Green low-carbon distribution network planning method

Abstract

The invention discloses a green low-carbon distribution network planning method, and the method comprises the following steps: S1, determining planning targets and constraint conditions, and constructing a multi-target optimization model, the planning targets including investment cost minimization, power supply reliability maximization and carbon emission minimization; the constraint conditions comprise the voltage level, the line current-carrying capacity, the equipment capacity, the network topology limitation and the renewable energy source permeability; s2, solving the constructed optimization model by adopting a multi-target particle swarm optimization algorithm; according to the method, the multi-target particle swarm optimization algorithm is introduced, the calculation efficiency of green low-carbon distribution network planning is greatly improved, the optimal solution set of the multi-target problem can be found in a short time through cooperation between the multi-target particle swarm optimization algorithm and multi-population collaborative optimization, the planning process is accelerated, the time cost is reduced, and the planning efficiency is improved through multi-target optimization. On the premise of ensuring the power supply quality, the energy utilization rate is improved, and the energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network planning, and in particular to a green and low-carbon distribution network planning method. Background Art

[0002] Against the backdrop of growing energy demand and increasing awareness of environmental protection, traditional distribution network planning methods mainly focus on the economy and reliability of the system. The consumption of traditional energy brings about serious environmental problems, including climate change and resource depletion, which necessitates the transformation of the power industry.

[0003] However, this method often ignores the impact of environmental factors such as carbon emissions during the planning process. Traditional methods usually optimize the configuration and layout of power equipment to reduce system operating costs and improve power supply reliability. Distribution network planning methods are usually based on single-objective optimization, such as minimizing investment costs and maximizing power supply reliability. These methods often build mathematical models and use linear programming, nonlinear programming and other algorithms to solve them. However, these methods often find it difficult to balance the contradictions between different objectives when dealing with multi-objective problems, and fail to fully consider environmental factors such as carbon emissions. Summary of the invention

[0004] The purpose of the present invention is to provide a green and low-carbon distribution network planning method to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a green and low-carbon distribution network planning method, the green and low-carbon distribution network planning method comprising the following steps: S1: Determine planning objectives and constraints, and build a multi-objective optimization model. Planning objectives include minimizing investment costs, maximizing power supply reliability, and minimizing carbon emissions; constraints include voltage level, line current carrying capacity, equipment capacity, network topology restrictions, and renewable energy penetration; S2: Use multi-objective particle swarm optimization algorithm to solve the constructed optimization model; S3: Generate a distribution network planning solution based on the solution results.

[0006] Preferably, when constructing a multi-objective optimization model, carbon emission constraints are introduced to ensure that the planning scheme meets the preset carbon emission standards.

[0007] Preferably, the voltage level maintains the voltage of each node within a certain range; the current of each line in the line current carrying capacity cannot exceed its maximum safe current to prevent overload; the capacity of all transformers and generator equipment meets the load demand but does not exceed the designed maximum capacity; the renewable energy penetration rate ensures that a certain proportion of energy comes from renewable energy.

[0008] Preferably, the specific method of adopting the multi-objective particle swarm optimization algorithm comprises the following steps: S11: Initialize the particle swarm and determine the size of the particle swarm, inertia weight, and parameters of the learning factor; S12: Calculate the fitness value of each particle according to the objective function of the multi-objective programming model; S13: Update the global optimal solution and the individual optimal solution; S14: Update the speed and position of the particle according to the global optimal solution and the individual optimal solution; S15: Determine whether the termination condition is met. If so, output the optimal solution. Otherwise, return to step S12 to continue iteration.

[0009] Preferably, when updating the global optimal solution and the individual optimal solution, a congestion ranking method is used to screen the solutions in the external archive, and the congestion ranking method includes the following steps: S111: Calculate the crowding distance of each solution; S112: sorting the solutions according to the crowding distance; S113: retain the solution with the smallest crowding distance and remove the solution with the largest crowding distance; S114: Update external files.

[0010] Preferably, the termination conditions of the multi-objective particle swarm optimization algorithm include a maximum number of iterations, a fitness change of the optimal solution being less than a preset threshold, and a number of stable solutions exceeding a preset ratio.

