Model Of Plug-in Electric Vehicles Research Articles

Multivariate stochastic modeling of plugin electric vehicles charging profile and grid impact analysis

The electrification of the transportation sector presents a significant challenge to the power grid. The accurate modeling of the plug-in electric vehicle (PEV) charging load is essential since it can guide policymakers and grid operators to make informed decisions regarding grid reinforcement to facilitate the additional charging demand of PEVs. This research paper introduces a hybrid technique for estimating the aggregated PEV charging load based on a stochastic model and real-world dynamics. The proposed methodology includes multivariate stochastic modeling of PEV driving behaviors which is combined with the powertrain simulations performed on multiple models of PEVs operating under two different driving cycles to determine critical parameters such as energy consumption, the initial state of charge (SOC), and grid charging energy requirements. These parameters are used to estimate daily PEV charging demand based on real-world dynamics. The grid impact analysis is performed on the model of an actual distribution network, located at the National University of Sciences and Technology (NUST), through load flow analysis to identify the weakest nodes in the grid and the percentage of transformer loading due to additional PEV charging load. In addition, the research paper proposes a techno-economic grid reinforcement solution, performed on the Homer Pro, to minimize the detrimental impact of the additional PEV charging load on grid voltage stability. The proposed solution includes measures such as optimal placement of distributed generations (DGs), including photovoltaic (PV) panels, storage systems, and diesel generators, to ensure reliable and efficient charging infrastructure for PEVs.

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.

Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications

The charging profiles of plug-in electric vehicles (PEVs) have large volatility. It has brought great challenges for aggregator to accurately complete load identification and aggregated configuration. Therefore, an analysis and configuration method of responsive capacity based on clustering and the Markov model are proposed in this paper. Firstly, the adaptive two-scale clustering algorithm based on spectral clustering (ATCSC) is applied to the clustering of charging piles. The offset compensation of the extreme points is used to form the distance measurement in the clustering process. Then, the responsive aggregated power can be obtained after the change control of suitable charging piles. Finally, the variation characteristics of the actual charging profiles based on the Markov model are introduced to the reliability evaluation in the load curtailment service. Simulation results reveal the following. (1) The robustness of the proposed method is better especially for the PEV charging profiles with strong volatility. (2) The validity of the aggregated configuration is verified. Additionally, the sum of power deviation is 0.0707 kW when the change interval of control strategy is 15 min. (3) The maximum shortage of configuration is −98.0875 kW as the entropy of the volatility is 37.027.

A Multiagent Federated Reinforcement Learning Approach for Plug-In Electric Vehicle Fleet Charging Coordination in a Residential Community

The increasing penetration of distributed renewable energy and electric vehicles (EV) in local microgrids/residential-community has brought a great challenge to balancing system stability and economic benefits. This paper proposes a decentralized framework based on an efficient federated deep reinforcement learning method for plug-in electric vehicle (PEV) fleet charging management in a residential community, which is equipped with a photovoltaic and battery energy storage system and connected to a local transformer. Firstly, the framework of PEV charging management is described as a virtual EV charging station coordinating charging tasks through sharing public information with distributed agents. Then, an individual preference model of PEV is developed considering heterogenous PEV charging anxiety, battery degradation, and collective penalty. Subsequently, we propose an attention-weighted federated soft-actor-critic method to efficiently seek the co-ordinational scheduling of the PEV fleet charging in a distributed way, where scalability and privacy protection can be ensured with attention-based information sharing. Finally, a real-world case study is conducted to validate the effectiveness and feasibility of the proposed approach.

Application of a novel PIPDF controller in an improved plug-in electric vehicle integrated power system for AGC operation

Previously linearised plug-in electric vehicle (PEV) models are adopted in automatic generation control functioning. However, lesser attention is given towards the exact designing of a PEV unit contemplating the suitable model parameters. Therefore, this paper presents a novel approach for determining the PEV model parameters employing a Bode plot analysis in the primary frequency control operation. Furthermore, a novel proportional–integral internal proportional-derivative feedback controller is proposed for the secondary frequency control application asserting zero frequency and tie-line power deviations under steady-state condition. The proposed controller provides better dynamic stability compared to the existing controllers by adjusting open loop poles of the system. The moth flame optimisation technique is used to tune the gain parameters of the proposed controller. Simulation results, obtained from the case studies under different power scenarios, depict a significant improvement in the dynamic responses of frequency and tie-line power deviations in the presence of the proposed PEV model and optimised controller.

