Smart Charging Of Electric Vehicles Research Articles

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.

Smart electric vehicle charging strategies for sectoral coupling in a city energy system

The decarbonization of city energy systems plays an important role to meet climate targets. We examine the consequences of integrating electric cars and buses into the city energy system (60% of private cars and 100% of public buses), using three different charging strategies in a modelling tool that considers local generation and storage of electricity and heat, electricity import to the city, and investments to achieve net-zero emissions from local electricity and heating in 2050. We find that up to 85% of the demand for the charging of electric cars is flexible and that smart charging strategies can facilitate 62% solar PV in the charging electricity mix, compared to 24% when cars are charged directly when parked. Electric buses are less flexible, but the timing of charging enables up to 32% to be supplied by solar PV. The benefit from smart charging to the city energy system can be exploited when charging is aligned with the local value of electricity in the city. Smart charging for cars reduces the need for investments in stationary batteries and peak units in the city electricity and heating sectors. Thus, our results point to the importance of sectoral coupling to exploit flexibility options in the city electricity, district heating and transport sectors.

Combined PV–EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment

Photovoltaic (PV) systems and electric vehicles (EVs) integrated in local distribution systems are considered to be two of the keys to a sustainable future built environment. However, large-scale integration of PV generation and EV charging loads poses technical challenges for the distribution grid. Each grid has a specific hosting capacity limiting the allowable PV and EV share. This paper presents a combined PV–EV grid integration and hosting capacity assessment for a residential LV distribution grid with four different energy management system (EMS) scenarios: (1) without EMS, (2) with EV smart charging only, (3) with PV curtailment only, and (4) with both EV smart charging and PV curtailment. The combined PV–EV hosting capacity is presented using a novel graphical approach so that both PV and EV hosting capacity can be analyzed within the same framework. Results show that the EV smart charging can improve the hosting capacity for EVs significantly and for PV slightly. While the PV curtailment can improve the hosting capacity for PV significantly, it cannot improve the hosting capacity for EVs at all. From the graphical analysis, it can be concluded that there is a slight positive correlation between PV and EV hosting capacity in the case of residential areas. • Combined PV–EV grid integration study in a residential LV grid is presented. • A novel graphical analysis approach for combined PV–EV HC assessment is presented. • Both HC for PV and EV are analyzed in the same framework. • Smart EV charging and PV curtailment are included to assess possible HC enhancement. • Correlation between HC for PV and EV in the case of residential areas is obtained.

Smart charging of electric vehicles to minimize the cost of charging and the rate of transformer aging in a residential distribution network

Smart charging of electric vehicles to minimize the cost of charging and the rate of transformer aging in a residential distribution network

Hierarchical Volt-VAR Optimization Framework Considering Voltage Control of Smart Electric Vehicle Charging Stations Under Uncertainty

Smart electric vehicle charging stations (EVCSs) equipped with distributed energy resources (DERs), such as photovoltaic (PV) systems and energy storage systems (ESSs), are promising entities for maintaining voltage quality in power distribution networks through voltage regulation using the smart inverters of DERs. This study proposes a hierarchical Volt-VAR optimization (VVO) framework that reflects the voltage regulation capability of smart EVCSs, which consists of global and local voltage control stages. At the global stage, smart inverters of EVCSs cooperate with conventional voltage regulators, such as an on-load tap changer (OLTC) and capacitor banks (CBs)), and smart inverters of PV systems to minimize the total active power losses and voltage deviations along with the determination of optimal parameters for local droop control functions of the smart inverters. At the local stage, smart inverters of EVCSs and PV systems quickly mitigate local voltage violations using dynamically varying local droop control functions with their optimal parameters calculated from the global stage. Under uncertainties in PV generation outputs and driving patterns of electric vehicle users, the deterministic optimization-based VVO problem at the global stage is reformulated into the chance-constrained optimization-based VVO problem. A simulation study was performed in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and smart EVCSs. The results demonstrate the effectiveness of the proposed framework in terms of total active power loss/voltage deviation, optimized local droop control function, and probability level of chance constraints.

