Probability characterization of solar farm power output and impact on system reliability

Abdullah Alamri; Maad Alowaifeer; A. P.Sakis Meliopoulos

doi:10.1109/PMAPS.2018.8440299

Probability characterization of solar farm power output and impact on system reliability

Abdullah Alamri
, Maad Alowaifeer
, A. P.Sakis Meliopoulos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

The deployment of renewable-resources, such as solar, is expected to rise significantly within the coming few years. An issue with such sources is their variability. Hence, modeling and studying their probabilistic behavior become a crucial matter. This paper presents three different methods for computing the generated power probability distribution function (pdf) of a photovoltaic (solar) generation farm (SF) containing large number of solar cell generators (SCGs). The methods are: (1) analytical method, (2) non-sequential Monte Carlo simulation, and (3) sequential Monte Carlo simulation. All three methods are utilized to calculate the pdf of a sample SF with 10 SCGs. Historical solar radiation data is utilized to perform the study. Further, force outage rates (FOR) of components are incorporated in the process of computing the SF generated power pdf. The results from each of the three methods are compared. While the analytical method is considered the benchmark method, all methods yield comparable results. The usefulness of the computed pdf is demonstrated by integrating them into a probabilistic production costing (PPC) model for assessing the reliability of a system comprising one or more SFs.

Original language	English
Title of host publication	2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538635964
DOIs	https://doi.org/10.1109/PMAPS.2018.8440299
State	Published - 17 Aug 2018
Externally published	Yes

Publication series

Name	2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Expected unserved energy
Loss of load probability
Monte Carlo simulation
Probabilistic analytical method
Probabilistic production costing
Solar cell generation
Solnr farm

ASJC Scopus subject areas

Computer Networks and Communications
Statistics, Probability and Uncertainty
Energy Engineering and Power Technology
Statistics and Probability

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/PMAPS.2018.8440299

Cite this

Alamri, A., Alowaifeer, M., & Meliopoulos, A. P. S. (2018). Probability characterization of solar farm power output and impact on system reliability. In 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings Article 8440299 (2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PMAPS.2018.8440299

Alamri, Abdullah ; Alowaifeer, Maad ; Meliopoulos, A. P.Sakis. / Probability characterization of solar farm power output and impact on system reliability. 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings).

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title = "Probability characterization of solar farm power output and impact on system reliability",

abstract = "The deployment of renewable-resources, such as solar, is expected to rise significantly within the coming few years. An issue with such sources is their variability. Hence, modeling and studying their probabilistic behavior become a crucial matter. This paper presents three different methods for computing the generated power probability distribution function (pdf) of a photovoltaic (solar) generation farm (SF) containing large number of solar cell generators (SCGs). The methods are: (1) analytical method, (2) non-sequential Monte Carlo simulation, and (3) sequential Monte Carlo simulation. All three methods are utilized to calculate the pdf of a sample SF with 10 SCGs. Historical solar radiation data is utilized to perform the study. Further, force outage rates (FOR) of components are incorporated in the process of computing the SF generated power pdf. The results from each of the three methods are compared. While the analytical method is considered the benchmark method, all methods yield comparable results. The usefulness of the computed pdf is demonstrated by integrating them into a probabilistic production costing (PPC) model for assessing the reliability of a system comprising one or more SFs.",

keywords = "Expected unserved energy, Loss of load probability, Monte Carlo simulation, Probabilistic analytical method, Probabilistic production costing, Solar cell generation, Solnr farm",

author = "Abdullah Alamri and Maad Alowaifeer and Meliopoulos, \{A. P.Sakis\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.",

year = "2018",

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doi = "10.1109/PMAPS.2018.8440299",

language = "English",

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Alamri, A, Alowaifeer, M & Meliopoulos, APS 2018, Probability characterization of solar farm power output and impact on system reliability. in 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings., 8440299, 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/PMAPS.2018.8440299

Probability characterization of solar farm power output and impact on system reliability. / Alamri, Abdullah; Alowaifeer, Maad; Meliopoulos, A. P.Sakis.
2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8440299 (2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Probability characterization of solar farm power output and impact on system reliability

AU - Alamri, Abdullah

AU - Alowaifeer, Maad

AU - Meliopoulos, A. P.Sakis

PY - 2018/8/17

Y1 - 2018/8/17

N2 - The deployment of renewable-resources, such as solar, is expected to rise significantly within the coming few years. An issue with such sources is their variability. Hence, modeling and studying their probabilistic behavior become a crucial matter. This paper presents three different methods for computing the generated power probability distribution function (pdf) of a photovoltaic (solar) generation farm (SF) containing large number of solar cell generators (SCGs). The methods are: (1) analytical method, (2) non-sequential Monte Carlo simulation, and (3) sequential Monte Carlo simulation. All three methods are utilized to calculate the pdf of a sample SF with 10 SCGs. Historical solar radiation data is utilized to perform the study. Further, force outage rates (FOR) of components are incorporated in the process of computing the SF generated power pdf. The results from each of the three methods are compared. While the analytical method is considered the benchmark method, all methods yield comparable results. The usefulness of the computed pdf is demonstrated by integrating them into a probabilistic production costing (PPC) model for assessing the reliability of a system comprising one or more SFs.

AB - The deployment of renewable-resources, such as solar, is expected to rise significantly within the coming few years. An issue with such sources is their variability. Hence, modeling and studying their probabilistic behavior become a crucial matter. This paper presents three different methods for computing the generated power probability distribution function (pdf) of a photovoltaic (solar) generation farm (SF) containing large number of solar cell generators (SCGs). The methods are: (1) analytical method, (2) non-sequential Monte Carlo simulation, and (3) sequential Monte Carlo simulation. All three methods are utilized to calculate the pdf of a sample SF with 10 SCGs. Historical solar radiation data is utilized to perform the study. Further, force outage rates (FOR) of components are incorporated in the process of computing the SF generated power pdf. The results from each of the three methods are compared. While the analytical method is considered the benchmark method, all methods yield comparable results. The usefulness of the computed pdf is demonstrated by integrating them into a probabilistic production costing (PPC) model for assessing the reliability of a system comprising one or more SFs.

KW - Expected unserved energy

KW - Loss of load probability

KW - Monte Carlo simulation

KW - Probabilistic analytical method

KW - Probabilistic production costing

KW - Solar cell generation

KW - Solnr farm

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DO - 10.1109/PMAPS.2018.8440299

M3 - Conference contribution

AN - SCOPUS:85053113756

SN - 9781538635964

T3 - 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings

BT - 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Alamri A, Alowaifeer M, Meliopoulos APS. Probability characterization of solar farm power output and impact on system reliability. In 2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8440299. (2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings). doi: 10.1109/PMAPS.2018.8440299

Probability characterization of solar farm power output and impact on system reliability

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this