Sensor network based PV power nowcasting with spatio-temporal preselection for grid-friendly control

© 2019 Elsevier Ltd The increasing penetration of photovoltaics (PV) systems introduces more uncertainties to the power system, and has drawn serious concern for maintaining the grid stability. Consequently, the PV power grid-friendly control (GFC) has been imposed by utilities to provide additional flexibilities for power system operations. Conventional GFC strategies show limitations to estimate real-time maximum available power, especially when fast moving clouds occur. In this regards, the spatio-temporal (ST) PV nowcasting using a sensor network provides a remedy to the above issue. However, current ST nowcasting methods suffer from the problems such as predictor mis-selection, inconsistent nowcasting, and poor model adaptability, which still hinder their practical use for GFC. In this paper, a novel ST PV power nowcasting method with predictor preselection is presented, which can be used for GFC. The proposed method enables a fast and precise predictor preselection in different scenarios, and provides consistent PV nowcasts with cloud information interpolated. The effectiveness of the proposed nowcasting method is evaluated in a real sensor network. The experimental results reveal that the proposed method has strong robustness in case of various weather conditions, with fewer training data used. Compared with the conventional methods, the proposed method shows an average nRMSE and nPMAE improvements over 13.5% and 41.3% respectively in the cloudy days. A practice of integrating the proposed nowcasting method to GFC operation is also demonstrated. The results show that the proposed method is promising to improve the performance of GFC

Pattern Classification and PSO Optimal Weights Based Sky Images Cloud Motion Speed Calculation Method for Solar PV Power Forecasting

The motion of cloud over a photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and particle swarm optimization optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. First, we use a k-means clustering method and texture features based on a gray-level co-occurrence matrix to classify the clouds. Second, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute are used for simulation; the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Deep learning-based hybrid short-term solar forecast using sky images and meteorological data

The global growth of solar power generation is rapid, yet the complex nature of cloud movement introduces significant uncertainty to short-term solar irradiance, posing challenges for intelligent power systems. Accurate short-term solar irradiance and photovoltaic power generation predictions under cloudy skies are critical for sub-hourly electricity markets. Ground-based image (GSI) analysis using convolutional neural network (CNN) algorithms has emerged as a promising method due to advancements in machine vision models based on deep learning networks. In this work, a novel deep network, ”ViT-E,” based on an attention mechanism Transformer architecture for short-term solar irradiance forecasting has been proposed. This innovative model enables cross-modality data parsing by establishing mapping relationships within GSI and between GSI, meteorological data, historical irradiation, clear sky irradiation, and solar angles. The feasibility of the ViT-E network was assessed the Folsom dataset from California, USA. Quantitative analysis showed that the ViT-E network achieved RMSE values of 81.45 W/m2 , 98.68 W/m2 , and 104.91 W/m2 for 2, 6, and 10-minute forecasts, respectively, outperforming the persistence model by 4.87%, 16.06%, and 19.09% and displaying performance comparable to CNN-based models. Qualitative analysis revealed that the ViT-E network successfully predicted 20.21%, 33.26%, and 36.87% of solar slope events at 2, 6, and 10 minutes in advance, respectively, significantly surpassing the persistence model and currently prevalent CNN-based model by 9.43%, 3.91%, and -0.55% for 2, 6, and 10-minute forecasts, respectively. Transfer learning experiments were conducted to test the ViT-E model’s generalisation under different climatic conditions and its performance on smaller datasets. We discovered that the weights learned from the three-year Folsom dataset in the United States could be transferred to a half-year local dataset in Nottingham, UK. Training with a dataset one-fifth the size of the original dataset achieved baseline accuracy standards and reduced training time by 80.2%. Additionally, using a dataset equivalent to only 4.5% of the original size yielded a model with less than 2% accuracy below the baseline. These findings validated the generalisation and robustness of the model’s trained weights. Finally, the ViT-E model architecture and hyperparameters were optimised and searched. Our investigation revealed that directly applying migrated deep vision models leads to redundancy in solar forecasting. We identified the best hyperparameters for ViT-E through manual hyperparameter space exploration. As a result, the model’s computational efficiency improved by 60%, and prediction performance increased by 2.7%

Random Finite Sets Based Very Short-Term Solar Power Forecasting Through Cloud Tracking

Tracking clouds with a sky camera within a very short horizon below thirty seconds can be a solution to mitigate the effects of sunlight disruptions. A Probability Hypothesis Density (PHD) filter and a Cardinalised Probability Hypothesis Density (CPHD) filter were used on a set of pre-processed sky images. Both filters have been compared with the state-of-the-art methods for performance. It was found that both filters are suitable to perform very-short term irradiance forecasting

Deep learning-based hybrid short-term solar forecast using sky images and meteorological data

The global growth of solar power generation is rapid, yet the complex nature of cloud movement introduces significant uncertainty to short-term solar irradiance, posing challenges for intelligent power systems. Accurate short-term solar irradiance and photovoltaic power generation predictions under cloudy skies are critical for sub-hourly electricity markets. Ground-based image (GSI) analysis using convolutional neural network (CNN) algorithms has emerged as a promising method due to advancements in machine vision models based on deep learning networks. In this work, a novel deep network, ”ViT-E,” based on an attention mechanism Transformer architecture for short-term solar irradiance forecasting has been proposed. This innovative model enables cross-modality data parsing by establishing mapping relationships within GSI and between GSI, meteorological data, historical irradiation, clear sky irradiation, and solar angles. The feasibility of the ViT-E network was assessed the Folsom dataset from California, USA. Quantitative analysis showed that the ViT-E network achieved RMSE values of 81.45 W/m2 , 98.68 W/m2 , and 104.91 W/m2 for 2, 6, and 10-minute forecasts, respectively, outperforming the persistence model by 4.87%, 16.06%, and 19.09% and displaying performance comparable to CNN-based models. Qualitative analysis revealed that the ViT-E network successfully predicted 20.21%, 33.26%, and 36.87% of solar slope events at 2, 6, and 10 minutes in advance, respectively, significantly surpassing the persistence model and currently prevalent CNN-based model by 9.43%, 3.91%, and -0.55% for 2, 6, and 10-minute forecasts, respectively. Transfer learning experiments were conducted to test the ViT-E model’s generalisation under different climatic conditions and its performance on smaller datasets. We discovered that the weights learned from the three-year Folsom dataset in the United States could be transferred to a half-year local dataset in Nottingham, UK. Training with a dataset one-fifth the size of the original dataset achieved baseline accuracy standards and reduced training time by 80.2%. Additionally, using a dataset equivalent to only 4.5% of the original size yielded a model with less than 2% accuracy below the baseline. These findings validated the generalisation and robustness of the model’s trained weights. Finally, the ViT-E model architecture and hyperparameters were optimised and searched. Our investigation revealed that directly applying migrated deep vision models leads to redundancy in solar forecasting. We identified the best hyperparameters for ViT-E through manual hyperparameter space exploration. As a result, the model’s computational efficiency improved by 60%, and prediction performance increased by 2.7%

SOLAR IRRADIANCE FORECASTING AND MODELING FOR SMART CITIES WITH HIGH PHOTOVOLTAIC PENETRATION

Ph.DDOCTOR OF PHILOSOPH