Plug-in Electric Vehicles Charging Demand 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.

Placement and Capacity of EV Charging Stations by Considering Uncertainties With Energy Management Strategies

At the present context, Plug-in electric vehicles (PEVs) are gaining popularity in the automotive industry due to their low CO2 emissions, simple maintenance, and low operating costs. As the number of PEVs on the road increases, the charging demand of PEVs affects distribution network features, such as power loss, voltage profile, and harmonic distortion. Furthermore, one more problem arises due to the high peak power demand from the grid to charge the PEVs at the charging station (CS). In addition, the location of CS also affects the behavior of EV users and CS investors. Hence, this paper applies CS investor, PEV user, and distribution network operator who could approach to CS's optimal location and capacity. Integrating renewable energy sources (RESs) at the charging station is suggested to lower the energy stress on the grid. Moreover, to keep down the peak power demand from the grid and utilize renewable energy more efficiently, energy management strategies (EMS) have been applied through the control of charging and discharging of the battery storage system (BSS). In addition, vehicle to grid (V2G) strategy is also applied to discharge the EV battery at charging station. Furthermore, the uncertainties related to PEV charging demand and PV power generation are addressed by the Monte Carlo Simulation (MCS) method.

Impact Assessment of Plug-in Electric Vehicle Charging Locations on Power Systems with Integrated Wind Farms Incorporating Dynamic Thermal Limits

The increased presence of electric vehicle charging locations in a power system with high penetration of intermittent wind power potentially leads to operation complexities resulting in abnormal impacts. This paper proposes an innovative framework for assessing the impact of plug-in electric vehicle (PEV) charging locations on a power system with integrated wind farms, incorporating dynamic thermal limits (DTLs). The framework comprises Monte Carlo simulation, which is embedded with stochastic modeling of various uncertainties under the key operating conditions. As part of the modeling framework, the transmission lines are ranked in accordance with the lowest level of expected energy not supplied. The PEV charging demand is then modeled by incorporating DTLs and applied to the least stressed transmission lines, following the IEEE 738-2006 standard. The new assessment framework is investigated using an IEEE 24-bus test system. The results demonstrate that applying DTLs on the least stressed transmission lines in conjunction with the integration of decentralized wind farms and strategic charging location of PEVs significantly improves the security of the energy supply and considerably reduces interruption costs, as opposed to not having such a framework.

A New Framework for Plug-In Electric Vehicle Charging Models Supported by Solar Photovoltaic Energy Resources Un nouveau cadre pour les modèles de recharge du véhicule électrique rechargeable supporté par des ressources d’énergie solaire photovoltaïque

This article proposes a new framework for modeling plug-in electric vehicle (PEV) charging demand supported by solar photovoltaic (PV) energy resources in a distribution system. The proposed work focuses on modeling the stochastic nature of both PEV loads and PV generation while considering the effect of the temporal-spatial characteristics of the driver's behavior, as well as solar irradiation and temperature. A trip chain, based on the Markov Chain Monte Carlo process, is developed to properly model PEV daily driving activities and the PV uncertainty. Charging facilities are assumed available at home, work, and fast-charging stations, having charging levels of 3.7, 6.6, and 50 kW, respectively. The proposed framework is examined, considering the National Household Travel Survey global data, as well as the city of Buffalo and New York state. The impact of varying the penetration levels of PEV and PV resources is also investigated. This work strengthens the proposed models in the literature by integrating the temporal-spatial characteristics of PEV charging demand into PV stochastic models.

Modeling the Future California Electricity Grid and Renewable Energy Integration with Electric Vehicles

This study focuses on determining the impacts and potential value of unmanaged and managed uni-directional and bi-directional charging of plug-in electric vehicles (PEVs) to integrate intermittent renewable resources in California in the year 2030. The research methodology incorporates the utilization of multiple simulation tools including V2G-SIM, SWITCH, and GridSim. SWITCH is used to predict a cost-effective generation portfolio to meet the renewable electricity goals of 60% in California by 2030. PEV charging demand is predicted by incorporating mobility behavior studies and assumptions charging infrastructure and vehicle technology improvements. Finally, the production cost model GridSim is used to quantify the impacts of managed and unmanaged vehicle-charging demand to electricity grid operations. The temporal optimization of charging sessions shows that PEVs can mitigate renewable oversupply and ramping needs substantially. The results show that 3.3 million PEVs can mitigate over-generation by ~4 terawatt hours in California—potentially saving the state up to about USD 20 billion of capital investment costs in stationary storage technologies.

