Enhancing renewable energy integration through strategic stochastic optimization planning of distributed energy resources (Wind/PV/SBESS/MBESS) in distribution systems

Abstract

This paper presents a comprehensive long-term stochastic mixed-integer single-level single-stage nonlinear multi-objective optimization planning model for integrating Distributed Energy Resources (DERs), including wind Distributed Generations (DGs), photovoltaic (PV) DGs, stationary Battery Energy Storage Systems (SBESSs), and mobile Battery Energy Storage Systems (MBESSs), over a 10-year project horizon. The model evaluates the efficiency and cost-effectiveness of hybrid SBESS-MBESS systems to enhance Renewable Energy Source (RES) integration within the electric power distribution system (DS) while addressing technical, environmental, and economic objectives. It minimizes total expected planning, operation, and emission costs, power loss, and voltage deviation by determining the optimal locations and capacities for wind DGs, PV DGs, and SBESSs, and by establishing a monthly transportation schedule for MBESSs. The optimization also coordinates the charging and discharging profiles of SBESSs and MBESSs to maximize green energy utilization and minimize system costs. Monte Carlo Simulation (MCS) models uncertainties in wind speed, solar irradiation, load power, and energy prices, while the backward reduction method (BRM) mitigates computational complexities. A hybrid optimization approach combining the non-dominated sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO) with a decision-making algorithm is proposed to solve the planning problem. Simulations on a 69-bus DS demonstrate significant reductions in long-term costs (37.72 %), power loss (41.58 %), and voltage deviation (47.07 %) achieved by the hybrid SBESS-MBESS system compared to other configurations, underscoring its potential to enhance renewable energy integration and system performance in transitioning energy systems.