Distributionally robust optimization for energy and reserve toward a low-carbon electricity market
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2017 Elsevier B.V. This paper proposes a two-stage distributionally robust model for the optimization of energy and reserve under uncertain wind power. The first-stage model considers a day-ahead market that determines the nominal generation and reserves before the realization of wind power uncertainty. The second-stage decisions are made in a realtime market, after the observation of uncertainty, so that the expected emission factor is constrained below a target level. Case studies are conducted to demonstrate that the proposed method is capable of effectively capturing the ambiguous distribution of wind power generation, and can be tractably solved. The influence of different emission constraints is also discussed, showing the trade-off between lowering the total operating cost and reducing the long-term impact of carbon emissions.
