scholarly journals Forecasting Photovoltaic Power Generation Using Satellite Images

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6603
Author(s):  
Dukhwan Yu ◽  
Seowoo Lee ◽  
Sangwon Lee ◽  
Wonik Choi ◽  
Ling Liu
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Power Plants ◽  
Satellite Images ◽  
Cloud Amount ◽  
Weather Forecast ◽  
Percentage Error ◽  
Virtual Power Plants ◽  
Forecast Data ◽  
Pv Power Generation

As the relative importance of renewable energy in electric power systems increases, the prediction of photovoltaic (PV) power generation has become a crucial technology, for improving stability in the operation of next-generation power systems, such as microgrid and virtual power plants (VPP). In order to improve the accuracy of PV power generation forecasting, a fair amount of research has been applied to weather forecast data (to a learning process). Despite these efforts, the problems of forecasting PV power generation remains challenging since existing methods show limited accuracy due to inappropriate cloud amount forecast data, which are strongly correlated with PV power generation. To address this problem, we propose a PV power forecasting model, including a cloud amount forecasting network trained with satellite images. In addition, our proposed model adopts convolutional self-attention to effectively capture historical features, and thus acquire helpful information from weather forecasts. To show the efficacy of the proposed cloud amount forecast network, we conduct extensive experiments on PV power generation forecasting with and without the cloud amount forecast network. The experimental results show that the Mean Absolute Percentage Error (MAPE) of our proposed prediction model, combined with the cloud amount forecast network, are reduced by 22.5% compared to the model without the cloud amount forecast network.

scholarly journals Very-Short-Term Power Prediction for PV Power Plants Using a Simple and Effective RCC-LSTM Model Based on Short Term Multivariate Historical Datasets

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 289 ◽  
Author(s):  
Biaowei Chen ◽  
Peijie Lin ◽  
Yunfeng Lai ◽  
Shuying Cheng ◽  
Zhicong Chen ◽  
...  
Keyword(s):  
Power Generation ◽  
Power Plants ◽  
Meteorological Factors ◽  
Training Dataset ◽  
Percentage Error ◽  
Short Term ◽  
Similar Time ◽  
Proposed Model ◽  
Time Periods ◽  
Pv Power Generation

Improving the accuracy of very-short-term (VST) photovoltaic (PV) power generation prediction can effectively enhance the quality of operational scheduling of PV power plants, and provide a reference for PV maintenance and emergency response. In this paper, the effects of different meteorological factors on PV power generation as well as the degree of impact at different time periods are analyzed. Secondly, according to the characteristics of radiation coordinate, a simple radiation classification coordinate (RCC) method is proposed to classify and select similar time periods. Based on the characteristics of PV power time-series, the selected similar time period dataset (include power output and multivariate meteorological factors data) is reconstructed as the training dataset. Then, the long short-term memory (LSTM) recurrent neural network is applied as the learning network of the proposed model. The proposed model is tested on two independent PV systems from the Desert Knowledge Australia Solar Centre (DKASC) PV data. The proposed model achieving mean absolute percentage error of 2.74–7.25%, and according to four error metrics, the results show that the robustness and accuracy of the RCC-LSTM model are better than the other four comparison models.

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

scholarly journals A Study on the Improvement of Efficiency by Detection Solar Module Faults in Deteriorated Photovoltaic Power Plants

2021 ◽  
Vol 11 (2) ◽  
pp. 727 ◽  
Author(s):  
Myeong-Hwan Hwang ◽  
Young-Gon Kim ◽  
Hae-Sol Lee ◽  
Young-Dae Kim ◽  
Hyun-Rok Cha
Keyword(s):  
Power Generation ◽  
Power Plants ◽  
Detection System ◽  
Failure Detection ◽  
Solar Module ◽  
Thermal Images ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Manufacturing Technologies ◽  
Pv Power Generation

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects that form in PV power generation panels and propose a method for enhancing the productivity and efficiency of PV power stations by determining the defects of aging PV modules based on their temperature, power output, and panel images. The method proposed in the paper allows the replacement of individual panels that are experiencing a malfunction, thereby reducing the output loss of solar power generation plants. The aim is to develop a method that enables users to immediately check the type of failures among the six failure types that frequently occur in aging PV panels—namely, hotspot, panel breakage, connector breakage, busbar breakage, panel cell overheating, and diode failure—based on thermal images by using the failure detection system. By comparing the data acquired in the study with the thermal images of a PV power station, efficiency is increased by detecting solar module faults in deteriorated photovoltaic power plants.

