Journal
APPLIED ENERGY
Volume 233, Issue -, Pages 321-337Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2018.10.003
Keywords
Hybrid renewable energy; Hydrogen; Fuel cell; Economic optimization; Model predictive control
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Funding
- National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme
- MOE AcRF Grant in Singapore [R-279-000-537-114]
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While macroscopic modelling and dynamic analyses are topics approached by many to improve the efficiency of hybrid energy systems, the optimization and control of operational strategies need to be studied to allow practicality of such configurations in an industrial setting. Simple implementation and reliable operability of any critical sub-system component of a hybrid energy module are critical requirements for the development of their control algorithms. In this paper, a state-of-the-art hybrid renewable energy system consist of different subsystems is presented with resolved economically optimal dispatch strategies. The local control of one of its key exemplary sub-system, a Solid Oxide Fuel Cell (SOFC), is realized via two methods i.e. a model-based approach of shrinking-horizon Model Predictive Control (MPC) with constraints and a data-driven approach using Virtual Reference Feedback Tuning (VRFT) to identify controller parameters. Fixed current load and fixed conversion efficiency operations are assessed respectively for 1-h and 24-h spans. For both simulation cases, results show that both control methods provide robust power tracking performance within constrained boundaries with marginal to no overshoot and the VRFT method achieving stability at least 10 times faster than the MPC method. While the two juxtaposed methods are diametrically different, they are both technically viable and efficient under industrial power loads. The main contribution of this work is to demonstrate the feasibility of the hybrid energy system by providing the practitioners with a broad range of local control options, depending on operational realities and constraints.
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