A Real-Time Rolling Horizon Chance Constrained Optimization Model for Energy Hub Scheduling. (November 2020) - Hou, Weilin; Liu, Zhaoxi; Ma, Li; Wang, Lingfeng
Highlights: A comprehensive energy hub model is built with multiple energy carriers. An adaptive rolling-horizon optimization scheme is proposed. A chance constrained programming model is developed to achieve robust solutions. A mixed integer linear programming based method is applied to solve the problem. Abstract: Multi-energy systems are effective means to enhance energy efficiency and enable sustainable cities. This paper proposes an energy hub (EH) model that integrates the energy storage systems, photovoltaic arrays, combined cooling heating and power (CCHP) system and electric vehicles (EVs). Additionally, the vehicle-to-grid (V2G) technology is considered in the proposed model. The optimal real-time scheduling problem of the EH is formulated as a stochastic problem minimizing the total operation cost in a rolling horizon fashion. A chance constrained programming (CCP) model is developed in this paper to handle the uncertainties in the scheduling problem of the EH. Simulation results show that the proposed EH optimization scheduling model with CCP results in lower operation costs.