Optimal planning of renewable distributed generators and battery energy storage systems in reconfigurable distribution systems with demand response program to enhance renewable energy penetration
Saleh Ba-swaimi, Renuga Verayiah, Vigna K. Ramachandaramurthy, Ahmad K. ALAhmad, Sanjeevikumar Padmanaban
Abstract
The shift to renewable energy offers compelling advantages on both environmental and economic fronts. Our communities become cleaner and healthier as renewable power sources dramatically cut greenhouse gas (GHG) emissions and air pollution. While renewable systems require upfront investment, they prove financially advantageous in the long run by reducing operating costs and eliminating exposure to unpredictable fuel prices. Additionally, the expanding renewable energy sector generates valuable employment opportunities across installation, maintenance, and technology development roles. Driven by this motivation, this study presents a two-stage stochastic mixed-integer non-linear programming (MINLP) multi-objective optimization model for enhancing renewable energy integration in distribution systems (DS) over a ten-year planning horizon. The first stage employs Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize long-term objectives of minimizing system cost, power loss, and voltage deviation through strategic allocation of photovoltaic distributed generators (PV-DGs), wind distributed generators (W-DGs), and battery energy storage systems (BESSs). The second stage utilizes Multi-Objective Particle Swarm Optimization (MOPSO) to minimize hourly operational metrics through optimal coordination of BESS charging/discharging schedules, demand response programs (DRPs), and network reconfiguration (NR). The model incorporates uncertainties in load demand, energy pricing, solar irradiation, and wind speed through Monte Carlo Simulation (MCS) and backward reduction algorithm (BRA). The methodology was validated on a modified IEEE 69-bus system across five progressive cases: the baseline system, systems with only RDGs, RDGs integrated with BESSs, RDGs coupled with BESSs and DRPs, and RDGs incorporating BESSs, DRPs, and NR. The results demonstrate that the proposed work significant improvements system parameters, achieving reductions of 44.87%, in system cost, 63.65%, in power loss, and 70.72%, in voltage deviation compared to the Case I (base case). Additionally, optimal coordination of DR and NR improved renewable energy utilization by 21.62% compared to the Case III (with only BESSs).