Special Issue "Solar and Wind Power and Energy Forecasting Ⅱ"

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

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Dr. Emanuele Ogliari
E-Mail Website
Guest Editor
Dr. Alessandro Niccolai
E-Mail Website
Guest Editor
Department of Energy - Electrical Engineering, POLITECNICO DI MILANO, Via La Masa 34, 20156 Milano (MI), Italy
Interests: evolutionary computation techniques; neural networks; optimization of EM devices; reflectarray antennas; electrical microgrid
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Special Issue Information

Dear Colleagues,

The Special Issue “Solar and Wind Power and Energy Forecasting Ⅱ” is a continuation of the previous and successful Special Issue “Solar and Wind Power and Energy Forecasting”. Prof. Dr. Sonia Leva, Dr. Emanuele Ogliari and Dr. Alessandro Niccolai (Politecnico di Milano, Milano, Italy) are serving as Guest Editors for this issue. We think that you could make an excellent contribution based on your expertise.

The renewable-energy-based generation of electricity is currently experiencing rapid growth in electric grids. The intermittent input from renewable energy sources (RES), as a consequence, creates problems in balancing the energy supply and demand.

Thus, forecasting of RES power generation is vital to help grid operators to better manage the electric balance between power demand and supply and to improve the penetration of distributed renewable energy sources and, in stand-alone hybrid systems, for the optimum size of all its components and to improve the reliability of the isolated systems.

This Special Issue of Energies, “Solar and Wind Power and Energy Forecasting Ⅱ”, is intended to disseminate new promising methods and techniques to forecast the output power and energy of intermittent renewable energy sources.

Prof. Dr. Sonia Leva
Dr. Emanuele Ogliari
Dr. Alessandro Niccolai
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 papers will be 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 2000 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

  • RES integration
  • Forecasting techniques
  • Machine learning
  • Computational intelligence
  • Optimization
  • PV system
  • Wind system

Published Papers (3 papers)

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Research

Article
A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model
Energies 2021, 14(20), 6447; https://0-doi-org.brum.beds.ac.uk/10.3390/en14206447 - 09 Oct 2021
Viewed by 331
Abstract
Forecasting uncertainties limit the development of photovoltaic (PV) power generation. New forecasting technologies are urgently needed to improve the accuracy of power generation forecasting. In this paper, a novel ultra-short-term PV power forecasting method is proposed based on a deep belief network (DBN)-based [...] Read more.
Forecasting uncertainties limit the development of photovoltaic (PV) power generation. New forecasting technologies are urgently needed to improve the accuracy of power generation forecasting. In this paper, a novel ultra-short-term PV power forecasting method is proposed based on a deep belief network (DBN)-based Takagi-Sugeno (T-S) fuzzy model. Firstly, the correlation analysis is used to filter redundant information. Furthermore, a T-S fuzzy model, which integrates fuzzy c-means (FCM) for the fuzzy division of input variables and DBN for fuzzy subsets forecasting, is developed. Finally, the proposed method is compared to a benchmark DBN method and the T-S fuzzy model in case studies. The numerical results show the feasibility and flexibility of the proposed ultra-short-term PV power forecasting approach. Full article
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting Ⅱ)
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Article
A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
Energies 2021, 14(14), 4256; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144256 - 14 Jul 2021
Viewed by 350
Abstract
Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not [...] Read more.
Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved. Full article
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting Ⅱ)
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Article
Dynamical Operation Based Robust Nonlinear Control of DC Microgrid Considering Renewable Energy Integration
Energies 2021, 14(13), 3988; https://0-doi-org.brum.beds.ac.uk/10.3390/en14133988 - 02 Jul 2021
Cited by 4 | Viewed by 570
Abstract
The importance of microgrids has been acknowledged with the increasing amount of research in direct current (DC) microgrids. The main reason for this is the straightforward structure and efficient performance. In this research article, double integral sliding mode controllers (DISMCs) have been proposed [...] Read more.
The importance of microgrids has been acknowledged with the increasing amount of research in direct current (DC) microgrids. The main reason for this is the straightforward structure and efficient performance. In this research article, double integral sliding mode controllers (DISMCs) have been proposed for energy harvesting and DC microgrid management involving renewable sources and a hybrid energy storage system (HESS). DISMC offers a better dynamic response and reduced amount of chattering than the traditional sliding mode controllers. In the first stage, the state differential model for the grid was derived. Then, the nonlinear control laws were proposed for the PV system and hybrid energy storage system to achieve the main objective of voltage regulation at the DC link. In the later part, the system’s asymptotic stability was proven using Lyapunov stability criteria. Finally, an energy management algorithm was provided to ensure the DC microgrid’s smooth operation within the safe operating limit. The proposed system’s effectiveness was validated by implementing on MATLAB/Simulink software and comparing against sliding mode control and Lyapunov redesign. Moreover, to ensure the proposed controller’s practical viability for this scheme, it has been tested on real-time hardware-in-the-loop test bench. Full article
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting Ⅱ)
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