Water-wind-light micro-grid capacity configuration optimization method based on QILDE

Abstract

The invention discloses a QILDE-based capacity configuration optimization method for a water-wind-light micro-grid, and belongs to the technical field of power system energy storage. The method comprises the following steps: firstly, extracting one-year wind and light data and load data in a micro-grid region, and summarizing the information; according to the provided parameters, a capacity configuration optimization model of the'water-wind-light 'integrated micro-grid is established, a quadratic interpolation learning differential evolution algorithm is utilized to solve the model, and related reliability indexes are calculated; and finally, under the condition that the annual wind and light abandoning rate and the vacancy rate are met, selecting a configuration scheme with the minimum equivalent annual value comprehensive cost as the optimal capacity configuration scheme of the'water, wind and light 'integrated micro-grid. The method utilizes the quadratic interpolation learning differential evolution algorithm to optimize the capacity configuration of the'water-wind-light 'integrated micro-grid, can balance the annual wind and light abandoning rate, the annual vacancy rate and the economical efficiency, thereby obtaining an optimal distribution terminal layout planning scheme, and has a certain guiding effect on acceleration of rapid construction of the micro-grid in practical engineering practice.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system energy storage, and particularly relates to a water-wind-solar microgrid capacity configuration optimization method based on QILDE. BACKGROUND

[0002] In wind-solar resource-rich areas, due to the lack of stable and economical energy storage technology, the power grid system is not perfect or the power supply quality guarantee rate is low, which leads to the fact that wind-solar power generation resources cannot be fully utilized. In the current microgrid system, chemical energy storage technology is mostly used to solve the power supply problem at the load end, but the chemical energy storage has high construction cost, short service life and poor stability, so through the research on economical and reliable energy storage technology, the establishment of a distributed power grid and an energy storage system can solve the problem of independently, stably and long-term power supply to the load end. Pumped storage power station has multiple functions such as peak regulation, valley filling, energy storage, frequency regulation, phase regulation, emergency backup, black start and special load of the system, and is an effective way to ensure the safe, reliable, stable and economical operation of the power system, and plays an important role in the transformation and upgrading of energy and power systems, quality improvement and the construction of a new type of power system.

[0003] In recent years, the demand for clean energy around the world has shown an unprecedented urgency, which is not only due to the increasing concern for environmental protection, but also because the limitations of traditional energy and its unsustainability have become apparent. Under this background, the continuous innovation of renewable energy technology has become the key to breaking free from the shackles of traditional energy. The "water-wind-solar" integrated microgrid, as a new type of intelligent power system that integrates the use of multiple renewable energy sources, is considered as one of the effective ways to cope with energy challenges. SUMMARY

[0004] The purpose of the present application is to solve the problems of insufficient power supply and energy waste in traditional power grids through the research on "water-wind-solar" integrated microgrid. Traditional power grids mainly rely on fossil energy, and there are energy instability and waste in the power supply process. By integrating hydroelectric power generation, wind power generation, photovoltaic power generation and energy storage technology, an integrated microgrid system can effectively cope with the fluctuations in energy supply under different environmental conditions, achieve efficient use and reasonable distribution of energy, and improve the stability and reliability of the power grid. In this application, the total capacity of wind turbines, the total capacity of photovoltaic units and the total capacity of pumped storage power stations are used as decision variables, and the system power supply availability and related operating requirements are used as constraint conditions to establish an objective function with the minimum equal-year comprehensive cost. The present application comprehensively considers the annual wind and light abandonment rate and the economic efficiency, and only needs the wind and light data of the microgrid construction environment and the load data, which is low in cost and easy to implement in practical applications.

[0005] To solve the above technical problems, the present application provides a water-wind-solar microgrid capacity configuration optimization method based on QILDE, which comprises the following steps: S1, acquiring wind and light data and load data in a micro-grid region in a set time period; S2, constructing a water, wind and light micro-grid capacity configuration optimization model based on the wind and light data and the load data, taking minimum equivalent value comprehensive cost as an objective function, and considering system related constraints; S3, solving the water, wind and light micro-grid capacity configuration optimization model based on a quadratic interpolation learning differential evolution algorithm, obtaining a configuration scheme and selecting an optimal configuration scheme.

