Power transmission network planning multi-objective optimization method considering load uncertainty

Abstract

The invention discloses a power transmission network planning multi-objective optimization method considering load uncertainty. The power transmission network planning multi-objective optimization method comprises the following steps: (1) load uncertainty modeling; (2) constructing a multi-objective optimization model; (3) solving the multi-objective optimization model; and (4) planning scheme evaluation and decision making. The invention relates to the technical field of power transmission network planning, in particular to a power transmission network planning multi-objective optimization method considering load uncertainty, and the method has the following advantages: 1, the load uncertainty is dealt with, and the planning adaptability is improved; 2, multi-objective collaborative optimization is realized, and comprehensive benefits are improved; and 3, scientific evaluation decision is adopted, and the feasibility of the scheme is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission network planning, and in particular to a multi-objective optimization method for power transmission network planning taking load uncertainty into account. Background Art

[0002] With the continuous development of power systems and the growing demand for electricity, transmission network planning has become increasingly important. Traditional transmission network planning methods are often based on fixed load forecasts. However, in reality, loads are affected by a variety of factors, such as economic development, weather changes, and user behavior, resulting in significant uncertainty. This uncertainty can lead to planning schemes experiencing problems such as insufficient power supply capacity, wasted investment, or substandard reliability in actual operation. Therefore, how to effectively account for load uncertainty in transmission network planning and achieve multi-objective optimization is a pressing technical challenge. Summary of the Invention

[0003] In view of this, the present invention aims to provide a multi-objective optimization method for transmission network planning taking load uncertainty into account, so as to improve the adaptability, economy and reliability of transmission network planning and effectively cope with the challenges brought by load uncertainty.

[0004] The technical solution of the embodiment of the present invention is achieved as follows:

[0005] A multi-objective optimization method for transmission network planning taking into account load uncertainty includes the following steps:

[0006] (1) Load uncertainty modeling: Using probability distribution functions to describe the active load uncertainty of load nodes, generate and reduce load scenarios;

[0007] (2) Constructing a multi-objective optimization model: Taking economy, reliability, and environmental benefits as the goals, an optimization model including objective functions and constraints is established;

[0008] (3) Solving the multi-objective optimization model: using an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the optimization model and obtain the Pareto optimal solution set;

[0009] (4) Planning scheme evaluation and decision-making: Based on the comprehensive evaluation index system and decision-maker preferences, the optimal planning scheme is selected from the Pareto optimal solution set.

[0010] Preferably, in step (1), the active load of the load node obeys the normal distribution Determine the mean μ by analyzing historical load data and influencing factors i and variance And the scenario generation and reduction technology is used to obtain a limited number of load scenarios {ω S |s=1,2,···,S} and its occurrence probability ps ,satisfy

[0011] Preferably, the economic objective function in step (2) is: in, Active power loss based on scenario s Calculation, r ij is the line resistance, is the line current in scenario s.

[0012] Preferably, the reliability objective function in step (2) is: in, is the outage load under scenario s, and λ is the unit outage load loss cost.

[0013] Preferably, the environmental benefit objective function in step (2) is: F3 = η(P 总发电 -P 新能源 ), where η is the carbon emission factor of traditional energy generation, P 新能源 is the installed capacity of new energy, P 总发电 is the total power generated by the system.

[0014] Preferably, the constraints of step (2) include:

[0015] 6.1 Power flow constraints: satisfying the node active / reactive power balance equation and

[0016] 6.2 Line capacity constraints:

[0017] 6.3. Node Voltage Constraint: V i min ≤V i s ≤V i max .

[0018] Preferably, the improved NSGA-II algorithm in step (3) adopts an individual coding method that combines binary coding and real number coding, the binary code represents the new construction / expansion status of the line, and the real number code represents continuous variables such as substation capacity. A new generation of population is generated through selection, crossover, and mutation operations, and non-dominated sorting and congestion calculation are performed.

[0019] Preferably, the comprehensive evaluation index system in step (4) includes economic efficiency, reliability, environmental benefit and technical feasibility indicators, and the hierarchical analysis method is used to determine the index weights, and the planning scheme is evaluated by the fuzzy comprehensive evaluation method.

[0020] Preferably, the scenario generation and reduction technology is a Monte Carlo simulation combined with a fast forward selection method, which retains representative scenarios by reducing the number of load scenarios.

