Penetration Of Vehicles Research Articles

A space–time-energy flow-based integer programming model to design and operate a regional shared automated electric vehicle (SAEV) system and corresponding charging network

Shared automated vehicles are expected to be part of the supply of transportation systems in the future. Parallel to this evolution, there is the rapid penetration of battery electric vehicles (BEVs). The limitations in battery capacity and charging speed of BEVs can influence the planning and operation of shared automated electric vehicle (SAEV) systems. The design of such systems needs to include these limitations so that their viability is properly estimated. In this paper, we develop a space–time-energy flow-based integer programming (IP) model in support of the strategic design of a regional SAEV system. The proposed approach optimizes the fleet (size and composition) and charging facilities (number and location), while explicitly accounting for vehicle operations in aggregated terms (including movements with users, relocations, and charging times). The model is used to assess the impact of vehicle range and different types of chargers in the optimal design of an interurban SAEV transport system in the center of Portugal. Results show a reduction in profit as the vehicle range increases. In regards to energy, it is observed that the adoption of long-range vehicles reduces the energy spent in relocations, and increases the amount of energy charged at a lower price. Additionally, it is found that a system with long-range vehicles does not take advantage of having fast chargers. Concerning the chargers’ optimal location, systems using short-range vehicles have more chargers close to the main commuter trips attracting cities, while systems with long-range vehicles have the chargers nearby the homes of users.

Blockchain and Machine Learning for Future Smart Grids: A Review

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.

Operation Strategy of EV Aggregators Considering EV Driving Model and Distribution System Operation in Integrated Power and Transportation Systems

Recently, the penetration of electric vehicle (EV) has increased, and several motor companies have declared a paradigm shift to EVs. However, most existing studies focusing on EV charging scheduling in power system operations neglect the integrated operation of transportation and distribution systems. Because EVs affect both systems, it is necessary to consider the grid operation and the individual EV’s preferences (charging cost and time) simultaneously. In this study, an integrated power-transportation system structure and operation algorithms are developed to analyze the impact of EVs on the distribution system. In addition, this study proposes an optimal EV driving model that consists of two driving patterns: routine driving and long-distance driving. For routine driving patterns, the Markov chain model is implemented considering the mileage and conditions of individual EVs. Subsequently, long-distance driving patterns are developed to select the optimal route through reinforcement learning. In addition, the EV aggregator structure of EV charging stations can effectively predict local charge demands and perform charge control in real-time. An integrated power-transportation system is developed using the IEEE RBTS system and the integrated system architecture and algorithms are developed using MATLAB. It utilizes the ACOPPF function of MATPOWER to evaluate systematic stability and derive DLMP (Distributed Locational Marginal Price). This study conducts performance verification in various scenarios, confirming the effect of charging control of EV aggregators considering system stability, loss reduction (50kWh), and cost reduction of the entire system.

Impact of autonomous vehicles on air quality and energy efficiency of road traffic flows – a case study in an urban roundabout

Due to the likely market penetration of autonomous vehicles (AVs) in the short-medium term, several studies have analysed their environmental impact due to changes in demand patterns or market models. However, there is little research on the operational impact of AVs on air quality. This work aims to explore several scenarios of Avs penetration with different driving patterns and anticipate the potential impacts of traffic performance, emissions, and air quality. An urban intersection was used as a case study, namely a roundabout located in a critical context from the air pollution exposure point of view. Results show that the introduction of Avs may lead to higher travel times at peak hours and increased emissions, mainly due to AVs with defensive behaviour. Regarding air quality, the introduction of AVs led to a degradation of air quality, especially during peak hours.

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.

