DOCSLIB.ORG

Report on Forecasting, Concept of Renewable Energy Management Centres and Grid Balancing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Report on Forecasting, Concept of Renewable Energy Management Centres and Grid Balancing 1. GIZ - India Green Energy Corridors IGEN-GEC Report on Forecasting, Concept of Renewable Energy Management Centres and Grid Balancing Published by Consortium Partners University of Oldenburg, Germany Fraunhofer IWES, Germany FICHTNER GmbH & Co. KG, Germany Ernst & Young LLP, India Table of Contents Table of Contents 1 Table of Figures 3 List of Tables 4 List of Abbreviations 5 Executive Summary 6 1 Introduction 9 2 Overview of the Work Package 10 3 Wind & Solar Power Forecasting Infrastructure and Requirements 11 3.1 Existing Infrastructure and Practices for RE Forecasting in India 11 3.1.1 Operational Wind and Solar Power Forecasting in India Today 11 3.1.2 Operational Numerical Weather Prediction System of IMD 12 3.1.3 Role of Forecasting in New Regulation issued by CERC 14 3.1.4 Comments on Current Practice of Load Forecasting 15 3.2 State of the Art Operational Wind and Solar Power Forecasting 17 3.2.1 Overview 17 3.2.2 Numerical Weather Prediction 18 3.2.3 Measure of Accuracy of Wind and Solar Power Forecast 19 3.2.4 Wind Power Forecasting 21 3.2.5 Solar Power Forecasting 28 3.3 The Australian Wind Energy Forecasting System as reference implementation for India 38 3.3.1 Anemos wind power prediction platform 39 3.3.2 Extreme event warnings 41 3.3.3 Experience 41 3.4 Wind and Solar Power Forecasting Practice in Germany 44 3.5 Recommendations for Wind & Solar Power and Load Forecasting in India 46 4 Establishment of Renewable Energy Management Centres (REMC) 49 5 Balancing Capability Enhancement 52 5.1 Methodology and Introduction 52 5.1.1 Introduction 52 5.1.2 Methodology 53 6 Enhancing Balancing Capacity 54 6.1.1 Wrap-up of interview phase in India 54 6.1.2 Introduction 54 6.1.3 State perspective 54 6.1.4 Central perspective 59 1 | P a g e 6.1.5 Assessment of existing balancing capacity 61 6.1.6 Flexibility of power plants – literature overview 61 6.1.7 Turndown capability in India 61 6.1.8 Theoretical thermal balancing and ramping potential 62 6.1.9 Theoretical hydro balancing potential 65 6.1.10 Conclusion 65 6.1.11 Short-term solutions 66 6.1.12 Mid-term solutions 70 6.1.13 Long-term solutions 75 7 Cost analysis of balancing options 78 7.1.1 Regional balancing 78 7.1.2 Retrofitting 78 7.1.3 Storage options 79 8 Summary and Recommendations 82 8.1.1 Main outcomes and recommendations 82 9 Overall Strategy Roadmap & Recommendations 84 9.1 Summary of the strategy and the recommendations of all three work packages 84 Annexures 86 References 87 2 | P a g e Table of Figures Figure 1 - Outer and inner domain of the WRF model at 27 km and 9 km ........................................... 13 Figure 2 - WRF model domains with 3 km horizontal resolution at Regional Centres ......................... 14 Figure 3 - Typical pattern of geographical diversity for the daily load curve in the N, W, and S states of India on a single day (29 March 2014) .................................................................................................. 16 Figure 4: Wind power forecasting time scale ........................................................................................ 21 Figure 5: Temporal development of the one-day forecast error in the German control area of ‘E.On Netz’(blue) and for Germany (red) ........................................................................................................ 23 Figure 6: Very high resolution model domains, left: UM-4km (grey shaded area, UK Met Office, 70 vertical layers), right: COSMO-DE (DWD, 50 vertical layers). .............................................................. 24 Figure 7: Table of Wind power software models with international operation ...................................... 26 Figure 8 : Example of a ramp event following a shut-down due to high wind speeds .......................... 28 Figure 9: Forecasting methods used for different spatial and temporal scales .................................... 29 Figure 10: Overview of a regional PV power production scheme ......................................................... 29 Figure 11: Shortest-term forecasting scheme using cloud index images. ............................................ 32 Figure 12: Example of nested domains used in the WRF model......................................................... 34 Figure 13: Derivation of global irradiance on tilted surfaces from global horizontal irradiance. ........... 35 Figure 14: Forecast of global irradiance ............................................................................................... 37 Figure 15: RMSE of five forecasting approaches and persistence for three German stations for the first three forecast days. (1)–(3): different global models plus post-processing, (4)–(5): ............................ 37 Figure 16: Absolute (left) and relative (right) forecast errors ................................................................ 38 Figure 17: Indian wind farm example .................................................................................................... 39 Figure 18: Simplified flow chart of the Anemos short-term model chain (0-72 h) ................................. 40 Figure 19: Anemos. Live GUI for forecasts visualization ...................................................................... 