Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Ready To Roll: Southeastern Pennsylvania's Regional Electric Vehicle Action Plan

On-road internal combustion engine (ICE) vehicles are responsible for nearly one-third of energy use and one-quarter of greenhouse gas (GHG) emissions in southeastern Pennsylvania.1 Electric vehicles (EVs), including plug-in hybrid electric vehicles (PHEVs) and all-electric vehicles (AEVs), present an opportunity to serve a significant portion of the region's mobility needs while simultaneously reducing energy use, petroleum dependence, fueling costs, and GHG emissions. As a national leader in EV readiness, the region can serve as an example for other efforts around the country."Ready to Roll! Southeastern Pennsylvania's Regional EV Action Plan (Ready to Roll!)" is a comprehensive, regionally coordinated approach to introducing EVs and electric vehicle supply equipment (EVSE) into the five counties of southeastern Pennsylvania (Bucks, Chester, Delaware, Montgomery, and Philadelphia). This plan is the product of a partnership between the Delaware Valley Regional Planning Commission (DVRPC), the City of Philadelphia, PECO Energy Company (PECO; the region's electricity provider), and Greater Philadelphia Clean Cities (GPCC). Additionally, ICF International provided assistance to DVRPC with the preparation of this plan. The plan incorporates feedback from key regional stakeholders, national best practices, and research to assess the southeastern Pennsylvania EV market, identify current market barriers, and develop strategies to facilitate vehicle and infrastructure deployment

The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change

Electric Energy Management for Plug-in Electric Vehicles Charging in the Distribution System by a dual cascade scheduling algorithm

This paper presents an algorithm for plug-in electric vehicles (PEVs) charging in the three-phase distribution system for residential houses. It aims to prevent violent voltage level deviation and increasing losses on the three-phase distribution system due to uncontrolled charging and allocate power to each plug-in electric vehicle. The algorithm is comprised of two processes. The first process is power limitation and limited power of load imbalance by if-else rules, while the second process is power allocation to each PEV by the dual cascade scheduling algorithm which is the integration of tasking scheduling algorithms. A 100 kVA distribution transformer and 30 houses are defined in the simulation situation. Also, the available PEVs in single-phase, two-phase, and three-phase systems are assigned for verification of the proposed algorithm. Root-mean-square deviation (RMSD) referred to the satisfaction of PEV owners, total PEVs charged energy, and the average percentage of achieved charging time, as the result indicators. The results show the proposed algorithm can provide good results without rejected PEVs charging. Furthermore, this paper also displays the analysis of voltage level, percentage of voltage unbalance factor, and loss in the distribution system. In the future, coordination with home appliances to gain a high load margin or electric energy cost control will be improved in the proposed algorith

Grid-able Plug-in Electric Vehicles in Smart Grids: Incorporation into Demand Response

Electric transportation has attracted a great deal of interest within the transport sector because of its notable potential to become a low-carbon substitute for conventional combustion engine vehicles. However, widespread use of this form of transportation, such as plug-in electric vehicles (PEVs), will constitute a significant draw on power grids, especially when associated with uncontrolled charging schemes. In fact, electric utilities are unable to control individual PEVs in order to manage their charging and avoid negative consequences for distribution lines. However, a control strategy could be directed at a single vehicle or group of vehicles. One effective approach could be to build on a supervisory control system, similar to a SCADA system that manages the aggregation of PEVs, a role that could be filled by aggregators that exchange data and information among individual PEVs and energy service providers. An additional consideration is that advances in intelligent technologies and expert systems have introduced a range of flexible control strategies, which make smart grid implementation more attractive and viable for the power industry. These developments have been accompanied by the initiation of a new paradigm for controllable PEV loads based on a number of advantages associated with a smart grid context. One of the established goals related to smart grids is to build on their ability to take advantage of all available energy resources through efficient, decentralized management. To this end, utilities worldwide are using IT, communication, and sensors to provide enhanced incorporation of operational tools and thus create a more robust and interactive environment able to handle generation-demand dynamics and uncertainties. One of these tools is demand response (DR), a feature that adjusts customers’ electricity usage through the offer of incentive payments. Motivated by this background, the goal of the work presented in this thesis was to introduce new operational algorithms that facilitate the charging of PEVs and the employment of their batteries for short-term grid support of active power. To allow both public parking lots and small residential garages to benefit from smart charging for end-user DR, a framework has been developed in which the aggregator handles decision-making through real-time interactions with PEV owners. Two interaction levels are implemented. First, for charging coordination with only one-round interaction, a fuzzy expert system prioritizes PEVs to determine the order in which they will be charged. Next, for smart charging, which includes battery discharging, a multi-stage decision-making approach with two-round interaction is proposed. Real-time interaction provides owners with an appropriate scheme for contributing to DR, while avoiding the inconvenience of pre-signed long-term contracts. A new stochastic model predicts future PEV arrivals and their energy demand through a combination of an artificial neural network (ANN) and a Markov chain. A new method is proposed for promoting collaboration of PEVs and photovoltaic (PV) panels. This technique is based on a determination of the ways in which smart charging can support simultaneous efficient energy delivery and phase-unbalance mitigation in a three-phase LV system. Simulation results derived from 38-bus and 123-bus distribution test systems have verified the efficacy of the proposed methods. Through case-study comparisons, the inefficiency of conventional charging regimes has been confirmed and the effectiveness of real-time interactions with vehicle owners through DR has been demonstrated. The most obvious finding to emerge from this study is that the use of a scoring-based (SCR) solution facilitates the ability of an aggregator to address urgent PEV energy demands, especially in large parking lots characterized by high levels of hourly vehicle transactions. The results of this study also indicate that significantly greater energy efficiency could be achieved through the discharging of PEV batteries when PEV grid penetration is high