M. A. Rostami - Department of Power and Control Eng., School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
M. Raoofat - Department of Power and Control Eng., School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
A. R. Abunasri - Department of Power and Control Eng., School of Electrical and Computer Engineering Shiraz University Shiraz, Iran

In this paper, a novel stochastic operating strategy for Virtual Power Plant (VPP) to participate in day-ahead market is proposed. VPP is the owner and manager of a group of Distributed Generations (DGs) and end consumers which have random behavior. Apart from uncertainties associated with generation and demand, the market price is also a source ofuncertainty in VPP operation and planning. Specifically, the integration of Plug-in Hybrid Electric Vehicles (PHEV) is considered in this paper which imposes uncertainty to network mostly due to their mobility and driving pattern. Increased utilization of these vehicles may bring forward challenges to network and need to be mitigated by controlling their charging demand. Therefore, the stochastic scheduling of VPP is performed considering a coordinated framework for charging PHEVs. The proposed stochastic programming is a non-linear, non-deterministic, and large-scale optimization problem, which is solved by a modified version of Teaching-Learning Based Optimization (TLBO) algorithm. The uncertainties of renewable generation (wind generation), PHEV demand, and market price are handled by Point Estimation Method (PEM) and Monte Carlo Simulation (MCS) method. The efficacy and veracity of the proposed methodology is corroborated by numerical simulations on modified 18-bus distribution system.

Virtual Power Plant (VPP); Point Estimation Method (PEM); Plug-in Hybrid Electric Vehicle (PHEV)