40 Figure 20: Visualization example .......................................................................................................... 42 Figure 21: Example for the platform surveillance processes: Monitoring of SCADA data feed quality. .............................................................................................................................................................. 43 Figure 22 - Proposal of a load forecasting framework .......................................................................... 48 Figure 23: Focus of report and distinction between short-term (frequency control) and long-term balancing (scheduling) .......................................................................................................................... 52 Figure 24: Installed capacity and capacity penetration of RE in the analyzed states in India in 2014 . 54 Figure 25: Minimum load of power plants in Gujarat ............................................................................ 62 Figure 26: Total theoretical balancing potential for each state and comparison to installed RE capacity .............................................................................................................................................................. 63 Figure 27: Theoretical thermal balancing potential in RE rich-states compared to the potential of regions and all India ........................................................................................................................................... 63 Figure 28: Theoretical state wise ramping potential of all thermal power plants (left) and ramping demand in Gujarat (right) ...................................................................................................................... 64 Figure 29: Installed capacity ................................................................................................................. 65 Figure 30: Theoretical thermal balancing capability of RE-rich states today and up to 2022 ............... 66 Figure 31: Forecasting quality of three selected DISCOMs in 2014 ..................................................... 67 Figure 32: Overview of natural gas sector in India................................................................................ 70 Figure 33: Storage capacity .................................................................................................................. 71 Figure 34: Thermal Balancing Potential – Comparison between states, regions and India ................. 72 Figure 35: Potential and installed capacity of pump hydro storage in India ........................................ 73 Figure 36: Increase of transmission capacity under the Green Energy Corridor Project Plans ........... 74 Figure 37: Smoothing effects of wind energy supply from RE due to geographical diversification ...... 75 Figure 38: Overview of storage options and their typical storage capacity and possible cycle durationSource: Sterner/Stadler 2014 .................................................................................................. 76 Figure 39: Cost estimates of retrofitting gas turbines in single cycle and combined cycle plants ........ 79 Figure 40: Cost of electric load shifting for different storage options .................................................... 80 3 | P a g e List of Tables Table 1: Challenges for balancing and integrating RE in India – state perspective ............................. 58 Table 2: Problems in respect to grid operation and challenges of RE integration – central perspective .............................................................................................................................................................. 60 Table 3: Overview of flexibility parameters of power plants to be found in literature (international practice today / state-of-the-art) ......................................................................................................................... 61 Table 4: Starting capabilities of power plants ....................................................................................... 68 Table 5: Improvement potential ............................................................................................................ 70 Table 6:
Load more

Recommended publications
  • History and Trends in Solar Irradiance and PV Power Forecasting: a Preliminary Assessment and Review Using Text Mining
    History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining Dazhi Yanga,∗, Jan Kleisslb, Christian A. Gueymardc, Hugo T. C. Pedrob, Carlos F. M. Coimbrab aSingapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A∗STAR), Singapore bCenter for Renewable Resources and Integration, Center for Energy Research Department of Mechanical and Aerospace Engineering, University of California, San Diego, CA, USA cSolar Consulting Services, Colebrook, NH, USA Abstract Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and orga- nizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineer- ing topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.
    [Show full text]
  • World Bank Document
    July 2019 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized © 2019 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: [email protected]. Furthermore, the ESMAP Program Manager would appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above or to [email protected]. Cover Image A system in University of British Columbia (UBC) with pyranometer and pyrgeometer for meteorological observations related to solar insolation.
