A scheduling method and system for multi-energy complementarity optimization based on improved enterprise development optimization algorithm: water, wind, and solar energy.

Abstract

This invention belongs to the technical field of energy and hydropower systems, and discloses a method and system for optimal scheduling of hydropower, wind power, and solar power complementarity based on an enterprise development optimization algorithm. The method includes the following steps: acquiring historical data and characteristic parameters of hydropower stations in the power transmission system, load demand of the power transmission grid, output data of wind power and solar power, and their various possibilities and corresponding probabilities; taking the minimum peak-to-valley difference of the remaining load of the power transmission system as the optimization objective, the water level of the hydropower station at each time period as the decision variable, setting constraints based on the characteristic parameters of the hydropower station, and constructing an optimal scheduling model; using an improved enterprise development optimization algorithm to solve the optimization model, obtaining the optimal water level of the hydropower station at each time period, and using this optimal water level to calculate the optimal output of the hydropower station at each time period, thereby realizing the scheduling of the power transmission system. Through this invention, a more efficient and robust intelligent optimization algorithm is constructed in the field of optimal scheduling of hydropower, wind power, and solar power complementarity.

Description

Technical Field

[0001] This invention belongs to the technical field of energy and hydropower systems, and more specifically, relates to a scheduling method and system for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm. Background Technology

[0002] The optimal scheduling of hydropower-wind-solar hybrid systems is a high-dimensional, non-convex, and nonlinear optimization problem. Compared to the scheduling rule optimization problems of single-energy hydropower systems, the model is more complex, thus requiring the development of efficient solution algorithms. Traditional optimization algorithms often involve large computational costs and are prone to the "curse of dimensionality" when solving high-dimensional problems. Intelligent optimization algorithms, such as genetic algorithms, ant colony optimization, particle swarm optimization, simulated annealing, and neural network methods, offer new solutions to this problem. Intelligent algorithms have developed rapidly in the past two decades due to their strong adaptability, robustness, and ease of parallel computing.

[0003] Compared to traditional optimization algorithms, they do not require an exact model of the problem itself, nor do they require the function to be continuous or differentiable. They are suitable for solving problems with very complex objective functions or constraints, but they still have certain drawbacks. Genetic algorithms are simple in principle and easy to operate, but they are prone to getting trapped in local optima. Particle swarm optimization can be processed in parallel and has the advantages of fast computation speed and good convergence performance when solving high-dimensional optimization problems, but it also has the disadvantages of premature convergence and getting trapped in local optima. Simulated annealing is not affected by initial values ​​and is an optimization algorithm for finding global optima, but the complexity of this algorithm is positively correlated with the size of the system being solved, and the computation speed is slow when optimizing large-scale systems. Summary of the Invention

[0004] To address the above-mentioned deficiencies or improvement needs of existing technologies, this invention provides a scheduling method and system for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm, which solves the problems of getting trapped in local optima and being prone to premature convergence in existing optimization scheduling.

[0005] To achieve the above objectives, according to one aspect of the present invention, a scheduling method for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm is provided, the method comprising the following steps:

[0006] Acquire historical data and characteristic parameters of hydropower stations in the power transmission system, load demand of the power transmission grid, and output data of wind power and solar power respectively;

[0007] The optimization objective is to minimize the peak-to-valley difference of the remaining load of the power transmission system, and the water level of the hydropower station at each time period is used as the decision variable. Based on the historical data and characteristic parameters of the hydropower station, constraints are set to construct an optimization scheduling model.

[0008] The optimization model is solved using an enterprise development optimization algorithm to obtain the optimal water level change process of the hydropower station. The optimal output of the hydropower station at each time period is calculated using this optimal water level change process, thereby realizing the scheduling of the power transmission system.

[0009] More preferably, the optimization model is as follows:

[0010]

[0011] Where Pr(s) is the probability of scenario s; ΔLR(s) is the minimum residual load peak-to-valley difference calculated in scenario s; C t For the power grid load, P h P w P s These represent the output values ​​of hydropower, wind power, and solar power, respectively.

