Bengt J. Olsson
X: @bengtxyz
LinkedIn: beos
Contents:
Model and Objectives
Scenario 1: Wind + Solar + Hydrogen
Scenario 2: Nuclear + Hydrogen
Observations and Key findings
Fully optimized system
Appendix 1: Cost Assumptions
Appendix 2: Less flexibility
Sometimes it’s useful to take a step back for a broader perspective. Today’s energy system models are often highly complex and rely on a multitude of assumptions—some uncertain. While such models provide important insights, a simplified framework can be valuable for identifying major trends and structural characteristics.
This post presents such a simplified model to explore how a fossil-free European power system might be built by 2050. The model is based on aggregated EnergyCharts (EC) data for 2023–2024 and treats Europe as one unified country with no internal transmission constraints—a classic “copper-plate” assumption. All generation and consumption is summed to roughly 2750 TWh/year. Import and export flexibility, that typically is subject to large uncertainties, becomes void in this model, as Europe is treated as one closed system without borders.
Model and Objectives
Two fossil-free scenarios for 2050 are analyzed:
• Scenario 1 (WS): Expansion of wind and solar power, with nuclear and other power sources held constant at 2023-2024 levels according to EC
• Scenario 2 (NUC): Expansion of nuclear power, while wind, solar, and other sources are held constant instead
Both scenarios include hydrogen production and storage, as well as battery support. All fossil-based power generation is, of course, removed. The future electricity demand is assumed to be approximately 4125 TWh/year (50% above today’s level), plus an additional 1750 TWh/year of hydrogen for the Power-to-X sector. Half of this hydrogen is assumed to be imported, so the model must deliver 875 TWh or 100 GW of continuous hydrogen output to industry. The linear scaling of load is motivated by the assumption that many new demand sources—such as electrified transport, data centers, and industrial processes—will add relatively constant load throughout the year. But, in contrast, the shift from gas boilers to electric heat pumps will amplify the seasonal variations in electricity demand.
Electrolyzer efficiency (electricity → H₂) is set at 70%, and hydrogen turbine efficiency (H₂ → electricity) at 40%. Batteries are assumed to have a 4-hour capacity and 90% round-trip efficiency. The model is optimized using PyPSA to find the lowest total system cost under these conditions. The assumed costs for all components are listed in Appendix 1 below.
Scenario 1: Wind and Solar Expansion (WS)
In this scenario, a constraint is imposed such that onshore wind may be expanded to at most three times today’s installed capacity, and solar power to a maximum of ten times. These limits mean that the remaining demand must be met by offshore wind, despite its higher cost.
To estimate capacity factors, we use EnergyCharts (EC) data for installed capacity and actual generation in 2024. The results for all of Europe are:
• Onshore wind: 23% (EC) → 30.5% in 2050 (higher performance from newer turbines)
• Offshore wind: 43% (EC) → 39.5% in 2050 (lower correlation due to more distributed geography)
• Solar PV: 11% (EC) → 11% in 2050 (assumed unchanged)
For both onshore wind and solar, the maximum instantaneous output in 2024 was around 60% of installed capacity. This suggests low spatial and temporal correlation across Europe — production is spread out geographically and temporally. In the model, the effective capacity factor for onshore wind is adjusted upward to reflect improved technology, while the offshore wind factor is adjusted slightly downward to account for more dispersed and less correlated resources. The observed solar capacity factor remains unchanged in the projection.
Results for Scenario 1:
Scenario 2: Nuclear Expansion (NUC)
In the nuclear expansion scenario, the model introduces a slow form of load-following: nuclear power is allowed to ramp up from zero to full capacity over one week, and the same ramp-down time applies. In practice, however, the actual variation turns out to be significantly smaller than this theoretical maximum — for reasons discussed later. In the simulation, nuclear output varies by no more than about 10%.
An additional constraint in the model is that nuclear generation is limited to a maximum of 80% of installed capacity. This is a conservative assumption and does not reflect actual operating conditions — in winter, nuclear plants in Europe often operate at over 95% of capacity. However, this cap offers a simple way to account for reduced availability due to maintenance, outages, and inspections.
The resulting capacity factor for nuclear power in this scenario is 79.7%.
Results for Scenario 2:
Observations and Key Findings
Generation Mix
In Scenario 1 — wind and solar (WS) — the model builds out all permitted onshore wind capacity, which corresponds to a threefold increase over today’s installed base, reaching around 620 GW. At the same time, the assumed capacity factor increases by 33%, meaning more energy per unit capacity. Offshore wind is expanded to 163 GW, and solar power is scaled up to the model-imposed limit of 2.35 TW, or ten times the current level.