[0006] As preferred, annual wind speed and light intensity data in the micro-grid region are extracted, wind power output is calculated according to a cut-in wind speed, a rated wind speed and a cut-out wind speed of wind power, photovoltaic power generation capacity is calculated in combination with photovoltaic conversion efficiency, annual electric power load demand data of the micro-grid are acquired, and a load curve is recorded with an hour as a time step.

[0007] As preferred, the objective function is specifically represented as: ; ; ; ; In the formula, economic cost, penalty cost of renewable energy wind and light abandonment rate, section margin utilization rate.

[0008] 4. The water, wind and light micro-grid capacity configuration optimization method based on QILDE according to claim 3, characterized in that: ; ; In the formula, and are initial investment costs of unit power and capacity of the first type power generation unit respectively; and are rated power and capacity of the first type power generation unit respectively; is a discount rate; is a service life; and are operation and maintenance costs of unit power and capacity of the first type power generation unit respectively; is output of the first type power generation unit in the time period.

[0009] As preferred, the system-related constraints include a system power supply reliability constraint, a renewable energy consumption constraint, a power supply and demand balance constraint, and physical operation constraints of various devices.

[0010] As preferred, the quadratic interpolation learning differential evolution algorithm introduces a quadratic interpolation learning mechanism on the basis of a standard differential evolution algorithm.

[0011] As preferred, the optimization step of the quadratic interpolation learning differential evolution algorithm generally combines the basic framework of the differential evolution algorithm and the local approximation strategy of the quadratic interpolation, and includes the following optimization steps: S31, initializing a population, randomly generating an initial population, setting constraint conditions and algorithm parameters, specifically including a population size, a scaling factor, a crossover probability, a maximum number of iterations, each individual representing a candidate scheme including wind power capacity, photovoltaic capacity and pumped storage capacity; S32, calculating the fitness function value of each individual in the initial population; S33, iterative optimization, performing mutation operation, crossover operation and quadratic interpolation learning operation; in each iteration, performing mutation operation on the current population, and generating a trial individual by using the strategy of "current optimal solution + differential vector"; S34, constructing a parabolic model near the current optimal solution based on the quadratic interpolation method to locate the local optimal region; constructing a quadratic interpolation model by using the fitness values of the current individual, the mutated individual and the trial individual; generating a better candidate individual by solving the optimal solution of the quadratic interpolation model; S35, retaining high-quality individuals through selection operation; if the maximum number of iterations is reached or the optimal solution of the population meets the preset precision, the algorithm is terminated, and the individual with the best fitness value in the current population is output as the optimal solution.

[0012] As preferred, during the algorithm operation process, it is necessary to verify whether each candidate scheme meets the set constraint conditions in real time.

[0013] As preferred, the optimal configuration scheme is to select the configuration scheme with the minimum equal annual comprehensive cost from the converged population after iteration as the final optimization result, which gives the optimal capacity ratio of wind power, photovoltaic and pumped storage power station, and ensures the minimization of the system life cycle cost under the premise of meeting all operation constraints.

