Optimization of load capacity in microgrids (SmartGrid) using linear programming
DOI:
https://doi.org/10.51247/st.v9iS1.731Keywords:
energy optimization, Pyomo, mixed-integer linear programming, microgrids, renewable energy, battery storage.Abstract
This work presents the design, formulation, and implementation of an energy optimization model based on mixed-integer linear programming, applied to the annual operation of a residential microgrid with photovoltaic generation, battery energy storage, and grid connection. The proposed methodology is grounded in the mathematical formulation of an hourly energy dispatch problem, incorporating technical constraints related to energy balance, battery state-of-charge dynamics, power limits, and the non-simultaneity of charging and discharging processes. The model was implemented using the open-source Pyomo environment within Python, together with a freely available linear solver, ensuring reproducibility and adaptability to different contexts. To assess the performance of the proposed approach, the obtained results were compared with those of an annual heuristic strategy based on sequential dispatch rules. The results show that the mathematical programming model achieves an annual operating cost reduction of between 2% and 3%, significantly increases the utilization of photovoltaic energy, and reduces grid energy purchases by 30% to 40%, while maintaining 100% demand coverage in all cases. The study confirms the feasibility and potential of mathematical optimization techniques as decision-support tools for the efficient and sustainable management of residential microgrids.
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