A Decision-Making Framework for the Smart Charging of Electric Vehicles Considering the Priorities of the Driver

During the last decade, the technologies related to electric vehicles (EVs) have captured both scientific and industrial interest. Specifically, the subject of the smart charging of EVs has gained significant attention, as it facilitates the managed charging of EVs to reduce disturbances to the power grid. Despite the presence of an extended literature on the topic, the implementation of a framework that allows flexibility in the definition of the decision-making objectives, along with user-defined criteria is still a challenge. Towards addressing this challenge, a framework for the smart charging of EVs is presented in this paper. The framework consists of a heuristic algorithm that facilitates the charge scheduling within a charging station (CS), and the analytic hierarchy process (AHP) to support the driver of the EV selecting the most appropriate charging station based on their needs of transportation and personal preferences. The communications are facilitated by the Open Platform Communications–Unified Architecture (OPC–UA) standard. For the selection of the scheduling algorithm, the genetic algorithm and particle swarm optimisation have been evaluated, where the latter had better performance. The performance of the charge scheduling is evaluated, in various charging tasks, compared to the exhaustive search for small problems.

Impact of sector coupling on the market value of renewable energies – A model-based scenario analysis

Decarbonizing the energy supply by substituting fossil fuels with renewable energy sources (RES) is a key task for the coming decades in order to achieve the EU's ambitious climate protection targets. Information about the possible development and marketability of RES in the electricity sector is essential for assessing future funding needs. However, rising shares of fluctuating RES generation in the energy system reduce the average market prices and increase price volatility. Balancing price variations requires a considerable degree of flexibility. Additional flexibility in the electricity market through closer interconnection between the electricity sector and other demand sectors makes it possible to keep the market values of RES closer to the general market price level, irrespective of their shares. Such sector coupling can thus contribute to a cost-efficient transition to a low-carbon energy system. This paper examines the impact of efficient sector coupling on the market values of RES in a European energy system with ambitious decarbonization. We analyze different scenarios by applying the Enertile model, which uses an integrated cost optimization approach with flexibility options due to sector coupling and provides a detailed future development of RES. In our analysis, we examine three flexibility options: smart charging of electric vehicles, decentralized heat pumps in buildings, and multivalent district heating grids. We show that the flexible use of electricity in district heating has a significant impact on market values, while the impact of both flexible electric vehicle charging and flexible heating with heat pumps is rather small. Short-term flexibility due to load shifting of the charging or heating process shows only a limited effect on market values. Fuel switching in district heating, however, offers the possibility to change the absolute demand for electricity in direct response to RES feed-in and drastically reduces the curtailment of RES.

Research Group Introduction: IKEDA Fuka Laboratory, Dept.of Electrical Engineering, National Institute of Technology, Ube College

Our research focuses on the compensation performance of fundamental reactive, unbalanced active, and harmonics currents with constant dc-capacitor voltage control of the smart charger for electric vehicles in single-phase three-wire distribution feeders.

Smart charging of mobile electric vehicles

The paper proposes to create a digital economy for the development of electric charging networks of mass-use road transport in a business environment, in fact, these are mechanisms of public-private partnership. To go along the path of purchasing electric charging stations of Korean, Japanese, and German manufacturers and placing them for their own money on communications of Federal or territorial highways or in organizations and enterprises of various forms of ownership is a poorly managed and not rational process. It is proposed to move away from the schemes of location of specific electric charging stations and switch to the schemes of non-powered sockets. The technical solution refers to the technical means of electric charging (recharging) of mobile electric vehicles. The goal is to reduce the cost of creating a network electric charging infrastructure for electric vehicles. The technical result is the ability to ensure the charging mode of the electric vehicle directly from the existing distributed power supply system in space, by using the phase state modes of the electric connector cable that is powered or not powered by electric current. Based on the materials of the paper, patents of Russia and Kazakhstan were obtained.

Bidirectional smart charging of electric vehicles considering user preferences, peer to peer energy trade, and provision of grid ancillary services

Electric vehicles (EVs) are witnessing increased utilization throughout the world as an alternative to fossil–fueled vehicles. The extensive deployment of EVs can bring challenges to the grid if not properly integrated. Such challenges, however, could be exploited as opportunities if the huge unused capacity of the battery storage in millions of EVs are utilized for ancillary services to the grid and peer–to–peer (PtP) energy trade. Given that there is at least one human user per vehicle, human input must be considered to improve the scheduling process. To that end, this paper presents a new algorithm for bidirectional smart charging of EVs considering user preferences, PtP energy trade, and provision of ancillary services to the grid. The preferences of an EV user as input to the model are embedded into the scheduling process enabling the model to be adaptive to various conditions. Optimization slack variables are utilized for optimal management of EV battery SOC and energy allocation for multiple services. New indices are developed and introduced for quantification of the EV participation in ancillary services and PtP transactions. Real–world data has been collected and utilized for model specification and simulation to make the assumptions more realistic. The efficacy and feasibility of the proposed model are validated using numerical studies. The results indicate that incorporating users’ preferences into the scheduling process would improve the aggregated revenue generated by the EV scheduling model which in turn could offset the charging costs by up to 100%. Further, an increase of about 90% in peer-to-peer energy transactions among EVs and 11% in ancillary services provision to the grid are achieved through the developed user-centric smart charging model.

