Optimizing Grid-Connected Solar PV and Battery Systems for Prosumers: Social Group Optimization

This paper presents an investigation into the cost of operation of a grid-connected solar photovoltaic (PV) power generation system coupled with battery storage for prosumers within a smart grid framework. To predict the PV generation profile of prosumers during a day a code is provided in MATLAB program. A MATLAB program implementing a linear programming model for community energy management is utilized to optimize energy exchange between prosumers and an energy retailer over a 24-hour period. Matrices and vectors are employed to solve the linear programming problem, determining optimal power exchange and energy storage levels to minimize community cost. The study introduces the Social Group Optimization (SGO) algorithm for calculating optimum power levels, which minimizes total costs for prosumers transitioning to a smart grid system. Results from one-day test simulations reveal that the SGO algorithm yields a significant reduction in operational costs, approximately €10.532, compared to alternative optimization algorithms. Additionally, the SGO algorithm demonstrates faster convergence towards optimal solutions, achieving the minimum cost price within 30 iterations. Moreover, the paper proposes further optimization of total costs to prosumers through load scheduling, capitalizing on favorable grid tariffs at different times of the day. The integration of scheduling optimization with the SGO algorithm-based optimization enhances cost-effectiveness and improves overall system performance. The study concludes by suggesting the extrapolation of one-day simulation results to model variations in generation throughout the year, facilitating long-term planning for prosumers within smart grid systems. Overall, the research underscores the effectiveness of the SGO algorithm in optimizing smart grid operations, offering significant cost savings and improved efficiency.