Stochastic Modeling Method of Plug-in Electric Vehicle Charging Demand for Korean Transmission System Planning

The number of plug-in electric vehicles (PEVs) has rapidly increased owing to the government’s active promotion policy worldwide. Consequently, in the near future, their charging demand is expected to grow enough for consideration in the planning process of the transmission system. This study proposes a stochastic method for modeling the PEV charging demand, of which the time and amount are uncertain. In the proposed method, the distribution of PEVs is estimated by the substations based on the number of electricity customers, PEV expansion target, and statistics of existing vehicles. An individual PEV charging profile is modeled using the statistics of internal combustion engine (ICE) vehicles driving and by aggregating the PEV charging profiles per 154 kV substation, the charging demand of PEVs is determined for consideration as part of the total electricity demand in the planning process of transmission systems. The effectiveness of the proposed method is verified through case studies in the Korean power system. It was found that the PEV charging demand has considerable potential as the additional peak demand in the transmission system planning because the charging time could be concentrated in the evening peak time.

QoS-Aware Capacity Planning of Networked PEV Charging Infrastructure

Plug-in electric vehicle (PEV) charging infrastructure is necessary to accommodate the rapid increase in PEV penetration rate. Capacity planning of PEV charging infrastructure (EVCI) must ensure not only a satisfactory charging service for PEV users but also a reliable operation of the power grid. In this paper, we propose a quality-of-service (QoS) aware capacity planning of EVCI. In particular, the proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems (ESSs) and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors. We model the EVCI as a queuing network with finite capacity, and utilize the non-stationary queuing models to study the temporal variability of the PEV charging demand. A network of charging facilities is used to demonstrate the effectiveness of the proposed framework.

Hierarchical planning of PEV charging facilities and DGs under transportation-power network couplings

The increasing penetration level of plug-in electric vehicles (PEVs), as well as distributed generators (DGs), imposes significant challenges on power system planning. This work presents a coordinated approach for the planning of PEV charging facilities and DGs, including both the locations and the capacities, with the consideration of the transportation - power network couplings. First, the PEV charging demand is characterized by a temporal-SoC (State of Charge) analysis, and the DG generation uncertainties are modeled by K-means clustering using historical data. The M/M/s/N queuing model is used to formulate the dynamics of charging stations and then obtain the optimal station capacity, including the number of chargers and waiting spaces. Furthermore, the placement of charging stations is optimized by the Floyd Algorithm to minimize the total distance to obtain charging service. Finally, the sitting and sizing of DGs are optimized over multiple objectives, including active power losses, reactive power losses, and voltage deviation. The solution is evaluated through a case study of the IEEE 53-bus test feeder coupled with a 25-node transportation network. It is shown that the proposed solution enables the active and reactive power losses as well as the voltage deviation to be reduced by 37.6%, 44.3%, and 33.6%, respectively, after the optimal integration of PEV charging stations and DGs. The scalability and effectiveness of the solution are further validated in an IEEE 123-bus test feeder coupled with the same transportation network, and the result confirms the effectiveness and scalability of the proposed solution.

PEV Charging Infrastructure Siting Based on Spatial–Temporal Traffic Flow Distribution

Plug-in electric vehicles (PEVs) offer a solution to reduce greenhouse gas emissions and decrease fossil fuel consumption. PEV charging infrastructure siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. In this paper, we propose a spatial–temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial–temporal distribution of traffic flows. We utilize the dynamic traffic assignment model to estimate the time-varying traffic flows on the road transportation network. Then, we cluster the traffic flow dataset into distinct categories using the Gaussian mixture model and site each type of charging facilities to capture a specific traffic pattern. We formulate our siting model as an mixed integer linear programming optimization problem. The model is evaluated based on two benchmark transportation networks, and the simulation results demonstrate the effectiveness of the proposed model.

Optimal Sizing of PEV Fast Charging Stations With Markovian Demand Characterization

Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results.

