Special Issue "Solar Forecasting and the Integration of Solar Generation to the Grid"

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

Deadline for manuscript submissions: 10 February 2021.

Special Issue Editors

Prof. Dr. Stéphane Grieu
Website
Guest Editor
Processes, Materials and Solar Energy (PROMES) laboratory, Perpignan, France Université de Perpignan Via Domitia, Perpignan, France
Interests: solar resource assessment and forecasting; distributed generation management; smart grids; thermal/electrical microgrids; smart buildings; machine/deep learning; model-based predictive control; non-linear optimization.
Dr. Stéphane Thil
Website
Guest Editor
Processes, Materials and Solar Energy (PROMES) laboratory Université de Perpignan Via Domitia, Perpignan, France
Interests: solar resource assessment and forecasting; distributed generation management; smart grids; signal and image processing; machine/deep learning; system identification; model-based predictive control; non-linear optimization

Special Issue Information

Dear Colleagues,

Due to the scarcity of fossil fuels and increasing energy needs, the large-scale deployment of solar technologies (particularly the deployment of solar photovoltaics) 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 steady and reliable service to their customers. Over the past few years, 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 has caused a large number of stability, quality, and safety issues. Power injection by distributed generators results in bidirectional power flow, and is 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 monitoring and control problems 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 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 the large-scale deployment of solar technologies and the increasing penetration of renewable-energy-based distributed generation in the electrical system, particularly solar photovoltaic power generation. We therefore invite original papers (novel technical developments, reviews, and case studies) addressing solar resource forecasting, the smart management of the distributed generation, and the issues related with the penetration of such a generation in the electrical system.

Prof. Dr. Stéphane Grieu
Dr. Stéphane Thil
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

  • Solar technologies
  • Solar resource forecasting
  • Smart management of distributed generation
  • Penetration in the electrical system
  • Smart grid paradigm

Published Papers (3 papers)

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Research

Open AccessArticle
Assessment of PV Hosting Capacity in a Small Distribution System by an Improved Stochastic Analysis Method
Energies 2020, 13(22), 5942; https://doi.org/10.3390/en13225942 - 13 Nov 2020
Abstract
PV hosting capacity (PVHC) analysis on a distribution system is an attractive technique that emerged in recent years for dealing with the planning tasks on high-penetration PV integration. PVHC uses various system performance indices as judgements to find an available amount of PV [...] Read more.
PV hosting capacity (PVHC) analysis on a distribution system is an attractive technique that emerged in recent years for dealing with the planning tasks on high-penetration PV integration. PVHC uses various system performance indices as judgements to find an available amount of PV installation capacity that can be accommodated on existing distribution system infrastructure without causing any violation. Generally, approaches for PVHC assessments are implemented by iterative power flow calculations with stochastic PV deployments so as to observe the operation impacts for PV installation on distribution systems. Determination of the stochastic PV deployments in most of traditional PVHC analysis methods is automatically carried out by the program that is using random selection. However, a repetitive problem that exists in these traditional methods on the selection of the same PV deployment for a calculation was not previously investigated or discussed; further, underestimation of PVHC results may occur. To assess PVHC more effectively, this paper proposes an improved stochastic analysis method that introduces an innovative idea of using repetitiveness check mechanism to overcome the shortcomings of the traditional methods. The proposed mechanism firstly obtains all PV deployment combinations for the determination of all possible PV installation locations. A quick-sorting algorithm is then used to remove repetitive PV deployments that are randomly selected during the solution procedure. Finally, MATLAB and OpenDSS co-simulations implemented on a small distribution feeder are used to validate the performance of the proposed method; in addition, PVHC enhancement by PV inverter control is investigated and simulated in this paper as well. Results show that the proposed method is more effective than traditional methods in PVHC assessments. Full article
(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid)
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Open AccessArticle
Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study
Energies 2020, 13(20), 5509; https://doi.org/10.3390/en13205509 - 21 Oct 2020
Cited by 1
Abstract
The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy [...] Read more.
The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems. Full article
(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid)
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Open AccessArticle
Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study
Energies 2020, 13(16), 4184; https://doi.org/10.3390/en13164184 - 13 Aug 2020
Cited by 1
Abstract
In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). [...] Read more.
In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting. Full article
(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid)
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