Enhancing microgrid energy management through solar power uncertainty mitigation using supervised machine learning

Abstract This study addresses the inherent challenges associated with the limited flexibility of power systems, specifically emphasizing uncertainties in solar power due to dynamic regional and seasonal fluctuations in photovoltaic (PV) potential. The research introduces a novel supervised machine learning model that focuses on regression methods specifically tailored for advanced microgrid energy management within a 100% PV microgrid, i.e. a microgrid system that is powered entirely by solar energy, with no reliance on other energy sources such as fossil fuels or grid electricity. In this context, “PV” specifically denotes photovoltaic solar panels that convert sunlight into electricity. A distinctive feature of the model is its exclusive reliance on current solar radiation as an input parameter to minimize prediction errors, justified by the unique advantages of supervised learning. The performance of four well-established supervised machine learning models—Neural Networks (NN), Gaussian Process Regression (GPR), Support Vector Machines (SVM), and Linear Regression (LR)—known for effectively addressing short-term uncertainty in solar radiation, is thoroughly evaluated. Results underscore the superiority of the NN approach in accurately predicting solar irradiance across diverse geographical sites, including Cairo, Egypt; Riyadh, Saudi Arabia; Yuseong-gu, Daejeon, South Korea; and Berlin, Germany. The comprehensive analysis covers both Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), demonstrating the model’s efficacy in various solar environments. Additionally, the study emphasizes the practical implementation of the model within an Energy Management System (EMS) using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, showcasing high accuracy in microgrid energy management. This validation attests to the economic efficiency and reliability of the proposed model. The calculated range of error, as the median error for cost analysis, varies from 2 to 6%, affirming the high accuracy of the proposed model.