New energy consumption capability improvement method and system

Abstract

The invention discloses a new energy consumption capability improving method and system, and the method comprises the following steps: (1) building a new energy power grid simulation digital model according to the collected historical operation data of a power grid, and accurately simulating the operation state and constraint conditions of the power grid; (2) calculating the maximum consumption capability of the new energy power grid by using an improved simulated annealing algorithm; (3) optimizing a new energy access scheme by using an improved differential evolution algorithm; (4) during the execution of the algorithm in the step (3), automatically adjusting algorithm parameters according to the search progress and the quality of the solution; and (5) determining an optimal new energy access scheme for improving the new energy consumption capability by combining an improved simulated annealing algorithm and an improved differential evolution algorithm. Through combination of the improved simulated annealing algorithm and the differential evolution algorithm, the frequency and voltage of the power grid can be kept in a safe range even under the condition of large fluctuation of new energy power generation, and the overall power supply reliability is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system renewable energy power generation optimization, and specifically relates to a method and system for improving renewable energy consumption capacity. Background Art

[0002] With the rapid development of renewable energy technologies such as wind and solar energy, their share in the global energy structure has increased rapidly. However, the intermittent and uncertain nature of renewable energy generation has brought huge challenges to traditional power grids. How to efficiently and stably connect these unstable energy resources to the power grid and effectively dispatch them has become a key bottleneck restricting the development of renewable energy.

[0003] At present, the common technical means to improve the capacity of new energy consumption include but are not limited to smart grid technology, the application of energy storage systems, demand-side response and advanced scheduling algorithms. Smart grid improves the flexibility and interactivity of the power grid through two-way communication, automatic control and distributed energy management, so that the power grid can better adapt to the fluctuations of new energy generation. Energy storage technology, especially battery energy storage, can store energy when there is excess electricity for use during peak hours or when there is insufficient new energy generation, effectively balancing the contradiction between supply and demand. Demand-side response encourages users to adjust their electricity consumption behavior at specific times to match the power supply through incentive mechanisms. In addition, traditional optimization algorithms such as genetic algorithms and particle swarm optimization are also commonly used in new energy scheduling, but these algorithms are prone to fall into local optimality when dealing with large-scale complex systems, which limits the efficient use of new energy, especially when the power grid accepts a high proportion of new energy generation. There are problems with consumption and scheduling. Therefore, it is urgent to develop a method and system for improving the capacity of new energy consumption to optimize the access plan of new energy in the power grid and realize the maximum acceptance capacity of the power grid for new energy. Summary of the invention

[0004] Purpose of the invention: In order to solve the above technical problems, the present invention provides a method and system for improving the new energy consumption capacity, which combines the improved simulated annealing algorithm and differential evolution algorithm to overcome the limitations of a single algorithm.

[0005] Technical solution: The present invention provides a method for improving the new energy consumption capacity, comprising the following steps:

[0006] (1) Establish a new energy grid simulation digital model based on the collected historical grid operation data to accurately simulate the grid operation status and constraints;

[0007] (2) Improved simulated annealing algorithm (ISA) is used to calculate the maximum absorption capacity of the new energy grid;

[0008] (3) Improved differential evolution algorithm (DE) to optimize the new energy access solution;

[0009] (4) During the execution of the algorithm in step (3), the algorithm parameters are automatically adjusted according to the search progress and the quality of the solution;

[0010] (5) Determine the optimal renewable energy access solution to enhance renewable energy consumption capacity by combining the improved simulated annealing algorithm (ISA) and the improved differential evolution algorithm (DE).

[0011] Furthermore, the new energy power grid simulation digital model described in step (1) includes power grid structure, lines, transformers, loads and new energy power generation equipment. The new energy power grid simulation digital model can accurately simulate the power grid operation status and various constraints, such as voltage, line capacity and transformer capacity.

[0012] Furthermore, the improved simulated annealing algorithm (ISA) parameter settings in step (2) include initial temperature T0, initial solution of new energy access capacity, temperature attenuation function, termination temperature Tend, and upper limit of iteration number Nmax.

