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Solar Forecasting and the Integration of Solar Generation to the Grid

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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 (20 September 2021) | Viewed by 15403

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Prof. Dr. Stéphane Grieu

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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.
Special Issues, Collections and Topics in MDPI journals

Dr. Stéphane Thil

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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

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Keywords

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

Published Papers (6 papers)

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Research

15 pages, 2463 KiB

Open AccessArticle

Solar Irradiance Prediction with Machine Learning Algorithms: A Brazilian Case Study on Photovoltaic Electricity Generation

by Gabriel de Freitas Viscondi and Solange N. Alves-Souza

Energies 2021, 14(18), 5657; https://doi.org/10.3390/en14185657 - 08 Sep 2021

Cited by 13 | Viewed by 2251

Abstract

Forecasting photovoltaic electricity generation is one of the key components to reducing the impacts of solar power natural variability, nurturing the penetration of renewable energy sources. Machine learning is a well-known method that relies on the principle that systems can learn from previously measured data, detecting patterns which are then used to predict future values of a target variable. These algorithms have been used successfully to predict incident solar irradiation, but the results depend on the specificities of the studied location due to the natural variability of the meteorological parameters. This paper presents an extensive comparison of the three ML algorithms most used worldwide for forecasting solar radiation, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), aiming at the best prediction of daily solar irradiance in a São Paulo context. The largest dataset in Brazil for meteorological parameters, containing measurements from 1933 to 2014, was used to train and compare the results of the algorithms. The results showed good approximation between measured and predicted global solar radiation for the three algorithms; however, for São Paulo, the SVM produced a lower Root-Mean-Square Error (RMSE), and ELM, a faster training rate. Using all 10 meteorological parameters available for the site was the best approach for the three algorithms at this location. Full article

(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid)

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19 pages, 4220 KiB

Open AccessFeature PaperArticle

Effect of Two Different Heat Transfer Fluids on the Performance of Solar Tower CSP by Comparing Recompression Supercritical CO₂ and Rankine Power Cycles, China

by Ephraim Bonah Agyekum, Tomiwa Sunday Adebayo, Festus Victor Bekun, Nallapaneni Manoj Kumar and Manoj Kumar Panjwani

Energies 2021, 14(12), 3426; https://doi.org/10.3390/en14123426 - 10 Jun 2021

Cited by 19 | Viewed by 2681

Abstract

China intends to develop its renewable energy sector in order to cut down on its pollution levels. Concentrated solar power (CSP) technologies are expected to play a key role in this agenda. This study evaluated the technical and economic performance of a 100 MW solar tower CSP in Tibet, China, under different heat transfer fluids (HTF), i.e., Salt (60% NaNO₃ 40% KNO₃) or HTF A, and Salt (46.5% LiF 11.5% NaF 42% KF) or HTF B under two different power cycles, namely supercritical CO₂ and Rankine. Results from the study suggest that the Rankine power cycle with HTF A and B recorded capacity factors (CF) of 39% and 40.3%, respectively. The sCO₂ power cycle also recorded CFs of 41% and 39.4% for HTF A and HTF B, respectively. A total of 359 GWh of energy was generated by the sCO₂ system with HTF B, whereas the sCO₂ system with HTF A generated a total of 345 GWh in the first year. The Rankine system with HTF A generated a total of 341 GWh, while the system with B as its HTF produced a total of 353 GWh of electricity in year one. Electricity to grid mainly occurred between 10:00 a.m. to 8:00 p.m. throughout the year. According to the results, the highest levelized cost of energy (LCOE) (real) of 0.1668 USD/kWh was recorded under the Rankine cycle with HTF A. The lowest LCOE (real) of 0.1586 USD/kWh was obtained under the sCO₂ cycle with HTF B. In general, all scenarios were economically viable at the study area; however, the sCO₂ proved to be more economically feasible according to the simulated results. Full article

(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid)

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23 pages, 2463 KiB

Open AccessArticle

A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting

by Shab Gbémou, Julien Eynard, Stéphane Thil, Emmanuel Guillot and Stéphane Grieu

Energies 2021, 14(11), 3192; https://doi.org/10.3390/en14113192 - 29 May 2021

Cited by 22 | Viewed by 2585

Abstract

The proliferation of photovoltaic (PV) power generation in power distribution grids induces increasing safety and service quality concerns for grid operators. The inherent variability, essentially due to meteorological conditions, of PV power generation affects the power grid reliability. In order to develop efficient monitoring and control schemes for distribution grids, reliable forecasting of the solar resource at several time horizons that are related to regulation, scheduling, dispatching, and unit commitment, is necessary. PV power generation forecasting can result from forecasting global horizontal irradiance (GHI), which is the total amount of shortwave radiation received from above by a surface horizontal to the ground. A comparative study of machine learning methods is given in this paper, with a focus on the most widely used: Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANN). Two years of GHI data with a time step of 10

\min

are used to train the models and forecast GHI at varying time horizons, ranging from 10

\min

to 4

h

. Persistence on the clear-sky index, also known as scaled persistence model, is included in this paper as a reference model. Three criteria are used for in-depth performance estimation: normalized root mean square error (nRMSE), dynamic mean absolute error (DMAE) and coverage width-based criterion (CWC). Results confirm that machine learning-based methods outperform the scaled persistence model. The best-performing machine learning-based methods included in this comparative study are the long short-term memory (LSTM) neural network and the GPR model using a rational quadratic kernel with automatic relevance determination. Full article

(This article belongs to the Special Issue Solar Forecasting and the Integration of Solar Generation to the Grid)

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20 pages, 9383 KiB

Open AccessArticle

Assessment of PV Hosting Capacity in a Small Distribution System by an Improved Stochastic Analysis Method

by Yu-Jen Liu, Yu-Hsuan Tai, Yih-Der Lee, Jheng-Lung Jiang and Chen-Wei Lin

Energies 2020, 13(22), 5942; https://doi.org/10.3390/en13225942 - 13 Nov 2020

Cited by 10 | Viewed by 2223

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 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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18 pages, 4784 KiB

Open AccessArticle

Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

by Foster Lubbe, Jacques Maritz and Thomas Harms

Energies 2020, 13(20), 5509; https://doi.org/10.3390/en13205509 - 21 Oct 2020

Cited by 17 | Viewed by 3051

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 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/m

^{2}

. 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/m

^{2}

and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m

^{2}

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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23 pages, 1346 KiB

Open AccessArticle

Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

by Hanany Tolba, Nouha Dkhili, Julien Nou, Julien Eynard, Stéphane Thil and Stéphane Grieu

Energies 2020, 13(16), 4184; https://doi.org/10.3390/en13164184 - 13 Aug 2020

Cited by 9 | Viewed by 1852

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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