In deregulated electricity markets, hydropower producers must bid their production into the day-ahead market. For price-taking producers, it is optimal to offer energy according to marginal costs, which for hydropower are determined by the opportunity cost of using water that could have been stored for future production. At the time of bidding, uncertainty of future prices and inflows may affect the opportunity costs and thus also the optimal bids. This paper presents a model for hydropower bidding where the bids are based on optimal production schedules from a stochastic model. We also present a heuristic algorithm for reducing the bid matrix into the size required by the market operator. Results for the optimized bids and the reduction algorithm are analyzed in a case study showing how uncertain inflows may affect the bids.
This paper presents a framework for price forecasting in hydro-thermal power systems. The framework consists of a long-term strategic and a short-term operational model. The strategic model provides the end-of-horizon valuation of water in hydro storages as input to the operational model. We emphasize on the operational model, and discuss work in progress to facilitate more detailed fundamental market modeling to enable realistic multi-market price forecasting. A case study of the Nordic power system demonstrates the use of the framework, quantifying the impact of constraints on cable ramping and reserve capacity on prices.
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.
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