Fig 1 - uploaded by M. A. Awadallah
Content may be subject to copyright.
Source publication
This paper presents a study of the impact of the electric vehicle (EV) charger load on the capacity of distribution feeders and transformers of an urban utility. A residential neighborhood of the city of Toronto, Canada, is selected to perform the study based on survey results that showed a high tendency for EV adoption. The two most loaded distrib...
Contexts in source publication
Context 1
... single-phase transformers rated at 100 kVA, 16 kV on the HV side, and 120/240 V on the LV side. The transformers feed 35 houses, where 19 houses are con- nected to transformer OT1 and 16 houses are connected to transformer OT2. The transformers are located at the approx- imate load center of the household load. The system of study is shown in Fig. ...
Context 2
... paper aims at exploring the impact of EV charger load on the distribution network capacity under steady-state oper- ation. Different factors are taken into consideration including EV penetration, charging during peak loads, and charger size. Results are obtained through CYME simulation of the single- phase system shown in Fig. ...
Citations
... However, its widespread application has caused some new issues, such as huge burden to power grid. The discharging and charging of EVs will affect the power system, so EV load forecasting has become a research area that has attracted much attention [2][3][4]. Load forecasting is designed to learn about the charging and discharging of EV in the near future. The forecasting results can optimize the planning and scheduling of power systems, and improve energy efficiency and reduce costs. ...
Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data.
... Ref. [4] examines the effect of the EV charger load on distribution feeder capacity in Toronto, Canada. The charger sizes considered in that case study were 1.4, 1.9, 3.3, 6.6, 10, 16, and 20kW, whereas the number of charging EVs varied from 1 to 19. ...
... Different sizes (kW) chargers are available in the market to charge EVs with different charging times. Although the 3.3kW and 6.6kW chargers are currently dominating the EV industry, manufacturers tend to increase the charger rating [4]. ...
... Exploring EV charging load limits becomes imperative. A case study in Toronto by [12] highlighted this need, studying how EV charger loads impact distribution network capacity. By analysing a city neighbourhood with a high likelihood of EV adoption and selecting the two most heavily loaded transformers along with their associated feeders and loads for simulation, the study identified component overloads in the worst-case scenario of simultaneous EV charging during peak load hours. ...
... The purpose of the PSMDO is to determine the maximum phase power demand at each node with particle representation given in (10). The objective function in (11) is to maximise the total maximum demand in the system with consideration of the charging behaviour of EV users in terms of the charging opportunity of each node and time defined by (12), subject to the lower and upper bounds of node voltage given in (13) and (14), and feeder capacity shown in (15) and (16). ...
This research proposes a new comprehensive methodology aimed at optimally managing electric vehicle (EV) charging in an unbalanced distribution system with consideration of grid capacity and voltage quality constraints as well as the stochastic driving and charging behaviour of EV users. A data mining algorithm is designed to generate suitable spatial and temporal EV and charger input data for EV scheduling that is decomposed into two subproblems. The lower‐level subproblem identifies the maximum load at each node and time and the upper‐level subproblem allocates charging slots for each EV being charged. Both subproblems are solved by developed particle swarm optimisation (PSO) algorithms. The effectiveness and robustness of the algorithms have been thoroughly validated by conducting rigorous tests on a modified IEEE 37‐bus distribution system with different EV penetration scenarios. The test results have confirmed the algorithms' performance in accommodating the increasing load associated with EV charging and successfully improving the system performance by maximising the system load factor, minimising load unbalance, and reducing the system power loss. All operational constraints on node voltages, and distribution transformer and feeder capacities of the existing power distribution infrastructure are fully respected while maintaining high user satisfaction represented by the average state of charge (SoC) of all EVs.
... The integration of several EVs can have a potential impact on the power quality of the grid they are connected to: the magnitude of the impact depends on the number of EVs being charged at the same time, their location, and their charging rate [14]. The paper [15] examines the impact of V2G operation when multiple vehicles are connected. ...
