Optimal allocation of renewable energy source and charging station for PHEVs

Aljehane, Nojood O.; Mansour, Romany F.

doi:10.1016/j.seta.2021.101669

Cited by 21 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to V2G facilities, installing RESs into the seaport system (e.g. wind turbine and photovoltaic array) can also enhance seaport operation performance [19]. These sources emit zero greenhouse gas emissions, are sustainable, and are widely available, making them ideal for powering hydrogen refueling stations in seaports [20].…”

Section: Motivation and Rationalementioning

confidence: 99%

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Xie,

Dehghanian,

Estebsari

2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

The ability to withstand and recover from disruptions is essential for seaport energy systems, and in light of the growing push for decarbonization, incorporating clean energy sources has become increasingly imperative to ensure resilience. This paper proposes a resilience enhancement planning strategy for a seaport multi‐energy system that integrates various energy modalities and sources, including heating, cooling, hydrogen, solar, and wind power. The planning strategy aims to ensure the reliable operation of the system during contingency events, such as power outages, equipment failures, or extreme weather incidents. The proposed optimization model is designed as a mixed‐integer nonlinear programming formulation, in which McCormick inequalities and other linearization techniques are utilized to tackle the model nonlinearities. The model allocates fuel cell electric trucks (FCETs), renewable energy sources, hydrogen refueling stations, and remote control switches such that the system resilience is enhanced while incorporating natural‐gas‐powered combined cooling, heating, and power system to minimize the operation and unserved demand costs. The model considers various factors such as the availability of renewable energy sources, the demand for heating, cooling, electricity, and hydrogen, the operation of remote control switches to help system reconfiguration, the travel behaviour of FCETs, and the power output of FCETs via vehicle‐to‐grid interface. The numerical results demonstrate that the proposed strategy can significantly improve the resilience of the seaport multi‐energy system and reduce the risk of service disruptions during contingency scenarios.

show abstract

Section: Motivation and Rationalementioning

confidence: 99%

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Xie,

Dehghanian,

Estebsari

2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…In view of the ongoing scientific and technological revolution and industrial transformation, the electric vehicle (EV) industry has become a worldwide driving force in the promotion of the economic growth and improvement of the ecological environment 1‐4 . Integrating EVs into the power system via charging stations (CSs) based on optimal charging strategies is regarded to be an effective method of alleviating the negative impact of the uncontrolled charging of large‐scale EVs 5‐8 . Moreover, the battery used in battery charging‐swapping station (BCSS) can be reused in an energy storage station (ESS) to form charging‐swapping‐storage integrated station (CSSIS); it can not only meet different users' needs, but also realize the echelon utilization of batteries 9‐11 .…”

Section: Introductionmentioning

confidence: 99%

“…optimal charging strategies is regarded to be an effective method of alleviating the negative impact of the uncontrolled charging of large-scale EVs. [5][6][7][8] Moreover, the battery used in battery charging-swapping station (BCSS) can be reused in an energy storage station (ESS) to form charging-swapping-storage integrated station (CSSIS); it can not only meet different users' needs, but also realize the echelon utilization of batteries. [9][10][11] Furthermore, combining CSSIS, renewable power energy, and other loads in the integrated station microgrid (ISMG) can not only avoid the adverse effects caused by the EV and renewable power energy connected to the grid, but also reduce the cost of energy storage and improve the utilization rate of renewable energy.…”

mentioning

confidence: 99%

Nash bargaining‐based cooperative game for distributed economic scheduling of microgrid with charging‐swapping‐storage integrated station

Cheng

Zuo

Wei

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

Summary To stimulate cooperative transaction between different stakeholders and optimize the economic profits of each entity in the microgrid (MG) with charging‐swapping‐storage integrated station (CSSIS), this article establishes a Nash bargaining‐based cooperative game model between MG and CSSIS, and proposes an alternating direction method of multipliers (ADMM) based distributed computation to reach Nash equilibrium. At first, MG and CSSIS are regarded as two different stakeholders, and their transactions are captured by the Nash bargaining framework, which takes the Stackelberg equilibrium solution as the initial bifurcation point of Nash negotiation, and constructs a bi‐level Nash bargaining game model for the microgrid and CSSIS. Then, theoretical analysis of the existence and uniqueness of Nash equilibrium is developed based on the proposed game property. Following that, with application of augmented Lagrange function and ADMM, the constraints coupled Nash bargaining mathematical problem is divided into two independent subproblems, namely, the economic profit maximization of MG and the economic operation maximization of CSSIS, whose solutions can be determined in a distributed iteration. Numerical simulation results indicate that, compared with Stackelberg equilibrium solution identified by the noncooperative game model, the model can further improve the benefits of both game participants and achieve win‐win results. At the same time, it can more reasonably schedule resources in the system to maximize the economic profit of the overall system.

show abstract

“…This problem has grown and has become a significant difficulty since the minority class is frequently of critical importance, as it represents favorable examples that are rare in nature or expensive to obtain [6]. This is true when considering contexts such as Big Data analytics [7,8,9,10,11,12,13], Biometrics [14,15,16,17,18,19,20,21,22], gene profiling [23], credit card fraud detection [24,25], face image retrieval [24], content-based image retrieval [26,27], disease detection [28,29,30,31,32], internet of things [33,34,35,36,37,38,39,40,41,42,43], Natural Language Processing [44,45], network security [46,47,48,49,50,51,…”

Section: Introductionmentioning

confidence: 99%

Stop Oversampling for Class Imbalance Learning: A Critical Review

Hassanat¹,

Tarawneh²,

Altarawneh³

2022

Preprint

View full text Add to dashboard Cite

For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is, models trained on fictitious data may fail spectacularly when put to real-world problems. The fundamental difficulty with oversampling approaches is that, given a real-life population, the synthesized samples may not truly belong to the minority class. As a result, training a classifier on these samples while pretending they represent minority may result in incorrect predictions when the model is used in the real world. We analyzed a large number of oversampling methods in this paper and devised a new oversampling evaluation system based on hiding a number of majority examples and comparing them to those generated by the oversampling process. Based on our evaluation system, we ranked all these methods based on their incorrectly generated examples for comparison. Our experiments using more than 70 oversampling methods and three imbalanced real-world datasets reveal that all oversampling methods studied generate minority samples that are most likely to be majority. Given data and methods in hand, we argue that oversampling in its current forms and methodologies is unreliable for learning from class imbalanced data and should be avoided in real-world applications.

show abstract

Optimal allocation of renewable energy source and charging station for PHEVs

Cited by 21 publications

References 21 publications

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Fueling the seaport of the future: Investments in low‐carbon energy technologies for operational resilience in seaport multi‐energy systems

Nash bargaining‐based cooperative game for distributed economic scheduling of microgrid with charging‐swapping‐storage integrated station

Stop Oversampling for Class Imbalance Learning: A Critical Review

Contact Info

Product

Resources

About