On the simulation of aggregated solar PV forecast errors

Edgar Nuño Martinez; Matti Juhani Koivisto; Nicolaos Antonio Cutululis; Poul Sorensen

doi:10.1109/TSTE.2018.2818727

On the simulation of aggregated solar PV forecast errors

Edgar Nuño Martinez^*, Matti Juhani Koivisto, Nicolaos Antonio Cutululis, Poul Sorensen

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

The uncertainty arising from high levels of solar photovoltaic (PV) penetration can have a substantial impact on power system operation. Therefore, there is a need to develop models capable of representing PV generation in a rigorous manner. This paper introduces a novel transformation-based methodology to generate stochastic solar area power forecast scenarios; easy to apply to new locations. We present a simulation study comparing day-ahead solar forecast errors covering regions with different geographical sizes, total installed capacities and climatic characteristics. The results show that our model can capture the spatio-temporal properties and match the long-term statistical properties of actual data. Hence, it can be used to characterize the PV input uncertainty in power system studies.

Original language	English
Journal	I E E E Transactions on Sustainable Energy
Volume	9
Issue number	4
Pages (from-to)	1889-1898
ISSN	1949-3029
DOIs	https://doi.org/10.1109/TSTE.2018.2818727
Publication status	Published - 2018

Keywords

Time series analysis
Power systems
Stochastic processes
Production
Predictive models
Autoregressive processes
Uncertainty
Solar power generation
Power system simulation
Forecasting

UN SDGs

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

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10.1109/TSTE.2018.2818727

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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

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title = "On the simulation of aggregated solar PV forecast errors",

abstract = "The uncertainty arising from high levels of solar photovoltaic (PV) penetration can have a substantial impact on power system operation. Therefore, there is a need to develop models capable of representing PV generation in a rigorous manner. This paper introduces a novel transformation-based methodology to generate stochastic solar area power forecast scenarios; easy to apply to new locations. We present a simulation study comparing day-ahead solar forecast errors covering regions with different geographical sizes, total installed capacities and climatic characteristics. The results show that our model can capture the spatio-temporal properties and match the long-term statistical properties of actual data. Hence, it can be used to characterize the PV input uncertainty in power system studies.",

keywords = "Time series analysis, Power systems, Stochastic processes, Production, Predictive models, Autoregressive processes, Uncertainty, Solar power generation, Power system simulation, Forecasting",

author = "{Nu{\~n}o Martinez}, Edgar and Koivisto, {Matti Juhani} and Cutululis, {Nicolaos Antonio} and Poul Sorensen",

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doi = "10.1109/TSTE.2018.2818727",

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T1 - On the simulation of aggregated solar PV forecast errors

AU - Nuño Martinez, Edgar

AU - Koivisto, Matti Juhani

AU - Cutululis, Nicolaos Antonio

AU - Sorensen, Poul

PY - 2018

Y1 - 2018

N2 - The uncertainty arising from high levels of solar photovoltaic (PV) penetration can have a substantial impact on power system operation. Therefore, there is a need to develop models capable of representing PV generation in a rigorous manner. This paper introduces a novel transformation-based methodology to generate stochastic solar area power forecast scenarios; easy to apply to new locations. We present a simulation study comparing day-ahead solar forecast errors covering regions with different geographical sizes, total installed capacities and climatic characteristics. The results show that our model can capture the spatio-temporal properties and match the long-term statistical properties of actual data. Hence, it can be used to characterize the PV input uncertainty in power system studies.

AB - The uncertainty arising from high levels of solar photovoltaic (PV) penetration can have a substantial impact on power system operation. Therefore, there is a need to develop models capable of representing PV generation in a rigorous manner. This paper introduces a novel transformation-based methodology to generate stochastic solar area power forecast scenarios; easy to apply to new locations. We present a simulation study comparing day-ahead solar forecast errors covering regions with different geographical sizes, total installed capacities and climatic characteristics. The results show that our model can capture the spatio-temporal properties and match the long-term statistical properties of actual data. Hence, it can be used to characterize the PV input uncertainty in power system studies.

KW - Time series analysis

KW - Power systems

KW - Stochastic processes

KW - Production

KW - Predictive models

KW - Autoregressive processes

KW - Uncertainty

KW - Solar power generation

KW - Power system simulation

KW - Forecasting

U2 - 10.1109/TSTE.2018.2818727

DO - 10.1109/TSTE.2018.2818727

M3 - Journal article

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VL - 9

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EP - 1898

JO - I E E E Transactions on Sustainable Energy

JF - I E E E Transactions on Sustainable Energy

IS - 4

ER -

On the simulation of aggregated solar PV forecast errors

Abstract

Keywords

UN SDGs

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Projects

NSON-DK: North Sea Offshore Network - Denmark

Cite this