[0012] More preferably, the constraints are as follows:

[0013]

[0014] V t+1 =V t +(R t -Q t -S t )Δt

[0015]

[0016] Among them, Q t min , These represent the minimum and maximum outflow limits of the hydropower station during time period t. These represent the power output of the hydropower station, wind power output, and solar power output during time period t. These represent the minimum and maximum output limits of the hydropower station in time period t, respectively. These represent the minimum and maximum power output limits of the wind farm in time period t, respectively. These represent the minimum and maximum output limits of the photoelectric field at time t, respectively. These represent the minimum and maximum water level limits for the hydropower station during time period t, respectively. t V t+1 These represent the initial and final reservoir capacities of the hydropower station in time period t; R t Q t S t These correspond to the inflow, power generation, and discharge of the hydropower station at time t, respectively; Δt is the duration of time t; ΔZ and ΔQ are the maximum variation constraints of water level and flow rate in adjacent time periods, respectively; Z t Z t+1are the initial and final water levels of the hydropower station at time period t; Q t and Q t+1 are the initial and final discharge flows out of the hydropower station at time period t, respectively.

[0017] Further preferably, the steps of solving the optimization model by using the enterprise development optimization algorithm are as follows:

[0018] S1 Generate an initial population containing N individuals, each individual representing a water level change process of a hydropower station, calculate the fitness function value of the current individual, determine the global optimal solution of the initial population and the optimal water level change process corresponding to the global optimal solution;

[0019] S2 Update each individual in the current population to form a new population, compare the fitness function values of each individual before and after the update. If the fitness function value of the updated individual is better than that before the update, then retain the update of this individual; otherwise, this individual is not updated; calculate the global optimal solution of the new population and compare it with the global optimal solution of the population before the update. If the global optimal solution of the new population is better than the global optimal solution of the population before the update, then retain the global optimal solution of the new population; otherwise, do not update the global optimal solution;

[0020] S3 If rand(0, 1) < p1, execute the task update mechanism, obtain the individual with the worst fitness in the current population, regenerate the water level change of this individual with the worst fitness, and calculate the fitness value of the regenerated individual;

[0021] If rand(0, 1) ≥ p1, calculate When c(t) = 1, execute the structure update mechanism; when c(t) = 2, execute the technology update mechanism; when c(t) = 3, execute the personnel update mechanism;

[0022] S4 Repeat steps S1 - S3 until the maximum number of iterations is reached, output the global optimal solution and the corresponding water level change process of the hydropower station, where rand(0, 1) is a random number uniformly distributed on the interval [0, 1], p1 is a pre-set probability, and x iter is the number of iterations.

[0023] Further preferably, in step S1, the generation of the initial population containing N individuals is initialized and generated through Tent mapping.

[0024] Further preferably, in step S2, the formation of the new population is obtained by guiding and optimizing the current optimal individual by using Gaussian random walk.

[0025] Further preferably, in step S3, the regeneration of the water level change is regenerated by using Tent mapping.

[0026] More preferably, the historical data and characteristic parameters of the hydropower station include the normal water level, the reservoir's water level-capacity curve, the discharge capacity curve, and the inflow during the scheduling period.

[0027] According to another aspect of the present invention, a scheduling system for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm is provided. The system includes an actuator for executing the aforementioned scheduling method for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm.

[0028] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described scheduling method for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm.

[0029] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0030] 1. This invention employs an enterprise development optimization algorithm to optimize scheduling problems in the field of multi-energy complementary scheduling of water, wind and solar power. This algorithm optimizes and solves problems by simulating the enterprise development process, and has the advantages of strong optimization ability and simple process. The optimization process covers tasks, structure, technology and personnel, and determines each step by switching activities, and gradually updates the searched solutions, effectively solving the problems of premature convergence and getting trapped in local optima in existing algorithms.

[0031] 2. To further improve the performance of the enterprise development optimization algorithm in the optimal scheduling of water, wind and solar multi-energy complementarity, this invention combines the enterprise development optimization algorithm with tent mapping and Gaussian walk to improve the algorithm. The aim is to enhance the algorithm's exploration and development capabilities, and thus build a more efficient and robust intelligent optimization algorithm in the field of optimal scheduling of water, wind and solar multi-energy complementarity.

