Time Horizon-Based Model Predictive Volt/VAR Optimization for Smart Grid Enabled CVR in the Presence of Electric Vehicle Charging Loads

This paper investigates the need of coordinated operation of conservation voltage reduction (CVR) in the presence of electric vehicle (EV) penetration in the active distribution network. In order to analyze the impact of both the technologies (CVR and EV), a time horizon-based model predictive Volt/VAR optimization (VVO) methodology has been introduced in smart grid framework. The proposed VVO methodology operates in centralized as well as local controls under different time scale of operation, including cloud transient effects on solar photovoltaic (PV) power output. Moreover, the control algorithms also consider the uncertainties in load demand and PV power generation. The VVO methodology has been validated with and without presence of EV loads in the distribution network. The VVO includes the impact of different EV charging loads having the ability of participation in reactive power support at selected charging points. This is also referred to as vehicle-to-grid operation in terms of reactive power dispatch only. Besides, the voltage and VAR regulation through smart inverters of PVs and EV charging station has been fruitfully utilized in global as well as local domain. A real-time Volt/VAR droop based controller has been introduced to control the smart inverters reactive power dispatch. To validate the developed methodology, a real-time cosimulation framework, using real-time digital simulator and Python interface, has been built. The proposed model predictive VVO algorithm has been tested and validated on a modified IEEE 34 bus test system. The simulated results reveal that significant CVR energy savings and losses reduction has been achieved without violating the system constraints. The voltage control algorithm works well in both slow and fast time scales.