Voltage Control in Smart Distribution Network with Deep Penetration of Electric Vehicle

Voltage Control in Smart Distribution Network with Deep Penetration of Electric Vehicle

A Decentralized Dynamic Pricing Model for Demand Management of Electric Vehicles

Transportation electrification is considered a green alternative to internal combustion vehicles. However, higher penetration of electric vehicles (EVs) can cause several technical challenges to the power systems, including local equipment overloading. This is especially challenging for distribution systems due to the direct connection of EVs with them. To mitigate equipment overloading, by managing EV loads locally, a dynamic pricing model is proposed in this study. First, a satisfaction function is devised for EVs considering the sensitivity of different EV owners to the battery state of charge and urgency of recharge. Then, a welfare function is developed for the EV fleet operator (EFO) considering the net load of EVs and the electricity price of the upstream grid. To this end, a welfare maximization problem is formulated considering the welfare of EVs and EFO. The problem is then decomposed into the EFO sub-problem and EV sub-problem to preserve privacy. Finally, a distributed mechanism is developed to solve the sub-problems iteratively without revealing private information. The performance of the proposed method is analyzed for a residential apartment complex in terms of load management and convergence for different day types (working days and holidays). Simulation results have shown the efficacy of the proposed method in mitigating the overloading of transformers during peak load hours.

A novel stability index-based virtual synchronous machine droop scheme for performance improvement of hybrid AC/DC microgrid under volatile loading conditions

The large-scale electric vehicle penetration into the grid and the volatile nature of the electric vehicle loading adversely affect grid stability. This paper presents a novel index-based droop scheme for improving the performance of the hybrid AC/DC microgrid under volatile loading conditions. The novelty of the proposed control scheme is that the adaptiveness in selecting active droop gain based on the volatile stability index generates an accurate power reference rather than a constant power reference as in conventional droop gain methods. The state–space modelling of the proposed controller and index-based approach is used to determine the theoretical stability margin. The simulation experiment results agree with the theoretical stability margin limit. The efficacy of the control scheme is validated using real-time experiments on a hardware-in-the-loop real-time simulator, OPAL-RT. The results show that the proposed control scheme improves the performance of the hybrid AC/DC microgrid by maintaining the frequency/voltage fluctuations in the AC and DC sub-grids within the acceptable operating limits of ± 2% and ± 10% as per IEEE 1547 standards. As a result, the percentage improvement in voltage profile is 2.68% at the AC sub-grid and 5.55% at the DC sub-grid. • A novel stability index to reflect volatile loading conditions of HMG. • HMG stability enhancement using HVI-based VSM-V droop controller. • Mathematical modelling and analysis of VSM-V droop scheme. • Real-time experimental verification using OPAL-RT.

Sequence-to-Sequence Recurrent Graph Convolutional Networks for Traffic Estimation and Prediction Using Connected Probe Vehicle Data

Traffic estimation is imperative for conducting fundamental transportation engineering tasks such as transportation planning and traffic safety studies. Additionally, traffic prediction is vital for many data-driven intelligent transportation system applications. Most traffic estimation and prediction methods rely on infrastructure-based sensors to collect traffic parameters. However, infrastructure-based data collection can be costly and time consuming to set up and maintain. Additionally, the spatial distribution of the collected traffic data is limited by the location of the deployed hardware sensors. Probe vehicle data can be used to overcome these limitations. Traffic modeling for estimation and prediction is a complex task due to the stochastic nonlinear spatiotemporal dependencies exhibited by traffic parameters. In this paper, a deep learning-based sequence-to-sequence architecture called Seq2seq GCN-LSTM was proposed to estimate and predict network-wide traffic volume and speed. The proposed method utilizes short-term historical traffic data collected from a low-penetration rate probe vehicle fleet to estimate and predict traffic parameters up to 60-minutes ahead. The method utilizes Graph Convolutional Networks for spatial dependency extraction and Long Short-Term Memory networks to model temporal dependencies. The proposed method generated superior traffic results compared to the baseline models. Additionally, the model demonstrated robustness against perturbations caused by the low rate. Furthermore, the probe vehicle penetration rate was varied to test its effect on the proposed method’s modeling capability. The model was able to maintain traffic volume and speed estimation and prediction performance within a reasonable margin of error using penetration rates as low as 1.5% and 0.5%, respectively.

Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization

Optimal Switching Operations of Soft Open Points in Active Distribution Network for Handling Variable Penetration of Photovoltaic and Electric Vehicles Using Artificial Rabbits Optimization

SMES-GCSC Coordination for Frequency and Voltage Regulation in a Multi-Area and Multi-Source Power System with Penetration of Electric Vehicles and Renewable Energy Sources

Frequency, tie-line power, and the terminal voltages of synchronized generators must all be kept within prescribed limits to ensure the stability of an interconnected power grid through combined automatic generation control (AGC) and automatic voltage regulator (AVR) loops. Thermal power plants, electric vehicles, and renewable energy sources—including solar and wind, geothermal, and solar thermal power plants—form the two-area integrated power system in present research. A new cascade controller named the cascaded proportional integral derivative (PID) and fractional-order PID (CPID-FOPID) controller is proposed for the first time, whose performance is compared with the PID and FOPID controller. The results show that the proposed cascade controller outperforms PID and FOPID in delivering superior dynamic characteristics, including short settling times and low oscillation amplitudes. A new metaheuristic algorithm named the coot algorithm was applied to optimize the parameters of these controllers. The suggested controller outperforms FOPID in the combined AGC and AVR problem under uncertain conditions (random load disturbance, variable input of solar irradiation, and wind power). Robustness of the controller is tested with significant variation in the turbine time constant of the thermal and geothermal power plant. In this study, authors also investigated the best possible coordination between the superconducting magnetic energy storage (SMES) and gate-controlled series capacitor (GCSC) devices to control both voltage and frequency simultaneously. The effect of communication time to the power system is analyzed in this study. Additionally, the obtained results are satisfactorily validated using OPAL-RT real-time digital simulator.

Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review

The high penetration of electric vehicles (EVs) will burden the existing power delivery infrastructure if their charging and discharging are not adequately coordinated. Dynamic pricing is a special form of demand response that can encourage EV owners to participate in scheduling programs. Therefore, EV charging and discharging scheduling and its dynamic pricing model are important fields of study. Many researchers have focused on artificial intelligence-based EV charging demand forecasting and scheduling models and suggested that artificial intelligence techniques perform better than conventional optimization methods such as linear, exponential, and multinomial logit models. However, only a few research studies focused on EV discharging scheduling (i.e., vehicle-to-grid, V2G) because the concept of EV discharging electricity back to the power grid is relatively new and evolving. Therefore, a review of existing EV charging and discharging-related studies is needed to understand the research gaps and to make some improvements in future studies. This paper reviews EV charging and discharging-related studies and classifies them into forecasting, scheduling, and pricing mechanisms. The paper determines the linkage between forecasting, scheduling, and pricing mechanism and identifies the research gaps in EV discharging scheduling and dynamic pricing models.

Methodology to Determine the Management of Demand in Recharging Electric Vehicles in Vertically Integrated Markets Includes Photovoltaic Solar Generation

The high penetration of photovoltaic solar generation and electric vehicles in developing countries and with vertically integrated electricity markets with restrictive regulatory policies enhance demand management and the participation of prosumers in optimizing their resources. In this sense, the research presents a demand management methodology based on the prosumer model for recharging electric vehicles through optimization based on linear programming to minimize recharging costs, considering the stochasticity of the solar radiation variables, vehicular mobility patterns, consumer preferences, and optimal location of charging stations through surveys and predictive tools such as PVsyst and GAMS, in such a way that the energy demand for recharging electric vehicles is met. This way, the methodology reduces power demand peaks and mitigates the economic and technical impact on distribution networks. This case study has been modelled with real information from electric vehicles, distribution networks, and surveys in Cuenca, Ecuador.

Energy‐efficient multiple cooperative moving relay selection for heterogeneous nonorthogonal‐multiple access systems

SummaryIn a high‐speed vehicular scenario, due to vehicle penetration loss (VPL) and fast signal fluctuations, frequent handovers occur, which results in more battery consumption. So power saving is a critical concern in high‐speed wireless networks. In the past, moving relay (MR)‐based heterogeneous network (HetNet) models were proposed and studied to overcome the effect of VPL, but the issue of power saving has not been studied till now for vehicular HetNet. In this paper, we propose an energy‐efficient algorithm that selects the best number of cooperative MRs among multiple MRs installed on the top of the train for efficient utilization of transmission power in a NOMA‐based wireless network. A half‐duplex amplify‐and‐forward relaying protocol is used for the investigation. We define the optimization problem of energy efficiency for cooperative MR. The problem of energy maximization is stated as an optimization problem, and an energy‐efficient MR selection algorithm is proposed as the solution to the optimization problem. The simulation results show that the proposed algorithm enhances the energy efficiency of vehicular and nonvehicular users and it also decreases the outage probability at fixed spectral efficiency in comparison with the fixed MR‐based selection combining and maximum ratio combining diversity techniques. This result will be beneficial in vehicular communication to achieve maximum energy efficiency.

