基于改进鸟群算法的多目标微电网优化调度研究

Multi-objective Microgrid Optimal Scheduling Based on Improved Bird Flock Algorithm

为克服传统智能算法在微电网多目标优化调度中存在局部最优、过早收敛等问题,提高微电网调度的准确性、稳定性和收敛速度,提出了1种基于改进鸟群算法的多目标微电网优化调度策略。首先,分析了微电网多目标调度的影响因素,建立了基于最低经济成本及最小环境影响的微电网调度模型。其次,通过随机均匀分布自适应选择惯性权重系数,运用线性调整策略及学习系数平衡全局和局部搜索能力,提高鸟群算法的收敛速度和搜索精度,并基于Lévy飞行策略更新鸟类群体的空间位置,扩大搜索范围、丰富种群多样性,从而使所提方法跳出局部最优实现精准收敛。最后,通过搭建并网运行条件下的典型微电网场景进行仿真实验,并使用典型的测试函数将所提方法与其他成熟算法进行对比分析。实验结果表明,所提方法的全局优化性能、收敛精度、稳定性和收敛速度均优于其他对比方法,且具有良好的经济性和环境友好性,能够实现良好的多目标优化平衡。

In order to overcome the local optimum and premature convergence of traditional intelligent algorithms in multi-objective optimal scheduling of microgrids,improve the accuracy,stability and convergence speed of microgrid scheduling,a multi-objective optimal scheduling strategy for microgrids based on improved bird swarm algorithm is proposed.Firstly,the influencing factors of microgrid multi-objective scheduling are analyzed,the microgrid scheduling model based on minimum economic cost and minimum environmental impact is established.Secondly,by adaptively selecting inertia weight coefficients through random uniform distribution,linear adjustment strategy and learning coefficients are applied to balance global and local search ability,improve the convergence speed and search accuracy of bird flock algorithm,and update the spatial position of bird population based on Lévy flight strategy to expand the search range and enrich the population diversity,so that the proposed method can jump out of the local optimum and achieve accurate convergence.Finally,simulation experiments are conducted by building a typical microgrid scenario under grid-connected operation conditions,and the proposed method is compared and analyzed with other mature algorithms by using typical test functions.The experimental results show that the global optimization performance,convergence accuracy,stability and convergence speed of the proposed method are better than other comparative methods,and the proposed method has good economic and environmental friendliness,and can achieve good multi-objective optimization balance.