 0.9]) and mutation probability (Pm ∈ [0.01, 0.1]) based on the population convergence state. When the population diversity decreases, Pm is increased to introduce new individuals; when the convergence speed is too fast, Pc is decreased to avoid getting trapped in local optima.

[0079] In the reference point guidance, 10 uniformly distributed reference points are introduced in the three-dimensional target space of "cost-reliability-absorption rate" to guide the population to be uniformly distributed on the Pareto front.

[0080] In the constraint handling mechanism, the "feasibility priority principle" combined with the penalty function method is adopted to impose penalties on individuals that violate the energy storage system operation constraints (such as state of charge ≤ 20% or ≥ 80%), so as to ensure the engineering feasibility of the final solution set.

[0081] Therefore, the optimal decision variable values ​​of the mathematical model can be obtained. Under these optimal decision variable values, the total investment cost, wind and solar power absorption rate, and power supply reliability can be minimized when configuring wind, solar and energy storage capacity for the current project.

[0082] In summary, this invention can obtain the data content corresponding to each data item of the project configuration planning parameters for a newly set wind-solar-storage capacity optimization configuration project, thereby determining a specific mathematical model based on the data content. After solving the mathematical model through a multi-objective optimization algorithm, the optimal decision variable values ​​corresponding to minimizing total investment cost, maximizing wind-solar absorption rate, and maximizing power supply reliability are obtained. This enables the acquisition of a better configuration scheme in a shorter time, improves the diversity of configuration schemes, and provides reliable theoretical support and practical guidance for practical engineering applications.

[0083] Computer device embodiment: The computer device in this embodiment includes a processor and a memory. The memory stores a computer program. When the processor executes the computer program, it implements the above-described embodiment of the wind, solar and storage capacity optimization configuration method based on multi-objective optimization.

[0084] A computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that a computer device may include more or fewer components, or a combination of certain components, or different components. For example, a computer device may also include input / output devices, network access devices, buses, etc. For instance, a processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microcontroller, or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device via various interfaces and lines.

[0085] The memory can be used to store computer programs and / or modules. The controller implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. For example, the memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (e.g., sound receiving function, sound-to-text function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (e.g., audio data, text data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0086] Computer-readable storage medium embodiment: If the modules integrated in the computer device of the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the process of the embodiment of the wind-solar-storage capacity optimization configuration method based on multi-objective optimization can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the controller, it can implement the steps of the embodiment of the wind-solar-storage capacity optimization configuration method based on multi-objective optimization. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The storage medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in computer-readable media may be appropriately added to or subtracted from the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, computer-readable media may not include electrical carrier signals and telecommunication signals, in accordance with legislation and patent practice.

[0087] Computer program product embodiment: The computer program product of this embodiment includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, executes the computer instructions, and causes the computer device to perform the various steps of the above-described embodiment of the wind-solar-storage capacity optimization configuration method based on multi-objective optimization.

[0088] Finally, it should be emphasized that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the allocation of wind, solar, and energy storage capacity based on multi-objective optimization, characterized in that, Includes the following steps: Obtain the data content corresponding to each data item of the project configuration planning parameters; construct a mathematical model based on the data content corresponding to each data item of the project configuration planning parameters; solve the mathematical model according to a set method to obtain the optimal decision variable values, wherein the set method is a multi-objective optimization algorithm; wherein, the objective function of the mathematical model includes minimizing total investment cost, maximizing wind and solar power absorption rate, and maximizing power supply reliability; the decision variables of the mathematical model include wind power installed capacity, photovoltaic installed capacity, rated power of energy storage, and rated energy of energy storage; the constraints of the mathematical model include wind curtailment constraints, solar curtailment constraints, power shortage constraints, wind power installed capacity constraints, photovoltaic installed capacity constraints, lower limit constraint of self-consumption ratio, upper limit constraint of self-consumption ratio, upper limit constraint of grid connection ratio, and energy storage capacity constraints.

2. The wind-solar-storage capacity optimization configuration method based on multi-objective optimization as described in claim 1, characterized in that: The project planning parameters include project planning condition data and project planning curve data, wherein the project planning curve data is historical data or forecast data.

3. The wind-solar-storage capacity optimization configuration method based on multi-objective optimization as described in claim 2, characterized in that: The project planning conditions data include the project planning type, which is either off-grid or grid-connected.

