High Penetration Of Electric Vehicles Research Articles

Scenario-based modelling of the potential for solar energy charging of electric vehicles in two Scandinavian cities

In order to contribute to the reduction of greenhouse gas emissions, electric vehicles (EVs) should be charged using electricity from renewable energy sources. This paper describes a study of photovoltaics (PV) utilization for EV charging in two Scandinavian cities: Tromsø in Norway and Uppsala in Sweden, with the objective to evaluate self-sufficiency and self-consumption.The suitable areas for PV were determined using building area statistics and utilization factors. The PV yield was simulated for integration scenarios of 10%–100% of the suitable area. EV charging patterns were generated using a stochastic model based on travel survey data. The scenarios include EV penetration of 10%–100% of the personal vehicle fleet.The results show that the PV energy yield could cover the EV load in most of the scenarios, but that the temporal load match could be improved. The energy balance was positive for all seasons and EV levels if the PV integration was over 50%. The highest self-sufficiency was achieved in Tromsø during summer, due to the longer days. For high EV penetration and low PV integration, the self-sufficiency was higher in Uppsala, indicating that installed PV power is more important than yield profile above a certain number of EVs.

How policy measures succeeded to promote electric mobility – Worldwide review and outlook

Electric vehicles (EVs) have been increasingly promoted through policy measures by governments across the world. This study investigates the effectiveness of these measures in 20 countries by measuring the influence of monetary incentives, traffic regulations favoring EVs as well as the charging infrastructure on the market share of EVs in these countries. Results from a covariance-based structural equation model show that all policy measures positively influence the percentage of EVs, specifically monetary measures in interaction with the charging infrastructure. Moreover, findings indicate that governmental measures promoting electric mobility reflect consumers' preferences in the respective countries and that countries with a high purchasing power also have a higher EV penetration. An analysis of the ratio between sold battery electric vehicles (BEVs) versus sold plug-in hybrid electric vehicles (PHEVs) further shows that consumers are purchasing more BEVs (vs. PHEVs) over time. A closer examination of Norway, the Netherlands, Germany and Brazil, where EVs have been adopted more or less successfully, further highlights additional factors influencing EV adoption. It also emphasizes the need for collaboration among stakeholders from the public and private sectors in order to promote EVs. Finally, a worldwide outlook predicts a growing acceptance of EVs over time.

Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm

High energy losses and voltage instability issues in distribution system are prevalent for energy planners and utilities due to high penetration of Electric Vehicles (EVs). Battery Swapping Stations (BSS) are important component for energizing EVs equipped with battery charging mode. In this paper, energy loss reduction and voltage stability factor are enhanced by optimal allocation of Distributed Generation (DG) and BSS, using new Grasshopper Optimizer Algorithm (GOA). The zone based strategy with dispersed DG ensures sufficient distance among BSS's to adequately serve the motorists and enhance system performance. The operational constraints of BSS, DG and loads in each zone for 33-and 69-bus networks are considered. GOA method is compared with established and well-known techniques, namely Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Particle Swarm Optimization (PSO). Using GOA, energy losses are reduced to 82% and 92%, while loadability is increased to 13% and 19% in 33- and 69-bus systems, respectively. The results have shown superiority of GOA in terms of system performance and convergence characteristics. Optimal sites and sizes for DG-BSS placement in the system are beneficial to EV users, BSS developers, electricity grid and utility.

Dynamic Subsidy Method for Congestion Management in Distribution Networks

Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network of the Roy Billinton test system with high penetration of electric vehicles and heat pumps. The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity to cheap energy prices and has no rebound effect.