    [Show full text]
  • Probabilistic Solar Power Forecasting: Long Short-Term Memory Network Vs Simpler Approaches
    Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches Vinayak Sharmaa,∗, Jorge Angel´ Gonz´alezOrdianob,c, Ralf Mikutb, Umit Calid aUniversity of North Carolina at Charlotte bInstitute for Automation and Applied Informatics, Karlsruhe Institute of Technology cColorado State University dNorwegian University of Science and Technology Abstract The high penetration of volatile renewable energy sources such as solar, make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14). Keywords: GEFCom14, Neural Networks, Quantile Regressions, LSTM, Probabilistic Forecasting 1. Introduction arXiv:2101.08236v1 [cs.LG] 20 Jan 2021 Over the past couple of years solar power has become one of the most pop- ular renewable energy sources (RES). Unfortunately, the generation of solar power depends completely on the Sun [5]. This dependency on weather adds uncertainty and variability to the generation of solar power.
    [Show full text]
  • Solar Forecasting: Maximizing Its Value for Grid Integration
    Solar Forecasting: Maximizing its value for grid integration Introduction The forecasting of power generated by variable energy resources such as wind and solar has been the focus of academic and industrial research and development for as long as significant amounts of these renewable energy resources have been connected to the electric grid. The progress of forecasting capabilities has largely followed the penetration of the respective resources, with wind forecasting having achieved a more mature state-of-the-art compared to its solar equivalent [Lew 2010]. Still in the last 5 years, there has been substantial and material progress in the state-of-the-art of solar forecasting [Kleissl 2016]. Numerical Weather Prediction (NWP) models became more sophisticated in assessing cloud interactions with aerosols; infrared satellite imagery allowed discovery of pre-sunrise cloud formations; advanced data processing methods such as deep machine learning became increasingly accessible; probabilistic forecasts began replacing deterministic ones; and, in balancing areas with high PV penetration, solar forecasts are now used operationally. Bulk grid integration As solar electricity penetration in the distribution grid is increasing, the power generated by those Distributed Energy Resources (DERs) needs to be taken into account in the operations and planning of IPPs, ISOs, and Balancing Authorities [Mills 2013]. Since the reliable performance of the bulk grid depends on the balancing of a continuously varying load with equal amounts of generation, knowledge of the load ahead of time (forecasting) is necessary for the economically optimal – and technically feasible – dispatch of generation sources. Solar electricity generation presents two challenges to the process above. First, a very large fraction of it comes from distributed PV systems (DPV) that are connected behind-the-meter (BTM) and are thus only visible to the system operator as load.
    [Show full text]
  • Wind Generation Forecasting Methods and Proliferation of Artificial Neural
    sustainability Review Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend Muhammad Shahzad Nazir 1,* , Fahad Alturise 2 , Sami Alshmrany 3, Hafiz. M. J Nazir 4, Muhammad Bilal 5 , Ahmad N. Abdalla 6, P. Sanjeevikumar 7 and Ziad M. Ali 8,9 1 Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China 2 Computer Department, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Saudi Arabia; [email protected] 3 Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; [email protected] 4 Institute of Advance Space Research Technology, School of Networking, Guangzhou University, Guangzhou 510006, China; [email protected] 5 School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 6 Faculty of Information and Communication Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 7 Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark; [email protected] 8 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia; [email protected] 9 Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt * Correspondence: [email protected] or [email protected]; Tel.: +86-1322-271-7968 Received: 8 April 2020; Accepted: 23 April 2020; Published: 6 May 2020 Abstract: To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries.
    [Show full text]
  • Photovoltaic and Solar Forecasting: State of the Art
    Photovoltaic and Solar Forecasting: State of the Art Forecast PV power Actual PV power Report IEA PVPS T14‐01:2013 Photo credits cover page Upper left image: Environment Canada, Data courtesy of NOAA (February 27, 2013) Upper right image: Dave Turcotte, CanmetENERGY, Natural Resources Canada INTERNATIONAL ENERGY AGENCY PHOTOVOLTAIC POWER SYSTEMS PROGRAMME Photovoltaic and Solar Forecasting: State of the Art IEA PVPS Task 14, Subtask 3.1 Report IEA‐PVPS T14‐01: 2013 October 2013 ISBN 978‐3‐906042‐13‐8 Authors: Sophie Pelland, [email protected] Jan Remund, [email protected] Jan Kleissl, [email protected] Takashi Oozeki, [email protected] Karel De Brabandere, [email protected] Contents Contents ................................................................................................................................................... 1 Foreword .................................................................................................................................................. 2 Acknowledgements .................................................................................................................................. 3 Executive summary .................................................................................................................................. 3 1. Introduction ..................................................................................................................................... 4 2. Photovoltaic forecasting and link to solar forecasting ...................................................................
