Abstract
In this paper, a practical approach for benchmarking
different bidding strategies towards the day-ahead market
has been evaluated. A rolling horizon simulation framework is
developed and closely integrated in the daily operations of a
hydropower producer. The power producer’s existing framework
of decision support models and data for prices and inflow has
been used to simulate the use of alternative strategies on a real
life case. In the simulation procedure, a mixed-integer stochastic
optimization model is used to determine the bids to the electricity
market and the production schedule.
It has been demonstrated that simulation over a long timehorizon
can be used to evaluate different bidding strategies.
Results from the case study show that one single strategy not
necessarily will be the optimal one under all conditions, because
the optimal strategy will depend on the the state of the system
different bidding strategies towards the day-ahead market
has been evaluated. A rolling horizon simulation framework is
developed and closely integrated in the daily operations of a
hydropower producer. The power producer’s existing framework
of decision support models and data for prices and inflow has
been used to simulate the use of alternative strategies on a real
life case. In the simulation procedure, a mixed-integer stochastic
optimization model is used to determine the bids to the electricity
market and the production schedule.
It has been demonstrated that simulation over a long timehorizon
can be used to evaluate different bidding strategies.
Results from the case study show that one single strategy not
necessarily will be the optimal one under all conditions, because
the optimal strategy will depend on the the state of the system
