| Abstract |
Utilizing renewable energy sources (RESs) offers a pathway towards a cleaner and more sustainable future by reducing carbon emissions, enhancing energy generation independently from conventional methods, and driving innovation in green technologies. Motivated by these goals, this paper introduces a long-term Mixed-Integer Nonlinear Programming (MINLP) multi-objective stochastic optimization planning model to increase the penetration of green energy in the distribution system (DS). The model integrates wind and solar Photovoltaic (PV) distributed generations (DGs) and battery energy storage systems (BESSs). It simultaneously minimizes three long-term objectives: total cost, power loss, and voltage deviation by determining the optimal locations and sizes for wind-DGs, PV-DGs, and BESSs. Additionally, the model incorporates a demand response program (DRP) to enhance green energy integration further. To address uncertainties in wind speed, solar irradiation, load demands, and energy prices, Monte Carlo Simulation (MCS) is employed. Scenario reduction through the Backward Reduction Algorithm (BRA) manages computational complexity. To solve the proposed model, a hybrid approach combining Non-Dominated Sorting Genetic Algorithm II (NSGAII) and Multi-Objective Particle Swarm Optimization (MOPSO) is employed. The proposed model has been considered planning for ten years, and this was simulated and validated on the IEEE 33-bus radial DS using MATLAB R2023b. Four cases were studied to demonstrate the proposed model's effectiveness: base case, DGs, DGs-BESSs, and DGs-BESSs-DRP. The results showed that the model substantially reduces total system cost by 26.27 %, power loss by 50.79 %, and voltage deviation by 47.53 % compared to the base case. Moreover, the integration of DRP significantly increased the green energy penetration by 6.52 % compared to the case without DRP. |