The electric vehicle (EV) market is growing rapidly due to the necessity of shifting from fossil fuel-based mobility to a more sustainable one. Smart charging paradigms (such as vehicle-to-grid (V2G), vehicle-to-building (V2B), and vehicle-to-home (V2H)) are currently under development, and the existing implementations already enable a bidirectional energy flow between the vehicles and the other systems (grid, buildings, or home appliances, respectively). With regard to grid connection, the increasingly higher penetration of electric vehicles must be carefully analyzed in terms of negative impacts on the power quality; and hence, the effects of electric vehicle charging stations (EVCSs) must be considered. In this work, the interactions of multiple electric vehicle charging stations have been studied through laboratory experiments. Two identical bidirectional DC chargers, with a rated power of 11 kW each, have been supplied by the same voltage source, and the summation phenomenon of the current harmonics of the two chargers (which leads to an amplification of their values) has been analyzed. The experiment consisted of 100 trials, which considered four different combinations of power set-points in order to identify the distribution of values and to find suitable indicators for understanding the trend of the harmonic interaction. By studying the statistical distribution of the Harmonic Summation Index, defined in the paper, the impact of the harmonic distortion caused by the simultaneous charging of multiple electric vehicles has been explored. Based on this study, it can be concluded that the harmonic contributions of the electric vehicle charging stations tend to add up with increasing degrees of similarity of the power set-points, while they tend to cancel out the more the power set-points differ among the chargers.
... Correspondingly, charging schemes can increase EV HC if EV owners are willing to adjust their charging habits and the research of [78] concluded that slow charging raised HC (301 EVs) as compared to fast charging (42 EVs). Similarly, the impact of charger size on HC and network performance was observed as reduced outage probability with slow charging in [79] and a low-rating charger allowed simultaneous charging of more EVs than a high-rating charger in [80]. ...
The electrification of the transport sector to control the carbon footprint has been gaining momentum over the last decade with electric vehicles (EVs) seen as the replacement for conventional internal combustion engines. Economic incentives, subsidies, and tax exemptions are also paving the way for rising EV penetration in the power distribution networks. However, the exponential EV adoption requires careful technical and regulatory analysis of traditional networks to satisfy the network reliability constraints. Therefore, it would be vital to find EV hosting capacity (HC) limits of networks from a multifaceted approach involving various market players, mainly distribution system operators and EV owners. This review provides a systematic categorization of EV hosting capacity evaluation and improvement methods, thus enabling researchers and industry personnel to navigate the advancing landscape of EVs. This novel framework extends beyond the theoretical implication of diverse objective functions and HC improvement methods to the actual numerical values of EV HC across varying geographical settings. Therefore, this unique synthesis of varying aspects of EV HC facilitates the in‐depth understanding of the integration of sustainable energy and transport sector.
... This may cause challenges for the DTs to operate over their expected lifespan. The frequent EV charging is potentially overloading DTs, which were initially taken into operation without considering EV penetration in the power system, thus causing early failure and premature replacement of DTs [1], [2]. ...
A major portion of a power system's asset portfolio comprises distribution transformers on residential premises. The rapid and massive acceptance of electric vehicles is posing challenges for distribution transformers to operate over their expected lifespan. This work proposes a four-layer framework to assess the real-time and anticipated aging of a distribution transformer and estimate the remaining useful life of a distribution transformer. The first layer stores residential smart meter data to be utilized for the kVA load estimation of a distribution transformer in the second layer. The performance of two powerful forecasting tools, i.e., Time Series Decomposition and Hidden Markov Model, is compared in the third layer. The historical and forecast data, along with the distribution transformer's thermal parameters, are used for its remaining useful life assessment. Numerical validation is conducted on real-world data utilizing electricity consumption and ambient temperature of fifteen households in London, Ontario, Canada. This work also includes the penetration of the most popular electric vehicles in Canada, along with service drop cable data and practical secondary distribution circuit configuration.
... Another consideration that would likely happen in the early phase of shifting from ICE to EV is the addicted manner of fueling different types of vehicles, so-called 'uncontrolled' mode charging based on user needs and preferences only at any time and place, which could happen in a residential or commercial in either urban or rural area. As a consequence, these may produce abrupt load pattern charging characteristics and impact the overall load profile, particularly in the feeder to which the EVCS is linked 16,17) . In most cases, it affects the voltage stability, stresses the thermal asset capacity, and jeopardizes the loading factor 18) . ...
... In this article, the multi-uncertainties that arose from the charging process, which includes each plug of two and four-wheelers (2|4-Ws) EVCS estimating the power intake fluctuation, have not yet been discussed or publicly available. Further, notable literature values that align with this research purpose are found in 6,7,17,18,20) . Despite those facts, our paper fills the critical gap in the literature by which considering the unknown entities (i.e., the timeframe of occurrence and the sense of user conducts and preferences) with the high possibility of diversities combining (i.e., technical specifications of EVs and supportive peripherals) but still in logically and reasonable sense. ...
