A novel two-stage multi-objective optimization strategy for enhanced network planning and operation

Abstract

The significant growth in the utilization of electric vehicles (EVs) and adoption of distributed energy resources (DERs) have transformed the landscape of the energy sector. Despite the advantages offered by EVs and DERs, they introduce additional challenges to system operators. Therefore, this paper proposes a multi-objective optimization strategy for enhancing network operation and planning, focusing on the allocation and sizing of electric vehicle charging stations (EVCSs), DERs, and capacitor banks (CBs), along with dynamic network reconfiguration. For this purpose, a novel two-stage methodology is introduced to address planning and operation separately, in which decision variables that remain constant over time (e.g., location of DERs, EVCSs, and CBs) are distinguished from those that can be adjusted in real-time (e.g., network configuration, CB taps, and DER operating points). The main objective is to minimize overall costs, voltage deviation, and power losses while ensuring compliance with network constraints. The optimization problem is addressed through the multi-objective cuckoo search, and the final solution is chosen using the fuzzy decision-making method. Finally, the effectiveness of the proposed approach is demonstrated through a comprehensive comparison with prior studies in the field and with the well-established non-dominated sorting genetic algorithm II.