Simulation of regional day-ahead PV power forecast scenarios

Edgar Nuno; Matti Juhani Koivisto; Nicolaos Antonio Cutululis; Poul Sorensen

doi:10.1109/PTC.2017.7981155

Simulation of regional day-ahead PV power forecast scenarios

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

Abstract

Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records.

Original language	English
Title of host publication	PowerTech, 2017 IEEE Manchester
Number of pages	6
Publisher	IEEE
Publication date	2017
ISBN (Print)	978-1-5090-4238-8
ISBN (Electronic)	978-1-5090-4237-1
DOIs	https://doi.org/10.1109/PTC.2017.7981155
Publication status	Published - 2017
Event	12th IEEE Power and Energy Society PowerTech Conference: Towards and Beyond Sustainable Energy Systems - University Place, University of Manchester., Manchester, United Kingdom Duration: 18 Jun 2017 → 22 Jun 2017

Conference

Conference	12th IEEE Power and Energy Society PowerTech Conference
Location	University Place, University of Manchester.
Country/Territory	United Kingdom
City	Manchester
Period	18/06/2017 → 22/06/2017

UN SDGs

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

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/PTC.2017.7981155

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NSON-DK: North Sea Offshore Network - Denmark
Sørensen, P. E. S. (Project Coordinator), Das, K. (Project Participant), Koivisto, M. J. (Project Participant), Pade, L.-L. (Project Participant), Skytte, K. (Project Participant), Gea-Bermudez, J. (Project Participant), Papakonstantinou, A. (Project Participant), Boscán Flores, L. R. (Project Participant), Kanellas, P. (Project Participant), Plakas, K. (Project Participant), Kitzing, L. (Project Participant) & Bergaentzlé, C. M. (Project Participant)
01/04/2016 → 31/03/2020
Project: Research

Cite this

@inproceedings{8a85150fd28846e68d66a3445deafc51,

title = "Simulation of regional day-ahead PV power forecast scenarios",

abstract = "Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records.",

author = "Edgar Nuno and Koivisto, \{Matti Juhani\} and Cutululis, \{Nicolaos Antonio\} and Poul Sorensen",

year = "2017",

doi = "10.1109/PTC.2017.7981155",

language = "English",

isbn = "978-1-5090-4238-8",

booktitle = "PowerTech, 2017 IEEE Manchester",

publisher = "IEEE",

address = "United States",

note = "12th IEEE Power and Energy Society PowerTech Conference : Towards and Beyond Sustainable Energy Systems, PowerTech Manchester 2017 ; Conference date: 18-06-2017 Through 22-06-2017",

}

TY - GEN

T1 - Simulation of regional day-ahead PV power forecast scenarios

AU - Nuno, Edgar

AU - Koivisto, Matti Juhani

AU - Cutululis, Nicolaos Antonio

AU - Sorensen, Poul

PY - 2017

Y1 - 2017

N2 - Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records.

AB - Uncertainty associated with Photovoltaic (PV) generation can have a significant impact on real-time planning and operation of power systems. This obstacle is commonly handled using multiple forecast realizations, obtained using for example forecast ensembles and/or probabilistic forecasts, often at the expense of a high computational burden. Alternatively, some power system applications may require realistic forecasts rather than actual estimates; able to capture the uncertainty of weatherdriven generation. To this end, we propose a novel methodology to generate day-ahead forecast scenarios of regional PV production matching the spatio-temporal characteristics while preserving the statistical properties of actual records.

U2 - 10.1109/PTC.2017.7981155

DO - 10.1109/PTC.2017.7981155

M3 - Article in proceedings

SN - 978-1-5090-4238-8

BT - PowerTech, 2017 IEEE Manchester

PB - IEEE

T2 - 12th IEEE Power and Energy Society PowerTech Conference

Y2 - 18 June 2017 through 22 June 2017

ER -

Simulation of regional day-ahead PV power forecast scenarios

Abstract

Conference

UN SDGs

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Projects

NSON-DK: North Sea Offshore Network - Denmark

Cite this