[0011] Preferably, the multi-objective particle swarm optimization algorithm introduces multi-swarm collaborative optimization when updating the speed and position of particles. The multi-swarm collaborative optimization method includes the following steps: S21: Divide the particle swarm into multiple sub-swarms, each sub-swarm has a different optimization objective; S22: Each subgroup performs optimization independently and shares the global optimal solution; S23: Adjust the optimization direction of the subgroups according to the collaborative relationship between the subgroups.

[0012] Preferably, the carbon emissions, economic costs and power supply reliability parameters in the planning model can be determined by the following method: S31: Collect historical operation data of the distribution network; S32: Analyze historical data to determine the carbon emissions, costs and reliability parameters of each device; S33: Construct an objective function of the planning model based on the analysis results.

[0013] Preferably, the distribution network planning scheme is generated by designing the grid architecture according to the selected solution, including the location and scale of new transmission lines, distribution stations, and renewable energy generation facilities; designing smart grid demand response strategies to optimize load curves and promote better utilization of renewable energy; and conducting environmental impact assessments to ensure that the planning scheme complies with local environmental standards and minimizes the impact on the ecosystem.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention introduces a multi-objective particle swarm optimization algorithm to greatly improve the computational efficiency of green and low-carbon distribution network planning. The cooperation between the multi-objective particle swarm optimization algorithm and multi-swarm collaborative optimization can find the optimal solution set of multi-objective problems in a short time, thereby speeding up the planning process and reducing time costs. In addition, through multi-objective optimization, it can improve energy utilization and reduce energy waste while ensuring power supply quality.

[0015] 2. The multi-objective particle swarm optimization algorithm of the present invention can effectively solve multi-objective problems. The algorithm can obtain a more reasonable and economical distribution network planning scheme. Compared with the traditional single-objective optimization method, the multi-objective particle swarm optimization algorithm can achieve dual optimization of economic and environmental benefits while ensuring the safety and stability of the distribution network.

[0016] 3. The present invention fully considers the requirements of low-carbon and environmental protection by constructing a multi-objective planning model. The application of the multi-objective particle swarm optimization algorithm enables the distribution network planning to significantly reduce carbon emissions and environmental pollution while meeting technical and economic requirements, which helps to achieve the goal of green and low-carbon sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of the green and low-carbon distribution network planning method.

[0018] Figure 2 The present invention is a flowchart of a specific method of the multi-objective particle swarm optimization algorithm.

[0019] Figure 3 Flow chart of the method for multi-population collaborative optimization of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figures 1 to 3The present invention provides a technical solution: a green and low-carbon distribution network planning method, the green and low-carbon distribution network planning method comprises the following steps: S1: Determine planning objectives and constraints, and build a multi-objective optimization model. Planning objectives include minimizing investment costs, maximizing power supply reliability, and minimizing carbon emissions; constraints include voltage level, line current carrying capacity, equipment capacity, network topology restrictions, and renewable energy penetration; S2: Use multi-objective particle swarm optimization algorithm to solve the constructed optimization model; S3: Generate a distribution network planning solution based on the solution results.

[0022] When constructing a multi-objective optimization model, carbon emission constraints are introduced to ensure that the planning scheme meets the preset carbon emission standards.

[0023] Objective function: Investment cost minimization objective: [ \text{min} C = \sum_{i=1}^{n} c_i x_i ], where ( c_i ) represents the investment cost of the ( i )th project, and ( x_i ) represents the investment decision variable of the ( i )th project.

[0024] Power supply reliability maximization objective: [ \text{max} R = \sum_{i=1}^{n} r_i x_i ] Among them, (r_i) represents the power supply reliability of the (i)th project, and (x_i) represents the investment decision variable of the (i)th project.

[0025] Carbon emission minimization goal: [ \text{min} E = \sum_{i=1}^{n} e_i x_i ] Among them, (e_i) represents the carbon emissions of the (i)th project, and (x_i) represents the investment decision variable of the (i)th project.

[0026] The voltage level ensures that the voltage of each node is maintained within a certain range; the current of each line in the line current carrying capacity cannot exceed its maximum safe current to prevent overload; the capacity of all transformers and generator equipment meets the load demand but does not exceed the designed maximum capacity; the renewable energy penetration rate ensures that a certain proportion of energy comes from renewable energy.

[0027] The specific method of using the multi-objective particle swarm optimization algorithm includes the following steps: S11: Initialize the particle swarm and determine the size of the particle swarm, inertia weight, and parameters of the learning factor; S12: Calculate the fitness value of each particle according to the objective function of the multi-objective programming model; S13: Update the global optimal solution and the individual optimal solution; S14: Update the speed and position of the particle according to the global optimal solution and the individual optimal solution; S15: Determine whether the termination condition is met. If so, output the optimal solution. Otherwise, return to step S12 to continue iteration.

