Optimal Planning and Decision-Making for Hybrid Microgrids: Integrating Diverse Renewable Sources with Battery Systems

Microgrids as modern power system facilities must be optimally designed to secure their efficient techno-economic feasibility. The uncertainty in renewable energy and electricity demand growth which can be managed by appropriate design and demand-side management methods. In this research work, a hybrid microgrid comprising 3 ^rd generation Perovskite Solar Cells / Wind Turbines / Micro turbines and battery system is modeled in Homer Pro software 3.14.2 to determine the optimal decisions for different demand-side management cases. Furthermore, the Perovskite Solar Cells devices were fabricated and experimentally examined and integrated to microgrid under investigation. This paper investigates the techno-economic modeling and optimization-based demand-side management of microgrids to maintain the load demand of a rural community on the coast of Aswan, Egypt. Various load demand scenarios involving baseloads without demand-side management, with loads shifting-based demand-side management, and with combined loads shifting and peak clipping-based demand-side management, are implemented, including their impacts on the microgrids' cost, reliability, and emissions are evaluated and compared. The design objective is to minimize the levelized cost of energy subject to various operational constraints based on Homer Pro software. The results have indicated a significant reduction in the Perovskite Solar Cells output power (89.0 kW) with Case-1(base load). While applying Case-2 (demand-side management based on loads shifting technique) the power reduced to 78.9 KW. Added to that the implementing of Case-3 (demand-side management based on loads shifting and loads clipping techniques) the power reduced to 77.6 KW. The results revealed that at Case-2 the demand side management was able to attain an attractive reduction of cost of energy by 14% in comparison with Case-1. Moreover, emissions at Case-3 decreased by 15% in comparison with Case-1. Finally, the proposed approach is to assist decision-makers to provide costs- effective and emissions-conscious planning for microgrids efficiently.