Wind-solar-storage integrated energy base optimization planning method considering multi-scene adjustment requirements of receiving-end power grid

Abstract

The invention discloses a wind and light storage integrated energy base optimization planning method considering receiving end power grid multi-scene adjustment demands, and relates to the technical field of power system planning and operation, and the method comprises the following steps: constructing an energy base investment decision model taking the maximum total income in a planning period as a target function; a multi-population evolution genetic algorithm is adopted to solve the wind and light storage system output optimization model and the receiving end power grid time sequence operation simulation model, and an optimal wind and light storage output plan under the typical day and income and cost data of the typical day under the plan are obtained; and based on the income and cost data of the typical day, solving the energy base investment decision model, and after the maximum evolution algebra is reached through selection, crossover, variation and inter-population information sharing, outputting a wind and light storage capacity configuration scheme enabling the objective function value of the decision model to be maximum. The method is used for solving the problem that a planning scheme obtained by a traditional method is disjointed with actual operation requirements of a power grid.

Description

Technical Field

[0001] This invention relates to the field of power system planning and operation technology, and more specifically, to an optimized planning method for integrated wind, solar and energy storage energy bases that takes into account the multi-scenario regulation needs of the receiving-end power grid. Background Technology

[0002] Against the backdrop of the ongoing advancement of "dual carbon" targets and the construction of new power systems, the proportion of installed capacity of new energy sources is rapidly increasing. The volatility and uncertainty of wind and solar power output significantly amplify the pressure on the safety and economic dispatch of the power system. During peak hours, the deviation between planned and actual power output at the power base can easily lead to increased costs for receiving-end reserve and deep peak shaving. Furthermore, the synergistic effect of energy storage in "stable power supply during peak hours and flexible adjustment during off-peak hours" still lacks a unified modeling and cost-sharing mechanism consistent with grid-side constraints. This results in a disconnect between planning schemes and actual grid operation, as well as insufficient absorption capacity and safety margin. Therefore, this paper proposes an optimized planning method for integrated wind, solar, and energy storage energy bases that addresses the multi-scenario adjustment needs of the receiving-end grid. This method is of great significance for improving the effectiveness of flexible resources and the operational safety of high-proportion new energy power systems.

[0003] Traditional methods for integrated wind, solar, and energy storage planning typically employ deterministic scenarios or simplified single-objective optimization models to find the optimal strategy. These methods often treat the receiving-end grid as a fixed load or simple boundary conditions, neglecting the flexible adjustment needs (such as peak shaving, frequency regulation, and reserve) across multiple time scales in actual operation. Furthermore, traditional methods have limited capacity to handle the uncertainty of wind and solar power output, often relying on typical days or single scenarios, making it difficult to capture their inherent randomness and volatility. In addition, when solving large-scale, high-dimensional, and nonlinear planning models, traditional optimization algorithms (such as standard genetic algorithms and linear programming) are prone to getting trapped in local optima, resulting in premature convergence. This leads to a planning scheme that is "optimal" in terms of both economy and reliability. These factors collectively cause the planning scheme obtained by traditional methods to become disconnected from the actual operational needs of the grid, potentially leading to unreasonable energy storage configurations, wasted investment, or failure to meet grid security constraints in real-time operation, resulting in problems such as wind and solar curtailment or power shortages. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an optimized planning method for integrated wind, solar and energy storage energy bases that considers the multi-scenario regulation needs of the receiving-end power grid, in order to solve the problems of disconnect between the planning scheme and the actual operation of the power grid, and insufficient absorption capacity and safety margin.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An optimization planning method for integrated wind, solar, and energy storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid includes the following steps: Constructing an energy base investment decision model with the objective function of maximizing total revenue within the planning period; employing a multi-population evolutionary genetic algorithm to solve the wind, solar, and energy storage system output optimization model and the receiving-end power grid time-series operation simulation model, obtaining the optimal wind, solar, and energy storage output plan for a typical day, and the revenue and cost data for a typical day under the plan; based on the revenue and cost data for the typical day, solving the energy base investment decision model, and through selection, crossover, mutation, and inter-population information sharing until reaching the maximum number of generations, outputting the wind, solar, and energy storage capacity configuration scheme that maximizes the objective function value of the decision model; wherein, the optimization model includes an objective function that minimizes total cost and day-ahead predicted output constraints, day-ahead energy storage operation constraints, energy base output constraints under uncertain intraday scenarios, and intraday energy storage operation constraints; the operation simulation model includes an objective function that minimizes total cost and power balance constraints, minimum unit start-up capacity constraints, thermal power unit operation constraints, new energy power plant operation constraints, and energy storage power plant operation constraints.