Earthquake Experience Data Point to Benefit of Diverse Backup Power

2014 ◽  
Author(s):  
David J. Calhoun ◽  
Mark A. Gake
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Nuclear Power ◽  
Power Plants ◽  
Electrical Power ◽  
Electrical Power Systems ◽  
Common Mode ◽  
Failure Risk ◽  
Diesel Generators ◽  
The Individual

Operating nuclear power plants typically have backup electrical power supplied by diesel generators. Although backup power systems are designed with redundant trains, each capable of supplying the power requirements for safe shutdown equipment, there is a common-mode seismic failure risk inherent in these customary backup power arrangements. In an earthquake, multiple equipment trains with similar, if not identical, components located side-by-side are exposed to inertial forces that are essentially identical. In addition, because of their similar subcomponent configurations, seismic fragilities are approximately equal. In that case, the probability of multiple backup power system failures during an earthquake is likely to be dependent on, and nearly the same as, the individual seismic failure probability of each equipment train. Post-earthquake inspections at conventional multiple unit power stations over the last 40 years identified this common-mode seismic failure risk long before the tsunami-related common-mode failures of diesel generators at Fukushima Daiichi in March 2011. Experience data from post-earthquake inspections also indicate that failure probabilities of diverse sets of power generation equipment are independent and inherently less susceptible to common-mode failures. This paper demonstrates that employing diverse backup power designs will deliver quantifiable improvements in electrical system availability following an earthquake. These improvements are illustrated from available literature of post-earthquake inspection reports, along with other firsthand observations. A case study of the seismic performance of similarly configured electrical power generation systems is compared to the performance of diverse sets of electrical power systems. Seismic probabilistic risk analyses for several system configurations are presented to show the benefit of improved post-earthquake availability that results from designing new backup power systems with greater diversity.

scholarly journals Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4490 ◽  
Author(s):  
Jaeik Jeong ◽  
Hongseok Kim
Keyword(s):  
Neural Network ◽  
New York ◽  
Convolutional Neural Network ◽  
Smart Grids ◽  
Power Plants ◽  
Temporal Correlation ◽  
Space Time ◽  
Percentage Error ◽  
Virtual Power Plants ◽  
Pv Generation

The accurate forecasting of photovoltaic (PV) power generation is critical for smart grids and the renewable energy market. In this paper, we propose a novel short-term PV forecasting technique called the space-time convolutional neural network (STCNN) that exploits the location information of multiple PV sites and historical PV generation data. The proposed structure is simple but effective for multi-site PV forecasting. In doing this, we propose a greedy adjoining algorithm to preprocess PV data into a space-time matrix that captures spatio-temporal correlation, which is learned by a convolutional neural network. Extensive experiments with multi-site PV generation from three typical states in the US (California, New York, and Alabama) show that the proposed STCNN outperforms the conventional methods by up to 33% and achieves fairly accurate PV forecasting, e.g., 4.6–5.3% of the mean absolute percentage error for a 6 h forecasting horizon. We also investigate the effect of PV sites aggregation for virtual power plants where errors from some sites can be compensated by other sites. The proposed STCNN shows substantial error reduction by up to 40% when multiple PV sites are aggregated.

scholarly journals Integration Capability Evaluation of Wind and Photovoltaic Generation in Power Systems Based on Temporal and Spatial Correlations

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 171 ◽  
Author(s):  
Hua Zhou ◽  
Huahua Wu ◽  
Chengjin Ye ◽  
Shijie Xiao ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Power Systems ◽  
Wind Power ◽  
Energy Generation ◽  
Copula Model ◽  
Spatial Correlations ◽  
Temporal And Spatial ◽  
Renewable Energy Generation ◽  
Pv Power Generation

With the rapid growth of renewable energy generation, it has become essential to give a comprehensive evaluation of renewable energy integration capability in power systems to reduce renewable generation curtailment. Existing research has not considered the correlations between wind power and photovoltaic (PV) power. In this paper, temporal and spatial correlations among different renewable generations are utilized to evaluate the integration capability of power systems based on the copula model. Firstly, the temporal and spatial correlation between wind and PV power generation is analyzed. Secondly, the temporal and spatial distribution model of both wind and PV power generation output is formulated based on the copula model. Thirdly, aggregated generation output scenarios of wind and PV power are generated. Fourthly, wind and PV power scenarios are utilized in an optimal power flow calculation model of power systems. Lastly, the integration capacity of wind power and PV power is shown to be able to be evaluated by satisfying the reliability of power system operation. Simulation results of a modified IEEE RTS-24 bus system indicate that the integration capability of renewable energy generation in power systems can be comprehensively evaluated based on the temporal and spatial correlations of renewable energy generation.

