Load Balancing With EV Chargers and PV Inverters in Unbalanced Distribution Grids

Abstract

Balanced three-phase four-wire distribution grids can host significantly more distributed generation and electric vehicles. Three-phase PV inverters and EV chargers can be adapted to transfer power from highly loaded to less loaded phases, without overloading the inverter or charger. Grid conditions will be improved due to a more balanced operation of the network and more PV panels and EVs can be connected before the limits of the network are reached. A classic coordinated charging strategy for EVs is adapted in this paper. It is shown that the charging of EVs can be improved when power can be transferred from one phase to another. By using PV inverters with a balancing inverter the power injected in each phase will become a controllable variable, as the total amount of produced power does not necessarily need to be equally divided across the three phases. The improvements made by using EV chargers and PV inverters that can balance the network are investigated. Several load flow simulations with realistic data show a positive effect on the system losses, the grid voltage and voltage unbalance. Finally a local controller is proposed to control the balancing between the phases when a real-time communication channel is not available.

A Centralised Control Method for Tackling Unbalances in Active Distribution Grids

“…Lately, new schemes have been proposed to control inverters either providing negative and zero sequence currents [9], [10] or transferring power between the phases [11]- [13] to balance the network. However, most of the references examined consider only one type of control measures and ignore the coordination potential of various active measures available to the DSO.…”

“…However, most of the references examined consider only one type of control measures and ignore the coordination potential of various active measures available to the DSO. For example, [11], [12] use only active power control of balancing inverters, ignoring reactive power or On Load Tap Changing (OLTC) transformers, while reference [13], focusing on the design of Battery Energy Storage Systems (BESS), considers only active and reactive power exchange of the inverters. Furthermore, none of the examined papers considers the unbalance requirement within an OPF framework, but they evaluate the grid conditions using power flow calculations.…”

Smart Charging of EVs in Residential Distribution Systems Using the Extended Iterative Method

“…As for the first and second models, the constraints are Equations (9)- (18). As for the third model, the constraints are Equations (9)- (18).…”

PV-based EV charging station with vehicle-to-grid services for business premises

“…Deploy BES [124,[127][128][129] Reduce losses [36,128] Deploy both [30,36,[125][126][127] Reduce unbalance [36,119] Reduce loading [36] Reduce costs [36,86,118,123,126] Reduce GHG emissions [86,117] Reduce transformer, feeder capacities required [36,129] Improve voltages [36,75,116,127] Improve QoS [36] Even though these papers form a comprehensive realm of studies regarding whether to augment grid or deploy PV and BES, the following limitations can be pointed out,  The papers above have assumed that either augmenting/reconfiguring the grid/phase or deploying onsite PV and BES is the best solution for reducing the impact, costs, GHG and increasing QoS. However, in practice, their various combinations (e.g., grid augmentation and PV; grid augmentation, PV, and BES; etc.)…”

“…Random PV output and EV load samples are generated from their supplied PDF [29,75,142] Predicted PV output Statistical smoothing techniques and quantile regression [171] Short-term forecasting engine supported by machine learning methods [149] MZS-s algorithm [142] Real-time PV output - [33,74,119,122] DC link voltage measurements [172][173][174] Since the inherent drawbacks limit the applicability of the direct measurement and probabilistic charging strategies as above, predicted PV output based charging strategies could be the appropriate avenues [142,168]. PV output for such charging approaches is predicted by statistical smoothing techniques and quantile regression, a short-term forecasting engine supported by machine learning methods, and MZS-s algorithm [142].…”