[0006] In a preferred embodiment, the objective function of the energy base investment decision model consists of electricity sales revenue, operation and maintenance costs, deviation penalty costs, peak-shaving sharing costs, and the investment costs of wind power, photovoltaic, and energy storage power stations in the energy base, as shown in the following formula:

[0007] In the formula, The total revenue within the system planning period; BE(y), CO(y), C DP (y) and C PS (y) represents the system's electricity sales revenue, operation and maintenance costs, peak-hour output deviation penalty costs, and peak-shaving sharing costs of the receiving-end grid in year y, respectively; MY is the planning period year; y is the year index; CI W CI P and CI ESS The investment cost for wind power, photovoltaic and energy storage power stations in the energy base.

[0008] In a preferred embodiment, the optimization model, after solving, yields the optimal wind, solar, and energy storage power output plan for a typical day, as well as the electricity sales revenue and deviation penalty cost for a typical day under the plan; the operation simulation model, based on the plan, yields the peak-shaving sharing cost for a typical day.

[0009] In a preferred embodiment, the wind-solar-storage system output optimization model includes linearizing the absolute value term in the objective function by introducing intermediate variables. The transformed objective function formula is as follows:

[0010] And satisfy the constraints:

[0011] In the formula, Ω represents the set of uncertain scenarios for new energy power output; t is the time period index; s is the new energy scenario index; pro s The probability of the s-th uncertain new energy scenario; C e and C p These are the penalty cost coefficients for the electricity sales price and peak energy storage capacity deviation of the energy base, respectively; T pea This refers to the collection of peak load periods; To contribute to the energy base in scenario s during time period t; Contribute to the energy base plan; The intermediate variable introduced represents the absolute value of the power deviation of the energy base in scenario s at time t during the peak load period of the receiving-end power grid.

[0012] In a preferred embodiment, the objective function formula of the receiving-end power grid time-series operation simulation model is:

[0013]

[0014] In the formula, the objective function The total operating cost of the receiving-end power grid on a typical day, including the electricity purchase cost F. EN And peak shaving cost F PS G represents the set of all peak-shaving generating units within the receiving-end power grid; T represents the total number of time periods within the dispatch cycle. and C represents the electricity purchase cost and peak-shaving cost paid to unit g at a typical time t on a given day; g Pg,t represents the power purchase cost coefficient of unit g; Pg,t represents the output of unit g at time t on a typical day.

[0015] In a preferred embodiment, the peak-shaving cost is calculated using the following formula:

[0016] In the formula, and These represent the peak-shaving costs of the receiving-end power grid before and after a typical daily energy base is connected to the grid; rd is the discount rate; and y is the year index. The duration of the scenario represented by a typical day d, satisfying... =365 days, MD is the total typical days.

[0017] In a preferred embodiment, the peak-shaving cost of the receiving-end power grid includes the peak-shaving cost of thermal power units, and the formula for the peak-shaving cost coefficient of thermal power units is as follows: = + +

[0018] In the formula, The peak-shaving loss cost coefficient for thermal power units at depth g. , This refers to the oil input cost coefficient and the environmental additional cost coefficient.

[0019] In a preferred embodiment, solving the objective function of the investment decision model based on the revenue and cost data includes the following steps: multiplying the electricity sales revenue, deviation penalty cost, peak shaving sharing cost, and calculated operation and maintenance cost for each typical day by the actual number of days in the system planning cycle represented by the corresponding typical day, and then summing them up to obtain annual data; substituting the annual data and the calculated one-time investment data into the objective function of the investment decision model to obtain the objective function value within the system planning cycle.

