Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market

There is a trend toward direct participation of wind farms in electricity markets. However, wind power is inherently intermittent and cannot be accurately predicted even in short time; thus increasing the imbalance costs paid by wind farm owners. To cope with these problems, some techniques have been proposed in literature including wind farm coupling to hydro units, energy storage facilities, and constructing a virtual power plant (VPP). This paper presents a stochastic profit-based model for day-ahead operational planning of a combined wind farm–cascade hydro system. The generation company (GenCo) that owns the VPP considers a portion of its hydro plants capacity to compensate the wind power forecast errors. The proposed optimization problem is a mixed integer linear programming (MILP), formulated as a two-stage stochastic programming model. The day-ahead scheduling is a here and now decision and the optimal operations of facilities are resources variables. In order to protect the GenCo against low price scenarios and wind power variation, the conditional value at risk (CVaR) is used as the risk aversion criterion. The proposed model is successfully applied to a real case study and the results are presented and discussed. The results are illustrated varying in the risk aversion level and the penalty coefficients for negative/positive imbalances. It is shown that the bidding strategy of the GenCo varies significantly depending on the chosen penalty market mechanism.

► A model for day-ahead operational planning of a virtual power plant is proposed. ► The optimization problem is formulated as a two stage stochastic programming model. ► The conditional value at risk (CVaR) is used as the risk aversion criterion. ► The bidding strategy of a GenCo is investigated with respect to various scenarios.