Optimizing renewable energy and green technologies in distribution systems through stochastic planning of distributed energy resources

This paper introduces a short-term stochastic Mixed Integer Non-Linear Programming (MINLP) multi-objective optimization model designed for collaborative planning. It integrates renewable-based distributed generations (RDGs) like wind and photovoltaic (PV) sources, plug-in electric vehicle parking lots (PEV-PLs), stationary battery energy storage systems (SBESSs), and mobile battery energy storage systems (MBESSs). The main goal is to enhance the penetration of green energy and Plug-in Electric Vehicles (PEVs) within the distribution system (DS). This objective is pursued through the optimization of various planning and operational decision variables, encompassing the number, location, and size of wind DGs, PV DGs, PEV-PLs, and SBESSs, as well as the number, sizing, and monthly transportation schedule of MBESSs, along with the hourly scheduling of charging and discharging profiles for PEVs, SBESSs, and MBESSs. The decision variables are concurrently minimized across three constrained objective functions: total annual expected investment, maintenance and operational costs, power loss, and voltage fluctuation. To validate the efficacy of the collaborative planning model, a 69-bus DS is selected as the case study, with five different configurations proposed: Base case, wind/PV/PEV-PL, wind/PV/PEV-PL/ SBESS, wind/PV/PEV-PL/MBESS, and wind/PV/PEV-PL/SBESS/MBESS. The research findings, summarized in the conclusion sections, highlight the advantages and disadvantages of each proposed configuration regarding economic, environmental, and technical objectives and their impact on green energy and PEV penetration levels.