[0020] In a preferred embodiment, the output of the wind-solar-storage capacity configuration scheme that maximizes the objective function value of the decision model includes the following steps: Each subpopulation is independently evolved using parallel computing instructions; during the evolution, the scheduling stochastic optimization model and the running simulation model are sequentially called to obtain the revenue and cost data; based on the revenue and cost data, the objective function of the investment decision model is solved to obtain the objective function value used to characterize maximizing the total revenue during the system planning cycle; within each subpopulation, selection, crossover, and mutation are performed according to the objective function value for a preset number of independent evolution generations, and information sharing operations are conducted between subpopulations to update the subpopulations; when the accumulated number of independent evolution generations reaches the maximum number of evolution generations, the individual with the largest objective function value among all subpopulations is determined and output as the optimal wind-solar-storage capacity configuration scheme.

[0021] The technical effects and advantages of the wind-solar-storage integrated energy base optimization planning method of this invention, which considers the multi-scenario regulation needs of the receiving-end power grid, are as follows: This invention constructs an optimization model for the multi-scenario regulation demand of the receiving-end power grid and energy base planning. At three levels—investment decision-making, time-series operation simulation, and revenue and cost calculation model—it employs a multi-population parallel evolutionary genetic algorithm (MPGA). By maintaining the parallel evolution of multiple subpopulations and periodically migrating individuals, it effectively preserves population diversity and greatly enhances the global exploration capability of complex solution spaces. This successfully overcomes the problem that traditional methods fail to meet the actual operation requirements of the power grid, which may lead to unreasonable energy storage configuration, wasted investment, or failure to meet power grid security constraints in real-time operation, resulting in wind and solar power curtailment or power shortages. Attached Figure Description

[0022] Figure 1A schematic diagram of the optimization planning method for integrated wind, solar and energy storage energy bases, considering the multi-scenario regulation needs of the receiving-end power grid, provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1, Figure 1 This invention presents an optimized planning method for integrated wind-solar-storage energy bases that considers the multi-scenario regulation needs of the receiving-end power grid, comprising the following steps: S1, Construct an energy base investment decision model with the objective function of maximizing total revenue within the planning period; S2, construct the power output optimization model of the wind, solar and energy storage system, including the objective function of minimizing the total cost and the day-ahead forecast power output constraints of the energy base, day-ahead energy storage operation constraints, energy base power output constraints under intraday uncertain scenarios and intraday energy storage operation constraints; A time-series operation simulation model of the receiving-end power grid is constructed, including an objective function that minimizes the total cost, as well as power balance constraints, minimum capacity constraints for unit startup, operating constraints for thermal power units, operating constraints for new energy power plants, and operating constraints for energy storage power plants.

[0025] S3. Using a multi-population evolutionary genetic algorithm, the output optimization model of the wind-solar-storage system and the time-series operation simulation model of the receiving-end power grid are solved to obtain the optimal wind-solar-storage output plan for a typical day, as well as the revenue and cost data for a typical day under the plan. Based on the revenue and cost data of the typical day, the energy base investment decision model is solved. Through selection, crossover, mutation and inter-population information sharing, until the maximum number of evolution generations is reached, the wind, solar and storage capacity configuration scheme that maximizes the objective function value of the decision model is output.

[0026] In this embodiment, step S1 is implemented as follows: An energy base investment decision-making model is constructed with the goal of maximizing total revenue within the planning period. The objective function of the energy base investment decision-making model is composed of electricity sales revenue, operation and maintenance costs, deviation penalty costs, peak-shaving sharing costs, and investment costs of wind power, photovoltaic, and energy storage power stations in the energy base. Its core lies in incorporating the dynamic revenue of long-term operation and one-time investment costs into a unified framework for optimization decision-making. The specific calculation formula is as follows: , In the formula, The total revenue within the system planning period; BE(y), CO(y), C DP (y) and C PS (y) represents the system's electricity sales revenue, operation and maintenance costs, peak-hour output deviation penalty costs, and peak-shaving sharing costs of the receiving-end grid in year y, respectively; MY is the planning period year, and y is the year index; CIW, CIP, and CIESS are the investment costs of wind power, photovoltaic, and energy storage power stations in the energy base, which are invested in a lump sum at the beginning of the project. The formula for calculating electricity sales revenue is as follows: , In the formula: PE represents the electricity price; rd represents the discount rate; and y represents the year index. E base ( y This represents the base's annual power generation. The formula for calculating operation and maintenance costs is as follows: , In the formula, , and These are the maintenance cost coefficients for wind power, photovoltaic, and energy storage power stations, respectively. The formula for calculating the penalty cost for output deviation during peak hours is as follows: , In the formula, For typical days d Penalty costs for output deviation during peak hours; For typical days d The number of days the scenario represents ,satisfy =365 days MD For the total number of typical days, The formula for calculating the peak-shaving cost sharing of the receiving-end power grid is as follows: , In the formula, and Typical days d Before and after the energy base is connected to the grid, the peak-shaving cost of the receiving-end power grid. This step transforms the complex engineering planning problem into a clear mathematical optimization problem by establishing an objective function that encompasses full lifecycle revenue data and key operational risk costs, thereby ensuring that the final planning scheme achieves optimal economic and financial feasibility.