[0021] Preferably, during the solution of the multi-objective optimization model, the power loss, node voltage and line transmission power in each scenario are obtained through power flow calculation as input parameters of the objective function and constraint conditions.

[0022] The embodiment of the present invention adopts the above technical solution, which has the following advantages:

[0023] 1. Addressing load uncertainty and improving planning adaptability: By modeling loads using probability distribution functions and constructing representative load scenarios using scenario generation and reduction techniques, load uncertainty is quantitatively incorporated into the planning process. This allows planning schemes to no longer be limited to a single fixed load forecast value, but rather to operate stably under a variety of possible load scenarios. This significantly improves the adaptability of transmission network planning to complex and variable load environments and reduces the risk of planning failures due to load forecast deviations.

[0024] 2. Achieve multi-objective collaborative optimization and improve comprehensive benefits: An innovative multi-objective optimization model that includes economic, reliability, and environmental benefits was constructed. In terms of economics, by considering construction investment, operating costs, and maintenance costs, it effectively avoids investment waste and reduces long-term operating costs. In terms of reliability, reliability is measured by power outage loss costs, fully considering the risk of power outages under load uncertainty to ensure stable power supply. In terms of environmental benefits, carbon emission costs are incorporated into the objective function to promote the access of new energy and low-carbon development. The model is solved by the improved NSGA-II algorithm, and the resulting Pareto optimal solution set provides decision makers with multiple options that take into account the benefits of multiple parties, thereby maximizing the comprehensive benefits of transmission network planning.

[0025] 3. Adopt scientific assessment and decision-making to enhance the feasibility of the plan: Establish a comprehensive evaluation index system covering multi-dimensional indicators such as economic efficiency, reliability, environmental benefits, and technical feasibility. Use the Analytic Hierarchy Process to determine the index weights, and combine the fuzzy comprehensive evaluation method to scientifically evaluate the planning scheme. This method can objectively and comprehensively reflect the characteristics and advantages and disadvantages of different schemes. In combination with the decision-maker's preferences, the optimal solution is selected from the Pareto optimal solution set, ensuring that the final planning scheme is not only feasible in theory but also meets the actual project needs and development strategy, effectively improving the feasibility of the planning scheme.

[0026] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0028] Figure 1 is a flow chart of the overall method of the present invention;

[0029] Figure 2 A flow chart for load uncertainty modeling of the present invention;

[0030] Figure 3 A flowchart of constructing a multi-objective optimization model for the present invention;

[0031] Figure 4 This is a flow chart of solving the multi-objective optimization model of the present invention. DETAILED DESCRIPTION

[0032] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0033] It should be noted that the terms "first," "second," "symmetrical," "array," etc. are used only to distinguish descriptions from positional descriptions and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, limitations on features such as "first" and "symmetrical" may explicitly or implicitly include one or more of these features; similarly, when the number of certain features is not limited in the form of words such as "two" or "three," it should be noted that these features also explicitly or implicitly include one or more of the number of features.

[0034] In the present invention, unless otherwise expressly specified or limited, terms such as "installation," "connection," and "fixation" should be understood broadly; for example, they may refer to fixed connection, detachable connection, or integral molding; they may refer to mechanical connection, direct connection, welding, or indirect connection through an intermediate medium; they may refer to internal communication between two components or interaction between two components. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on the specification and drawings in conjunction with specific circumstances.

[0035] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0036] like Figure 1-4 The present invention provides a multi-objective optimization method for transmission network planning taking into account load uncertainty, comprising the following steps:

[0037] (1) Load uncertainty modeling: Using probability distribution functions to describe the active load uncertainty of load nodes, generate and reduce load scenarios;

[0038] (2) Constructing a multi-objective optimization model: Taking economy, reliability, and environmental benefits as the goals, an optimization model including objective functions and constraints is established;

[0039] (3) Solving the multi-objective optimization model: Using the improved non-dominated sorting genetic algorithm (NSGA-II) to solve the optimization model and obtain the Pareto optimal solution set;

[0040] (4) Planning scheme evaluation and decision-making: Based on the comprehensive evaluation index system and decision-maker preferences, the optimal planning scheme is selected from the Pareto optimal solution set.

