Pith. sign in

REVIEW 6 major objections 8 minor 1 cited by

Do we actually understand the impact of renewables on electricity prices? A causal inference approach

T0 review · 6 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Forecast wind and solar generation have distinct, state-dependent causal effects on UK wholesale electricity prices: wind's price reduction is U-shaped across penetration levels, solar's is consistently negative, and both have grown…

desk verdict Plausible new CATE curves for UK renewables' price impact, but the inference ignores time-series dependence and the headline U-shape is not formally tested. read the letter →

arxiv 2501.10423 v1 pith:K4TB5MLD submitted 2025-01-10 stat.AP cs.LG

Davide Cacciarelli , Pierre Pinson , Filip Panagiotopoulos , David Dixon , Lizzie Blaxland This is my paper · ORCID
classification stat.APcs.LG MSC 62D2062G0862P20
keywords causalinferencedoublemachinelearningconditionalaveragetreatmenteffectmerit-orderelectricitywholesalepriceswindpowersolarUKmarket
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

The paper sets out to answer a causal question: what happens to UK wholesale electricity prices when forecast wind or solar generation is one GWh higher? It claims the answer is not a single number but a state-dependent curve, and it estimates the curve with a local form of double machine learning. The headline findings are that wind's price-reducing effect is U-shaped across penetration levels, solar's effect is consistently negative, and both effects have grown between 2018 and 2024. If the causal reading is right, regression-based constant-effect estimates mislead policy about how much renewables actually depress prices.

What carries the argument

The object that carries the argument is the local partially linear double machine-learning estimator for the conditional average treatment effect (CATE). A boxcar kernel slices the half-hourly data into overlapping windows of 10,000 observations centred on each penetration level; inside each window, machine-learning models residualize price and forecast generation against a long list of confounders (load, gas price, carbon permits, time-of-day and season, installed capacity, daylight), and an OLS regression of the residualized price on residualized treatment gives the local effect. One hundred bootstrap repetitions per window plus a Gaussian smoother convert these local slopes into the reported non-linear curves. The economic mechanism the paper invokes is the merit-order effect: extra low-marginal-cost renewables displace the marginal generator, so the size of the price cut is governed by the local slope of the supply curve.

What would settle it

Run the same local DML procedure with a placebo treatment that cannot plausibly affect the day-ahead price at gate closure—for instance, the wind forecast issued one week before the settlement period instead of the forecast used at gate closure. Under unconfoundedness the placebo CATE should be zero at every penetration level; if it tracks the reported U-shape or the solar curve, the residualization is still leaking confounders and the paper's causal curves are not identified.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that forecast renewable generation has a non-linear, causal, time-varying effect on day-ahead prices in the UK. Over 2018–2024, a 1 GWh increase in forecast wind reduces wholesale prices by up to about 7 GBP/MWh at low penetration, the effect fades to near zero at 20–30% penetration, and it strengthens again beyond that. Solar shows a consistently price-reducing effect, largest at low penetration (up to about 9 GBP/MWh per 1 GWh) and diminishing quickly, which directly contradicts the raw observational pattern that appears to show prices rising with moderate solar penetration. The paper interprets this as the merit-order effect operating through the slope of the supply curve, and it further shows both effects becoming more pronounced over time. These are conditional average treatment effects: the price impact of one more forecast GWh, holding a rich set of market confounders fixed.

Load-bearing premise

The whole causal story stands or falls on the assumption that after controlling for the listed market variables, forecast wind and solar generation are as good as randomly assigned with respect to price shocks, and that the half-hourly observations do not influence each other; the paper does not test either condition.

