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REVIEW 4 major objections 8 minor 24 references

PyPSA-Spain: an extension of PyPSA-Eur to model the Spanish energy system

T0 review · 4 major / 8 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read PyPSA-Spain, a single-country extension of PyPSA-Eur with corrected renewable profiles and price-based interconnections, yields a more balanced 2030 mix for Spain and suggests the official energy plan overestimates exports to France.

desk verdict Useful open Spanish power-system model with genuinely new national datasets, but the headline Q2Q profile correction is fitted and validated on the same year, so the 2030 transfer rests on untested assumptions. read the letter →

arxiv 2412.06571 v1 pith:KVUVNFDY submitted 2024-12-09 physics.comp-ph

Cristobal Gallego-Castillo , Marta Victoria This is my paper · ORCID
classification physics.comp-ph PACS 89.30.-g
keywords energysystemmodelPyPSA-Eurquantile-to-quantilecorrectionrenewablecapacityfactorsSpanishelectricity2030decarbonisationinterconnectionmodellingnationalandclimateplan
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

PyPSA-Spain is an open-source model of the Spanish electricity system built on the European model PyPSA-Eur. The paper argues that a single-country model with national data improves the representation of Spain compared to treating it as one node inside a European optimisation. Three features carry the improvement: a quantile-to-quantile (Q2Q) correction of wind and solar hourly capacity factors calibrated on 2022 historical generation, regional demand profiles at NUTS 2 and NUTS 3 levels, and interconnections to France and Portugal whose imports and exports respond to hourly electricity prices. In a 2030 case study, the corrected model yields a cost-optimal mix with roughly 70 GW each of solar PV and onshore wind, and indicates that the official Spanish energy plan overestimates exports to France because it assumes no decarbonisation in neighbouring countries.

What carries the argument

The central mechanism is the quantile-to-quantile (Q2Q) transformation $\bar g^* = Q(\bar g) = \mathrm{CDF}_{H}^{-1}(\mathrm{CDF}_{PE}(\bar g))$, which maps the cumulative distribution of the modelled normalised capacity factor to that of historical national generation; three normalisation schemes are tested, and the paper chooses scheme 3 for wind and scheme 1 for solar, then applies the fitted transform to the hourly capacity factor of every Voronoi cell, the area assigned to each network node. The second load-bearing piece is the nested interconnection model: hourly price series for France and Portugal, obtained from a lower-resolution European optimisation with a consistent carbon target, are attached to border nodes so that imports and exports are decided by price arbitrage. The Q2Q transform corrects the systematic under/overestimation in the renewable time series without requiring knowledge of its physical cause, while the nested pricing keeps the single-country model consistent with a decarbonising Europe.

What would settle it

Compare the Q2Q-transformed wind and solar profiles for a non-2022 historical year, such as 2018 or 2023, against actual Spanish generation; if bias and RMSE are not lower than in the uncorrected PyPSA-Eur profiles, the claimed improvement does not generalise.

Watch

Extended reading notes

Core claim

The central claim is that the single-country model PyPSA-Spain, by replacing PyPSA-Eur's default Spanish representation with national-specific inputs, produces a more accurate picture of the Spanish power system. The Q2Q transformation reshapes the distribution of modelled hourly capacity factors for wind and solar so that it matches the distribution of historical national generation; the paper selects normalisation scheme 3 for wind and scheme 1 for solar, and applies the resulting function cell-by-cell. Demand is represented with regional hourly profiles and regional annual shares, and interconnections are modelled with border nodes whose generators and loads carry precomputed hourly prices from a European PyPSA-Eur run. When optimising the 2030 mix under the decarbonisation target of the Spanish NECP, the model finds about 70 GW of solar PV and about 70 GW of onshore wind; it also finds that Spain would be a net exporter to France and Portugal, and that the NECP's assumed French exports are larger than what emerges when France is also assumed to decarbonise.

Load-bearing premise

The load-bearing premise is that the Q2Q correction, calibrated on 2022 national historical generation, remains valid for the 2030 scenario and transfers from the country scale to every individual grid cell, so that a change in the bias structure over time or space would turn the correction into a distortion.