[0027] In this embodiment, step S2 is implemented as follows: Unlike traditional planning methods that oversimplify or completely disconnect operational processes, this technology, through the aforementioned energy base investment decision-making model, achieves precise quantification of each candidate planning scheme throughout the entire planning cycle, ensuring that the planning results are both economical and reliable. The aforementioned wind-solar-storage system output optimization model, considering the uncertainty of new energy output, formulates an optimal scheduling plan that maximizes the average daily expected operating efficiency of the energy base, and accurately calculates the corresponding electricity sales revenue and deviation penalty cost. This model improves the model's solution efficiency by introducing intermediate variables to linearize the absolute value term in the objective function. The transformed objective function is as follows: , And satisfy the constraints: , In the formula, Ω represents the set of uncertain scenarios for renewable energy output, t is the time period index, s is the renewable energy scenario index, pros is the probability of the s-th renewable energy uncertainty scenario, Ce and Cp are the electricity sales price of the energy base and the penalty cost coefficient for peak energy storage power deviation, respectively, and Tpea is the set of peak load periods. Contribute to the energy base in scenario s during time period t. Contribute to the energy base plan, The intermediate variable introduced represents the absolute value of the power deviation of the energy base in scenario s at time t during the peak load period of the receiving-end power grid. The receiving-end power grid time-series operation simulation model is used to quantify the impact of energy base access on the economic operation of the power grid. Its objective function is to minimize the total operating cost of the receiving-end power grid in a typical day, as shown in the following formula: , , In the formula, the objective function The total operating cost of the receiving-end power grid on a typical day, including the electricity purchase cost F. EN And peak shaving cost F PS G represents the set of all peak-shaving generating units within the receiving-end power grid, and T represents the total number of time periods within the dispatching cycle. and C represents the electricity purchase cost and peak-shaving cost paid to unit g at a typical time t on a given day; g Let Pg,t be the power purchase cost coefficient for unit g, and Pg,t be the output of unit g at time t on a typical day. The peak-shaving cost sharing method is calculated using the following formula: , In the formula, and These represent the peak-shaving costs of the receiving-end power grid before and after a typical daily energy base is connected to the grid, where rd is the discount rate and y is the year index. The duration of the scenario represented by a typical day d, satisfying... =365 days, MD is the total number of typical days. The peak-shaving cost of the receiving-end power grid includes the peak-shaving cost of thermal power units, and the formula for the peak-shaving cost coefficient of thermal power units is as follows: = + + , In the formula, The peak-shaving loss cost coefficient for thermal power units at depth g. , For oil input cost coefficient and environmental additional cost coefficient, The constraints of the wind-solar-storage system output optimization model primarily ensure the physical feasibility of the scheme. Specifically, these include day-ahead forecast output constraints for the energy base, day-ahead energy storage operation constraints (covering upper and lower limits of charging and discharging power and capacity), energy base output constraints under uncertain intraday scenarios (ensuring equipment output does not exceed limits), and intraday energy storage operation constraints (maintaining a reasonable energy state). The constraints of the receiving-end grid time-series operation simulation model ensure the safe and stable operation of the grid. Specifically, these include power balance constraints (real-time balance between system power generation and load demand), minimum unit start-up capacity constraints (ensuring system reserve), thermal power unit operation constraints (such as upper and lower limits of output and ramp rate), renewable energy power plant operation constraints (allowing for wind and solar curtailment), and energy storage power plant operation constraints (charge and discharge mutual exclusion and energy conservation). The specific formulas are as follows: The formula for calculating the power output constraint of the energy base is as follows: , In the formula, , and Contribute to the forecasted power output of wind and solar power and the planning of energy bases. and This refers to the planned curtailment of power generated by the recent wind power project. and For the planned charging and discharging power of energy storage, The calculation formula for the energy storage operation constraints of the current power output plan is as follows: , In the formula, Maximum charging and discharging power of energy storage; and Representing time respectively t 0-1 variables representing the charging and discharging states of energy storage; , For energy storage timet Charging and discharging power; For a moment t Energy state; and These are the maximum and minimum energy states; and For charging and discharging efficiency, The formula for calculating the output constraints of an energy base under uncertain scenarios is as follows:

