Dynamic charging strategy of electric vehicles in the distribution network integrated with renewable energy sources

Electric vehicle (EV) smart charging, which regulates the charging rates of EVs in response to the availability of surplus solar photovoltaics (PV) power or the electricity prices, can effectively enhance the local power demand–supply balance and help improve the PV power local utilization. In this regard, researchers have developed two categories of EV charging controls: (i) B2V or G2V ((i.e., building-to-vehicle or grid-to-vehicle) power flow in which the EV can only be charged; and (ii) B2V2B or G2V2G (i.e., building to vehicle to building or grid-vehicle-to-grid) in which the vehicles can be both charged and discharged. The frequent charging/discharging could potentially accelerate the EV battery degradation, which might make such applications not economical. However, systematic investigation has rarely been conducted for the impact of various EV usage and charging factors (including the EV charging strategy, different EV charging forms, EV charging limits, and commuting distance) on the power balancing performances and EV battery cycling degradation. As a result, the EV owners may not be willing to join the smart charging demand response due to the concerns of accelerated battery degradation, and this hinders the applications of EVs in the power regulation in the future energy system. This chapter aims to investigate the effect of different ways of using EVs on the demand response performances and the EV battery degradation. A parametric study considering a set of different scenarios combining various EV charging forms, EV charging limits and commuting distances will be conducted in Sweden. A smart charging control method of the EV will be developed, which can optimize the EV charging and discharging rates to minimize the grid interactions. A degradation model, which can evaluate the EV battery degradation due to charging/discharging cycling, will be constructed to investigate the EV battery degradation under typical scenarios. The performances of each scenario will be analyzed and compared to draw conclusions. The study results can help improve researchers’ understanding of the impacts of smart EV charging in the building community performances. The obtained impacts on the battery degradation can also support decision makers in selecting suitable EV charging and usage strategies.

The number of Electric vehicle (EV) users is expected to increase in the future. The driving profile of EV users is unpredictable, necessitating the design of charging scheduling protocols for EV charging stations servicing multiple EVs. A large EV charging load affects the grid in terms of peak load demand. Electric vehicle charging stations with solar panels can help to reduce the grid impact of EV charging events. With reference to the increasing number of EVs, new technology needs to be developed for charging station and management to create a stable system for users, and electric utilities. The load of a total EV charge can affect the grid, degrading quality and system stability. In this paper, a charging station scheduling strategy is proposed based on the game theoretic approach. In the proposed strategy, with respect to the grid load demand minimization, charging stations have scheduled EV charging times to prevent sudden peak load on the grid the proposed game theory strategy is sudden peak load on the grid. The proposed game theory strategy is defined on the basis of priority so that both grid operators and EV users can maximize their profit by setting priorities for charging and discharging. This work provides a strategy for grid peak load minimization.

Electric Vehicle (EV) technology is one of the most promising solutions to reduce dependence on fossil fuels and greenhouse gas (GHG) emissions in the transportation sector. However, a large increase of EVs raises concerns about negative impacts on electricity generation, transmission, and distribution systems. This study analyzes the benefits and trade-offs for EV penetration in Thai road transport based on EV penetration scenarios from 2019 to 2036. Two charging strategies are considered to assess the impact of EV charging: free charging and off-peak charging. Uncertainty variables are considered by a stochastic approach based on Monte-Carlo simulation (MCS). The simulation results shown that the adoption of EVs can reduce both energy consumption and GHG emissions. The results also indicate that the increased load due to EV charging demand in all scenarios is still within the buffer level, compared to the installed generation capacity in the Power Development Plan 2018 revision 1 (PDP2018r1), and the off-peak charging strategy is more beneficial than the free-charging strategy. However, the increased load demand caused by all EV charging strategies has a direct impact on the power generating schedule, and also decreases the system reliability level.

The growing popularity of electric vehicles (EVs) has the potential to complicate distribution network operations. When a large number of electric vehicles are charging at the same time, the system load can significantly increase. This problem is exacerbated when charging is done concurrently in the evening, which coincides with peak load times. To prevent the increase in peak load and distribution operation stress, EV charging must be coordinated to achieve financial and technical objectives. This study seeks to evaluate the impact of financially driven EV charging scheduling algorithms. The contribution of this study is that the scheduling algorithm considers EV usage behavior based on real data as well as considers the state-of-charge (SoC) target set by EV owners. The proposed algorithm seeks to minimize the total charging cost incurred by EV owners using mixed-integer linear programming (MILP). The impact of the coordinated charging scheduling on the system demand profile and real distribution system operation metrics are also evaluated. The simulation result tested on the Bantul Feeder 05 system demonstrates that coordinated charging can reduce the charging costs by 57.3%. Furthermore, the peak load is reduced by 5.2% while also improving the load factor by 3.5% as compared to uncoordinated scheduling. Based on the power flow simulation, the proposed algorithm can reduce distribution transformer loading by 0.5% and improve voltage quality by 0.1% during peak load. This demonstrates that coordinated EV charging benefits not only the EV users but also the distribution system operator by preventing system operation issues.