    [Show full text]
  • Solar Photovoltaic Forecasting of Power Output Using LSTM Networks
    atmosphere Article Solar Photovoltaic Forecasting of Power Output Using LSTM Networks Maria Konstantinou, Stefani Peratikou and Alexandros G. Charalambides * Department of Chemical Engineering, Cyprus University of Technology, Corner of Athinon and Anexartisias, 57, Lemesos 3603, Cyprus; [email protected] (M.K.); [email protected] (S.P.) * Correspondence: [email protected]; Tel.: +357-25-002306; Fax: +357-25-002668 Abstract: The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world’s energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, PV power output is affected by several factors, such as location, clouds, etc. As PV plants proliferate and represent significant contributors to grid electricity production, it becomes increasingly important to manage their inherent alterability. Therefore, solar PV forecasting is a pivotal factor to support reliable and cost-effective grid operation and control. In this paper, a stacked long short-term memory network, which is a significant component of the deep recurrent neural network, is considered for the prediction of PV power output for 1.5 h ahead. Historical data of PV power output from a PV plant in Nicosia, Cyprus, were used as input to the forecasting model. Once the model was defined and trained, the model performance was assessed qualitative (by graphical tools) and quantitative (by calculating the Root Mean Square Error (RMSE) and by applying the k-fold cross-validation method). The results showed that our model can predict well, since the RMSE gives a value of 0.11368, whereas when applying the k-fold cross-validation, the mean of the resulting RMSE values is 0.09394 with a standard deviation 0.01616.
    [Show full text]
  • Arxiv:2010.04715V2 [Cs.LG] 14 Oct 2020
    Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models Eric Zelikman∗1, Sharon Zhou∗1, Jeremy Irvin∗1 Cooper Raterink1, Hao Sheng1, Anand Avati1 Jack Kelly2, Ram Rajagopal1, Andrew Y. Ngy 1, David Gagney 3 1Stanford University, 2Open Climate Fix, 3National Center for Atmospheric Research {ezelikman,sharonz,jirvin16,crat,avati}@cs.stanford.edu,[email protected] [email protected], [email protected], [email protected], [email protected] Abstract Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting. 600 True Predicted 500 400 300 200 100 Global Horizontal Irradiance (GHI) 0 Time (≈ 8 days in January 2018) 0 200 400 600 800 1000 Figure 1: Several days of 30-min horizon NGBoost irradiance forecasts on the Sioux Falls station [1]. 1 Introduction arXiv:2010.04715v2 [cs.LG] 14 Oct 2020 Increasing adoption of renewable energy in the electricity sector is essential to the reduction of anthropogenic greenhouse gas emissions, but significant production increases must occur in order to phase out fossil fuel use [2, 3].
    [Show full text]
  • Day-Ahead Forecasting of Solar Power Output from Photovoltaic Plants in the American Southwest
    Renewable Energy 91 (2016) 11e20 Contents lists available at ScienceDirect Renewable Energy journal homepage: www.elsevier.com/locate/renene Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest * David P. Larson, Lukas Nonnenmacher, Carlos F.M. Coimbra Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center of Excellence in Renewable Energy Integration and Center for Energy Research, University of California San Diego, La Jolla, CA 92093, USA article info abstract Article history: A forecasting method for hourly-averaged, day-ahead power output (PO) from photovoltaic (PV) power Received 2 July 2015 plants based on least-squares optimization of Numerical Weather Prediction (NWP) is presented. Three Received in revised form variations of the forecasting method are evaluated against PO data from two non-tracking, 1 MWp PV 30 November 2015 plants in California for 2011e2014. The method performance, including the inter-annual performance Accepted 8 January 2016 variability and the spatial smoothing of pairing the two plants, is evaluated in terms of standard error Available online xxx metrics, as well as in terms of the occurrence of severe forecasting error events. Results validate the performance of the proposed methodology as compared with previous studies. We also show that the Keywords: Forecasting bias errors in the irradiance inputs only have a limited impact on the PO forecast performance, since the Solar power output method corrects for systematic errors in the irradiance forecast. The relative root mean square error Photovoltaics (RMSE) for PO is in the range of 10.3%e14.0% of the nameplate capacity, and the forecast skill ranges from Numerical weather prediction 13% to 23% over a persistence model.
