Cost-Effective and Low-Carbon Emission Deployment of PV-DG Integration in Distribution Networks Using Self-Adaptive Bonobo Optimizer

Abstract

This study presents an advanced optimization approach, the self-adaptive bonobo optimization technique (SABOT), designed specifically to facilitate the seamless integration of photovoltaic-distributed generation (PV-DG) in distribution networks. While retaining the foundational principles of the standard BOT, SABOT incorporates four distinct mating strategies: promiscuous, restrictive mating, consortship, and extra-group mating. To enhance its capabilities, SABOT introduces advanced features such as a memory mechanism and a repulsion-based learning technique for dynamic parameter adjustment across successive iterations. These enhancements significantly improve the algorithm’s exploration potential, enabling more effective identification of optimal solutions. The developed SABOT seeks to minimize the costs associated with carbon dioxide (CO₂) emissions from the power grid, operational expenses of PV units, and energy losses. To accurately model the variability of solar power generation, the beta probability density function (PDF) is employed, capturing the daily fluctuations in solar irradiation. The improved SABOT was rigorously evaluated on two test systems: a real-world Ajinde Nigerian distribution network and the widely-used IEEE 69-bus system. The simulation results highlight SABOT’s superior performance, demonstrating substantial decreases in emissions and losses of energy, thereby underscoring its effectiveness as a robust optimization tool for sustainable energy solutions. The aggregate yearly costs of emissions and lost energy for the Ajinde system are significantly reduced by 31% using the suggested SABOT version in comparison to the original scenario. It also achieves a significant 35% decrease for the IEEE 69-bus system. Additionally, the simulation results demonstrate the competitive performance of the proposed SABOT version in comparison to differential evolution (DE), particle swarm optimizer (PSO), the techniques, and the conventional BOT.