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Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model

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  • Fan, Linyuan
  • Ji, Dandan
  • Lin, Geng
  • Lin, Peng
  • Liu, Lixi

Abstract

With the advent of multi-carrier energy systems in the context of local energy markets, e.g., thermal market, virtual energy hub plants have become more prominent in many countries to achieve sustainable energy supply networks. The former researches on techno-economic operation of virtual energy hub plants did not consider the promoted power-to-X (P2X) conversion facilities alongside hydrogen market to assess the energy efficiency, greenhouse gas emissions, and economic issues in an integrated manner. To this end, this paper presents a risk-averse multi-objective method to tackle the self-scheduling problem of the virtual energy hub plant and address the mentioned issues. The considered virtual energy hub plant is operated based on the different energy carriers, i.e., electricity, heating, cooling, and hydrogen, P2X units, electric vehicles, and developed demand response (DR) strategy. The virtual energy hub plant operator tries to maximize its revenue by participating in the local energy markets and minimizing CO2 emission rates considering different uncertain sources. The combined heat and power (CHP) unit and DR strategy are utilized as practical solutions for the instability of renewable power output as well as exploiting opportunities in various local energy markets. To model the proposed strategy, a non-probabilistic information gap decision theory (IGDT) is developed. The presented model is applied to a sample test case, and numerical results confirm that the profit of the virtual energy hub plant is increased by up to 17.54%, and the CO2 emission rate is reduced by up to 10.24% considering the up-to-date energy storage systems, developed DR strategy, and P2X units.

Suggested Citation

  • Fan, Linyuan & Ji, Dandan & Lin, Geng & Lin, Peng & Liu, Lixi, 2023. "Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223008563
    DOI: 10.1016/j.energy.2023.127462
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    3. Mei, Shufan & Tan, Qinliang & Liu, Yuan & Trivedi, Anupam & Srinivasan, Dipti, 2023. "Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction," Energy, Elsevier, vol. 284(C).
    4. Zhao, Kaifang & Qiu, Kai & Yan, Jian & Shaker, Mir Pasha, 2023. "Technical and economic operation of VPPs based on competitive bi–level negotiations," Energy, Elsevier, vol. 282(C).
    5. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).
    6. Liao, Zitian & Liao, Xiaoqun & Khakichi, Aroos, 2024. "Optimum planning of energy hub with participation in electricity market and heat markets and application of integrated load response program with improved particle swarm algorithm," Energy, Elsevier, vol. 286(C).
    7. Zheng, Yangbing & Xue, Xiao & Xi, Sun & Xin, Wang, 2024. "Balancing Possibilist-probabilistic risk assessment for smart energy hubs: Enabling secure peer-to-peer energy sharing with CCUS technology and cyber-security," Energy, Elsevier, vol. 304(C).
    8. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).

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