Balancing Operation Cost and Carbon Emissions in Multi-Energy Microgrid Scheduling Using Joint Operator Evolutionary Algorithm

A multi-energy microgrid (MEMG), incorporating a combined heat and power (CHP) system and electric vehicles (EVs), is an important infrastructure to meet multiple energy demands and reduce carbon emissions. However, operation cost minimization and carbon emissions minimization in the MEMG conflict with each other. Therefore, this paper proposes a multi-objective optimal scheduling model for the MEMG, aimed at achieving a balance between operation cost and carbon emissions. Moreover, a novel multi-objective evolutionary algorithm (MOEA) called joint operator evolutionary algorithm (JOEA) is developed to solve this model. Experimental results indicate that JOEA has excellent performance on the proposed multi-objective optimal scheduling problem. In addition, JOEA is compared with several well-known MOEAs on this engineering problem and 20 benchmark problems. The comparison results show that JOEA outperforms other MOEAs and has competitive performance.