[0032] 3. This invention, considering the uncertainty of wind and solar power output, takes minimizing the peak-to-valley difference of the remaining load in the power transmission system as the objective function. The core objective of hydropower-wind-solar complementarity is to fully utilize the flexible regulation capabilities and large storage capacity of hydropower station reservoirs to perform "primary power compensation" regulation for wind and solar power, thus mitigating the inherent randomness, volatility, and intermittency of wind and solar power output. The process of hydropower stations mitigating wind and solar power output fluctuations is essentially a compensation for new energy sources such as wind and solar. Its peak-shaving optimization scheduling needs to ensure that the output fluctuations after hydropower and wind-solar complementarity are as consistent as possible with the grid load fluctuations, in order to minimize the fluctuations of the remaining load. Attached Figure Description

[0033] Figure 1This is a flowchart of a scheduling method for multi-energy complementarity optimization based on improved enterprise development optimization algorithm, constructed according to a preferred embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the structure of a water-wind-solar hybrid system constructed according to a preferred embodiment of the present invention;

[0035] Figure 3 The results are the operation results of a typical solar-water-wind-solar hybrid system constructed according to the preferred embodiment of the present invention, wherein (a) is the solution result of the typical solar-water-wind-solar hybrid system based on the standard enterprise development optimization algorithm, and (b) is the solution result of the typical solar-water-wind-solar hybrid system based on the improved enterprise development optimization algorithm.

[0036] Figure 4 The results are the operation results of a typical solar-water-wind-solar hybrid system constructed according to the preferred embodiment of the present invention, wherein (a) is the solution result of the typical solar-water-wind-solar hybrid system based on the standard enterprise development optimization algorithm, and (b) is the solution result of the typical solar-water-wind-solar hybrid system based on the improved enterprise development optimization algorithm.

[0037] Figure 5 The results are the operation results of a typical solar-water-wind-solar hybrid system constructed according to the preferred embodiment of the present invention, wherein (a) is the solution result of the typical solar-water-wind-solar hybrid system based on the standard enterprise development optimization algorithm, and (b) is the solution result of the typical solar-water-wind-solar hybrid system based on the improved enterprise development optimization algorithm.

[0038] Figure 6 The results are the operation results of a typical solar-water-wind-solar hybrid system constructed according to the preferred embodiment of the present invention, wherein (a) is the solution result of the typical solar-water-wind-solar hybrid system based on the standard enterprise development optimization algorithm, and (b) is the solution result of the typical solar-water-wind-solar hybrid system based on the improved enterprise development optimization algorithm. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0040] like Figure 1 and 2 As shown, a scheduling method for multi-energy complementarity optimization based on improved enterprise development optimization algorithm includes the following steps:

[0041] Step 1: Read wind and solar power output data, historical data and characteristic parameters of hydropower stations, and load demand of the power grid.

[0042] The wind and solar power output data includes wind power output scenarios and solar power output scenarios. Within a scheduling period, multiple time periods are divided. The output of wind power and solar power in each time period corresponds to multiple possibilities. In this embodiment, a scheduling period is one day, and a 24-hour day is divided into 12 time periods. The output of wind power and solar power in each time period has 10 possibilities, and each possibility corresponds to an occurrence probability.

[0043] Historical data and characteristic parameters of hydropower stations include: normal water level, reservoir water level-capacity curve, discharge capacity curve, and inflow during the scheduling period.

[0044] Step 2: Construct an optimized scheduling model. The objective function is set as minimizing the peak-to-valley difference of the remaining load of the system formed by hydropower stations, wind power, solar power, and the power transmission grid. The fitness is the value of the objective function, i.e., the peak-to-valley difference of the remaining load after peak-shaving optimization of the hydropower-wind-solar hybrid system. The decision variable is the water level of the hydropower station at each time period. Constraints are constructed using historical basic data of the hydropower station.

