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Cross-domain Pareto optimization of heterogeneous domains for the operation of smart cities

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  • Stoyanova, Ivelina
  • Monti, Antonello

Abstract

This paper presents a tool for multi-objective cross-domain optimization of heterogenous energy and non-energy domains for urban areas in a comprehensive optimization approach. The multi-objective optimization concept facilitates the definition of any number of objective functions, versatile in their origin and properties, flexibly adaptable to area specifics or regional and communal priorities. The applied optimization for several domains at the lowest system level yields a comprehensive holistic solution optimal for the particular subsystem, as it is fitted for this subsystem according to its characteristics. The main advantage is the exploitation of cross-domain interactions and synergies and, therefore, the improved overall performance of the system. However, the cross-domain approach requires high-level modeling in order to overcome compatibility issues, which decreases the accuracy of domain-specific simulation and optimization compared to methods which apply detailed models. Based on the mathematical concept of multi-objective Pareto optimization, its adaptation, implementation and application in the context of Smart Cities are presented in detail. Several optimization formulations and methods are applied to the test scenario to demonstrate the concept, compare the results and evaluate the computational performance of the application. The ε-constraint method appears to be better suitable for heterogeneous domains, as the weighted sum method is highly sensitive to weight setting for heterogeneous objectives. The computational performance is very good in terms of computation time but meets numerical limitations for samples of around 500 buildings, leading to numerical infeasibility for samples of 1300 buildings. This is solved with the presented method for model aggregation. The tool is available on GitHub.

Suggested Citation

  • Stoyanova, Ivelina & Monti, Antonello, 2019. "Cross-domain Pareto optimization of heterogeneous domains for the operation of smart cities," Applied Energy, Elsevier, vol. 240(C), pages 534-548.
    • Handle: RePEc:eee:appene:v:240:y:2019:i:c:p:534-548
    DOI: 10.1016/j.apenergy.2019.02.010
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    References listed on IDEAS

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    1. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Liu, Xuezhi & Wu, Jianzhong & Jenkins, Nick & Bagdanavicius, Audrius, 2016. "Combined analysis of electricity and heat networks," Applied Energy, Elsevier, vol. 162(C), pages 1238-1250.
    3. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
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    Cited by:

    1. 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).
    2. Nie, Yonghui & Qiu, Yu & Yang, Annan & Zhao, Yan, 2024. "Risk-limiting dispatching strategy considering demand response in multi-energy microgrids," Applied Energy, Elsevier, vol. 353(PA).

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