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Inventions
Special Issues
Distribution Renewable Energy Integration and Grid Modernization
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Distribution Renewable Energy Integration and Grid Modernization

A special issue of Inventions (ISSN 2411-5134). This special issue belongs to the section "Inventions and Innovation in Electrical Engineering/Energy/Communications".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 4055

Special Issue Editors

Dr. Harsha Vardhana Padullaparti

E-Mail Website
Guest Editor
Power Systems Engineering Center, National Renewable Energy Laboratory (NREL), Golden, CO 80401, USA
Interests: distribution grid planning and operations; advanced distribution management systems (ADMS); distributed energy resource management systems (DERMS); electric vehicle grid integration; machine learning applications; power system modeling and simulation; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Surya Santoso

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Interests: power quality; power system modeling; power system simulation; distributed generation; renewable energy; photovoltaics; wind turbine; energy storage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In response to the dynamic landscape of renewable energy integration and the evolving role of emerging technologies in reshaping electric power systems, we are pleased to announce a Special Issue dedicated to the theme of “Distribution Renewable Energy Integration and Grid Modernization.” This Special Issue aims to provide a comprehensive platform for researchers, practitioners, and industry experts to present and discuss cutting-edge research, innovations, and practical applications in the field.

The Special Issue will encompass a wide spectrum of topics within the realm of renewable energy integration into transmission and distribution grids, with a focus on addressing challenges and harnessing opportunities for a more sustainable and efficient energy ecosystem. Submissions are encouraged to explore various facets, including the integration of distributed energy resources (DERs) such as solar and wind power, the role of electric vehicles (EVs) in demand response and grid support, and the advancements in advanced distribution management system (ADMS) applications and distributed energy resource management system (DERMS) applications for enhanced grid operations.

Authors are encouraged to submit original research articles, review papers, case studies, and innovative applications that shed light on the interdisciplinary nature of these topics. We seek to promote the exchange of ideas and insights that will pave the way for sustainable energy integration and grid management. The Special Issue will serve as a valuable resource for academics, professionals, and policymakers striving to navigate the complexities of modern energy systems. This Special Issue of Inventions will include (but is not limited to) the following topics of interest:

  • Distribution and grid edge planning and operations;
  • Advanced distribution management system (ADMS) applications: distribution state estimation, Volt/VAR optimization, fault location, isolation, and service restoration (FLISR);
  • Distributed energy resource management systems (DERMS): behind-the-meter DER management, demand response, voltage support, peak demand management, DER flexibility;
  • DER aggregation for ancillary service provision and virtual power plant (VPP) controls;
  • Sensor data analytics and applications in distribution operations;
  • Integrated transmission and distribution planning;
  • Grid integration of electric vehicles (EVs), EV charging infrastructure planning, and vehicle-to-grid (V2G) operation;
  • Battery energy storage system (BESS) applications in grid operations;
  • Cybersecurity in distributed energy resource (DER) communications.

Dr. Harsha Vardhana Padullaparti
Prof. Dr. Surya Santoso
Guest Editors

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. Inventions 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 1800 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

  • advanced distribution management system (ADMS)
  • battery energy storage system (BESS)
  • cybersecurity
  • distributed energy resource management system (DERMS)
  • distribution planning
  • demand response
  • DER flexibility
  • electric vehicles (EV)
  • grid modernization
  • machine learning
  • sensors
  • virtual power plants

Published Papers (2 papers)

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Research

18 pages, 4900 KiB  
Article
Flexible Synthetic Inertia Optimization in Modern Power Systems
by Peter Makolo, Ramon Zamora, Uvini Perera and Tek Tjing Lie
Inventions 2024, 9(1), 18; https://doi.org/10.3390/inventions9010018 - 26 Jan 2024
Viewed by 1445
Abstract
Increasing the replacement of conventional synchronous machines by non-synchronous renewable machines reduces the conventional synchronous generator (SG) inertia in the modern network. Synthetic inertia (SI) control topologies to provide frequency support are becoming a new frequency control tactic in new networks. However, the [...] Read more.
Increasing the replacement of conventional synchronous machines by non-synchronous renewable machines reduces the conventional synchronous generator (SG) inertia in the modern network. Synthetic inertia (SI) control topologies to provide frequency support are becoming a new frequency control tactic in new networks. However, the participation of SI in the market of RES-rich networks to provide instant frequency support when required proposes an increase in the overall marginal operation cost of contemporary networks. Consequently, depreciation of operation costs by optimizing the required SI in the network is inevitable. Therefore, this paper proposes a flexible SI optimization method. The algorithm developed in the proposed method minimizes the operation cost of the network by giving flexible SI at a given SG inertia and different sizes of contingency events. The proposed method uses Box’s evolutionary optimizer with a self-tuning capability of the SI control parameters. The proposed method is validated using the modified New England 39-bus network. The results show that provided SIs support the available SG inertia to reduce the RoCoF values and maintain them within acceptable limits to increase the network’s resilience. Full article
(This article belongs to the Special Issue Distribution Renewable Energy Integration and Grid Modernization)
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20 pages, 5632 KiB  
Article
Predicting Energy Generation in Large Wind Farms: A Data-Driven Study with Open Data and Machine Learning
by Matheus Paula, Wallace Casaca, Marilaine Colnago, José R. da Silva, Kleber Oliveira, Mauricio A. Dias and Rogério Negri
Inventions 2023, 8(5), 126; https://doi.org/10.3390/inventions8050126 - 11 Oct 2023
Viewed by 2162
Abstract
Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a [...] Read more.
Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state’s electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November. Full article
(This article belongs to the Special Issue Distribution Renewable Energy Integration and Grid Modernization)
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