An Integrated Approach to Optimal Charging Scheduling of Electric Vehicles Integrated with Improved Medium-Voltage Network Reconfiguration for Power Loss Minimization

Abstract

The uncoordinated integration of electric vehicles (EVs) severely deteriorates the operational performance of a distribution network. To optimize distribution network performance in an EV charging environment, this paper presents a two-stage optimization approach, which integrates coordinated EV charging with network reconfiguration. A formulation to minimize system power loss is presented, and an optimal solution is obtained using a binary particle swarm optimization algorithm. The proposed approach is tested on a modified IEEE 33-bus medium-voltage node test system, coupled with a low voltage distribution network. Results of the coordinated and uncoordinated EV charging are compared with those of the developed integrated approach, and the operational performance of the system is studied. The results show that the integration of network reconfiguration with coordinated EV charging significantly decreases network power losses and fairly improves voltage profile. Thus, the proposed strategy can lead to improved operational performance of the system while dealing with the growing penetration of EVs in the network.

A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network

“…The SG network is an intelligent electricity grid equipped with information and communication (ICT) facilities. The SG network provides a controlled environment to coordinate EVs' charging operation [15,16], enable large integration of renewable energy sources and flatten their variability [17], and support the vehicle to grid (V2G) feature for grid support services including frequency tuning and load regulation [18]. Various attributes of Smart Grid at different levels of electricity network are summarized in Figure 2.…”

A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization

“…From a grid point of view, it seems these modern vehicles are as an electric load on the system during the charging period. A frequent and uncontrolled charging strategy can cause negative effects such as an increase of power losses, voltage deviations, transformers, and line saturations [5][6][7][8][9]. As a result, the distribution grid's safe and reliable operation may be under high risk.…”

“…In this context, the researchers have considered various objectives such as power loss minimization, voltage profile improvement, charging cost minimization, etc., and these objectives are optimized by applying different optimization techniques. In the study of [5], the authors aimed to minimize distribution network power losses by optimally managing charging requests from the customers. In this work, a typical driving pattern without having any charging preference was considered.…”

Hierarchical Distributed Framework for Optimal Dynamic Load Management of Electric Vehicles With Vehicle-to-Grid Technology

“…One key observation from the reviews presented in [20][21][22], is that most of EV charging strategies focuses on one or two of the following power grid issues (total power losses, excessive voltage drops, load/ frequency fluctuations or peak shaving), regardless of the other issues. For instance, authors of [23] tried to minimize the active power losses and voltage regulation specially in the MV networks, in [24] only the peak shaving and valley filling were addressed, while in [25] only the reduction of the energy cost was considered. Thus, it is quite clear that the main shortcoming in the previous methods is that no technique has been designed to address all these power grid issues at medium voltage (MV) and low voltage (LV) levels, taking into consideration the owners' satisfaction factor, battery degradation, charging cost, computational time, and the stochastic behavior of the system.…”