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Relative optimization potential: A novel perspective to address trade-off challenges in urban energy system planning

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  • Lin, Jian
  • Zhong, Xiaoyi
  • Wang, Jing
  • Huang, Yuan
  • Bai, Xuetao
  • Wang, Xiaonan
  • Shah, Nilay
  • Xie, Shan
  • Zhao, Yingru

Abstract

Urban energy systems often contain multiple optimization sub-objectives with different dimensions. They often need to be normalized in many multi-objective optimization problems. This process may result in the loss of trade-off characteristics of each sub-objectives. This paper introduces a novel indicator, relative optimization potential, to quantify the difference in sensitivity of sub-objectives to the trade-off process, which is easily ignored in previous studies. An integrated multi-objective optimization framework for urban energy systems with flexible decision-making approaches is further proposed to match the differentiated requirements of various scenarios. This framework includes a partial a posterior approach to tackle high-dimensional multi-objective optimization problems, which reduces the computational cost while obtaining equivalent or better results through a reasonable simplification of the decision process. Moreover, a results evaluation method consistent with the perspective of decision-making is proposed to improve the interpretability and consistency of the entire trade-off process. The case studies illustrate the preference characteristics of the proposed approach under different scenarios. And its feasibility is verified by the cross-sectoral planning problem of integrated energy system and building retrofit in different urban areas and scenarios. The proposed approach favors different sub-objectives under different building environments, demand profiles and grid-connection conditions while seeking the maximization of overall relative benefits. Overall, the proposed approach can be applied flexibly in various optimization scenarios to rationally trade-off multi-objective according to different optimization conditions without causing a significant increase in computational cost. This study provides a new tool for multi-objective optimization of urban energy systems with different perspectives.

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  • Lin, Jian & Zhong, Xiaoyi & Wang, Jing & Huang, Yuan & Bai, Xuetao & Wang, Xiaonan & Shah, Nilay & Xie, Shan & Zhao, Yingru, 2021. "Relative optimization potential: A novel perspective to address trade-off challenges in urban energy system planning," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921010874
    DOI: 10.1016/j.apenergy.2021.117741
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    2. Zhao, Xiangming & Guo, Jianxiang & He, Maogang, 2023. "Multi-objective optimization and improvement of multi-energy combined cooling, heating and power system based on system simplification," Renewable Energy, Elsevier, vol. 217(C).
    3. Faustino, Fausta J. & Lopes, José Calixto & Melo, Joel D. & Sousa, Thales & Padilha-Feltrin, Antonio & Brito, José A.S. & Garcia, Claudio O., 2023. "Identifying charging zones to allocate public charging stations for electric vehicles," Energy, Elsevier, vol. 283(C).
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