scholarly journals A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4256
Author(s):  
Taeyoung Kim ◽  
Jinho Kim
Keyword(s):  
Power Systems ◽  
Absolute Error ◽  
Support Vector ◽  
Capacity Estimation ◽  
Pv Systems ◽  
Estimation Capacity ◽  
Load Pattern ◽  
Pv Generation ◽  
Net Load

Rooftop photovoltaic (PV) systems are usually behind the meter and invisible to utilities and retailers and, thus, their power generation is not monitored. If a number of rooftop PV systems are installed, it transforms the net load pattern in power systems. Moreover, not only generation but also PV capacity information is invisible due to unauthorized PV installations, causing inaccuracies in regional PV generation forecasting. This study proposes a regional rooftop PV generation forecasting methodology by adding unauthorized PV capacity estimation. PV capacity estimation consists of two steps: detection of unauthorized PV generation and estimation capacity of detected PV. Finally, regional rooftop PV generation is predicted by considering unauthorized PV capacity through the support vector regression (SVR) and upscaling method. The results from a case study show that compared with estimation without unauthorized PV capacity, the proposed methodology reduces the normalized root mean square error (nRMSE) by 5.41% and the normalized mean absolute error (nMAE) by 2.95%, It can be concluded that regional rooftop PV generation forecasting accuracy is improved.

A Multi-Dimensional Method for Nodal Load Forecasting

2014 ◽  
Vol 1070-1072 ◽  
pp. 708-717
Author(s):  
Zhi Yuan Pan ◽  
Chao Nan Liu ◽  
Jing Wang ◽  
Yong Wang
Keyword(s):  
Energy Flow ◽  
Load Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Load Pattern ◽  
And Control

The intelligent dispatch and control of future smart grid demands grasping of any nodal load pattern in the general great grid, therefore to realize the load forecasting of any nodal load is quite important. To solve this problem, focusing on overcoming the weakness of isolated nodal load forecasting and based on the correlation analysis, this paper proposes a multi-dimensional nodal load forecast system and corresponding method for effective prediction of any nodal load of the grid. This system includes topology partitioning of the grid energy flow according to layers and regions, basic forecasting unit composed of each layer’s total amount of load and its nodal loads, and combination forecasting for any node. The forecasting method is based on techniques including the multi-output least square support vector machine, Kalman filtering and the approximate optimal prediction. A case study shows that the multi-dimensional nodal load forecasting model helps to improve the forecasting accuracy, and has practical prospects.

Reliability modeling and performance evaluation of solar photovoltaic system using Gumbel–Hougaard family copula

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anas Sani Maihulla ◽  
Ibrahim Yusuf ◽  
Muhammad Salihu Isa
Keyword(s):  
Power Systems ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Content Type ◽  
Reliability Indices ◽  
Pv Systems ◽  
Sensitivity Tests ◽  
Pv Generation ◽  
The Impact

PurposeSolar photovoltaic (PV) is commonly used as a renewable energy source to provide electrical power to customers. This research establishes a method for testing the performance reliability of large grid-connected PV power systems. Solar PV can turn unrestricted amounts of sunlight into energy without releasing carbon dioxide or other contaminants into the atmosphere. Because of these advantages, large-scale solar PV generation has been increasingly incorporated into power grids to meet energy demand. The capability of the installation and the position of the PV are the most important considerations for a utility company when installing solar PV generation in their system. Because of the unpredictability of sunlight, the amount of solar penetration in a device is generally restricted by reliability constraints. PV power systems are made up of five PV modules, with three of them needing to be operational at the same time. In other words, three out of five. Then there is a charge controller and a battery bank with three batteries, two of which must be consecutively be in operation. i.e. two out of three. Inverter and two distributors, all of which were involved at the same time. i.e. two out of two. In order to evaluate real-world grid-connected PV networks, state enumeration is used. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems. Every module's test results on a realistic 10-kW PV system. To see how the model works in practice, many scenarios are considered. Tables and graphs are used to show the findings.Design/methodology/approachThe system of first-order differential equations is formulated and solved using Laplace transforms using regenerative point techniques. Several scenarios were examined to determine the impact of the model under consideration. The calculations were done with Maple 13 software.FindingsThe authors get availability, reliability, mean time to failure (MTTF), MTTF sensitivity and gain feature in this research. To measure the reliability of PV systems, a collection of reliability indices has been created. Furthermore, detailed sensitivity tests are carried out to examine the effect of various factors on the efficiency of PV power systems.Originality/valueThis is the authors' original copy of the paper. Because of the importance of the study, the references are well-cited. Nothing from any previously published articles or textbooks has been withdrawn.

