基于灰狼多目标算法的电网碳-能复合流优化调度

Grey Wolf Multi-objective Optimizer for Optimal Carbon-energy Combined-flow

针对低碳经济发展过程中电网应承担的碳排放责任,本文将实际的潮流和虚拟的碳流结合,通过追溯网损对应的碳排放建立起电网碳-能复合流优化模型,并采用多目标灰狼算法对该模型进行求解。通过在原始灰狼算法中加入Pareto存档、α、β和δ狼选择机制及灰狼的游荡行为,使之能够应用到多目标优化中,并得到分布性能较好的Pareto前沿。最后引入改进TOPSIS法进行折中解选择。IEEE118节点仿真结果表明多目标灰狼算法在电网碳-能复合流优化模型中具有很好的适用性。

Under the consideration of the responsibility for carbon emissions to be borne by power grid during the low-carboneconomic development, this paper proposes a grey wolf multi-objective optimizer( GWMO) to achieve an optimal carbonenergy combined-flow( OCECF), which is based on the combination of the actual energy flow and virtual carbon flow as wellas the trace of the reactive power dispatch of the power grid. The proposed algorithm introduce Pareto archived, α, β and δ wolfselection mechanism and wandering behavior of gray wolf into original grey wolf optimizer to realize a multi-objective optimizationand an excellent distributed Pareto front. Moreover, an improved TOPSIS is adopted to select the compromise solution.IEEE 118-bus system simulation results show that the GWMO for OCECF has a good applicability.