The uncertainty parameters driven by diverse user behavior, battery types, and charger instruments of electric mobility (e-mobility) would pose risk challenges to existing grid assets'
adequacy and reliability issues. Using the pseudorandom of the Monte Carlo method, this research constructs an estimation framework to generate power intake of public electric vehicle charging stations (EVCSs) for a typical 20kV|0.4kV distribution grid urban area. This study suggested that those multi-uncertainties potentially threaten daily operations with a significant impact in low voltage grids with voltage magnitude dropping to 0.884 p.u. Nevertheless, recognizing and alerting
to the future-changing and performance-preserving electric grid infrastructure should raise awareness as gradual employment is outpacing the national target of the energy transition.
... Reference [5] presents a technical evaluation model to address the maximum ability of the system to serve EV demand as well as adjusting the impact of EV load on total system profile. Reference [6] presents a case study of the impact of EV charger load on the capacity of two transformers in the Toronto distribution network; a worst-case scenario is assumed when all houses of the neighbourhood simultaneously charge their EVs during the summer and winter peak load hour. Reference [7] assess the maximum number of EVs that could circulate in a district considering the physical limits of the MV distribution network. ...
... With regards to HC assessment, studies are conducted considering test networks [13] as well as real and existing DSs [6]. Assessment of DSs hosting capacity is mainly done either in a deterministic [13,6,7], or in a probabilistic way [8][9], some comparisons are also shown in [14][15]. ...
... With regards to HC assessment, studies are conducted considering test networks [13] as well as real and existing DSs [6]. Assessment of DSs hosting capacity is mainly done either in a deterministic [13,6,7], or in a probabilistic way [8][9], some comparisons are also shown in [14][15]. ...
... Te hosting capacity of distribution feeders and transformers can be represented in the form of loci of operating conditions inside which no overloads of distribution transformer and secondary drop lead are guaranteed [36]. 8 International Transactions on Electrical Energy Systems Te methodology was tested by the network of a neighborhood in the city of Toronto, Canada with the assumption of the worst-case scenario that all houses simultaneously charge, although very unlikely, their EVs during the summer and winter peak load hours. ...
This paper presents the impact of uncoordinated and coordinated charging management of electric vehicles (EVs) on the loading capability of major distribution system equipment, voltage quality, and energy loss in a distribution system. The main emphasis is given to the overloading of distribution transformers, primary feeders, and a substation transformer. The voltage quality of load points along the feeders and the system energy loss are also underlined. The load profile for uncoordinated EV charging is simulated by a Monte Carlo method with several deterministic and stochastic variables involved. To mitigate the overloading of the system components, a coordinated charging (also known as smart charging) model formulated as a linear programming problem is proposed with the objective of maximizing the total amount of energy consumption by EVs and the sum of all individual final states of charge (SoCs), and minimizing the sum of the absolute deviation of individual SoCs from the overall average SoC. The optimization problem is subject to equipment capability loading and planning criteria constraints with low, medium, and high EV penetration levels. The voltage quality problem and energy loss are also analyzed by an unbalanced three-phased power flow model. A case study of a real and practical 115/22 kV distribution system of the Provincial Electricity Authority (PEA) with a 50 MVA substation transformer, 5 feeders, and 732 distribution transformers shows that the possibility of overloaded system components, voltage drops along the feeders, and the system energy loss can be identified in the uncoordinated charging scenario and offer the readiness for equipment replacement and network reinforcement planning. The proposed smart charging model allows the distribution system to accommodate more EVs by appropriately managing the power and the start times of charging for the individual EVs over the timeslots of a day. The study results confirm no violation of the system components and voltage regulation imposed by the system planning guidelines. In addition, the system peak demand and the system energy loss are significantly lower in the smart charging scenario, thus deferring investment upgrades, offering better asset utilization, and retaining network security and service quality.
... However, the utilization of both solar PVs and EVs in many homes leads to major issues due to their uncertainties. This situation may negatively impact the distribution system, i.e., reliability problems [10], transformer overloading due to EVs charged simultaneously [11,12], and voltage violations due to output power fluctuations from solar PV generation [13]. To mitigate the aforementioned problems, the aggregator is assigned to control the power consumption of a small/median number of prosumers because the electricity utility manages the energy inadequately for a large number of prosumers [8]. ...
... P AG B,t and P PS ev,i,t denote the charging/discharging powers (kW) of the central BESS of the aggregator and the EV battery of the prosumer i at time t, respectively. In Equations (11) and (12), P PS i,t and P AG t can be both positive (received power) and negative (injected power) values depending on the behaviors of home baseload, solar PV generation, and battery scheduling at that time. ...
Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the proposed method can reduce mean power consumption by 9.04% and 39.57% compared with consumption using the system without EV usage and the system that applies the conventional energy price, respectively. Also, it can decrease the costs of the prosumer by between 1.67% and 24.57%, and the aggregator can generate revenue by 0.065 USD per day, which is higher than that generated when employing conventional energy prices.