A model predictive control approach for matching uncertain wind generation with PEV charging demand in a microgrid

The matching between uncertain wind power supply and plug-in electric vehicles (PEVs) charging demand has the potential to reduce the greenhouse gas emission and the fossil fuel pollution. In view of this, we propose a hierarchical model predictive control approach to coordinate the wind generation and PEV charging in the context of microgrid. The proposed control approach consists of two control layers. In the top layer, a stochastic model predictive controller computes the optimal power references for the wind generator and the PEV fleet. These references are fed to the bottom layer, and are further executed by the wind generator controller and PEV fleet controller, respectively. A salient feature of this approach is that it comprehensively incorporates the uncertainties in both sides of supply and demand, i.e., the uncertainties associated with the maximum available wind generation, and the uncertainties associated with the PEVs charging demand. Using the Chebyshev inequality and the chance constraints, the corresponding stochastic optimization problem is approximated as a quadratic programming problem. By doing so, the proposed approach not only keeps the microgrid power balance, but also ensures the PEV users’ quality of experience. Furthermore, it can bring the power flow between microgrid and utility power system to a predefined trajectory. Simulation results based on real-world wind and PEV data validate the effectiveness of the proposed approach.

Hierarchical optimal planning approach for plug‐in electric vehicle fast charging stations based on temporal‐SoC charging demand characterisation

Fast charging stations are critical infrastructures to enable a high penetration level of plug-in electric vehicles (PEVs) into future distribution networks. The fast charging stations need to be carefully planned to meet the PEV charging demand as well as reduce costs. This study addresses this technical challenge and proposes a hierarchical planning solution for both sitting and sizing of PEV fast charging stations based on a temporal-SoC (state-of-charge) characterisation and modelling of PEV fast charging demand. The optimal sitting of fast charging stations is firstly determined to ensure minimising the total number of stations and meeting the PEV fast charging demand considering the constraints of transportation networks and the expected PEV remaining mileage. Then the sizing (number of chargers and waiting spaces) of fast charging station is optimised by the use of M/M/s/N queuing model, so as to maximise the expected profit of the operator. The proposed solution is evaluated through a set of case studies for a range of scenarios, and numerical simulation results have confirmed the effectiveness of the proposed solution.

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified.

Optimal temporal-spatial PEV charging scheduling in active power distribution networks

BackgroundThe increasing penetration of a massive number of plug-in electric vehicles (PEVs) and distributed generators (DGs) into current power distribution networks imposes obvious challenges on power distribution network operation.MethodsThis paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs. The solution is designed to ensure the reliable and secure operation of the active power distribution networks, the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand, as the PEVs can be considered as mobile energy storage units.ResultsAs a result, the charging demands of PEVs are optimally scheduled temporally and spatially, which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing (RTP).ConclusionsThe proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.

A semi-cooperative decentralized scheduling scheme for plug-in electric vehicle charging demand

The paper proposes a decentralized control scheme for scheduling the flexible charging demand of plug-in electric vehicles in residential distribution networks. This control scheme is designed for execution by a multi-agent system at two consecutive stages of static and dynamic scheduling. The distinctive attributes of the developed control scheme are (i) to realistically prioritize both the customers’ and the utility’s objectives, (ii) to incorporate the uncertainty in the forecasted demand, (iii) to account for customers’ flexibility in their charging demand, and (iv) to specify a fair pricing scenario to all customers while protecting their privacy. The paper includes extensive numerical studies using a set of recorded real-world driving data, representing heterogeneous vehicular demand. In order to assess the efficacy of the proposed scheduling scheme, comparative assessments are also presented against an optimization-based charging scheduling scheme.

Distribution system planning to accommodate distributed energy resources and PEVs

With deregulation of the power industry, environmental policy changes, advancements in technology, and the transformation to smart grid, the distribution planning paradigm has gone through significant changes in recent years. Concurrently, with increase in gas prices, driven by a foreseeable fossil fuel depletion in the future, developments in the automotive sector, and environmental concerns, penetration of plug-in electric vehicles (PEVs) has been increasing. These changes will continue to drive the distribution planning problem to evolve in the coming years. This paper presents a comprehensive long-term distribution planning framework from the perspective of local distribution companies (LDCs) considering distributed generation (DG), substations, capacitors, and feeders. Apart from considering the usual demand profile, the proposed framework considers uncontrolled and controlled (smart) PEV charging demand, as well as demand response (DR) options. Based on a back-propagation algorithm combined with cost-benefit analysis, a novel approach is proposed to determine the optimal upgrade plan, allocation, and sizing of the selected components in distribution systems, to minimize the total capital and operating cost. A new iterative method is proposed which involves post-processing the plan decisions to guarantee acceptable adequacy levels for each year of the planning horizon. The performance of the proposed framework is examined considering several case studies on the 33-bus and 69-bus test systems. It is noted from the studies that the presence of uncontrolled PEV charging loads results in much higher plan costs as compared to the case without PEVs, impacts the distribution plan significantly, and hence these loads indeed need be considered in the planning process. On the other hand, smart charging of PEVs have a much reduced impact on the plan cost, and helps alleviate some investments which were needed with uncontrolled charging PEVs. Therefore, the LDCs and policy makers need to encourage customers to adapt these options in the long-run.

