Two-stage stochastic programming for risk-aware scheduling of energy hubs participating in day-ahead and real-time electricity markets

Abstract

Energy hubs play an important role in sustainable cities; they are modern energy systems that couple different energy carriers in order to enhance energy efficiency and reliability and decrease the cost of supplying energy demands. In this research, a two-stage stochastic model is proposed for operation of energy hubs in conjunction with both day-ahead and real-time electricity markets, considering the uncertainties in demands, renewable power and real time electricity prices. In order to prevent high operation costs in the worst scenarios, value-at-risk, as a risk metric, is incorporated into model. A multi-objective optimisation problem is formulated and the operating point of hub components as well as transactions of electricity and natural gas are determined. The results show that the consideration of value-at-risk improves the operation cost at the worst scenarios, but may worsen the operation cost at other scenarios. The results indicate that with increase in weight factor, expected cost increases and value-at-risk decreases; for instance, the increase of weight factor from 1 to 2 increases expected cost from $379.97 to $380.13 and decreases value-at-risk from $393.63 to $393.51. The results also show that any increase in confidence level increases value-at-risk; for instance, the increase of confidence level from 0.9 to 0.95 increases value-at-risk from $392.01 to $393.63.