Distributed energy sources including renewable energy (RE) sources and electric vehicle (EV) discharging offer opportunities to improve grid performance. Although uncoordinated EV charging can perfectly meet users’ driving needs, it brings great challenges in maintaining the quality of low voltage (LV) distribution grids. Most existing EV control strategies do not take into account the heterogeneous nature of EV charging, including the various users’ travel needs and the intermittent nature of RE sources. This article proposes a hierarchical control method that simultaneously coordinates EV charging by considering a dynamic energy tariff, RE sources, EV hardware characteristics, and particularly the EV’s driving needs. The proposed local controller sends the EV’s charging priority and required energy to the central controller based on the EV’s hardware characteristics and users’ driving needs. The central controller then uses this information along with the energy tariff from the retailer and the present grid performance to control EV’s charging or discharging power. The efficacy of this hierarchical control method is evaluated using an Australian LV grid. The obtained results show that this method can reduce the neutral current and voltage imbalance with the maximized usage of renewable energy resources while the EV users’ driving needs are met.

A Blockchain Based Electric Vehicle Smart Charging System with Flexibility

This article explores the potential impacts of integrating electric vehicles (EVs) and variable renewable energy (VRE) on power system operation. EVs and VRE are integrated in a production cost model with a 5 min time resolution and multiple planning horizons to deduce the effects of variable generation and EV charging on system operating costs, EV charging costs, dispatch stacks, reserves and VRE curtailment. EV penetration scenarios of the light-duty vehicle fleet of 10%, 20%, and 30% are considered in the RTS-GMLC test system, and VRE penetration is 34% of annual energy consumption. The impacts of EVs are investigated during the annual peak in the summer and during the four weeks of the year in which high VRE and low loads lead to overgeneration. Uncoordinated and coordinated EV charging scenarios are considered. In the uncoordinated scenario, charging is undertaken at the convenience of the EV owners, modeled using data from the Idaho National Laboratory’s EV Project. Coordinated charging uses an “aggregator” model, wherein EV charging is scheduled to minimize operating costs while meeting the daily charging requirements subject to EV availability and charging constraints. The results show that at each EV penetration level, the uncoordinated charging costs were higher than the coordinated charging costs. During a high-VRE, low-load week, with uncoordinated EV charging at 30% penetration (3% energy penetration), the peak load increased by as much as 27%. Using coordinated charging, the EV load shifts to hours with low prices, coincident with either low load, high VRE, or both. Furthermore, coordinated charging substantially reduces the curtailment of PV by as much as nine times during the low-load seasons, and the curtailment of wind generation by more than half during the summer peak season, compared to the scenarios with no EVs and uncoordinated EV charging. Using a production cost model with multiple planning cycles, load and VRE forecasts, and a “look ahead” period during scheduling and dispatching units was crucial in creating and utilizing the flexibility of coordinated EV charging.

A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning

The goal of this research is to study the control approaches of the electric vehicle – grid integration systems in a power distribution grid to benefit the electric vehicle charging customers and the grid operation. To find the balance of both aspects, a series of modeling and simulation have been conducted to validate the effectiveness of the developed control approaches. The impact of the plug-in electric vehicle (PEV) charging activity on the distribution power grid is analyzed. The PEV charging behavior model is established through the statistical analysis of the National Highway Travel Survey. This charging behavior model is used to generate the PEV charging load profiles for the investigation of the PEV charging impacts on the local distribution transformer aging and the network voltage deviation. The simulation results indicate that the high penetration rate of PEV charging potentially leads to serious distribution transformer aging and voltage problems to the end users in the remote side of a distribution line. To mitigate the PEV charging impacts, scheduling methods are investigated based on the existing utility programs, such as the Time of Use and Direct Load Control, and optimization techniques. The charging scheduling shifts the PEV charging load to the grid off-peak period to flatten the load profile. Therefore, it mitigates the PEV charging resulted overloading, transformer aging, and voltage deviation problems. To improve the customer acceptance of PEV charging control, the PEV charging in a distribution grid form a competition game, which allows individual PEV charging xiii customers to pursue charging cost minimization while various grid and customer constraints are met. Due to that the individual objectives and constraints are related to the PEVs charging strategy selection, the charging game is considered as a generalized Nash Equilibrium problem (GNEP). The Nikaido-Isoda reformulation function is used to solve the GNEP. The obtained PEV charging strategies lead to the lowest PEV charging cost and guarantee the grid operation safety and customer requirement. The obtained solution strategies of this problem are regarded as the Nash Equilibriums of the game. To explore the possibility of providing grid services by using the PEV smart charging method, a study of distribution grid level vehicle - grid integration control with voltage regulation is conducted. In this study, the PEV charging control is designed at both Microgrid-level and distribution-level. The Microgrid controllers aim to optimally allocate the limited power to the charging PEVs. When a distribution grid voltage

