Industrial Energy Flexibility Scheduling Based on Conditional Value at Risk

Energy flexibility helps industrial companies to reduce their electricity costs, contributing to grid stability and limiting renewables curtailment. This paper proposes a cost-optimized scheduling model for energy flexibility considering risk by integrating the Conditional Value at Risk (CVaR) as a risk measure in stochastic programming. The optimization aims to reduce energy costs by trading energy flexibility on the day-ahead energy market (DAM). The optimization results are an optimized load profile, overall cost reduction, and the CVaR. The paper presents a case study of an industrial facility with energy-flexible units. Here, a sensitivity analysis is carried out. Results show that the scheduling model successfully optimized energy costs while considering the uncertainties and the risk appetite of the consumer.