Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing

Ana Turk, Qiuwei Wu*, Menglin Zhang, Jacob Østergaard

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Secure operation of the power system is challenged by the high level of uncertainty and fluctuation introduced by renewable energy sources. More flexibility is needed to cope with the uncertainty and improve the utilization of renewable energy. A prominent solution to provide flexibility, and simultaneously increase the efficiency of the system, is the integration of different energy sectors. This paper proposes a two-stage stochastic scheduling scheme of an integrated multi-energy system, which considers the wind power uncertainty and the synergy of different energy sectors to achieve the optimal economic operation of the whole system with minimum curtailment of wind power. In the first stage, energy and reserve scheduling of generating units is performed, while accommodation of wind power production is realized through reserves in the second stage. In the proposed scheme, the electric power system, natural gas system, and district heating system are coordinated to achieve more flexibility, both in the day-ahead and real-time stage. The stochasticity of the wind power uncertainty is represented by realistic scenarios with corresponding probabilities, which are obtained from a scenario generation algorithm based on historical observations taking into account the temporal correlation of wind power. The simulation results on a smallscale test system show that both, the economic efficiency and wind power utilization, have been improved with more flexibility and more reliable scenario set. It is shown, that the total system cost is reduced and reserves are optimized.
Original languageEnglish
Article number117130
Number of pages17
Publication statusPublished - 2020


  • Integrated multi-energy system
  • Temporal-correlated scenario set
  • Two-stage stochastic programming
  • Wind power uncertainty

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