Control of an isolated microgrid using hierarchical economic model predictive control

Will Challis Clarke, Michael John Brear and Chris Manzie

Applied Energy, 2020, vol. 280, issue C, No S0306261920314148

Abstract: This article experimentally demonstrates a novel, microgrid control algorithm based on a two-layer economic model predictive control framework that was previously developed by the authors. This algorithm is applied to an isolated microgrid with a solar photovoltaic system, a battery bank and a gasoline-fuelled generator. The control system performance is experimentally compared to a baseline algorithm over 5 min and 10 h periods, while an experimentally validated model is used to compare performance over a full year. The results indicate that applying the proposed, two-layer economic model predictive control algorithm can reduce operating costs and CO2 emissions by 5%–10% relative to conventional, rule based methods, and by 10%–15% if improved solar and demand forecasts are available. Furthermore, the proposed two-level algorithm can achieve reductions of up to 5% compared with current state-of-the-art methods which only attempt to optimize performance in the energy management system.

Keywords: Microgrid; Hybrid power plant; Remote power system; Economic model predictive control; Energy storage; Optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2020.115960

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