Editorial extensions

If this is right

  • A single 'merit-order effect' estimate is misleading: the same extra forecast GWh of wind lowers prices by up to about 7 GBP/MWh at low penetration, almost nothing at 20–30% penetration, and more again at higher penetration.
  • Solar's causal price-reducing effect is negative at every penetration level, so the bump in raw associations around 4–7% penetration is a confounding artefact, not a real economic effect.
  • Both wind and solar effects have strengthened between 2018 and 2024, so historical estimates of renewables' price impact cannot be extrapolated to future penetration levels.
  • The same qualitative patterns appear in the NordPool day-ahead market, while in the UK intraday market solar's effect is much smaller, showing the day-ahead results do not carry over to all trading floors.
  • Wind's U-shape implies the market value of additional wind capacity is non-monotonic: cannibalisation is strongest at moderate penetration and partly reverses at high penetration, which matters for revenue forecasts and investment signals.

Reading between the lines

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

  • If the causal reading is right, policy instruments that pay every renewable MWh the same price will overvalue output at mid-penetration and undervalue it at low and high penetration; the efficient price signal would track the penetration level and time of day.
  • The treatment is forecast generation, not realized output, so the curves describe the market's anticipated reaction at gate closure; improving forecast accuracy or moving gate closure could shift the whole curve, a testable extension the paper does not pursue.
  • The re-strengthening of wind's effect at high penetration is plausibly tied to the gas price level: in years with low gas prices the U-shape should be shallower, so splitting the sample by gas-price regime would test the mechanism.
  • The bootstrap and cross-fitting treat half-hourly observations as independent despite strong serial correlation in prices; re-estimating with block-bootstrap or time-series DML would probably widen the confidence bands and show whether the U-shape is robust.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

6 major / 8 minor

Summary. The paper proposes a local partially linear double machine learning (DML) estimator to estimate the conditional average treatment effect (CATE) of forecast wind and solar generation on UK wholesale electricity prices, conditional on predicted penetration. Using half-hourly data from 2018 to 2024, the authors report a U-shaped price-reducing effect for wind (strong at low penetration, near zero at 20-30%, and stronger again at higher penetration), a consistently price-reducing but rapidly weakening effect for solar, and an increasing price-reducing effect over time. The manuscript includes supplementary results for NordPool and intraday markets and provides code and data on GitHub.

Significance. The substantive claims, if they held up, would be policy-relevant: they indicate that a single average effect masks a state-dependent and time-varying price impact of renewables. The methodological idea of applying DML with a local boxcar kernel to obtain nonlinear CATEs is a useful adaptation, and the authors deserve credit for releasing code and data. However, the evidential basis for the headline claims is currently weak: the inference ignores temporal dependence, the non-linear shape claims are not formally tested, and the identification assumptions are not stated or defended. The significance of the findings therefore hinges on substantial revision of the statistical analysis.