[0014] Compared with the prior art, the present application has the following advantages: 1, The scheme first extracts the wind and light data and load data in the micro-grid area for one year, and the information is summarized; according to the provided parameters, the water, wind and light integrated micro-grid capacity configuration optimization model is established, the model is solved by using the quadratic interpolation learning differential evolution algorithm, and the related reliability indexes are calculated; finally, under the premise of meeting the annual wind and light abandonment rate and the shortage rate, the configuration scheme with the minimum equal-year comprehensive cost is selected as the optimal water, wind and light integrated micro-grid capacity configuration scheme. For the nonlinear and discrete optimization problem of water, wind and light integrated micro-grid capacity configuration optimization, the present application provides a water, wind and light integrated micro-grid capacity configuration optimization method based on quadratic interpolation differential evolution, taking the total capacity of wind turbine, the total capacity of photovoltaic unit and the total capacity of pumped storage power station as the decision variable, taking the system power supply availability and related operation requirements as the constraint condition, and establishing the minimum equal-year comprehensive cost as the objective function. The method has high accuracy and stability, and has certain guiding effect on accelerating the rapid construction of micro-grid in actual engineering practice. The present application comprehensively considers the annual wind and light abandonment rate and the shortage rate and economy, and only needs the wind and light data and load data of the micro-grid construction environment, which is low in cost and easy to realize in practical application.

[0015] 2, The present application takes the annual wind and light abandonment rate and the shortage rate as the constraint condition, considers the equipment full life cycle cost, establishes the minimum equal-year comprehensive cost objective function, more accurately measures the economy of different configuration schemes, so as to select the optimal water, wind and light integrated micro-grid capacity configuration scheme, realizes the water, wind and light integrated micro-grid capacity configuration optimization considering the annual wind and light abandonment rate and the shortage rate and economy, and reasonably and effectively plays the important role of micro-grid. At the same time, the method of the present application is simple in calculation process, convenient to use, and has high engineering practical value. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The method flowchart provided by an embodiment of the present application is shown in the figure; Figure 2 The annual system load demand diagram provided by an embodiment of the present application is shown in the figure; Figure 3 The annual average wind speed diagram provided by an embodiment of the present application is shown in the figure; Figure 4 The annual light intensity diagram provided by an embodiment of the present application is shown in the figure; Figure 5 The power allocation information result style diagram provided by an embodiment of the present application is shown in the figure; Figure 6 The power allocation information result of case 1 provided by an embodiment of the present application is shown in the figure; Figure 1 Figure 7 The power allocation information result of case 1 provided by an embodiment of the present application is shown in the figure;​Figure 2 Figure 8 The 1-year power allocation information result provided for an embodiment of the present application Figure 3 Figure 9 The 1-year power allocation information result provided for an embodiment of the present application Figure 4 Figure 10 The 2-year power allocation information result provided for an embodiment of the present application Figure 1 Figure 11 The 2-year power allocation information result provided for an embodiment of the present application Figure 2 Figure 12 The 2-year power allocation information result provided for an embodiment of the present application Figure 3 Figure 13 The 2-year power allocation information result provided for an embodiment of the present application Figure 4 Figure 14 The 3-year power allocation information result provided for an embodiment of the present application Figure 1 Figure 15 The 3-year power allocation information result provided for an embodiment of the present application Figure 2 Figure 16 The 3-year power allocation information result provided for an embodiment of the present application Figure 3 Figure 17 The 3-year power allocation information result provided for an embodiment of the present application Figure 4 Figure 18 The case 1 iteration convergence effect comparison chart of the quadratic interpolation learning differential evolution algorithm and the original differential evolution algorithm provided for an embodiment of the present application Figure 19 The case 2 iteration convergence effect comparison chart of the quadratic interpolation learning differential evolution algorithm and the original differential evolution algorithm provided for an embodiment of the present application Figure 20 The case 3 iteration convergence effect comparison chart of the quadratic interpolation learning differential evolution algorithm and the original differential evolution algorithm provided for an embodiment of the present application. DETAILED DESCRIPTION