[0041] like Figure 1-4 As shown, in step (1), the active load of the load node obeys the normal distribution Determine the mean μ by analyzing historical load data and influencing factors i and variance And the scenario generation and reduction technology is used to obtain a limited number of load scenarios {ω S |s=1,2,···,S} and its occurrence probability p s ,satisfy The economic objective function in step (2) is: in, Active power loss based on scenario s Calculation, r ij is the line resistance, is the line current under scenario s, and the reliability objective function in step (2) is: in, is the power outage load under scenario s, λ is the unit power outage load loss cost, and the environmental benefit objective function in step (2) is: F3=η(P 总发电 -P 新能源 ), where η is the carbon emission factor of traditional energy generation, P 新能源 is the installed capacity of new energy, P 总发电 is the total power generated by the system.

[0042] like Figure 1-4 As shown, the constraints of step (2) include:

[0043] 6.1 Power flow constraints: satisfying the node active / reactive power balance equation and

[0044] 6.2 Line capacity constraints:

[0045] 6.3. Node Voltage Constraint: V i min ≤V i s ≤V i max .

[0046] like Figure 1-4 As shown in Figure 3, the improved NSGA-II algorithm in step (3) adopts an individual coding method that combines binary coding and real number coding. Binary coding represents the new construction / expansion status of the line, and real number coding represents continuous variables such as substation capacity. A new generation of population is generated through selection, crossover, and mutation operations, and non-dominated sorting and congestion calculation are performed.

[0047] like Figure 1-4 As shown, the comprehensive evaluation index system in step (4) includes economic efficiency, reliability, environmental benefits and technical feasibility indicators. The hierarchical analysis method is used to determine the index weights, and the planning scheme is evaluated through the fuzzy comprehensive evaluation method.

[0048] like Figure 1-4 As shown in Figure 3, the scenario generation and reduction technology is a Monte Carlo simulation combined with a fast forward selection method, which retains representative scenarios by reducing the number of load scenarios.

[0049] like Figure 1-4 As shown in Figure 2, during the solution of the multi-objective optimization model, the power loss, node voltage, and line transmission power in each scenario are obtained through power flow calculation as input parameters of the objective function and constraints.

[0050] In this embodiment, the present invention is specifically designed to work as follows:

[0051] First, load uncertainty modeling is performed: relevant information is collected from historical load data, economic development indicators, weather data, and other channels. Based on the collected data, the normal distribution parameters of the active load at the load node, namely the mean and variance, are determined. Monte Carlo simulation is used to generate a large number of load scenarios. Scenario reduction techniques such as fast forward selection are then used to obtain a limited number of representative load scenarios and their occurrence probabilities, eliminating redundant scenarios.

[0052] Then, we enter the stage of building a multi-objective optimization model: determine the economic goal, calculate the investment cost of transmission network construction, the operating cost under different load scenarios (the power loss is calculated through flow calculation and then the cost is calculated), and the maintenance cost, and combine them to form an economic objective function; determine the reliability goal, and build a reliability objective function based on the power outage load and unit power outage load loss cost under each load scenario; determine the environmental benefit goal, calculate the carbon emission cost based on the total power generation power of the system, the installed capacity of new energy and the carbon emission factor, and obtain the environmental benefit objective function. At the same time, clarify the flow constraints, line capacity constraints, node voltage constraints and other conditions to build a complete multi-objective optimization model;

[0053] Next comes the process of solving the multi-objective optimization model: Initializing the parameters of the improved non-dominated sorting genetic algorithm (NSGA-II), including population size and genetic generations. Individuals are encoded using a combination of binary and real numbers, with binary encoding representing the status of new or expanded lines and real encoding representing substation capacity. The population is continuously updated through genetic operations such as selection, crossover, and mutation. Non-dominated sorting and congestion calculations are performed on the population, retaining outstanding individuals and gradually approaching the Pareto optimal solution set.

[0054] The final stage is planning scheme evaluation and decision-making: a comprehensive evaluation index system covering economic efficiency, reliability, environmental benefits, and technical feasibility is established. Experts are invited to rate the importance of each indicator, and the analytic hierarchy process is used to determine the weight of each indicator. A fuzzy evaluation matrix is ​​constructed, and a fuzzy comprehensive evaluation method is used to evaluate each planning scheme obtained from the Pareto optimal solution set. Based on the evaluation scores, combined with actual project requirements and decision-maker preferences, the optimal transmission network planning scheme is selected.