[0013] Among them, the initial temperature T0: is set according to the complexity of the problem, usually high to ensure the extensiveness of the initial search. The value range can be set to 800-1200, and further can be set to 1000.

[0014] Temperature decay function: Adopts an adaptive cooling strategy to dynamically adjust the temperature decay rate according to the solution quality during the search process to more effectively balance exploration and development. The initial decay coefficient is high and gradually decreases with iterations.

[0015] The temperature update formula of the temperature decay function is:

[0016] T k+1 =T k α

[0017] Where: α is the cooling coefficient, which is adaptively adjusted according to the quality of the solution.

[0018] Termination temperature T end : Set a sufficiently low temperature value and stop iteration when the temperature is lower than this value.

[0019] Furthermore, the improved simulated annealing algorithm in step (2) introduces a multi-level perturbation strategy, and applies different perturbation ranges to different search stages, so as to improve the efficiency of local search and global search.

[0020] The perturbation generation formula is:

[0021] x′=x+Δ·rand()

[0022] Where: Δ is the disturbance range, rand() is a random number.

[0023] Furthermore, the improved simulated annealing algorithm in step (2) introduces an intelligent neighborhood search mechanism, which guides the search based on the information of the local gradient, performs a more detailed search near the local optimal solution, and improves the quality of the solution. The neighborhood search formula is as follows:

[0024]

[0025] Where: γ is the search step size, is the local gradient of the solution.

[0026] Furthermore, the improved simulated annealing algorithm described in step (2) introduces a composite temperature adjustment function, combined with the linear decrease and exponential decay strategies, to ensure that the algorithm can not only quickly explore the initial solution space, but also conduct detailed searches in the later stage to avoid falling into the local optimum too early.

[0027] The compound temperature adjustment formula is as follows: T k+1 =T0·(α k +β k )

[0028] Where: α and β are the linear and exponential decay coefficients respectively.

[0029] Furthermore, an improved Metropolis criterion is adopted, which not only considers the fitness difference between the current solution and the new solution, but also introduces the information of the historical optimal solution to enhance the memory of the algorithm.

[0030] In this example, the relevant acceptance probability formula is as follows:

[0031]

[0032] Where: e and e′ are the fitness of the current solution and the new solution respectively, and T is the current temperature.

[0033] Furthermore, the termination condition is set. When the temperature drops to the termination temperature T end Or when the maximum number of iterations is reached, the current optimal solution is recorded as the maximum absorption capacity of the new energy grid.

[0034] For each solution x′, calculate its fitness value. Decide whether to accept the new solution based on the current temperature T, and gradually lower the temperature until it reaches T end or the maximum number of iterations is reached.

[0035] Furthermore, the improved differential evolution algorithm (DE) parameter settings in step (3) include population size, mutation factor, crossover probability, and maximum number of iterations. After each round of simulated annealing, differential evolution is used to update the population, generate new solutions through crossover and mutation operations, and evaluate the fitness of the new solutions.

[0036] Population size: Usually set to 200-300 individuals. The specific value needs to be adjusted according to the problem scale and computing resources.

[0037] Mutation factor F: Initially set to 0.5 or 0.8, it can be dynamically adjusted according to the convergence of the population during algorithm operation.

[0038] Crossover probability CR: The general value range is 0.5-0.9, which affects the exploration and utilization capabilities of the algorithm.

[0039] Maximum number of iterations: This value is set based on the problem complexity and expected accuracy to ensure that the algorithm has enough time to search the solution space. In this example, it is set to the same as simulated annealing.

[0040] A certain number of individuals are randomly generated as the initial population, and each individual represents a configuration of a new energy access solution.

[0041] The mutation factor F is dynamically adjusted according to the diversity of the current population to improve the flexibility of the search and the global optimization capability.

[0042] The improved optimization formula is as follows:

[0043] v i =x r1 +F·(x r2 -x r3 )

[0044] Where: x r1 、x r2 、x r3 are randomly selected individuals.

[0045] Furthermore, a hierarchical crossover operation is introduced, which first performs local crossover within the population and then performs global crossover to balance local optimization and global search.

