Comparing Microgrid Planning and Management Methods for Energy Resilience and Sustainability
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Abstract
As the global energy landscape continues to evolve, microgrids have emerged as a pivotal solution to address the increasing demand for reliable and sustainable power generation. With a growing emphasis on renewable energy sources, this research explores the critical role of microgrids in modern energy systems. The importance of microgrids and renewable energy sources in the current energy landscape cannot be overstated. In an era marked by climate change concerns and a heightened awareness of environmental sustainability, microgrids offer a means to decentralize power generation and promote local resilience. The paper tackles challenges like grid stability, load management, and cyber security, offering real-world case studies to demonstrate the practical applications of these strategies in various settings, from remote communities to industrial facilities and smart cities. This research addresses these challenges by implementing optimization strategies that included advanced control algorithms, real-time data analytics, and load management techniques. Therefore, in this paper we investigate the most recent developments in microgrid design and control strategies to achieve sustainable energy management. It emphasizes key elements of microgrid systems, including the integration of renewable energy, energy storage, and efficient control mechanisms. It explores innovative approaches such as incorporating artificial intelligence, machine learning, and real-time data analytics to optimize energy generation, storage, and distribution within microgrids by using integration of both qualitative and quantitative techniques to provide a comprehensive understanding of microgrid design and control strategies for sustainable energy management. Quantitative data analysis involves the application of statistical techniques to analyze the collected data, enabling the assessment of key performance metrics related to microgrid deployments.