Multi-objective stochastic techno-economic-environmental optimization of distribution networks with G2V and V2G systems

Plug-in electric vehicles (PEVs) are one of the most promising technologies for decarbonizing the transportation sector towards the global Net-zero target. However, charging/discharging of PEVs impacts the electricity network's stability, increases the operating costs, and affects the voltage profile. This paper proposes a flexible multi-objective optimization approach to evaluate and deploy vehicle-to-grid and grid-to-vehicle technologies considering techno-economical and environmental factors. Furthermore, life cycle of PEV batteries, charg-ing/discharging pattern, and driving behaviours of the PEV owners are considered. The simulations are run over a modified IEEE 69-bus radial distribution test system to minimize two objective functions including the operating costs and CO2 emissions using the heuristic-based Firefly Algorithm in a stochastic optimization framework considering renewable generations, load consumption, and charging/discharging timing of PEVs as the uncertain parameters. The results demonstrate significant reductions in the operating costs and CO2 emissions, and the voltage profile of the network is improved properly. Besides, by implementing the discharging facility of PEVs in the network, the PEV owners save a considerable amount in operating costs.