[0045] (1) Optimize the scheduling model

[0046] Considering the uncertainty of wind and solar power output, the objective function is set as minimizing the peak-to-valley difference of the system's remaining load, as shown in the formula:

[0047]

[0048] Where Pr(s) is the probability of scenario s; ΔLR(s) is the minimum residual load peak-to-valley difference calculated in scenario s; C t For the power grid load, P h P w P s These represent the output values ​​of hydropower, wind power, and solar power, respectively. The fitness of the algorithm is the objective function value, which is the peak-valley difference of the remaining load after peak-shaving optimization of the hydro-wind-solar hybrid system. The decision variable is the water level at each time period, which is the value of each individual in the population in each dimension of the algorithm.

[0049] (2) The constraints include:

[0050] The flow constraint is: These represent the minimum and maximum outflow limits for the hydropower station during time period t.

[0051] The output constraint is: These represent the power output of the hydropower station, wind power output, and solar power output during time period t. These represent the minimum and maximum output limits of the hydropower station in time period t, respectively. These represent the minimum and maximum power output limits of the wind farm in time period t, respectively. These represent the minimum and maximum output limits of the photoelectric field at time t, respectively.

[0052] Water level constraints are: These represent the minimum and maximum water level limits of the hydropower station's reservoir during time period t.

[0053] The water balance constraint is: V t+1 =V t +(R t -Q t -S t )Δt, R t Q t S t These correspond to the inflow, power generation, and discharge of the hydropower station in time period t, respectively; Δt is the duration of time period t.

[0054] Water level / flow rate variation constraints: ΔZ and ΔQ are the constraints on the maximum variation of water level and flow rate in adjacent time periods, respectively; Z t Z t+1 These represent the initial and final water levels of the hydropower station at time t; Q t Q t+1 These represent the initial and final outflow rates of the hydropower station during time period t.

[0055] Step 3: Solve the optimal scheduling model using the enterprise development optimization algorithm.

[0056] A. Enterprise Development Optimization Algorithm

[0057] Enterprise Development Optimization (ED) is a novel metaheuristic optimization algorithm proposed by Dinh-Nhat Truong et al. from the National Taiwan University of Science and Technology, based on the enterprise development process, and published in Engineering Structures in August 2024. Every company must strive for continuous development, and this development relies on experimentation and resources. According to Leavitt's research on industrial organizations over more than 20 years, complex organizational systems depend on the interaction of four variables: task, structure, technology, and personnel, which can be considered periodic.

[0058] Tasks refer to the organization's primary objectives, encompassing many different but operationally meaningful sub-tasks that may exist within a complex organization. Structure refers to the systems used for communication, authority (or other roles), and workflows. Technology refers to the tools, equipment, and methodologies used to accomplish tasks and facilitate operations within the organization. People primarily refer to individuals. Because these four variables are interdependent, a change in one variable often leads to compensatory (or counteracting) changes in another. Different solutions to any one variable will produce different organizational performance values. Therefore, when organizational performance is quantitatively assessed and compared, the optimal management solutions can be identified.

[0059] 1) Population initialization

[0060] A population with an individual dimension of D and a population size of N is randomly generated, as shown in the following formula:

[0061] Similar to other metaheuristic optimization algorithms, the ED optimization algorithm randomly generates a uniformly distributed initial population for optimization. The initial population is generated as shown in the following equation:

[0062] x i =l b +rand(0,1)×(u b -l b )

[0063] In the formula, x i The i-th water level value of the hydroelectric power station, u b l b These refer to the upper and lower boundaries of the water level during the optimization process, respectively, and rand(0,1) is a random number that follows a uniform distribution on the interval [0,1].

[0064] 2) Update mechanism

[0065] a task update

[0066] During task management, the individual with the worst fitness is subjected to a process of regenerating the water level change process:

[0067] x wrost (t)=l b +rand(0,1)×(u b -l b )

[0068] In the formula, x wrost It is the worst individual solution in the search space.

