An hourly energy management strategy based on the development of a system dynamics model for microgrid systems

• Developed a System Dynamics model for precise hourly microgrid energy balance. • Coordinated renewables, diesel generators, and batteries for real-time power adjustments. • Achieved 30 % peak-hour error cost reduction using a penalty-weighted approach. • Validated model adaptability through simulations, including a real-world Iranian microgrid. • Demonstrated scalability for diverse microgrid setups with data-driven control methods. This study develops a System Dynamics based (SD-based) energy management strategy for microgrids to achieve precise hourly power balance by coordinating renewable resources, diesel generators, and battery storage. Addressing the volatility of renewable generation, the model exploits real-time adjustments in power generation and storage to match dynamic demand levels. Simulations were run on three cases, including a practical case using data from an Iranian microgrid, to study the cost-effectiveness and flexibility of the proposed strategy. The results demonstrate the capability of the model in following complicated power changes with sustained energy balance at reduced operating costs. For Case III, under fast changes in demand, the strategy reached a total cost of 233.33 M.U (Monetary Unit). The use of a penalty-weighted strategy had the effect of reducing peak-hour error costs by 30 %, reflecting good cost management even during increased variability. Utilizing the Vensim software, the S.D. model responds by shifting to diesel generation in periods of shortage and harvesting renewable energy in surplus periods, reflecting an optimal system of real-time control. Additionally, the simulations also indicate that greater energy change magnitudes, especially abrupt changes, increase overall costs, which justifies the application of strategic control for high-variability scenarios. In conclusion, this SD-based method offers a low-cost, dependable microgrid energy management solution that maximizes the utilization of renewables with a continuous energy supply. The success in handling various demand profiles indicates the potential for scalability in other microgrid setups, thereby enhancing sustainable and resilient decentralized energy systems via flexible, data-driven control strategies.