Real-Time Optimal Voltage Regulation for Distribution Networks Incorporating High Penetration of PEVs

This paper proposes a vehicle-to-grid reactive power support (V2GQ) strategy for optimal coordinated voltage regulation in distribution networks with high distributed generation (DG) penetration. The proposed algorithm employs plug-in electric vehicles (PEVs), DG, and on-load tap changer (OLTC) to satisfy PEV charging demand and grid voltage requirements with relaxed tap operation, and minimum DG active power curtailment. The voltage regulation problem is formulated as a nonlinear programming and consists of three consecutive stages, in which successive stages apply the outputs of their preceding stages as constraints. The first stage aims to maximize the energy delivered to PEVs to assure PEV owner satisfactions. The second stage maximizes the DG extracted active power. Third stage minimizes the voltage deviation from its nominal value utilizing the available PEV and DG reactive powers. The main implicit objective of the third stage problem is relaxing the OLTC tap operation. In addition, the conventional OLTC control is replaced by a proposed centralized controller that utilizes the output of the third stage to set its tap position. Real-time simulations are developed to demonstrate the effectiveness of the proposed optimal coordinated algorithm on a typical distribution network using OPAL-RT real-time simulator (RTS) in a hardware-in-the-loop (HIL) application.

Probabilistic estimation of plug-in electric vehicles charging load profile

Plug-in electric vehicles (PEVs) are widely considered as a sustainable mode of transport by countries worldwide due to high efficiency and low or zero carbon emissions. However, PEVs will add significant additional load to the existing power distribution system and it will be a challenge to meet the new demand. In this study, probabilistic modelling has been presented to estimate the system-wide PEV charging load within domestic grids. U.S. national household travel survey data set has been utilized to quantitatively determine the mobility behaviour of PEVs.Uncertain nature of the problem in modelling and data preparation should be taken into account. Due to the existence of complex interdependencies between the system inputs, the problem definition leads to a multivariate uncertainty analysis problem. The modelling procedure is decomposed into two basic components: the modelling of the marginal distributions; and that of the stochastic dependence structure. In addition, Copula theory is presented for the multivariate modelling of dependent random variable. The results indicate that the PEVs can contribute to increase the load demand at certain hours, although the charging demand is very limited most of the time. Moreover, the probabilistic distribution of aggregated PEV charging demand is compared with that obtained by the Monte Carlo simulation. The numerical results have shown the effectiveness of the proposed methodology.

Online coordinated charging decision algorithm for electric vehicles without future information

The large-scale integration of plug-in electric vehicles (PEVs) to the power grid spurs the need for efficient charging coordination mechanisms. It can be shown that the optimal charging schedule smooths out the energy consumption over time so as to minimize the total energy cost. In practice, however, it is hard to smooth out the energy consumption perfectly, because the future PEV charging demand is unknown at the moment when the charging rate of an existing PEV needs to be determined. In this paper, we propose an online coordinated charging decision (ORCHARD) algorithm, which minimizes the energy cost without knowing the future information. Through rigorous proof, we show that ORCHARD is strictly feasible in the sense that it guarantees to fulfill all charging demands before due time. Meanwhile, it achieves the best known competitive ratio of 2.39. By exploiting the problem structure, we propose a novel reduced-complexity algorithm to replace the standard convex optimization techniques used in ORCHARD. Through extensive simulations, we show that the average performance gap between ORCHARD and the offline optimal solution, which utilizes the complete future information, is as small as 6.5%. By setting a proper speeding factor, the average performance gap can be further reduced to 5%.

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.