[0069] b. Structural Update

[0070] Restricting the structure to a workflow, the new structure will be influenced by other workflow structures and the current optimal workflow. The new structure is simulated using the following formula to generate new individual solutions:

[0071]

[0072] In the formula, For the new structure, that is, the new individual, x best (t-1) is the current optimal solution. To influence the center position of other working structures in the new structure, The solution is randomly selected from the population solution. rand(0,1) is a random number in the interval [-1,1]. m is the number of workflows that affect the new structure. In this paper, m=3 is chosen so as to obtain the optimal result in a shorter computation time.

[0073] c. Technological updates

[0074] Technology plays a crucial role in facilitating organizational change. Often, organizational restructuring isn't a direct response to extraordinary ideas, but rather a consequence of technological advancements making these ideas feasible. This paper uses the following formula to simulate the balance between exploration and utilization in technological updates, aiming to optimize individual processes:

[0075]

[0076] In the formula, This indicates the exploration phase, while This indicates the utilization stage.

[0077] d. Personnel turnover

[0078] Enterprises must cultivate a participatory work culture that fosters individual creativity and teamwork by respecting individuals and stakeholders. Such a work culture influences employees' commitment to and engagement in sustainability. Care is crucial for success. This invention assumes a single-dimensional feature and simulates the personnel update process by randomly selecting and updating features using the following formula to generate new individual scenarios:

[0079]

[0080] In the formula, m represents the number of people affecting the individual; in this paper, m = 3 is chosen to obtain the optimal result in a shorter computation time; d is a random characteristic of the people. The calculation method for this characteristic is as follows:

[0081] d = [rand(0,1) × n d ]

[0082] In the formula, n d The dimension of the solution.

[0083] 3) Update switching mechanism

[0084] In the proposed TGED algorithm, it is assumed that only one update mechanism is considered at a time. Therefore, at any given time t, only one of the four update mechanisms (i.e., task, structure, technology, and personnel) occurs, controlled by an update switching mechanism. When rand(0,1) < p1 (where p1 = 0.1, indicating a 10% probability of executing this step), the task update mechanism is executed. The switching between the structure, technology, and personnel update mechanisms is controlled by a function c(t), as shown in the following equation. When the value of c(t) is 1, 2, and 3, the structure, technology, and personnel update mechanisms are executed respectively.

[0085]

[0086] In the formula, max iter This represents the maximum number of iterations, and t represents the t-th iteration.

[0087] (2) Enterprise Development Optimization Algorithm Improvement Strategies

[0088] The ED algorithm optimizes solutions to problems by simulating the development process of enterprises, and has advantages such as strong optimization capabilities. However, the algorithm also has some shortcomings, such as being prone to getting trapped in local optima and having poor stability. To address these shortcomings, the following improvement strategies are proposed.

[0089] a. Chaotic initialization

[0090] Chaotic systems are used to describe irregularly distributed systems in mathematics. Chaotic search has characteristics such as nonlinearity, ergodicity, randomness, and sensitivity to initial conditions, which are beneficial for improving the search ability of algorithms. There are many types of chaotic mappings, such as the Logistic mapping, Lozi mapping, Chebyshev mapping, Tent mapping, and Cubic mapping. This invention uses the Tent mapping to initialize the population, making the initial solutions as uniformly distributed as possible in the solution space. The Tent mapping first generates a chaotic sequence according to the following formula:

[0091]

[0092] The chaotic sequence is then mapped to the value space of the decision variables in the problem to be optimized. The specific steps are as follows:

[0093] ① According to the above formula, n individuals with dimension m are generated, x1 = rand(0,1), α is taken as 0.49, and the element-valued individuals are X = (x1, x2, ..., x m ), x i ∈(0,1), i=1,2,...,m;

[0094] ②According to the formula

[0095] y i =a i +x i ×(b i -a i )

[0096] The initial population is obtained by mapping the individual elements to the value space of the decision variables. In the formula, a... i b i These are the lower and upper bounds of the value space, respectively, x i Let y be the numerical value of an individual in the i-th dimensional space. i Let be the numerical value of the initial solution individual in the i-th dimensional space.

