Plug-in Electric Vehicles Penetration Levels Research Articles

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

Probabilistic operational costs assessment of combined PV–PEV connections in LV distribution networks

Decarbonization stimulates the increased penetration of photovoltaic (PV) systems and plug-in electric vehicles (PEV) in low-voltage (LV) distribution networks. However, the individual or combined PV and PEV massive connections may incur significant economic costs for distribution utilities due to technical issues caused by these connections. This paper proposes a probabilistic approach for operational costs assessment of combined PV–PEV penetration in LV distribution networks catering to the uncertainties associated with PV, PEV and households. In addition, to identify the relationships between the technical issues with each other and the increased combined PV and PEV penetration levels, a correlation analysis is developed to help utility engineers to estimate the operational costs of combined PV and PEV connections. The proposed methodology is applied to a real Brazilian LV distribution network showing that the costs of real power losses decrease in reduced penetration levels of combined PV–PEV. However, they increase under high combined penetration levels representing financial losses to utilities. Also, operational costs related to steady-state voltage level compliance indicators increase with PV connection, and the combined PV–PEV connections have beneficial results, reducing these costs. Further, the proposed methodology can be used by utility engineers responsible for studies of LV distribution network reinforcement actions. • Proposed methodology converts technical parameters into operational costs. • Operational costs in utility’s point-of-view due to combined PV–PEV connection. • Proposed approach considering stochastic uncertainties of PV, PEV and households. • Identifies high-risk consumer units, allowing LV network reinforcements. • Proposed methodology uses technical correlation analysis and Monte Carlo Simulation.

A unit commitment model for optimal vehicle-to-grid operation in a power system

Integrating plug-in electric vehicles (PEVs) into a smart grid can pose some challenges, particularly when a significant number of these vehicles are simultaneously charged and discharged. However, smart management of PEVs in a vehicle-to-grid (V2G) system can result in benefits to the grid such as load leveling, and cost reduction. This paper proposes a unit commitment model for a V2G system connected to a smart power grid. The model considers different penetration levels of PEVs and investigates the economic and technical effects of using PEVs to support the grid. The proposed methodology incorporates controlled charging and discharging as well as accounting for battery degradation in the unit commitment problem. The model is tested using an IEEE 24 bus network to determine the impact of high PEV penetration on generation cost. A comparison between a system without V2G and a system with V2G is presented to highlight the benefits of the proposed approach. The results show that the optimal scheduling of PEVs leads to reduction in generation cost and is effective in leveling the load profile through valley filling and peak load reduction.

Investigation on Using Low Voltage Automatic Regulation to Minimize the Impacts of Charging Plug-in Electric Vehicles in Distribution Systems

The growth of micro and mini distributed generation and, more recently, the use of electric energy storage systems and the incentives for electric mobility are important examples of the transformations that distribution networks have been going through. In this context, this paper firstly presents the impacts of uncoordinated plug-in electric vehicles (PEVs) charging in a real Brazilian distribution system. Four scenarios were elaborated with different PEVs penetration levels and the results show increased voltage unbalance, system losses, and violations of the steady-state voltage limits, even in the presence of an automatic voltage regulator installed in the medium voltage network. Then, as the main contribution, the potential usage of automatic voltage regulation at the low voltage network was investigated to minimize the negative impacts of uncontrolled PEV charging on distribution system steady-state operation. It is important to highlight that this is not a common practice of utilities in Brazil. The obtained results showed that regulating the voltage at the low voltage side could be an effective solution to keep the voltages within statutory limits.

Charging management of plug‐in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

The charging management of plug-in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle-to-grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four-minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum-inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results.

Public Plug-in Electric Vehicles + Grid Data: Is a New Cyberattack Vector Viable?

High-wattage demand-side appliances such as Plug-in Electric Vehicles (PEVs) are proliferating. As a result, information on the charging patterns of PEVs is becoming accessible via smartphone applications, which aggregate real-time availability and historical usage of public PEV charging stations. Moreover, information on the power grid infrastructure and operations has become increasingly available in technical documents and real-time dashboards of the utilities, affiliates, and the power grid operators. The research question that this study explores is: Can one combine high-wattage demand-side appliances with public information to launch cyberattacks on the power grid? To answer this question and report a proof of concept demonstration, the study scrapes data from public sources for Manhattan, NY, USA using the electric vehicle charging station smartphone application and the power grid data circulated by the U.S. Energy Information Administration, New York Independent System Operator, and the local utility in New York. It then designs a novel data-driven cyberattack strategy using state-feedback based partial eigenvalue relocation, which targets frequency stability of the power grid. The study establishes that while such an attack is not possible at the current penetration level of PEVs, it will be practical once the number of PEVs increases.

