A Computationally Efficient Method for Risk Averse Scheduling of Hybrid Power Plants

Abstract

Day-ahead scheduling of hybrid power plants with renewable energy resources is inherently associated with uncertainties. We therefore show how to formulate the problem as a two-stage stochastic optimization. To hedge against low profits or even losses, risk averse scheduling is achieved by including risk measures like the (conditional) value at risk into the objective. However, a standard formulation of this approach entails multiple drawbacks, e.g., large sample sizes are required to correctly capture the tails of the profit distribution. To overcome these deficiencies we propose two new methods based on the principles of the recently introduced Robust Common Rank Approximation. The methods are based on efficient scenario reduction with the aid of a simplified, quickly computable proxy model of the full system. We demonstrate dramatically reduced computation times at similar or even superior solution quality with simulations of a hybrid power plant located at the French Antilles.