[0097] b. Gaussian walk

[0098] Gaussian random walk is a typical type of random walk model, possessing strong exploitation capabilities. Therefore, this invention utilizes the characteristics of Gaussian random walk by using the current best individual as a guide to generate a new random population, thus balancing its exploration and exploitation capabilities. The following formula represents the Gaussian random walk strategy based on the current best individual:

[0099] GW1=Gaussian(μ BP ,σ)+(ε×BP-ε′×P i )

[0100] GW2=Gaussian(μ P ,σ)

[0101] In the formula, GW1 and GW2 are two Gaussian random walk methods, and one can be selected based on the specific optimization problem. ε and ε′ are random numbers between [0,1], BP is the best individual in the population, and P... i It is the position of individual i in the population, μ BP μ P σ and σ are Gaussian distribution parameters, where the mean μ BP Equal to |BP|, mean μ P Equal to |P i The standard deviation σ can be calculated using the following formula:

[0102]

[0103] In the algorithm, as the number of iterations increases, its local search capability should be strengthened to find the optimal solution. This reduces the step size of the Gaussian jump, where g is the number of algorithm iterations.

[0104] B. The specific solution process is as follows:

[0105] (1) Set the IMED algorithm parameters: population size is N, individual dimension is D, and the number of algorithm iterations is max. iter The initial population of N individuals is generated through Tent mapping. Each individual represents the water level change of a hydroelectric power station over time. The fitness value of each individual is calculated through the objective function to determine the initial global optimal solution. The current best particle is found by comparing the fitness values ​​of all particles, and the fitness value of that particle is recorded.

[0106] (2) A Gaussian random walk is used to guide the optimization of the current best individual, generating a new random population. For each particle i, the updated fitness value fitness(i) is calculated and compared with the previous fitness value. If the new fitness value fitness(i) is better than the old value Fitness(i), the current position of the particle is updated, moving it towards the new solution. If fitness(i) is better than the current global optimal solution global_best_fitness, the global optimal solution value global_best_fitness = fitness(i) is updated, and the global optimal water level change process global_best_pos is updated to the water level change process corresponding to particle i.

[0107] (3) Determine whether rand(0,1)<p1(p1=0.1) is true. If it is true, execute the task update mechanism, obtain the individual with the worst fitness in the current population, regenerate the water level change process of the individual over time based on the Tent mapping, and calculate the updated fitness value.

[0108] If rand(0,1) < p1 (p1 = 0.1) is not true, calculate the value of c(t). When c(t) = 1, execute the structural update mechanism; when c(t) = 2, execute the technology update mechanism; and when c(t) = 3, execute the personnel update mechanism. The value of c(t) is related to the number of iterations. In the early stages, the number of iterations t is much smaller than the maximum number of iterations max. iter The probability of c(t) being 3 is relatively high. Then, as the number of iterations increases, it gradually transitions to taking 2 and 1, eventually achieving the switching between different update mechanisms.

[0109] Whether it's a task update or an update to the structure, technology, or personnel, after each update, the water level process is corrected to ensure the reasonableness of the results. The fitness values ​​of all individuals are recalculated and updated, and it is determined whether to update the global optimal solution and its corresponding water level process. As the number of iterations increases, the global optimal solution is continuously updated, ensuring that the algorithm converges towards the optimal direction.

[0110] (4) Determine whether the maximum number of iterations has been reached. iterIf the maximum iteration count is not reached, the iteration count is increased, and the process returns to (2) to recalculate. If the maximum iteration count is reached, the maximum iteration count is reached. iter Then, the globally optimal position is determined, and the globally optimal solution is output as the scheme for the optimized scheduling of water-wind-solar hybrid systems.

[0111] The present invention will be further described below with reference to specific embodiments.