[0017] ​​​​​​​​​​​In order to make the objects, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the drawings and examples. It should be understood that the specific implementation described herein is only one of the best embodiments of the present application, which is only used to explain the present application and does not limit the protection scope of the present application. All other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0018] Embodiment 1: as shown in the figure, a water, wind and light micro-grid capacity configuration optimization method based on QILDE, comprising: Figure 1 - Figure 20 The water, wind and light micro-grid capacity configuration optimization method based on the quadratic interpolation learning differential evolution of the present application comprises the following steps: 1: as shown in the figure, the water, wind and light micro-grid capacity configuration optimization method based on the quadratic interpolation learning differential evolution of the present application comprises the following steps: Figure 1 S1, data acquisition and processing stage. Extract the wind speed and light intensity data of the micro-grid region in the micro-grid region throughout the year. Calculate the wind power output according to the cut-in wind speed, rated wind speed and cut-out wind speed of the wind power, and calculate the photovoltaic power generation capacity in combination with the photovoltaic conversion efficiency. Obtain the annual power load demand data of the micro-grid, and record the load curve with an hourly time step. S2, optimization model construction stage.

[0019] Based on the complete data obtained in step S1, the water, wind and light integrated micro-grid capacity configuration optimization model is established. The core of the model is to build a mathematical optimization problem with the minimum equal annual comprehensive cost as the objective function.

[0020] The objective function specifically includes the following cost items: initial investment cost of various types of power generation equipment (including unit power cost and unit capacity cost), operation and maintenance cost (including fixed maintenance fee and variable maintenance fee), and penalty cost (including wind and light abandonment penalty and power supply shortage penalty).

[0021] In step S2, the minimum equal annual comprehensive cost is the objective function, and its characteristics are described as follows:

[0022] ; ; ; ; ; economic cost, penalty cost of renewable energy wind and light abandonment rate, section margin utilization rate, and respectively, the first ​Initial investment cost per unit power and capacity of the nth generation unit; and respectively the rated power and capacity of the nth generation unit; respectively the rated power and capacity of the nth generation unit; is the discount rate; is the service life, 25 for wind turbines and photovoltaic cells, 10 for batteries, and 30 for pumped storage power stations and thermal power plants; and respectively the rated power and capacity of the nth generation unit; respectively the rated power and capacity of the nth generation unit; is the output of the nth generation unit in the tth period; is the output of the nth generation unit in the tth period; ; wherein, is the period of curtailed wind, light or water; is the penalty cost; is the curtailed wind, light or water power in the tth period; ; wherein, is the period of cross-section existence margin; is the penalty cost; is the cross-section existence margin in the tth period.

[0023] In terms of constraint setting, three types of constraints are mainly considered: system power supply reliability constraint, to meet the given power supply availability index; renewable energy consumption constraint, to limit the annual curtailed wind and light rate to not more than a set threshold; power supply and demand balance constraint, to ensure that the load demand in each period is met, and the shortage rate is controlled within the allowed range. In addition, physical operation constraints of various types of equipment are also included, such as the maximum installation capacity limit of wind power and photovoltaic, the storage capacity limit and charge-discharge power limit of pumped storage power station.

[0024] Specifically, each type of constraint is specifically expressed as follows: (1) Wind power maximum installation capacity constraint ; wherein, is the local maximum available wind power resource.

[0025] (2) Photovoltaic maximum installation capacity constraint ; wherein, is the local maximum available wind power resource.

[0026] (3) Battery maximum installation capacity constraint ; wherein, is the installed capacity of the battery, is the maximum installed capacity of the battery.

[0027] (4) Battery storage power constraint ; wherein, , is the minimum storage power of the battery, and is the maximum storage power of the battery, is the current storage capacity of the battery.

[0028] (5) Maximum charging and discharging continuous power constraint of the battery ; ; wherein, , is the current charging power and discharging power, , is the maximum charging and discharging continuous power.

[0029] (6) Maximum reservoir capacity constraint of the pumped storage power station ; wherein, is the reservoir capacity of the pumped storage power station, is the maximum reservoir capacity of the pumped storage power station (7) Pumped storage power station capacity constraint ; wherein, is the current capacity of the pumped storage power station, , is the maximum capacity and minimum capacity of the pumped storage power station.

[0030] (8) Maximum pumping and power generation continuous power constraint of the pumped storage power station ; ; wherein, , is the pumping and power generation continuous power of the pumped storage power station, , is the maximum pumping and maximum power generation continuous power of the pumped storage power station.

