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From bidding strategy in smart grid toward integrated bidding strategy in smart multi-energy systems, an adaptive robust solution approach

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  • Aghamohamadi, Mehrdad
  • Mahmoudi, Amin

Abstract

The integration of different energy carriers may result in significant cross effects among multi-energy players in both decision making and energy provision perspectives. Accordingly, the consumption of other energy types, would be subject to an hourly-based pattern along with electric energy. Therefore, the proactive role of energy hub system (EHS), employed to represent the interactions between different energy carriers, can be developed not only for electricity market, but also for different types of energy. However, due to load/energy price forecast errors, EHSs might not be able to efficiently participate in day-ahead market as a prosumer. Therefore, a proper bidding strategy is required to effectively characterize the associated uncertainties. To do so, this paper proposes an adaptive robust integrated bidding strategy for EHS to participate in day-ahead energy markets. Accordingly, the EHS operator can bid in multiple energy markets at a time to maximize/minimize its benefit/operation costs. The proposed model has been developed as a min-max-min problem in the context of adaptive robust optimization, which is solved with a new solution consisting of bi-level decomposition, duality theory, primal cutting planes and a post-event analysis. A comprehensive case study has been also conducted to evaluate the performance of the proposed model.

Suggested Citation

  • Aghamohamadi, Mehrdad & Mahmoudi, Amin, 2019. "From bidding strategy in smart grid toward integrated bidding strategy in smart multi-energy systems, an adaptive robust solution approach," Energy, Elsevier, vol. 183(C), pages 75-91.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:75-91
    DOI: 10.1016/j.energy.2019.06.106
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    Cited by:

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    3. Zheng, Shunlin & Sun, Yi & Li, Bin & Qi, Bing & Zhang, Xudong & Li, Fei, 2021. "Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects," Applied Energy, Elsevier, vol. 283(C).
    4. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    5. Sobhani, Seyed Omid & Sheykhha, Siamak & Madlener, Reinhard, 2020. "An integrated two-level demand-side management game applied to smart energy hubs with storage," Energy, Elsevier, vol. 206(C).
    6. Yang, Peiwen & Dong, Jun & Lin, Jin & Liu, Yao & Fang, Debin, 2021. "Analysis of offering behavior of generation-side integrated energy aggregator in electricity market:A Bayesian evolutionary approach," Energy, Elsevier, vol. 228(C).
    7. Liu, Yangyang & Zhou, Jiangxin & Zhou, Qihui & Liu, Chuanquan & Yu, Feng, 2023. "Bidding strategy of integrated energy system considering decision maker’s subjective risk aversion," Applied Energy, Elsevier, vol. 341(C).
    8. Siqin, Zhuoya & Niu, DongXiao & Wang, Xuejie & Zhen, Hao & Li, MingYu & Wang, Jingbo, 2022. "A two-stage distributionally robust optimization model for P2G-CCHP microgrid considering uncertainty and carbon emission," Energy, Elsevier, vol. 260(C).

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