Intelligent Energy System and Power Metering Optimization Based on the Energy Plan Model
DOI:
https://doi.org/10.13052/spee1048-5236.43310Keywords:
Energy Plan, Energy structure, Energy and environmental benefits, NSGA-II, electric power measurementAbstract
With the national carbon neutrality and carbon peaking policies proposed one after another, this paper proposes intelligent Energy system design and power metering based on the Energy Plan model. In this paper, the Energy PLAN model is proposed as the basic model for energy development planning during the 14th Five-Year Plan period, and the simulation calculation is carried out for the energy system in 2020. The research shows that there is a large gap between the peak and valley of power load, the peak time of power supply and demand does not match, and the response-ability of the energy supply department is poor. Thus, the problems to be solved in the development of energy are determined. In addition, the accuracy of the model is further verified by comparing the actual data with the simulation results of Energy PLAN, and the direction of energy structure reform is determined. By coupling the NSGA-II (Non-dominated Sorting Genetic Algorithms-II) algorithm with the development model, a dual-objective multi-time scale scheduling model is established, which aims at the minimum operating cost and the best energy and environmental benefit coefficient. Secondly, the proposed model is solved using the NSGA-II algorithm. In order to ensure the diversity of solutions and promote the Pareto front approach to the ideal Pareto front, the fuzzy dominance method is used to perform a fast non-dominated sorting of the population, and the maximum satisfaction method is used to select the Pareto optimal compromise solution. Example analysis shows that when the power demand level is L*-L*-L*, the power input under “H-M”, “M-L” and “H-L” scenarios is 106.02 × 103, 102.73 × 103 and 94.2 × 103 GWh, respectively. However, when the power demand level is H*-L*-L*, the power input is always 142.64 × 103 GWh. With the decrease in the transmission rate, the measurement data obtained by the user’s electric acquisition platform becomes less and less, and the performance of state estimation becomes worse. When λ=50%, the proposed model still maintains a high estimation accuracy.
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