Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids

Mohammad Rayati, Pasquale De Falco, Daniela Proto, Mokhtar Bozorg and Mauro Carpita
Additional contact information
Mohammad Rayati: Institut d’Energie et Systèmes Electriques (IESE), Haute École d’Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), Haute École Spécialisée de Suisse Occidentale (HES-SO), 1041 Yverdon-les-Bains, Switzerland
Pasquale De Falco: Department of Engineering, University of Naples Parthenope, 80133 Naples, Italy
Daniela Proto: Department of Electrical Engineering, Università Federico II of Napoli, 80138 Naples, Italy
Mokhtar Bozorg: Institut d’Energie et Systèmes Electriques (IESE), Haute École d’Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), Haute École Spécialisée de Suisse Occidentale (HES-SO), 1041 Yverdon-les-Bains, Switzerland
Mauro Carpita: Institut d’Energie et Systèmes Electriques (IESE), Haute École d’Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), Haute École Spécialisée de Suisse Occidentale (HES-SO), 1041 Yverdon-les-Bains, Switzerland

Energies, 2021, vol. 14, issue 16, 1-21

Abstract: In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth’s orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The CSI data of each group are divided into bins of magnitudes and the transition probabilities among the bins are identified to deliver a Markov Chain (MC) model to track the intraday weather condition variations. The proposed model is tested on the measurements of two PV systems located at two different climatic regions: (a) Yverdon-les-Bains, Switzerland; and (b) Oahu, Hawaii, USA. The model is compared with a previously published N -state MC model and the performance of the proposed model is elaborated.

Keywords: data analysis; electrical distribution grids; Markov Chain (MC) model; numerical simulation; photovoltaic (PV) systems; solar irradiance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/16/4734/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/16/4734/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:16:p:4734-:d:608282

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().