Abstract: Uncertainties in wind and solar power outputs reduce their market competitiveness. Participation of cascade hydropower, wind, solar, and storage systems in energy and frequency regulation markets has become an effective means to achieve high-quality renewable energy supply. This study first develops a day-ahead bidding model and a real-time adjustment and frequency regulation response model for such hybrid systems that participate in these regulation markets. To address the uncertainty in wind and solar outputs, we develop a distributionally robust chance-constrained model, based on the Wasserstein distance fuzzy set for the systems' market participation. Then, a bi-objective optimization is made to maximize day-ahead market revenue and minimize real-time adjustment deviations. And a normalized normal constraint (NNC) method is used to obtain Pareto frontier solutions; the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to select the optimal solution from the Pareto front. Application to a case study of a river basin in southwestern China verifies our method and the models prove effective, rational, and applicable.

Key words: cascade hydropower station, joint operation, frequency regulation ancillary services market, distributionally robust chance constraint, normalized normal constraint method