OPTIMIZATION OF STORAGE CAPACITY AND SOLAR PV SIZING ON HOME ENERGY MANAGEMENT SYSTEM USING GRID COMPUTING

The optimization of storage capacity and solar PV sizing in a home energy management system (HEMS) using grid computing is a topic of increasing importance in achieving energy efficiency and sustainability. This research paper aims to address the challenges associated with storage capacity optimization and solar PV sizing in HEMS by integrating grid computing techniques. Through a comprehensive literature review, the study explores existing research on energy management systems, storage capacity optimization, solar PV sizing, and grid computing. The gaps in the literature are identified, emphasizing the need for efficient optimization techniques in HEMS. The research methodology involves data collection, analysis, and the implementation of optimization algorithms within the framework of grid computing. Mathematical models and algorithms are developed to optimize the storage capacity and solar PV sizing of the HEMS. The objective is to minimize energy consumption, maximize renewable energy utilization, and optimize the interaction with the grid. The results obtained from the optimization process demonstrate the effectiveness of the proposed approach. The optimized storage capacity and solar PV sizing lead to significant energy savings, reduced reliance on the grid, and enhanced costeffectiveness. Moreover, the integration of grid computing techniques improves the efficiency and performance of the HEMS, allowing for real-time adjustments and better utilization of available resources. The findings of this research paper contribute to the field of home energy management by providing insights into the optimization of storage capacity and solar PV sizing using grid computing. The optimized HEMS can empower homeowners to make informed decisions about energy consumption, reduce their environmental impact, and achieve greater energy independence. Furthermore, the integration of grid computing techniques paves the way for scalable and sustainable energy management systems. Future research directions could include the development of advanced optimization algorithms, the integration of additional renewable energy sources, and the consideration of dynamic pricing models. These advancements will further enhance the optimization process and expand the applicability of HEMS in various contexts.