The future of forecasting for renewable energy

Forecasting for wind and solar renewable energy is becoming more important as the amount of energy generated from these sources increases. Forecast skill is improving, but so too is the way forecasts are being used. In this paper, we present a brief overview of the state‐of‐the‐art of forecasting wind and solar energy. We describe approaches in statistical and physical modeling for time scales from minutes to days ahead, for both deterministic and probabilistic forecasting. Our focus changes then to consider the future of forecasting for renewable energy. We discuss recent advances which show potential for great improvement in forecast skill. Beyond the forecast itself, we consider new products which will be required to aid decision making subject to risk constraints. Future forecast products will need to include probabilistic information, but deliver it in a way tailored to the end user and their specific decision making problems. Businesses operating in this sector may see a change in business models as more people compete in this space, with different combinations of skills, data and modeling being required for different products. The transaction of data itself may change with the adoption of blockchain technology, which could allow providers and end users to interact in a trusted, yet decentralized way. Finally, we discuss new industry requirements and challenges for scenarios with high amounts of renewable energy. New forecasting products have the potential to model the impact of renewables on the power system, and aid dispatch tools in guaranteeing system security. This article is categorized under: Energy Infrastructure > Systems and Infrastructure Wind Power > Systems and Infrastructure Photovoltaics > Systems and Infrastructure

[1]  H. Madsen,et al.  From probabilistic forecasts to statistical scenarios of short-term wind power production , 2009 .

[2]  Simon T. K. Lang,et al.  Stochastic representations of model uncertainties at ECMWF: state of the art and future vision , 2017 .

[3]  Li Fang,et al.  An Assessment of the Impact of Land Thermal Infrared Observation on Regional Weather Forecasts Using Two Different Data Assimilation Approaches , 2018, Remote. Sens..

[4]  J. Kleissl,et al.  Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed , 2011 .

[5]  Munther A. Dahleh,et al.  A Marketplace for Data: An Algorithmic Solution , 2018, EC.

[6]  Henrik Madsen,et al.  Space-Time Trajectories of Wind Power Generation: Parametrized Precision Matrices Under a Gaussian Copula Approach , 2015 .

[7]  Henrik Madsen,et al.  Space-time trajectories of wind power generation: Parameterized precision matrices under a Gaussian copula approach - DTU Orbit (10/11/2017) , 2014 .

[8]  Mark Landry,et al.  Probabilistic gradient boosting machines for GEFCom2014 wind forecasting , 2016 .

[9]  Henrik Madsen,et al.  Ensemble-based Probabilistic Forecasting at Horns Rev , 2009 .

[10]  Shaobu Wang,et al.  Capturing Dynamics in the Power Grid: Formulation of Dynamic State Estimation through Data Assimilation , 2014 .

[11]  B. F. Sule,et al.  Stochastic dynamic programming models for reservoir operation optimization , 1984 .

[12]  Kostas Philippopoulos,et al.  Improved very short‐term spatio‐temporal wind forecasting using atmospheric regimes , 2019 .

[13]  A. Besir Kurtulmus,et al.  Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain , 2018, ArXiv.

[14]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[15]  Martin Kühn,et al.  On the Use of Dual-Doppler Radar Measurements for Very Short-Term Wind Power Forecasts , 2018, Remote. Sens..

[16]  Silas C. Michaelides,et al.  Mesoscale Resolution Radar Data Assimilation Experiments with the Harmonie Model , 2018, Remote. Sens..

[17]  Robin Girard,et al.  Forecasting ramps of wind power production with numerical weather prediction ensembles , 2013 .

[18]  Qing Wang,et al.  Assessing the mechanisms governing the daytime evolution of marine stratocumulus using large‐eddy simulation , 2019, Quarterly Journal of the Royal Meteorological Society.

[19]  Cristobal Gallego-Castillo,et al.  A review on the recent history of wind power ramp forecasting , 2015 .

[20]  Göran Lindström,et al.  Interpretation of runoff processes in hydrological modelling—experience from the HBV approach , 2015 .

[21]  Tapan Kumar Saha,et al.  Minimum Synchronous Inertia Requirement of Renewable Power Systems , 2018, IEEE Transactions on Power Systems.

[22]  V. Miranda,et al.  Wind power forecasting uncertainty and unit commitment , 2011 .

[23]  Craig Gentry,et al.  Computing arbitrary functions of encrypted data , 2010, CACM.

[24]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[25]  Manuel Matos,et al.  Multi-period flexibility forecast for low voltage prosumers , 2017 .

[26]  Nils Gustafsson,et al.  Survey of data assimilation methods for convective‐scale numerical weather prediction at operational centres , 2018 .

[27]  J. Wyngaard Toward Numerical Modeling in the “Terra Incognita” , 2004 .

[28]  Emma M. Stewart,et al.  Estimating Behind-the-meter Solar Generation with Existing Measurement Infrastructure: Poster Abstract , 2016, BuildSys@SenSys.

