Risk-Based Two-Stage Stochastic Model for Optimal Scheduling Problem of an Energy Hub in Day-Ahead and Real-Time Energy Market for Improve Reliability

Abstract

Supplying local electrical and heat demands through the energy hub (EH) system and renewable sources such as photovoltaic (PV) can increase the reliability system. The EH includes combined heat and power units, PV arrays, and storage units and it can trade energy with the wholesale energy markets. The EH operator (EHO) purchases the required energy from the day-ahead market regarding the forecasted amount of demand and the output power of PV system. Then, the EHO can trade energy with the real-time energy markets regarding the uncertainties of PVs and real-time energy prices to minimize its total operation cost. Although the EHO needs to determine the optimal scheduling of its resources considering the participation in the both DA and RT energy markets, this problem is yet needs to the appropriate models. Therefore, a risk-based two-stage stochastic optimization problem is proposed in this paper to model the decision making problem of the reliability EHO in the DA and RT energy markets considering the uncertainties. For this purpose, the uncertainties of PV system and RT energy prices are modeled using the two-stage stochastic approach where the risk of EHO’s decisions is managed using Tail-Value-at-Risk (TVaR). The results show that with increasing the risk parameters, the EHO increases the purchased power from the DA market as the first-stage decision regarding which the trading energy with RT market decreases. Therefore, an energy at a reasonable price and with high reliability is provided to energy hub.