[0055] The following are several other specific embodiments of the present invention:

[0056] Example 1: Transmission Network Planning for New Urban Areas

[0057] Load uncertainty modeling phase: For new urban areas, we collected documents such as economic development planning documents and population growth forecast reports for the next few years. By analyzing the planned land area, the types and expected number of businesses in different functional areas (commercial, residential, and industrial) within the new area, and combining historical load data from similar areas, we determined the mean and variance of the normal distribution of active load at each load node. We used Monte Carlo simulation to generate a large number of load scenarios, and then used the fast forward selection method to reduce the scenarios. We obtained representative load scenarios and their probability of occurrence, such as low-load scenarios at night in commercial areas, high-load scenarios during weekdays, and high-load scenarios in residential areas during holidays.

[0058] During the multi-objective optimization model construction phase: Regarding economic efficiency, the construction investment cost was calculated based on the planned substation location and line routing for the new district, combined with local power equipment procurement prices and construction cost standards. Power flow calculations under different load scenarios determined line power losses, and operating costs were calculated based on local electricity prices. Maintenance costs were estimated based on the number of equipment and maintenance standards, forming an economic objective function. Regarding reliability, the distribution of key enterprises, hospitals, and government agencies within the new district, which require high power supply reliability, was considered. The power outage load caused by line failures and other factors was evaluated under different load scenarios. The unit power outage loss cost was then combined to construct a reliability objective function. Regarding environmental benefits, the proportion of new energy access in the new district plan was calculated, and the carbon emission cost was calculated based on the carbon emission factors of traditional energy generation to form an environmental benefit objective function. Constraints such as power flow, line capacity, and node voltage were also clarified.

[0059] During the multi-objective optimization model solution phase, the improved NSGA-II algorithm parameters were initialized, with a population size of 100 and a genetic generation number of 50. Individuals were coded using binary codes to represent new line construction in the new district plan (0 for no construction, 1 for construction), and real numbers to represent the capacity of the planned new substations. Through continuous iteration of the population through selection, crossover, and mutation operations, non-dominated sorting and congestion calculation were performed to gradually obtain a Pareto optimal solution set.

[0060] Planning Scheme Evaluation and Decision-Making Phase: A comprehensive evaluation index system was established, inviting urban planning experts, power company engineers, and environmental protection department representatives to score indicators such as economic efficiency, reliability, environmental benefits, and technical feasibility. The analytic hierarchy process was used to determine the weights of each indicator. A fuzzy evaluation matrix was constructed to conduct a fuzzy comprehensive evaluation of each planning scheme within the Pareto optimal solution set. Ultimately, based on the evaluation scores and the new district's development positioning (such as the requirement to be a green and low-carbon demonstration new district), the most suitable transmission network planning scheme for the urban new district was selected.

[0061] Example 2: Transmission network reconstruction planning in old urban areas

[0062] Load uncertainty modeling phase: Monthly and quarterly load data for the past 5-10 years in older urban areas was collected to analyze load variations with seasons, holidays, and other factors. Furthermore, factors that could influence load variations, such as renovation plans for older residential communities and the renewal of commercial stores within these areas, were investigated. After determining the normal distribution parameters for the active load at each load node, Monte Carlo simulation was used to generate a large number of load scenarios. The scenarios were then reduced using a fast forward selection method. Representative load scenarios and their probabilities were identified, such as a significant increase in air conditioning load during high summer temperatures and a decrease in load during the Spring Festival when residents return home.

[0063] During the multi-objective optimization model construction phase, the following objectives were considered: Regarding economic efficiency, the construction investment costs for replacing aging lines and upgrading substation equipment during the transformation of the transmission network in older urban areas were calculated. The operating costs were calculated based on the reduction in power loss and operating time of the transformed lines under different load scenarios, combined with electricity prices. Maintenance costs were estimated based on equipment maintenance cycles and maintenance fee standards, and the economic objective function was determined. Regarding reliability, the impact of power outages on residents' lives and public services under different load scenarios was evaluated, considering the dense population and concentration of public service facilities such as hospitals and schools in older urban areas. The outage load and unit load loss costs were determined, and a reliability objective function was constructed. Regarding environmental benefits, the impact of measures such as adopting energy-saving equipment and increasing renewable energy absorption capacity during the transformation on carbon emissions was evaluated, and the environmental benefit objective function was calculated using the carbon emission factor. Constraints such as power flow, line capacity, and node voltage were clearly defined.