Energy storage for mitigating grid congestion caused by electric vehicles: A techno-economic analysis using a computationally efficient graph-based methodology

This paper studies the impact of electric vehicle charging on congestion in low-voltage networks and the economic feasibility of energy storage as an alternative to conventional network upgrades. To this end, we propose a computationally efficient graph-based methodology and employ it on a GIS data set of over 6,500 low-voltage networks. We provide insights regarding the types of networks that are most congested due to electric vehicle charging, the typical location of overloaded network components, and the types of networks in which a storage-based upgrade is profitable. A main result is that the number of networks in which it is profitable to use energy storage grows exponentially as the cost of energy storage decreases, regardless of the electric vehicle penetration level and charging power. Our findings also show that using energy storage to alleviate congestion can incur substantial savings, conditioned with a cost reduction of stationary energy storage to 200 US$/kWh and below.

PV assisted electric vehicle charging station considering the integration of stationary first- or second-life battery storage

In recent years, the wider penetration of Renewable Energy Sources (RES) and Electric Vehicles (EV) has required the introduction of new sources of flexibility and adequate control models to increase the reliability of the energy sector. Indeed, the optimal management at the local scale of the match between energy demand and production is key to dealing with the technical and economic issues caused by the high variability of RES and EV charging demand. In this study, an environmentally oriented optimal design of a photovoltaic (PV) powered EV Charging Station (EVCS) integrated with an Electric Energy Storage System (ESS) is presented. Batteries characteristics and their role on life cycle emissions are also addressed. The system design is determined with the double aim of minimizing the carbon emissions due to the charging process and carbon footprint of the installed technologies and maximizing the self-consumption of local energy production. A bi-level optimization approach is adopted: a Multi-Objective Genetic Algorithm is used to optimally define the number of slow and medium EV Supply Equipment (EVSE), PV peak power and battery capacity, while a mixed integer linear programming (MILP) algorithm is used for the optimal control of the charging process. A comparison of different solutions is carried out over an eight-year time horizon, accounting for uncertainties in renewable generation and EV charging demand. The results highlight that the installation of Second-Life batteries as energy storage media in sustainable terms. Indeed, the cumulative CO2 emissions over the whole time horizon of the Electric Vehicle Charging Station are decreased by 15% and 10% respectively if compared with First-Life batteries and a baseline case without any stationary storage system. These results confirm that long-term evaluation of techno-environmental performance is crucial to make sustainable and conscious choices.

Driver behavioural adaptations to simulated automated vehicles, potential implications for traffic microsimulation

The ongoing technological development of automated vehicles is bringing their implementation ever closer. Until all vehicles are completely automated there will be a mix of human driven and automated vehicles. To assess impacts of such automated vehicles on traffic safety and flow, techniques involving traffic microsimulations are often used. Here mixed traffic scenario’s are simulated by assigning different behavioural models to automated and to human driven vehicles. The human driver models generally remain unchanged when simulating different penetration rates of automated vehicles. In the real world, however, phenomena like social contagion could result in changes in human driving behaviour when automated vehicle penetration rates increase. To investigate such adaptive behaviour a simulator study was conducted where participants were asked to drive the same route three times, each time with a different penetration rate of automated vehicles. The simulated automated vehicles were assigned driving behaviour that resulted in a shorter time-headway, stricter speed control and a reduced reaction time compared to the simulated human drivers. The results show that when the penetration rate of automated vehicles increased the participants adopted shorter time-headways, smaller following distances, less variable velocity more closely resembling the maximum allowed speed, and reduced reaction times with faster acceleration at traffic lights. These findings suggest that driving behaviour changes when the composition of surrounding traffic changes. In order to more accurately use traffic microsimulation to estimate effects of mixed traffic it is therefore important to ensure that human driver models are updated to incorporate social contagion effects.

