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This paper first presents a generic model to characterize a variety of flexible demand-side resources (e.g., plug-in electric vehicles and distributed generation). Key sources of uncertainty affecting the modeling results are identified and are characterized via multiple stochastic scenarios. We then propose a risk-averse optimal bidding formulation for the resource aggregator at the demand side based on the conditional value-at-risk (VaR) theory. Specifically, this strategy seeks to minimize the expected regret value over a subset of worst-case scenarios whose collective probability is no more than a threshold value. Our approach ensures the robustness of the day-ahead (DA) bidding strategy while considering the uncertainties associated with the renewable generation, real-time price, and electricity demand. We carry out numerical simulations against three benchmark bidding strategies, including a VaR-based approach and a traditional scenario based stochastic programming approach. We find that the proposed strategy outperforms the benchmark strategies in terms of hedging high regret risks, and results in computational efficiency and DA bidding costs that are comparable to the benchmarks.
Xu et al. (Thu,) studied this question.