Entropic Risk-Based Predictive Control for Wind Integrated Energy Systems Under Market Participation

Energy systems with a significant share of renewable energy production require control strategies that can handle the effects of uncertainty to ensure stable and reliable operation. Simultaneously, liberalized energy markets balance supply and demand on a large scale. In an effort towards renewable energy integration, this paper proposes a stochastic model predictive control framework to handle the energy management of an offshore wind energy system while trading on the day-ahead spot and intraday markets. Furthermore, the framework is extended with the entropic risk measure (ERM) to handle risk-sensitive decision-making. The uncertainty is modeled in discrete scenarios, which propagate uncertainty through a nonlinear model. The proposed framework is validated in simulation with historical data. Results show that the proposed method balances demand and supply while minimizing operational costs through market participation. Furthermore, the framework is compared to itself with different degrees of risk aversion to illustrate the effects of risk sensitivity on the system.