Optimal integration of Demand Response Programs and electric vehicles into the SCUC

Abstract Security Constrained Unit Commitment (SCUC) is carried out by power system operators to determine the optimal participation of generating units to meet the load demand while satisfying units’ and systems’ constraints as well as the desired level of security. With technological advances in the realm of communications and development in the infrastructure, a proper environment for the active contribution of customers has been formed. This new structure has enabled the consumers to play a vital role in the market via Demand Response Programs (DRPs) and coordinated aggregated Plug-In Electric Vehicle (PEV) fleets. These new resources could be a merit for the power system provided that they are employed correctly and in coordination with the rest of the system. The SCUC is where DRPs and PEVs could be optimally addressed to exploit their full potential. This paper tackles the problem of SCUC in the presence of DRPs and aggregated PEV fleets. The objective is to lessen the operation cost provided that all constraints are satisfied. Different constraints including those related to the system, units, transmission system, DRPs, and PEVs as well as the security constraints are taken into consideration. The SCUC is modeled as a two-stage stochastic mixed-integer programming problem and an appropriate model of PEVs is derived to facilitate the integration of these resources into the grid while considering their charging and discharging characteristics as well as efficiency. Moreover, a suitable model of DRPs is introduced in this study based on the concept of price elasticity of demand. The proposed approach is practiced over the IEEE 24-bus Reliability Test System (RTS) to analyze the effect of the smart grid on SCUC. Obtained results demonstrate that with proper deployment of DRPs and PEVs the operation cost could be lessened while the security of the system is improved.

Probabilistic Analysis of Impacts on Distribution Networks due to the Connection of Diverse Models of Plug-in Electric Vehicles

This work analyzes impacts on distribution networks produced by the connection of diverse types of Plug-in Electric Vehicles (PEVs), into a probabilistic framework. Some of the uncertainty sources are related with technical parameters of PEVs. Therefore, a review of PEVs currently available in the market is reported. Other uncertain parameters are related with the behavior of the PEV owners, for instance, the arrival and departure times to home, and the state of charge of the PEV when it is plugged to the grid. These parameters are modeled by using probability density functions, to then generate random numbers and perform Monte Carlo simulations. Each Monte Carlo simulation corresponds to the calculation of a power-flow in the analyzed network. The proposed methodology is tested on the IEEE 33-bus test distribution network, with the purpose of quantifying the influence of diversity of PEVs. The analysis is performed, specifically, for identify those transformers and lines that could be overloaded. Two scenarios of PEV penetration by 2025 and 2030 were assessed, i.e., considering that 10 and 30 percent of the residential customers will have at least one PEV, respectively. Obtained results reveal the importance of considering diversity of PEV model to conduct in a suitable manner this type of studies. The proposed methodology is expected to be useful for network planning expansion and to support the design of time of use tariffs.

Multi-objective optimal energy management of microgrids including plug-in electric vehicles with the vehicle to grid capability for energy resources scheduling

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases.

Optimal Allocation of Inverter-Based WTGS Complying With Their DSTATCOM Functionality and PEV Requirements

Recently, the integration of inverter-based wind turbine generation systems (WTGS) and plug-in electric vehicles (PEV) has remarkably been expanded into distribution systems throughout the world. These distributed resources could have various technical benefits to the grid. However, they are also associated with potential operation problems due to their stochastic nature, such as high power losses and voltage deviations. An optimization-based approach is introduced in this paper to properly allocate multiple WTGS in distribution systems in the presence of PEVs. The proposed approach considers 1) uncertainty models of WTGS, PEV, and loads, 2) DSTATCOM functionality of WTGS, and 3) various system constraints. Besides, the realistic operational requirements of PEVs are addressed, including initial and preset conditions of their state of charge (SOC), arriving and departing times, and various controlled/uncontrolled charging schemes. The WTGS planning paradigm is established as a bi-level optimization problem which guarantees the optimal integration of multiple WTGS, besides optimized PEV charging in a simultaneous manner. For this purpose, a bi-level metaheuristic algorithm is developed for solving the planning model. Intensive simulations and comparisons with various approaches on the 69-bus distribution system interconnected with four PEV charging stations are deeply presented considering annual datasets. The results reveal the effectiveness of the proposed approach.

