A Flexible Stochastic Optimization Method for Wind Power Balancing With PHEVs

This paper proposes a flexible optimization method, based on state of the art algorithms, for the smart control of plug-in hybrid electric vehicles (PHEVs) to balance wind power production. The problem is approached from the perspective of a balance responsible party (BRP) with a large share of wind power in its portfolio. The BRP uses controllable PHEVs to minimize the imbalance of its portfolio resulting from wind power forecast errors. A Markov Decision Process (MDP) formulation in combination with dynamic programming is used to solve the multistage stochastic problem. The main difficulty for applying MDPs to this problem is to efficiently include time interdependence of the wind power forecast error. In the presented approach, the probability distribution and time interdependence of the forecast error are represented by a scenario tree. Because of the MDP formulation, the algorithm is adaptable to deal with different transition models and constraints. This feature enables to use the algorithm in a dynamic environment such as the future smart grid. To demonstrate this, a generic charging model for PHEVs is used in the BRP wind balancing case. The flexibility of the algorithm is shown by investigating the solution for different degrees of complexity in the charging model.