Optimization and simulation of a grid-connected PV system using load forecasting methods: A case study of a university building

Abstract

Distributed generation represents a paradigm shift from the traditional electric grid to localized generation of electric power along with the capability of incorporating renewable energy (RE) sources into the energy mix. Responding to the need for sufficient analysis, simulation, and study of feasibility of distributed generation, this study aims to design a hybrid energy system for a university building and analyze its economic benefits. The viability of existing load forecasting methodologies for energy systems is also presented in this paper. The energy system design was determined through predictive modeling of the load profile of the building using historical data and optimization using machine learning methods, namely auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM). Simulations were run in HOMER Pro. Results show that a grid-connected solar photovoltaic (PV) system installed on the roof coupled with an energy storage system (ESS) will provide the most economic benefits because it yields a reduced cost of energy (COE) per kilowatt-hour (kWh) for the building. This study shows that in line with efforts to transition towards clean energy, hybrid energy systems using RE not only have economic benefits, but can also ensure energy security and environmental sustainability.