Optimal Scheduling of Plug-in Electric Vehicle Charging Including Time-of-Use Tariff to Minimize Cost and System Stress

Abstract

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.

Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles

“…According to the above equations, the coordinates of intersection H and E can be calculated as shown in Equations (6) and (7).…”

“…The existing charging guidance strategies of EVs have been mainly studied from the grid system and the users. The research from the grid system mainly focuses on the aspects of the power grid load balance [6][7][8] and efficient energy flow management [9,10] when vehicles connect to the power grid system. With the goals of optimizing the power grid load and energy balance, the users' charging requests are reasonably scheduled to achieve peak load shifting and load balance of the power grid.…”

Combined analytic hierarchy process and binary particle swarm optimization for multiobjective plug-in electric vehicles charging coordination with time-of-use tariff

“…To address these issues, recent researches have studied the integration of PEVs in the distribution system using different strategies and optimization methods. Those studies focus on PEV charging coordination that relies on the availability of a two-way smart grid communication infrastructure [5]. A multiagent distribution system operator and aggregator-based PEV charging management are implemented in laboratory microgrid to avoid distribution transformer overloading [6].…”

Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids

“…Several other models have been proposed for BEV charging/discharging and pricing scheduling [34][35][36][37][38][39]. The impact of variable prices on the behavior of BEV users is studied in Reference [34], where the variable prices are determined based on the calculation of distribution locational marginal price (DLMP) and updated continuously based on the users' trips and behavior.…”

“…In Reference [35], an optimal Time-of-Use (TOU) schedule and a controlled BEV-charging algorithm are used to maximize both customer and utility benefits, and a controlled charging algorithm is also proposed to improve thier voltage quality at the EV load locations while avoiding customer inconvenience. In Reference [36], meta-heuristic techniques are used for charging coordination of BEVs with simultaneous operation of capacitor switching to minimize power losses and voltage deviation in a distribution system. Furthermore, a TOU electricity tariff was included in the proposed charging coordination to reduce the PEV charging cost.…”