[0112] This study focuses on the hydro-wind-solar multi-energy complementary system formed by integrating wind and solar resources into the Baihetan Hydropower Station and its power transmission grid. The installed wind power capacity and photovoltaic installed capacity are 3470MW and 1805MW, respectively. The receiving-end grids of the Baihetan Hydropower Station are the Zhejiang Power Grid and the Jiangsu Power Grid. This invention transmits 50% of the power output of the Baihetan Hydropower Station to Zhejiang Province. Taking four typical days (February 5, 2022 as a typical day of strong wind and weak sunlight during the dry season 1; August 4, 2021 as a typical day of strong sunlight and weak wind during the flood season 2; October 19, 2021 as a typical day of strong wind and weak sunlight during the flood season 3; and May 10, 2022 as a typical day of strong sunlight and weak wind during the dry season 4) as examples, a 24-hour scheduling cycle is used, with each scheduling period lasting 2 hours, divided into 12 scheduling periods. Simulated scheduling analysis is conducted under the peak-shaving scheme of the hydro-wind-solar complementary system to evaluate the regulation performance of the hydro-wind-solar complementary optimal scheduling model based on the improved enterprise development optimization algorithm constructed in this invention on wind and solar power output and grid load. The DE, SFS, ED, and IMED algorithms are used to solve the model, with the following algorithm parameters: population size set to 50, individual dimension set to 13, and number of iterations set to 100. One typical day corresponds to one scheduling period.

[0113] like Figures 3-6 As shown in the bar chart, the combined output of hydropower, wind power, and solar power varies, with different colors corresponding to the output of hydropower, wind power, and solar power, respectively. The solid black line represents the grid load change process, while the dashed red line represents the surplus load change process after peak shaving by the hydropower-wind-solar complementary system. Analysis of the results from four typical days reveals that the combined output of hydropower, wind power, and solar power decreases during off-peak periods. At this time, the hydropower station raises the water level by storing water, reserving sufficient capacity to lower the water level, increase outflow, and reserve output for peak periods, effectively tracking load fluctuations. Further observation of the surplus load process after peak shaving by the hydropower-wind-solar complementary system on the four typical days shows that, compared to the original load, the surplus load value after peak shaving is smaller overall, and the change process is more stable, verifying the effectiveness of the model constructed in this paper.

[0114] Table 1 Calculation results of peak-valley difference of residual load on a typical day

[0115]

[0116] Further comparison of the peak-shaving effects of the IMED algorithm with three other algorithms is shown in Table 1. On typical days 1 and 4 of the two dry seasons, the IMED algorithm yielded the smallest peak-to-valley difference in residual load. On typical day 1, the IMED algorithm reduced the peak-shaving results of the hydro-wind-solar hybrid system by approximately 633MW, 862MW, and 286MW compared to DE, SFS, and ED, respectively. On typical day 4, the IMED algorithm reduced the peak-shaving results of the hydro-wind-solar hybrid system by approximately 634MW, 576MW, and 84MW compared to DE, SFS, and ED, respectively. This indicates that the application of the IMED algorithm not only enhances the applicability of the model in complex scheduling environments but also improves the quality of the optimization results. On typical days 2 and 3 of the two flood seasons, the results obtained by the four algorithms were almost identical. This indicates that when the inflow is large during the flood season, hydropower stations have a strong regulating effect, and the optimization effects of different algorithms are roughly the same. However, for typical flood season days and dry season days with relatively small average inflows, the regulation range of hydropower output is limited, further testing the optimization ability of the algorithms.

[0117] Under the influence of fluctuating wind and solar power output, the stable operation of the power grid load requires effective peak-shaving strategies to reduce the impact of wind and solar power on the system. The results of four typical days verify the effectiveness of the hydro-wind-solar complementary optimal scheduling model constructed in this paper in terms of wind and solar power output regulation and power grid load balance. The solution quality of the IMED algorithm is higher than that of the unimproved original algorithm and other comparative algorithms, which can provide reference value for the short-term intraday scheduling operation of hydro-wind-solar complementary systems.