Electric Taxi Charging Guidance Strategy Based on Electric Vehicle Agent-based Simulation

Electric vehicles connected to the electricity grid will have a great impact on the power system, especially with the high penetration of electric vehicles, the safety and stability of the power system could be threatened. In order to accurately simulate the electric taxi operation behavior and load characteristic, this paper proposes a mothed based on multi-agent technique and reinforcement learning algorithm to study the operation behavior of electric taxis. Different charging strategies on the operation of electric taxi are studied. Based on the charging strategy of shortest path, a charging guidance strategy model is proposed, which considers the charging distance, the charging queue time and the charging equilibrium degree of the charging equipment. The simulation results show that the electric taxi adopting the charging guidance strategy will not only decrease the charging queue time, improve the operation time and income of the electric taxi, but also help improve the equilibrium degree and utilization rate of the charging equipment, reduce the power grid loss and voltage offset.

Analysis of the contribution of CCS to achieve the objectives of Mexico to reduce GHG emissions

Mexico has a strong commitment to reduce its greenhouse gas (GHG) emissions. The electrical and the industrial sectors have been the second and third largest contributors to GHG emissions–excluding fugitive emissions-. This paper analyzes the potential contributions of Carbon Capture and Storage (CCS) systems on the electrical sector, as well as the participation of the cement, metal and chemical industries. This study was carried out using a computational mathematical model, the MEM70, which is a partial equilibrium model that represents the Mexican energy system. It was found that, even considering energy efficiency measures, a high penetration of electric vehicles and the electrical sector with high participation of low carbon emission technologies such as renewable and nuclear sources. Further, it is necessary to implement CCS to achieve the goal for reducing national greenhouse gas emissions.

Robust Planning of Electric Vehicle Charging Facilities With an Advanced Evaluation Method

The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multistage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multistage planning. Then, the well-established data envelopment analysis is utilized to evaluate the planning schemes while the novel EV expected energy not supplied cost is defined to measure the service ability of CFs. The final planning result obtained by the proposed framework will not only have good performance in the current stage but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.

Cost‐based optimal siting and sizing of electric vehicle charging stations considering demand response programmes

Here, the optimal placement and sizing of electric vehicle charging stations (EVCSs) are presented. High penetration of electric vehicles (EVs) and resulted losses in network would consequently impose more complexity to solution of application problem of EVCSs. To overcome this problem, the model would consider the incentive-based demand response programmes (DRPs), which is handled by particle swarm optimisation algorithm. Minimising investment cost, connection cost, total cost of losses, and demand response (DR) cost are the objective functions of this problem here. Finally, the proposed model is applied to a test system and results are discussed. By comparing the results obtained through different scenarios, it is concluded that the application of DRP results in a distinct reduction in grid losses and total costs.

Dynamic Power Tariff for Congestion Management in Distribution Networks

This paper proposes dynamic power tariff (DPT), a new concept for congestion management in distribution networks with high penetration of electric vehicles, and heat pumps. The DPT concept is proposed to overcome a drawback of the dynamic tariff (DT) method, i.e., DPT can replace the price sensitivity parameter in the DT method, which is relatively unrealistic in practice. Based on the control theory, a control model with two control loops, i.e., the power flow control and voltage control, is established to analyze the congestion management process by the DPT method. Furthermore, an iterative method based on distributed optimization is proposed to determine the DPT rates, which enables active participation of aggregators in the congestion management. The case studies demonstrate the efficacy of the DPT method for congestion management in distribution networks, and show its ability to save congestion management cost compared to the DT methods.

Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Through Chance Constrained Mixed-Integer Programming

This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming (MIP) designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic characteristics of the EV driving patterns, a chance constrained optimization of the EV charging is proposed and formulated through MIP. With the chance constraints in the optimization formulations, it guarantees that the failure probability of the EV charging plan fulfilling the driving requirement is below the predetermined confidence parameter. The efficacy of the proposed approach was demonstrated by case studies using a 33-bus distribution system of the Bornholm power system and the Danish driving data. The case study results show that the DLMP method through chance constrained MIP can successfully alleviate the congestion in the distribution network due to the EV charging while keeping the failure probability of EV charging not meeting driving needs below the predefined confidence.