Editorial extensions

If this is right

  • The Q2Q-corrected profiles change the 2030 optimal mix from a solar-dominated configuration to a balanced one, with roughly 70 GW of solar PV and 70 GW of onshore wind, and lower battery and floating-offshore needs.
  • The refined regional demand representation alone has a negligible effect on aggregate optimal capacities, but it makes it possible to construct future demand scenarios with different sectoral shares across regions.
  • With price-based interconnections, the model yields net exports of similar order to historical balances, and suggests that the Spanish NECP overestimates exports to France because it does not assume French decarbonisation.
  • A spatial resolution of at least 35–50 nodes is required to stabilise the optimal onshore wind capacity and avoid underestimating wind potential.
  • The model's endogenous carbon price for the 2030 target is 71.2–77.1 EUR/tCO2, close to the NECP's exogenous 79 EUR/tCO2.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial extension: The Q2Q transform is calibrated and evaluated on the same year, 2022; applying the fitted transform to a different historical weather year would test whether it reduces bias generally or merely memorises that year.
  • Editorial extension: The negligible aggregate effect of the demand refinement suggests its value may lie in grid-bottleneck analysis rather than capacity totals; examining line loadings with the regional demand profiles could reveal that value.
  • Editorial extension: The one-way nested price treatment could be iterated, using the Spanish model's dispatch to update border prices, yielding a stronger equilibrium without running a full European optimisation.
  • Editorial extension: Because the Q2Q correction is purely statistical, its applicability to future scenarios with different turbine technology or siting patterns is uncertain; a physical calibration would be needed for reliable extrapolation.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The paper introduces PyPSA-Spain, an open-source single-country extension of PyPSA-Eur for the Spanish power system. It adds three main functionalities: a quantile-to-quantile (Q2Q) correction of wind and solar capacity factors calibrated to Spanish 2022 historical generation, NUTS2/NUTS3 demand profiles from Datadis, and price-based interconnections with France and Portugal via virtual border nodes fed by a one-node-per-country PyPSA-Eur optimisation. The model is used to compute cost-optimal 2030 mixes, finding roughly 70 GW each of onshore wind and solar PV, and to compare with the Spanish NECP, concluding that the NECP may overestimate exports to France.

Significance. If the validation issues are resolved, this is a valuable open modelling contribution. The paper ships open code and data, documents a reproducible workflow, exploits national institutional data (REE, Datadis), and provides a transparent spatial-resolution analysis. The Q2Q correction is a simple and potentially transferable idea, and the nested interconnection approach is a pragmatic alternative to full co-optimisation. The paper also produces falsifiable quantitative predictions (capacity mixes, interconnector flows, CO2 prices) that can be checked against future outcomes.