The growth of Electric Vehicle (EV) users is expected to rise in the future. The driving behavior of EV users is inherently unpredictable, necessitating the development of a scheduling framework for EV charging stations, particularly when the number of EVs exceeds one. The substantial load imposed on the grid by a large number of EVs charging concurrently can impact the grid during peak hours. The integration of solar panels into Electric Vehicle Charging Stations offers a viable priority to mitigate the grid impact resulting from EV charging events. In light of the expanding adoption of EVs, there is a critical need to address peak load technological challenges associated with EV charging management. This paper introduces effective solutions and strategies to overcome the problems. The results give a comprehensive solution to EVCS, considering the profit for both EV users and the grid by providing the Time of charging (TOC) based cost for scheduled charging priorities. The effectiveness of the implemented system by analyzing the load shifting of 12.7 Kwh for 100% acceptance, based on EV user responsiveness to the priority. This provides a cost reduction benefit of 22.63 Rs for off-peak hours to EV users, with the additional benefit of fast charging at off-peak hours by selecting a proper strategy for charging according to the requirements of EV users.

This paper proposes a new approach for forecasting the coordinated Electric Vehicles (EVs) charging and discharging that minimizes the EVs charging cost, based on the day-ahead electricity price (DAEP) subject to the EVs state of charge (SOC) limits, the EVs maximum power charger, the EVs batteries full charging at the end of the charging period. Besides, the EVs initial state of charge (SOC 0 ) has been calculated based on the EVs daily driving mileage, while Latin Hypercube Sampling (LHS) has been applied to deal with the EVs arrival, departure time and SOC 0 uncertainties. The proposed optimal strategy enables EVs users to make a profit of 14.79€ while they need 2.17€ to charge their EVs in the uncoordinated scenario. Furthermore, the comparison between the real and the estimated results show that the charging cost based on the real SOC 0 values is 2.88% and 27% higher than the charging cost based on the estimated SOC 0 values for coordinated and uncoordinated scenarios respectively.

The growing use of electric vehicles presents a challenge of grid overload due to simultaneous, large-scale charging. Implementing smart charging strategies, which require careful consideration of unique regional conditions, isn't straightforward. Additionally, a systematic method for assessing strategy feasibility based on local needs is missing in the current literature, hindering effective deployment. Therefore, this study conducts a comprehensive exploration and evaluation of smart electric vehicle charging strategies with the scoping review and multi-criteria evaluation methods to critically investigate existing smart electric vehicle charging strategies, and assesses their feasibility of implementation in specific regional contexts, using Denmark's electric vehicle home charging as a case study. The scoping review is based on 95 full-text articles and analyzes 43 commonly discussed electric vehicle charging strategies, with most literature focusing on real-time pricing and time-of-use pricing strategies. We categorize electric vehicle charging strategies into four key dimensions: charging architecture, data requirements, intelligence level, and applied methods. Of the 27 identified methods, linear programming and the heuristic method are most prevalently utilized, showing a close correlation between data requirement levels and the chosen methods. Literature reflects a discourse on both operational and monetary objectives of electric vehicle charging strategies from the vantage point of grid operators, aggregators, and electric vehicle users. The results from the multi-criteria feasibility evaluation of the Danish case show that time-of-use pricing, real-time pricing, and timed charging strategies rank among the seven most feasible strategies for electric vehicle home charging in Denmark.

Investigation of electric vehicle smart charging characteristics on the power regulation performance in solar powered building communities and battery degradation in Sweden

A bi-level optimization model for electric vehicle charging strategy based on regional grid load following

Emission mitigation potential from coordinated charging schemes for future private electric vehicles