[0046] The crossover formula is as follows:

[0047]

[0048] Where: u i，j The individual after the cross

[0049] Furthermore, the population size is dynamically adjusted according to the progress of the algorithm. A larger population is used in the initial stage to promote diversity, and then it is gradually reduced in the later stage to focus on more potential solution areas and improve computational efficiency.

[0050] The population size adjustment formula is as follows:

[0051]

[0052] Where: N kis the population size of the kth generation, and K is the maximum number of iterations.

[0053] Furthermore, the idea of ​​co-evolution is introduced to divide the population into multiple subgroups. Different mutation strategies and crossover rules are applied to each subgroup. Excellent individuals are exchanged regularly between subgroups to promote the combination of global search and local refinement.

[0054] The subgroup exchange formula is as follows:

[0055]

[0056] Where: and They are the individuals in subgroup 1 and subgroup 2 respectively, and best represents the excellent individual in the subgroup.

[0057] Furthermore, an elite retention strategy is introduced in the selection operation to ensure that the individuals with the best fitness can enter the next generation, thereby accelerating the convergence speed and maintaining the quality of the solution.

[0058] Furthermore, a termination condition is set. When the maximum number of iterations is reached or the convergence standard is met, the current optimal solution is recorded as the optimized new energy access solution.

[0059] Furthermore, the algorithm parameter adaptive adjustment formula in step (4) is:

[0060]

[0061] Where: α k is the cooling coefficient of the kth generation, Δe is the fitness difference between the current solution and the historical optimal solution, and is the fitness of the historical optimal solution.

[0062] During the execution of the algorithm, the algorithm parameters (such as the cooling coefficient of SA, the variation factor and crossover probability of DE) are automatically adjusted according to the current search progress and the quality of the solution to achieve online learning and self-optimization of the algorithm.

[0063] The present invention also discloses a system for improving the new energy consumption capacity, comprising:

[0064] Construction module, used to construct the digital model of power grid simulation of new energy power grid and simulation analysis models of multiple types of new energy;

[0065] The calculation module uses the improved simulated annealing algorithm to calculate the maximum absorption capacity of the current new energy grid, and uses the improved differential evolution algorithm to optimize the new energy access plan;

[0066] The determination module determines the optimal new energy access plan to improve the new energy absorption capacity based on the calculated maximum absorption capacity of the current new energy power grid by combining the improved differential evolution algorithm.

[0067] Among them, the improved differential evolution algorithm in the calculation module can automatically adjust the algorithm parameters according to the search progress and the quality of the solution.

[0068] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following significant advantages: (1) By integrating the improved simulated annealing algorithm (SA) with the differential evolution algorithm (DE), the solution can not only conduct extensive global search and avoid local optimal traps, but also achieve refined adjustment of the solution through intelligent neighborhood search and hierarchical crossover operations, thereby improving the global optimization capability and practical application value of the new energy access solution; (2) The introduction of adaptive cooling strategy, adaptive mutation factor, dynamic population management and other mechanisms enables the algorithm to automatically adjust parameters according to the search process and the quality of the solution. This online learning and adaptive adjustment strategy significantly improves the adaptability and real-time optimization performance of the algorithm, and ensures the optimal solution search capability under different conditions; (3) By real-time monitoring of the algorithm execution process and adjusting the optimization target weight according to the power grid operation data, the algorithm can learn the real-time status of the power grid and make adaptive adjustments, thereby enhancing the long-term applicability and reliability of the system; (4) It can effectively increase the proportion of new energy power such as wind power and photovoltaic power in the total power, reduce the phenomenon of "wind abandonment" and "light abandonment", and even in the case of large fluctuations in new energy power generation, it can keep the power grid frequency and voltage within a safe range, thereby improving the overall power supply reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a schematic diagram of the working process of the method for improving the new energy consumption capacity of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is further described in detail below through specific embodiments. However, it is necessary to point out that the following embodiments are only used to describe the content of the invention and do not constitute a limitation on the protection scope of the present invention.

