Building optimal generation bids of a hydro chain in the day-ahead electricity market under price uncertainty

Abstract

This paper presents a model to build the optimal generation bids of a set of connected hydro plants in a deregulated system organized as a day-ahead market. The generation company is assumed to be price-taker, and therefore, market prices are considered exogenous variables. The main problem of the utility is to find the optimal hourly energy blocks that should submit to the market for each one of its units in order to: 1) maximize the expected profit taking into account some risk aversion criterion, 2) ensure that, after the auction, the obtained cleared schedule is technically feasible. Price uncertainty is introduced via scenarios generated by an input/output hidden Markov model (IOHMM). In order to be protected against low prices scenarios, a minimum conditional value-at-risk (CVaR) constraint has been included. The model takes into account a very detailed representation of the generating units, and it is formulated as a MILP optimization problem. Its application to a real-size example case is presented and discussed in this paper with satisfactory results