Source-linked AI summary
Modelling the Nordic power system expansion with PyPSA-Eur: a comparison with the Nordic Grid Development Perspective 2025
Leonardo Rydin Gorjão, Heidi S. Nygård
TL;DR
The Nordic power system faces rising demand and the challenge of integrating variable renewables, while hydrogen’s system role remains uncertain. This study compares NGDP targets with a net-zero least-cost PyPSA-Eur pathway, finding broad capacity alignment but a later hydrogen ramp in the model.
Problem
Hydrogen’s deployment, integration, and system impact in a Nordic system balancing variable renewables remain unresolved amid expected demand growth.
Source: §I · p. 1Method
PyPSA-Eur models a least-cost Nordic transition from 2025 to 2050 under a net-zero constraint and compares the pathway with NGDP targets.
Source: §I · p. 1, C. NGDP scenario design and modelling… · p. 3Results
Broadly, simulated solar, offshore-wind, onshore-wind, and hydropower capacities match NGDP targets, but NGDP hydrogen demand starts in 2030 while PyPSA-Eur ramps deployment in 2045 and 2050.
Source: §III · pp. 3–4Takeaways & Limitations
The authors identify the need for better-aligned industrial demand representation and for hydrogen roles to be assessed against expected costs.
Source: §IV · p. 6Future work should add detailed demand categories, higher temporal and spatial resolution, and clearer industrial-demand representation aligned with Nordic government targets.
Source: §IV · p. 6
Abstract
from arXiv · showhide
We compare the Nordic Grid Development Perspective 2025 (NGDP) with a least-cost transition pathway for the Nordic power system obtained with PyPSA-Eur for the period 2025-2050 under a net-zero CO$_2$ constraint. The model co-optimises generation, storage, and transmission expansion with hourly system operation while constraining the Nordic generation mix to remain consistent with NGDP milestone ranges. Overall, the optimised capacity pathways reproduce the broad NGDP expansion of wind and solar power, with Denmark emerging as the main locus of offshore wind and solar deployment, Finland as a major site for onshore wind expansion, and Norway and Sweden relying more strongly on existing hydropower and nuclear capacity. The main discrepancy between the two pathways does not lie in the broad build-out of variable renewables, but in the timing and role of hydrogen. Whereas the NGDP treats hydrogen as an early and steadily increasing component of industrial demand and system development already from 2030, PyPSA-Eur delays substantial hydrogen deployment until 2045-2050, when it becomes cost-effective primarily as a flexibility and seasonal storage option in a strongly variable-renewable-energy-dominated system. This contrast suggests that early hydrogen build-out in the Nordics is not a robust outcome of cost optimisation alone, but depends strongly on assumptions regarding industrial demand growth, sector coupling, and anticipatory infrastructure planning. We therefore argue that hydrogen remains the principal strategic uncertainty in the Nordic transition, with the NGDP representing a more anticipatory planning perspective and PyPSA-Eur a more conservative least-cost timing of deployment.
I. Introduction
The Nordic transition must meet rising electricity demand while balancing variable renewables across spatially heterogeneous resources. This work compares a net-zero least-cost PyPSA-Eur pathway with NGDP targets.
I. Introduction
- The Nordics face expected electricity-demand growth driven by electrification and new industrial loads.The passage frames this alongside the drive to decarbonise and meet the 2050 net-zero target.Source: §I · p. 1
- Variable renewables must complement existing Nordic nuclear and hydropower while addressing seasonal variability, heterogeneous resources, and grid needs.The cited passage describes hydrogen as a possible seasonal-storage and balancing technology whose deployment and system impact remain to be uncovered.Source: §I · p. 1
- The study compares NGDP wind, solar, and hydrogen plans with PyPSA-Eur transition pathways from 2025 to 2050 under a net-zero constraint.It also examines system balance, technology roles in meeting demand growth, and differences between least-cost development and NGDP expectations.Source: §I · pp. 1–2
A. Model framework and optimisation problem
PyPSA-Eur co-optimises investment and operation under physical and emissions constraints. The setup also specifies data inputs and maximum capacity densities for renewable technologies.
A. Model framework and optimisation problem
- PyPSA-Eur co-optimises generation, storage, and transmission investment with hourly operation to minimise annual system costs under a strict CO2 budget.The model covers a full weather year and enforces nodal power balance, network constraints, and technology-specific operating limits.Source: A. Model framework and optimisation… · p. 2
- The model combines grid and plant data with ERA5-derived wind and solar capacity factors and land-use constraints for renewable deployment.Its network topology uses ENTSO-E map data with OpenStreetMaps geospatial referencing; onshore land eligibility uses CORINE and Natura 2000 exclusions.Source: A. Model framework and optimisation… · p. 2
- Maximum capacity densities are 6.2 MW/km2 for onshore wind, 5.5 MW/km2 for offshore wind, 43 MW/km2 for fixed-tilt and rooftop solar, and 43 MW/km2 for HSAT solar.The optimisation also sequentially reduces CO2 emissions by year, reaching net-zero emissions in 2050.Source: A. Model framework and optimisation… · p. 2
B. Technology representation and constraints
The technology portfolio includes conventional and renewable generators alongside multiple storage options. NGDP bounds constrain selected generation technologies, while demand follows population and GDP growth estimates rather than NGDP targets.
