Economic Feasibility Study Of Using An Electric Vehicle And Photovoltaic Microgeneration In A Smart Home

Sausen, Jordan Passinato; Binelo, Marcia de Fatima Brondani; Campos, Maurício de; Sausen, Airam; Sausen, Paulo Sérgio

doi:10.1109/tla.2018.8447356

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…Diversas pesquisas relacionadas aos VEs podem ser encontradas, tanto em relação às tecnologias de carregamento [9] como às estações de carregamento [10]. Em [11] é proposto um modelo de acoplamento de um VE em uma smart home, considerando um sistema de geração de energia fotovoltaica e as operações Vehicle-to-Home (V2H) e Home-to-Vehicle (H2V) com resultados analisados a partir de dados tarifários. Em [12] são indicados diferentes programas de resposta à demanda para evitar a formação de novos picos na rede de distribuição de energia elétrica.…”

Section: Fundamentação Teóricaunclassified

Análise Técnica da Inserção de Veículos Elétricos e Geração Fotovoltaica no Sistema de Distribuição

Piotrowski¹,

Neto²,

Abaide³

et al. 2021

Proceedings of the 13th Seminar on Power Electronics and Control (SEPOC 2021)

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A análise do sistema de distribuição de energia elétrica (SD) perante a crescente inserção de veículos elétricos (VEs) e o constante crescimento da geração distribuída fotovoltaica (GD) permite fornecer uma nova viabilidade técnica para a disseminação desse meio de transporte diante dessa fonte de energia renovável. O principal objetivo deste artigo está em analisar o impacto técnico do carregamento dos VEs juntamente com inserção da GD considerando diferentes níveis de implantação no SD. Logo, um estudo de caso é simulado no software OpenDSS utilizando o circuito Ckt 5 para identificar os impactos no SD em relação à integração de VEs e GD. As simulações são realizadas para um horizonte de dez anos considerando a expectativa de crescimento dos VEs e sua inserção em conjunto com painéis fotovoltaicos residenciais. Os resultados mostram que a combinação de VEs e GD pode ser uma excelente alternativa para uma maior disseminação dessas tecnologias.

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Section: Fundamentação Teóricaunclassified

Análise Técnica da Inserção de Veículos Elétricos e Geração Fotovoltaica no Sistema de Distribuição

Piotrowski¹,

Neto²,

Abaide³

et al. 2021

Proceedings of the 13th Seminar on Power Electronics and Control (SEPOC 2021)

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“…The algorithm is mainly made to be very efficient when the selling price/purchase ratio is greater than one, allowing the user to earn money. In [28], Sausen et al proposed a model for coupling an EV to a smart home containing PV and batteries, having the objective of achieving cost minimization that was conditional on maximizing vehicle efficiency. Four scenarios are considered: in the first one, uncontrolled EV charging was adopted; in the second one, economic charging was applied; in the third one, economic EV charging was adopted, including distributed generation; and in the fourth one, economic EV charging was considered, with distributed generation and V2H operation.…”

Section: Introductionmentioning

confidence: 99%

Home Energy Management Considering Renewable Resources, Energy Storage, and an Electric Vehicle as a Backup

et al. 2022

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The vehicle-to-grid concept emerged very quickly after the integration of renewable energy resources because of their intermittency and to support the grid during on-peak periods, consequently preventing congestion and any subsequent grid instability. Renewable energies offer a large source of clean energy, but they are not controllable, as they depend on weather conditions. This problem is solved by adding energy storage elements, implementing a demand response through shiftable loads, and the vehicle-to-grid/vehicle-to-home technologies. Indeed, an electric vehicle is equipped with a high-capacity battery, which can be used to store a certain amount of energy and give it back again later when required to fulfill the electricity demand and prevent an energy shortage when the main-grid power is limited for security reasons. In this context, this paper presents a comparative study between two home microgrids, in one of which the concept of vehicle-to-home is integrated to provide a case study to demonstrate the interest of this technology at the home level. The considered microgrid is composed of renewable energy resources, battery energy storage, and is connected to the main grid. As the vehicle is not available all day, in order to have consistent results, its intervention is considered in the evening, night, and early morning hours. Two case studies are carried out. In the first one, the vehicle-to-home concept is not taken into account. In this case, the system depends only on renewable resources and the energy storage system. Subsequently, the electric vehicle is considered as an additional energy storage device over a few hours. Electric vehicle integration brings an economic contribution by reducing the cost, supporting the other MG components, and relieving the main grid. Simulation results using real weather data for two cities in France, namely Brest and Toulon, show the effectiveness of the vehicle-to-home concept in terms of cost, energy self-sufficiency, and continuity of electrical service.