major comments (6)
  1. [Algorithm 2, Figures 3 and 4] The uncertainty quantification in Algorithm 2 (Supplementary Notes) uses an iid bootstrap and random-fold cross-fitting on half-hourly data, but prices and forecasts are strongly autocorrelated at daily, weekly, and seasonal scales. The iid bootstrap will understate the variance of the CATE estimates, and random-fold cross-fitting can leak future information into the nuisance-model training, biasing the residualized treatment and outcome. The reported 80% confidence intervals in Figures 3 and 4 are therefore not reliable, and it is unclear whether the U-shape for wind, the rapid decay for solar, and the growing influence over 2018-2024 survive proper time-series inference. The authors should use a block bootstrap or HAC standard errors and a blocked or purged cross-fitting scheme, and they should report how conclusions change under those choices.
  2. [Figure 3 and Section 'Evolution of causal effects over time'] The central shape claims—the U-shape for wind and the decay for solar—are based on visual inspection of smoothed point estimates. No formal test is provided for whether the CATE at mid-penetration differs significantly from the CATE at low or high penetration, nor for the temporal trend claimed in Figure 4. A formal test (e.g., testing a quadratic term in a regression of the CATE on penetration, or a permutation test for the time trend) is needed; with current 80% intervals, the evidence remains suggestive rather than demonstrative.
  3. [Equations (5)-(9) and Table S1] The causal interpretation of the CATE requires explicit identification assumptions: unconfoundedness given the variables in Table S1, overlap, and no interference. These assumptions are not stated. Moreover, some plausible confounders for 30-minute wholesale prices are absent from Table S1, including interconnector flows, transmission constraints, and generation outages. The paper should state the assumptions, discuss their plausibility for the UK electricity market, and include robustness checks (e.g., adding further covariates, or a placebo test using a treatment that should have no causal effect).
  4. [Equation (8) and Algorithm 2] There is an inconsistency in the definition of the boxcar kernel: Equation (8) uses a bandwidth h on the conditioning variable x (penetration), but the supplementary text states that 'kernels with a size h of 10,000 observations' were used. Since x is measured in percent penetration, h cannot simultaneously be a distance in x and a number of observations. The exact procedure by which a window is defined—whether by distance in x or by a nearest-neighbour count—must be clarified because it directly affects the estimated CATE curve and the reproducibility of the results.
  5. [Section 'Additionally, a Gaussian filter was applied' and Figures S4/S5] The main CATE curves in Figures 3 and 4 are smoothed with a Gaussian filter whose bandwidth is set to 1.5 times the standard deviation of the mean effect estimates. No sensitivity analysis is provided for this smoothing parameter, and the raw bootstrap estimates shown in Figures S4 and S5 exhibit substantial variability. Without evidence that the U-shape and the time trend are robust to the smoothing choice, these shape claims may be artifacts of the smoothing procedure.
  6. [Figure 4] The temporal analysis using sliding windows of two financial years is potentially confounded by the changing distribution of penetration within each window: later periods have both higher renewable penetration and possibly different market conditions. The claim that the per-MWh effect 'has significantly increased' should be based on comparisons at comparable penetration levels or should be explicitly conditioned on penetration. The paper should clarify what quantity is plotted in Figure 4 (e.g., the CATE at a fixed penetration, an average over penetration, or the whole curve) and provide tests for the trend.
minor comments (8)
  1. [Abstract] In the abstract, 'impact electricity prices' should read 'impact on electricity prices'.
  2. [Introduction and Methods] There are several typos: 'Nord sPool' should be 'Nord Pool', 'Euclidian' should be 'Euclidean', and the quantile regression section defines the indicator as 1{ε < 0} but does not explain the notation; please clarify.
  3. [Figure 4] The caption and axis labels of Figure 4 should state explicitly whether the plotted curve is the CATE at a fixed penetration level, a penetration-weighted average, or the entire CATE function over time; the current description is ambiguous.
  4. [Methods, Data] The description of the 'estimated load' variable as 'estimated electricity load, generated from actual demand with noise' is terse; please explain how this variable is constructed and why a noisy version is used.
  5. [Algorithm 1] The implementation uses LightGBM but no hyperparameters are provided; specifying the configuration would improve reproducibility.
  6. [Supplementary Table S1] The check marks in Table S1 are not explained clearly enough in the caption; consider using separate columns for 'used in price residualisation' and 'used in treatment residualisation' to avoid ambiguity.
  7. [References] Reference 38 contains a typo ('F oundations'); please correct it.
  8. [Discussion] The claim of providing 'the first robust causal evidence' is too strong given the identification and inference concerns; suggest softening to 'new causal evidence' or similar.

Circularity Check

0 steps flagged · score 1.0 of 10

No load-bearing circularity: the CATE curves are data-driven outputs of a two-stage DML residualization, not artifacts of self-citation or definitional identities.