Sustainable Energy System Planning for an Industrial Zone by Integrating Electric Vehicles as Energy Storage

Abstract Renewable energy sources and electric vehicles are promising solutions for reducing fossil fuel consumption and environmental impacts within the electricity and transportation sectors. In this study, a new electric vehicle aggregator framework is proposed and four different electric vehicle charging scenarios have been modelled to analyse the impact of electric vehicles on the considered industrial microgrid. The remarkable point of this aggregator is reducing the computational time in the presence of a high penetration level of EVs. The EV charging scenarios include; no electric vehicle, uncoordinated electric vehicle charging, unidirectional and bi-directional vehicle to the grid. Diverse types of renewable energy sources such as wind turbines, solar PV panels, and geothermal units are considered, and an energy scheduling model is implemented. Uncertainties of renewable energy sources and electric vehicles behaviour (arrival time, departure time, and travelled distance) are modelled with their probability distribution function from related historical data. Also, for demand-side management, the Time of Use (TOU) strategy is implemented and three cost levels are defined, which contain off-peak, mid-peak, on-peak, and their cost levels. Results show that the suggested vehicle to grid model is effectively reduced the operation cost and maximize renewable energy share. Eventually, a sensitivity analysis is executed, and the result illustrates that a model with an increase in the penetration level of electric vehicles from (100 to 2000) in the industrial microgrid have better effectiveness in relative operation cost in electric vehicle smart charging schemes. It means relative operation cost, which is related to the unidirectional and bi-directional decrease from 0.996 to 0.95 and 0.988 to 0.831, respectively.

Conic Optimization for Electric Vehicle Station Smart Charging With Battery Voltage Constraints

This article proposes a new convex optimization strategy for coordinating electric vehicle charging, which accounts for battery voltage rise and the associated limits on maximum charging power. Optimization strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage, which occurs as the battery is charged. However, battery voltage rise is an important consideration since it imposes limits on the maximum charging power. This is particularly relevant for dc fast charging, where the maximum charging power may be severely limited, even at the moderate state of charge levels. First, a reduced-order battery circuit model is developed, which retains the nonlinear relationship between the state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as the second-order cone constraints. These constraints are integrated with a linearized power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimization strategy for electric vehicle smart charging. The resulting optimization is a second-order cone program and, thus, can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive.

Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: A review

Photovoltaics (PV) and electric vehicles (EVs) are two emerging technologies often considered as cornerstones in the energy and transportation systems of future sustainable cities. They both have t ...

Stochastic Modeling and Integration of Plug-In Hybrid Electric Vehicles in Reconfigurable Microgrids With Deep Learning-Based Forecasting

This paper investigates the impact of uncoordinated, coordinated, and smart charging of plug-in hybrid electric vehicles (PHEVs) on the optimal operation of microgrids (MGs) incorporating the dynamic line rating (DLR) security constraint. The DLR constraint, particularly in the islanding mode, influences the ampacity of MG feeders, when distribution lines reach their maximum capacity. To overcome any line outage or contingency situation, smart PHEVs are utilized to help improve the grid security. However, using PHEVs can cause higher power losses and feeder overloading issues. To address these concerns, a reconfiguration technique is employed in this paper. A heuristic algorithm, known as the collective decision-based optimization algorithm, is utilized to overcome the non-convexity and nonlinearity of the problem. The unscented transform technique is employed to model DLR uncertainties caused by solar radiation, load demand, and weather temperature, as well as PHEVs' uncertainties caused by varying charging strategies, numbers of PHEVs being charged, charging start time, and charging duration. Moreover, a deep learning gated recurrent unit technique is designed to forecast renewable power output for mitigating the uncertainties in renewable energy components. A modified IEEE 33-bus test network is deployed to evaluate the efficiency and performance of the proposed model.

Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles

The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared.

Electric Vehicles and Their Role in the Energy System

Electric vehicles (EVs) can help decarbonise both transport and electricity supply. This is both via reduced tailpipe emissions and due to the flexibility in charge and discharge that EV batteries can offer to the electricity system. For example, smart charging of EVs could enable the storage of roughly one fifth of the solar generation of Great Britain for when this energy is needed. However, to do this, the market needs to align vehicle charging behaviour to complement renewable generation and meet system needs.

