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<p>At present, renewable energy represented by landscape is developing rapidly, but it has the characteristics of randomness, volatility and uncertainty, which not only increases the regulation pressure of conventional power supply, but also brings great challenges to the economic operation of power grid. In this paper, combining the characteristics of multi-energy complementarity of the electric-gas-thermal integrated energy system, and considering the coupling relationship between different energy sources and economic operation, a multi-objective optimization model is constructed to minimize the system operation cost and maximize the wind and photovoltaic consumption. Aiming at the nonlinear, non-differentiable and multi-constraint characteristics of the proposed multi-objective model, a hybrid non-dominated sorting genetic algorithm and multi-objective particle swarm optimization (NSGA-II-MOPSO) algorithm are proposed. The method calculates the crowding distance and sorts according to the elite strategy, and then updates the particle velocity and position to obtain the optimal Pareto front. On the basis of improving the absorption of wind and photovoltaic, the system operation cost is effectively reduced. Finally, the electric-gas-thermal IES 39-20-6 system is used for verification, the simulation results show that the proposed model is correct and the algorithm is effective.</p>

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Multi-Objective Optimization of Power-Gas-Heat Integrated Energy System Based on NSGA-II-MOPSO Hybrid Intelligent Algorithm

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Jiayu Li,
  • Ziyi Gong,
  • Guixi Miao,
  • Xin Wang,
  • Liang Yuan,
  • Xuefa Jia,
  • Hui Ma

摘要

At present, renewable energy represented by landscape is developing rapidly, but it has the characteristics of randomness, volatility and uncertainty, which not only increases the regulation pressure of conventional power supply, but also brings great challenges to the economic operation of power grid. In this paper, combining the characteristics of multi-energy complementarity of the electric-gas-thermal integrated energy system, and considering the coupling relationship between different energy sources and economic operation, a multi-objective optimization model is constructed to minimize the system operation cost and maximize the wind and photovoltaic consumption. Aiming at the nonlinear, non-differentiable and multi-constraint characteristics of the proposed multi-objective model, a hybrid non-dominated sorting genetic algorithm and multi-objective particle swarm optimization (NSGA-II-MOPSO) algorithm are proposed. The method calculates the crowding distance and sorts according to the elite strategy, and then updates the particle velocity and position to obtain the optimal Pareto front. On the basis of improving the absorption of wind and photovoltaic, the system operation cost is effectively reduced. Finally, the electric-gas-thermal IES 39-20-6 system is used for verification, the simulation results show that the proposed model is correct and the algorithm is effective.