full rationale

The paper's causal estimates are produced by a two-stage residualization: confounders are used to predict price and wind/solar forecast separately (Supplementary Algorithm 2), and the CATE is the OLS slope of residualized price on residualized treatment within each boxcar window (Eq. 10). The U-shaped wind curve, the rapidly decaying solar curve, and the increasing magnitude over time are outputs of this estimation, not constraints imposed by the model or by any fitted parameter that already encodes these shapes. The conditioning variable (predicted penetration) is not identical to the treatment (predicted production), so the local slope is not equal to the definition of the window by construction. The only self-citations are contextual or methodological (e.g., Jonsson et al. 2010 for the local polynomial baseline, and Morales et al. 2013 for market operations); none is used as a uniqueness theorem or to justify the central CATE estimates. The paper even flags additional confounding when penetration itself is used as an input (Supplementary Figure S3), which is a validity caveat, not a circular step. Concerns about the iid bootstrap under temporal dependence affect the widths of confidence intervals but do not make the point estimates definitionally equivalent to the inputs. No circular step can be exhibited from the text, so the derivation chain is self-contained.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central empirical claims rest on the causal identification assumptions of DML and on the validity of the market data. These assumptions are not explicitly stated or tested. No new theoretical entities are introduced. The main free parameters are local-window and smoothing hyperparameters, which can materially affect the estimated shape of the effect curves.

free parameters (5)
  • Boxcar kernel size h = 10,000 observations (formal definition as bandwidth inconsistent)
    Controls the width of the local windows in which the partially linear model is estimated; no sensitivity analysis is reported.
  • Step size s = 1,000 observations
    Determines spacing between kernel centers; arbitrary choice affects the smoothness of the CATE curve.
  • Gaussian smoothing standard deviation = 1.5 times the standard deviation of the mean effect estimates
    Applied to the raw bootstrap CATEs; this can alter the apparent U-shape and is not varied in the paper.
  • Number of bootstrap replications B = 100
    Used for uncertainty quantification; small and does not account for serial dependence.
  • Number of DML folds K = not stated
    Required by Algorithm 1; without a value and LightGBM hyperparameters, exact replication is not possible.
assumptions (6)
  • domain assumption Unconfoundedness: conditional on the confounders in Table S1, predicted renewable production is independent of potential price outcomes.
    Required for DML to estimate a causal effect; not stated in the paper. The residualization in Eqs (5)-(6) simply assumes this.
  • domain assumption Positivity/overlap: within each local window, all levels of treatment remain possible for every confounder value.
    Required for local CATE estimation; no diagnostic checks are provided.
  • domain assumption SUTVA/no interference: the potential outcome for one half-hour period is unaffected by treatments in other periods.
    Electricity prices are serially correlated and market outcomes depend on the whole supply stack; not discussed.
  • domain assumption Partially linear local specification: within each window, price is linear in treatment with additive non-linear confounder effects.
    Eq (5) states this; no specification test is given. This is a strong assumption for a highly non-linear electricity market.
  • domain assumption Data reliability: EnAppSys and NESO variables, including derived penetration values, are measured without error.
    All estimates rely on these data; no validation or data-cleaning details beyond Table S1 are provided.
  • standard math Standard DML consistency and asymptotic normality of the cross-fitted residualized OLS estimator.
    The paper invokes the DML framework of Chernozhukov et al. but does not verify its conditions (e.g., Neyman orthogonality holds for the partially linear model, nuisance rates).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Do we actually understand the impact of renewables on electricity prices? A causal inference approach." pith.science (2026). https://pith.science/paper/K4TB5MLD

@misc{pith2026250110423,
  author       = {Pith},
  title        = {Pith review of: Do we actually understand the impact of renewables on electricity prices? A causal inference approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K4TB5MLD}},
  note         = {Machine review of arXiv:2501.10423}
}
read the original abstract

The energy transition is profoundly reshaping electricity market dynamics. It makes it essential to understand how renewable energy generation actually impacts electricity prices, among all other market drivers. These insights are critical to design policies and market interventions that ensure affordable, reliable, and sustainable energy systems. However, identifying causal effects from observational data is a major challenge, requiring innovative causal inference approaches that go beyond conventional regression analysis only. We build upon the state of the art by developing and applying a local partially linear double machine learning approach. Its application yields the first robust causal evidence on the distinct and non-linear effects of wind and solar power generation on UK wholesale electricity prices, revealing key insights that have eluded previous analyses. We find that, over 2018-2024, wind power generation has a U-shaped effect on prices: at low penetration levels, a 1 GWh increase in energy generation reduces prices by up to 7 GBP/MWh, but this effect gets close to none at mid-penetration levels (20-30%) before intensifying again. Solar power places substantial downward pressure on prices at very low penetration levels (up to 9 GBP/MWh per 1 GWh increase in energy generation), though its impact weakens quite rapidly. We also uncover a critical trend where the price-reducing effects of both wind and solar power have become more pronounced over time (from 2018 to 2024), highlighting their growing influence on electricity markets amid rising penetration. Our study provides both novel analysis approaches and actionable insights to guide policymakers in appraising the way renewables impact electricity markets.

