PV Grid Integration – System Management Issues and Utility Concerns

Hudson, Raymond; Heilscher, Gerd

doi:10.1016/j.egypro.2012.07.012

Cited by 38 publications

(13 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The common acquisition granularity varies from 1 second to 60 minutes. [8][9][10] If the acquisition granularity of the data is too large, part of the information will be omitted, affecting the extraction of the effective information and even leading to the emergence of reverse conclusions; if the acquisition granularity is too close, it is necessary to have more adequate storage space and effective management system, resulting in the waste of resources and the increase of costs. If the sampling span decreases, the amount of the overall data will be linearly reduced, resulting in the overall sample number insufficient and the increase of the uncertainty; if the sampling span increases, the amount of the overall data growths, it will produce the information redundancy and increase the cost of the data management.…”

Section: Discussionmentioning

confidence: 99%

“…At present, there are no uniform standards for the acquisition granularity and sampling span of PV output power in the planning and designing of energy storage stations and lack of theoretical basis at home and abroad. The common acquisition granularity varies from 1 second to 60 minutes . If the acquisition granularity of the data is too large, part of the information will be omitted, affecting the extraction of the effective information and even leading to the emergence of reverse conclusions; if the acquisition granularity is too close, it is necessary to have more adequate storage space and effective management system, resulting in the waste of resources and the increase of costs.…”

Section: Introductionmentioning

confidence: 99%

“…Without losing the characteristics of the system, the study on the output trajectory of the intermittent energy is the foundation to accurately grasp the system characteristics and optimal use. Hudson and Heilscher believed that the acquisition granularity of 1 second could face the power fluctuations caused by complex changes of the clouds, but the theoretical basis was insufficient. Lin et al used the acquisition granularity of the data with 15 minutes to realize the prediction of the PV output power according to the probability statistics theory, and the prediction errors caused by the fluctuations of the PV output power were compensated by energy storage systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Han

Liu

et al. 2017

Int J Energy Res

View full text Add to dashboard Cite

Summary The acquisition granularity (time feature quantity) and sampling span (spatial feature quantity) of the data are the feature factors to analyze the active power of renewable energy power stations. According to the time and space characteristics of photovoltaic (PV) power stations, the acquisition granularity and sampling span calibration methods of PV output power based on data mining technology are proposed this paper. The initial range of the acquisition granularity is determined by analyzing the maximum‐order difference components of the PV output power. Through deeply mining the continuous change state of the PV output power, an acquisition granularity calibration method of the PV output power based on the multiobjective optimization model is proposed from time characteristics. The particle swarm optimization algorithm is used to solve the model to obtain the optimal the acquisition granularity of the PV power station. Through the analysis of the sample information entropy change trend of the PV output power, a sampling span calibration method of the PV output power based on the information entropy theory is proposed from space characteristics. The sensitivity analysis of the acquisition granularity and sampling span of the data to the capacity of energy storage systems is realized by the smooth control of the PV output power using first‐order low filters. The simulation tests of the annual history operating data at a PV power station with the installed capacity of 40 MW in China verify the validity of the provided methods. The simulation results show when the acquisition granularity takes 60 seconds and the sampling span takes 33 days, it can satisfy the accuracy of the required data of energy storage systems to realize the smooth control of the PV output power.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Han

Liu

et al. 2017

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…The unpredictability of intermittent RE energy sources creates reliability challenges for utilities aiming at balancing power supplies and demands across centralized grid networks. Thus, balancing more of the variable input of renewables become a major challenge for TNSP, and prediction (forecasting) methods and tools are required in order to facilitate the integration of high penetration of variable RE input [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machines for Solar Photovoltaic Power Predictions

et al. 2018

View full text Add to dashboard Cite

The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task.

show abstract

“…Some materials exhibit a property known as the photoelectric effect that causes them to absorb photons of light and release electrons. When these free electrons are captured, an electric current results that can be used as electricity [1,2,4,5].…”

Section: Introductionmentioning

confidence: 99%

Soft Computing Techniques FUZZY and ANN based MPPT for Grid Tied PV

Singh¹,

Shahid²

2016

IJERT

View full text Add to dashboard Cite

Photovoltaic system (PV) are affected by fast changing irradiation and due these characteristics, there is an operating point in which the maximum available power of PV is obtained. Artificial Neural Network(ANN) and Fuzzy logic controller (FLC) is the artificial intelligent based maximum power point tracking (MPPT) method for obtaining the maximum power point (MPP). This paper presents a detailed comparative study between ANN Controller and FLC in DC-DC boost converter. Both method is implemented using ANN and Fuzzy logic toolbox of MATLAB and detailed model and results is carried out in SIMULINK. The both proposed method is able to track the maximum power point in minimum time with small oscillations and the highest system efficiency. This investigation provides valuable results for all users who want to implement the reliable ANN and fuzzy logic subset for their works.

show abstract

PV Grid Integration – System Management Issues and Utility Concerns

Cited by 38 publications

References 1 publication

Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Sensitivity analysis of PV output power to capacity configuration of energy storage systems from time and space characteristics

Extreme Learning Machines for Solar Photovoltaic Power Predictions

Soft Computing Techniques FUZZY and ANN based MPPT for Grid Tied PV

Contact Info

Product

Resources

About