Multi-objective optimization method and system for off-grid micro-grid construction

Abstract

The invention discloses a multi-objective optimization method and system for off-grid micro-grid construction, and relates to the technical field of power grid construction planning. Comprising the following steps that basic data of off-grid type micro-grid construction are obtained, and the basic data comprise but are limited to geographical location information, meteorological data, user electrical load data, candidate distributed power supply equipment parameters and energy storage equipment parameters; according to the method, the multi-objective optimization model including the construction cost, the power supply reliability and the environmental friendliness is constructed, the intelligent optimization algorithm is adopted for solving, multiple key factors in micro-grid construction are comprehensively considered, and compared with the prior art, the method has remarkable progress in the aspects of comprehensiveness of optimization objectives and effectiveness of the optimization method, and the method is suitable for popularization and application. The comprehensive performance of the micro-grid can be effectively improved, meanwhile, a more reasonable scheme can be provided for micro-grid construction through multi-objective optimization, the construction cost is reduced, the power supply reliability is improved, and the influence on the environment is reduced.

Description

Technical Field

[0001] This invention relates to the field of power grid construction planning technology, specifically to a multi-objective optimization method and system for off-grid microgrid construction. Background Technology

[0002] In the face of the global energy crisis and rapid deterioration of the global environment, developing renewable energy sources such as solar and wind power is of paramount importance. While renewable energy offers advantages such as sustainability and environmental friendliness, its unpredictable and intermittent nature hinders power generation. Fortunately, the complementarity of solar and wind power across different seasons and daytime periods allows them to be combined into a hybrid renewable energy system (HRES), which can reduce the impact of uncertainty and provide a more reliable power supply.

[0003] With the transformation of the energy structure and the increasing demand for renewable energy, off-grid microgrids are widely used in scenarios such as power supply in remote areas and emergency power supply. In the construction of existing off-grid microgrids, single-objective optimization methods are typically used, such as considering only construction costs or focusing solely on power supply reliability, which makes it difficult to achieve optimal overall microgrid performance. Some multi-objective optimization methods suffer from problems such as incomplete optimization objectives, low computational efficiency, and inability to adapt to the complex operating environment of microgrids. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-objective optimization method and system for the construction of off-grid microgrids, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-objective optimization method for the construction of off-grid microgrids, comprising the following steps: Acquire basic data for the construction of off-grid microgrids, including but not limited to geographic location information, meteorological data, user electricity load data, parameters of candidate distributed power generation equipment, and parameters of energy storage equipment; Based on the acquired basic data, a multi-objective optimization model for off-grid microgrids is constructed. This model includes a construction cost objective function, a power supply reliability objective function, and an environmental friendliness objective function. The calculation formula for the construction cost objective function C is as follows: ,in, Let be the cost of the i-th distributed power supply device, and n be the data of the distributed power supply device. Let m represent the cost of the j-th energy storage device, and m be the number of energy storage devices. The formula for calculating the power supply reliability objective function R is: ,in, This represents the time of the k-th power outage, where l is the number of power outages. Total downtime; The formula for calculating the environmental friendliness objective function E is: ,in This represents the environmental impact coefficient of the i-th distributed power generation unit. This represents the power generation of the i-th distributed power source; The multi-objective optimization model is solved using intelligent optimization algorithms to obtain multiple Pareto optimal solutions; Based on preset decision rules, the optimal solution is selected from multiple Pareto optimal solutions as the optimization scheme for the construction of off-grid microgrids.

[0006] Preferably, the geographic location information is obtained through GPS, the meteorological data is obtained through meteorological monitoring equipment or a meteorological data service platform, and the user's electricity load data is collected through smart meters and uploaded to the data processing center.

[0007] Preferably, the intelligent optimization algorithm is a non-dominated sorting genetic algorithm, specifically including the following operations: Initialize the population and randomly generate a certain number of individuals, each representing an off-grid microgrid construction scheme; Calculate the fitness of each individual, and calculate the fitness value of each individual under each objective according to the construction cost objective function, power supply reliability objective function, and environmental friendliness objective function in the multi-objective optimization model; Perform non-dominated sorting to divide individuals in the population into different non-dominated layers; Crowding is calculated for individuals in the same non-dominated layer to ensure population diversity. New populations are generated through selection, crossover, and mutation operations; Repeat the steps until the preset termination condition is met, and output multiple Pareto optimal solutions.

[0008] Preferably, the preset decision rule includes: according to the user's preference weight, weighted summation of the scores of multiple Pareto optimal solutions under the three objectives of construction cost, power supply reliability and environmental friendliness, and selecting the solution with the highest score as the optimal solution.

[0009] Preferably, the method further includes simulation verification of the optimization scheme. If the simulation results do not meet the preset performance indicators, the multi-objective optimization model is rewritten.

