TY - JOUR
T1 - Regionalization of a national integrated energy system model
T2 - A case study of the northern Netherlands
AU - Sahoo, Somadutta
AU - van Stralen, Joost N.P.
AU - Zuidema, Christian
AU - Sijm, Jos
AU - Yamu, Claudia
AU - Faaij, André
N1 - Funding Information:
We acknowledge the support provided by the ESTRAC Integrated Energy System Analysis Project financed by the New Energy Coalition (finance code: 656039). Additionally, we would like to thank experts from The Netherlands Organization for Applied Research (TNO in Dutch) Energy Transition and the Netherlands Environmental Assessment Agency (PBL in Dutch) working in different energy subsectors. We would especially like to thank Jeffrey Sipma and Joost Gerdes from TNO Energy Transition for their respective inputs regarding the built environment and the generation of Sankey diagrams. Lastly, we would like to thank Amirhossein Fattahi, Manuel Sanchez Dieguez, and Rafael Martínez Gordón for their assistance in editing this document and providing suggestions for modifying the modeling framework.
Funding Information:
We acknowledge the support provided by the ESTRAC Integrated Energy System Analysis Project financed by the New Energy Coalition (finance code: 656039). Additionally, we would like to thank experts from The Netherlands Organization for Applied Research (TNO in Dutch) Energy Transition and the Netherlands Environmental Assessment Agency (PBL in Dutch) working in different energy subsectors. We would especially like to thank Jeffrey Sipma and Joost Gerdes from TNO Energy Transition for their respective inputs regarding the built environment and the generation of Sankey diagrams. Lastly, we would like to thank Amirhossein Fattahi, Manuel Sanchez Dieguez, and Rafael Mart?nez Gord?n for their assistance in editing this document and providing suggestions for modifying the modeling framework.
Publisher Copyright:
© 2021 The Authors
PY - 2022/1/15
Y1 - 2022/1/15
N2 - Integrated energy system modeling tools predominantly focus on the (inter)national or local scales. The intermediate level is important from the perspective of regional policy making, particularly for identifying the potentials and constraints of various renewable resources. Additionally, distribution variations of economic and social sectors, such as housing, agriculture, industries, and energy infrastructure, foster regional energy demand differences. We used an existing optimization-based national integrated energy system model, Options Portfolio for Emission Reduction Assessment or OPERA, for our analysis. The modeling framework was subdivided into four major blocks: the economic structure, the built environment and industries, renewable energy potentials, and energy infrastructure, including district heating. Our scenario emphasized extensive use of intermittent renewables to achieve low greenhouse gas emissions. Our multi-node, regionalized model revealed the significant impacts of spatial parameters on the outputs of different technology options. Our case study was the northern region of the Netherlands. The region generated a significant amount of hydrogen (H2) from offshore wind, i.e. 620 Peta Joule (PJ), and transmitted a substantial volume of H2 (390 PJ) to the rest of the Netherlands. Additionally, the total renewable share in the primary energy mix of almost every northern region is ∼90% or more compared to ∼70% for the rest of the Netherlands. The results confirm the added value of regionalized modeling from the perspective of regional policy making as opposed to relying solely on national energy system models. Furthermore, we suggest that the regionalization of national models is an appropriate method to analyze regional energy systems.
AB - Integrated energy system modeling tools predominantly focus on the (inter)national or local scales. The intermediate level is important from the perspective of regional policy making, particularly for identifying the potentials and constraints of various renewable resources. Additionally, distribution variations of economic and social sectors, such as housing, agriculture, industries, and energy infrastructure, foster regional energy demand differences. We used an existing optimization-based national integrated energy system model, Options Portfolio for Emission Reduction Assessment or OPERA, for our analysis. The modeling framework was subdivided into four major blocks: the economic structure, the built environment and industries, renewable energy potentials, and energy infrastructure, including district heating. Our scenario emphasized extensive use of intermittent renewables to achieve low greenhouse gas emissions. Our multi-node, regionalized model revealed the significant impacts of spatial parameters on the outputs of different technology options. Our case study was the northern region of the Netherlands. The region generated a significant amount of hydrogen (H2) from offshore wind, i.e. 620 Peta Joule (PJ), and transmitted a substantial volume of H2 (390 PJ) to the rest of the Netherlands. Additionally, the total renewable share in the primary energy mix of almost every northern region is ∼90% or more compared to ∼70% for the rest of the Netherlands. The results confirm the added value of regionalized modeling from the perspective of regional policy making as opposed to relying solely on national energy system models. Furthermore, we suggest that the regionalization of national models is an appropriate method to analyze regional energy systems.
KW - Built environment
KW - District heating
KW - Industries
KW - Optimization
KW - Regionalization
KW - Renewable energy potentials
UR - http://www.scopus.com/inward/record.url?scp=85118527239&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2021.118035
DO - 10.1016/j.apenergy.2021.118035
M3 - Article
AN - SCOPUS:85118527239
VL - 306
JO - Applied Energy
JF - Applied Energy
SN - 0306-2619
M1 - 118035
ER -