Optimal energy management in decentralized systems

Abstract

South Africa is currently grappling with a significant energy crisis, marked by increasing electricity demand, deteriorating coal-based infrastructure, and frequent instances of load shedding. With coal accounting for more than 80% of electricity production, this dependency results in elevated carbon emissions, power outages, and economic instability. Shifting towards renewable energy offers a viable solution to these issues by harnessing South Africa’s plentiful solar and wind resources, thereby promoting sustainability and cost-effectiveness. Nevertheless, critical challenges such as energy intermittency, management of excess energy, and high infrastructure expenses must be overcome to facilitate a dependable and equitable energy transition. This research introduces a comprehensive and optimized energy-sharing framework aimed at addressing these challenges, in alignment with Sustainable Development Goal 7 (SDG7), which seeks to ensure affordable, reliable, sustainable, and modern energy for all. A hybrid renewable energy system is modeled using HOMER Pro, integrating solar, wind, and battery storage systems to achieve cost-effective energy generation. To enhance energy demand forecasting, a hybrid Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) model is utilized, allowing for data-driven planning and minimizing supply-demand discrepancies. Thesizing of system componentsisfurther refined through a hybrid Genetic Algorithm-Particle Swarm Optimization (GA-PSO) method, aimed at reducing costs and enhancing performance. An energy-sharing model based on Linear Programming (LP) guarantees equitable energy distribution among consumers, with a focus on prioritizing surplus renewable energy. This framework is further strengthened by incorporating Game Theory (GT) to encourage cooperative energy trading, enhance equitable energy access, and maximize cost savings. Key performance indicators, such as reduced grid dependence, cost savings, integration of renewable energy, and decreased carbon emissions, are assessed to validate the framework’s effectiveness. The results indicate that this hybrid, data-driven strategy significantly improves energy efficiency, resilience, and sustainability.