TY  - JOUR
AU  - Parrado-Hernando, G.
AU  - Herc, Luka
AU  - Pfeifer, Antun
AU  - Capellán-Perez, Iñigo
AU  - Batas Bjelić, Ilija
AU  - Duić, Neven
AU  - Frechoso-Escudero, Fernando
AU  - Miguel González, Luis Javier
AU  - Gjorgievski, Vladimir
PY  - 2022
UR  - https://dais.sanu.ac.rs/123456789/13495
AB  - Long term-energy planning has gradually moved towards finer temporal and spatial resolutions of the energy system to design the decarbonization of the society. However, integrated assessment models (IAMs), focusing on a broader concept of sustainability transition, are typically yearly-resolution models which complicates capturing the specific supply-demand dynamics, relevant in the transition towards renewable energy sources (RES). Different methods for introducing sub-annual information are being used in IAMs, but the hourly representation of variable RES remains challenging. This article presents a method to translate the main dynamics of an hourly-resolution energy model into a yearly-resolution model. Here we test our method with the current European Union region (EU-27) by configuring and applying the hourly-resolution EnergyPLAN. Multiple linear regression analysis is applied to 174960 simulations (set by varying 39 inputs by clusters and reaching 100% renewable systems), relating the adjusted capacity factors of the technologies as well as the variation of electricity demand and natural gas consumption as a function of the options installed to manage the variable RES. The obtained results allow validation of the developed approach, which shows to be flexible and easily generalizable enough to be applied to any couple of hourly and annual-resolution models and/or country. © 2022 Elsevier Ltd
T2  - Renewable Energy
T1  - Capturing features of hourly-resolution energy models through statistical annual indicators
SP  - 1192
EP  - 1223
VL  - 197
DO  - 10.1016/j.renene.2022.07.040
UR  - https://hdl.handle.net/21.15107/rcub_dais_13495
ER  - 

@article{
author = "Parrado-Hernando, G. and Herc, Luka and Pfeifer, Antun and Capellán-Perez, Iñigo and Batas Bjelić, Ilija and Duić, Neven and Frechoso-Escudero, Fernando and Miguel González, Luis Javier and Gjorgievski, Vladimir",
year = "2022",
abstract = "Long term-energy planning has gradually moved towards finer temporal and spatial resolutions of the energy system to design the decarbonization of the society. However, integrated assessment models (IAMs), focusing on a broader concept of sustainability transition, are typically yearly-resolution models which complicates capturing the specific supply-demand dynamics, relevant in the transition towards renewable energy sources (RES). Different methods for introducing sub-annual information are being used in IAMs, but the hourly representation of variable RES remains challenging. This article presents a method to translate the main dynamics of an hourly-resolution energy model into a yearly-resolution model. Here we test our method with the current European Union region (EU-27) by configuring and applying the hourly-resolution EnergyPLAN. Multiple linear regression analysis is applied to 174960 simulations (set by varying 39 inputs by clusters and reaching 100% renewable systems), relating the adjusted capacity factors of the technologies as well as the variation of electricity demand and natural gas consumption as a function of the options installed to manage the variable RES. The obtained results allow validation of the developed approach, which shows to be flexible and easily generalizable enough to be applied to any couple of hourly and annual-resolution models and/or country. © 2022 Elsevier Ltd",
journal = "Renewable Energy",
title = "Capturing features of hourly-resolution energy models through statistical annual indicators",
pages = "1192-1223",
volume = "197",
doi = "10.1016/j.renene.2022.07.040",
url = "https://hdl.handle.net/21.15107/rcub_dais_13495"
}

Parrado-Hernando, G., Herc, L., Pfeifer, A., Capellán-Perez, I., Batas Bjelić, I., Duić, N., Frechoso-Escudero, F., Miguel González, L. J.,& Gjorgievski, V.. (2022). Capturing features of hourly-resolution energy models through statistical annual indicators. in Renewable Energy, 197, 1192-1223.
https://doi.org/10.1016/j.renene.2022.07.040
https://hdl.handle.net/21.15107/rcub_dais_13495

Parrado-Hernando G, Herc L, Pfeifer A, Capellán-Perez I, Batas Bjelić I, Duić N, Frechoso-Escudero F, Miguel González LJ, Gjorgievski V. Capturing features of hourly-resolution energy models through statistical annual indicators. in Renewable Energy. 2022;197:1192-1223.
doi:10.1016/j.renene.2022.07.040
https://hdl.handle.net/21.15107/rcub_dais_13495 .

Parrado-Hernando, G., Herc, Luka, Pfeifer, Antun, Capellán-Perez, Iñigo, Batas Bjelić, Ilija, Duić, Neven, Frechoso-Escudero, Fernando, Miguel González, Luis Javier, Gjorgievski, Vladimir, "Capturing features of hourly-resolution energy models through statistical annual indicators" in Renewable Energy, 197 (2022):1192-1223,
https://doi.org/10.1016/j.renene.2022.07.040 .,
https://hdl.handle.net/21.15107/rcub_dais_13495 .