Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints

Jing, Rui, Wang, Meng, Liang, Hao, Wang, Xiaonan, Li, Ning, Shah, Nilay and Zhao, Yingru 2018. Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints. Applied Energy 231 , pp. 534-548. 10.1016/j.apenergy.2018.09.151

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Abstract

By connecting stand-alone energy systems, the neighborhood-level urban energy network can serve several buildings in a more economic and ecological manner. In some cases, in order to achieve the best performance of the entire network, the benefit of some buildings within the network may not be guaranteed. Few attentions have been paid to the benefit allocation fairness for energy networks. This study proposes novel cost and emission benefit allocation constraints inspired from cooperative Game theory to ensure that each involved building shares the benefit together. A Mixed Integer Linear Programming (MILP) model is developed to investigate the impacts of benefit allocation constraints. The model offers different network topologies, i.e., centralized mode and distributed mode. Multi-objective optimization and decision-making are further conducted to assess the trade-offs between different objectives via generating the Pareto frontier. Through an illustrative case study, a three-building neighborhood-level energy network is optimal designed in Shanghai, China. The results indicate that when benefit allocation is considered, the solution space will slightly shrink compared to the scenario not considering benefit allocation. Meanwhile, distributed mode achieves better performance than the centralized mode. Overall, the analyses provide a solid approach to enhance infrastructure planning for urban energy networks particularly when the stakeholders of each participating building within a network are different.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0306-2619
Date of Acceptance:	16 September 2018
Last Modified:	04 Aug 2022 02:12
URI:	https://orca.cardiff.ac.uk/id/eprint/138970

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