    [Show full text]
  • SMART GRIDS and RENEWABLES: a Guide for Effective Deployment TABLE of CONTENTS
    IRENA International Renewable Energy Agency SMART GRIDS AND RENEWABLES A Guide for Effective Deployment Working Paper Working November 2013 Copyright © IRENA 2013 Unless otherwise indicated, material in this publication may be used freely, shared or reprinted, so long as IRENA is acknowledged as the source. About IRENA The International Renewable Energy Agency (IRENA) is an intergovernmental organisation that supports countries in their transition to a sustainable energy future, and serves as the principal platform for international cooperation, a centre of excel- lence, and a repository of policy, technology, resource and financial knowledge on renewable energy. IRENA promotes the widespread adoption and sustainable use of all forms of renewable energy, including bioenergy, geothermal, hydropower, ocean, solar and wind energy in the pursuit of sustainable development, energy access, energy security and low-carbon eco- nomic growth and prosperity. www.irena.org Acknowledgements The work for the preparation of this paper was led by Paul Komor and Anderson Hoke, University of Colorado, Boulder, Colo- rado, USA. The paper benefitted from very valuable comments from Rainer Bacher (Bacher Energie, Switzerland), Melissa Chan (Navigant Consulting, USA), Kazuyuki Takada (New Energy and Industrial Technology Development Organization, Japan), Matt Wakefield (EPRI, USA). Authors: Ruud Kempener (IRENA), Paul Komor and Anderson Hoke (University of Colorado) For further information or to provide feedback, please contact: Ruud Kempener, IRENA Innovation and Technology Centre. E-mail: [email protected] or [email protected]. Disclaimer The designations employed and the presentation of materials herein do not imply the e xpression of any opinion whatsoever on the part of the International Renewable Energy Agency concerning the legal status of any country, territory, city or area, or concerning their authorities or the delimitation of their frontiers or boundaries.
    [Show full text]
  • A Suite of Metrics for Assessing the Performance of Solar Power Forecasting
    Available online at www.sciencedirect.com ScienceDirect Solar Energy 111 (2015) 157–175 www.elsevier.com/locate/solener A suite of metrics for assessing the performance of solar power forecasting Jie Zhang a, Anthony Florita a, Bri-Mathias Hodge a,⇑, Siyuan Lu b, Hendrik F. Hamann b, Venkat Banunarayanan c, Anna M. Brockway c a National Renewable Energy Laboratory, Golden, CO 80401, USA b IBM TJ Watson Research Center, Yorktown Heights, NY 10598, USA c U.S. Department of Energy, Washington, D.C. 20585, USA Received 8 August 2014; received in revised form 2 October 2014; accepted 12 October 2014 Communicated by: Associate Editor Jan Kleissl Abstract Forecasting solar energy generation is a challenging task because of the variety of solar power systems and weather regimes encoun- tered. Inaccurate forecasts can result in substantial economic losses and power system reliability issues. One of the key challenges is the unavailability of a consistent and robust set of metrics to measure the accuracy of a solar forecast. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, and applications) that were developed as part of the U.S. Department of Energy SunShot Initiative’s efforts to improve the accuracy of solar forecasting. In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design-of-experiments methodology in conjunction with response surface, sen- sitivity analysis, and nonparametric statistical testing methods. The three types of forecasting improvements are (i) uniform forecasting improvements when there is not a ramp, (ii) ramp forecasting magnitude improvements, and (iii) ramp forecasting threshold changes.
    [Show full text]
  • Day-Ahead Hourly Forecasting of Power Generation From
    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Senior Member, IEEE, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri, Debora Mucci Abstract—The ability to accurately forecast power generation • Monitoring: mismatches between power forecasts and from renewable sources is nowadays recognised as a fundamental the actually generated power may be also used by energy skill to improve the operation of power systems. Despite the producers to monitor the plant operation, to evaluate the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, natural degradation of the efficiency of the plant due and infer the impact of single components in providing accu- to the aging of some components (see [4]) or for early rate predictions. In this paper we extensively compare simple detection of incipient faults. forecasting methodologies with more sophisticated ones over 32 For the previous reasons, the topic of renewable energy photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather conditions forecasting has been also object of some recent textbooks like and weather forecasts on the prediction of PV power generation. [5] and [6] that provide overviews of the state-of-the-art of the most recent technologies and applications of renewable Index Terms—PV plants; Machine Learning algorithms; power energy forecasting. In this context, the objective of this paper generation forecasts. is to compare different methodologies to predict day-ahead hourly power generation from PV power plants.
    [Show full text]