[0031] (9) Maximum transmission capacity constraint of the section ; wherein, is the maximum transmission capacity of the section, which is 10000 MW; is the load of the time period; and respectively the charging, discharging state of the battery, satisfying the following constraints: ; and respectively the generation, pumping state of the pumped storage power station, satisfying the following constraints: .

[0032] S3, model solving and optimization phase.

[0033] A quadratic interpolation learning differential evolution algorithm (QILDE) is used to solve the optimization model. The algorithm innovatively introduces a quadratic interpolation learning mechanism on the basis of the standard differential evolution algorithm.

[0034] The specific implementation process is as follows: first, initialize the population, and each individual represents a candidate scheme containing wind power capacity, photovoltaic capacity and pumped storage capacity; in each iteration, perform mutation operation on the current population, and use the strategy of "current optimal solution + difference vector" to generate trial individuals; then construct a parabolic model near the current optimal solution based on the quadratic interpolation method to quickly locate the local optimal region; finally, retain high-quality individuals through selection operation. During the algorithm running process, it will be checked whether each candidate scheme meets the constraints set in step S2, especially the strict monitoring of the wind and light abandonment rate and the shortage rate. After iteration, the configuration scheme with the minimum equal-year comprehensive cost is selected from the converged population as the final optimization result, which gives the optimal capacity ratio of wind power, photovoltaic and pumped storage power station, and ensures the minimization of the system life cycle cost under the premise of meeting all operating constraints.

[0035] The quadratic interpolation learning differential evolution algorithm described in step S3 mainly introduces a simple and effective direct approximation strategy of quadratic interpolation learning (QIL) on the basis of the existing differential evolution algorithm, which is used to quickly find the optimal solution of the parabola defined by three different points.

[0036] At the same time, exploration and exploitation are also important for metaheuristic algorithms, and proper trade-off between the two is essential. DE performs well in exploration but poorly in exploitation. It is noted that quadratic interpolation (QI) shows a better exploitation ability, which can quickly find the optimal solution of the parabola. Therefore, the QILDE (quadratic interpolation learning differential evolution) algorithm combining DE and QI will be used for solving.

[0037] Specifically, in the DE algorithm, the first The th iteration Each individual can be represented as ,in , , For population size, The maximum number of iterations, To solve for the dimension of variables, the basic idea of ​​the DE algorithm is to first generate experimental individuals through mutation and crossover operators, and then generate a new generation of superior individuals from the parent individuals and experimental individuals through a greedy selection operator, eliminating inferior individuals. This allows the population to have the characteristics of remembering the optimal solution of individuals and sharing information within the population, thereby ensuring that the algorithm approaches the optimal solution.

[0038] (1) Mutation operator The mutation operator is the core of DE, and its purpose is to provide each target individual with... Generate a mutation vector The mutation strategy is as follows: ; In the formula, , , Not equal to And all distinct integers; This is a scaling factor used to control the scaling degree of the difference vector.

[0039] (2) Crossover operator Generate mutation vectors using mutation operators. Then, the mutation is used to target individuals. and mutation vector Crossover is performed to generate experimental individuals. .

[0040] ; In the formula, The crossover probability; A random number uniformly distributed between [0,1]. It is a random integer between [1, D], used to ensure that at least one dimension of the experimental individual comes from the mutation vector.

[0041] (3) Selection Operator Test individuals to be generated Finally, the target individual is compared using a "greedy" selection operator. and test individuals This ensures that individuals with better fitness enter the next generation of the population, thereby ensuring the consistency of the evolutionary process.

[0042] ; where, denotes the objective function value.

[0043] QIL is a simple and effective direct approximation method for quickly finding the optimal solution of a parabola defined by three different points.

[0044] 1) Generally, a parabola is formulated as: ; where , and are coefficients. Assume that there are three points on the curve of ( , ), ( , ) and ( , ): The values of , and can be obtained by solving the above equations: ; ; where d = 1, 2, 3,..., D.

