Optimal charging of electric vehicles taking distribution network constraints into account

Abstract

The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer.

Co-Optimization of Distribution Transformer Aging and Energy Arbitrage Using Electric Vehicles

“…By considering the alreadyestablished loadings on the transformer (shown in Figs. [5][6][7] and by varying the transformer rating, it can be seen that the present cost of perpetual replacements in the decentralized strategy is much higher than in the centralized strategy, due to the lack of coordination between consumers. In addition, the optimal transformer rating that minimizes this cost is 35 kVA in the centralized strategy regardless of the mode of operations, whereas in the decentralized strategy, the optimal rating varies from 35 to 45 kVA, with V2G requiring the largest capacity.…”

“…Studies on distribution lines [3], transformers [4], [5], and voltage stability [6], [7] emphasize the need of proper management and possible capacity investments in the distribution system in order to accommodate the EVs' load. Since it is expected for consumers to mostly charge EVs at their homes [8], the major impact will be on the local pole-top distribution transformers.…”

Control of EV Charging Points for Thermal and Voltage Management of LV Networks

“…These studies propose the use of complex optimization techniques (e.g., linear [4]- [6], nonlinear [7], dynamic [8], Manuscript quadratic [9], [10], mix-integer linear programming [11], and receding-horizon [12]) that require extensive information/visibility of the network (e.g., voltages and currents); the state of charge (SOC) of the EVs; and, typically, the electricity market (e.g., real-time pricing). In practice, however, real-time data is limited and many interoperability challenges (e.g., data exchanges between EV and charging point) need to be addressed [14].…”

“…Hence, not only the LV network capacity and voltage constraints must be satisfied, but it is also critical to quantify the effects that any EV charging strategy has on the expected charging time/level of EVs. This, however, has not been assessed in the above works, which are limited to price aspects only [6], [7], [11], [12].…”

ARIMA-based demand forecasting method considering probabilistic model of electric vehicles' parking lots

“…In [16] a simplified model is utilized for calculating the output of EV parking lots. Recent studies focus on the optimal charging of EVs and try to shift the EV charging time to off-peak periods [17]. According to [18] authors proposed a model for EV charging demand and assumed that the electricity demand curve for the distribution network is known.…”