Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

Koubaa, Rayhane; Yoldaş, Yeliz; Goren, Selcuk; Krichen, Lotfi; Önen, Ahmet

doi:10.1177/0958305x20965158

Cited by 5 publications

(4 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…V2G can be viewed as an efficient solution for peak shaving and valley filling. Cost-benefit studies and the feasibility of V2G in different scenarios were part of several studies [42][43][44][45]. A case study of EVs participating in peak shaving is conducted in Shanghai using four brands of EVs-BYD e6, Roewe e50, Nissan Leaf, and Tesla Model S [42].…”

Section: B Peak Shaving and Valley Fillingmentioning

confidence: 99%

See 1 more Smart Citation

Review of Challenges and Opportunities in the Integration of Electric Vehicles to the Grid

Vishnu,

Kaliyaperumal,

Jayaprakash

et al. 2023

WEVJ

View full text Add to dashboard Cite

Electric vehicle (EV) technology has revolutionized the transportation sector in the last few decades. The adoption of EVs, along with the advancement of smart grid technologies and Renewable Energy Sources (RES), has introduced new concepts in the automobile and power industries. Vehicle-Grid Integration (VGI) or Vehicle-to-Grid (V2G) is a technology revolutionizing both the transport and electric power sectors. From a V2G perspective, these sectors are complementary and mutually beneficial. For the power sector, mitigation of voltage and frequency excursions and the prospect of grid stabilization on the brink of uncertainties owing to the dynamics in the grid scenario are very important. This article focuses on various aspects of EV-power grid integration. The tremendous benefits of this technology, as presented in the literature, are reviewed. Furthermore, the concerns and the implementation challenges are reviewed in detail in this work.

show abstract

Section: B Peak Shaving and Valley Fillingmentioning

confidence: 99%

“…The metaheuristic BAT algorithm is used to solve the problem. An adaptive charging/discharging scheme for peak shaving is discussed in [44]. In [45], a case study conducted at a parking garage in Dansmästaren, Uppsala, Sweden, is detailed.…”

Section: B Peak Shaving and Valley Fillingmentioning

confidence: 99%

Review of Challenges and Opportunities in the Integration of Electric Vehicles to the Grid

Vishnu,

Kaliyaperumal,

Jayaprakash

et al. 2023

WEVJ

View full text Add to dashboard Cite

show abstract

“…Num max represents the maximum number of crossings. Individual P i can be expressed in the form of real number matrix, as shown in formula (10) [26], [27], [28].…”

Section: Optimization Based On Improved Psomentioning

confidence: 99%

Optimization Effect Analysis of ACM-PSO Integrating Individual Adjustment and Cross Operation on Microgrid DG Technology

Tang,

Shang,

Chen

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Microgrid, as a distributed power technology, has deep potential at present. This study deeply researches microgrid and electric vehicles. A grid-connectedmicrogrid power optimization management model is established, and an adaptive crossover multi particle swarm optimization (ACM-PSO) is proposed, which can adjust and operate individually. The proposed algorithm introduces individual adjustment operation and considers the start and stop state of the micro power supply. Besides, it does not need to decompose the unit commitment problem into two levels of optimization problems, which reduces the complexity of the optimization problem. A-PSO algorithm is prone to fall into local optimum. This problem can be avoided and its global search ability can be improved by introducing the crossover operation of ACM-PSO. According to the analysis of examples, the ACM-PSO algorithm has the best economic effect in different operation schemes among experimental methods.

show abstract

“…[4][5][6] With continuous global fossil fuel shortages and environmental pollution issues, EVs are portrayed as an effective way to reducing carbon dioxide (CO 2 ) emissions. 7,8 Their relative impact on ecology is more persistent where countries are adopting renewable energy as a primary source. 9,10 EVs represent a massive leap toward a clean and sustainable energy system.…”

Section: Introductionmentioning

confidence: 99%

A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability

Ullah

Liu

Yamamoto

et al. 2021

Energy & Environment

View full text Add to dashboard Cite

The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles’ energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles’ energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination ( R2), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R2, lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption.

show abstract

Implementation of cost benefit analysis of vehicle to grid coupled real Micro-Grid by considering battery energy wear: Practical study case

Cited by 5 publications

References 51 publications

Review of Challenges and Opportunities in the Integration of Electric Vehicles to the Grid

Review of Challenges and Opportunities in the Integration of Electric Vehicles to the Grid

Optimization Effect Analysis of ACM-PSO Integrating Individual Adjustment and Cross Operation on Microgrid DG Technology

A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability

Contact Info

Product

Resources

About