Intelligent microgrid energy management with improved solar pv forecasting and optimal load scheduling

Abstract

Energy consumption is rapidly increasing due to population growth and the rising reliance on technology. This surge, coupled with the scarcity of energy resources, poses significant challenges for nations striving for sustainable development and progress. Renewable energy sources are the preferred way to generate electricity in the future, which will lead to a reduction in dependence on traditional grids and a more sustainable energy management approach. Accurate, rapid, and reliable solar PV forecasting is essential for informed decision-making, effective energy management, and strategic planning. It ensures a resilient and secure power grid, empowering participants to adeptly navigate the complexities of energy supply and consumption with confidence. Inaccurate solar power forecasting leads to several critical issues: grid instability due to power fluctuations, electricity shortages or wastage from underestimating or overestimating solar output, and suboptimal resource allocation. These challenges ultimately reduce the overall efficiency of energy systems. In this context, a dynamic forecasting model that adeptly captures assertive stochastic and non linear behaviors is crucial. Additionally, each technique, both conventional and modern, grapples with its unique limitations that can impede its overall effectiveness. A comprehensive evaluation of diverse forecasting models has emerged, covering everything from traditional techniques to cutting-edge methods, all aimed at effectively tackling the complex challenges of non-linear solar PV prediction. Developing advanced hybrid models has become a key strategy to address these challenges, as they effectively combine the strengths of individual forecasting methods. This integrated approach substantially enhances performance and accuracy in solar power forecasting. As a result, many research efforts focus on creating forecasting models that provide reliable, consistent, and accurate solar PV predictions to improve energy management efficiency. newline