[0045] After solving the minimum value, the improvement strategy of the mutation strategy is: ; The mutation scheme of DE is mainly responsible for exploring new search space. QI is mainly responsible for exploring the local solution space around the best individual. Therefore, this can achieve a good balance between development and exploration. Compared with DE algorithm, QILDE shows extremely competitive performance in terms of solution quality, extraction accuracy, robustness and stability. The specific steps are as follows: 1) Input basic data. Including wind and light data, load data, wind and light conversion efficiency, construction and maintenance cost parameters, etc.

[0046] 2) Generate initial population. Take wind turbine capacity, photovoltaic capacity, and pumped storage power station capacity as 1 × 3-dimensional decision variables. Initialize the individuals in each population, randomly generate a number within the value range to form an initial individual for each individual in the population, and a certain number of initial individuals constitute the initial population. Each individual in the population represents a capacity configuration scheme, including wind turbine capacity, photovoltaic capacity and pumped storage power station capacity.

[0047] 3) Calculate the fitness function. According to the equal annual value comprehensive cost minimum objective function fitness, realize the reasonable distribution of wind and photovoltaic resources, pumped storage power station charging and discharging and annual load demand within one year, and obtain the annual comprehensive cost. According to the evolution algebra, the mutation operator and the crossover operator are calculated, new individuals are generated by using quadratic interpolation, new individuals of the next generation are selected according to the smaller fitness in the crossover, and the optimal individual is reserved.

[0048] 4) Determine whether the population meets the above constraints such as annual abandoned wind and light rate, shortage amount, etc. If it meets, stop iteration after 1000 iterations, if it does not meet, return to step 2) and start again.

[0049] The pseudo code of the QILDE algorithm can be as follows: The scheme first extracts the wind and light data and load data in the microgrid area for one year, and summarizes the information; according to the provided parameters, an integrated water-wind-light microgrid capacity configuration optimization model is established, the model is solved by using the quadratic interpolation learning differential evolution algorithm and the related reliability indexes are calculated; finally, under the condition of meeting the annual abandoned wind and light rate and the shortage rate, the configuration scheme with the minimum equal annual value comprehensive cost is selected as the optimal water-wind-light integrated microgrid capacity configuration scheme. For the nonlinear and discrete optimization problem of the water-wind-light integrated microgrid capacity configuration optimization problem, the present application provides a water-wind-light integrated microgrid capacity configuration optimization method based on quadratic interpolation differential evolution, takes the total capacity of wind turbine, the total capacity of photovoltaic unit and the total capacity of pumped storage power station as the decision variable, takes the system power supply availability and related operation requirements as the constraint condition, and establishes the minimum equal annual value comprehensive cost as the objective function. The method has high accuracy and stability, and has certain guiding effect on speeding up the rapid construction of microgrid in actual engineering practice. The present application comprehensively considers the annual abandoned wind and light rate and the shortage rate and the economy, only needs the wind and light data and the load data of the microgrid construction environment, and has low cost and is easy to realize in actual application.

[0050] Example 2: For the embodiment of the present application, the annual system load demand diagram is as shown in Figure 2 The annual average wind speed diagram is as shown in Figure 3 When the wind speed is converted into wind power output, the cut-in wind speed is taken as 3m / s, the rated wind speed is taken as 11m / s, and the cut-out wind speed is taken as 25m / s; the annual light intensity diagram is as shown in Figure 4As shown, the photoelectric conversion efficiency is 85%. The wind power investment cost is 7000 yuan / kW, the wind power operation and maintenance cost is 50 yuan / MWh, the wind turbine operation life is 20 years; the photovoltaic investment cost is 3600 yuan / kW, the photovoltaic operation and maintenance cost is 60 yuan / MWh, the photovoltaic component operation life is 25 years; the pumped storage power station unit investment cost is 1500 yuan / kW, the pumped storage power station unit maintenance cost is 20 yuan / kWh / year, the pumped storage power station unit operation life is not less than 40 years, taking 40 years, the pumped storage power station unit power generation efficiency and pumping efficiency is 80%, the continuous full-load hours is 24, and the discount rate is taken as 6.5%.

