Planning and optimization of a residential microgrid utilizing renewable resources and integrated energy storage

This paper introduces a strategic planning and optimization framework for residential microgrids, integrating renewable energy resources and advanced energy storage systems. The framework aims to improve energy management efficiency, reliability, and sustainability within residential microgrids. It focuses on seamlessly incorporating photovoltaic (PV) systems, air conditioning water heaters (ACWH), and battery storage, leveraging historical climate data, diverse control strategies, and various operational scenarios to manage uncertainties inherent in energy systems. At the framework's core is a nonlinear programming computation that achieves the trade-offs between the sizes of solar panels, ACWH units, and batteries against the system's installation investment. This algorithm seeks to configure key microgrid components optimally, balancing reliability and costeffectiveness. The economic viability of the proposed system is a key highlight, as it is evaluated using annualized life-cycle costing methods, offering a promising assessment of potential financial benefits. The study investigates different tariff structures and consumption patterns, stressing the flexibility and effectiveness of the framework in enhancing system performance and instilling confidence in the system's adaptability and efficiency. Furthermore, the research highlights the critical role of sophisticated energy management systems (EMS) in enabling real-time monitoring, control, and optimization of energy generation and storage. EMS technologies facilitate optimized energy dispatch and cost minimization, contributing significantly to developing sustainable residential microgrid solutions. Integrating electrical and heat energy storage systems in a well-balanced manner can significantly improve energy utilization efficiency. Under specific tariff and load scenarios, this enhanced efficiency can significantly reduce the investment payback period, potentially by up to 46.99 %, 45.82 %, and 48.91 % for Load Profiles A, B, and C, respectively. Increasing the battery size for Load Profile A under Case 3 tariffs by 7.54 % compared to Case 1 tariffs significantly reduces payback; similarly, for Load Profile C under Case 3 tariffs, a 10.41 % larger battery size compared to Case 1 tariffs reduces payback, emphasizing the advantages of increased battery capacity. This paper offers a robust strategy for planning and optimizing the integration of renewable resources and energy storage in residential microgrids, paving the way for more resilient and costeffective energy systems.