Figures

Figures reproduced from arXiv: 2501.10423 by the authors.

Figure 1
Figure 1. Price variation across predicted renewable penetration levels. Bar plots show the mean APX prices for wind and solar penetration levels, over 2018-2024, with error bars representing associated 95% confidence intervals. The predicted wind power penetration intervals (a) are defined in 10% increments, while those for predicted solar power penetration (b) are defined in 1% increments, reflecting the lower installed sol… view at source ↗
Figure 2
Figure 2. Regression analysis of predicted renewable penetration and APX wholesale prices. The top row shows the mean spot price modelled as a function of the hour of the day, as well as predicted (a) wind power penetration and (b) solar power penetration. The bottom row presents the results of quantile regression models, with the solid line representing the mean spot price, and the shaded region representing central 80%-cove… view at source ↗
Figure 3
Figure 3. Causal impact of predicted renewable power production on electricity prices. Comparison of observational mean and causal effects of wind (a) and solar (b) power production on wholesale electricity prices. The solid lines represent the non-linear CATE estimates derived using our local partially linear DML framework, capturing the price impact (in GBP/MWh) of a 1 GWh increase in renewable energy generation. The dashed… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Temporal evolution of the causal impact of renewable power production on electricity prices. non-linear CATE estimates of (a) wind and (b) solar power production over time, obtained using a sliding window spanning two financial years, with each window containing approx…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Mean Field Game for Capacity Expansion Modeling

    math.OC 2025-07 conditional novelty 6.0 of 10

    A finite-horizon mean field game of renewable capacity expansion yields a threshold time and a capacity threshold that separate investing from non-investing producers.

Reference graph

Works this paper leans on

43 extracted references · 42 canonical work pages · cited by 1 Pith paper

  1. [1]

    Net zero roadmap: A global pathway to keep the 1.5 °c goal in reach – 2023 update

    IEA. Net zero roadmap: A global pathway to keep the 1.5 °c goal in reach – 2023 update. Technical Report, International Energy Agency (2023)

    work page 2023

  2. [2]

    How much of the uk’s energy is renewable (2023)

    National Grid. How much of the uk’s energy is renewable (2023). Accessed: 2023-04-12

    work page 2023

  3. [3]

    Climate change synthesis report 2023 – summary for policymakers

    IPCC. Climate change synthesis report 2023 – summary for policymakers. In Lee, H. & Romero, J. (eds.) Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1–34 (IPCC, Geneva, Switzerland, 2023)

    work page 2023

  4. [4]

    M., Conejo, A

    Morales, J. M., Conejo, A. J., Madsen, H., Pinson, P. & Zugno, M. Integrating renewables in electricity markets: operational problems, vol. 205 (Springer Science & Business Media, 2013)

    work page 2013

  5. [5]

    & Muesgens, F

    Antweiler, W. & Muesgens, F. On the long-term merit order effect of renewable energies. Energy Econ. 99, 105275 (2021)

    work page 2021

  6. [6]

    & Holttinen, H

    Miettinen, J. & Holttinen, H. Impacts of wind power forecast errors on the real-time balancing need: a Nordic case study. IET Renew. Power Gener. 13, 227–233 (2019)

    work page 2019

  7. [7]

    Goodarzi, S., Perera, H. N. & Bunn, D. The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices. Energy Policy 134, 110827 (2019)

    work page 2019

  8. [8]

    Morthorst, P. E. Wind power and the conditions at a liberalized power market. Wind. Energy 6, 297–308 (2003)

    work page 2003

Show all 43 references
  1. [9]