major comments (4)
  1. [Section 3.1, Eq. (3), Figures 5, 7, 26, 28] The headline validation of the Q2Q correction is in-sample and partly tautological. Because Eq. (3) is \bar g* = CDF_H^{-1}(CDF_PE(\bar g)), the marginal distribution of the transformed series equals the target CDF by construction, so the PDF agreement in Figures 5 and 26 is a consistency check rather than independent evidence. The bias and RMSE statistics in Figures 7 and 28 are computed on the same 2022 data used to estimate CDF_H, CDF_PE, and to select among normalisation schemes 1-3, so they measure in-sample fit. This is load-bearing because Section 4 shows the profile correction is the functionality with the largest effect on the optimal mix, and the 2030 case study in Section 5 inherits these profiles. The text itself warns at the end of Section 3.1 that changing configuration parameters or meteorological year may reduce performance, but that warning is not operationalised. Please add a genuine out-of-sample evaluation, e.g., calibrate on 2022 and validate on 2023, or perform split-sample cross-validation over several years, and report whether the normalisation-scheme ranking and the RMSE reductions persist.
  2. [Section 3.1, scale transfer from country to Voronoi cells] The country-level Q2Q transform is applied to every Voronoi cell, but the cell-level validity of this transfer is not demonstrated. The normalisation analysis in Section 3.1 compares only country-level aggregates, and no NUTS2 or cell-level validation is reported. A monotone pointwise transform cannot correct phase errors or spatial-correlation errors in the modelled time series, so improved country-level bias and RMSE need not translate into improved timing of renewable availability at individual nodes, which is what matters for storage and interconnection sizing in the 2030 optimisations. Please validate the corrected profiles at regional scale using the NUTS2 installed-capacity data already exploited in Section 3.4.1, and report temporal-coherence metrics (e.g., lagged correlation or event-timing errors) in addition to marginal-distribution statistics.
  3. [Section 3.3, Figures 11, 17, 21] The nested-interconnection price series are produced by a one-node-per-country PyPSA-Eur model under a single -70% CO2 target, and the conclusion in Section 5 that the NECP may overestimate exports to France depends directly on those prices. No sensitivity of the price series or of the resulting net flows to the neighbouring-country CO2 target, to the clustering resolution of the European model, or to the weather year is reported. Because the virtual border-node formulation makes Spain a price-taker on each interconnection, the export/import balance is exactly as robust as the assumed price series. Please stress-test the boundary prices (e.g., alternative CO2 targets or small price offsets) and show whether the qualitative conclusion about NECP exports to France survives.
  4. [Sections 4-5, Table 4, Figures 18-21] The 2030 scenario results are presented as point estimates without uncertainty quantification. The optimal capacities, battery requirements, and interconnector flows are conditional on several assumptions that the paper itself shows are influential, notably the Q2Q correction, the solar PV investment cost of 440 EUR/kWe in Table 4, the 1 MW/km2 power density cap, and the CO2 limit. The paper provides a spatial-resolution sensitivity but no sensitivity over these techno-economic parameters. At minimum, a one-at-a-time sensitivity analysis over the solar PV cost and the power density cap, together with a statement of how the NECP comparison changes, would be needed to support the policy-relevant claims in Section 5.
minor comments (8)
  1. [Abstract] The abstract contains a typo: 'a entire year' should read 'an entire year'.
  2. [Section 1] There are several typos: 'stablish' should be 'establish', and 'The reminder of this paper' should be 'The remainder of this paper'.
  3. [Section 2.2] In the description of the capacity limit, 'This cup is used during the optimisation phase' should read 'This cap is used during the optimisation phase'.
  4. [Section 3.1 and Section 4] Typos include 'Lets consider' (should be 'Let us consider'), 'subestimation' (should be 'underestimation'), and 'strit' (should be 'strict').
  5. [Sections 3.4.2 and 4] The text refers to 'Figure 3.4.2' where it should refer to Figure 12, and uses 'NCEP' instead of 'NECP' in Section 4.
  6. [Appendix B, Figures 27 and 28] The captions of Figures 27 and 28 say 'wind power generation time series' but the analysis in Appendix B is for solar PV generation; the captions should be corrected accordingly.
  7. [Figures 14-16] The bar charts in the Case 1, Case 2, and Case 3 figures appear to retain the legend label 'Reference case'; this is confusing because the figures are meant to show the cases with individual functionalities. The legends should be updated to match each figure.
  8. [Section 3.3] Minor wording: 'a node located in the border' should be 'a node located at the border', and 'a node located in the shore' should be 'a node located on the shore'.

Circularity Check

2 steps flagged · score 6.0 of 10

Renewable-profile Q2Q validation is partly tautological and fully in-sample; 2030 results inherit an untested transfer.

  1. self definitional [Section 3.1, Eq. (3); Figures 5 and 26]
    "The Q2Q transform is a nonlinear function between the input normalised generation, ¯g and the transformed normalised generation, g∗, given by: ¯g∗ = Q(¯g) = CDF −1 w,H (CDF w,P E(¯g)). (3) ... Thus, the PDF of the transformed time series, ¯g∗ w,P E,t, replicates that of the normalised version of the historical wind power generation time series, ¯g w,R,t."

    Because Eq. (3) defines Q as the quantile-matching map from the PyPSA-Eur distribution to the historical distribution, the equality of the transformed PDF to the historical PDF is a property of the definition, not an empirical finding. The paper presents Figures 5 and 26 as evidence of improved accuracy, and the abstract and conclusions describe the profiles as 'fitted and validated with historical data'. The distributional component of that validation is circular: when the same year's data are used both to define the target CDF and to test the match, success is guaranteed by construction.

  2. fitted input called prediction [Section 3.1, normalisation schemes and Figs. 7/28; abstract/conclusions 'fitted and validated' claim]
    "The three Q2Q transforms obtained under the defined normalisation schemes, Q1(¯g), Q2(¯g) and Q3(¯g), are applied to the hourly capacity factors at each Voronoi cell. The resulting wind power generation time series aggregated at country level are then compared with real data to check the accuracy of the Q2Q transformation with each normalisation scheme. ... However, it should be noted that using different configuration parameters than those used to obtain the Q2Q transformations (e.g."