This enormous amount of solar capacity results in sharp production spikes, with peak output reaching 1,715 GW. Such high peaks pose a serious challenge for transmission infrastructure, both in terms of capacity and system stability.
In contrast, the nuclear scenario (NUC) yields a much more stable electricity output. Peak generation reaches 712 GW — over 1 TW less than in the WS scenario. Nuclear capacity is expanded by a factor of six compared to today’s level, although this figure is somewhat overstated due to the 80% utilization constraint in the model.
Batteries
Batteries play a critical role in both scenarios. In the WS case, 240 GW / 1 TWh of battery capacity is deployed — a substantial volume, alson in 2050. The batteries help shave solar production peaks, easing the load on the transmission grid. This enables a lower capacity of electrolyzers to be installed, while also improving their utilization. On the demand side, batteries help reduce the need for hydrogen gas turbines. Altogether, this significantly lowers the LCOE in the WS scenario. The peak-shaving functionality also improves grid operability.
Still, the overall production dynamics in WS remain highly volatile, with rapid changes and steep peaks as mentioned above.
In the NUC case, battery deployment is much more modest — 16 GW / 65 GWh — but the need for flexibility is also much lower due to the steadier output. A particularly noteworthy observation is that all gas turbines in the model could theoretically be replaced with batteries at relatively low cost. In that case, all hydrogen production could be dedicated to the Power-to-X sector.
In addition to bulk balancing, batteries also offer important ancillary services such as frequency control, congestion relief, and local balancing — though these functions fall outside the scope of this model.
Flexibility
At the all-European level, only hydrogen offers sufficient flexibility to balance generation and growing demand in 2050. Other sources such as batteries, pumped hydro, demand-side management (DSM), and hydro reservoirs play important local roles but are comparatively minor on a system-wide scale — particularly in the WS scenario. (For reference, see this post about balancing contributions to the present pan-European power system).
In the WS case, flexibility requires:
• Production flexibility via high-capacity electrolyzers (~500 GW)
• Storage capacity (~300 TWh)
• Hydrogen turbines (~200 GW) to reconvert hydrogen into electricity when needed
Despite this large infrastructure, approximately 3% of total electricity production is curtailed — i.e., cannot be utilized.
In the NUC scenario, the flexibility requirement is much smaller. The system is almost entirely balanced through variations in hydrogen production over time. Gas turbines play a negligible role. As shown in the output graphs, it is the hydrogen production rate that varies most in this scenario.
Although the model allows for slow load-following by nuclear power, this ability is barely utilized in practice. Nuclear output remains nearly constant throughout the year. The reason is economic: it is cheaper to adjust hydrogen production than to lower nuclear capacity factors. When there is excess power, hydrogen production is ramped up rather than throttling nuclear output. This increases storage needs slightly, but the cost is much lower than leaving nuclear reactors idle.
This creates a highly efficient synergy between nuclear and hydrogen: nuclear provides stable baseload power, while hydrogen production offers the system’s flexibility. The total balancing infrastructure required in the NUC scenario is far smaller than in WS — about 200 GW of electrolyzers, 100 TWh of storage, and 27 GW of gas turbines (which, as noted, could feasibly be replaced by batteries).
The figure below illustrates how both systems perform during a four-day Dunkelflaute period in December 2024 — a scenario designed to test maximum flexibility needs.
System Characteristics
This model does not account for technical system attributes such as rotational inertia or the costs of balancing forecast deviations and rapid fluctuations in supply and demand. However, the two scenarios differ significantly in how such challenges would likely be managed in practice.
Nuclear power inherently provides synchronous inertia through rotating machinery, which supports frequency stability. In the WS scenario, such stability must instead be achieved via synthetic inertia — for example, through batteries or advanced power electronics — which is feasible but requires more sophisticated control systems.
Overall, the NUC scenario results in a more stable and gradually varying system, while the WS scenario exhibits more pronounced variability in power flows. This increases demands on the transmission grid and on ancillary services. Both approaches are technically viable but differ in their operational characteristics and system requirements.
Cost and System LCOE
The model minimizes total system cost under defined technical and economic constraints. However, the resulting cost — expressed here as System LCOE — is highly sensitive to input assumptions, such as technology costs and performance parameters. For this reason, the results should be interpreted with very much caution.