    & Madsen, H

    Jónsson, T., Pinson, P. & Madsen, H. On the market impact of wind energy forecasts. Energy Econ. 32, 313–320 (2010)

    2010

  2. [10]

    J., Boerema, N

    Cutler, N. J., Boerema, N. D., MacGill, I. F. & Outhred, H. R. High penetration wind generation impacts on spot prices in the Australian national electricity market. Energy Policy 39, 5939–5949 (2011)

    2011

  3. [11]

    & MacGill, I

    Forrest, S. & MacGill, I. Assessing the impact of wind generation on wholesale prices and generator dispatch in the Australian National Electricity Market. Energy Policy 59, 120–132 (2013)

    2013

  4. [12]

    P., Wild, P., Foster, J

    Bell, W. P., Wild, P., Foster, J. & Hewson, M. Revitalising the wind power induced merit order effect to reduce wholesale and retail electricity prices in Australia. Energy Econ. 67, 224–241 (2017)

    2017

  5. [13]

    & Linares, P

    Würzburg, K., Labandeira, X. & Linares, P. Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria. Energy Econ. 40, S159–S171 (2013)

    2013

  6. [14]

    Ketterer, J. C. The impact of wind power generation on the electricity price in Germany. Energy economics 44, 270–280 (2014)

    2014

  7. [15]

    & Paulsen, T

    Gürtler, M. & Paulsen, T. The effect of wind and solar power forecasts on day-ahead and intraday electricity prices in Germany. Energy Econ. 75, 150–162 (2018)

    2018

  8. [16]

    Do, L. P. C., Lyócsa, Š. & Molnár, P. Impact of wind and solar production on electricity prices: Quantile regression approach. J. Oper. Res. Soc. 70, 1752–1768 (2019)

    2019

  9. [17]

    R., Matsumoto, K., Tanaka, K., Sugiawan, Y

    Keeley, A. R., Matsumoto, K., Tanaka, K., Sugiawan, Y . & Managi, S. The impact of renewable energy generation on the spot market price in Germany: ex-post analysis using boosting method. The Energy J. 42, 1–22 (2021)

    2021

  10. [18]

    & Denny, E

    O’Mahoney, A. & Denny, E. The merit order effect of wind generation on the irish electricity market. MPRA Pap. (2011)

    2011

  11. [19]

    & Ahern, C

    O’Flaherty, M., Riordan, N., O’Neill, N. & Ahern, C. A quantitative analysis of the impact of wind energy penetration on electricity prices in Ireland. Energy Procedia 58, 103–110 (2014)

    2014

  12. [20]

    & Zoppoli, P

    Clò, S., Cataldi, A. & Zoppoli, P. The merit-order effect in the Italian power market: The impact of solar and wind generation on national wholesale electricity prices. Energy Policy 77, 79–88 (2015)

    2015

  13. [21]

    & Lee, W

    Shcherbakova, A., Kleit, A., Blumsack, S., Cho, J. & Lee, W. Effect of increased wind penetration on system prices in Korea’s electricity markets. Wind. Energy 17, 1469–1482 (2014)

    2014

  14. [22]

    & Brand, A

    Nieuwenhout, F. & Brand, A. The impact of wind power on day-ahead electricity prices in the Netherlands. In 2011 8th International Conference on the European Energy Market (EEM) , 226–230 (IEEE, 2011)

    2011

  15. [23]

    & Kažukauskas, A

    Hu, X., Jarait ˙e, J. & Kažukauskas, A. The effects of wind power on electricity markets: A case study of the Swedish intraday market. Energy Econ. 96, 105159 (2021)

    2021

  16. [24]

    & Honkapuro, S

    Spodniak, P., Ollikka, K. & Honkapuro, S. The impact of wind power and electricity demand on the relevance of different short-term electricity markets: The Nordic case. Appl. energy 283, 116063 (2021)

    2021

  17. [25]