[0028] When updating the global optimal solution and the individual optimal solution, the crowding ranking method is used to screen the solutions in the external archive. The crowding ranking method includes the following steps: S111: Calculate the crowding distance of each solution; S112: sorting the solutions according to the crowding distance; S113: retain the solution with the smallest crowding distance and remove the solution with the largest crowding distance; S114: Update external files.

[0029] The termination conditions of the multi-objective particle swarm optimization algorithm include the maximum number of iterations, the fitness change of the optimal solution is less than the preset threshold, and the number of stable solutions exceeds the preset ratio.

[0030] The multi-objective particle swarm optimization algorithm introduces multi-swarm collaborative optimization when updating the speed and position of particles. The multi-swarm collaborative optimization method includes the following steps: S21: Divide the particle swarm into multiple sub-swarms, each sub-swarm has a different optimization objective; S22: Each subgroup performs optimization independently and shares the global optimal solution; S23: Adjust the optimization direction of the subgroups according to the collaborative relationship between the subgroups.

[0031] The carbon emissions, economic costs and power supply reliability parameters in the planning model can be determined by the following methods: S31: Collect historical operation data of the distribution network; S32: Analyze historical data to determine the carbon emissions, costs and reliability parameters of each device; S33: Construct an objective function of the planning model based on the analysis results.

[0032] In this embodiment, the multi-objective optimization model is expanded, and the fuzzy analytic hierarchy process is introduced to perform weight allocation on the multi-objective optimization model.

[0033] The specific method of weight allocation includes the following steps: S41: Construct a judgment matrix: Construct a judgment matrix based on the decision maker’s preferences for each goal; S42: Calculate weight vector: Calculate the weight vector of each target using fuzzy analytic hierarchy process; S43: Substitute the weight distribution result into the multi-objective optimization model and solve it using the multi-objective particle swarm optimization algorithm; S44: A set of optimization solutions is obtained by combining the multi-objective optimization model with the weight distribution results.

[0034] Generating distribution network planning solutions includes designing the grid architecture based on the selected solution, including the location and scale of new transmission lines, distribution stations, and renewable energy generation facilities; designing smart grid demand response strategies to optimize load curves and promote better use of renewable energy; and conducting environmental impact assessments to ensure that the planning scheme meets local environmental standards and minimizes the impact on the ecosystem.

[0035] According to the distribution of buildings and energy demand in the region, the layout of the distributed energy system is designed, the existing power grid is upgraded, and an intelligent control system is added to achieve effective connection with distributed energy. Construction is carried out according to the planned design, and photovoltaic panels, wind turbines and energy storage equipment are installed. After the installation is completed, the system is tested to ensure that all equipment is working properly and seamlessly connected to the power grid. A monitoring system is established to track energy output, consumption and loss, evaluate system performance, collect operating data, analyze it, and identify inefficient links; based on the results of data analysis, the power generation plan and load distribution are adjusted to optimize system performance.

[0036] Select a specific region as the implementation target and collect basic information on the region’s distribution network data, energy demand forecasts, and renewable energy potential; Use a multi-objective particle swarm optimization algorithm to generate distribution network planning schemes that differ in cost, reliability, and carbon emissions, and use simulation tools to evaluate the performance of each scheme; Select the final solution from the optimal solution set through trade-off analysis, cost-benefit analysis or multi-criteria decision-making method, and implement the distribution network upgrade or new construction project according to the selected solution; After implementation, a monitoring system will be established to track the performance of the plan, especially in terms of carbon emissions and reliability, and the operation and maintenance strategy will be adjusted or the next step of planning will be carried out based on the feedback.

