11 - Risk-averse scheduling of virtual power plants considering electric vehicles and demand response

Abstract

In electricity grids, distributed generation units such as electric vehicles, energy storage systems, and renewable energy sources are non-dispatchable due to their intermittent and uncertain nature. The non-dispatchable energy units are integrated with dispatchable power sources through a VPP, which is a critical concept to solve this challenge. Accordingly, this chapter deals with a scheduling framework for virtual power plants (VPPs) by considering the related operational and security constraints. The studied VPP includes photovoltaic systems, wind turbines, energy storage systems, electric vehicles, and diesel generators. Demand response is also considered in this chapter for flexible loads to improve the economic operation of the studied VPP. Furthermore, the uncertainty in the electricity market is taken into account and the resulting risk due to the uncertainty is controlled by the conditional value-at-risk (CVaR) measure. CVaR is used to improve the cost of the worst-case scenarios and solve the problem for different risk levels. The problem is formulated as mixed-integer non-linear programming and implemented in the general algebraic modeling system (GAMS) software package for optimization. Numerical simulations are accomplished to evaluate the model, and the obtained results are discussed in detail.