Plug-in Electric Vehicles Owners Research Articles

Plug-in electric vehicle dynamic pricing strategies for bidirectional power flow in decentralized and centralized environment

With the increasing number of plug-in electric vehicles (PEV) power demand management without increasing existing facilities is becoming challenging. To address the issue two novel pricing strategies one for centralized and other for decentralized environments considering bidirectional flow of power in a dynamic power pricing market are proposed in this paper. The two proposed strategies aim to fulfil the requirement of PEV owners and the power system operated by the Distribution System Operator (DSO). In the proposed approach load demand management with peak saving and valley filling for charging/discharging of PEV is done, while the base load connected to the system is not affected. While availing Vehicle to Grid (V2G) services battery degradation cost is considered for increasing service effectiveness and encouraging PEV owners to participate. The randomness of PEV in terms of time, SOC, battery capacity and level of charging/discharging is considered to be in accordance with the real-time scenario. The two proposed strategies are compared based on objective achievement, loading on the distribution transformer and finding a real-time suitable environment for a 24-hour duration. The numerical case study of the proposed strategies are carried out by considering Indian environment.

Advanced control strategies to manage electric vehicle drivetrain battery health for Vehicle-to-X applications

The demand for local power resilience is promoting the installation of renewable-based microgrids. Plug-in electric vehicles (PEVs) are rapidly increasing and can play a critical role in microgrid service provision. The capability of bi-directional charging and discharging will transition PEVs from a simple means of transportation to multi-purpose value generating assets. They can balance the inherently fluctuating generation of renewable energy resources and shape customer’s electricity demand, while still providing the core service of transportation. Value stacking through those additional services allows PEV owners to draw from a number of revenue streams to offset investment costs. However, accelerated battery degradation is often cited as concern when using PEV batteries for purposes other than driving, which presents a major barrier for broad market adoption. This study seeks to assess the economics and trade-offs of bi-directional use of PEVs when utilized as fleet vehicles. In particular, the usage patterns and benefits for PEV fleet deployment at three U.S. military bases is presented. The results show operational cost benefits for bi-directional application of up to 60.8 % annual return on investment without managing battery health, and up to 106.0 % when actively managing battery health through advanced control strategies. The one year payback period far exceeds the expected installation lifespan of at least ten years. Given the results, utilizing PEV fleets for additional services at U.S. military bases can largely offset the additional cost of bi-directional charging hardware and software compared to standard uni-directional equipment.

Transactive Charging Control of Electric Vehicles Considering Voltage Unbalance and Transformers’ Loss of Life

This paper presents a novel transactive energy (TE) management model for real-time coordination of plug-in electric vehicles (PEVs) in the distribution network. The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers’ loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model.

Optimal location of fast charging stations alongside pricing design from distribution company viewpoint considering renewable energy resource: A modified capacitated flow-capturing location model

Investigating the optimal location of fast charging stations (FCSs) followed by optimizing the electricity sale prices simultaneously for plug-in electric vehicles (PEVs) are considered as critical challenges in a power grid. This paper proposes the improved flow-capturing location model (FCLM) with mix-integer linear programming (MILP), in which the location of FCSs, alongside the electricity sale price to PEVs, is optimized so that the profit of the distribution company (DISCO) is maximized. Also, it considers renewable energy resources (RESs) to support FCSs. DISCO is assumed to be in charge of the cost of stations, the expansion network, RESs, and the electricity purchase from the network. The goal is to determine with how much price DISCO should sell the electricity to PEVs for the return on investment (ROI) rate to be positive at a specified time. Moreover, the PEV owners’ non-utilization function is considered in such a way that the electricity sale price to PEVs in stations has a direct relationship with the PEV owners’ non-utilization. In addition, the growth of PEVs, electric load, and electricity purchase from the network are considered. The proposed model has been implemented on a 25-node urban traffic network fed by a 33-bus distribution network. The results demonstrate that the investor provides electricity for PEVs with less price in the proposed model because the investment costs of FCSs are reduced by 34.4%. Also, by raising the PEVs’ charging demand, the electricity sale price in the station is reduced. In this condition, the utilization of PEV owners is maximized. Moreover, considering RESs reduces voltage drop and electricity sales price to PEVs.

