Electric vehicles in smart grid: a survey on charging load modelling (original) (raw)
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IET Electrical Systems in Transportation, 2018
Electric vehicle (EV) integration into the power grids is increasing rapidly. To analyse the effect of charging of EVs on the distribution system, most of the literature considered EV load as constant power load (CPL) which do not represent the exact behaviour of these uncertain loads. An accurate EV load modelling is developed by determining the relationship between power consumption by EV, grid voltage and state of charges of fast charging EV load. The derived relationship is validated by simulating a realistic fast charging system to obtain a battery charging behaviour characteristics and is curve fitted on standard exponential load model. Further the impact of stochastic 24-h load profile of fast charging EVs considering the exponential load model is investigated on IEEE 123 bus distribution system and is compared with the constant impedance-constant currentconstant power (ZIP) load model and CPL model. The stochastic 24-h load is developed using queuing analysis-based method. The results show that the exponential load model is the better representation of fast charging EV load and 10.19% of the reduction in annual energy demand and 11.19% of the reduction in annual energy loss is observed for exponential load model compared to the existing CPL model.
Analysis of EV Cost-Based Charging Load Profiles
Proceedings, 2020
During the last few decades, electric vehicles (EVs) have emerged as a promising sustainable alternative to traditional fuel cars. The work presented here is carried out in the context of the Horizon 2020 project MERLON and targets the impact of EVs on electrical grid load profiles, while considering both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation modes. Three different charging policies are considered: the uncontrolled charging, which acts as a reference scenario, and two strategies that fall under the umbrella of individual charging policies based on price incentive strategies. Electricity prices along with the EV user preferences are taken into account for both charging (G2V) and discharging (V2G) operations, allowing for more realistic scenarios to be considered.
Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach
Energies
Electric vehicles (EVs) are becoming increasingly popular, and it is important for utilities to understand their charging characteristics to accurately estimate the demand on the electrical grid. In this work, we developed simulation models for different EV charging scenarios in the home sector. We used them to predict maximum demand based on the increasing penetration of EV consumers. We comprehensively reviewed the literature on EV charging technologies, battery capacity, charging situations, and the impact of EV loads. Our results suggest a method for visualizing the impact of EV charging loads by considering factors such as state of charge, arrival time, charging duration, rate of charge, maximum charging power, and involvement rate. This method can be used to model load profiles and determine the number of chargers needed to meet EV user demand. We also explored the use of a time-of-use (TOU) tariff as a demand response strategy, which encourages EV owners to charge their vehic...
The Role of Charging Infrastructure in Electric Vehicle Implementation within Smart Grids
Energies
In the integration of electric vehicle (EV) fleets into the smart grid context, charging infrastructure serves as the interlinkage between EV fleets and the power grid and, as such, affects the impacts of EV operation on the smart grid. In this study, the impacts of charging infrastructure on the effectiveness of different EV operational modes were simulated using a multi-component modelling approach, which accounts for both stochastic EV fleet charging behaviors as well as optimal energy vector dispatch operation. Moreover, a campus microgrid case study was presented to demonstrate the various design factors and impacts of charging infrastructure implementation affecting EV fleet adoption and operation. Based on results from the study, it was shown that charging infrastructure should be adopted in excess of the minimum required to satisfy EV charging for driving needs. In addressing uncontrolled charging behaviors, additional charging infrastructure improves EV owner convenience an...
Energy Management and Optimization of Large-Scale Electric Vehicle Charging on the Grid
World Electric Vehicle Journal
The sustainability of a clean energy transition for electric vehicle transportation is clearly affected by increased energy consumption cost, which is associated with large-scale electric vehicles (EVs) charging on a fossil-fuel dependent electricity grid. This places a potential threat on the safe operations and stability of the grid and increases the emissions of greenhouse gases (GHGs) from the power stations that generate the electricity. Furthermore, the uncontrolled large-scale integration of EVs charging on the grid will increase exponentially in the coming years. Because of this, new peaks on the grid will be generated due to the EV charging load variance, and a significant impact on the transformer limit and substation capacity violation will occur. To mitigate the significant impact of the high cost of energy consumption by large-scale EVs charging on the grid, and to reduce the emissions of GHGs, there is a need to provide a multi-level optimization approach that is robus...