Smart Charging of Electric Vehicles

Problem Definition: By providing an environmentally friendly alternative to traditional vehicles, electric vehicles will transform urban mobility, particularly in smart cities. In practice, once an electric vehicle is plugged in, the charging station completes charging as soon as possible. Given that the procurement cost of electricity and associated emissions vary significantly during a day, substantial savings can be achieved by smart charging—delaying charging until the cost is lower. In this paper, we study smart charging as an innovative business model for utility firms. Practical Relevance: Utility firms are already investing in charging stations and they can achieve significant cost savings through smart charging. Methodology: We consider a mechanism design problem in which a utility firm first announces pairs of charging price and completion time. Then, each customer selects the pair that maximizes their utility. Given the selected completion times, the utility firm solves the optimal control problem of determining the charging schedule that minimizes the cost of charging under endogenous, time-varying electricity procurement cost. We assume that there are ample parking spots with chargers in the charging station. Results: We devise an intuitive and easy-to-implement policy for scheduling charging of electric vehicles under given completion times. We prove that this policy is optimal if all customers arrive at the station simultaneously. We also characterize the optimal pairs of charging price and completion time. By using real electricity demand and generation data from the largest electricity market in the U.S., we find that cost and emissions savings from smart charging are approximately 20% and 15%, respectively, during a typical summer month. Managerial Implications: In contrast to the current practice of charging vehicles without delay, we show that it is economically and environmentally beneficial to delay charging some vehicles and to set charging prices based on customers' inconvenience cost due to delays. We also find that most of the savings of smart charging can be achieved by implementing it only a few peak-demand days in a month, highlighting its practical relevance.

Large Scale Smart Charging of Electric Vehicles in Practice

The energy system is changing due to a steady increase in electric vehicles on the demand side and local production (mostly through solar panels) on the production side. Both developments can put the energy grid under stress during certain timeframes, while there might be enough capacity on the grid most of the day. Smart charging of electric vehicles might be a solution to time dependent congestion. In this study, a smart charging strategy was developed and tested in large scale with 1000 public chargers, operated in the real word. We developed and tested protocols to temporarily limit the charger capacity based on the transformer data and the number of running sessions. Over 150,000 sessions were handled, of which almost half were influenced by the smart charging strategy applied. We found that we were able to keep within the grid limits by using these controls, without hindering the driver experience. Further improvements to the smart charging strategy can be made as soon as car manufacturers share information about the car battery such as the state of charge.

A Blockchain Based Electric Vehicle Smart Charging System with Flexibility

The increase in development of electric vehicle(EV) will have a strong impact on the power distribution grid if adequate care is not taken on the high power demand required for charging EV. Consequently, there is need to create a platform to enable charging point operators to effectively manage the EV user’s charging requests and ensure that there charging needs are satisfied while not exceeding the distribution grid capacity. If this is not done, there is no doubt that in few a years, EV owners will be faced with the problems of unavailability of charging stations and congestion in grid sequel to simultaneous charging of many EV’s. This work proposes a smart EV charging infrastructure based on a blockchain platform. With the charging demand (kWh) and maximum duration of the charging event provided by the EV user, the EV load flexibility is determined and utilized through smart charging to achieve a stable grid. EV owners and charging stations are linked through the platform thereby reducing the actors in EV charging ecosystem from six to four. Flexibility (power and time) in charging of EV is traded within the blockchain platform. By this, additional investors will be attracted into the business of EV charging station and through flexible offers, EV loads are shifted from the peak load hours. Consequently, the simulations shows that the acceptance rate of EV users increased by more than 50% when our smart charging system was adopted compared to the normal charging scenario.

Cybersecurity of Smart Electric Vehicle Charging: A Power Grid Perspective

With the roll-out of electric vehicles (EVs), the automobile industry is transitioning away from conventional gasoline-fueled vehicles. As a result, the EV charging demand is continuously growing and to meet this growing demand, various types of electric vehicle charging stations (EVCSs) are being deployed for commercial and residential use. This nexus of EVs, EVCSs, and power grids creates complex cyber-physical interdependencies that can be maliciously exploited to damage each of these components. This paper describes and analyzes cyber vulnerabilities that arise at this nexus and points to the current and emerging gaps in the security of the EV charging ecosystem. These vulnerabilities must be addressed as the number of EVs continue to grow worldwide and their impact on the power grid becomes more viable. The purpose of this paper is to list and characterize all backdoors that can be exploited to seriously harm either EV and EVCS equipments, or power grid, or both. The presented issues and challenges intend to ignite research efforts on cybersecurity of smart EV charging and enhancing power grid resiliency against such demand-side cyberattacks in general.