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“…13,[32][33][34][35][36][37][38][39][40] However, the disadvantage of these methods is the need for a high sampling frequency, which will always depend upon the time needed to perform the calculations of the parameters adaptation. 32 Their use for a long runtime is, therefore, not feasible, as performed in Wang et al, 18 Zhang et al, 41 Gruosso and Bandeira, 42 Sausen et al 43 Artificial neural networks (ANNs) have also been employed in the prediction of battery lifetime, serving as an instant estimation model of the SOC in systems identification models or even coupled to other models, to cover up some deficiency of the original model. But when using ANNs directly as a prediction model, important and desirable characteristics of the electrical models and other model classes are lost.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, on the one hand there are electrical models capable of simulating battery dynamics for a long period of time in an accurate way, being able to predict its electrical characteristics according to different usage scenarios, as the Tremblay model, 18,[41][42][43] but the model parameters often depend on visual analysis of discharge curves or are based on AI methods, so the high computational costs prevent them from being used at runtime on the device. On the other hand, some models employed to estimate the instantaneous SOC accept methods such as the Kalman filter or least squares to estimate and adapt the parameters to the battery in use, but are not able to predict the characteristics of the battery for a long period time, since the types of equivalent circuits and the parameter adaptation methods depend on a short time interval between each adaptation iteration, usually in the order of 1 second.…”

Section: Introductionmentioning

confidence: 99%

“…However, the disadvantage of these methods is the need for a high sampling frequency, which will always depend upon the time needed to perform the calculations of the parameters adaptation 32 . Their use for a long runtime is, therefore, not feasible, as performed in Wang et al, 18 Zhang et al, 41 Gruosso and Bandeira, 42 Sausen et al 43 …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐phase method of estimation and adaptation of parameters of electrical battery models

Binelo

Sausen

et al. 2020

Int J Energy Res

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Mathematical modeling of the battery lifetime is an important tool for the design of more efficient batteries, as well as for the optimization of their use. The electrical models class is among the classes of mathematical models used for this purpose, and a fundamental step to their application is the correct estimation of their parameters. This paper performs the mathematical modeling of Lithium-Ion Polymer batteries lifetime through the electrical model of Tremblay, in which a multi-phase method of estimation and adaptation of parameters is proposed, divided into three phases: discovery, learning, and inference. The multi-phase method is based on two Artificial Intelligence techniques: genetic algorithms and artificial neural networks. The proposed method is validated by the simulation and experimental studies. From the results, it is concluded that the application of the multi-phase method improves the effective accuracy of the Tremblay model, when it comes to adapt its parameters to the battery during runtime. For constant discharge currents, the average error reduction was 79%, when compared to the best set of parameters obtained by GA without the adaptation process. For variable current discharge curves, the method was able to reduce the error more than 35%. This method can be applied to other battery lifetime prediction models.

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Economic Feasibility Study Of Using An Electric Vehicle And Photovoltaic Microgeneration In A Smart Home

Cited by 12 publications

References 11 publications

Análise Técnica da Inserção de Veículos Elétricos e Geração Fotovoltaica no Sistema de Distribuição

Análise Técnica da Inserção de Veículos Elétricos e Geração Fotovoltaica no Sistema de Distribuição

Home Energy Management Considering Renewable Resources, Energy Storage, and an Electric Vehicle as a Backup

Multi‐phase method of estimation and adaptation of parameters of electrical battery models

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