Real‐time agent‐based control of plug‐in electric vehicles for voltage and thermal management of LV networks: formulation and HIL validation

Ensuring a stable and reliable operation of current and future distribution networks represents a major challenge for system operators aggravated by the global proliferation of plug-in electric vehicles (PEVs). While the introduction of a controlled charging process would be advantageous to minimise the impacts PEVs cause in the system, a suitable, efficient and ready to be implemented solution is still missing. The present work addresses this issue by proposing a smart charging management solution capable to simultaneously combat the main network impacts derived from the energy needs of the vehicles. This is done by means of an agent-based hierarchical real-time algorithm which combines a local decentralised nodal voltage management with a centralised thermal control conceived to minimise the impact upon participating users. The effectiveness of the proposed system is tested both using a simulation environment considering multiple PEV penetration levels and employing commercially available charging stations and cars through hardware-in-the-loop simulations. The results reveal how all network violations are successfully attenuated by peak shaving the total aggregated charging demand and ensuring a correct system operation for all penetration scenarios while inflicting no impact on the participating users.

Multi-objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles

The increasing penetration of solar distributed generations (SDGs) and wind distributed generations (WDGs) together with plug-in electric vehicles (PEVs) will lead to a promising amount of reduction in greenhouse gas emissions. Nevertheless, they bring about adversities such as uncertainty in production-load sides, augmented power loss, and voltage instability in the power system, which should be carefully addressed to increase the reliability. In this concern, this paper proposes a multi-objective optimization methodology for sizing and siting of SDGs, WDGs, and capacitor banks (CBs) in the power system considering uncertainties stemmed from PEVs load demand, solar irradiance, wind speed, and the conventional load. The understudy objectives are the voltage stability index, green-house gas emissions, and the total cost. An unconventional point estimate method (PEM) is used to handle the related uncertainties, and chance-constrained programming method is deployed to deal with smooth constraints. The corresponding probability distribution functions of output variables are estimated by the maximum entropy method. Furthermore, robustness analysis is made by Monte Carlo simulation (MCS). The proposed methodology is applied to a typical radial distribution network. The results show that the presence of PEV’s significantly increases the load demand, which results in voltage collapse in the distribution system without the presence of distributed generations. However, the proposed probabilistic method ensures the safe operation of the distribution system with the optimal allocation of renewable distributed generations and CBs. Moreover, the results of deterministic and probabilistic cases are compared under different penetration levels of PEVs. The best tradeoff solution of the Pareto front is selected by the fuzzy satisfying method.

Optimal DG Sizing and Location in Modern Power Grids using PEVs Load Demand Probability

The integration of plug-in electric vehicles (PEVs) to the conventional distribution system has had a major impact upon consumption of energy in the past year. This paper presents optimal distributed generator (DG) sizing and location in the power system using PEVs load demand probability. The MATLAB m-file scripts and OpenDSS were applied to solve the proposed study by varying the percentage penetration level of PEVs. A genetic algorithm optimization technique was used to find the best solution of DG installation. The simulation results showed thatthe PEVs were directly connected to the power grid with 100 PEVs (13.84%), 200 PEVs (27.68%) and 500 PEVs (69.19%), respectively. It was found that the DG sizing also varied with 1.773 MW, 1.663 MW and 1.996 MW, respectively. While the position of the DG also changes according to the sizing of DG. The position of DG was installed at bus No.738, bus No.741 and bus No.711, respectively. Therefore, the optimal DG placement helped to improve and reduce the total line loss and total energy demand from the power grid. The grid increased the power system stability and reduced the impact from the large scale of PEV penetration.

