TY - JOUR
T1 - Impact of operational details and temporal representations on investment planning in energy systems dominated by wind and solar
AU - Helistö, Niina
AU - Kiviluoma, Juha
AU - Morales-España, Germán
AU - O'Dwyer, Ciara
N1 - Funding Information:
N.H. has received funding from the Jenny and Antti Wihuri Foundation. This work has been supported by the Strategic Research Council at the Academy of Finland, project ?Transition to a Resource Efficient and Climate Neutral Electricity System (EL-TRAN)? (grant number 314319). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 864276. Ciara O'Dwyer has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 774629.
Publisher Copyright:
© 2021 The Authors
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/5/15
Y1 - 2021/5/15
N2 - Planning of future energy systems with higher prevalence of wind and solar energy requires a careful representation of the temporal and operational characteristics of the system in the investment planning model. This study aims to identify the aspects that should be considered when selecting the representation for a particular system. To demonstrate the impacts that various model representations have in terms of model accuracy and computational effort, we carry out case studies on two test systems implemented within the Backbone energy systems modelling framework. The results show that the temporal and operational representations have different benefits and weaknesses in different system types. The findings provide general guidelines on the relative importance of different model details, depending on the characteristics of the system under study. For example, some temporal sampling strategies can better capture long-term storage needs, while others are more suitable for short-term storage modelling. Likewise, solar-dominated and wind-dominated systems differ in their methodological requirements. Furthermore, the interactions between energy sectors and the operational limits of the technologies for sector coupling should be correctly captured, as they significantly impact on the value of different technologies and their flexibility. Finally, we recommend testing several temporal and technical representations for each particular system in order to ensure the feasibility of the selected method for that purpose. The findings and recommendations inform energy system modellers about improvements that will facilitate higher quality planning results.
AB - Planning of future energy systems with higher prevalence of wind and solar energy requires a careful representation of the temporal and operational characteristics of the system in the investment planning model. This study aims to identify the aspects that should be considered when selecting the representation for a particular system. To demonstrate the impacts that various model representations have in terms of model accuracy and computational effort, we carry out case studies on two test systems implemented within the Backbone energy systems modelling framework. The results show that the temporal and operational representations have different benefits and weaknesses in different system types. The findings provide general guidelines on the relative importance of different model details, depending on the characteristics of the system under study. For example, some temporal sampling strategies can better capture long-term storage needs, while others are more suitable for short-term storage modelling. Likewise, solar-dominated and wind-dominated systems differ in their methodological requirements. Furthermore, the interactions between energy sectors and the operational limits of the technologies for sector coupling should be correctly captured, as they significantly impact on the value of different technologies and their flexibility. Finally, we recommend testing several temporal and technical representations for each particular system in order to ensure the feasibility of the selected method for that purpose. The findings and recommendations inform energy system modellers about improvements that will facilitate higher quality planning results.
KW - Energy system optimization
KW - Operational constraints
KW - Power system planning
KW - Representative periods
KW - Time series reduction
KW - Unit commitment
KW - Variable renewable energy
UR - http://www.scopus.com/inward/record.url?scp=85101921562&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2021.116712
DO - 10.1016/j.apenergy.2021.116712
M3 - Article
SN - 0306-2619
VL - 290
JO - Applied Energy
JF - Applied Energy
M1 - 116712
ER -