Modeling the impact of wind and solar power forecasting errors on intraday electricity prices

2017 ◽  
Author(s):  
Florian Ziel
Keyword(s):  
Solar Power ◽  
Electricity Prices ◽  
Solar Power Forecasting ◽  
The Impact ◽  
Forecasting Errors ◽  
Power Forecasting

scholarly journals Adaptive Solar Power Forecasting based on Machine Learning Methods

2018 ◽  
Vol 8 (11) ◽  
pp. 2224 ◽  
Author(s):  
Yu Wang ◽  
Hualei Zou ◽  
Xin Chen ◽  
Fanghua Zhang ◽  
Jie Chen
Keyword(s):  
Power Systems ◽  
Solar Power ◽  
Machine Learning Methods ◽  
Load Demand ◽  
Forecasting Method ◽  
Combining Data ◽  
Data Updating ◽  
Solar Power Forecasting ◽  
Forecasting Errors ◽  
Power Forecasting

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

scholarly journals Development of a sky imaging system for short-term solar power forecasting

2014 ◽  
Vol 7 (5) ◽  
pp. 4859-4907 ◽  
Author(s):  
B. Urquhart ◽  
B. Kurtz ◽  
E. Dahlin ◽  
M. Ghonima ◽  
J. E. Shields ◽  
...  
Keyword(s):  
Solar Power ◽  
Dynamic Range ◽  
Imaging System ◽  
High Dynamic Range ◽  
The United States ◽  
Camera System ◽  
High Dynamic ◽  
Solar Power Forecasting ◽  
The Impact ◽  
Power Forecasting

Abstract. To facilitate the development of solar power forecasting algorithms based on ground-based visible wavelength remote sensing, we have developed a high dynamic range (HDR) camera system capable of providing hemispherical sky imagery from the circumsolar region to the horizon at a high spatial, temporal, and radiometric resolution. The University of California, San Diego Sky Imager (USI) captures multispectral, 16 bit, HDR images as fast as every 1.3 s. This article discusses the system design and operation in detail, provides a characterization of the system dark response and photoresponse linearity, and presents a method to evaluate noise in high dynamic range imagery. The system is shown to have radiometrically linear response to within 5% in a designated operating region of the sensor. Noise for HDR imagery is shown to be very close to the fundamental shot noise limit. The complication of directly imaging the sun and the impact on solar power forecasting is also discussed. The USI has performed reliably in a hot, dry environment, a tropical coastal location, several temperate coastal locations, and in the great plains of the United States.

scholarly journals Development of a sky imaging system for short-term solar power forecasting

2015 ◽  
Vol 8 (2) ◽  
pp. 875-890 ◽  
Author(s):  
B. Urquhart ◽  
B. Kurtz ◽  
E. Dahlin ◽  
M. Ghonima ◽  
J. E. Shields ◽  
...  
Keyword(s):  
Solar Power ◽  
Dynamic Range ◽  
Imaging System ◽  
High Dynamic Range ◽  
The United States ◽  
Camera System ◽  
High Dynamic ◽  
Solar Power Forecasting ◽  
The Impact ◽  
Power Forecasting

Abstract. To facilitate the development of solar power forecasting algorithms based on ground-based visible wavelength remote sensing, we have developed a high dynamic range (HDR) camera system capable of providing hemispherical sky imagery from the circumsolar region to the horizon at a high spatial, temporal, and radiometric resolution. The University of California, San Diego Sky Imager (USI) captures multispectral, 16 bit, HDR images as fast as every 1.3 s. This article discusses the system design and operation in detail, provides a characterization of the system dark response and photoresponse linearity, and presents a method to evaluate noise in high dynamic range imagery. The system is shown to have a radiometrically linear response to within 5% in a designated operating region of the sensor. Noise for HDR imagery is shown to be very close to the fundamental shot noise limit. The complication of directly imaging the sun and the impact on solar power forecasting is also discussed. The USI has performed reliably in a hot, dry environment, a tropical coastal location, several temperate coastal locations, and in the great plains of the United States.

MPF-Net: A computational multi-regional solar power forecasting framework

2021 ◽  
Vol 151 ◽  
pp. 111559
Author(s):  
Faiza Mehmood ◽  
Muhammad Usman Ghani ◽  
Muhammad Nabeel Asim ◽  
Rehab Shahzadi ◽  
Aamir Mehmood ◽  
...  
Keyword(s):  
Solar Power ◽  
Solar Power Forecasting ◽  
Power Forecasting

scholarly journals Building the Sun4Cast System: Improvements in Solar Power Forecasting

2018 ◽  
Vol 99 (1) ◽  
pp. 121-136 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Branko Kosović ◽  
Tara Jensen ◽  
Jeffrey K. Lazo ◽  
Jared A. Lee ◽  
...  
Keyword(s):  
Decision Making ◽  
Value Chain ◽  
Solar Irradiance ◽  
Collaborative Design ◽  
Solar Power ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Solar Array ◽  
Solar Power Forecasting ◽  
Power Forecasting

Abstract As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

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