[0071] A system for improving the consumption capacity of new energy, comprising:

[0072] Construction module, used to construct the digital model of power grid simulation of new energy power grid and simulation analysis models of multiple types of new energy;

[0073] The calculation module uses the improved simulated annealing algorithm to calculate the maximum absorption capacity of the current new energy grid, and uses the improved differential evolution algorithm to optimize the new energy access plan;

[0074] The determination module determines the optimal new energy access plan to improve the new energy absorption capacity based on the calculated maximum absorption capacity of the current new energy power grid by combining the improved differential evolution algorithm.

[0075] Among them, the improved differential evolution algorithm in the calculation module can automatically adjust the algorithm parameters according to the search progress and the quality of the solution.

[0076] The present invention selects a regional power grid as an application example. The region includes multiple new energy sources such as wind power generation and photovoltaic power generation, and the power grid load fluctuates greatly. The following method of the present invention is used to perform a one-month scheduling simulation.

[0077] A method for improving the new energy consumption capacity comprises the following steps:

[0078] Step (1): Build a digital model for new energy grid simulation

[0079] Build a detailed digital model of power grid simulation, including grid structure, lines, transformers, loads, and renewable energy power generation equipment. The model needs to accurately simulate the grid operation status and various constraints, such as voltage, line capacity, and transformer capacity.

[0080] Step (2): Improved simulated annealing algorithm (ISA) optimizes the new energy consumption capacity

[0081] First, set the parameters for ISA. The specific settings are as follows:

[0082] Initial temperature T0: set according to the complexity of the problem, usually high to ensure the extensiveness of the initial search. In this example, it is set to T0=1000.

[0083] Initial solution: initial configuration of new energy access capacity.

[0084] Temperature decay function: Adopts an adaptive cooling strategy to dynamically adjust the temperature decay rate according to the solution quality during the search process to more effectively balance exploration and development. The initial decay coefficient is high and gradually decreases with iterations.

[0085] The temperature update formula is: T k+1 =T k α

[0086] Wherein: α is the cooling coefficient, which is adaptively adjusted according to the quality of the solution and is set to 0.995 in this embodiment.

[0087] Termination temperature T end : Set a sufficiently low temperature value. When the temperature is lower than this value, the iteration stops. In this example, it is set to T end =10 -6 The upper limit of the number of iterations is N max =1000.

[0088] A multi-level perturbation strategy is introduced to apply different perturbation ranges for different search stages to improve the efficiency of local and global search.

[0089] The perturbation generation formula is: x′=x+Δ·rand()

[0090] Where: Δ is the disturbance range, rand() is a random number.

[0091] An intelligent neighborhood search mechanism is introduced to guide the search based on local gradient information, and a more refined search is performed near the local optimal solution to improve the quality of the solution. The neighborhood search formula is as follows:

[0092] Where: Υ is the search step length, is the local gradient of the solution.

[0093] A composite temperature adjustment function is designed, combining linear decrease and exponential decay strategies to ensure that the algorithm can not only quickly explore the initial solution space, but also conduct detailed searches in the later stages to avoid falling into local optimality too early.

[0094] The compound temperature adjustment formula is as follows: T k+1 =T0·(α k +β k )

[0095] Where: α and β are the linear and exponential decay coefficients respectively.

[0096] The improved Metropolis criterion is adopted, which not only considers the fitness difference between the current solution and the new solution, but also introduces the information of the historical optimal solution to enhance the memory of the algorithm.

[0097] In this test case, the relevant acceptance probability formula is as follows:

[0098]

[0099] Where: e and e′ are the fitness of the current solution and the new solution respectively, and T is the current temperature.

[0100] Set the termination condition. When the temperature drops to the termination temperature T end Or when the maximum number of iterations is reached, the current optimal solution is recorded as the maximum absorption capacity of the new energy grid.

[0101] For each solution x′, calculate its fitness value. Decide whether to accept the new solution based on the current temperature T, and gradually lower the temperature until it reaches T end or the maximum number of iterations is reached.

[0102] Step (3): Improved differential evolution algorithm (DE) optimizes new energy access solution

[0103] After each round of simulated annealing, differential evolution is used to update the population, generate new solutions through crossover and mutation operations, and evaluate the fitness of the new solutions.

[0104] First, set the parameters such as population size, mutation factor F, crossover probability CR, and maximum number of iterations.