Impact of plug-in electric vehicles and distributed generation on reliability of distribution systems

The use of Plug-in electric vehicles (PEV) are increasing in recent years because of its environmental friendly nature over the conventional vehicles. In the upcoming years, the use of PEVs will increase significantly. The connection of PEVs to the distribution system will lead to the new challenges. This paper evaluates the impact of PEV connection on system reliability. Studies are carried out for different PEV penetration levels to analyze the impact. Some PEVs are operated in vehicle to grid (V2G) mode to support the system peak loads. Distributed generation (DG) units are integrated with charging stations to reduce the PEVs charging impact on the system. In the present study, charging stations are integrated with solar PV units which magnifies the intention of PEV usage. Along with the system reliability evaluation, Expected Energy Not Charged (EENC) is proposed to measure the reliability of the PEVs in the system. The combined effects of PEV charging and DGs are also studied. The impact assessment is done for two practical systems.

Distributed Control of PEV Charging Based on Energy Demand Forecast

This paper presents a new distributed smart charging strategy for grid integration of plug-in electric vehicles (PEVs). The main goal is to smooth the daily grid load profile while ensuring that each PEV has a desired state of charge level at the time of departure. Communication and computational overhead, and PEV user privacy are also considered during the development of the proposed strategy. It consists of two stages: 1) an offline process to estimate a reference operating power level based on the forecasted mobility energy demand and base loading profile, and 2) a real-time process to determine the charging power for each PEV so that the aggregated load tracks the reference loading level. Tests are carried out both on primary and secondary distribution networks for different heuristic charging scenarios and PEV penetration levels. Results are compared to that of the optimal solution and other state-of-the-art techniques in terms of variance and peak values, and shown to be competitive. Finally, a real vehicle test implementation is done using a commercial-of-the-shelf charging station and an electric vehicle.

A Novel Markov-Based Temporal-SoC Analysis for Characterizing PEV Charging Demand

The integration of a massive number of plug-in electric vehicles (PEVs) into current power distribution networks brings direct challenges to network planning, control, and operation. To increase the PEV penetration level with minimal negative impact, the dynamical PEV travel behaviors and charging demand need to be better understood. This paper presents a Markov-based analytical approach for modeling PEV travel behaviors and charging demand. The travel behaviors of individual PEVs are expressed mathematically through Monte Carlo simulation considering two essential factors: temporal travel purposes and state of charge (SoC). Markov model and hidden Markov model (HMM) are adopted to explicitly formulate the probabilistic correlation between multiple PEV states and SoC ranges. This modeling approach provides an efficient and generic tool for analyzing PEV travel behaviors and charging demand based on available PEV statistics. The analytical model is further adopted in the impact assessment of two PEV normal charging scheduling strategies for a range of PEV penetration levels in an IEEE 53-bus test network with field data (network parameters and realistic PEV statistics). The results demonstrate the benefit of the proposed modeling approach in network analysis considering PEV integration.

Spinning Reserve Requirement Optimization Considering Integration of Plug-In Electric Vehicles

The increasingly widespread utilization enables plug-in electric vehicles (PEVs) to provide ancillary service and support economic and secure operation of power systems. This paper proposes a novel model to optimize day-ahead spinning reserve requirement (SRR) considering PEVs’ contribution in providing operating reserve. Based on the probabilistic criteria, the cost of expected energy supplied by PEV (EESEV) is formulated. The costs of reserves from thermal generators and PEVs as well as expected energy not supplied (EENS) are considered together to determine optimal SRR. The post-contingency PEV reaction time is taken into account in the formation of EENS and EESEV. The capacities of PEV interruptible charging demand and vehicle to grid service are calculated, respectively, under the conditions of both immediate charging and smart charging strategies. The effects of PEVs on system SRR quantification, unit commitment are comprehensively analyzed using IEEE reliability test system. Numerical results systematically demonstrate the effectiveness of PEVs’ participation on the reduction of operation costs and the improvement of power system reliability. Sensitivity analysis of PEV penetration level and compensation cost to PEV owners is compressively conducted to provide meaningful reference for future implementation.

Scenario-Based Comprehensive Expansion Planning for Distribution Systems Considering Integration of Plug-in Electric Vehicles

Widespread utilization of plug-in electric vehicles (PEVs) would incur more uncertainties, and thus challenge the traditional planning strategies for distribution systems. In this paper, a scenario-based comprehensive expansion planning (SCEP) approach for distribution systems is presented to cope with future uncertainties. This approach can not only take into account the initial investment and operation costs but also consider the expected adaptation cost to other future scenarios, and in this way the flexibility of an initial planning scheme can be achieved. Future scenarios are identified in line with three uncertainties, namely the conventional load levels, penetration levels of PEVs and whether or not with successful implementation of a coordinated charging strategy. The charging profile for dump charging mode has been determined straightforwardly, while that for coordinated charging mode is obtained by solving the proposed day-ahead dispatching model (DDM). Furthermore, a priority-based real time dispatching strategy (PRTDS) is proposed to obtain direct charging instructions for PEVs, and thus to demonstrate the rationality of utilizing the charging profile acquired from DDM in distribution system expansion planning (DSEP). Finally, the performance of the proposed methodology is demonstrated by numerical studies.

