Stochastic risk-constrained scheduling of smart energy hub in the presence of wind power and demand response

Abstract

Energy hub represents a coupling among different energy networks and plays an undeniable role as the interface between energy producers and consumers. Therefore, energy hub creates a great opportunity to achieve more efficient and reliable energy systems. Considering rapid progress of wind energy in power system generation, it is crucial to treat with this resource as a part of future energy infrastructures. This paper attempts to develop a general stochastic optimization and modeling framework for solving the wind integrated smart energy hub (SEH) scheduling problem. The electrical and thermal loads of the energy hub have been served in presence of demand response (DR) programs. In the proposed DR program, the amount of responsive load can change during operation time slots of SEH. Stochastic programming method is used to deal with the impact of uncertainties related to wind power generation and load forecasting on the scheduling problem of SEH. The Monte Carlo sampling approach is used to generate the wind power and the customer demand scenarios. The scenario reduction method is also introduced to make this problem tractable. Furthermore, an appropriate risk measurement, the conditional value-at-risk (CVaR) methodology, is incorporated with the model to mitigate the risk of expected cost due to market price and load forecast volatilities.