[0010] Preferably, the preset performance indicators include construction cost not exceeding 110% of the budget, power supply reliability not less than 99%, and environmental friendliness objective function value within a preset range.

[0011] A multi-objective optimization system for off-grid microgrid construction includes: The data acquisition module is used to acquire basic data for the construction of off-grid microgrids; The model building module is used to construct a multi-objective optimization model for off-grid microgrids based on the acquired basic data. The solver module is used to solve multi-objective optimization models using intelligent optimization algorithms to obtain multiple Pareto optimal solutions; The decision module is used to select the optimal solution from multiple Pareto optimal solutions as the optimization scheme for the construction of off-grid microgrids according to preset decision rules.

[0012] Preferably, the system further includes a simulation verification module for simulating and verifying the optimization scheme. If the simulation results do not meet the preset performance indicators, the solution module is triggered to re-solve the multi-objective optimization model.

[0013] Preferably, the data acquisition module includes a GPS unit, a meteorological data acquisition unit, and a smart meter data acquisition unit.

[0014] Preferably, the solution module uses a non-dominated sorting genetic algorithm to solve the multi-objective optimization objective.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This multi-objective optimization method for off-grid microgrid construction constructs a multi-objective optimization model that includes construction cost, power supply reliability, and environmental friendliness, and solves the model using an intelligent optimization algorithm. It comprehensively considers multiple key factors in microgrid construction and, compared with existing technologies, shows significant progress in the comprehensiveness of optimization objectives and the effectiveness of optimization methods. It can effectively improve the overall performance of microgrids. At the same time, multi-objective optimization can provide a more reasonable solution for microgrid construction, reduce construction costs, improve power supply reliability, and reduce environmental impact. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the multi-objective optimization system for the construction of off-grid microgrids according to the present invention.

[0017] Figure 2 This is a multi-objective optimization system module diagram for the construction of off-grid microgrids according to the present invention. Detailed Implementation

[0018] 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.

[0019] like Figure 1 As shown, the present invention provides a technical solution: a multi-objective optimization method for the construction of off-grid microgrids, comprising the following steps: Acquire basic data for the construction of off-grid microgrids, including but not limited to geographic location information, meteorological data, user electricity load data, parameters of candidate distributed power generation equipment, and parameters of energy storage equipment; Specifically, the basic data required for the construction of off-grid microgrids is obtained through diverse means. Geographical location information is obtained using GPS positioning equipment. For example, in special geographical environments such as mountainous areas and islands, the specific coordinates of the microgrid construction can be accurately determined, providing a basis for subsequent analysis of meteorological conditions such as sunlight and wind speed. Meteorological data is collected in real time through on-site meteorological monitoring equipment, such as anemometers, solar radiation meters, and temperature and humidity sensors, collecting data on wind speed, sunlight intensity, temperature, and humidity. In addition, historical and real-time meteorological data can also be obtained from a meteorological data service platform. The meteorological data collection frequency can be set to once every 15 minutes to meet the needs of microgrid operation analysis. User electricity load data is collected through smart meters. Smart meters are installed on the user side and have metering and communication functions. They can upload the collected real-time electricity data, such as active power and reactive power at different times, to the data processing center via a wireless network at a 1-minute interval, facilitating the analysis of user electricity consumption patterns.

[0020] Based on the acquired basic data, a multi-objective optimization model for off-grid microgrids is constructed. This model includes a construction cost objective function, a power supply reliability objective function, and an environmental friendliness objective function. The calculation formula for the construction cost objective function C is as follows: ,in, Let be the cost of the i-th distributed power supply device, and n be the data of the distributed power supply device. Let m represent the cost of the j-th energy storage device, and m be the number of energy storage devices. The formula for calculating the power supply reliability objective function R is: ,in, This represents the time of the k-th power outage, where l is the number of power outages. Total downtime; The formula for calculating the environmental friendliness objective function E is: ,in This represents the environmental impact coefficient of the i-th distributed power generation unit. This represents the power generation of the i-th distributed power source; The multi-objective optimization model is solved using intelligent optimization algorithms to obtain multiple Pareto optimal solutions; Based on preset decision rules, the optimal solution is selected from multiple Pareto optimal solutions as the optimization scheme for the construction of off-grid microgrids.

[0021] The intelligent optimization algorithm is a non-dominated sorting genetic algorithm, which specifically includes the following operations: Initialize the population and randomly generate a certain number of individuals, each representing an off-grid microgrid construction scheme; Calculate the fitness of each individual, and calculate the fitness value of each individual under each objective according to the construction cost objective function, power supply reliability objective function, and environmental friendliness objective function in the multi-objective optimization model; Perform non-dominated sorting to divide individuals in the population into different non-dominated layers; Crowding is calculated for individuals in the same non-dominated layer to ensure population diversity. New populations are generated through selection, crossover, and mutation operations; Repeat the steps until the preset termination condition is met, and output multiple Pareto optimal solutions.