[0105] Population size: usually set to 20-100 individuals. The specific value needs to be adjusted according to the problem scale and computing resources. In this embodiment, it is set to NP=50.

[0106] Mutation factor F: Initially set to 0.5 or 0.8, it can be dynamically adjusted according to the convergence of the population during the algorithm operation. In this example, it is set to F = 0.8.

[0107] Crossover probability CR: generally takes a value in the range of 0.5-0.9, which affects the exploration and utilization capabilities of the algorithm. In this embodiment, it is set to CR=0.5.

[0108] Maximum number of iterations: This value is set based on the problem complexity and expected accuracy to ensure that the algorithm has enough time to search the solution space. In this example, it is set to the same as simulated annealing.

[0109] A certain number of individuals are randomly generated as the initial population, and each individual represents a configuration of a new energy access solution.

[0110] The mutation factor F is dynamically adjusted according to the diversity of the current population to improve the flexibility of the search and the global optimization capability.

[0111] The improved optimization formula is as follows:

[0112] v i =x r1 +F·(x r2 -x r3 )

[0113] Where: x r1 、x r2 、x r3 are randomly selected individuals.

[0114] Furthermore, a hierarchical crossover operation is introduced, which first performs local crossover within the population and then performs global crossover to balance local optimization and global search.

[0115] The crossover formula is as follows:

[0116]

[0117] Where: u i，j The individual after the cross

[0118] Furthermore, the population size is dynamically adjusted according to the progress of the algorithm. A larger population is used in the initial stage to promote diversity, and then it is gradually reduced in the later stage to focus on more potential solution areas and improve computational efficiency.

[0119] The population size adjustment formula is as follows:

[0120]

[0121] Where: N k is the population size of the kth generation, and K is the maximum number of iterations.

[0122] The idea of ​​co-evolution is introduced to divide the population into multiple subgroups. Different mutation strategies and crossover rules are applied to each subgroup. Excellent individuals are exchanged regularly between subgroups to promote the combination of global search and local refinement.

[0123] The subgroup exchange formula is as follows:

[0124]

[0125] Where: and They are the individuals in subgroup 1 and subgroup 2 respectively, and best represents the excellent individual in the subgroup.

[0126] An elite retention strategy is introduced in the selection operation to ensure that the individuals with the best fitness can enter the next generation, thereby accelerating the convergence speed and maintaining the quality of the solution.

[0127] Set the termination condition. When the maximum number of iterations is reached or the convergence criteria are met, the current optimal solution is recorded as the optimized new energy access solution.

[0128] Step (4): Real-time feedback and adaptive adjustment

[0129] During the execution of the algorithm, the algorithm parameters (such as the cooling coefficient of SA, the variation factor and crossover probability of DE) are automatically adjusted according to the current search progress and the quality of the solution to achieve online learning and self-optimization of the algorithm.

[0130] The parameter adaptive adjustment formula is:

[0131]

[0132] Where: α k is the cooling coefficient of the kth generation, Δe is the fitness difference between the current solution and the historical optimal solution, e best is the fitness of the historical optimal solution.

[0133] Step (5): Determine the optimal new energy access plan for improving the new energy absorption capacity by combining the improved simulated annealing algorithm and the improved differential evolution algorithm.

[0134] The present invention selects the regional power grid as an application example. The region contains multiple new energy sources such as wind power generation and photovoltaic power generation, and the power grid load fluctuates greatly. The method of the present invention is used to conduct a one-month dispatch simulation. The results show that the new energy consumption rate is increased by about 15% compared with the traditional method; the system frequency is controlled within a narrower range, and the stability is improved; the economic benefit evaluation shows that the dispatch cost is reduced by about 10%.