    The Q2Q correction is constructed from the 2022 historical national wind and solar time series and from the 2022 PyPSA-Eur country-level time series; the choice among normalisation schemes 1-3 is also made by comparing the same 2022 country-level results to the same historical data. The bias/RMSE reductions in Figures 7 and 28 are therefore in-sample diagnostics: they show how well the fitted transform reproduces the data from which CDF_H and the normalisation selection were derived, not that the profiles are generally more accurate. The authors' own caveat that changing turbine model or meteorological year may reduce performance confirms that the 2022 agreement does not, by itself, validate the 2030 case study or the transfer to Voronoi cells.

full rationale

The strongest non-circular content is the demand re-scaling from Datadis, the NUTS2/NUTS3 demand allocation, and the price-based interconnection model, which are independent of the Q2Q fit. However, the paper's central evidence for improved renewable profiles is not self-contained: Eq. (3) defines Q as the quantile-matching map, so the PDF matches in Figures 5 and 26 are true by definition, and the bias/RMSE statistics are computed on the same 2022 data used to fit the transform and to choose among the three normalisation schemes. Because Section 4 identifies the Q2Q correction as the functionality with the largest impact on the optimal mix, the 2030 capacities and the NECP export comparison inherit the validity of this in-sample correction plus an untested stationarity/scale-transfer assumption. The paper itself warns that other configuration parameters or meteorological years may reduce performance. No load-bearing self-citation, uniqueness argument, or renaming of a known result was found; the demand and interconnection results are not circular. The score reflects a partial but real definitional/in-sample circularity in the central renewable-profile validation.

Assumptions & free parameters 5 free parameters · 7 assumptions · 1 invented entities

The central scenario depends on many externally supplied and fitted inputs. The Q2Q corrections and gdp/pop weights are fitted to 2022 Spanish data, while costs, power densities, CO2 limit, demand and interconnection capacities are policy and engineering choices. The paper clearly states many of these, but the stationarity and transferability of the Q2Q correction is the least externally grounded input. No new physical entities are proposed; the virtual border nodes are modeling constructs.

free parameters (5)
  • Q2Q transform for onshore wind (normalization scheme 3) = Empirical CDF mapping fitted to 2022 Spanish wind generation; scheme 3 (each series normalized by its own maximum)
    Section 3.1, Figs. 5-7: scheme 3 chosen because it gave best PDF fit and smallest bias on 2022 data; transform is then applied to all Voronoi cells in the 2030 optimization.
  • Q2Q transform for solar PV (normalization scheme 1) = Empirical CDF mapping fitted to 2022 Spanish PV generation; scheme 1 (both series normalized by installed capacity)
    Appendix B selects scheme 1 based on same-year RMSE/bias; transform is applied for 2030 capacity factor correction.
  • GDP and population weights for annual demand distribution = gdp=0.16, pop=0.82
    Section 3.4.2 linear regression on Spanish NUTS3 2022 electricity demand; replaces PyPSA-Eur weights 0.60/0.40.
  • Solar PV investment cost = 440 EUR/kWe
    Section 4: hand-specified weighted average of utility (380) and rooftop (620) costs, used in 2030 optimal mix. The paper states this value is assumed.
  • Renewable power density limit = 1 MW/km2
    Section 4: conservative cap for solar PV, onshore and floating wind capacity expansion; directly limits installed capacities in each Voronoi cell.
assumptions (7)
  • domain assumption Long-term market equilibrium, perfect competition and perfect foresight are assumed in the optimization.
    Section 2 states the problem formulation relies on these assumptions; inherited from PyPSA-Eur.
  • domain assumption Linearized DC power flow (Kirchhoff's laws) is an adequate representation of the transmission network.
    Section 2.2 objective and constraints use linearised power flow; standard in PyPSA-Eur.
  • domain assumption Existing renewable capacity is spatially distributed proportionally to annual capacity factor where plant locations are unknown.
    Section 2.2 describes this heuristic; it affects initial capacity layout used for Q2Q validation and optimization.
  • ad hoc to paper The 2022 quantile-to-quantile bias correction remains valid for the 2030 scenario and transfers from country scale to Voronoi cell scale.
    Section 3.1 applies the country-level Q2Q transform to every cell; no out-of-sample or future-year validation is provided, and the paper only tests three normalizations on the same year.
  • ad hoc to paper Nested boundary prices from a one-node-per-country PyPSA-Eur optimization with a -70% CO2 target adequately represent France and Portugal in the Spanish 2030 optimization.
    Section 3.3 uses these price series as inputs; this is an assumption that neighboring countries' prices do not respond to Spanish dispatch detail, and the target differs from Spain's electricity-sector CO2 limit.
  • domain assumption Historical demand data from Datadis and ESIOS, and the NUTS2/NUTS3 allocations, are accurate and representative for 2030 demand profiles.
    Sections 3.2 and 3.4.1 rely on these national datasets; they are treated as ground truth.
  • domain assumption The NECP 2030 scenario inputs (demand 344 TWh, CO2 limit 11.986 Mt, interconnection capacities 5000 MW France and 4000 MW Portugal) are appropriate scenario settings.
    Section 4 sets these from the Spanish NECP; they are external policy inputs rather than derived quantities.
invented entities (1)
  • Virtual border node with virtual demand and virtual generator for each interconnection
    purpose: Represents imports and exports with France and Portugal: virtual demand consumes exported electricity; virtual generator with hourly price cost produces imported electricity.
    Section 3.3 introduces these purely as modeling devices to enable price-arbitrage flows; they are not physical infrastructure and have no external falsifiable handle.

