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In this paper, we propose a two-stage electricity market framework to explorethe participation of distributed energy resources (DERs) in a day-ahead (DA)market and a real-time (RT) market. The objective is to determine the optimalbidding strategies of the aggregated DERs in the DA market and generate onlineincentive signals for DER-owners to optimize the social welfare taking intoaccount network operational constraints. Distributionally robust optimizationis used to explicitly incorporate data-based statistical information ofrenewable forecasts into the supply/demand decisions in the DA market. Weevaluate the conservativeness of bidding strategies distinguished by differentrisk aversion settings. In the RT market, a bi-level time-varying optimizationproblem is proposed to design the online incentive signals to tradeoff the RTimbalance penalty for distribution system operators (DSOs) and the costs ofindividual DER-owners. This enables tracking their optimal dispatch to providefast balancing services, in the presence of time-varying network states whilesatisfying the voltage regulation requirement. Simulation results on both DAwholesale market and RT balancing market demonstrate the necessity of thistwo-stage design, and its robustness to uncertainties, the performance ofconvergence, the tracking ability, and the feasibility of the resulting networkoperations.

Incorporate Day-ahead Robustness and Real-time Incentives for Electricity Market Design