多目标粒子群算法的动态多燃料经济环境负荷分配

Dynamic multiple-fuels economic environmental dispatch using multi-objective particle swarm optimization

考虑动态的负荷需求和多种燃料资源,以经济成本和环境成本为优化指标,建立动态多燃料经济环境负荷分配的多目标优化模型,并提出一种多目标粒子群优化算法求解该类优化模型.模型采用动态负荷需求和多种燃料资源,更有利于节约电能成本和提高能源利用效率,但高维数、复杂非线性和多目标成为求解该优化模型的难点,故在算法中引入多目标解集更新策略和变邻域搜索策略.实验仿真结果表明,该模型是有效的,且采用所提算法求解这类模型时所获得的近似Pareto前端的精度明显优于其他算法.

Aiming at economic cost and environmental cost, and considering dynamic load demand and multiple fuels, a multi-objective dynamic multi-fuel economic environmental dispatch model is established and a multi-objective particle swarm optimization is proposed to solve the optimization model. Dynamic load and multiple fuels benefit energy cost saving and the improvement of energy utilization efficiency of the model. However, it is a hard task to solve it due to the high dimensions, complex nonlinearity and multiple objectives. In the algorithm, an update strategy of multi-objective set and variable neighborhood search are introduced. Simulation results show that the model is valid and the approximate Pareto front obtained by the proposed algorithm is superior to other algorithms in terms of the degree of approximation for solving the problem.