Computational intelligence based PEVs aggregator scheduling with support for photovoltaic power penetrated distribution grid under snow conditions

• A snow loss-aware deep learning-based short-term PV yield predictor is developed. • Stochastic day-ahead scheduling of an independent PEVs aggregator is investigated. • A novel local balancing service offered by the PEVs aggregator to DSO is proposed. • The service benefits PEV owners through providing extra energy demand of the grid. This paper addresses the issue of optimal day-ahead scheduling of a plug-in electric vehicles (PEVs) aggregator that participates in the electricity market and offers an out-of-market balancing service to the local renewable power penetrated distribution system in a snow-prone area. The proposed balancing service provides a reliable source of flexibility for the extra real-time energy demand of the distribution system operator (DSO) which originates from the difference between its day-ahead bids and the actual demand. The problem is investigated on a snowy day when the DSO's day-ahead decisions encounter more uncertainty due to the considerable effect of snow loss on the DSO's photovoltaic plant performance. The aggregator's scheduling is formulated as two-stage stochastic programming which minimizes the PEVs’ charging cost. Monte Carlo simulation and K-means clustering are implemented to generate scenarios of driving patterns and real-time energy market prices, respectively. Offering the balancing service requires day-ahead predictions of the photovoltaic power and the grid load demand which are modeled using long short-term memory networks. The problem is formulated as mixed-integer linear programming. The results show that the proposed scheduling approach reduces the PEVs’ charging cost by 53% and guarantees the grid normal operation. Moreover, the balancing service can reduce the expected PEVs’ charging cost and the DSO's real-time cost by 12% and 14%, respectively. © 2017 Elsevier Inc. All rights reserved.

Stochastic Optimal Sizing of Plug-in Electric Vehicle Parking Lots in Reconfigurable Power Distribution Systems

Charging demand of plug-in electric vehicles (PEVs) can cause reliability and operational challenges in power distribution systems. The aggregated charging control of PEVs in the parking lots (PLs) may alleviate the challenges, if the place and size of PEV PLs are optimally determined. This paper develops a stochastic framework for finding optimal location and sizing of PLs as well as optimal charging profile of PEV PLs with the vehicle to grid capability in a reconfigurable distribution system. The main aims are to reduce distribution system losses and enhance network reliability, subject to numerous constraints of the power distribution system, PLs, and PEVs. To guarantee the global optimum solutions, the proposed optimization problem is linearized to achieve a mixed-integer linear program model. Furthermore, kernel density estimator (KDE) is presented to model the temporal uncertainties associated with PEV owners’ behavior with small number of iterations and no necessary assumptions. Various scenarios and sensitivity analysis are conducted to show the efficacy of the proposed method.

Electric Vehicle Trip Chain Information-Based Hierarchical Stochastic Energy Management With Multiple Uncertainties

This paper proposes a vehicle trip chain information-based hierarchical stochastic energy management system (Hi-SEMS) for a microgrid integrated with highly uncertain plug-in electric vehicles (PEVs) fleet, renewable energy sources, and loads. The two-layer Hi-SEMS scheme aims at optimal scheduling of PEV fleet to counteract the operational uncertainties encountered in real-time operation and minimizing the microgrid daily operating cost comprising the fuel cost and the emission cost. In the proposed work, the realistic behavior of PEVs fleet is modeled using a stochastic trip chain information to accurately forecast the charge/discharge demand taking into account the driver’s behavior as foundation. Realistic statistical trip data, multi-location charge/discharge of PEVs fleet to include coupled spatial-temporal dynamics of heterogeneous PEVs fleet is considered for intelligent charge/discharge scheduling. The effectiveness of the proposed strategy is verified on modified LV CIGRE and modified IEEE 33 bus radial distribution test networks and is compared with four different cases on each of the two test systems. The simulation results exhibit that the proposed Hi-SEMS outperforms the other four cases in terms of daily operating cost, the incentive to the PEVs owner, and robustness to multiple uncertainties.

To purchase or lease: Investigating the finance decision of plug-in electric vehicle owners in California

In this paper we explore the factors that influence plug-in electric vehicle (PEV) buyers’ decision to lease or purchase a PEV. We estimate a binary logistic regression model using survey data of 9,472 PEV owners in California, surveyed between 2015 and 2018. Several factors influencing the purchase versus lease decision are identified; battery electric vehicles (BEVs) are more likely to be leased over plug-in hybrid electric vehicles (PHEVs). Results show a positive association between electric range and likelihood of purchasing BEVs, but a negative association for PHEVs. Sociodemographic factors such as living in a single-family home, homeownership, and having at home solar panels are associated with purchasing. An increase in the stated importance of high occupancy vehicle/carpool lane access is associated with the decision to lease, but an increase in state rebate importance is associated with the decision to purchase. The results highlight different factors that influence the decision to purchase or lease a PEV, both of which will be important strategies for growing the PEV market.