[0022] Specifically, a certain number of individuals are randomly generated, each representing an off-grid microgrid construction scheme. Assuming the initial population size is 100, each individual consists of the configuration parameters of distributed power devices (such as device type, quantity, and rated power) and energy storage devices (such as device type, quantity, and battery capacity). The fitness value of the individual under each objective is calculated according to the construction cost objective function, power supply reliability objective function, and environmental friendliness objective function in the multi-objective optimization model. For the construction cost objective, the fitness value can be set to 1 / C; for the power supply reliability objective, the fitness value is R; and for the environmental friendliness objective, the fitness value is set to 1 / E. The individuals in the population are divided into different non-dominated layers. If individual A is not inferior to individual B in all objectives and is superior to individual B in at least one objective, then individual A is said to dominate individual B. Individuals not dominated by any other individual constitute the first non-dominated layer. After removing individuals from the first non-dominated layer from the population, the remaining individuals not dominated by other individuals constitute the second non-dominated layer, and so on, until all individuals are assigned to the corresponding non-dominated layer. Crowding density is calculated for individuals in the same non-dominated layer to ensure population diversity. The crowding density calculation formula is as follows: ,in, This represents the crowding level of individual i on target d. This represents the function value of individual i on the objective d. and Let represent the maximum and minimum values ​​of objective d in the non-dominated layer, respectively. The crowding degree of an individual is the sum of its crowding degrees across all objectives. Individuals are selected from the current population using a roulette wheel selection method. Crossover and mutation operations are performed based on crossover and mutation probabilities to generate a new population. The crossover probability is between 0.7 and 0.9, and the mutation probability is between 0.01 and 0.05. The above steps are repeated until a preset termination condition is met, such as reaching the maximum number of iterations or population convergence. At this point, multiple Pareto optimal solutions are output. A Pareto optimal solution is a set of solutions in a multi-objective optimization problem where no other solution is superior to this solution across all objectives.

[0023] The preset decision-making rules include: based on the user's preference weights, weighted summation of the scores of multiple Pareto optimal solutions under the three objectives of construction cost, power supply reliability, and environmental friendliness, and selecting the solution with the highest score as the optimal solution.

[0024] Preferably, the method further includes simulation verification of the optimization scheme. If the simulation results do not meet the preset performance indicators, the multi-objective optimization model is rewritten.

[0025] Preferred performance indicators include construction cost not exceeding 110% of the budget, power supply reliability not less than 99%, and environmental friendliness objective function value within a preset range.

[0026] Specifically, based on a pre-defined decision rule, the optimal solution is selected from multiple Pareto optimal solutions as the optimized scheme for off-grid microgrid construction. The pre-defined decision rule is to calculate the weighted sum of the scores of multiple Pareto optimal solutions under three objectives: construction cost, power supply reliability, and environmental friendliness, according to the user's preference weights, and select the solution with the highest score as the optimal solution. Assuming that the user's preference weights for construction cost, power supply reliability, and environmental friendliness are 0.3, 0.4, and 0.3, respectively, and for a given Pareto optimal solution, its construction cost objective score is set as... The power supply reliability target score is set as follows: The environmental friendliness target score is set as follows: Then the total score S of the solution is: .

[0027] The selected optimization scheme is verified by simulation. The preset performance indicators include construction cost not exceeding 110% of the budget, power supply reliability not less than 99%, and environmental friendliness objective function value within the preset range (e.g., not exceeding the threshold set according to the local environmental capacity). If the simulation results do not meet the preset performance indicators, the multi-objective optimization model is solved again until an optimization scheme that meets the performance indicators is obtained.

[0028] like Figure 2As shown, the multi-objective optimization system for off-grid microgrid construction of the present invention includes: The data acquisition module is used to acquire basic data for the construction of off-grid microgrids. This module includes a GPS unit, a meteorological data acquisition unit, and a smart meter data acquisition unit. The GPS unit is responsible for acquiring geographical location information. Meteorological data is acquired through meteorological monitoring equipment or a meteorological data service platform. The smart meter data acquisition unit collects user electricity load data and uploads it to the data processing center.

[0029] The model building module is used to construct a multi-objective optimization model for off-grid microgrids based on the acquired basic data; based on the basic data acquired by the data acquisition module, a multi-objective optimization model for off-grid microgrids is constructed, including construction cost objective function, power supply reliability objective function, and environmental friendliness objective function.

[0030] The solution module is used to solve the multi-objective optimization model using intelligent optimization algorithms to obtain multiple Pareto optimal solutions. This module includes functional sub-modules such as population initialization, fitness calculation, non-dominated sorting, crowding calculation, selection, crossover and mutation operations, and termination condition judgment.

[0031] The decision module is used to select the optimal solution from multiple Pareto optimal solutions as the optimization scheme for the construction of off-grid microgrids according to preset decision rules.