Distribution planning with hourly profiles for analysing electric vehicle charging strategies

This paper analyses the impact of plug-in electric vehicles (PEVs) on distribution networks. The reference network model (RNM) is used to model the grids and to carry out sensitivity analysis for several scenarios. The set of scenarios selected includes PEV penetration levels, charging strategies and location distributions. Each scenario is characterised in terms of the number of electric vehicles, the vehicles' class, technical parameters and charging time. Five large-scale real distribution networks are modelled and analysed. The topologies selected are intended to be representative of some typical European distribution networks. Results show that the charging strategies are critical. At least, new multi-tariff schemes should be established to encourage charging at valley hours. Depending on the PEV location distribution, smart charging may also be necessary to reduce the distribution network costs further.

Fuzzy Approach for Online Coordination of Plug-In Electric Vehicle Charging in Smart Grid

This paper proposes an online fuzzy coordination algorithm (OL-FCA) for charging plug-in electric vehicles (PEVs) in smart grid networks that will reduce the total cost of energy generation and the associated grid losses while maintaining network operation criteria such as maximum demand and node voltage profiles within their permissible limits. A recently implemented PEV coordination algorithm based on maximum sensitivity selection (MSS) optimization is improved using fuzzy reasoning. The proposed OL-FCA considers random plug-in of vehicles, time-varying market energy prices, and PEV owner preferred charging time zones based on priority selection. Impacts of uncoordinated, MSS, and fuzzy coordinated charging on total cost, gird losses, and voltage profiles are investigated by simulating different PEV penetration levels on a 449-node network with three wind distributed generation (WDG) systems. The main advantage of OL-FCA compared with the MSS PEV coordination is the reduction in the total cost it introduces within the 24h.

Optimal Coordination of Plug-in Electric Vehicles in Power Grids With Cost-Benefit Analysis—Part II: A Case Study in China

Continuing with a set of enabling techniques for the optimal coordination of plug-in electric vehicles (PEVs) in Part I, we present a case study in this paper using techniques based on the data collected in the Beijing–Tianjin–Tangshan Region (BTTR) China to discover optimal PEV coordination strategies and assess the attractiveness of these strategies. In Part II, we first present the charging characteristics for different categories of PEVs in BTTR and predict the optimal seasonal daily loads with PEVs under different PEV penetration levels using a two-stage optimization model in both 2020 and 2030. The simulation results indicate that optimal PEV coordination effectively reduces the peak load and smooths the load curve. Finally, we present a cost-benefit analysis of optimal coordination strategies by taking a social welfare approach. The analysis shows that the optimal coordination strategies are beneficial in terms of the reduction in capital investment in power grid expansion and that the attractiveness of a coordination strategy is related to the coordination level. The results also show that the fully coordinated charging and vehicle to grid are not the most attractive strategies. This case study is useful for better understanding the costs and benefits of PEV coordination strategies and for supporting PEV-related decision and policy making from a power system planning perspective.

Stochastic Modeling of Plug-in Electric Vehicle Distribution in Power Systems

This paper proposes a stochastic modeling of plug-in electric vehicles (PEVs) distribution in power systems, and analyzes the corresponding clustering characteristic. It is essential for power utilities to estimate the PEV charging demand as the penetration level of PEV is expected to increase rapidly in the near future. Although the distribution of PEVs in power systems is the primary factor for estimating the PEV charging demand, the data currently available are statistics related to fuel-driven vehicles and to existing electric demands in power systems. In this paper, we calculate the number of households using electricity at individual ending buses of a power system based on the electric demands. Then, we estimate the number of PEVs per household using the probability density function of PEVs derived from the given statistics about fuel-driven vehicles. Finally, we present the clustering characteristic of the PEV distribution via case studies employing the test systems.