Abstract:

The main contribution of this thesis is to consider the integration of PBDR (Price-based Demand Response) and risk averse G2V (Grid-to-Vehicle) scheduling of EVA (EV Aggregator) to support grid stability. A DR (Demand Response) integrated stochastic programming G2V scheduling framework in which the EVA seeks to maximize its expected profits while the EV owners minimize their costs in response to designed TOU (Time-of-Use) prices, and additional congestion management constraint applied to mitigate potential transformer overloads in the distribution system. The results highlight that, for risk-averse EVA, charge schedules are obtained with less charging rates as compared to risk-neutral EVA. Besides, risk-averse EVA provides more regulation to SO than a risk-neutral EVA. The proposed pragmatic model is efficient to provide peak demand control, the regulation capacity to SO for supporting grid stability and consequently avoiding inevitable contingencies. The work can be extended with the inclusion of V2G in the charging algorithm of EVs of such a smart distribution network.