Enhanced Prosumer Energy Management Using Modified Social Group Optimization for Cost-Effective and Sustainable Energy Utilization

Abstract

Effective energy management in prosumer communities is significant for optimizing renewable energy usage and cutting down costs. The research develops an optimization framework to analyze the impact of scaling up photovoltaic (PV) generation and demand on self-consumption, storage utilization, and grid interaction. A linear programming approach can be used to minimize total energy costs by optimizing energy purchases, storage operation, and grid sales. Additionally, the Modified Social Group Optimization (MSGO) algorithm improves the optimization efficiency, taking into account the variations in demand, storage restriction, and the limits of grid exchanges. Simulation results show that by increasing PV generation, self-consumption and energy export are maximized, while high demand requires efficient storage and thus large reliance on grids. The system generates 182.58 kW PV energy and the consumption of 343.20 kW requires import of 262.80 kW. The storage systems manage surplus power 109.23 kW; of that stored, 72.63 kW is released during low solar periods. Economically, contribution of PV sales reaches €41.29, and that of storage adds up to €18.45, resulting in partial offsetting of total costs amounting to €340. Findings highlight that proper scaling of PV and managing demand could enhance energy efficiency as well as reduce dependence on the grid while unlocking better economic returns, thus making this framework a very advantageous tool in making sustainable energy plans for prosumer communities.

Keywords

• Energy cost optimization
• Modified Social Group Optimization
• Prosumer energy management
• Renewable energy utilization
• Smart grids
• Social Group Optimization
• PV

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