Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging

This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.

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“…However, large on-board batteries increase vehicle cost, size, and energy consumption. It also requires fast charger 35 – 37 .…”

Section: Electric Vehicle Charging Station (Evcs) and Its Impact On Smart Distribution Gridmentioning

confidence: 99%

Operational planning steps in smart electric power delivery system

Jayachandran

Reddy

Padmanaban³

et al. 2021

Sci Rep

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“…Research has shown that the use of time-of-use electricity prices for off-peak power consumption can effectively reduce the burden on the grid and greatly save costs for each EV user. Due to the difference in electricity prices, the charging and discharging preferences of vehicle users are also different, so it is necessary to consider the impact of electricity price fluctuations on the optimization results (Clairand et al, 2020;Li et al, 2020). Therefore, in order to facilitate the formation of a more complete electricity price policy theory, sensitivity analysis of peak and valley electricity price fluctuations is needed.…”

Section: Comparison Of Vehicle To Grid Response Under Different Electricity Price Modesmentioning

confidence: 99%

V2G Multi-Objective Dispatching Optimization Strategy Based on User Behavior Model

Tao

et al. 2021

Front. Energy Res.

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V2G (Vehicle to Grid) technology can adjust the grid load through the unified control of the charging and discharging of electric vehicles (EVs), and achieve peak shaving and valley filling to smooth load fluctuations. Aiming at the random and uncertain problem of EV users travel and behavior decision-making, this paper proposes a V2G multi-objective dispatching strategy based on user behavior. First, a V2G behavior model was established based on user behavior questionnaire surveys, and the effective effect of EV load was simulated through Monte Carlo simulation. Then, combined with the regional daily load curve and peak-valley time-of-use electricity prices, with the goal of stabilizing grid load fluctuations and increasing the benefits of EV users, a multi-objective optimal dispatching model for EV clusters charging and discharging is established. Finally, Considering the needs of EV users and the operation constraints of the microgrid, the genetic algorithm is used to obtain the Pareto optimal solution. The results show that when dispatching with the maximum benefit of users, the peak-to-valley ratio of the grid side can be reduced by 2.99%, and the variance can be reduced by 9.52%. The optimization strategy can use peak and valley time-of-use electricity prices to guide the intelligent charging and discharging of EVs while meeting user needs, so as to achieve the optimal multi-objective benefit of V2G participation in power response.

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“…Indeed, the paper's main focus was on theoretical findings; that is, presenting a methodology for energy management, without much discussion of real charging behavior or the practical implications. In [14,15], a smart charging algorithm was presented to minimize the frequency deviation of the network by considering the user preferences as one input. The customers can select between three different tariffs options that vary in terms of the maximum charging power: 1.5 kW, 2.5 kW, or 7.2 kW.…”

Section: Introductionmentioning

confidence: 99%

“…The customers can select between three different tariffs options that vary in terms of the maximum charging power: 1.5 kW, 2.5 kW, or 7.2 kW. As in [11], the focus of these studies [14,15] was on presenting a new methodology for energy management rather than focusing primarily on the customer impacts. In these studies, no real commercial charging data were used; rather synthetic data were assessed.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicles as a Flexibility Provider: Optimal Charging Schedules to Improve the Quality of Charging Service

et al. 2021

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The sub-aggregation of electric vehicles provides significant potential to power systems in the form of ancillary services. This means with smart charging it is possible to shift loads from peak to off-peak hours. For the flexibility from privately owned electric vehicles to be offered to the electricity market, customer participation is crucial; however, the impacts of sub-aggregation on customers have not been studied thoroughly. In this paper, charging data covering over 80,000 real-world charging sessions from various commercial charging sites are introduced and the charging characteristics are analyzed. Importantly, a method for finding an optimal pattern for load reduction with the least impact on customers is presented. We suggest an optimal schedule for load reduction from the customer viewpoint at different types of charging sites, including public car parks, offices, residential sites, and shopping centers. The findings indicate that residential and office charging sites offer the greatest potential for load reduction with the least impact on customers. The most flexibility is available during peak charging hours, which on average are at 08:00 at car parks, 07:30 at office sites, 19:00 at residential sites, and 10:00 at shopping centers.

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Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging

Cited by 57 publications

References 35 publications

Operational planning steps in smart electric power delivery system

Operational planning steps in smart electric power delivery system

V2G Multi-Objective Dispatching Optimization Strategy Based on User Behavior Model

Electric Vehicles as a Flexibility Provider: Optimal Charging Schedules to Improve the Quality of Charging Service

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