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Energies
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Enhanced Variable Renewable Energies Forecasts for Optimal Grid Operations
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Enhanced Variable Renewable Energies Forecasts for Optimal Grid Operations

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

Deadline for manuscript submissions: closed (30 December 2020) | Viewed by 10399

Special Issue Editors

Prof. Dr. Philippe Lauret

E-Mail Website
Guest Editor
Laboratory of Physics and Mathematical Engineering for Energy and the Environment (PIMENT), University of La Réunion, 97715 Réunion, France
Interests: energy meteorology; solar forecasting and resource asssesment; statistical learning
Prof. Dr. Mathieu David

E-Mail Website
Guest Editor
Laboratory of Physics and Mathematical Engineering for Energy and the Environment (PIMENT), University of La Réunion, 97715 Réunion, France
Interests: energy meteorology, solar forecasting and resource assessment, smart grids, green buildings

Special Issue Information

Dear Colleagues,

High penetration of Variable Renewable Energies (VRE – Solar and Wind) into electricity grids can lead to management difficulties as the inherent variability and lack of predictability of these sources affect the grid supply/demand balance. One of the strategies for mitigating the grid imbalance is to generate enhanced forecasts of the corresponding Wind/PV power output at different time horizons and spatial scales. This will provide valuable information for grid operators that can take the necessary actions to mitigate the intermittency at lower cost and ensuring the electricity system stability. More specifically, these enhanced forecasts, and particularly probabilistic forecasts, can be considered as optimal inputs to a large class of decision-making problems related to grid operations. Therefore, the improvement of VRE forecasting methods is essential for increasing the value of VRE power generation and enabling a higher penetration of these technologies into electricity grids.

Authors are particularly encouraged to submit research papers, reviews and case studies on the following subjects:

  • VRE probabilistic forecasts
  • Multivariate VRE predictions
  • Use of deterministic and/or probabilistic VRE for the optimal control of electrical grid and storage systems operations
  • Use of VRE forecasts to mitigate the cost of the grid imbalance in the case of high VRE penetration

Prof. Dr. Philippe Lauret
Prof. Dr. Mathieu David
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

  • Wind forecasting techniques
  • PV power/solar irradiance forecasting methods
  • VRE (wind/solar) probabilistic forecasts
  • Optimal grid operations
  • Optimal operating reserve assessment

Published Papers (4 papers)

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Research

33 pages, 3304 KiB  
Article
From Firm Solar Power Forecasts to Firm Solar Power Generation an Effective Path to Ultra-High Renewable Penetration a New York Case Study
by Richard Perez, Marc Perez, James Schlemmer, John Dise, Thomas E. Hoff, Agata Swierc, Patrick Keelin, Marco Pierro and Cristina Cornaro
Energies 2020, 13(17), 4489; https://doi.org/10.3390/en13174489 - 31 Aug 2020
Cited by 18 | Viewed by 3028
Abstract
We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, and hence entirely remove load balancing uncertainty emanating from grid-connected solar [...] Read more.
We introduce firm solar forecasts as a strategy to operate optimally overbuilt solar power plants in conjunction with optimally sized storage systems so as to make up for any power prediction errors, and hence entirely remove load balancing uncertainty emanating from grid-connected solar fleets. A central part of this strategy is the plant overbuilding that we term implicit storage. We show that strategy, while economically justifiable on its own account, is an effective entry step to achieving least-cost ultra-high solar penetration where firm power generation will be a prerequisite. We demonstrate that in the absence of an implicit storage strategy, ultra-high solar penetration would be vastly more expensive. Using the New York Independent System Operator (NYISO) as a case study, we determine current and future costs of firm forecasts for a comprehensive set of scenarios in each ISO electrical region, comparing centralized vs. decentralized production and assessing load flexibility’s impact. We simulate the growth of the strategy from firm forecast to firm power generation. We conclude that ultra-high solar penetration enabled by the present strategy, whereby solar would firmly supply the entire NYISO load, could be achieved locally at electricity production costs comparable to current NYISO wholesale market prices. Full article
(This article belongs to the Special Issue Enhanced Variable Renewable Energies Forecasts for Optimal Grid Operations)
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27 pages, 7859 KiB  
Article
The Value of PV Power Forecast and the Paradox of the “Single Pricing” Scheme: The Italian Case Study
by Marco Pierro, David Moser, Richard Perez and Cristina Cornaro
Energies 2020, 13(15), 3945; https://doi.org/10.3390/en13153945 - 01 Aug 2020
Cited by 15 | Viewed by 2319
Abstract
One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In [...] Read more.
One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms’ generation is important both for the producers/traders to minimize the imbalance costs and for the transmission system operators to ensure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale photovoltaic (PV) plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of the forecast accuracy and accuracy improvement in the context of the Italian energy market’s regulatory framework. Through this study, we further point out several important criticisms of the Italian “single pricing” system that brings paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome the undesired effects of the current dispatching regulations. Full article
(This article belongs to the Special Issue Enhanced Variable Renewable Energies Forecasts for Optimal Grid Operations)
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16 pages, 1214 KiB  
Article
Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control
by Jean-Laurent Duchaud, Cyril Voyant, Alexis Fouilloy, Gilles Notton and Marie-Laure Nivet
Energies 2020, 13(14), 3565; https://doi.org/10.3390/en13143565 - 10 Jul 2020
Cited by 6 | Viewed by 1643
Abstract
With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-step is an important parameter [...] Read more.
With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-step is an important parameter affecting not only its accuracy but also the optimal control time discretization, hence its efficiency and computational burden. To quantify this trade-off, four machine learning forecast models are tested on two geographical locations for time-steps varying from 2 to 60 min and horizons from 10 min to 6 h, on global irradiance horizontal and tilted when data was available. The results are similar for all the models and indicate that the error metric can be reduced up to 0.8% per minute on the time-step for forecasts below one hour and up to 1.7% per ten minutes for forecasts between one and six hours. In addition, it is shown that for short term horizons, it may be advantageous to forecast with a high resolution then average the results at the time-step needed by the energy management system. Full article
(This article belongs to the Special Issue Enhanced Variable Renewable Energies Forecasts for Optimal Grid Operations)
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11 pages, 3633 KiB  
Article
An Ensemble Forecasting Model of Wind Power Outputs Based on Improved Statistical Approaches
by Yeojin Kim and Jin Hur
Energies 2020, 13(5), 1071; https://doi.org/10.3390/en13051071 - 01 Mar 2020
Cited by 27 | Viewed by 2770
Abstract
The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a [...] Read more.
The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a viable alternative to conventional single-model-based forecasting for improving the forecasting accuracy. In this work, we propose the day-ahead ensemble forecasting of wind power using statistical methods. The ensemble forecasting model consists of three single forecasting approaches: autoregressive integrated moving average with exogenous variable (ARIMAX), support vector regression (SVR), and the Monte Carlo simulation-based power curve model. To apply the methodology, we conducted forecasting using the historical data of wind farms located on Jeju Island, Korea. The results were compared between a single model and an ensemble model to demonstrate the validity of the proposed method. Full article
(This article belongs to the Special Issue Enhanced Variable Renewable Energies Forecasts for Optimal Grid Operations)
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