[0028] In the formula, , and For uncertain scenarios s Wind power, solar power, and energy base output; and For the scene s The power curtailment of wind and solar power, and For the scene s The charging and discharging power of energy storage The formula for calculating the output constraints of an energy base under uncertain scenarios is as follows: , In the formula, and Representing time respectively t Uncertainty scenarios s 0-1 variables representing the charging and discharging states of energy storage; , For a moment t Scene s Energy storage charging and discharging power; For a moment t Scene s Energy storage status Before the start of peak load periods, the energy storage status must be consistent with the day-ahead plan under any uncertain intraday scenario, as specified in the following formula: , The formula for calculating power balance constraints is as follows: , In the formula, and They are time points t wind farm w and photovoltaic power station p time t contribution; and Energy storage power stations e time t The charging and discharging power; For the receiving end of the power grid t Forecasted load power; This refers to the power exchange between the receiving-end power grid and external sources; power flowing into the receiving-end power grid is positive, and power flowing out is negative. The formula for calculating the minimum start-up capacity constraint of the generating unit is as follows: , In the formula, R Reserve backup power for the receiving-end power grid; For thermal power units g Maximum power generation capacity, The calculation formula for the operating constraints of thermal power units is as follows: , The calculation formula for the operating constraints of new energy power plants is as follows: , , In the formula, and For a moment t wind farm w The predicted wind power and wind curtailment power; and For a moment t Photovoltaic power station p Forecasted photovoltaic power and curtailment power, The formula for calculating the operating constraints of an energy storage power station is as follows: , In the formula, Maximum charging and discharging power of energy storage; and These represent the characteristics of energy storage power stations. e time t 0-1 variables representing the charging and discharging states of energy storage; , For energy storage power stations e time t Charging and discharging power; For energy storage power stations e time t Energy state; and These represent the maximum and minimum energy states.