Distributed Optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks

This paper proposes a distributed optimization-based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles and heat pumps. The DDT method employs a decomposition-based optimization method to have aggregators explicitly participate in congestion management, which gives more certainty and transparency compared to the normal DT method. With the DDT method, aggregators reveal their final aggregated plan and respect the plan during operation. By establishing an equivalent overall optimization, it is proven that the DDT method is able to minimize the overall energy consumption cost and line loss cost, which is different from the previous decomposition-based methods such as multiagent system methods. In addition, a reconditioning method and an integral controller are introduced to improve convergence of the distributed optimization where challenges arise due to multiple congestion points, multiple types of flexible demands, and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks.

Wind Generation and Electric Vehicles coordination in Microgrids for Peak Shaving purposes

Nowadays, the urgent need to reduce emissions has led to the development of Renewable Energy Sources (RES). Wind energy via Wind Generators is a very common RES, which, however, is characterized by great intermittency. Consequently, wind energy production may vary from zero to excessive (way above the load demand), which makes wind power plants’ penetration very difficult, making the need for energy storage essential. On the other hand, Electric Vehicles (EVs) offer a new, eco-friendly and more effective means of transportation. The energy stored in their batteries is used to move the EVs. Nevertheless, the penetration of EVs will increase the energy demand from Thermal Plants, and will therefore harm the environment unless it is combined with an increased RES integration. The advantages of combined use of RES and EVs are evident. EVs can augment Wind Power Plants penetration through their storage capabilities and use some of their excessive wind energy output for environmental-friendly mobility. In this paper, the combination of EVs, RES (Wind Generators) and Thermal Plants of an island is examined as an alternative for the future grids. Two scenarios are being studied with focus on peak shaving. Taking into account the Medium Voltage (MV) line load demand, wind power and thermal units output hourly data in a yearly basis, a special algorithm is developed so as to study the case of absorbing the excess energy via off peak charging and reintroducing it into the Microgrid scheme in peak demand periods. The studies are conducted for two cases: a) low penetration and b) high penetration of EVs. All findings are thoroughly discussed in conclusions section.

Integration of Electric Vehicles into the Power Distribution Network with a Modified Capacity Allocation Mechanism

The growing penetration of electric vehicles (EVs) represents an operational challenge to system operators, mainly at the distribution level by introducing congestion and voltage drop problems. To solve these potential problems, a two-level coordination approach is proposed in this study. An aggregation entity, i.e., an EV virtual power plant (EV-VPP), is used to facilitate the interaction between the distribution system operator (DSO) and EV owners considering the decentralized electricity market structure. In level I, to prevent the line congestion and voltage drop problems, the EV-VPP internally respects the line and voltage constraints when making optimal charging schedules. In level II, to avoid power transformer congestion problems, this paper investigates three different coordination mechanisms, or power transformer capacity allocation mechanisms, between the DSO and the EV-VPPs, considering the case of EVs charging and discharging. The three mechanisms include: (1) a market-based approach; (2) a pro-rata approach; and (3) a newly-proposed constrained market-based approach. A case study considering a 37-bus distribution network and high penetration of electric vehicles is presented to demonstrate the effectiveness of the proposed coordination mechanism, comparing with the existing ones.

An Integrated eVoucher Mechanism for Flexible Loads in Real-Time Retail Electricity Market

This paper proposes an innovative economic and engineering coupled framework to encourage typical flexible loads or load aggregators, such as parking lots with high penetration of electric vehicles, to participate directly in the real-time retail electricity market based on an integrated eVoucher program. The integrated eVoucher program entails demand side management, either in the positive or negative direction, following a popular customer-centric design principle. It provides the extra economic benefit to end-users and reduces the risk associated with the wholesale electricity market for electric distribution companies (EDCs), meanwhile improving the potential resilience of the distribution networks with consideration for frequency deviations. When implemented, the eVoucher program allows typical flexible loads, such as electric vehicle parking lots, to adjust their demand and consumption behavior according to financial incentives from an EDC. A distribution system operator (DSO) works as a third party to hasten negotiations between such parking lots and EDCs, as well as the price clearing process. Eventually, both electricity retailers and power system operators will benefit from the active participation of the flexible loads and energy customers.