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Pith. "Pith review of PyPSA-Spain: an extension of PyPSA-Eur to model the Spanish energy system." pith.science (2026). https://pith.science/paper/KVUVNFDY

@misc{pith2026241206571,
  author       = {Pith},
  title        = {Pith review of: PyPSA-Spain: an extension of PyPSA-Eur to model the Spanish energy system},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KVUVNFDY}},
  note         = {Machine review of arXiv:2412.06571}
}
read the original abstract

This work presents PyPSA-Spain, an open-source model of the Spanish energy system based on the European model PyPSA-Eur. It aims to leverage the benefits of single-country modelling over a multi-country approach. In particular, several databases provided by Spanish institutions are exploited to improve the estimation of solar photovoltaic (PV) and onshore wind generation hourly profiles, as well as the spatio-temporal description of the electricity demand. PyPSA-Spain attains hourly resolution for a entire year and represents the Spanish energy system using a configurable number of nodes, while selecting around 35-50 nodes is identified as a good compromise between spatial resolution and model simplicity. To accommodate cross-border interactions, a nested model approach with PyPSA-Eur was used, wherein time-dependent electricity prices from neighbouring countries were precomputed through the optimisation of the European energy system. As a case study, the optimal electricity mix for 2030 was obtained and compared with the latest update of the Spanish National Energy and Climate Plan (NECP) from September 2024.

Figures

Figures reproduced from arXiv: 2412.06571 by the authors.