[0029] In this embodiment, step S3 is implemented as follows: A multi-population parallel evolutionary genetic algorithm is adopted as the core solution algorithm to coordinate the solution of the high-dimensional, nonlinear optimization problem of wind-solar-storage capacity planning. The core of this method lies in decomposing the complex planning problem into multiple subpopulations for parallel exploration, and guiding the search direction through detailed simulation evaluation. The specific steps are as follows: Step 1: Initialize MPGA algorithm parameters, subpopulation number M, subpopulation number N, independent evolution generation IE, maximum evolution generation ME, subpopulation crossover rate PCs, mutation rate PMs, s=1, 2, …, M; initialize energy base planning parameters, and generate time-series output coefficient sequences and uncertainty scenario sets for MD typical days of wind power and photovoltaic power. Step 2: Randomly generate M initial populations to represent the capacity of wind power, photovoltaic power, and energy storage in the energy base, and set the number of generations Lp1=1; Step 3: Using the spmd (Single Program Multi Data) parallel computing command in Matlab software, create M sub-computation processes, corresponding to each subpopulation, and perform the following operations: Step 3.1: Set the independent evolutionary generation Lp2=1; Step 3.2: Set typical day d = 1; Step 3.3: Using the energy optimization scheduling model in 2.2, calculate the energy base output plan for a typical day d, and the electricity sales revenue and the peak-hour power deviation penalty cost of the receiving-end grid; Step 3.4: Using the receiving-end power grid operation simulation model in 3.1, calculate the power purchase cost and peak-shaving cost of the receiving-end power grid before the energy base is connected to the grid; Step 3.5: Using the receiving-end power grid operation simulation model in 3.1, calculate the power purchase cost and peak-shaving cost of the receiving-end power grid after the energy base is connected; Step 3.6: Calculate the peak-shaving cost allocated to the energy base on a typical day (d); Step 3.7, d=MS? Yes, proceed to step 3.8; No, d = d + 1, proceed to step 3.3; Step 3.8: Calculate the objective function; Step 3.9: Perform selection, crossover, and mutation operations on the subpopulations according to their respective parameters; Step 3.10: Determine if Lp2 = IE? If yes, proceed to step 4; otherwise, Lp2 = Lp2 + 1, proceed to step 3.2. Step 4: Find the overall best individual among the M subpopulations and determine if Lp1 = ME. If yes, return the best individual and stop; otherwise, propagate the current best individual to each subpopulation, Lp1 = Lp1 + IE, and go to step 3. The total revenue within the system planning period is obtained by weighted integration of the revenue and cost data. Specifically, the electricity sales revenue, deviation penalty cost, and peak-shaving sharing cost obtained from simulations on each typical day are multiplied by the actual number of days in the system planning period represented by that typical day, and then summed to aggregate into annual data. Finally, the data is substituted into the objective function of the investment decision model to calculate the objective function value within the system planning period. This value is the fitness of the individual in the genetic algorithm. After obtaining the fitness of all individuals, each subpopulation performs selection, crossover, and mutation operations for a preset number of independent generations based on this fitness. The selection operation retains superior individuals probabilistically based on their fitness. The crossover operation generates new feasible solutions by exchanging some gene information of different individuals. The mutation operation randomly changes the values ​​of some genes in individuals with a small probability to maintain population diversity, thereby updating and generating a new generation of subpopulations. After that, the algorithm executes an information sharing mechanism, that is, it finds the historical global best individual in all subpopulations and propagates it to other subpopulations to guide the entire search direction and avoid getting trapped in local optima. This process is repeated until the set maximum number of generations is reached. Finally, the individual with the highest objective function value, that is, the fitness, is selected from all subpopulations, and its corresponding wind, solar and storage capacity configuration scheme is output as the optimal solution. This step constructs an optimization framework based on a multi-population parallel evolutionary genetic algorithm, deeply embedding physical operational constraints and economic objectives into the planning and decision-making process. This effectively overcomes the problems of improper energy storage configuration, low investment efficiency, and high system operation risk caused by neglecting operational details in traditional methods. Thus, it efficiently and robustly searches for the globally optimal or near-optimal planning scheme in the vast solution space.

[0030] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0031] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0032] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0033] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0034] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0035] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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 planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid, characterized in that, Includes the following steps: Construct an energy base investment decision model with the objective function of maximizing total revenue within the planning period; A multi-population evolutionary genetic algorithm is used to solve the power output optimization model of the wind-solar-storage system and the time-series operation simulation model of the receiving-end power grid, so as to obtain the optimal wind-solar-storage power output plan for a typical day, as well as the revenue and cost data of the typical day under the plan. Based on the revenue and cost data of the typical day, the energy base investment decision model is solved. Through selection, crossover, mutation and inter-population information sharing, until the maximum number of evolution generations is reached, the wind, solar and storage capacity configuration scheme that maximizes the objective function value of the decision model is output. The optimization model includes an objective function that minimizes the total cost, as well as day-ahead forecast output constraints, day-ahead energy storage operation constraints, energy base output constraints under intraday uncertain scenarios, and intraday energy storage operation constraints. The operational simulation model includes an objective function for minimizing total cost and power balance constraints, minimum start-up capacity constraints for generating units, operational constraints for thermal power units, operational constraints for new energy power plants, and operational constraints for energy storage power plants.

2. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 1, characterized in that, The objective function of the energy base investment decision model consists of electricity sales revenue, operation and maintenance costs, deviation penalty costs, peak-shaving sharing costs, and investment costs of wind power, photovoltaic, and energy storage power stations in the energy base, as shown in the following formula: In the formula, y represents the total revenue within the system planning period; BE(y), CO(y), CDP(y), and CPS(y) represent the electricity sales revenue, operation and maintenance costs, peak-hour output deviation penalty costs, and receiving-end grid peak-shaving sharing costs of the system in year y, respectively; MY represents the planning period year; y is the year index; CIW, CIP, and CIESS represent the investment costs of wind power, photovoltaic, and energy storage power stations in the energy base.

3. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 2, characterized in that, After solving the optimization model, the optimal wind, solar and energy storage power output plan for a typical day is obtained, along with the electricity sales revenue and deviation penalty cost for a typical day under the plan. The operational simulation model, based on the plan, yields the peak-shaving cost for a typical day.

4. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 3, characterized in that, The power output optimization model for the wind-solar-storage system includes linearizing the absolute value term in the objective function by introducing intermediate variables. The transformed objective function formula is as follows: And satisfy the constraints: In the formula, Ω represents the set of uncertain scenarios for renewable energy output; t represents the time period index; s represents the renewable energy scenario index; pros represents the probability of the s-th renewable energy uncertainty scenario; Ce and Cp represent the electricity sales price of the energy base and the penalty cost coefficient for peak energy storage power deviation, respectively; Tpea represents the set of peak load periods. To contribute to the energy base in scenario s during time period t; Contribute to the energy base plan; The intermediate variable introduced represents the absolute value of the power deviation of the energy base in scenario s at time t during the peak load period of the receiving-end power grid.

5. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 4, characterized in that, The objective function formula for the receiving-end power grid time-series operation simulation model is: In the formula, the objective function G represents the total operating cost of the receiving-end power grid on a typical day, including the electricity purchase cost (FEN) and the peak-shaving cost (FPS); G is the set of all peak-shaving generating units in the receiving-end power grid; and T is the total number of time periods within the dispatch cycle. and Let Cg and Pg,t be the electricity purchase cost and peak-shaving cost paid to unit g at a typical time t on a given day, respectively; Cg is the electricity purchase cost coefficient of unit g; and Pg,t is the output of unit g at a typical time t on a given day.

6. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 5, characterized in that, The peak-shaving cost sharing method is calculated using the following formula: In the formula, and These represent the peak-shaving costs of the receiving-end power grid before and after a typical daily energy base is connected to the grid; rd is the discount rate; and y is the year index. The duration of the scenario represented by a typical day d, satisfying... =365 days, MD is the total typical days.

7. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 6, characterized in that, The peak-shaving cost of the receiving-end power grid includes the peak-shaving cost of thermal power units, and the formula for the peak-shaving cost coefficient of thermal power units is as follows: = + + In the formula, The peak-shaving loss cost coefficient for thermal power units at depth g. , This refers to the oil input cost coefficient and the environmental additional cost coefficient.

8. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 7, characterized in that, Solving the objective function of the investment decision model based on the revenue and cost data includes the following steps: The electricity sales revenue, deviation penalty cost, peak shaving sharing cost, and calculated operation and maintenance cost for each typical day are multiplied by the actual number of days in the system planning cycle represented by the corresponding typical day, and then summed to obtain annual data. Substituting the annual data and the calculated one-time investment data into the objective function of the investment decision model, the objective function value within the system planning period is obtained.

9. The method for optimizing the planning of integrated wind-solar-storage energy bases considering the multi-scenario regulation needs of the receiving-end power grid as described in claim 8, characterized in that, The output of the wind-solar-storage capacity configuration scheme that maximizes the objective function value of the decision model includes the following steps: Parallel computing instructions are used to enable each subpopulation to evolve independently. During the evolution, the scheduling stochastic optimization model and the running simulation model are called sequentially to obtain the benefit and cost data. Based on the revenue and cost data, the objective function of the investment decision model is solved to obtain the objective function value used to characterize the maximization of total revenue during the system planning cycle; Within the subpopulation, based on the objective function value, a preset number of independent evolutionary generations are performed, including selection, crossover, mutation, and inter-population information sharing, and the subpopulation is updated. Once the accumulated number of independent evolutionary generations reaches the maximum number of evolutionary generations, the individual with the largest objective function value among all subpopulations is selected as the optimal wind-solar-storage capacity configuration scheme and output.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-9.

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