Optimal Charging Infrastructure Portfolio for Minimizing Grid Impact of Plug-In Electric Vehicles

Charging infrastructure (CI) is crucial for the widespread adoption of plug-in electric vehicles (PEVs). The optimal portfolio of CI is beneficial for PEV owners, power systems, and policymakers. Therefore, an optimal CI portfolio estimation method is proposed to minimize the grid impacts of PEVs. All possible CI types (residential, workplace, public, and commercial) along with the three commonly used charging levels are considered and seven PEV charging profiles are estimated. Then, an optimization model is developed to determine the optimal portfolio of CI. An index is proposed to compare the performance of the proposed method with other CI portfolios. Various cases are simulated to analyze the performance under diverse conditions such as the presence of renewables and specific policy scenarios. The proposed method can be used by policymakers for making new policies, in new PEV markets, and to incentivize the use of specific CI types, in mature/developing markets.

Transactive Energy Management of V2G-Capable Electric Vehicles in Residential Buildings: An MILP Approach

This paper proposes a new energy management model for residential buildings to handle the uncertainties of demand and on-site PV generation. For this purpose, the building energy management system (BEMS) organizes a transactive energy (TE) market among plug-in electric vehicles (PEVs) to determine their charge/discharge scheduling. According to the proposed TE framework, the PEV owners get reimbursed by the BEMS for the flexibility they offer. In this regard, the PEV owners submit their response curves for reimbursement upon arrival. Then, the BEMS solves an optimization problem to maximize its own profit and determine the real-time TE market-clearing price. Afterward, based on the clearing price, the real-time scheduling of PEV batteries and the reimbursements to the PEV owners for their responses are determined. Additionally, the original mixed-integer non-linear optimization problem is reformulated as a mixed-integer linear programming one using a set of linearization techniques. Finally, the proposed model is applied to a residential building with 50 PEV charging piles, and the simulation results show that the proposed model decreases the actual charging payment of PEV owners by 17.6% and 52.3%, and the total cost of BEMS by 5.1% and 10.8% compared to demand response concept-based and uncontrolled charging models, respectively.

Electric Energy Management for Plug-in Electric Vehicles Charging in the Distribution System by a dual cascade scheduling algorithm

This paper presents an algorithm for plug-in electric vehicles (PEVs) charging in the three-phase distribution system for residential houses. It aims to prevent violent voltage level deviation and increasing losses on the three-phase distribution system due to uncontrolled charging and allocate power to each plug-in electric vehicle. The algorithm is comprised of two processes. The first process is power limitation and limited power of load imbalance by if-else rules, while the second process is power allocation to each PEV by the dual cascade scheduling algorithm which is the integration of tasking scheduling algorithms. A 100 kVA distribution transformer and 30 houses are defined in the simulation situation. Also, the available PEVs in single-phase, two-phase, and three-phase systems are assigned for verification of the proposed algorithm. Root-mean-square deviation (RMSD) referred to the satisfaction of PEV owners, total PEVs charged energy, and the average percentage of achieved charging time, as the result indicators. The results show the proposed algorithm can provide good results without rejected PEVs charging. Furthermore, this paper also displays the analysis of voltage level, percentage of voltage unbalance factor, and loss in the distribution system. In the future, coordination with home appliances to gain a high load margin or electric energy cost control will be improved in the proposed algorithm

Multi-agent DRL-based data-driven approach for PEVs charging/discharging scheduling in smart grid

This paper studies the charging/discharging scheduling problem of plug-in electric vehicles (PEVs) in smart grid, considering the users’ satisfaction with state of charge (SoC) and the degradation cost of batteries. The objective is to collectively determine the energy usage patterns of all participating PEVs so as to minimize the energy cost of all PEVs while ensuring the charging needs of PEV owners. The challenges herein are mainly in three folds: 1) the randomness of electricity price and PEVs’ commuting behavior; 2) the unknown dynamics model of SoC; and 3) a large solution space, which make it challenging to directly develop a model-based optimization algorithm. To this end, we first reformulate the above energy cost minimization problem as a Markov game with unknown transition probabilities. Then a multi-agent deep reinforcement learning (DRL)-based data-driven approach is developed to solve the Markov game. Specifically, the proposed approach consists of two networks: an extreme learning machine (ELM)-based feedforward neural network (NN) for uncertainty prediction of electricity price and PEVs’ commuting behavior and a Q network for optimal action-value function approximation. Finally, the comparison results with three benchmark solutions show that our proposed algorithm can not only adaptively decide the optimal charging/discharging policy by on-line learning process, but also yield a lower energy cost within an unknown market environment.