[29]  Julie K. Lundquist,et al.  Incorporation of the Rotor-Equivalent Wind Speed into the Weather Research and Forecasting Model’s Wind Farm Parameterization , 2019, Monthly Weather Review.

[30]  S. E. Haupt,et al.  WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction , 2016 .

[31]  Clifford F. Mass,et al.  Impacts of Assimilating Smartphone Pressure Observations on Forecast Skill during Two Case Studies in the Pacific Northwest , 2018, Weather and Forecasting.

[32]  Malte Siefert,et al.  Use of Forecast Uncertainties in the Power Sector: State-of-the-Art of Business Practices , 2016 .

[33]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[34]  Kayo Ide,et al.  Progress in Forecast Skill at Three Leading Global Operational NWP Centers during 2015–17 as Seen in Summary Assessment Metrics (SAMs) , 2018 .

[35]  S. Haan,et al.  High‐resolution wind and temperature observations from aircraft tracked by Mode‐S air traffic control radar , 2011 .

[36]  Philippe Blanc,et al.  Short-term solar power forecasting based on satellite images , 2017 .

[37]  James W. Taylor,et al.  Probabilistic forecasting of wind power ramp events using autoregressive logit models , 2017, Eur. J. Oper. Res..

[38]  Henrik Madsen,et al.  Automatic Classification of Offshore Wind Regimes With Weather Radar Observations , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  Brett Candy,et al.  Assessment and Assimilation of FY-3 Humidity Sounders and Imager in the UK Met Office Global Model , 2018, Advances in Atmospheric Sciences.

[40]  S. E. Haupt,et al.  A regime-dependent artificial neural network technique for short-range solar irradiance forecasting , 2016 .

[41]  M A Matos,et al.  Setting the Operating Reserve Using Probabilistic Wind Power Forecasts , 2011, IEEE Transactions on Power Systems.

[42]  Hoay Beng Gooi,et al.  Polyhedral Predictive Regions for Power System Applications , 2018, IEEE Transactions on Power Systems.

[43]  Vijay Gupta,et al.  Distributed Energy Management for Networked Microgrids Using Online ADMM With Regret , 2018, IEEE Transactions on Smart Grid.

[44]  A. H. Murphy,et al.  Time Series Models to Simulate and Forecast Wind Speed and Wind Power , 1984 .

[45]  R. Beare,et al.  The Onset of Resolved Boundary-Layer Turbulence at Grey-Zone Resolutions , 2019, Boundary-Layer Meteorology.

[46]  Frank S. Marzano,et al.  Ingestion of Sentinel-Derived Remote Sensing Products in Numerical Weather Prediction Models: First Results of the ESA Steam Project , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[47]  Henryk Modzelewski,et al.  On Producing Reliable and Affordable Numerical Weather Forecasts on Public Cloud-Computing Infrastructure , 2019, Journal of Atmospheric and Oceanic Technology.

[48]  S. E. Haupt,et al.  Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation , 2017 .

[49]  David M. Siuta,et al.  Benefits of a multimodel ensemble for hub‐height wind prediction in mountainous terrain , 2018 .

[50]  Bernard Multon,et al.  Energy storage sizing for wind power: impact of the autocorrelation of day‐ahead forecast errors , 2013 .

[51]  R. Bannister A review of operational methods of variational and ensemble‐variational data assimilation , 2017 .

[52]  Henrik Madsen,et al.  Weather radars - the new eyes for offshore wind farms? , 2014 .

[53]  Anton Kaifel,et al.  Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36 , 2019, Energies.

[54]  Larry K. Berg,et al.  The Wind Forecast Improvement Project (WFIP): A Public–Private Partnership Addressing Wind Energy Forecast Needs , 2015 .

[55]  Martin Kühn,et al.  Very short-term forecast of near-coastal flow using scanning lidars , 2018, Wind Energy Science.

[56]  David McMillan,et al.  Leveraging Turbine-Level Data for Improved Probabilistic Wind Power Forecasting , 2020, IEEE Transactions on Sustainable Energy.

[57]  Anthony Papavasiliou,et al.  Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network , 2013, Oper. Res..

[58]  Michael Devetsikiotis,et al.  Blockchains and Smart Contracts for the Internet of Things , 2016, IEEE Access.

[59]  A. V. Frolov,et al.  Can a quantum computer be applied for numerical weather prediction? , 2017, Russian Meteorology and Hydrology.

[60]  Fei Teng,et al.  Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications , 2019, Applied Energy.

[61]  Ricardo J. Bessa,et al.  Improving Renewable Energy Forecasting With a Grid of Numerical Weather Predictions , 2017, IEEE Transactions on Sustainable Energy.

[62]  David D. Turner,et al.  Shallow Cumulus in WRF Parameterizations Evaluated against LASSO Large-Eddy Simulations , 2018, Monthly Weather Review.

[63]  Ning Zhang,et al.  Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV , 2018, IEEE Transactions on Power Systems.

[64]  Jimy Dudhia,et al.  Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model , 2012 .

