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Solar and Wind Power and Energy Forecasting Ⅱ

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 12611

Special Issue Editors


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Guest Editor
Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Interests: photovoltaic system; grid; power sharing; inverters; forecasting; nowcasting; machine learning; degradation; battery management systems; polymer solar cells; organic photovoltaics; electric vehicle; vehicle-to-grid; microgrid; energy systems; maximum power point trackers; electric power plant loads; electricity price; power markets; heterogeneous networks; base stations; energy efficiency; life cycle assessment; wind power; regenerative braking; bicycles; motorcycles; car sharing; autonomous vehicles
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Guest Editor
Department of Energy–Electrical Engineering, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy
Interests: evolutionary computation techniques; neural networks; optimization of EM devices; reflectarray antennas; electrical microgrid
Special Issues, Collections and Topics in MDPI journals

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 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

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

Published Papers (5 papers)

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Research

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23 pages, 8815 KiB  
Article
Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model
by Eduardo Rangel-Heras, César Angeles-Camacho, Erasmo Cadenas-Calderón and Rafael Campos-Amezcua
Energies 2022, 15(8), 2842; https://doi.org/10.3390/en15082842 - 13 Apr 2022
Cited by 3 | Viewed by 1394
Abstract
In this paper, a methodology for short-term forecasting of power generated by a photovoltaic module is reported. The method incorporates a nonlinear autoregressive with exogenous inputs (NARX) fed by the solar radiation and temperature times series, as well as an estimation of power [...] Read more.
In this paper, a methodology for short-term forecasting of power generated by a photovoltaic module is reported. The method incorporates a nonlinear autoregressive with exogenous inputs (NARX) fed by the solar radiation and temperature times series, as well as an estimation of power time series obtained by implementing an ideal single diode model. This synthetic time series was validated against an actual photovoltaic module. The NARX model has been implemented in conjunction with the corrective vector multiplier (CVM) technique, which uses solar radiation under clear sky conditions to adjust the forecasting results. In addition, collinearity and the Granger causality tests were used to choose the input variables. The forecasting horizon was 24-h-ahead. The hybrid NARX-CVM model was compared to a nonlinear autoregressive neural network and persistence model using the typic forecasting error measures such as the mean bias error, mean squared error, root mean squared error and forecast skill. The results showed that the forecasting skills of the hybrid model are about 34% against the NAR model and about 42% against the Persistence model. The model was validated by blind forecasting. The results demonstrated evidence of the quality of the conformed forecasting model and the convenience of its implementation and building. Full article
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting Ⅱ)
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10 pages, 1490 KiB  
Article
A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model
by Ling Liu, Fang Liu and Yuling Zheng
Energies 2021, 14(20), 6447; https://doi.org/10.3390/en14206447 - 09 Oct 2021
Cited by 8 | Viewed by 1249
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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22 pages, 4284 KiB  
Article
A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation
by Taeyoung Kim and Jinho Kim
Energies 2021, 14(14), 4256; https://doi.org/10.3390/en14144256 - 14 Jul 2021
Cited by 3 | Viewed by 2006
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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23 pages, 6332 KiB  
Article
Dynamical Operation Based Robust Nonlinear Control of DC Microgrid Considering Renewable Energy Integration
by Ammar Armghan, Muhammad Kashif Azeem, Hammad Armghan, Ming Yang, Fayadh Alenezi and Mudasser Hassan
Energies 2021, 14(13), 3988; https://doi.org/10.3390/en14133988 - 02 Jul 2021
Cited by 18 | Viewed by 2165
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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Review

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24 pages, 3163 KiB  
Review
Renewable Energy Status in Azerbaijan: Solar and Wind Potentials for Future Development
by Feyruz Mustafayev, Przemyslaw Kulawczuk and Christian Orobello
Energies 2022, 15(2), 401; https://doi.org/10.3390/en15020401 - 06 Jan 2022
Cited by 11 | Viewed by 4826
Abstract
Azerbaijan has a well-developed hydrocarbon industry backed with abundant domestic resources. Oil and gas have played a crucial role in the economic revival of the country since independence was regained back in 1991. The legal foundation of the transition to carbon-zero energy generation [...] Read more.
Azerbaijan has a well-developed hydrocarbon industry backed with abundant domestic resources. Oil and gas have played a crucial role in the economic revival of the country since independence was regained back in 1991. The legal foundation of the transition to carbon-zero energy generation was laid in the 1990s with a number of acts mentioning the importance of the shift. The government has an ambitious plan to improve the situation, though an action plan with targeted renewables share in production and consumption is still to be prepared. This study, based on systematic review methodology for qualitative research, analyzes the potential of renewables in Azerbaijan with a focus on solar and wind power, discusses the deficiencies hindering the development of the renewables industry, and develops recommendations on applicable actions to improve the situation in this regard. It also includes legislative acts of the Republic concerning renewable energy. The main objective of the study is to explore renewable energy potentials and assess the readiness of the country to make a shift towards green energy. The findings of the article demonstrate enough potential to increase the share of renewables. The potential, however, is obscured with a relatively less solid legal framework and a lack of expertise in the industry. Full article
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting Ⅱ)
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