B. Technology representation and constraints
- The model allows conventional and renewable generation, hydropower reservoirs, pumped hydro, batteries, hydrogen, and methanolisation.Nuclear, solar, wind, and hydropower capacities are bounded using NGDP values from the preceding and following decades; other technologies are not NGDP-constrained.Source: B. Technology representation and… · p. 2
- Non-renewable technologies are unconstrained by NGDP capacity targets but phase out quickly under high costs and CO2 budget constraints.The footnote attributes their rapid phase-out to both factors.Source: B. Technology representation and… · p. 2
- Norway’s lack of Natura 2000 designation prevents applying the corresponding land exclusions in the same dataset framework.The stated simulation impact is minute given weak onshore-wind expansion in Norway.Source: B. Technology representation and… · p. 2
- Demand follows population and GDP growth estimates, not NGDP targets; transport other than electric vehicles and data-centre demand are not modelled.The demand estimates cover agriculture, industry, and distribution-level consumption such as households.Source: B. Technology representation and… · p. 3
C. NGDP scenario design and modelling of the North Sea-centric European electricity system
The scenario evaluates the Nordic NGDP pathway in five-year intervals and embeds it in a wider European model domain to represent cross-border links and North Sea development.
C. NGDP scenario design and modelling of the North Sea-centric European electricity system
- The model includes North Sea-relevant European countries and the Baltics and Poland, aggregating each included non-Nordic country to a single bus.The domain includes Great Britain, Northern Ireland, the Netherlands, Belgium, France, Germany, Estonia, Latvia, Lithuania, and Poland.Source: C. NGDP scenario design and modelling… · p. 3
- The NGDP supplies targets for 2025, 2030, 2040, and 2050; the study evaluates five-year intervals for Finland, Sweden, Norway, and Denmark, collectively termed the Nordics.The NGDP targets include installed capacity, required transmission reinforcements, and anticipated demand growth.Source: C. NGDP scenario design and modelling… · p. 3
III. Results and discussion
The modeled renewable capacity mix broadly matches NGDP targets, but the pathways differ sharply in hydrogen timing: PyPSA-Eur deploys it substantially later, chiefly for flexibility and seasonal storage.
III. Results and discussion
- Solar, offshore wind, onshore wind, and hydropower capacities broadly match NGDP targets, though Norwegian hydropower is difficult to account for accurately.The model allows five-year capacity ranges between adjacent NGDP milestones, and the largest discrepancies involve Norway’s distributed hydropower fleet.Source: §III · p. 3
- Denmark leads solar and offshore wind expansion, while Finland leads onshore wind; rooftop solar reaches 30% or more of installed solar capacity outside Denmark.The model emphasizes HSAT solar in Denmark, while rooftop technologies play a larger role in Finland, Norway, and Sweden.Source: §III · pp. 3–4, Figure 3 · p. 4
- Finnish industrial electricity demand rises from 40 TWh in 2025 to 96 TWh in 2050 in NGDP, versus 38.91 to 53.08 TWh in PyPSA-Eur.The passage says whether Finland’s industrial demand should double remains debatable.Source: §III · p. 4
- In Norway, NGDP industry demand rises from 56 to 78 TWh, versus PyPSA-Eur’s 17.25 to 35.19 TWh; the model treats gas and oil industry separately.Accounting for that separation, the authors describe a doubling of non-gas-and-oil energy consumption in Norway as aligned with national targets.Source: §III · p. 4
- NGDP projects steadily increasing hydrogen demand from 2030, while PyPSA-Eur deployment ramps up only in 2045–2050, mainly for flexibility and seasonal storage in a VRE-dominated system.The passage attributes the timing shift to large-scale VRE creating excess energy to store and hydrogen costs falling enough to become economically attractive.Source: §III · p. 4
- In 2050, annualised capital expenditure reaches up to 8.17 e bn/year for offshore wind, 1.94 e bn/year for solar, and 3.74 e bn/year for hydrogen.The passage identifies Denmark’s offshore wind, solar, and late hydrogen deployment as the main capital-expenditure drivers.Source: Figure 4 · p. 5, §III · p. 5
- The model reinforces continental HVDC links in line with NGDP, but does not capture NGDP’s extensive internal grid upgrades or represent internal transmission voltage levels in detail.The model instead strongly reinforces HVDC connections between Nordic countries and Continental Europe.Source: §III · pp. 4–5
- Almost 40 TWh net exports in 2050, peaking at 55 TWh in 2045, are projected for the Nordic system, alongside questionable distribution-grid consumption declines in Denmark and Finland.The authors point to likely Finnish electricity-demand underestimation and call halving distribution-grid electricity needs between 2040 and 2050 doubtful.Source: §III · p. 5
IV. Conclusions and Outlook
The NGDP and PyPSA-Eur differ most in hydrogen’s timing and system role: the NGDP anticipates early growth, while the least-cost pathway delays substantial deployment. The authors identify hydrogen as the Nordic transition’s key uncertainty, shaped by demand assumptions and planning expectations.
IV. Conclusions and Outlook
- The NGDP anticipates hydrogen growth from 2030, whereas PyPSA-Eur delays substantial uptake until 2045–2050, when it becomes a flexibility and seasonal-storage option.The authors frame early hydrogen deployment as a planning choice rather than a robust outcome of cost optimisation alone.Source: §IV · p. 5
- PyPSA-Eur omits data centres and parts of transport demand and appears to underestimate industrial electricity growth, limiting the modeled need for early hydrogen.The authors caution that NGDP may also overstate how quickly projected hydrogen demand materialises.Source: §IV · p. 6
- Future work should add demand categories, finer temporal and spatial resolution, and industrial demand aligned with government targets, while testing hydrogen roles against expected costs.Source: §IV · p. 6