Modeling and Simulating of Private EVs Charging Load
As an important part of the smart grid, Electric Vehicles (EVs) could be a good measure against energy shortages and environmental pollutions. In this paper, based on the relevant EVs development policy, the private EVs charging load is investigated. Based on statistical data, the Monte Carlo method is applied to determine the one-trip driven distance for the private EV. And by analyzing the EVs driving habit and the charging characteristics of EVs battery, we derive the initial state-of-charge (SOC) of charging, charging power and initial charging time. As a result, a more accurate mathematical model of computing the charging load accused by private EVs is proposed. Furthermore, the EVs charging loads in 2015 and 2020 are computed and compared in plug-in charging and wireless charging mode. The results of simulation show that the daily load peak of private EVs charging caused by wireless charging mode is significantly lower than that of plug-in charging mode. And the charging load of large-scale EVs would have significant impacts on the planning and operation of power grid. It is very important to predict and analyze the EVs charging load for the construction and scheduling of the smart grid in the future.
Charging Electric Vehicles in the Smart Grid
Power Systems, 2016
High level challenges that motivate the evolution towards smart grids include (i) the anticipated electrification of transportation, including electrical vehicles (EVs), and (ii) the increasing penetration of distributed renewable energy sources (DRES). This chapter will discuss how the extra grid load stemming from the EVs can be handled, including the context of reduced control over power generation in light of DRES adoption (especially solar and wind power). After a basic introduction to common EV charging technology, we give two illustrative examples of controlling EV charging: avoiding peaks, and balancing against renewable generation. We then qualitatively present possible demand response (DR) strategies to realize such control. Finally, we highlight the need for, and underlying principles of, (smart grid) simulation tools, e.g., to study the effectiveness of such DR mechanisms.
ELECTRIC VEHICLE CHARGING AND UNLOADING EFFECT ANALYSIS ON THE POWER GRID
IAEME PUBLICATION, 2020
The global energy efficiency and environmental protection have become more and more important at this time, and the production of electric vehicles (EVs) is therefore accelerating. The efficiency of electric vehicles is a strong one and other kinds of electricity can be used as fuel as a prominent feature, and electric vehicles are seen as eco transport for the 21st century. The electric vehicle is being gradually taken care of by car manufacturers, policymakers and environmental organizations. Electric vehicles can reduce pollution by draining vehicles as zero-emission vehicles dramatically to boost the environment and change the electricity system. The author, therefore, agrees that in our country electric vehicles should be actively developed, but that large-scale EV charging is a practical issue in the grid service and the distribution system planning. It could have a significant impact on the performance of the network, including overloading, output reduction, decreased service quality and increased power losses. With the movement of the light vehicle fleet to electric movement, a "vehicle to grid" (V2G) power is offered. V2G makes sense only if there is a connection between the car and the electricity market. This article discusses briefly the impact of the charge on the dispatch system, the contributing factors, the methods of charging control which decrease the impact on the distribution network and V2G techniques. Address the EV phenomenon based on the current state of development and the challenges
Plug-in Hybrid Electric Vehicles and Smart Grid: Investigations Based on a Micro Simulation
Introduction of Plug-in Hybrid Electric Vehicles (PHEVs) could potentially trigger a stepwise electrification of the whole transportation sector. But the impact on the electric grid by electrical vehicle charging is still not fully known. This paper investigates several PHEV charging schemes, including smart charging, using a novel iterative approach. An agent based traffic demand model is used for modeling the electrical demand of PHEVs over the day. For modeling the different parts of the electric grid, an approach based on interconnected multiple energy carrier systems is used. For a given charging scheme the power system simulation gives back a price signal indicating whether grid constraints, such as maximum power output at hub transformators, have been violated. This leads to a corrective step in the iterative process, until a charging pattern is found, which does not violate grid constraints. The proposed system allows to investigate existing electric grids, whether they are capable of meeting increased electricity demand by certain future PHEV penetration. Furthermore, in the future, different types of smart charging schemes can be added into the system for comparison.