LAUSR

Abstract This paper derives distributionally robust optimal bidding strategies for a wind-storage aggregator that participates as a price-maker in the day-ahead market, and a deviator in the balancing market. The market power is modeled by using a bi-level structure, which is finally transformed into a mixed integer linear programming model that can be solved using off-the-shelf solvers. The uncertainty in the distribution of wind generation forecast error is incorporated using distributionally robust optimization, which optimizes the decisions against the expectation over the worst-case distribution of the uncertainty. An autoregressive-moving-average model is used to learn the historical forecast error, to generate forecast error scenarios and then to extract the statistical information of these scenarios to describe the potential distributions of the uncertain forecast error. A case study based on one-year Nordpool data validates the effectiveness of the proposed model, shows the impacts of market power, renewable subsidies and aggregation levels, and compares its performance to that of the perfect information case and the case of bidding at day-ahead forecast.