In this case, the WS scenario yields a slightly lower system cost than NUC, but it’s worth emphasizing that higher grid and system costs associated with WS were not included. With different assumptions — for example, on capacity factors, cost trajectories, or component lifetimes — the ranking between scenarios could reverse entirely. For example, if the CAPEX for nuclear power is reduced from 6 to 4 MUSD/MW, the NUC scenario becomes clearly more cost-effective than the WS scenario, with a resulting system LCOE of 59.5 USD/MWh.
Thus, System LCOE should not be interpreted as an absolute cost comparison for investment decisions, but rather as a tool for exploring how changes within a scenario affect total system economics.
For instance, replacing all gas turbines in the NUC scenario with batteries increases the System LCOE by about 1%, or approximately 1 USD/MWh (1 öre/kWh).
value of heat
Heat is a by-product of nuclear power generation, hydrogen production via electrolysis, and hydrogen combustion in gas turbines. In a future power system, this heat should be utilized—for example, for district heating or industrial process heat—which gives it a tangible value.
In the model, an alternative LCOE is calculated where the value of recovered heat is subtracted from the annualized cost, thereby reflecting the cost of electricity alone. A conservative assumption is applied to nuclear power: only one-third of the generated heat is credited in order to avoid interference with electricity production. For electrolyzers and gas turbines, however, the full amount of excess heat is considered recoverable.
Assuming an electricity-to-heat value ratio of 1:4, the adjusted LCOE values become:
- LCOE(WS, with heat value): 64.2 USD/MWh (down from 65.7)
- LCOE(NUC, with heat value): 67.4 USD/MWh (down from 76.9)
Fully Optimized System
Finally, a scenario is tested in which wind, solar, and nuclear capacities are all treated as free variables in the optimization. As expected, the result is a hybrid solution combining features of both extreme scenarios.
Key Findings in the Fully Optimized Scenario
The most striking outcome in this scenario is that the mix of onshore wind, solar, and nuclear effectively eliminates the need for offshore wind. Onshore wind is built out to the model’s upper limit, while solar lands at roughly 8× today’s installed capacity — a level that aligns closely with several professional projections for 2050.
Overall, the resulting system resembles the WS scenario, but with some differences:
- Includes twice today’s nuclear capacity
- A reduced hydrogen infrastructure
- Roughly half the battery storage capacity
It should be stressed, however, that these outcomes are highly sensitive to the underlying cost assumptions. Different investment priorities or cost estimates could lead to different conclusions.
Still, the broader takeaway remains robust:
A diversified mix of technologies consistently outperforms single-path strategies.
Appendix 1: Cost Assumptions
Capex
| Technology | CAPEX |
|---|---|
| Onshore Wind | 1.2 MUSD/MW |
| Offshore Wind | 3.0 MUSD/MW |
| Solar PV | 0.3 MUSD/MW |
| Nuclear | 6.0 MUSD/MW |
| Gas Turbine (H₂) | 1.0 MUSD/MW |
| Electrolyzer | 0.5 MUSD/MW |
| Hydrogen Storage | 1000 USD/MWh |
| Battery (4h) | 0.2 MUSD/MWh |
OPEX
| Technology | CAPEX Lifetime (years) | FOM (% of CAPEX) | Discount rate |
|---|---|---|---|
| Onshore Wind | 25 | 2% | 6% |
| Offshore Wind | 25 | 2% | 6% |
| Solar PV | 25 | 2% | 6% |
| Nuclear | 60 | 2% (+ VOM/Fuel 10 USD/MWh) | 6% |
| Gas Turbine (H₂) | 25 | 2% | 6% |
| Electrolyzer | 25 | 2% | 6% |
| Hydrogen Storage | 40 | 2% | 6% |
| Battery (4h) | 15 | 2% | 6% |
cost for other power
“Fixed” or legacy power dispatch (with values retained from from EnergyChart’s data) is given a cost of 50 USD/MWh.
Appendix 2: What if storage was much more expensive?
The fully optimized hybrid solution is appealing because wind and solar naturally complement each other on a seasonal scale. Solar dominates in summer, wind in winter — together they provide a relatively smooth annual power profile.
If solar were reduced somewhat in the mix, wind and solar combined could better match Europe’s seasonal electricity demand curve. With nuclear and other base-load sources included, this results in a more balanced supply — potentially reducing the need for massive seasonal storage.
To test this, I ran a simulation where the cost of hydrogen storage was increased by a factor of 100, effectively discouraging the system from relying on large-scale flexibility. The result?
- The power mix was reshaped to better follow the load curve directly.
- Total hydrogen storage dropped to just 6 TWh.
- No gas turbines at all, all hydrogen to Power-to-X
- More nuclear was added to provide stable output and reduce variability.
- But higher LCOE…