Integrating plug-in electric vehicles (PEVs) into household fleets- factors influencing miles traveled by PEV owners in California

Household vehicle miles traveled (VMT) has traditionally been studied in the context of gasoline vehicles. In this study, we analyze VMT of Plug-in Electric Vehicles (PEVs) to understand PEV use and the factors that influence their use in a household. We use data from a unique repeat survey of PEV owners in California who were originally surveyed when they first bought their vehicle. Having two survey responses per household allows us to obtain more reliable VMT data and analyze the change in VMT over the vehicle ownership period. The results show that PEV VMT is correlated with traditional factors like population density, built environment, attitudes towards technology, and lifestyle preferences. Specific to PEVs, electric driving range and access to infrastructure, have a major influence on PEV VMT. Moreover, while lower electricity price at home may lead to a higher share of PEV VMT in total household VMT, we do not identify the presence of “rebound effect”. Overall, we observe that factors influencing PEV VMT are like those observed for conventional gasoline vehicles. We find that PEVs drive a similar amount as conventional vehicles, not less (due to range anxiety) as some have suggested. The results have implications for emissions impact assessments and travel demand models that depend on assumptions of average annual VMT for policymaking.

Optimal coordination of plug-in hybrid electric vehicles: A stochastic market-based approach

The existing models implemented to coordinate the plug-in electric vehicles (PEV) based on the price of electricity almost exclusively use the uniform price methods. These methods are based on price-taker mechanisms where prices are exogenous and unaffected by the PEVs activity. This standpoint, however, suffers from two major issues. First, the important indexes of network operation are neglected, e.g., power losses, network reliability, cost/benefit of PEVs operation. Second, coordination with distributed generations (DG) is not possible, efficiently. In this paper, a new viewpoint based on locational marginal price (LMP) is formulated for coordination of PEVs and DGs in electricity day-ahead markets. The LMPs at fleet and DG connected busses are used as a signal to motivate PEV and DG owners to participate in coordination problem. Also, coordination problem is inserted in the optimal operation problem of the network and an efficient algorithm based on Chaotic Crow Search algorithm (CCSA) is used to meet the optimal operation of the network. To make a bidirectional power flow, the vehicle-to-grid (V2G) is used as the PEVs; and, for modeling random behavior of loads, market price, and PEVs, a stochastic modeling framework based on PEM is implemented. The feasibility and satisfying performance of the proposed method has been tested on an 84-bus test system, and simulation results reveal promising results.

Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model

This study investigates the stochastic optimal electricity procurement problem of an electricity retailer in a smart grid environment. Various energy provision means, including distributed generations (DGs), pool market (PM), forward contracts (FC), photovoltaic (PV) system, wind turbine (WT), and Battery energy storage system (BESS) are considered. A novel plug-in electric vehicle (PEV) based demand response program (DRP) is integrated into the problem considering economic incentives for PEV owners, which is computationally fast, and it is based on the uncertainties derived from PEVs, such as arrival/departure time, daily traveled miles and vehicle type. Moreover, the uncertainty of PM price, solar irradiation, wind speed, and conventional loads are taken into account by an artificial neural network (ANN)-based scenario generation method. Furthermore, an analytical linear BESS degradation cost model is integrated into the objective function to ensure fast global optimization. To confirm the functionality of the proposed method, different operation schemes, such as with and without DRP, with and without battery degradation cost, are thoroughly inspected. Additionally, the sensitivity of the problem for the number of PEVs is analyzed. The proposed two-stage stochastic mixed-integer linear problem (MILP) is solved by commercially available optimization software.

