Grey-box identification for photovoltaic power systems via particle-swarm algorithm

Naji Al-Messabi, Cindy Goh, Yun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

2 Citations (Scopus)

Abstract

Amongst renewable generators, photovoltaics (PV) are becoming more popular as the appropriate low cost solution to meet increasing energy demands. However, the integration of renewable energy sources to the electricity grid possesses many challenges. The intermittency of these non-conventional sources often requires accurate forecast, planning and optimal management. Many attempts have been made to tackle these challenges; nonetheless, existing methods fail to accurately capture the underlying characteristics of the system. There exists scope to improve present PV yield forecasting models and methods. This paper explores the use of apriori knowledge of PV systems to build clear box models and identify uncertain parameters via heuristic algorithms. The model is further enhanced by incorporating black box models to account for unmodeled uncertainties in a novel grey-box forecasting and modeling of PV systems.

Original languageEnglish
Title of host publication2015 21st International Conference on Automation and Computing
Subtitle of host publicationAutomation, Computing and Manufacturing for New Economic Growth, ICAC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
DOIs
Publication statusPublished - 30 Oct 2015
Event21st International Conference on Automation and Computing, ICAC 2015 - Glasgow, United Kingdom
Duration: 11 Sep 201512 Sep 2015

Conference

Conference21st International Conference on Automation and Computing, ICAC 2015
CountryUnited Kingdom
CityGlasgow
Period11/09/1512/09/15

Fingerprint

Particle Swarm Algorithm
Photovoltaic System
Power System
Forecasting
Renewable Energy
Uncertain Parameters
Intermittency
Heuristic algorithms
Black Box
Electricity
Model
Heuristic algorithm
Forecast
Planning
Generator
Grid
Uncertainty
Energy
Modeling
Costs

Keywords

  • photovoltaic power systems
  • power generation planning
  • particle swarm optimisation,
  • forecasting theory
  • grey systems

Cite this

Al-Messabi, N., Goh, C., & Li, Y. (2015). Grey-box identification for photovoltaic power systems via particle-swarm algorithm. In 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015 [7313980] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IConAC.2015.7313980
Al-Messabi, Naji ; Goh, Cindy ; Li, Yun. / Grey-box identification for photovoltaic power systems via particle-swarm algorithm. 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc., 2015.
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Al-Messabi, N, Goh, C & Li, Y 2015, Grey-box identification for photovoltaic power systems via particle-swarm algorithm. in 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015., 7313980, Institute of Electrical and Electronics Engineers Inc., 21st International Conference on Automation and Computing, ICAC 2015, Glasgow, United Kingdom, 11/09/15. https://doi.org/10.1109/IConAC.2015.7313980

Grey-box identification for photovoltaic power systems via particle-swarm algorithm. / Al-Messabi, Naji; Goh, Cindy; Li, Yun.

2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7313980.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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AB - Amongst renewable generators, photovoltaics (PV) are becoming more popular as the appropriate low cost solution to meet increasing energy demands. However, the integration of renewable energy sources to the electricity grid possesses many challenges. The intermittency of these non-conventional sources often requires accurate forecast, planning and optimal management. Many attempts have been made to tackle these challenges; nonetheless, existing methods fail to accurately capture the underlying characteristics of the system. There exists scope to improve present PV yield forecasting models and methods. This paper explores the use of apriori knowledge of PV systems to build clear box models and identify uncertain parameters via heuristic algorithms. The model is further enhanced by incorporating black box models to account for unmodeled uncertainties in a novel grey-box forecasting and modeling of PV systems.

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Al-Messabi N, Goh C, Li Y. Grey-box identification for photovoltaic power systems via particle-swarm algorithm. In 2015 21st International Conference on Automation and Computing: Automation, Computing and Manufacturing for New Economic Growth, ICAC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7313980 https://doi.org/10.1109/IConAC.2015.7313980