A novel day-ahead bidding method considering real-time operational characteristic and strategy for wind-hydro hybrid power systems

Two-stage stochastic model is the state-of-the-art method for making the day-ahead bid for wind–hydro hybrid power systems (WHHPS) considering both the day-ahead and the real-time balancing markets. However, the real-time operational characteristic and strategy of WHHPS are ignored in current studies which limits their applications in day-ahead bidding. To fill this gap, in this paper, a novel day-ahead bidding method considering real-time operational characteristic and strategy for WHHPS is proposed. First, using Kantorovich distance, a scenario tree generation algorithm is introduced to capture the stage-wise unfolding nature of the wind power and balancing market price realisations in the real-time operation. Then, a bilevel bidding method is proposed, whose upper level aims to maximise the expected income while managing the income risk of the WHHPS, and the lower level operates the WHHPS considering the real-time operational characteristic and strategy. Due to the chronological order in real-time operation, the lower level contains sequentially nested problems which makes the whole model difficult to solve. For this reason, a weighted reformulation method is proposed to equally reformulate the sequentially nested problems in the lower level into a single LP problem. Finally, the bilevel model is reformulated as MILP. Comparisons are made with the current state-of-the-art models in a case study. The results show that the bidding strategies derived from the conventional two-stage stochastic bidding model tend to be overly optimistic regarding both the expected income and the associated risk. Moreover, using CVaR may fail to accurately reflect the operator’s risk preference in bidding. In comparison, the proposed method establishes a monotonic decreasing relationship between income risk and the coefficient P, providing a direct approach for risk management.