Electricity market modeling considering a high penetration of flexible heating systems and electric vehicles

The electrification of the heating and transportation sectors is a major building block in Germany’s effort to reduce carbon dioxide emissions. The additional loads and flexibilities resulting from the anticipated large-scale integration of the heating and transportation sectors necessitate an adequate representation in electricity market modeling. First, the paper presents a comprehensive modeling approach of the heating sector in a pan-European electricity market simulation considering both small-scale building heat pumps and large-scale conversion units with thermal energy storages in district heating networks. The operation of small-scale building heat pumps is emulated using an electro-thermal 1R1C building model integrated into the economic dispatch formulation while the operation of units in district heating networks is restricted by additional constraints resulting from the combined heat and power output, local heat load coverage and possible heat power transfer between heating networks. The transportation sector is mapped using previously published models by the authors including both controlled and fast charging processes. Subsequently, the load shift potentials resulting from the market-integration of the heating and transportation sectors and their impact on the pan-European electricity market with focus on Germany based on the three target years 2030, 2040 and 2045 are examined. Furthermore, by applying a Monte Carlo simulation to map possible future primary energy source prices a major uncertainty dimension is addressed. Considering the current German electricity market design, the integration of the two sectors lead to a moderate increase in future annual electricity demands and a substantial increase in market-based peak load. The resulting annual peak load from power-to-heat devices account to 22 GW (2030), 53 GW (2040) and 102 GW (2045) and from electric vehicles to 12 GW (2030), 36 GW (2040) and 96 GW (2045) for the target years. Further results demonstrate the larger load-shift potentials of electric vehicles compared to building heat pumps by comparing the weighted annual average market price of the flexible loads. The primary fuel price uncertainty is examined based on the 0.25, 0.50 and 0.75 quantiles for the target years which result in annual average electricity market price spreads of over 2.16 (2030), 2.97 (2040) and 3.44 (2045) between the 0.25 and 0.75 quantile scenarios.

Multiobjective optimal operation strategy for electric vehicle battery swapping station considering battery degradation

The study aims to analyze a futuristic view of the automobile industry conducive to the much-needed penetration of Electric Vehicles (EVs) as per the current environmental and economic scenario. The study suggests the roll-out of EVs in tandem with the supporting Charging Infrastructure, which is a prerequisite for adopting the former. Although transport electrification is a much-accentuated and researched solution to the deteriorating environment and plummeting conventional resources, the design, production, manufacturing, use, degradation, and disposal of an exponential number of lithium-ion batteries for the same have environmental, economic, and social impacts. Thus, emphasis has been made on the sustainable use of charging infrastructure that curbs unnecessary and early battery aging from fast charging technology. Battery swap requests at a Battery Swapping Station (BSS) can be served via batteries from either available battery stock or by charging previously incoming discharged batteries. The study suggests an optimal strategy for the same via a mathematical model representing the operation cost of a BSS consisting of three components, namely, cost of battery utilization, damage cost associated with different charging methods, and dynamic electricity cost. The solution to the multiobjective optimization problem gave the optimum number of batteries that should be used from the battery stock and the charging decision for incoming discharged batteries, given the possible charging options and the constraints on demand satisfaction. Finally, the results from two different optimization tools, Solver in MS Excel and Lingo software, were compared.

Analysis of Unsymmetrical Customer and Electric Vehicle Connection on Voltage Profiles and Voltage Unbalance - Case Study Real Low Voltage Distribution Network

Abstract In the past couple of years, the integration of electrical vehicles (EV) in the power system has been capturing the attention of many researchers, which has led to this being a frequently analysed topic in scientific papers and seminars. Some of the main parameters related to power quality that are observed, while integrating EVs, are voltage variations and voltage unbalance. The low-voltage rural network, of a small settlement located on the outskirts of Zavidovici, has been modelled for the purpose of this paper. Several cases were considered. As the first base case, an analysis was performed when there were no connected vehicles in the network, then the case with 20% penetration, 35% penetration, and 80% vehicle penetration. Practical analysis was done in DIgSILENT Power Factory software. It has been concluded that electrical vehicles cause a voltage drop in the network, while at the same time causing an increase in voltage unbalances.