Optimizing day-ahead bid curves in hydropower production

Abstract

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.

Financial storage rights for hydroelectricity

“…Bids for all time periods of the following day are typically submitted simultaneously with no provision for contingent bids on their reservoir levels. This imposes a daunting task on these producers to determine their optimal bidding strategies [1], [2], since it requires them to base their decisions on projections of market supply and demand dynamics to compute the expected prices for each of the bidding time periods of the next day. This becomes particularly difficult in a market with large share of renewable energy sources (RES), increasing stochastic supply-side variations.…”

Hydropower Bidding Using Linearized Start-Ups

“…This method has been extended to a stochastic programming model for optimization of bids in [14]. For the current topic, it is important to emphasize that the method involves solving stochastic MILPs in iterations and that each model instance thus should be solved within a few minutes.…”

“…Use of continuous variables means that it is possible to turn a generator on by a fractional amount. This does not correspond to any feasible operating point, but it might be a reasonable approximation when calculating marginal costs, especially since it is currently not possible to use the full MILP formulation due to time constraints [14]. In [15], it is found that using a linear approximation to start-ups gives an average deviation of 0.01% from the objective function value of the equivalent binary formulation.…”

“…The size and complexity of the problem depends on the number of scenarios as well as the size and characteristics of the river system(s) to be optimized. Details about the bidding model may be found in [6,14].…”

“…The MILP and LP formulation are implemented in the existing framework for short-term scheduling that is used by the Nordic hydropower industry [13,14]. This model is implemented in C and can be solved with CPLEX, Gurobi or COIN solvers.…”