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Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework

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  • Wang, Jian
  • Ilea, Valentin
  • Bovo, Cristian
  • Xie, Ning
  • Wang, Yong

Abstract

This paper addresses a self-scheduling model for a multi-energy virtual power plant (MEVPP) to optimize its day-ahead energy and reserve schedules considering the participation in joint markets. The coordination of energy and reserve services is realized by developing a holistic market framework. MEVPP trades electric energy in day-ahead market and natural gas in natural gas market under the uniform price scheme. MEVPP provides reserve in ancillary service market under the pay-as-bid scheme considering uncertain market clearing prices. Reserve regulations are modeled for the reserve quality provided by MEVPP. MEVPP can sign contracts in capacity market for capacity adequacy. The electricity and natural gas imbalance payments and unsupplied reserve penalty resulting from uncertain PV generation are calculated in real-time. The case studies, based on the practical data from Italian power exchange and transmission system operator, show the economic achievements of MEVPP with multiple markets participation. The advantages of multi-energy coupling in improving flexibility and economic profit are numerically analyzed. MEVPP is proven to be a promising reserve service supplier for TSO. Because through reasonable regulations, the reserve quality of MEVPP can be improved with little impact on its total cost.

Suggested Citation

  • Wang, Jian & Ilea, Valentin & Bovo, Cristian & Xie, Ning & Wang, Yong, 2023. "Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework," Energy, Elsevier, vol. 278(PB).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pb:s0360544223012975
    DOI: 10.1016/j.energy.2023.127903
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    Cited by:

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    2. Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).

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