Incorporate Day-Ahead Robustness and Real-Time Incentives for Electricity Market Design

Yi Guo, Xuejiao Han, Xinyang Zhou, Gabriela Hug

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations


In this paper, we propose a two-stage electricity market framework to explore the participation of distributed energy resources (DERs) in a day-ahead (DA) market and a real-time (RT) market. The objective is to determine the optimal bidding strategies of the aggregated DERs in the DA market and generate online incentive signals for DER-owners to optimize the social-welfare taking into account network operational constraints. Distributionally robust optimization is used to explicitly incorporate data-based statistical information of renewable forecasts into the supply/demand decisions in the DA market. We evaluate the conservativeness of bidding strategies distinguished by different risk aversion settings. In the RT market, a bi-level time-varying optimization problem is proposed to design the online incentive signals to tradeoff the RT imbalance penalty for distribution system operators (DSOs) and the costs of individual DER-owners. This enables tracking their optimal dispatch to provide fast balancing services, in the presence of time-varying network states while satisfying the voltage regulation requirement. Simulation results on both DA wholesale market and RT balancing market demonstrate the necessity of this two-stage design, and its robustness to uncertainties, the performance of convergence, the tracking ability and the feasibility of the resulting network operations.

Original languageAmerican English
Article number120484
Number of pages14
JournalApplied Energy
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2022 The Authors

NREL Publication Number

  • NREL/JA-5D00-84942


  • Distribution networks
  • Electricity market mechanism
  • Online optimization
  • Power systems
  • Stochastic optimization


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