[0032] Preferably, the system also includes a simulation verification module for simulating and verifying the optimization scheme. If the simulation results do not meet the preset performance indicators, the solution module is triggered to re-solve the multi-objective optimization model.

[0033] Preferably, the data acquisition module includes a GPS unit, a meteorological data acquisition unit, and a smart meter data acquisition unit.

[0034] The preferred solution module uses a non-dominated sorting genetic algorithm to solve the multi-objective optimization objective.

[0035] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. A multi-objective optimization method for the construction of off-grid microgrids, characterized in that, Includes the following steps: Acquire basic data for the construction of off-grid microgrids, including geographic location information, meteorological data, user electricity load data, parameters of candidate distributed power generation equipment, and parameters of energy storage equipment; Based on the acquired basic data, a multi-objective optimization model for off-grid microgrids is constructed. This model includes a construction cost objective function, a power supply reliability objective function, and an environmental friendliness objective function. The calculation formula for the construction cost objective function C is as follows: ,in, Let be the cost of the i-th distributed power supply device, and n be the data of the distributed power supply device. Let represent the cost of the j-th energy storage device, and m be the number of energy storage devices; the formula for calculating the power supply reliability objective function R is: ,in, This represents the time of the k-th power outage, where l is the number of power outages. The total downtime is given; the formula for calculating the environmental friendliness objective function E is: ,in This represents the environmental impact coefficient of the i-th distributed power generation unit. This represents the power generation of the i-th distributed power source; The multi-objective optimization model is solved using intelligent optimization algorithms to obtain multiple Pareto optimal solutions; Based on preset decision rules, the optimal solution is selected from multiple Pareto optimal solutions as the optimization scheme for the construction of off-grid microgrids.

2. The multi-objective optimization method for off-grid microgrid construction according to claim 1, characterized in that, The geographic location information is obtained through GPS, the meteorological data is obtained through meteorological monitoring equipment or meteorological data service platform, and the user electricity load data is collected through smart meters and uploaded to the data processing center.

3. The multi-objective optimization method for off-grid microgrid construction according to claim 1, characterized in that, The intelligent optimization algorithm is a non-dominated sorting genetic algorithm, which specifically includes the following operations: Initialize the population and randomly generate a certain number of individuals, each representing an off-grid microgrid construction scheme; Calculate the fitness of each individual, and calculate the fitness value of each individual under each objective according to the construction cost objective function, power supply reliability objective function, and environmental friendliness objective function in the multi-objective optimization model; Perform non-dominated sorting to divide individuals in the population into different non-dominated layers; Crowding is calculated for individuals in the same non-dominated layer to ensure population diversity. New populations are generated through selection, crossover, and mutation operations; Repeat the steps until the preset termination condition is met, and output multiple Pareto optimal solutions.

4. The multi-objective optimization method for off-grid microgrid construction according to claim 1, characterized in that, The preset decision rules include: according to the user's preference weights, weighted summation of the scores of multiple Pareto optimal solutions under the three objectives of construction cost, power supply reliability, and environmental friendliness, and selecting the solution with the highest score as the optimal solution.

5. The multi-objective optimization method for off-grid microgrid construction according to claim 4, characterized in that, It also includes performing simulation verification on the optimization scheme. If the simulation results do not meet the preset performance indicators, the multi-objective optimization model is rewritten.

6. The multi-objective optimization method for off-grid microgrid construction according to claim 5, characterized in that, The preset performance indicators include construction cost not exceeding 110% of the budget, power supply reliability not less than 99%, and environmental friendliness objective function value within the preset range.

7. A multi-objective optimization system for off-grid microgrid construction, characterized in that, include: The data acquisition module is used to acquire basic data for the construction of off-grid microgrids; The model building module is used to construct a multi-objective optimization model for off-grid microgrids based on the acquired basic data; The solver module is used to solve multi-objective optimization models using intelligent optimization algorithms to obtain multiple Pareto optimal solutions; The decision-making module is used to select the optimal solution from multiple Pareto optimal solutions as the optimization scheme for the construction of off-grid microgrids according to preset decision-making rules.

8. The multi-objective optimization system for off-grid microgrid construction according to claim 7, characterized in that, It also includes a simulation verification module, which is used to perform simulation verification on the optimization scheme. If the simulation results do not meet the preset performance indicators, the solution module is triggered to re-solve the multi-objective optimization model.

9. The multi-objective optimization system for off-grid microgrid construction according to claim 7, characterized in that, The data acquisition module includes a GPS unit, a meteorological data acquisition unit, and a smart meter data acquisition unit.

10. The multi-objective optimization system for off-grid microgrid construction according to claim 7, characterized in that, The solution module uses a non-dominated sorting genetic algorithm to solve the multi-objective optimization objective.

Images