Figure 1
Figure 1. Spatial domain considered in PyPSA-Eur and PyPSA-Spain. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Transmission network and Voronoi cells (top), wind power annual capacity factor [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Distribution of characteristic lengths (squared root of the Voronoi cell areas) for [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (32 more)
Figure 4
Figure 4. Figure 4: Wind power generation in Spain in 2022. Left: time series; right: PDF. Subindices: [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: PDFs of the wind power generation time series in Spain in 2022. Subindices: [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: CF of the wind power generation time series in Spain in 2022. Subindices: [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Bias and RMSE of the simulated wind power generation time series in Spain in [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: Hourly electricity demand in three NUTS 2 regions in Spain in 2022 [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Electricity demand density aggregated at NUTS 3 level obtained with the current [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]
Figure 10
Figure 10. Figure 10: Clustered network with 15 nodes and the default interconnections included in [PITH_FULL_IMAGE:figures/full_fig_p020_10.png]
Figure 11
Figure 11. Figure 11: Hourly electricity prices in France and Portugal obtained with a one-node per [PITH_FULL_IMAGE:figures/full_fig_p021_11.png]
Figure 12
Figure 12. Figure 12: Normalised electricity demand versus normalised GDP and population for [PITH_FULL_IMAGE:figures/full_fig_p022_12.png]
Figure 13
Figure 13. Figure 13: shows the optimised capacities for the Reference case. For each technology, the set of bars corresponds to a different number of clusters of the modelled network. To provide context, the horizontal dashed lines represent the actual capacities in 2024 for peninsular Sp…
Figure 14
Figure 14. Figure 14: shows the optimal capacities when the Q2Q transformations obtained in Section 3.1 are considered. Solar PV Onshore wind Floating wind CCGT Battery 0 20 40 60 80 100 120 GW Historical 2 8 15 24 35 50 65 80 100 Reference case [PITH_FULL_IMAGE:figures/full_fig_p026_14.png]
Figure 15
Figure 15. Figure 15: Optimal configuration for Case 2. Solar PV Onshore wind Floating wind CCGT Battery 0 20 40 60 80 100 120 GW Historical 2 8 15 24 35 50 65 80 100 Reference case [PITH_FULL_IMAGE:figures/full_fig_p027_15.png]
Figure 16
Figure 16. Figure 16: Optimal configuration for Case 3. electricity exchanges for each interconnection, as it is shown in [PITH_FULL_IMAGE:figures/full_fig_p027_16.png]
Figure 17
Figure 17. Figure 17: Annual electricity exchanges through interconnections. [PITH_FULL_IMAGE:figures/full_fig_p028_17.png]
Figure 18
Figure 18. Figure 18: Optimal configuration for 2030 estimated with PyPSA-Spain. Horizontal lines [PITH_FULL_IMAGE:figures/full_fig_p029_18.png]
Figure 19
Figure 19. Figure 19: Optimal geographical capacity distribution for solar PV (left) and batteries [PITH_FULL_IMAGE:figures/full_fig_p030_19.png]
Figure 20
Figure 20. Figure 20: Optimal geographical capacity distribution for onshore wind (left) and floating [PITH_FULL_IMAGE:figures/full_fig_p030_20.png]
Figure 21
Figure 21. Figure 21: Annual electricity exchanges through the interconnections. [PITH_FULL_IMAGE:figures/full_fig_p032_21.png]
Figure 22
Figure 22. Figure 22: Percentile 90 of the hourly line loading for the network model with 100 nodes. [PITH_FULL_IMAGE:figures/full_fig_p032_22.png]
Figure 23
Figure 23. Figure 23: Spatial subdivisions of Spain referred in this paper. Top left: Voronoi cells in the [PITH_FULL_IMAGE:figures/full_fig_p037_23.png]
Figure 24
Figure 24. Figure 24: Surface not protected by Natura 2000 network. [PITH_FULL_IMAGE:figures/full_fig_p038_24.png]
Figure 25
Figure 25. Figure 25: Eligible terrain for onshore wind (top) and solar PV (bottom). [PITH_FULL_IMAGE:figures/full_fig_p039_25.png]
Figure 26
Figure 26. Figure 26: PDFs of the solar PV generation time series in Spain in 2022. Subindices: [PITH_FULL_IMAGE:figures/full_fig_p040_26.png]
Figure 27
Figure 27. Figure 27: CF of the wind power generation time series in Spain in 2022. Subindices: [PITH_FULL_IMAGE:figures/full_fig_p040_27.png]
Figure 28
Figure 28. Figure 28: Bias and RMSE of the simulated wind power generation time series in Spain in [PITH_FULL_IMAGE:figures/full_fig_p041_28.png]
Figure 29
Figure 29. Figure 29: Hourly electricity demand in Andaluc´ıa (NUTS 2 region in South Spain) [PITH_FULL_IMAGE:figures/full_fig_p042_29.png]
Figure 30
Figure 30. Figure 30: Hourly electricity demand in Balearic Islands (NUTS 2 region in East Spain) [PITH_FULL_IMAGE:figures/full_fig_p042_30.png]
Figure 31
Figure 31. Figure 31: Hourly electricity demand in Navarra (NUTS 2 region in North Spain) according [PITH_FULL_IMAGE:figures/full_fig_p043_31.png]
Figure 32
Figure 32. Figure 32: Optimal onshore wind power capacity for the 2030 CO2 target, by Spanish [PITH_FULL_IMAGE:figures/full_fig_p044_32.png]
Figure 33
Figure 33. Figure 33: Optimal solar PV capacity for the 2030 CO2 target, by Spanish provinces (NUTS [PITH_FULL_IMAGE:figures/full_fig_p044_33.png]
Figure 34
Figure 34. Figure 34: Optimal batteries power capacity for the 2030 CO2 target, by Spanish provinces [PITH_FULL_IMAGE:figures/full_fig_p045_34.png]
Figure 35
Figure 35. Figure 35: Optimal batteries energy capacity for the 2030 CO2 target, by Spanish provinces [PITH_FULL_IMAGE:figures/full_fig_p045_35.png]

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.