Electric passenger vehicles sales and carbon dioxide emission reduction potential in China’s leading markets

Abstract This study summarizes the annual sales and market trends of plug-in electric vehicles in China from 2011 to 2017 at the national and ronal levels. The major performance characteristics of top-selling plug-in electric vehicle (PEV) models sold in China are summarized. Then, incentive policies of PEV adoption are explored at the provincial level in China. Approximately 70% of the total PEV model sales have lower all-electric ranges and battery capacity than average in the top 5 provinces that have sold the most battery electric vehicles (BEVs) or plug-in hybrid electric vehicles (PHEVs). Sales by make and model in these top 5 provinces are used to estimate ronal electricity consumption, gasoline replacement and carbon dioxide reductions. From 2011 to 2017, 682,047 PEVs have been sold in the 5 provinces, with 18.3 billion electric vehicle kilometers traveled (VKT). 3.0 TWh of electricity were consumed, reducing gasoline consumption by 1.6 billion liters and CO2 emissions by 611,824 tons. Approximately 70% of the total gasoline replacement and 80% of the total CO2 emissions reduction were contributed by BEVs. A sensitivity analysis is also conducted to assess the impacts of certain parameters on the results.

Planning and Operation of Parking Lots Considering System, Traffic, and Drivers Behavioral Model

The planning and operation problems of parking lots of plug-in electric vehicles (PEVs) are studied in this paper. Herein, each distribution company (DISCO) allocates the parking lots to the electrical feeders to minimize the power loss and expected energy not supplied of the system, and consequently minimize the total cost of the planning problem over the given time horizon. In addition, the generation company (GENCO) manages the charging time of PEVs parked in the parking lots to defer the more expensive and pollutant generation units, and as a result maximize its daily profit. In both planning and operation problems, the behavioral model of PEVs’ drivers are modeled with respect to the value of incentive and their distance from the parking lots. To achieve the realistic results in the planning problem of each DISCO, several economic and technical factors including yearly inflation and interest rates, hourly and daily variations of the load demand, yearly load growth of the system, and yearly growth rate of PEVs’ application are considered. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing algorithm and genetic algorithm, respectively. It is demonstrated that the behavioral model of drivers, their driving patterns, and even the type of PEVs can remarkably affect the outcomes of planning and operation problems. It is shown that the optimal allocation of parking lots can minimize every DISCO’s planning cost and optimal charging management of PEVs can increase the GENCO’s daily profit.

PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect

The load demand modeling of Plug-in Electric Vehicles (PEVs) has been taken more attention in today's power system studies. Big-data should be handled for accurate modeling of PEVs load demand. Therefore, the utilization of data mining tools will be helpful for PEVs data analytics and clustering. In this paper, a Factor Analysis (FA) based method is introduced for the PEVs data mining. The load profiles of PEVs that are extracted by Monte Carlo Simulation (MCS) are clustered in some groups optimally using FA method. The clustered data is applied on Energy Storage Systems (ESSs) and Distributed Generation (DGs) planning procedure, separately. The simulation results show the power demand of PEVs effect on both ESSs and DGs planning, however, the temporal feature of PEVs profiles affects only on ESS planning, but not considerably on DG planning. This temporal feature, here called Storage to Storage (S2S) effect, reflects the nature of PEVs and ESS long-term memory which is discussed in this paper. The simulation results show that the optimal ESSs capacity is reduced if the PEVs data are clustered especially in high PEVs penetration. However, the optimal capacities of DGs is the same with and without PEVs data clustering scenarios.