Decentralized Energy Management System for LV Microgrid Using Stochastic Dynamic Programming With Game Theory Approach Under Stochastic Environment

In this article, we propose a decentralized energy management system to minimize the daily operating cost of microgrid and the net charging cost of individual plug-in electric vehicles (PEVs) while maintaining comfort level and privacy. A novel approach based on stochastic dynamic programming (SDP) with N-person noncooperative game-theoretic approach is used for real-time scheduling of heterogeneous PEVs both ways-in the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) mode. We use the SDP method for full enumeration of all possible decisions while game theory provides quick convergence to Nash-equilibrium. The proposed approach considers uncertainties of renewable generation as well as load demand and offers autonomy to the microgrid operators and PEVs owners to optimize their objectives individually. The effectiveness of the proposed approach has been tested on modified LV CIGRE and modified IEEE 33 bus test networks. The results have been compared with the deterministic and stochastic bi-level optimization. The simulation results exhibit that the proposed algorithm outperforms the other two algorithms in terms of operating cost, incentive to the PEVs owner, robustness to uncertainties, and convergence. The operating cost reduction achieved for the modified CIGRE model and modified IEEE 33 bus with the proposed approach is 20.02% and 36.53%, respectively, as compared to the deterministic framework, whereas 7.64% and 16.35% are the values obtained in comparison with the bi-level approach.

Plug‐in electric vehicles smart charging mechanisms for cost minimization and ancillary service provision

The increasing number of Plug-in Electric Vehicles (PEV) connected to the grid will create challenges to be addressed and opportunities that can be ceased. One challenge is the load shape dynamics, which might result in increasing the loading level on the distribution lines. On the other hand, there is an opportunity to use the PEV stored energy to provide ancillary services to the grid. Therefore, this paper proposes a new PEV charging mechanisms that not only aid in ancillary service provision, but also help in reducing the charging cost and avoiding the extra investment in peak generation units while maximizing the integrated PEVs. The proposed approach utilizes three stages to ensure the satisfaction of PEV owners with different preferences and to respect the grid limitations. The main objective is to develop different charging mechanisms for charging PEV's in the parking lots to ensure the maximum satisfaction of the PEV owners by fulfilling their charging requirements with the minimum possible cost. The developed algorithm relies on a moving time window to address the real-time changes and considers the participation of PEVs in the frequency regulation and peak shaving. The effectiveness of the proposed algorithm is verified through simulation using real data.

Network-Constrained Transactive Coordination for Plug-In Electric Vehicles Participation in Real-Time Retail Electricity Markets

Uncoordinated adoption of plug-in electric vehicles (PEVs) imposes further load on the distribution network, and therefore may result in disruptive impacts on the grid. Transactive coordination of PEVs has been introduced as an effective approach to mitigate these negative consequences. This paper furthers efforts enabling PEVs to participate in a real-time retail electricity market under a transactive energy (TE) paradigm. In this regard, PEV owners will estimate their willingness to pay/accept using a user-friendly strategy and submit the estimated values to the retail market operator. Then using a network-constrained market clearing mechanism, the clearing prices, i.e., dual variables of active power balance constraints, will be calculated. Finally, these calculated clearing prices will be sent to whole PEVs and their actions have been determined. The proposed model is applied to the modified IEEE 33-bus test system combined with several low voltage feeders, and the results illustrate the effectiveness of the proposed model from viewpoints of PEV owners and utility.

The potential for community financed electric vehicle charging infrastructure

Charging infrastructure is one of the key ingredients necessary to further increase the market uptake of plug-in electric vehicles (PEVs). Most public charging infrastructure in Europe is not profitable, and 42% of European households reside in dwellings with no possibility to install a private charger, which are key limitations to the build-up of charging infrastructure. This work presents and analyzes an alternative business model for ‘community-owned charging points’ that combines many of the convenience advantages of private charging with the cost savings of a joint purchase. Survey data from 3,131 households in Austria is used to identify barriers to PEV charging and charging infrastructure usage, and investigate consumers’ preferences with respect to community-owned charging points. Four key target groups of customers for such a business model are identified based on their socio-economic characteristics, purchase history, and expressed level of environmental concerns, including current PEV owners, high income households, photovoltaic (PV) owners, and environmentalists. Identified distinct preferences for the configuration of a collective charging station amongst these groups include lower importance attached to flexibility to charge the vehicles at any time by current PEV owners, higher sensitivity to cost per charge of PV owners, and strong preferences for comfort and flexibility demonstrated by high income households.

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.