Scenario reduction for risk-averse electricity trading

Stochastic optimisation models used to identify risk-averse decisions in electricity futures markets are usually hard to solve because of the large number of scenarios representing the uncertain parameters involved. A novel scenario reduction technique is proposed to select those scenarios that, considering the risk aversion of the decision maker, best represent the original scenario set and make the optimisation problem tractable. Two case studies illustrate the performance of the proposed technique to reduce scenarios pertaining to both continuous and discrete uncertain parameters. The advantage of the proposed technique against the existing ones is apparent in highly risk-averse cases.