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Solar Forecasting and the Integration of Solar Generation to the Grid: Volume II

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A2: Solar Energy and Photovoltaic Systems".

Deadline for manuscript submissions: closed (7 July 2023) | Viewed by 4407

Special Issue Editor

Prof. Dr. Stéphane Grieu

E-Mail Website
Guest Editor
PROMES-CNRS Laboratory, University of Perpignan Via Domitia, Perpignan, France
Interests: solar resource assessment and forecasting; distributed generation management; smart buildings; smart grids; thermal/electrical microgrids; machine/deep learning; reinforcement learning; model-based predictive control; non-linear optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the scarcity of fossil fuels and increasing energy needs, the large-scale deployment of solar technologies is accelerating. This deployment comes within the fight against climate change, in a context of sustainable development.

According to national and international regulations, mainly concerned with voltage constraints, current levels, and voltage drop gradients, grid operators are contractually obligated to maintain a steady and reliable service to their customers. Over the past few years, low-voltage and medium-voltage power distribution grids have been undergoing major changes due to the increasing penetration of renewable-energy-based distributed generation, particularly photovoltaic power generation. This penetration causes a large number of stability, quality, and safety issues. In addition, power injection by distributed generators results in bidirectional power flow and is highly irregular. This is mainly due to the intermittent nature of renewable energy sources.

Within this context, the smart grid paradigm has emerged as a solution to the aforementioned issues facing power distribution grid operators. The enhancement of grid observability, through an advanced metering infrastructure and the forecasting of grid load and distributed generation, paves the way for smart management strategies, among which predictive management strategies, that keep the balance between supply and demand.

This Special Issue (entitled “Solar Forecasting and the Integration of Solar Generation to the Grid”) therefore focuses on solutions to promote the integration of solar power generation in power distribution grids. We therefore invite original papers (novel technical development, review, and case study papers) addressing solar resource and solar power forecasting and the smart management of distributed generation.

Prof. Dr. Stéphane Grieu
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. 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

  • solar technologies
  • solar forecasting
  • solar resource
  • smart management of distributed generation
  • smart grid paradigm

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Published Papers (2 papers)

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Research

17 pages, 679 KiB  
Article
Probabilistic Intraday PV Power Forecast Using Ensembles of Deep Gaussian Mixture Density Networks
by Oliver Doelle, Nico Klinkenberg, Arvid Amthor and Christoph Ament
Energies 2023, 16(2), 646; https://doi.org/10.3390/en16020646 - 5 Jan 2023
Cited by 7 | Viewed by 1985
Abstract
There is a growing interest of estimating the inherent uncertainty of photovoltaic (PV) power forecasts with probability forecasting methods to mitigate accompanying risks for system operators. This study aims to advance the field of probabilistic PV power forecast by introducing and extending deep [...] Read more.
There is a growing interest of estimating the inherent uncertainty of photovoltaic (PV) power forecasts with probability forecasting methods to mitigate accompanying risks for system operators. This study aims to advance the field of probabilistic PV power forecast by introducing and extending deep Gaussian mixture density networks (MDNs). Using the sum of the weighted negative log likelihood of multiple Gaussian distributions as a minimizing objective, MDNs can estimate flexible uncertainty distributions with nearly all neural network structures. Thus, the advantages of advances in machine learning, in this case deep neural networks, can be exploited. To account for the epistemic (e.g., model) uncertainty as well, this study applies two ensemble approaches to MDNs. This is particularly relevant for industrial applications, as there is often no extensive (manual) adjustment of the forecast model structure for each site, and only a limited amount of training data are available during commissioning. The results of this study suggest that already seven days of training data are sufficient to generate significant improvements of 23.9% in forecasting quality measured by normalized continuous ranked probability score (NCRPS) compared to the reference case. Furthermore, the use of multiple Gaussian distributions and ensembles increases the forecast quality relatively by up to 20.5% and 19.5%, respectively. Full article
(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid: Volume II)
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21 pages, 7330 KiB  
Article
Solar Irradiance Nowcasting System Trial and Evaluation for Islanded Microgrid Control Purposes
by Remember Samu, Satya Girdhar Bhujun, Martina Calais, GM Shafiullah, Moayed Moghbel, Md Asaduzzaman Shoeb and Bijan Nouri
Energies 2022, 15(17), 6100; https://doi.org/10.3390/en15176100 - 23 Aug 2022
Cited by 2 | Viewed by 1899
Abstract
The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. [...] Read more.
The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system’s accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system’s accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich. Full article
(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid: Volume II)
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