scholarly journals A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1750 ◽  
Author(s):  
Fei Wang ◽  
Kangping Li ◽  
Xinkang Wang ◽  
Lihui Jiang ◽  
Jianguo Ren ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
System Capacity ◽  
Support Vector ◽  
Capacity Estimation ◽  
Pv System ◽  
Load Curve ◽  
Net Load

scholarly journals Forecasting Error Modelling for Improving PV Generation Prediction

2019 ◽  
Vol 2 (1) ◽  
pp. 27-36
Author(s):  
Happy Aprillia ◽  
Hong-Tzer Yang
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Weather Condition ◽  
Absolute Error ◽  
Density Estimator ◽  
Forecasting Error ◽  
Uncertainty Model ◽  
Pv Generation

Accurate forecasting of Photovoltaic (PV) generation output is important in operation of high PV-penetrated power systems. In this paper, an adaptive uncertainty modelling method for forecasting error is proposed to improve the prediction accuracy of PV generation. The proposed method models the uncertainty in forecast data using Kernel Density Estimator and guarantee the provision of accurate expected value. Neural Network model is then constructed by the developed uncertainty model to forecast the PV output. The actual confidence level is traced within the day and injected as an input to the Neural Network model by observing the Mean Absolute Prediction Error (MAPE) and Unscaled Mean Bounded Relative Absolute Error (UMBRAE). The proposed method is tested with various significant changes of weather condition and proved to have promising performance on PV generation forecasting. Thus, the developed adaptive uncertainty model can be further used in power system planning that have high-penetration energy sources with stochastic behavior.

scholarly journals Forecasting Daily River Flow Using an Artificial Flora–Support Vector Machine Hybrid Modeling Approach (Case Study: Karkheh Catchment, Iran)

2020 ◽  
Vol 13 ◽  
pp. 117862212096965
Author(s):  
Reza Dehghani ◽  
Hassan Torabi Poudeh ◽  
Hojatolah Younesi ◽  
Babak Shahinejad
Keyword(s):  
Support Vector Machine ◽  
River Flow ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Flow Modeling ◽  
Machine Model ◽  
Daily Discharge

In this study, the hybrid support vector machine–artificial flora algorithm method was developed and the obtained results were compared with those of the support vector–wave vector machine model. Karkheh catchment area was considered as a case study to estimate the flow rate of rivers using the daily discharge statistics taken from hydrometric stations located upstream of the dam in the statistical period of 2008 to 2018. Necessary criteria including coefficient of determination, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe coefficient were used to evaluate and compare the models. The results illustrated that the combined structures provided acceptable results in terms of river flow modeling. Also, a comparison of the models based on the evaluation criteria and Taylor’s diagram demonstrated that the proposed hybrid method with the correlation coefficient of R2 = 0.924 to 0.974, RMSE = 0.022 to 0.066 m3/s, MAE = 0.011 to 0.034 m3/s, and Nash-Sutcliffe (NS) coefficient = 0.947 to 0.986 outperformed other methods in terms of estimating the daily flow rates of rivers.

scholarly journals Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4733
Author(s):  
Andi A. H. Lateko ◽  
Hong-Tzer Yang ◽  
Chao-Ming Huang ◽  
Happy Aprillia ◽  
Che-Yuan Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Weather Data ◽  
Support Vector ◽  
Short Term ◽  
Learner Model ◽  
Average Maximum ◽  
Power Forecasting

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

Data Analytics to Help Improve Hydraulic Fracture Geometry Prediction Using Support Vector Regression: Case Study

