Efficient Online Heuristic Approach for Handling Fluctuation in Renewable Energy in a Microgrid

Abstract

Renewable resources are prime contributors to energy in a microgrid. Fluctuation in the renewable outcome is inevitable because of strong dependence on the atmospheric condition. Energy storage systems (ESSs) are often used to mitigate variability issues. Development of efficient methodology to supply necessary electrical energy to the load from a combination of ESS and utility grid with minimal cost is the need of the hour. The variable pricing scheme adds another dimension to the problem. In this article, we propose a mixed-integer linear programming (MILP) formulation to find an optimal operation schedule of charging/discharging of an ESS offline. As MILP formulation fails to scale for large inputs, we propose a heuristic algorithm, namely, intelligent day-ahead schedule with predicted renewable energy heuristic algorithm, to quickly find a good solution. We propose another online heuristic strategy, namely, online scheduling algorithm (OSA), which can take account of real-time fluctuation. We provide an extensive comparative study of the different proposed approaches using the Pecan Street dataset. We observe that the OSA can provide a reasonably acceptable solution considering its minimal requirement of computing resources. Under a significant fluctuation in the renewable outcome, a maximum of 18% deviation was observed for the OSA compared to every slot optimal results, i.e., OSET <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\mathtt {on}$</tex-math></inline-formula> .