[0135] In summary, the present invention proposes a method and system for improving the absorption capacity of new energy. By combining the improved simulated annealing algorithm and the differential evolution algorithm, the access scheme of the new energy of the power grid is optimized to realize the maximum acceptance capacity of the power grid for new energy. The advantages of the two are complementary to each other to overcome the limitations that may exist in a single algorithm. The simulated annealing algorithm enhances the global search ability and convergence speed of the algorithm through adaptive cooling strategy and multi-level perturbation strategy; the differential evolution algorithm effectively balances the breadth and depth of the search through adaptive mutation factors and hierarchical crossover operations, thereby improving the optimization effect. By combining the improved simulated annealing algorithm and the differential evolution algorithm, the proportion of new energy power such as wind power and photovoltaic power in the total power can be effectively increased, and the phenomenon of "abandoning wind" and "abandoning light" can be reduced. Even in the case of large fluctuations in new energy power generation, the frequency and voltage of the power grid can be kept within a safe range, thereby improving the overall power supply reliability.

Claims

1. A method for improving the new energy consumption capacity, characterized in that: The following steps are involved: (1) Establish a new energy grid simulation digital model based on the collected historical grid operation data to accurately simulate the grid operation status and constraints; (2) The improved simulated annealing algorithm is used to calculate the maximum absorption capacity of the new energy grid; (3) Improved differential evolution algorithm to optimize new energy access solutions; (4) During the execution of the algorithm in step (3), the algorithm parameters are automatically adjusted according to the search progress and the quality of the solution; (5) Determine the optimal renewable energy access solution to enhance renewable energy absorption capacity by combining the improved simulated annealing algorithm and the improved differential evolution algorithm.

2. The method for improving the new energy consumption capacity according to claim 1, characterized in that: The new energy grid simulation digital model described in step (1) includes grid structure, lines, transformers, loads and new energy power generation equipment, and the simulation constraints of the new energy grid simulation digital model include voltage, line capacity and transformer capacity.

3. The method for improving the new energy consumption capacity according to claim 1, characterized in that: The improved simulated annealing algorithm parameter settings in step (2) include the initial temperature T0, the initial solution of the new energy access capacity, the temperature decay function, the termination temperature T end , the upper limit of the number of iterations N max ; The temperature attenuation function is: T k+1 =T k ·α Where: α is the cooling coefficient, which is adaptively adjusted according to the quality of the solution during the search process.

4. The method for improving the new energy consumption capacity according to claim 1, characterized in that: The improved simulated annealing algorithm described in step (2) introduces a multi-level perturbation strategy, and applies different perturbation ranges to different search stages.

5. The method for improving the new energy consumption capacity according to claim 1, characterized in that: The improved simulated annealing algorithm described in step (2) introduces an intelligent neighborhood search mechanism, which guides the search based on the information of the local gradient, performs a more detailed search near the local optimal solution, and improves the quality of the solution; the neighborhood search formula is as follows: Where: γ is the search step length, is the local gradient of the solution.

6. The method for improving the new energy consumption capacity according to claim 1, characterized in that: The improved simulated annealing algorithm described in step (2) introduces a composite temperature adjustment function, and the composite temperature adjustment formula is as follows: T k+1 =T0·(α k +β k ) Where: α and β are the linear and exponential decay coefficients respectively.

7. The method for improving the new energy consumption capacity according to claim 1, characterized in that: The improved differential evolution algorithm parameter settings in step (3) include population size, mutation factor F, crossover probability CR, and maximum number of iterations.

8. The method for improving the new energy consumption capacity according to claim 1, characterized in that: The algorithm parameter adaptive adjustment formula in step (4) is: Where: α k is the cooling coefficient of the kth generation, Δe is the fitness difference between the current solution and the historical optimal solution, e best is the fitness of the historical optimal solution.

9. A system for improving the capacity of absorbing new energy, characterized in that: include: Construction module, used to construct the digital model of power grid simulation of new energy power grid and simulation analysis models of multiple types of new energy; The calculation module uses the improved simulated annealing algorithm to calculate the maximum absorption capacity of the current new energy grid, and uses the improved differential evolution algorithm to optimize the new energy access plan; The determination module determines the optimal new energy access plan to improve the new energy absorption capacity based on the calculated maximum absorption capacity of the current new energy power grid by combining the improved differential evolution algorithm.

10. The new energy consumption capacity system according to claim 9, characterized in that: The improved differential evolution algorithm in the calculation module can automatically adjust the algorithm parameters according to the search progress and the quality of the solution.

Images