2020 ◽  
Author(s):  
Avinash Wesley ◽  
Bharat Mantha ◽  
Ajay Rajeev ◽  
Aimee Taylor ◽  
Mohit Dholi ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Hydraulic Fracture ◽  
Data Analytics ◽  
Support Vector ◽  
Fracture Geometry

Reduction of the Low Voltage Substation Constraints by Inserting Photovoltaic Systems in Underserved Areas

2019 ◽  
Vol 12 (2) ◽  
pp. 105-112
Author(s):  
Benbouza Naima ◽  
Benfarhi Louiza ◽  
Azoui Boubekeur
Keyword(s):  
Low Voltage ◽  
Voltage Drop ◽  
Electrical Energy ◽  
Utilization Rate ◽  
Photovoltaic Systems ◽  
Voltage Distribution ◽  
Medium Voltage ◽  
Pv Systems ◽  
Transformer Substation ◽  
Load Pattern

Background: The improvement of the voltage in power lines and the respect of the low voltage distribution transformer substations constraints (Transformer utilization rate and Voltage drop) are possible by several means: reinforcement of conductor sections, installation of new MV / LV substations (Medium Voltage (MV), Low Voltage (LV)), etc. Methods: Connection of mini-photovoltaic systems (PV) to the network, or to consumers in underserved areas, is a well-adopted solution to solve the problem of voltage drop and lighten the substation transformer, and at the same time provide clean electrical energy. PV systems can therefore contribute to this solution since they produce energy at the deficit site. Results: This paper presents the improvement of transformer substation constraints, supplying an end of low voltage electrical line, by inserting photovoltaic systems at underserved subscribers. Conclusion: This study is applied to a typical load pattern, specified to the consumers region.

scholarly journals State-Space Model of Quasi-Z-Source Inverter-PV Systems for Transient Dynamics Studies and Network Stability Assessment

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4150
Author(s):  
Lluís Monjo ◽  
Luis Sainz ◽  
Juan José Mesas ◽  
Joaquín Pedra
Keyword(s):  
Power Systems ◽  
State Space ◽  
State Space Model ◽  
Pv System ◽  
Pv Systems ◽  
Renewable Power ◽  
Ac Networks ◽  
Transient Interactions ◽  
The Stability ◽  
Averaged Model

Photovoltaic (PV) power systems are increasingly being used as renewable power generation sources. Quasi-Z-source inverters (qZSI) are a recent, high-potential technology that can be used to integrate PV power systems into AC networks. Simultaneously, concerns regarding the stability of PV power systems are increasing. Converters reduce the damping of grid-connected converter systems, leading to instability. Several studies have analyzed the stability and dynamics of qZSI, although the characterization of qZSI-PV system dynamics in order to study transient interactions and stability has not yet been properly completed. This paper contributes a small-signal, state-space-averaged model of qZSI-PV systems in order to study these issues. The model is also applied to investigate the stability of PV power systems by analyzing the influence of system parameters. Moreover, solutions to mitigate the instabilities are proposed and the stability is verified using PSCAD time domain simulations.

scholarly journals Taking into Consideration the Inclusion of Wind Generation in Hybrid Microgrids: A Methodology and a Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4082
Author(s):  
Luis Arribas ◽  
Natalia Bitenc ◽  
Andreo Benech
Keyword(s):  
Power Systems ◽  
Design Process ◽  
Research Community ◽  
Wind Generation ◽  
Generation System ◽  
Economic Barriers ◽  
Wind Generation System

During the last decades, there has been great interest in the research community with respect to PV-Wind systems but figures show that, in practice, only PV-Diesel Power Systems (PVDPS) are being implemented. There are some barriers for the inclusion of wind generation in hybrid microgrids and some of them are economic barriers while others are technical barriers. This paper is focused on some of the identified technical barriers and presents a methodology to facilitate the inclusion of wind generation system in the design process in an affordable manner. An example of the application of this methodology and its results is shown through a case study. The case study is an existing PVDPS where there is an interest to incorporate wind generation in order to cope with a foreseen increase in the demand.

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