Modeling and optimization of energy management systems with solar-load balancing in a smart campus: Huye campus as a case study

Abstract

This study addresses the critical challenge of optimizing energy management in a smart campus envi- ronment through the integration of solar photovoltaic (PV) systems, energy storage, and dynamic load balancing. Focusing on the Huye campus of the University of Rwanda, the project develops a robust energy management system (EMS) that takes advantage of predictive analytics, stochastic and robust optimization techniques, and real-time Model Predictive Control (MPC) to minimize grid reliance, re- duce operational costs, and enhance sustainability. By analyzing historical energy consumption pat- terns (2019–2023) and simulating scenarios such as sunny, cloudy, and grid outage conditions, the EMS demonstrates a reduction of 60–92% in energy costs through prioritized solar utilization, demand re- sponse (DR) strategies, and optimization of energy storage. A Decision Support Tool (DST) is integrated to provide actionable insights, enabling campus managers to make data-driven decisions about energy efficiency. Key results include an 81% reduction in grid dependency, a validated photovoltaic capacity of 848 kWp, and a scalable framework applicable to educational institutions in solar-rich regions. Key innovations include a scenario-based resilience framework for cloudy or rainy days and a Decision Support Tool (DST) that uses data-driven insights, predictive analytics, and actionable recommenda- tions to enhance system efficiency, reliability, and sustainability. Simulations demonstrate a 4.2-hour backup during grid outages and a projected 6.2-year payback period for the 848-kWp PV system. The work aligns with Rwanda’s National Energy Policy (2023) and offers a replicable model for regional educational institutions.