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Optimization of demand response-oriented electrolytic and fuel cell cogeneration system for community residents: uncovering flexibility and gaps

Zhang, X., Ramírez-Mendiola, J. L. ORCID: https://orcid.org/0000-0001-7666-7440, Lai, Y., Su, J. ORCID: https://orcid.org/0000-0003-1757-4900, Li, M. and Guo, L. (2023) Optimization of demand response-oriented electrolytic and fuel cell cogeneration system for community residents: uncovering flexibility and gaps. Energy Conversion and Management, 287. 117099. ISSN 0196-8904

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To link to this item DOI: 10.1016/j.enconman.2023.117099

Abstract/Summary

Low carbon energy systems are dependent on renewable power sources, which present challenges in controllability compared to conventional sources. This poses difficulties in maintaining grid balance. To address these challenges, demand response mechanisms and low-carbon technologies are being implemented, particularly in the residential sector, which is a significant consumer of electricity and heat. In this respect, on-site hydrogen production by alkaline electrolytic cell (AEC) and proton exchange membrane fuel cell - combined heat and power (PEMFC-CHP) systems are of particular interest for their potential to improve grid flexibility. For uncovering the flexibility and techno-economic gaps of the hydrogen based system, this study compares the performance of an AEC-PEMFC-CHP system with a market-competitive heat pump (HP) system for a residential community scenario. Results from a two-step capacity-operation optimization using advanced methods demonstrate that the AEC-PEMFC-CHP system offers greater flexibility but at the expense of higher power consumption and lower efficiency. In detail, the cost of the AEC-PEMFC-CHP system is 2.3 times higher than that of the typical HP system, implying that it would require a 0.6 times higher efficiency and a 1/10th lower equipment cost to compete. Furthermore, load prediction plays a critical role in optimizing both systems, with a longer prediction horizon of 16–20 h proving effective even with larger prediction errors.

Item Type:Article
Refereed:Yes
Divisions:Science > School of the Built Environment > Construction Management and Engineering
ID Code:112153
Publisher:Elsevier

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