[0051] The related parameters of the original differential evolution algorithm are set as follows: the population size is set to 50, the iteration number is set to 2000 generations, the scaling factor F=0.1+0.9 rand() (in which rand() represents a random number from 0 to 1), and the crossover probability factor CR is a random number from 0 to 1; the parameter calculation process of the quadratic interpolation learning differential evolution algorithm is consistent with the foregoing description.

[0052] Suppose that the annual abandoned wind and light rate and the shortage rate change: Case 1: The annual abandoned wind and light rate is 10%, and the annual shortage rate is 10%; Case 2: The annual abandoned wind and light rate is 20%, and the annual shortage rate is 10%; Case 3: The annual abandoned wind and light rate is 10%, and the annual shortage rate is 0%.

[0053] Compared with the original differential evolution algorithm, the optimal planning scheme result obtained by the water-wind-light integrated microgrid capacity configuration optimization method based on the quadratic interpolation learning differential evolution of the application is shown in Table 1, the annual power allocation information result of case 1 is shown in Figures 6-9 The annual power allocation information result of case 2 is shown in Figures 10-13 The annual power allocation information result of case 3 is shown in Figures 14-17

[0054] Table 1 Economic benefits and capacity configuration comparison of QILDE and DE algorithm in different cases Note: The bold indicates that a better economic benefit is obtained As can be seen from the running results in different cases, when the annual abandoned wind and light rate is increased, the total capacity of the pumped storage power station is decreased, the photovoltaic unit capacity and the pumped storage power station capacity are increased under the condition that the annual shortage rate is 0 and the economic efficiency is optimal, and the final cost is also higher than that in the first two cases.

[0055] Figures 18-20 For the iteration convergence effect comparison of the quadratic interpolation learning differential evolution algorithm and the original differential evolution algorithm in the three cases, one hundred data points are uniformly taken as references.​

[0056] From the convergence curve, it can be seen that the convergence speed of the quadratic interpolation learning differential evolution algorithm proposed in the application is faster than that of the original differential evolution algorithm, and the original differential evolution algorithm is more difficult to eliminate the penalty cost and meet the constraint. At the same time, it can be seen from the results in Table 1 that the quadratic interpolation learning differential evolution algorithm improves the success rate of the capacity configuration scheme, and for the capacity configuration optimization problem of 'water, wind and light', the quadratic interpolation learning differential evolution algorithm has a faster convergence speed in the solving process and can find a better solution set.

[0057] In summary, the quadratic interpolation learning differential evolution algorithm is used for the capacity configuration optimization of the 'water, wind and light' integrated microgrid, which can balance the annual wind and light curtailment rate, annual shortage rate and economy, so as to obtain the optimal distribution terminal layout planning scheme, and has a certain guiding role for speeding up the construction of microgrid in actual engineering practice.

[0058] The above specific embodiments are the preferred embodiments of the application, and the specific implementation range of the application is not limited by this. The scope of the application includes but is not limited to the above specific embodiments, and equivalent changes made according to the shape, structure and method of the application are within the protection scope of the application.

Claims

1. A method for optimizing the capacity configuration of hydro-wind-solar microgrids based on QILDE, characterized in that, Includes the following steps: S1. Obtain wind and solar data and load data within a specified time period in the microgrid area; S2. Based on the wind and solar data and load data, with the objective function of minimizing the comprehensive cost per year, and considering system-related constraints, construct a capacity configuration optimization model for hydro-wind-solar microgrids. S3. Solve the capacity configuration optimization model of the hydro-wind-solar microgrid based on the quadratic interpolation learning differential evolution algorithm to obtain the configuration scheme and select the optimal configuration scheme.

