Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging

The climate change is addressing unprecedented pressures on conventional power system regarding the significant fossil fuel consumptions and carbon emissions, which largely challenges the conventional power system operation. This paper proposes a novel dynamic non-dominated sorting multi-objective biogeography-based optimization (Dy-NSBBO) to solve multi-objective dynamic economic emission load dispatch considering the mass integration of plug-in electric vehicles (PEVs), namely MO-DEELDP problem. First, a real-world economic emission load dispatch considering PEVs charging is first formulated as a constrained dynamic multi-objective optimization problem. Then a new multi-objective BBO is proposed adopting the non-dominated solution sorting technique, change detection and memory-based selection strategies in the multi-objective BBO method to strengthen the dynamic optimization performance. The proposed Dy-NSBBO is applied to solve three different dynamic economic emission load dispatch cases integrating four plug-in electric vehicle charging scenarios respectively. Comprehensive analysis shows that the novel algorithm is promising to bring considerable economic and environmental benefits to the power system operators and provides competitive charging strategies for policy makers and PEVs aggregators.