Dual-level consensus-based frequency regulation using vehicle-to-grid service

To coordinate plug-in electric vehicles (PEVs) to support the primary frequency control, this paper presents a dual-level consensus-based frequency control method. The upper-level control aims to minimize the frequency deviation of multi-area power system, and the lower-level control aims to minimize the frequency regulation cost and battery degradation cost for individual PEVs. The incremental cost functions are formulated based on the objectives of the dual control levels, in this case, no objective function needs to be solved, and instead, the optimal results can be achieved by reaching a consensus incremental cost among neighboring individuals. The graph theory is applied to build the communication network among neighboring control areas in upper-level control, and among neighboring PEVs in lower-level control. The consensus algorithm is applied to specify the information updating and exchange among neighboring individuals. In addition, the dynamic models of multi-area power system and PEVs are developed for controller implementation and investigation. The simulation with different case studies are conducted to validate the performance of the proposed dual-level frequency control. The simulation results show that, with limited communication, the connected PEVs can be efficiently coordinated via dual-level controller to provide primary frequency regulation service, and meanwhile meet the battery charging/discharging constraints.

Reaching 30% plug-in vehicle sales by 2030: Modeling incentive and sales mandate strategies in Canada

Plug-in electric vehicles (PEVs) could play a strong role in decarbonizing the transportation sector, leading some governments to set the goal of PEVs accounting for 30% of new sales by 2030 (e.g., the “EV30@30” campaign). To explore the feasibility of this goal, we use a behaviourally-realistic vehicle adoption model (REPAC) to simulate the impacts of incentives and vehicle mandates on PEV sales over this time frame, using the case study of Canada. We consider a range of technology assumptions, including optimistic and pessimistic battery cost scenarios ($CDN 85/kWh and $CDN 125/kWh, respectively, by 2030). We find that the country’s present policies can only induce PEVs to reach 5–11% new market share by 2030. Without changes in PEV supply, we find that purchase incentives can boost PEV new market share, where a $CDN 6000/vehicle subsidy is needed for 13 years to reach the 2030 goal (in the median technology assumption scenario). We also model ZEV mandate scenarios where automakers must reach 30% or 40% PEV sales by 2030, finding that compliance with both is achievable even in pessimistic technology scenarios, through a combination of increased PEV model availability and intra-firm cross-price subsidies. While incentive-based or mandate-based strategies (or some combination thereof) can achieve 2030 goals, results demonstrate the high government expenditure involved in an incentive-based strategy -- $CDN 15–48 billion undiscounted ($10–28 billion discounted), or around $9000–10,000 per added PEV sale. Policymakers ought to consider these tradeoffs, among others, when designing PEV-supportive policies to achieve long-term climate goals.

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

This paper proposes the impact of plug-in electric vehicles (PEVs) integrated into a power distribution system based on voltage-dependent control. The gasolinegate situation has many people turning to electric vehicles as a more environmentally friendly option, especially in smart community areas. The advantage of PEVs is modern vehicles that can use several types of fuel cells and batteries as energy sources. The proposed PEVs model was developed as a static load model in power distribution systems under balanced load conditions. The power flow analysis was determined by using certain parameters of the proposed electrical network. The main research objective was to determine the voltage magnitude profiles, the load voltage deviation, and total power losses of the electrical power system by using the new proposed methodology. Furthermore, it investigated the effects of the constant power load, the constant current load, the constant impedance load, and the plug-in electric vehicles load model. The IEEE 33 bus system was selected as the test system. The proposed methodology assigned the balanced load types in a steady state condition and used the new methodology to solve the power flow problem. The simulation results showed that increasing the plug-in electric vehicles load had an impact on the grids when compared with the other four load types. The lowest increased value for the plug-in electric vehicles load had an effect on the load voltage deviation (0.062), the total active power loss (120 kW) and the total reactive power loss (80 kVar), respectively. Therefore, this study verified that the load of PEVs can affect the electrical power system according to the time charging and charger position. Therefore, future work could examine the difference caused when PEVs are attached to the electrical power system by means of the conventional or complex load type.

Evaluation of Aggregate Models of Plug-in Electric Vehicles for Primary Frequency Control

This paper summarises and evaluates in detail aggregate models of plug-in electric vehicles (PEVs) for primary frequency control (PFC) through dynamic simulations. A basic aggregate model of PEVs for PFC is introduced and then gradually developed in the following steps: 1) technical characteristics of PEVs are incorporated into the model; 2) technical characteristics of distribution networks are formulated and added; 3) a strategy is described to well-design the frequency-droop controller of PEVs for PFC. Moreover, from an economic point of view, a method is presented to assess the benefits which could result from PEVs for PFC. Four simulation scenarios are defined to evaluate the impact of: 1) different PEV's penetration levels; 2) PEV's operating modes and constraints; 3) power consumed in the network during the PFC; 4) well-designed frequency droop controller, on the frequency response following a contingency event. Simulation results show that aggregate PEVs have a great potential not only to improve the frequency response, while preserving the overall stability, but also to save some costs associated with PFC.

