Abstract
Hydropower producers need to schedule when to release
water from reservoirs and participate in wholesale electricity markets where the day-ahead production is physically traded. A mixed-integer linear stochastic model for bid optimization and short-term production allocation is developed and tested through a simulation procedure implemented for a complex real-life river system. The stochastic bid model sees uncertainty in both spot market prices and inflow to the reservoirs. The same simulation
procedure is also implemented for a practice-based deterministic heuristic method similar to what is currently used for bid determination in the industry, and the results are compared. The stochastic approach gives improvements in terms of higher obtained average price and higher total value than the deterministic alternative. It also performs well in terms of startup costs. In the presence of river flow travel delay, the practice-based method is even more outperformed by the stochastic model.
water from reservoirs and participate in wholesale electricity markets where the day-ahead production is physically traded. A mixed-integer linear stochastic model for bid optimization and short-term production allocation is developed and tested through a simulation procedure implemented for a complex real-life river system. The stochastic bid model sees uncertainty in both spot market prices and inflow to the reservoirs. The same simulation
procedure is also implemented for a practice-based deterministic heuristic method similar to what is currently used for bid determination in the industry, and the results are compared. The stochastic approach gives improvements in terms of higher obtained average price and higher total value than the deterministic alternative. It also performs well in terms of startup costs. In the presence of river flow travel delay, the practice-based method is even more outperformed by the stochastic model.