2. The method for optimizing the capacity configuration of a hydro-wind-solar microgrid based on QILDE according to claim 1, characterized in that, Extract wind speed and solar intensity data for the entire year within the microgrid area. Calculate wind power output based on the cut-in wind speed, rated wind speed, and cut-out wind speed. Combine this with photovoltaic conversion efficiency to calculate photovoltaic power generation. Obtain the annual power load demand data for the microgrid and record the load curve with an hourly time step.

3. The method for optimizing the capacity configuration of a hydro-wind-solar microgrid based on QILDE according to claim 1, characterized in that, The objective function is specifically expressed as follows: ； ； ； ； In the formula, Economic costs Penalty costs for the curtailment rate of renewable energy sources such as wind and solar power. This refers to the cross-sectional margin utilization rate.

4. The capacity configuration optimization method for a hydro-wind-solar microgrid based on QILDE according to claim 3, characterized in that: ； ； In the formula, and The first Initial investment cost per unit power and capacity of the power generation unit; and The first Rated power and capacity of the power generation unit; The discount rate; This refers to the service life; and The first The operation and maintenance cost per unit power and capacity of the power generation unit; For the first The output of the power generation unit in the first time period.

5. The method for optimizing the capacity configuration of a hydro-wind-solar microgrid based on QILDE according to claim 1, characterized in that, The system-related constraints include system power supply reliability constraints, which meet the given power supply availability index; renewable energy consumption constraints, which limit the annual wind and solar curtailment rate to no more than a set threshold; power supply and demand balance constraints, which ensure that load demand is met in each period and the deficit rate is controlled within the allowable range; and physical operation constraints of various equipment, specifically including the maximum installation capacity limit for wind power and photovoltaic power, the reservoir capacity limit and charging and discharging power limit of pumped storage power stations.

6. The method for optimizing the capacity configuration of a hydro-wind-solar microgrid based on QILDE according to claim 1, characterized in that, The quadratic interpolation learning differential evolution algorithm introduces a quadratic interpolation learning mechanism on the basis of the standard differential evolution algorithm.

7. The method for optimizing the capacity configuration of a hydro-wind-solar microgrid based on Qildex according to claim 6, characterized in that, The optimization steps of the quadratic interpolation learning differential evolution algorithm typically combine the basic framework of the differential evolution algorithm with the local approximation strategy of quadratic interpolation, including the following optimization steps: S31. Initialize the population. Randomly generate the initial population and set constraints and algorithm parameters, including population size, scaling factor, crossover probability, and maximum number of iterations. Each individual represents a candidate scheme that includes wind power capacity, photovoltaic capacity, and pumped storage capacity. S32. Calculate the fitness function value for each individual in the initial population; S33. Iterative optimization involves performing mutation, crossover, and quadratic interpolation learning operations. In each iteration, mutation is performed on the current population, and experimental individuals are generated using a strategy of "current optimal solution + difference vector". S34. Construct a parabolic model near the current optimal solution based on the quadratic interpolation method to locate the local optimum region; construct a quadratic interpolation model using the fitness values ​​of the current individual, the mutated individual, and the experimental individual; generate better candidate individuals by solving the optimal solution of the quadratic interpolation model. S35. Select high-quality individuals through selection operations; if the maximum number of iterations is reached or the optimal solution of the population meets the preset precision, terminate the algorithm and output the individual with the best fitness value as the optimal solution.

8. The method for optimizing the capacity configuration of a hydro-wind-solar microgrid based on QILDE according to claim 7, characterized in that, During the algorithm's operation, it is necessary to verify in real time whether each candidate solution meets the set constraints.

9. The method for optimizing the capacity configuration of a hydro-wind-solar microgrid based on QILDE according to claim 1, characterized in that, The optimal configuration scheme is the scheme with the minimum comprehensive cost of equal annual values ​​selected from the converged population after iteration as the final optimization result. This scheme gives the best capacity ratio of wind power, photovoltaic and pumped storage power stations, ensuring that the system's total life cycle cost is minimized while meeting all operational constraints.

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