Modelling and optimal management of renewable energy communities using reversible solid oxide cells

F.R. Bianchi, B. Bosio, F. Conte, S. Massucco, G. Mosaico, G. Natrella and M. Saviozzi

Applied Energy, 2023, vol. 334, issue C, No S0306261923000211

Abstract: The use of reversible solid oxide cells within a renewable energy community results a promising application which permits to balance the temporal mismatch between renewable energy production and users’ demand through hydrogen as energy vector. Differently from batteries and supercapacitors, this technology is characterized by a high stored energy density and a negligible daily self-discharge. Nevertheless, the system management is more complex requiring cell behaviour optimization and hydrogen storage control. Here this work proposes a control algorithm for reversible solid oxide cell operation coupled to renewable energy sources within a renewable energy community formed by an aggregation of fifteen residential customers. Based on forecasts of loads and renewable energy production, the proposed algorithm, a stochastic model predictive control, can optimize system operation aiming at economic benefit maximization. The transition between the fuel cell mode for power generation and the electrolysis mode for energy storage through hydrogen production was set considering the available renewable energy, the power demand of community members and the energy sell-back price in order to increase the auto-consumed amount as well as to favour the electricity exchange within the renewable energy community. Since the reversible solid oxide cell is the key-point in such a system, SIMFC-SIMEC (SIMulation of Fuel Cells and Electrolysis Cells), a physically based 2D model, allowed an effective prediction of cell behaviour deriving the efficiency of electricity and hydrogen production from local physicochemical feature and working parameter gradients on each stacked cell plane.

Keywords: Reversible Solid Oxide Cells; Green Hydrogen; Renewable Energy Communities; Stochastic Model Predictive Control (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2023.120657

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