Energy Conversion and Management: X (Oct 2024)
Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration
Abstract
This work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. The outer-stage problem simultaneously minimizes the long-term expected planning costs, power losses, and voltage deviations by determining the optimal sizing and placement of renewable energy resources (RESs), such as solar photovoltaic distributed generators (PV-DGS), wind-DGs, and battery energy storage systems (BESSs). In contrast, the inner-stage problem emphasizes the reduction of hourly operational expenses, power losses, and voltage deviations through the identification of optimal scheduling for demand response programs (DRPs) and network reconfiguration (NR). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to address the outer-stage optimization problem. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to address the inner-stage issue. In both phases, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is utilized at the conclusion of each iteration to identify the ideal solution from a collection of non-dominated solutions. Monte Carlo simulation (MCS) is utilized to model the system’s unknown factors, including solar radiation, wind speed, load demand, and energy pricing. Subsequently, the backward reduction algorithm (BRA) is employed to streamline the resulting scenarios into a more feasible and representative subset, therefore mitigating excessive computational effort. The suggested model is validated utilizing the IEEE 33-bus DS developed in MATLAB R2023b. Simulation outcomes from various case studies indicate that incorporating optimal DRP and NR scheduling into a hybrid system of RESs and BESSs enhances renewable energy penetration by 17.39% compared to the case utilizing just BESSs. Moreover, the established model, featuring a wind-DG/PV-DG/BESS/DRP/NR configuration, achieves significant improvements in all objective functions, including a 31.14% reduction in total system cost, a 61.67% decrease in power loss, and a 58.11% improvement in voltage deviation, compared to the base case.