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control.

Optimal Charging Strategy of Electric Vehicles in Unbalanced Three-Phase Distribution Network

Background: The increasing penetration of Electric Vehicles (EV) could lead to significant impact on the power systems, particularly for existing distribution networks. Methods: These impacts contain phase unbalance, excessive voltage deviations and overloading of equipment, which arise when a large numbers of Electric Vehicles are simultaneously charged. Results: In the current research, an optimal charging strategy is designed to control charging rates of Electric Vehicles in unbalanced three-phase distribution networks. A technique according to the Particle Swarm Optimization (PSO) is designed in order to reduce the charging cost of vehicles, with considering certain constraints. The constraint set involves transformer and line restrictions, unbalance of phase and voltage range. Conclusion: The proposed charging strategy is investigated on a real distribution network. Findings shows that high penetration of Electric Vehicles can be sustained in the existing distribution networks, demonstrating the effectiveness of the proposed method.

ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation

Large-scale utilization of electric vehicles (EVs) affects the total electricity demand considerably. Demand forecast is usually designed for the seasonally changing load patterns. However, with the high penetration of EVs, daily charging demand makes traditional forecasting methods less accurate. This paper presents an autoregressive integrated moving average (ARIMA) method for demand forecasting of conventional electrical load (CEL) and charging demand of EV (CDE) parking lots simultaneously. Our EV charging demand prediction model takes daily driving patterns and distances as an input to determine the expected charging load profiles. The parameters of the ARIMA model are tuned so that the mean square error (MSE) of the forecaster is minimized. We improve the accuracy of ARIMA forecaster by optimizing the integrated and auto-regressive order parameters. Furthermore, due to the different seasonal and daily pattern of CEL and CDE, the proposed decoupled demand forecasting method provides significant improvement in terms of error reduction. The impact of EV charging demand on the accuracy of the proposed load forecaster is also analyzed in two approaches: (1) integrated forecaster for CEL+CDE, and (2) decoupled forecaster that targets CEL and CDE independently. The forecaster outputs are used to formulate a chance-constrained day-ahead scheduling problem. The numerical results show the effectiveness of the proposed forecaster and its influence on the stochastic power system operation.

Impact of a High Penetration of Electric Vehicles and Photovoltaic Inverters on Power Quality in an Urban Residential Grid Part I – Unbalance

Impact of a High Penetration of Electric Vehicles and Photovoltaic Inverters on Power Quality in an Urban Residential Grid Part I – Unbalance

Impact of a High Penetration of Electric Vehicles and Photovoltaic Inverters on Power Quality in an Urban Residential Grid Part II – Harmonic Distortion

Impact of a High Penetration of Electric Vehicles and Photovoltaic Inverters on Power Quality in an Urban Residential Grid Part II – Harmonic Distortion

Optimal Reconfiguration-Based Dynamic Tariff for Congestion Management and Line Loss Reduction in Distribution Networks

This paper presents an optimal reconfiguration-based dynamic tariff (DT) method for congestion management and line loss reduction in distribution networks with high penetration of electric vehicles. In the proposed DT concept, feeder reconfiguration (FR) is employed through mixed integer programming when calculating the DT, leading to minimized energy cost and reduced DT as compared with the DT concept without FR. This paper further demonstrates that the line losses can be taken into account during the calculation of DT. As a result, the line loss reduction can be realized in a decentralized manner through the DT framework. Three case studies were conducted to validate the optimal reconfiguration-based DT method for congestion management and line loss reduction in distribution networks.