scholarly journals Survey Analysis of Solar Power Generation Forecasting

2021 ◽  
Vol 309 ◽  
pp. 01039
Author(s):  
Deekshitha Erlapally ◽  
K. Anuradha ◽  
G. Karuna ◽  
V. Srilakshmi ◽  
K. Adilakshmi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Power Generation ◽  
Deep Learning ◽  
Power Plants ◽  
Solar Power ◽  
Weather Condition ◽  
Series Data ◽  
Mean Bias Error ◽  
Percentage Error

Solar power is the conversion of sunlight into electricity using solar photovoltaic cells as a source of energy. There are various applications for solar power; here is information on PV cell generation. We seek to understand the behavior of solar power plants through the data generated by the photovoltaic modules and the power generation in different weather conditions in India. The goal of this survey is to give a thorough assessment and study of machine learning, deep learning and artificial intelligence. Artificial intelligence (AI) models as well as information preprocessing techniques, parameter selection algorithms and predictive performance evaluations are used in machine learning and deep learning models for predicting renewable energies. But in case of time series data we can predict only the errors using a linear regression model, we can also calculate things like root mean square error (RMSE), mean absolute error (MSE), mean bias error (MBE) and mean absolute percentage error (MAPE). By the analysis of weather condition also we can predict the consumption of current by solar for every 15 minutes, 1day, and 1week or even for 1 month and find the accuracy.

scholarly journals Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation

2020 ◽  
Vol 185 ◽  
pp. 01052
Author(s):  
Runjie Shen ◽  
Ruimin Xing ◽  
Yiying Wang ◽  
Danqiong Hua ◽  
Ming Ma
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Random Forest ◽  
Prediction Model ◽  
Power Plants ◽  
Time Series Prediction ◽  
Cloud Amount ◽  
Continuous Model ◽  
Short Term ◽  
Photovoltaic Power

As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.

Short-Term Generation Schedule Optimisation for Combined Heat and Power

2011 ◽  
Author(s):  
E. E. B. Gomes ◽  
P. Pilidis ◽  
A. L. Polyzakis
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Electricity Market ◽  
Power Plants ◽  
Hybrid Genetic Algorithm ◽  
Combined Heat And Power ◽  
End Users ◽  
Technical Literature ◽  
Priority List ◽  
Distributed Generators

In the last decades one of the most difficult problems in the electricity market has been how to dispatch and manage the electricity in power generation plants. Despite of all the benefits of distributed poly-generation and combined heat and power systems, their penetration in the power market worldwide is quite modest and one of the barriers against their increasing participation is the high fees for back-up supplies, which is one of the problems addressed in this investigation. This paper introduces a pool of distributed generation units (named nerve-centre) able to economically optimise the generation schedule of gas turbine power plants and end-users interconnected through a mini-grid. A hybrid genetic algorithm adapted priority list was developed to solve the multi unit generation schedule optimisation problem. The algorithm developed in this study leads the optimisation mechanism to a faster convergence and a very low risk of non-convergence to the optimal result. Despite the power generation optimisation studies reported in the technical literature, none of them has been modelled for such a pool of distributed generators trading electricity in the competitive market. This investigation shows that the proposed nerve-centre concept can result in significant savings to generators/end-users.

scholarly journals Mixed Integer Quadratic Programming Based Scheduling Methods for Day-Ahead Bidding and Intra-Day Operation of Virtual Power Plant

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1410 ◽  
Author(s):  
Rakkyung Ko ◽  
Daeyoung Kang ◽  
Sung-Kwan Joo
Keyword(s):  
Quadratic Programming ◽  
Power Systems ◽  
Power Plants ◽  
Power Grids ◽  
Mixed Integer ◽  
Virtual Power ◽  
Integer Quadratic Programming ◽  
Virtual Power Plants ◽  
Mixed Integer Quadratic Programming ◽  
The Impact

As distributed energy resources (DERs) proliferate power systems, power grids face new challenges stemming from the variability and uncertainty of DERs. To address these problems, virtual power plants (VPPs) are established to aggregate DERs and manage them as single dispatchable and reliable resources. VPPs can participate in the day-ahead (DA) market and therefore require a bidding method that maximizes profits. It is also important to minimize the variability of VPP output during intra-day (ID) operations. This paper presents mixed integer quadratic programming-based scheduling methods for both DA market bidding and ID operation of VPPs, thus serving as a complete scheme for bidding-operation scheduling. Hourly bids are determined based on VPP revenue in the DA market bidding step, and the schedule of DERs is revised in the ID operation to minimize the impact of forecasting errors and maximize the incentives, thus reducing the variability and uncertainty of VPP output. The simulation results verify the effectiveness of the proposed methods through a comparison of daily revenue.

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