    & Demetriades, E

    Tselika, K., Tselika, M. & Demetriades, E. Quantifying the short-term asymmetric effects of renewable energy on the electricity merit-order curve. Energy Econ. 132, 107471 (2024). 18/19

    2024

  18. [26]

    & Linares, P

    Gelabert, L., Labandeira, X. & Linares, P. An ex-post analysis of the effect of renewables and cogeneration on Spanish electricity prices. Energy economics 33, S59–S65 (2011)

    2011

  19. [27]

    Cruz, A., Muñoz, A., Zamora, J. L. & Espínola, R. The effect of wind generation and weekday on Spanish electricity spot price forecasting. Electr. Power Syst. Res. 81, 1924–1935 (2011)

    2011

  20. [28]

    & Saenz-Díez, J

    Azofra, D., Jiménez, E., Martínez, E., Blanco, J. & Saenz-Díez, J. Wind power merit-order and feed-in-tariffs effect: A variability analysis of the Spanish electricity market. Energy Convers. Manag. 83, 19–27 (2014)

    2014

  21. [29]

    & Pryor, S

    Barthelmie, R., Murray, F. & Pryor, S. The economic benefit of short-term forecasting for wind energy in the UK electricity market. Energy Policy 36, 1687–1696 (2008)

    2008

  22. [30]

    & Staffell, I

    Joos, M. & Staffell, I. Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany. Renew. Sustain. Energy Rev. 86, 45–65 (2018)

    2018

  23. [31]

    & Pacheco, A

    Woo, C.-K., Horowitz, I., Moore, J. & Pacheco, A. The impact of wind generation on the electricity spot-market price level and variance: The Texas experience. Energy Policy 39, 3939–3944 (2011)

    2011

  24. [32]

    Woo, C.-K. et al. Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets. Energy Policy 92, 299–312 (2016)

    2016

  25. [33]

    B., Brinkman, G

    Martinez-Anido, C. B., Brinkman, G. & Hodge, B.-M. The impact of wind power on electricity prices. Renew. energy 94, 474–487 (2016)

    2016

  26. [34]

    L., Steininger, K

    Prol, J. L., Steininger, K. W. & Zilberman, D. The cannibalization effect of wind and solar in the California wholesale electricity market. Energy Econ. 85, 104552 (2020)

    2020

  27. [35]

    L., Eicke, A

    Stiewe, C., Xu, A. L., Eicke, A. & Hirth, L. Cross-border cannibalization: Spillover effects of wind and solar energy on interconnected European electricity markets. arXiv preprint arXiv:2405.17166 (2024)

    2024 arXiv

  28. [36]

    Double/debiased machine learning for treatment and structural parameters (2018)

    Chernozhukov, V .et al. Double/debiased machine learning for treatment and structural parameters (2018)

    2018

  29. [37]

    Causality (Cambridge university press, 2009)

    Pearl, J. Causality (Cambridge university press, 2009)

    2009

  30. [38]

    & Schölkopf, B

    Peters, J., Janzing, D. & Schölkopf, B. Elements of Causal Inference: F oundations and Learning Algorithms (MIT Press, 2017)

    2017

  31. [39]

    & Papies, D

    Fuhr, J., Berens, P. & Papies, D. Estimating causal effects with double machine learning–a method evaluation. arXiv preprint arXiv:2403.14385 (2024)

    2024 arXiv

  32. [40]

    Ke, G. et al. LightGBM: A highly efficient gradient boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems , 3149–3157 (2017)

    2017

  33. [41]

    EnAppSys market data platform

    EnAppSys. EnAppSys market data platform. https://www.enappsys.com/ (2024)

    2024

  34. [42]

    https://www.neso.energy/data-portal (2024)

    NESO data portal. https://www.neso.energy/data-portal (2024)

    2024

  35. [43]

    Astral: a python package for calculating the times of various aspects of the sun and moon

    Kennedy, S. Astral: a python package for calculating the times of various aspects of the sun and moon. https://sffjunkie. github.io/astral/ (2023). Version 3.3.2, Python library for astronomical calculations. 19/19

    2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.