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Electronics
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Advances in Renewable Energy Integration and Smart Grids
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Advances in Renewable Energy Integration and Smart Grids

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 3835

Special Issue Editor

Dr. Balaji Venkatasubramanian

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, University of Cyprus, Nicosia 1678, Cyprus
Interests: power system analysis; power system planning; applications of AI/ML in power systems; renewable energy

Special Issue Information

Dear Colleagues,

Renewable energy sources have become indispensable for combating climate change and ensuring the security of energy supply, both of which pose a threat to socio-economic stability. That is why, despite the widely varying landscapes of political direction, market policies, and economic development, governments and policymakers around the world agree that the adoption of renewable energy sources is a top priority.

There has been an ever-growing global trend of transforming the electrical power sector to accommodate renewable energy integration and the challenges associated with it. Through the installation of digital communication, metering, and monitoring infrastructures, the rollout of demand-side management programs, and the development of new energy market mechanisms, power systems have evolved into modern smart grids. The development of smart electronics plays a major role in this transition, ranging from smart meters for individual consumers to smart inverters for the connection of renewable energy resources with the main electrical grid, to the expansion of SCADA systems necessary for the operation of smart grids.

The aim of this Special Issue is to showcase the most recent advances in exploiting the opportunities that smart grids offer to maximize the techno-economic benefits of renewable energy integration. Original research articles and reviews are both welcome. Research areas may include (but are not limited to) the following:

  • Renewable and distributed energy generation technologies;
  • Provision of ancillary services by renewable generation;
  • Microgrids and virtual power plants;
  • Demand-side management and demand response;
  • Energy markets, energy economics, and policies;
  • Energy conversion, storage, and power electronics;
  • Control centers and SCADA;
  • Dynamic grid stability and control;
  • Cyber-physical and multi-energy systems;
  • Resilience and reliability analysis of smart grids.

We look forward to receiving your contributions.

Dr. Balaji Venkatasubramanian
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart grids
  • renewable energy sources
  • sustainable energy
  • power system resilience
  • power system operation
  • power system planning
  • energy economics

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Published Papers (2 papers)

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Research

31 pages, 18255 KiB  
Article
Enhancing the Performance of a Renewable Energy System Using a Novel Predictive Control Method
by Mahmoud A. Mossa, Najib El Ouanjli, Olfa Gam and Ton Duc Do
Electronics 2023, 12(16), 3408; https://doi.org/10.3390/electronics12163408 - 11 Aug 2023
Cited by 5 | Viewed by 1588
Abstract
The current study concerns improving the performance of a renewable energy system using systematically designed control algorithms. The performance of the system under study is evaluated under two operating scenarios: the first in which the system consists of only a wind-driven synchronous generator [...] Read more.
The current study concerns improving the performance of a renewable energy system using systematically designed control algorithms. The performance of the system under study is evaluated under two operating scenarios: the first in which the system consists of only a wind-driven synchronous generator connected to the utility grid; in the second scenario, the generator is combined with a photo-voltaic solar system and a battery for supplying a load. Each system component is modeled and thoroughly described. To maximize the benefits of solar and wind energies, two separate maximum power point tracking procedures are adopted. Furthermore, to enhance the generator’s dynamics, a novel predictive control scheme is designed and validated by comparing its performance with traditional predictive control. The novel predictive controller utilized a simple and unique cost function to avoid the shortages of traditional predictive controllers. For standalone operation, an effective procedure is adopted to ensure the power balance between the generation, storage, and isolated load units. To evaluate the effectiveness of the designed controllers under different operating regimes, Matlab/Simulink is utilized for this task. The obtained results confirm the superiority of the novel predictive scheme used with the synchronous generator over the classic control approach for the two operating scenarios. This has been shown in the form of reduced ripples and reduced current harmonics. The obtained results are also confirming the validity of the adopted maximum power tracking strategies with solar panels and wind turbines as well. Furthermore, balanced power delivery is achieved thanks to the adopted management strategy for standalone operation, which enhances the overall system performance. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Integration and Smart Grids)
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14 pages, 2394 KiB  
Article
Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method
by Hongxi Wang, Hongtao Shen, Fei Li, Yidi Wu, Mengyu Li, Zhengang Shi and Fangming Deng
Electronics 2023, 12(3), 730; https://doi.org/10.3390/electronics12030730 - 1 Feb 2023
Cited by 4 | Viewed by 1740
Abstract
Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed. To improve communication efficiency and model generalization [...] Read more.
Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed. To improve communication efficiency and model generalization ability, FL is introduced to combine data from multiple locations without sharing to collaboratively train the prediction model. Furthermore, a hybrid LSTM-BPNN prediction model is designed to improve the accuracy of predictions. LSTM is used to extract important features from the time-series data, and BPNN maps the extracted high-dimensional features to the low-dimensional space and outputs the predicted values. Experiments show that the minimum MAPE of the hybrid prediction model constructed in this paper can reach 1.2%, and the prediction effect is improved by 30% compared with the traditional model. Under the FL mode, the trained prediction model not only improves the prediction accuracy by more than 20% but also has excellent generalization ability in multiple scenarios. Full article
(This article belongs to the Special Issue Advances in Renewable Energy Integration and Smart Grids)
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