[65]  Pierre Pinson,et al.  Wind Energy: Forecasting Challenges for Its Operational Management , 2013, 1312.6471.

[66]  Pierre Pinson,et al.  Statistical post‐processing of turbulence‐resolving weather forecasts for offshore wind power forecasting , 2020, Wind Energy.

[67]  Bri-Mathias Hodge,et al.  Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry , 2017 .

[68]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[69]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[70]  Stefan Wilbert,et al.  Short-term forecasting based on all-sky cameras , 2017 .

[71]  In-Jin Choi,et al.  Impacts of a newly-developed aerosol climatology on numerical weather prediction using a global atmospheric forecasting model , 2019, Atmospheric Environment.

[72]  Vladimiro Miranda,et al.  Wind Power Trading Under Uncertainty in LMP Markets , 2012, IEEE Transactions on Power Systems.

[73]  Ashish Sharma,et al.  Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework , 2014 .

[74]  Bri-Mathias Hodge,et al.  Recent Trends in Variable Generation Forecasting and Its Value to the Power System , 2015, IEEE Transactions on Sustainable Energy.

[75]  Kurt Schaldemose Hansen,et al.  Mesoscale to microscale wind farm flow modeling and evaluation , 2017 .

[76]  Andreas Svensson,et al.  Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes , 2018 .

[77]  L. L. Wendell,et al.  Report from a working group meeting on wind forecasts for WECS operation , 1978 .

[78]  Edmund Keith Stone A comparison of Mode‐S Enhanced Surveillance observations with other in situ aircraft observations , 2018 .

[79]  Thomas Auligné Multivariate Minimum Residual Method for Cloud Retrieval. Part II: Real Observations Experiments , 2014 .

[80]  Humphrey W. Lean,et al.  Statistics of convective cloud turbulence from a comprehensive turbulence retrieval method for radar observations , 2019, Quarterly Journal of the Royal Meteorological Society.

[81]  Xiaochen Zhang,et al.  A Data-Driven Approach for Detection and Estimation of Residential PV Installations , 2016, IEEE Transactions on Smart Grid.

[82]  Iain MacGill,et al.  Using nacelle-based wind speed observations to improve power curve modeling for wind power forecasting , 2012 .

[83]  Robert P. Broadwater,et al.  Current status and future advances for wind speed and power forecasting , 2014 .

[84]  Pierre Pinson,et al.  Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting , 2019, International Journal of Forecasting.

[85]  Hoay Beng Gooi,et al.  Ellipsoidal Prediction Regions for Multivariate Uncertainty Characterization , 2017, IEEE Transactions on Power Systems.

[86]  Joseph W. Boardman,et al.  The Moon Mineralogy Mapper (M3) imaging spectrometer for lunar science: Instrument description, calibration, on‐orbit measurements, science data calibration and on‐orbit validation , 2011 .

[87]  S. E. Haupt,et al.  A Wind Power Forecasting System to Optimize Grid Integration , 2012, IEEE Transactions on Sustainable Energy.

[88]  Henrik Madsen,et al.  Spatio-Temporal Forecasting by Coupled Stochastic Differential Equations: Applications to Solar Power , 2017, 1706.04394.

[89]  J.A. Ferreira,et al.  Wind turbines emulating inertia and supporting primary frequency control , 2006, IEEE Transactions on Power Systems.

[90]  Julia Gottschall,et al.  Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity , 2017 .

[91]  Andrea Baronchelli,et al.  Evolutionary dynamics of the cryptocurrency market , 2017, Royal Society Open Science.

[92]  Stefano Alessandrini,et al.  An Analog Technique to Improve Storm Wind Speed Prediction Using a Dual NWP Model Approach , 2018, Monthly Weather Review.

[93]  Ricardo J. Bessa,et al.  LASSO vector autoregression structures for very short-term wind power forecasting , 2017 .

[94]  Shian-Jiann Lin,et al.  What Is the Predictability Limit of Midlatitude Weather? , 2019, Journal of the Atmospheric Sciences.

[95]  João Peças Lopes,et al.  Probabilistic evaluation of reserve requirements of generating systems with renewable power sources: The Portuguese and Spanish cases , 2009 .

[96]  Pengwei Du,et al.  Forecast System Inertia Condition and Its Impact to Integrate More Renewables , 2018, IEEE Transactions on Smart Grid.

[97]  Peder Bacher Short-term Solar Power Forecasting , 2008 .

[98]  Saifur Rahman,et al.  Forecasting sub-hourly solar irradiance for prediction of photovoltaic output , 1987 .

[99]  Andrew C. Lorenc,et al.  A comparison of hybrid variational data assimilation methods for global NWP , 2018, Quarterly Journal of the Royal Meteorological Society.

[100]  Hugo Morais,et al.  Active Distribution Grid Management Based on Robust AC Optimal Power Flow , 2018, IEEE Transactions on Smart Grid.

[101]  Carlos F.M. Coimbra,et al.  Real-time forecasting of solar irradiance ramps with smart image processing , 2015 .