A sensitivity analysis of short-term hydropower planning using stochastic programming

Huge amount of uncertainties are being introduced to the power market because of the ongoing growth in the renewable energy sources like wind and solar power. The intermittent nature of these power sources increases the volatility of the day-ahead market prices. Therefore, improving planning tools and constructing an optimal bidding strategy to the day-ahead market is an essential task for the price-taker hydropower producer. This paper applies an optimal bidding model under the uncertainties of the day-ahead market prices and the water inflow level. Specifically, the model is built using a two-stage stochastic mixed integer linear programming approach. The uncertainties are handled by generating scenarios based on historical data. The model is tested by studying three reservoir test system. Profound sensitivity analysis is provided, in terms of volatility in day-ahead market prices and water inflow level as well as in terms of water opportunity cost and initial volume of the reservoir. Based on the comparison of the stochastic and corresponding deterministic problems, the result aims to show the impact of modeling the uncertainties explicitly.