基于改进粒子群算法的综合能源系统多目标优化运行

Multi-objective Optimal Operation of Integrated Energy System Based on Improved Particle Swarm Optimization Algorithm

“双碳”目标下,电力能源系统需要逐步向节能低碳的方向发展,综合能源系统(IES)是解决能源与环保问题的重要举措。目前对IES的研究主要集中在分布式能源、储能并网和多目标优化等方面,智能算法是处理优化问题的重要途径,但随着模型复杂化,传统的智能算法存在收敛性差、容易陷入局部最优的问题。基于此围绕IES经济、环保与稳定运行目标,构建了考虑经济性、环保性及出力不平衡性3个指标的基于改进粒子群算法的IES多目标优化模型。首先,以3个指标最优为目标搭建了IES模型;其次,采用隶属度函数和层次分析法(AHP)进行归一化处理并确定权重系数;最后,引入粒子浓度评价算子改进粒子群算法,对所提模型进行求解,并分析系统在单一目标和多目标情况下的运行结果,验证了模型和算法的有效性。改进算法显著提高了收敛速度,有效避免了粒子陷入局部最优。

Under the"dual carbon"goal,the electric energy systems need to gradually develop towards the way of energy saving and low carbon.The integrated energy system(IES)is an important measure to solve energy and environmental protection problems.At present,the research on IES mainly focuses on distributed energy,energy storage grid connection and multi-objective optimization.Intelligent algorithm is an essential way to deal with optimization problems.However,with the complexity of the model,the traditional intelligent algorithms have the problem of poor convergence and easy to fall into the local optimum.Centering on the objectives of economy,environmental protection and stable operation,a multi-objective optimization model of IES based on improved particle swarm optimization considering three indicators of economy,environmental protection and output imbalance was built.Firstly,the IES model was established with the goal of optimizing the three indicators.Secondly,the membership function and the analytic hierarchy process(AHP)were used to normalize and determine the weight coefficient.Finally,the particle concentration evaluation operator was introduced to improve the particle swarm algorithm to solve the proposed model,and the operating results of the system under singleobjective and multi-objective conditions were analyzed,which verifies the effectiveness of the model and algorithm.The improved algorithm significantly improve the convergence speed and effectively avoide the particles falling into the local optimum.