[0118] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A scheduling method for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm, characterized in that, The method includes the following steps: Obtain the historical data and characteristic parameters of the hydropower stations in the power transmission system, the load demand of the power transmission grid, and the output data of wind power and photovoltaic power respectively; Taking the minimum peak-valley difference of the remaining load in the power transmission system as the optimization objective, and the water levels of each period of the hydropower station as decision variables, set the constraint conditions based on the historical data and characteristic parameters of the hydropower station, and construct an optimal scheduling model; Use an improved enterprise development optimization algorithm to solve the optimal scheduling model, so as to obtain the optimal water level change process of the hydropower station, calculate the optimal output of each period of the hydropower station using this optimal water level change process, and thus achieve the scheduling of the power transmission system; The steps of using an improved enterprise development optimization algorithm to solve the optimal scheduling model are as follows: S1 Generate an initial population containing N individuals, each individual represents a water level change process of a hydropower station, calculate the fitness function value of the current individual, determine the global optimal solution of the initial population and the optimal water level change process corresponding to this global optimal solution; S2 Update each individual in the current population to form a new population. Compare the fitness function values of each individual before and after the update. If the fitness function value of the updated individual is better than that before the update, then retain the update of this individual; otherwise, this individual is not updated; Calculate the global optimal solution of the new population and compare it with the global optimal solution of the population before the update. If the global optimal solution of the new population is better than the global optimal solution of the population before the update, then retain the global optimal solution of the new population; otherwise, do not update the global optimal solution; S3 If rand(0, 1) < p1, execute the task update mechanism, obtain the individual with the worst fitness in the current population, regenerate the water level change of this individual with the worst fitness, and calculate the fitness value of the regenerated individual; If rand(0,1)≥p1, calculate ,when The structure update mechanism is executed in time; when Implement a technology update mechanism in a timely manner; when Timely personnel update mechanism; S4 Repeat steps S1 to S3 until the maximum number of iterations is reached, and output the global optimal solution and the corresponding hydropower station water level change process, where rand(0,1) is a random number uniformly distributed in the interval [0,1], p1 has a pre-set probability, and x iter It is the number of iterations; In step S1, the generation of the initial population containing N individuals is initialized by Tent mapping; In step S2, the formation of the new population is obtained by guiding the search of the current optimal individual using Gaussian random walk; In step S3, the regeneration of the water level change is regenerated by Tent mapping.

2. The scheduling method for multi-energy complementarity optimization based on improved enterprise development optimization algorithm of water, wind and solar as described in claim 1, characterized in that, The optimal scheduling model is as follows: in, For the scene The probability of; For the scene The minimum peak-to-valley difference of the remaining load calculated in the middle; For grid load, , , These represent the output values ​​of hydropower, wind power, and solar power, respectively.

3. A scheduling method for multi-energy complementarity optimization based on improved enterprise development optimization algorithm, as described in claim 1 or 2, characterized in that... The constraint conditions are as follows: in, , The time periods of the hydropower station Minimum and maximum outbound flow limits, , , These represent the power output of the hydropower station, wind power output, and solar power output during time period t. 、 The hydroelectric power station was in the first Minimum and maximum output limits for different time periods. , The wind farms were respectively in the 1st Minimum and maximum output limits for different time periods. , The photoelectric field at the th Minimum and maximum output limits for different time periods. , The water level of the hydropower station during the time period Minimum and maximum water level limits, , The time periods of the hydropower station reservoirs are respectively The initial and final storage capacities; , , These correspond to the time periods of the hydropower station. Inflow, power generation, and wastewater discharge; For time period Duration; and These are the constraints on the maximum variation of water level and flow rate in adjacent time periods; , These represent the initial and final water levels of the hydropower station during time period A; , These represent the initial and final outflow from the hydropower station during time period 𝑡.

4. The scheduling method for multi-energy complementarity optimization based on improved enterprise development optimization algorithm as described in claim 1, characterized in that, The historical data and characteristic parameters of the hydropower station include the normal storage level, the water level-storage capacity curve of the reservoir, the discharge capacity curve, and the inflow during the scheduling period.

5. A scheduling system for multi-energy complementarity optimization based on an improved enterprise development optimization algorithm, characterized in that: The system includes an actuator, which is used to execute a multi-energy complementary optimization scheduling method of water-wind-light based on an improved enterprise development optimization algorithm according to any one of claims 1-4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements a multi-energy complementary optimization scheduling method of water-wind-light based on an improved enterprise development optimization algorithm according to any one of claims 1-4.

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