[0064] During the multi-objective optimization model solution phase, the improved NSGA-II algorithm parameters were set to 80 populations and 40 genetic generations. Binary codes represented line replacements (0 for no replacement, 1 for replacement), while real numbers represented the capacity parameters of the substation equipment after the upgrade. Genetic operations were then iterated across the population, performing non-dominated sorting and congestion calculations to obtain the Pareto optimal solution set.

[0065] Planning Scheme Evaluation and Decision-Making Phase: A comprehensive evaluation index system was established, and community representatives, power operations and maintenance personnel, and urban construction department staff were invited to participate in determining index weights. The weights for each index were determined using the Analytic Hierarchy Process (AHP). Planning schemes within the Pareto optimal solution set were evaluated using the fuzzy comprehensive evaluation method. Considering the limited funding and construction difficulties in older urban areas, as well as residents' expectations for power supply reliability and stability, the most appropriate planning scheme for the transmission network renovation in older urban areas was selected.

[0066] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various modifications and substitutions within the technical scope disclosed in the present invention, and such modifications and substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A multi-objective optimization method for transmission network planning taking into account load uncertainty, characterized in that: The following steps are involved: (1) Load uncertainty modeling: Using probability distribution functions to describe the active load uncertainty of load nodes, generate and reduce load scenarios; (2) Constructing a multi-objective optimization model: Taking economy, reliability, and environmental benefits as the goals, an optimization model including objective functions and constraints is established; (3) Solving the multi-objective optimization model: using an improved non-dominated sorting genetic algorithm to solve the optimization model and obtain the Pareto optimal solution set; (4) Planning scheme evaluation and decision-making: Based on the comprehensive evaluation index system and decision-maker preferences, the optimal planning scheme is selected from the Pareto optimal solution set.

2. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 1, characterized in that: In the step (1), the active load of the load node obeys the normal distribution Determine the mean μ by analyzing historical load data and influencing factors i and variance And the scenario generation and reduction technology is used to obtain a limited number of load scenarios {ω S |s=1,2,···,S} and its occurrence probability p s ,satisfy 3. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 1, characterized in that: The economic objective function in step (2) is: in, Active power loss based on scenario s Calculation, r ij is the line resistance, is the line current in scenario s.

4. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 1, characterized in that: The reliability objective function in step (2) is: in, is the outage load under scenario s, and λ is the unit outage load loss cost.

5. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 1, characterized in that: The environmental benefit objective function in step (2) is: F3 = η(P 总发电 -P 新能源 ), where η is the carbon emission factor of traditional energy generation, P 新能源 is the installed capacity of new energy, P 总发电 is the total power generated by the system.

6. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 1, characterized in that: The constraints of step (2) include: 6.1 Power flow constraints: satisfying the node active / reactive power balance equation and 6.2 Line capacity constraints: 6.

3. Node voltage constraints:

7. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 1, characterized in that: The improved NSGA-II algorithm in step (3) adopts an individual coding method that combines binary coding and real number coding. The binary code represents the new construction / expansion status of the line, and the real number code represents continuous variables such as substation capacity. A new generation of population is generated through selection, crossover, and mutation operations, and non-dominated sorting and congestion calculation are performed.

8. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 1, characterized in that: The comprehensive evaluation index system in step (4) includes economic efficiency, reliability, environmental benefit and technical feasibility indicators. The index weights are determined using the hierarchical analysis method, and the planning scheme is evaluated using the fuzzy comprehensive evaluation method.

9. The multi-objective optimization method for transmission network planning taking load uncertainty into account according to claim 2, characterized in that: The scenario generation and reduction technology is a Monte Carlo simulation combined with a fast forward selection method, which retains representative scenarios by reducing the number of load scenarios.

10. A multi-objective optimization method for transmission network planning taking load uncertainty into account according to any one of claims 1 to 9, characterized in that: In the process of solving the multi-objective optimization model, the power loss, node voltage and line transmission power in each scenario are obtained through power flow calculation and used as input parameters of the objective function and constraint conditions.

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