Multi-objective optimal allocation of renewable distributed generation units in a distribution network under high penetration of plug-in hybrid electric vehicles

This paper presents a novel multi-objective framework for efficiently allocating renewable distribution generation (RDG) in electric distribution systems (EDSs) amidst high plug-in hybrid electric vehicle (PHEV) penetration. The study incorporates a practical PHEV demand model considering various stochastic parameters. It investigates the impact of PHEV adoption on EDS via simulations with three demand response levels, including grid-to-vehicle and vehicle-to-grid options. Unlike traditional RDG studies focusing solely on technical or economic objectives, the proposed framework optimizes both aspects simultaneously using a posteriori multi-objective methodology. The multi-objective problem is addressed using the multi-objective artificial hummingbird algorithm (MOAHA), and the best trade-off solution from Pareto optimal solutions is selected using the technique for order preference by similarity to the ideal solution. The framework is validated on 33-bus, 69-bus and 118-bus benchmark electric distribution test systems, offering three distinct optimal solutions for each system to accommodate diverse decision-maker preferences. The reduction in energy loss after allocation of RDGs in the presence of PHEVs is observed to be 70.65%, 75.84% and 31.30%, respectively, for 33-bus, 69-bus and 118-bus test systems. Finally, the effectiveness of MOAHA is demonstrated through comparative analysis with other leading optimization algorithms.