[0037] In summary, the present invention introduces a multi-objective particle swarm optimization algorithm, which greatly improves the computational efficiency of green and low-carbon distribution network planning. The cooperation between the multi-objective particle swarm optimization algorithm and the multi-swarm collaborative optimization can find the optimal solution set of the multi-objective problem in a short time, thereby speeding up the planning process and reducing the time cost. Through multi-objective optimization, it can improve energy utilization and reduce energy waste under the premise of ensuring the quality of power supply; the multi-objective particle swarm optimization algorithm can effectively solve multi-objective problems, and the algorithm can obtain a more reasonable and economical distribution network planning scheme. Compared with the traditional single-objective optimization method, the multi-objective particle swarm optimization algorithm can achieve dual optimization of economic and environmental benefits while ensuring the safety and stability of the distribution network; by constructing a multi-objective planning model, the requirements of low-carbon and environmental protection are fully considered. The application of the multi-objective particle swarm optimization algorithm enables the distribution network planning to significantly reduce carbon emissions and reduce environmental pollution while meeting technical and economic requirements, which helps to achieve the sustainable development goals of green and low-carbon.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A green and low-carbon distribution network planning method, characterized by: The green and low-carbon distribution network planning method includes the following steps: S1: Determine planning objectives and constraints, and build a multi-objective optimization model. Planning objectives include minimizing investment costs, maximizing power supply reliability, and minimizing carbon emissions; constraints include voltage level, line current carrying capacity, equipment capacity, network topology restrictions, and renewable energy penetration; S2: Use multi-objective particle swarm optimization algorithm to solve the constructed optimization model; S3: Generate a distribution network planning solution based on the solution results.

2. A green and low-carbon distribution network planning method according to claim 1, characterized in that: When constructing a multi-objective optimization model, carbon emission constraints are introduced to ensure that the planning scheme meets the preset carbon emission standards.

3. A green and low-carbon distribution network planning method according to claim 1, characterized in that: The voltage level is determined to maintain the voltage of each node within a certain range; the current of each line in the line current carrying capacity cannot exceed its maximum safe current to prevent overload; The capacity of all transformers and generator equipment shall meet the load requirements but not exceed the designed maximum capacity; Renewable energy penetration ensures that a certain percentage of energy comes from renewable sources.

4. A green and low-carbon distribution network planning method according to claim 1, characterized in that: The specific method of adopting the multi-objective particle swarm optimization algorithm comprises the following steps: S11: Initialize the particle swarm and determine the size of the particle swarm, inertia weight, and parameters of the learning factor; S12: Calculate the fitness value of each particle according to the objective function of the multi-objective programming model; S13: Update the global optimal solution and the individual optimal solution; S14: Update the speed and position of the particle according to the global optimal solution and the individual optimal solution; S15: Determine whether the termination condition is met. If so, output the optimal solution. Otherwise, return to step S12 to continue iteration.

5. A green and low-carbon distribution network planning method according to claim 4, characterized in that: When updating the global optimal solution and the individual optimal solution, the crowding ranking method is used to screen the solutions in the external archive. The crowding ranking method includes the following steps: S111: Calculate the crowding distance of each solution; S112: sorting the solutions according to the crowding distance; S113: retain the solution with the smallest crowding distance and remove the solution with the largest crowding distance; S114: Update external files.

6. A green and low-carbon distribution network planning method according to claim 4, characterized in that: The termination conditions of the multi-objective particle swarm optimization algorithm include the maximum number of iterations, the fitness change of the optimal solution is less than a preset threshold, and the number of stable solutions exceeds a preset ratio.

7. A green and low-carbon distribution network planning method according to claim 4, characterized in that: The multi-objective particle swarm optimization algorithm introduces multi-swarm collaborative optimization when updating the speed and position of particles. The multi-swarm collaborative optimization method includes the following steps: S21: Divide the particle swarm into multiple sub-swarms, each sub-swarm has a different optimization objective; S22: Each subgroup performs optimization independently and shares the global optimal solution; S23: Adjust the optimization direction of the subgroups according to the collaborative relationship between the subgroups.

8. A green and low-carbon distribution network planning method according to claim 1, characterized in that: The carbon emissions, economic costs and power supply reliability parameters in the planning model can be determined by the following methods: S31: Collect historical operation data of the distribution network; S32: Analyze historical data to determine the carbon emissions, costs and reliability parameters of each device; S33: Construct an objective function of the planning model based on the analysis results.

9. A green and low-carbon distribution network planning method according to claim 1, characterized in that: The scheme for generating distribution network planning includes designing the grid architecture, including the location and size of new transmission lines, distribution stations, and renewable energy generation facilities based on the selected solution; designing smart grid demand response strategies to optimize the load curve and promote better utilization of renewable energy; Conduct environmental impact assessments to ensure that planning schemes comply with local environmental standards and minimize impacts on the ecosystem.

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