Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids

Salvatti, Gabriel A.; Carati, Emerson Giovani; Cardoso, Rafael; Costa, Jean Patric da; Stein, Carlos Marcelo de Oliveira

doi:10.3390/en13051191

Cited by 44 publications

(14 citation statements)

References 21 publications

(45 reference statements)

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“…It made no reference to any real-world data collection. [23] Dynamic programming is used to govern the charging (G2V) and discharging of the storage device (V2G) in order to extend the life of the battery and minimizing grid reliance.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

2022

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To meet the world’s growing energy needs, photovoltaic (PV) and electric vehicle (EV) systems are gaining popularity. However, intermittent PV power supply, changing consumer load needs, and EV storage limits exacerbate network instability. A model predictive intelligent energy management system (MP-iEMS) integrated home area power network (HAPN) is being proposed to solve these challenges. It includes forecasts of PV generation and consumers’ load demand for various seasons of the year, as well as the constraints on EV storage and utility grid capacity. This paper presents a multi-timescale, cost-effective scheduling and control strategy of energy distribution in a HAPN. The scheduling stage of the MP-iEMS applies a receding horizon rule-based mixed-integer expert system.To show the precise MP-iEMS capabilities, the suggested technique employs a case study of real-life annual data sets of home energy needs, EV driving patterns, and EV battery (dis)charging patterns. Annual comparison of unique assessment indices (i.e., penetration levels and utilization factors) of various energy sources is illustrated in the results. The MP-iEMS ensures users’ comfort and low energy costs (i.e., relative 13% cost reduction). However, a battery life-cycle degradation model calculates an annual decline in the storage capacity loss of up to 0.013%.

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Section: Monte Carlo Simulationmentioning

confidence: 99%

Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

2022

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“…Therefore, power management and energy management are different concerning their objectives. While energy management is related to user goals, power management ensures instantaneous power balance [23][24][25]. The focus of this paper is dc nanogrids power management because this control layer is responsible for system stability.…”

Section: Introductionmentioning

confidence: 99%

Price-Response Matrices Design Methodology for Electrical Energy Management Systems Based on DC Bus Signalling

et al. 2021

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Prosumers’ electrical installations (PEIs), as nanogrids and low-voltage microgrids, have gained importance in recent years following the development of standards such as the IEC 60364-8 series. In these systems, all distributed energy resources (DERs) are usually integrated using dc bus coupling. The IEC 60364-8-3 predicts an electrical energy management system (EEMS) for power-sharing. The overall research framework of this paper is the nanogrid power management, where complex algorithms are required, as well as the conventional state machines and hierarchical controls. However, the addition of new DERs in such systems is not straightforward due to the complicated parameter settings for energy usage optimization. A different control strategy, named price-based power management, has been conceived to make the EEMS scalable to include new sources and simplify parameterization. Since it is analogous to economic markets, most users understand the concepts and feel comfortable tuning parameters according to their own cost/benefits goals. This paper proposes a price-based power management algorithm for EEMS to automatically design the price-response matrices (PRMs). The PRMs are a way to organize power management, considering new DERs and variable price of energy. The main contribution is the methodology to design the PRMs. Experimental results are carried out to demonstrate the effectiveness of the proposed strategy. The results were obtained with a 1.5 kW prototype composed of a PV generator, battery energy storage, loads, and grid connection.

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“…The latest research works were centered on discussing the reliability issues that are faced by power distribution networks due to electric vehicle charging [22][23][24]. Many researchers are currently working on the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) concepts [25][26][27]. However, no significant research work has been noticed addressing the RAM of the overall PEV system.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Reliability, Availability and Maintainability Analysis of a Plug-In Electric Vehicle

Talukdar¹,

Deka²

2021

WEVJ

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Electric vehicle technologies have seen rapid development in recent years. However, Reliability, Availability, and Maintainability (RAM) related concerns still have restricted large-scale commercial utilization of these vehicles. This paper presents an approach to carry out a quantitative RAM analysis of a plug-in electric vehicle. A mathematical model is developed in the Markov Framework incorporating the reliability characteristics of all significant electrical components of the vehicle system, namely battery, motor, drive, controllers, charging unit, and energy management unit. The study shows that the vehicle’s survivability can be increased by improving its components’ restoration rates. The paper also investigates the role of a charging station on the availability of the vehicle. It illustrates how the grid power supply’s reliability influences the operational effectiveness of a plug-in electric vehicle. The concepts that are presented in the article can support further study on the reliability design and maintenance of a plug-in electric vehicle.

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Electric Vehicles Energy Management with V2G/G2V Multifactor Optimization of Smart Grids

Cited by 44 publications

References 21 publications

Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

Electric Vehicle Battery Storage Concentric Intelligent Home Energy Management System Using Real Life Data Sets

Price-Response Matrices Design Methodology for Electrical Energy Management Systems Based on DC Bus Signalling

An Approach to Reliability, Availability and Maintainability Analysis of a Plug-In Electric Vehicle

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