Example of IEEE Transactions on Smart Grid format
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Example of IEEE Transactions on Smart Grid format Example of IEEE Transactions on Smart Grid format Example of IEEE Transactions on Smart Grid format Example of IEEE Transactions on Smart Grid format
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Example of IEEE Transactions on Smart Grid format Example of IEEE Transactions on Smart Grid format Example of IEEE Transactions on Smart Grid format Example of IEEE Transactions on Smart Grid format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access
recommended Recommended

IEEE Transactions on Smart Grid — Template for authors

Publisher: IEEE
Categories Rank Trend in last 3 yrs
Computer Science (all) #3 of 226 down down by 1 rank
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 1921 Published Papers | 37739 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 06/06/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

8.267

21% from 2018

Impact factor for IEEE Transactions on Smart Grid from 2016 - 2019
Year Value
2019 8.267
2018 10.486
2017 7.364
2016 6.645
graph view Graph view
table view Table view

19.6

20% from 2019

CiteRatio for IEEE Transactions on Smart Grid from 2016 - 2020
Year Value
2020 19.6
2019 16.4
2018 13.8
2017 14.4
2016 13.1
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 21% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 20% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

3.571

1% from 2019

SJR for IEEE Transactions on Smart Grid from 2016 - 2020
Year Value
2020 3.571
2019 3.59
2018 3.361
2017 2.854
2016 2.73
graph view Graph view
table view Table view

3.336

5% from 2019

SNIP for IEEE Transactions on Smart Grid from 2016 - 2020
Year Value
2020 3.336
2019 3.17
2018 3.645
2017 3.046
2016 2.845
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 1% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 5% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

IEEE Transactions on Smart Grid

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IEEE

IEEE Transactions on Smart Grid

Approved by publishing and review experts on SciSpace, this template is built as per for IEEE Transactions on Smart Grid formatting guidelines as mentioned in IEEE author instructions. The current version was created on 06 Jun 2020 and has been used by 912 authors to write and format their manuscripts to this journal.

Computer Science

i
Last updated on
06 Jun 2020
i
ISSN
1949-3053
i
Impact Factor
Very High - 4.79
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
IEEEtran
i
Citation Type
Numbered
[25]
i
Bibliography Example
C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1109/TSG.2010.2089069
Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

Abstract:

Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is en... Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges. read more read less

Topics:

Energy consumption (67%)67% related to the paper, Energy management (62%)62% related to the paper, Smart grid (60%)60% related to the paper, Best response (57%)57% related to the paper, Game theory (56%)56% related to the paper
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2,715 Citations
open accessOpen access Journal Article DOI: 10.1109/TSG.2010.2055903
Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments
A-H Mohsenian-Rad1, Alberto Leon-Garcia1

Abstract:

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, re... Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies. read more read less

Topics:

Electricity pricing (65%)65% related to the paper, Limit price (62%)62% related to the paper, Pricing schedule (60%)60% related to the paper, Energy consumption (54%)54% related to the paper, Electric power industry (53%)53% related to the paper
View PDF
1,782 Citations
Journal Article DOI: 10.1109/TSG.2017.2753802
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
Weicong Kong1, Zhao Yang Dong1, Youwei Jia2, David J. Hill3, Yan Xu4, Yuan Zhang1

Abstract:

As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operatio... As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households. read more read less

Topics:

Smart grid (52%)52% related to the paper, Smart meter (51%)51% related to the paper, Recurrent neural network (51%)51% related to the paper, Deep learning (51%)51% related to the paper, Electric power system (51%)51% related to the paper
1,415 Citations
Journal Article DOI: 10.1109/TSG.2012.2197425
Hierarchical Structure of Microgrids Control System
Ali Bidram1, Ali Davoudi1

Abstract:

Advanced control strategies are vital components for realization of microgrids. This paper reviews the status of hierarchical control strategies applied to microgrids and discusses the future trends. This hierarchical control structure consists of primary, secondary, and tertiary levels, and is a versatile tool in managing st... Advanced control strategies are vital components for realization of microgrids. This paper reviews the status of hierarchical control strategies applied to microgrids and discusses the future trends. This hierarchical control structure consists of primary, secondary, and tertiary levels, and is a versatile tool in managing stationary and dynamic performance of microgrids while incorporating economical aspects. Various control approaches are compared and their respective advantages are highlighted. In addition, the coordination among different control hierarchies is discussed. read more read less
1,234 Citations
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IEEE Transactions on Smart Grid format uses IEEEtran citation style.

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Frequently asked questions

1. Can I write IEEE Transactions on Smart Grid in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the IEEE Transactions on Smart Grid guidelines and auto format it.

2. Do you follow the IEEE Transactions on Smart Grid guidelines?

Yes, the template is compliant with the IEEE Transactions on Smart Grid guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in IEEE Transactions on Smart Grid?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the IEEE Transactions on Smart Grid citation style.

4. Can I use the IEEE Transactions on Smart Grid templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for IEEE Transactions on Smart Grid.

5. Can I use a manuscript in IEEE Transactions on Smart Grid that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper IEEE Transactions on Smart Grid that you can download at the end.

6. How long does it usually take you to format my papers in IEEE Transactions on Smart Grid?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in IEEE Transactions on Smart Grid.

7. Where can I find the template for the IEEE Transactions on Smart Grid?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Transactions on Smart Grid's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the IEEE Transactions on Smart Grid's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. IEEE Transactions on Smart Grid an online tool or is there a desktop version?

SciSpace's IEEE Transactions on Smart Grid is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like IEEE Transactions on Smart Grid?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like IEEE Transactions on Smart Grid?”

11. What is the output that I would get after using IEEE Transactions on Smart Grid?

After writing your paper autoformatting in IEEE Transactions on Smart Grid, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is IEEE Transactions on Smart Grid's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for IEEE Transactions on Smart Grid?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Transactions on Smart Grid. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Transactions on Smart Grid?

The 5 most common citation types in order of usage for IEEE Transactions on Smart Grid are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the IEEE Transactions on Smart Grid?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per IEEE Transactions on Smart Grid's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download IEEE Transactions on Smart Grid in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in IEEE Transactions on Smart Grid Endnote style according to Elsevier guidelines.

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