Risk management and stochastic optimization for industrial consumers

Abstract

To encourage industrial consumers to participate more actively in deregulated energy markets, it is necessary to provide them with optimization tools to manage the risk derived from energy price uncertainty. In this paper, we review several risk measures, formulate some of them within stochastic programming models and discuss those which better fit the risk attitude of industrial consumers. With the measures selected, safety-first and valueat-risk, two bi-objective mixed-integer linear stochastic problems are implemented. These models obtain, through a risk-aversion parameter, a tradeoff between the risk measure and the expected cost of the total energy supply cost of industrial consumers. The efficient frontiers obtained with the safety-first and value-at-risk models are compared in a realistic case example.