Evaluation of aggregate models of plug-in electric vehicles for primary frequency control

This paper summarises and evaluates in detail aggregate models of plug-in electric vehicles (PEVs) for primary frequency control (PFC) through dynamic simulations. A basic aggregate model of PEVs for PFC is introduced and then gradually developed in the following steps: 1) technical characteristics of PEVs are incorporated into the model; 2) technical characteristics of distribution networks are formulated and added; 3) a strategy is described to well-design the frequency-droop controller of PEVs for PFC. Moreover, from an economic point of view, a method is presented to assess the benefits which could result from PEVs for PFC. Four simulation scenarios are defined to evaluate the impact of: 1) different PEV's penetration levels; 2) PEV's operating modes and constraints; 3) power consumed in the network during the PFC; 4) well-designed frequency droop controller, on the frequency response following a contingency event. Simulation results show that aggregate PEVs have a great potential not only to improve the frequency response, while preserving the overall stability, but also to save some costs associated with PFC.

A Second-Order Cone Programming Model for Planning PEV Fast-Charging Stations

This paper studies siting and sizing of plug-in electric vehicle (PEV) fast-charging stations on coupled transportation and power networks. We develop a closed-form service rate model of highway PEV charging stations' service abilities, which considers heterogeneous PEV driving ranges and charging demands.We utilize a modified capacitated flow refueling location model (CFRLM) to explicitly capture time-varying PEV charging demands on the transportation network under driving range constraints. We explore extra constraints of the CFRLM to enhance model accuracy and computational efficiency.We then propose a stochastic mixed-integer second order cone programming (SOCP) model for PEV fast-charging station planning. The model considers the transportation network constraints of CFRLM and the power network constraints with AC power flow. Numerical experiments are conducted to illustrate the effectiveness of the proposed method.

Fuzzy Load Modeling of Plug-in Electric Vehicles for Optimal Storage and DG Planning in Active Distribution Network

Plug-in electric vehicle (PEV) charge challenges can be addressed by including their effects on the planning of distribution network components. The planning problem becomes more combinatorial when the uncertainty of PEVs is considered as well. In this paper, storage and distributed generation (DG) planning is considered as an option to deal with the problems arising from PEV uncertainty. The optimal location, capacity, and power rating of the stationary batteries, as well as the location and capacity of dispatchable DGs, are determined to minimize the cost objective function under technical constraints. Short-term scheduling and long-term planning, as optimization problems, are solved using Tabu Search and simulated annealing algorithms, respectively. Simulation results show that when connecting PEVs to the distribution network, both of the stationary battery and DG units are needed from technical and economic points of view. Moreover, the optimal penetration of stationary storage units increases if the uncertainty of PEVs is considered.

Evaluation of Achievable Vehicle-to-Grid Capacity Using Aggregate PEV Model

Large-scale plug-in electric vehicles (PEVs) utilizing vehicle-to-grid (V2G) technology can collectively behave as a storage system under the control of an aggregator, e.g., arbitraging in the energy market and providing ancillary services to the grid. Quantitatively evaluating V2G capacity, i.e., charging and discharging power ranges, for a PEV fleet utilizing V2G technology (which is referred to as a V2G fleet in this paper) ahead of time is of fundamental importance for V2G implementation. However, because of the stochastic characteristics of PEV driving behaviors, charging demands are difficult to forecast, which makes evaluating V2G capacity technically difficult. This paper first establishes an aggregate model of a V2G fleet that employs aggregated parameters to represent energy and power constraints of the entire V2G fleet and, therefore, reduces the difficulty of forecasting. Then, an evaluation method for V2G capacity of large-scale PEVs is developed based on the proposed aggregate model. To make the V2G capacity evaluated in advance achievable while guaranteeing charging demands during real-time operation, a heuristic smart charging strategy is designed. The application of the evaluation method in optimal charge and discharge scheduling for a V2G fleet providing power reserves is illustrated. Numerical simulations are conducted to validate the proposed method.