Regional PV power prediction for improved grid integration

Lorenz, Elke; Scheidsteger, Thomas; Hurka, Johannes; Heinemann, Detlev; Kurz, Christian

doi:10.1002/pip.1033

Cited by 224 publications

(111 citation statements)

References 14 publications

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“…For instance, Perez et al (2007) proposed an empirical solar radiation forecast model relating sky-cover predictions from the National Digital Forecast Database to the clear-sky index of global irradiance. The PV power forecasting approaches presented in Lorenz et al (2011) andPelland, Galanis, andKallos (2013) involve empirical models to derive the planeof-array (POA) irradiance as input for PV simulation models. Another common example of empirically-based post-processing is the transposition of the predicted GHI to POA irradiance in PV simulations.…”

Section: Post-processing To Derive Additional Parametersmentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

2017

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Designing, financing, and operating successful solar heating, concentrating solar power, and photovoltaic systems requires reliable information about the solar resource available and its variability over time. In the past, seasonal and daily variability has been studied and understood; however, with new solar technologies becoming more important in energy supply grids, small time-scale effects are critical to successful deployment of these important low carbon technologies. A vital part of the bankability of solar projects is to understand the variability of the solar resource so that supply and storage technologies can be optimized.This handbook is the result of 10 years of international collaboration carried out by experts from the International Energy Agency's (IEA's) Solar Heating and Cooling (SHC), Solar PACES, and Photovoltaic Power Systems Technology Collaboration Programmes. Under IEA SHC Task 46: Solar Resource Assessment and Forecasting, experts from 11 countries produced information products and best practices on solar energy resources that will greatly benefit project developers and system operators as well as assist policymakers in advancing renewable energy programs worldwide.Meteorologists, mathematicians, solar technology specialists, and other key solar resource experts from around the world joined forces to further our understanding of the sun's temporal and spatial variability through benchmarking satellite-derived solar resource data and solar forecasts, developing best practices for measuring the solar resource, and conducting research to improve satellite-based algorithms. The results of IEA SHC Task 46 are useful to a wide range of users of solar heating and cooling, photovoltaics, and concentrating solar power systems and of building developers and owners as well as anyone else who needs to understand and predict sunlight for agricultural or other purposes.The earlier edition of the handbook, which was published in 2015, is used worldwide as a reference for each stage of a solar energy project. Since that time, there has been substantial growth in the interest in high-quality "bankable" solar resource data. This revision adds significant new methods so it will be even more useful.This publication is a summary that details the fundamentals of solar resources as well as captures the state of the art. For those wanting more depth, it also provides the references where more detailed information can be found.I would like to acknowledge the leadership of the National Renewable Energy Laboratory and express appreciation to the U.S. Department of Energy for producing the handbook and incorporating results from IEA SHC Task 46. Ken Guthrie Chair, IEA Solar Heating and Cooling Technology Collaboration Programme June 2017This report is available at no cost from the National Renewable Energy Laboratory at www.nrel.gov/publications. Preface Dave Renné Dave Renné Renewables, LLCAs the world looks for carbon-free sources to meet final energy demand associated with heat, electrical power, and transport, ...

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Section: Post-processing To Derive Additional Parametersmentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

2017

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“…Let P α k and P β k are the α and β percentiles (α > β) for the PV power time series X for the k-length future time interval [t + 1, t + k]. The interval formed by them corresponds to the interval where |α − β| × 100% of the values of X are expected to lie [6]. In this work, we construct two types of 2D-interval forecasts: by using the 90th and 10th percentiles, as well as 75th and 25th percentiles.…”

Section: Problem Definitionmentioning

confidence: 99%

Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power

Rana

Koprinska

2016

Energies

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Solar energy generated from PhotoVoltaic (PV) systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties for the large-scale integration of PV power in the electricity grid and requires accurate forecasting of the electricity generated by PV systems. In this paper we consider 2D-interval forecasts, where the goal is to predict summary statistics for the distribution of the PV power values in a future time interval. 2D-interval forecasts have been recently introduced, and they are more suitable than point forecasts for applications where the predicted variable has a high variability. We propose a method called NNE2D that combines variable selection based on mutual information and an ensemble of neural networks, to compute 2D-interval forecasts, where the two interval boundaries are expressed in terms of percentiles. NNE2D was evaluated for univariate prediction of Australian solar PV power data for two years. The results show that it is a promising method, outperforming persistence baselines and other methods used for comparison in terms of accuracy and coverage probability.

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“…The mean of the error factor increases linearly up to a maximum intraday mean of 20 % [11]. The standard deviation is set to allow for a maximum overestimation at any given time of 40 % and a maximum underestimation of 30 % [12]. Errors for all past time steps are set to zero, and a noise factor is added [13].…”

Section: E Pv Forecastmentioning

confidence: 99%

PV integration in Low-Voltage feeders with Demand Response

Borsche

Andersson

2015

2015 IEEE Eindhoven PowerTech

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Increased distributed Photo-Voltaic (PV) generation leads to an increase in voltages and unwarranted backflows into the grid. This paper investigates Demand Response (DR) with Electric Water Heaters (EWHs) as a way to increase the PV hosting capacity of a low-voltage feeder. A control strategy relying only on power measurements at the transformer is proposed. Flexible loads are optimally dispatched considering energy acquisition costs, a PV shedding penalty, and power and energy constraints. Furthermore, grouping of loads and PV plants is investigated, and switching penalties are used to reduce the unnecessary switching of loads. It is shown that this strategy can substantially increase the PV hosting capacity of a Low-Voltage (LV) feeder, even when only basic controllability is available. However, grid integration of massive amounts of distributed, stochastic generation has introduced many technical challenges for power system operators. In the case of PV, one key issue that arose in Europe is voltage constraint violations in distribution networks. Approximately 80 % of PV power is fed into the 400 V Low-Voltage (LV) grid [4]. When generation in-feed exceeds local demand, voltage levels rise, causing damage to consumer and grid equipment if no action is taken. Safety concerns are also of importance as protection equipment now need to take into account bidirectional power flows.Staying within the allowed voltage band is a greater challenge in LV systems due to a comparatively smaller short circuit impedance and a larger R ⁄X ratio. This often causes voltage violations before line limits are reached [5]. With ambitious renewable energy targets to meet in the next few decades, many solutions have been proposed to increase the PV hosting capacity of LV grids. Seven key ways are outlined below. The first two methods aim to increase the system's ability to transfer power to higher voltage levels, while the remaining methods attempt to handle the surplus power locally [6]:

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Regional PV power prediction for improved grid integration

Cited by 224 publications

References 14 publications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Second Edition

Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power

PV integration in Low-Voltage feeders with Demand Response

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