4. The wind-solar-storage capacity optimization configuration method based on multi-objective optimization as described in claim 1, characterized in that: The setting method is the NSGA-II algorithm.

5. The wind-solar-storage capacity optimization configuration method based on multi-objective optimization as described in any one of claims 1 to 4, characterized in that: In the mathematical model, the total investment cost is expressed as: Initial Investment = C wind ·W cap +C pv ·S cap + C bat,p ·P bat +C conv ·(W cap +S cap + P bat Annual maintenance cost = Initial investment · r om Present value = Initial investment + Annual maintenance costs , where C wind This represents the unit cost of wind power (yuan / kW), W cap C represents the installed capacity of wind power. pv S represents the unit cost of photovoltaic power (yuan / kW). cap P represents the installed capacity of photovoltaic power. bat Indicates the rated power of energy storage C bat,p C represents the unit cost of energy storage power (yuan / kW). conv The converter cost factor (yuan / kW) is represented by r. om This indicates the annual maintenance cost rate. Indicates the project's lifespan (in years). The discount rate is represented by the absorption rate, which is expressed as: theoretical wind power generation = Theoretical photovoltaic power generation = Total abandoned electricity = The grid connection rate = 1 – (total abandoned power / (theoretical wind power generation + theoretical photovoltaic power generation)), where, This represents the theoretical wind power output (kW) at time t. This represents the theoretical photovoltaic output (kW) at time t. This represents the actual wind power output (kW) utilized. The actual photovoltaic output (kW) utilized is represented; power supply reliability is expressed as the power shortage rate obtained from the total power shortage and total load: Total power shortage = Total load = Power shortage rate = total power shortage / total load, where, This represents the load demand (kW) at time t. This represents the energy storage discharge power (kW) at time t. This represents the amount of electricity purchased online at time t (kW).

6. The wind-solar-storage capacity optimization configuration method based on multi-objective optimization as described in claim 5, characterized in that: The mathematical model constraints also include energy storage state of charge updates, which are expressed as follows: ,in, This represents the state of charge of the stored energy at time t. This represents the charging power (kW) at time t. This represents the discharge power (kW) at time t. Indicates charging efficiency. Indicates discharge efficiency. (Hourly time step); Constraints are: ， The value is 0.

1. The value is 0.

9.

7. The wind-solar-storage capacity optimization configuration method based on multi-objective optimization as described in claim 6, characterized in that: The wind curtailment constraint is expressed as: G1 = (Wind curtailment power / Theoretical wind power generation) - Y w,max ≤0, Y w,max This represents the maximum allowable wind curtailment rate; the solar curtailment constraint is expressed as: G2 = (curtailed solar power / theoretical photovoltaic power generation) - Y s,max ≤0, Y s,max This represents the maximum permissible light rejection rate; the power shortage constraint is expressed as: G3 = LPSP - LPSP max ≤0, LPSP max This represents the maximum allowable power shortage rate; the wind power installed capacity constraint is expressed as: G4 = W cap - W max ≤0, W max This represents the maximum installed capacity of wind power; the photovoltaic installation constraint is expressed as: G5 = S cap - S max ≤0, S max The maximum installed capacity of photovoltaic power is represented by: G6 = ZXZFZYZB – (Self-consumption electricity / Total power generation) ≤ 0, where ZXZFZYZB represents the minimum self-consumption ratio; the upper limit constraint of the self-consumption ratio is represented by: G7 = (Self-consumption electricity / Total power generation) - 1 ≤ 0; the upper limit constraint of the grid connection ratio is represented by: G8 = (Grid connection electricity / Total power generation) - ZDSWZB ≤ 0, where ZDSWZB represents the maximum grid connection ratio; the energy storage capacity constraint is represented by: G9 = min_ratio – (E bat / P bat )≤0, P bat E represents the rated power of energy storage. bat This represents the rated energy of the energy storage, and min_ratio represents the minimum ratio of energy storage to power.

8. A computer device comprising a processor and a memory, characterized in that: The memory stores a computer program, which, when executed by the processor, implements the wind, solar and storage capacity optimization configuration method based on multi-objective optimization as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the wind, solar and storage capacity optimization configuration method based on multi-objective optimization as described in any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, they implement the wind, solar and storage capacity optimization configuration method based on multi-objective optimization as described in any one of claims 1 to 7.

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