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Advanced Forecasting Methods for Sustainable Power Grid: 2nd Edition

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 114

Special Issue Editors


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Guest Editor
Department of Electrical Engineering and Computer Science, University of Catania, 95125 Catania, Italy
Interests: photovoltaic systems; forecasting for photovoltaic systems; photovoltaic/thermal systems; photovoltaic systems monitoring; fault detection in photovoltaic systems; distributed photovoltaic resources
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, University of Catania, 95125 Catania, Italy
Interests: circuit analysis and modeling applied to power systems and power electronics; application of stochastic optimization, machine learning, and computational electromagnetics in the field of electronic and electrical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many smart power grid systems currently deployed include various renewable energy sources, such as photovoltaic and wind energy. These renewable energy sources could have considerable impacts on power grid systems from both technical and environmental perspectives. The generated renewable energy can cause the discontinuity of energy production due to the non-programmable and unpredictable nature of renewable sources. The output of plants powered by non-programmable renewable energy sources (NPRESs) significantly changes the hourly pattern of zonal loads that need to be fulfilled by conventional generation plants. Thus, NPRESs introduce a stochastic component into the electricity demand related to the inherent variability of weather conditions, making the residual electricity load increasingly intermittent and harder to predict. As such, the rising implementation of NPRES plants results in both increasing imbalances between demand and generation and greater difficulty in building up the reserve margins needed to manage the randomness of the load while providing security and stability to the grid. For this reason, more effort has been made by the research community to establish accurate forecasting systems. This Special Issue aims to present advanced forecasting methods with applications that cover various practical challenges in sustainable power grids.

Topics to be covered in this Special Issue include, but are not limited to, the following:

  • Forecasting PV and wind power generation;
  • Energy demand forecasting;
  • Forecast models for wind speed and solar radiations;
  • Forecast models for grid connected MPPT;
  • Electric vehicle load forecasting;
  • Electricity price forecasting;
  • Forecasting techniques for smart grids;
  • Artificial intelligence and data-driven approaches;
  • The application of forecasting techniques in power systems;
  • Anomalies and faults prediction.

Dr. Cristina Ventura
Dr. Santi Agatino Rizzo
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. Energies 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 2600 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

  • renewable forecasting
  • renewable energy sources
  • solar radiation forecasting
  • wind speed forecasting
  • fault detection
  • power forecasting
  • MPPT forecasting
  • artificial intelligence

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Related Special Issue

Published Papers (1 paper)

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Research

23 pages, 3337 KiB  
Article
Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation
by Cristina Ventura, Giuseppe Marco Tina and Santi Agatino Rizzo
Energies 2025, 18(15), 4161; https://doi.org/10.3390/en18154161 - 5 Aug 2025
Abstract
In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability [...] Read more.
In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability makes them particularly sensitive to forecast accuracy. To address these challenges, a comprehensive methodology for assessing and mitigating imbalance penalties by integrating a short-term PV forecasting model with a battery energy storage system is proposed. Unlike conventional approaches that focus exclusively on improving statistical accuracy, this study emphasizes the economic and regulatory impact of forecast errors under the current Italian imbalance settlement framework. A hybrid physical-artificial neural network is developed to forecast PV power one hour in advance, combining historical production data and clear-sky irradiance estimates. The resulting imbalances are analyzed using regulatory tolerance thresholds. Simulation results show that, by adopting a control strategy aimed at maintaining the battery’s state of charge around 50%, imbalance penalties can be completely eliminated using a storage system sized for just over 2 equivalent hours of storage capacity. The methodology provides a practical tool for market participants to quantify the benefits of storage integration and can be generalized to other electricity markets where tolerance bands for imbalances are applied. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid: 2nd Edition)
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