Accommodation Capacity Analysis of the Large Scale PV Power Generation Access to Regional Power Grid

In this paper, the factors are analyzed,which affect the accommodation capacity of large scale PV power generation access to the grid . The analysis method mainly consider the peak load adjustment ability of the whole power system. Then a real case based on provincial grid is calculated and analyzed. The results show that: As an important technical style ,the generation and acommodation of the large scale photovoltaic power generation are influenced by the light resource, load level, adjustment ability, transmission channel. The planning of large-scale photovoltaic power plants must keep pace with the conventional power and grid planning.

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

Applied Sciences ◽

10.3390/app11020727 ◽

2021 ◽

Vol 11 (2) ◽

pp. 727 ◽

Cited By ~ 1

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.

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Large-scale penetration impact of photovoltaic power generation system on utility power plants

2009 Transmission & Distribution Conference & Exposition: Asia and Pacific ◽

10.1109/td-asia.2009.5356994 ◽

2009 ◽

Cited By ~ 1

Author(s):

T. Kato ◽

Y. Suzuoki

Keyword(s):

Power Generation ◽

Power Plants ◽

Large Scale ◽

Generation System ◽

Photovoltaic Power ◽

Power Generation System

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Forecast of photovoltaic power generation based on DBSCAN

E3S Web of Conferences ◽

10.1051/e3sconf/202123602016 ◽

2021 ◽

Vol 236 ◽

pp. 02016

Author(s):

Jiaying Zhang ◽

Yingfan Zhang

Keyword(s):

Power Generation ◽

Prediction Model ◽

Power Plants ◽

Large Scale ◽

Clustering Algorithm ◽

Renewable Energy Sources ◽

Stable Operation ◽

Power Prediction ◽

Photovoltaic Power ◽

Bidirectional Lstm

The power output of the photovoltaic power generation has prominent intermittent fluctuation characteristics. Large-scale photovoltaic power generation access will bring a specific impact on the safe and stable operation of the power grid. With the increase in the proportion of renewable energy sources such as wind power and photovoltaics, the phenomenon of wind abandonment and light abandonment has further increased. The photovoltaic power generation prediction is one of the critical technologies to solve this problem. It is of outstanding academic and application value to research photovoltaic power generation prediction methods and systems. Therefore, accurately carrying out the power forecast of photovoltaic power plants has become a research hot point in recent years. It is favored by scholars at home and abroad. First, this paper builds a simulation model of the photovoltaic cell based on known theoretical knowledge. Then it uses the density clustering algorithm (DBSCAN) in the clustering algorithm and classifies the original data. Finally, according to a series of problems such as the slow modeling speed of photovoltaic short-term power prediction, the bidirectional LSTM photovoltaic power prediction model, and CNN-GRU photovoltaic power prediction model based on clustering algorithm are proposed. After comparing the two models, it is concluded that the bidirectional LSTM prediction model is more accurate.

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Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation

Energies ◽

10.3390/en12010109 ◽

2018 ◽

Vol 12 (1) ◽

pp. 109 ◽

Cited By ~ 4

Author(s):

Jingjing Tu ◽

Yonghai Xu ◽

Zhongdong Yin

Keyword(s):

Power Generation ◽

Distribution System ◽

Large Scale ◽

Voltage Stability ◽

Distribution Network ◽

Peak Load ◽

Data Driven ◽

Support Vector ◽

Machine Method ◽

Photovoltaic Power

For the integration of distributed generations such as large-scale wind and photovoltaic power generation, the characteristics of the distribution network are fundamentally changed. The intermittence, variability, and uncertainty of wind and photovoltaic power generation make the adjustment of the network peak load and the smooth control of power become the key issues of the distribution network to accept various types of distributed power. This paper uses data-driven thinking to describe the uncertainty of scenery output, and introduces it into the power flow calculation of distribution network with multi-class DG, improving the processing ability of data, so as to better predict DG output. For the problem of network stability and operational control complexity caused by DG access, using KELM algorithm to simplify the complexity of the model and improve the speed and accuracy. By training and testing the KELM model, various DG configuration schemes that satisfy the minimum network loss and constraints are given, and the voltage stability evaluation index is introduced to evaluate the results. The general recommendation for DG configuration is obtained. That is, DG is more suitable for accessing the lower point of the network voltage or the end of the network. By configuring the appropriate capacity, it can reduce the network loss, improve the network voltage stability, and the quality of the power supply. Finally, the IEEE33&69-bus radial distribution system is used to simulate, and the results are compared with the existing particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM). The feasibility and effectiveness of the proposed model and method are verified.

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A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Energies ◽

10.3390/en12203817 ◽

2019 ◽

Vol 12 (20) ◽

pp. 3817 ◽

Cited By ~ 2

Author(s):

Fu ◽

Yang ◽

Yao ◽

Jiao ◽

Zhu

Keyword(s):

Power Generation ◽

Hierarchical Clustering ◽

Power Plants ◽

Large Scale ◽

Prediction Method ◽

Entire Region ◽

Elman Neural Network ◽

Large Power ◽

Power Dispatching ◽

Pv Power Generation

Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

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