The impact of wind power on day-ahead electricity prices in the Netherlands

Nieuwenhout, F.D.J.; Brand, A.J.

doi:10.1109/eem.2011.5953013

Cited by 17 publications

(12 citation statements)

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“…This is explained by the accounting unit for price effects, €/MWh for each additional GWh of renewable energy produced, as one GWh of production is a much smaller part of large electricity markets. For example, the Danish and Dutch hourly wind production in 2006 (a year analyzed by Nieuwenhout and Brand (2011), Ostergaard et al (2006) and Jonsson et al (2010)) was far below 1 GW on average; for Ireland it was even less. On the contrary, Spanish and German hourly wind 2006 production was 2.6 GW and 3.5 GW respectively, about three to five times bigger than it was in their smaller counterparts.…”

Section: A Comparative Assessment Of the Literaturementioning

confidence: 98%

“…This is probably due to the unusually high renewable penetration and also the small size of the Danish system 8 . Nieuwenhout and Brand (2011) use wind and weather data from the Netherlands to reconstruct day-ahead wind generation figures for 2006-2009, and divide the data to create groups that correspond to low or no-wind production intervals. They find that average day-ahead prices at the Dutch electricity exchange were roughly 5% higher during the no-wind intervals with respect to the average of the entire sample for the analyzed period.…”

Section: Empirical Studiesmentioning

confidence: 99%

“…For example, only some of the simulation studies use past weather data to estimate renewable output (e.g. Holttinen et al, 2001;Nieuwenhout and Brand, 2011), or consider the existence of an emission trading system (e.g. Sensfuss et al, 2008;.…”

Section: Comparability Of Existing Studiesmentioning

confidence: 99%

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Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria

2013

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Economic theory predicts that high renewable electricity production reduces the price of electricity, also referred to as the "merit-order effect". Although the merit-order effect is only one of several consequences of renewable production on the electricity system, it is crucial to determine its size for the economic evaluation of renewable energies. In this paper we present a comprehensive overview of relevant past research results on the price effect of renewables. Additionally, we conduct a new empirical analysis of the price effect of renewable production for the Austrian-German region, a market that clearly qualifies for a merit-order effect analysis given its characteristics. Based on the review and our own analysis, we show that the merit-order effect varies depending on the region and the assessment method chosen. We also find that the size of this effect is less dispersed throughout different markets than previously suggested by the literature.

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Section: A Comparative Assessment Of the Literaturementioning

confidence: 98%

Section: Empirical Studiesmentioning

confidence: 99%

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Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria

2013

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“…The importance of predictors is likely to change over time. For instance, in many countries, the share of renewables remains subject to growth in the coming years and, hence, their feed-in volume will affect electricity markets accordingly (MacCormack et al, 2010;Nieuwenhout and Brand, 2013;Ryu et al, 2010;Schleicher-Tappeser, 2012).…”

Section: Predictor Sensitivity Analysismentioning

confidence: 99%

Forecasting electricity prices with machine learning: predictor sensitivity

Naumzik

Feuerriegel

2020

IJESM

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Purpose Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, the purpose of this paper is to provide accurate forecasts.. Design/methodology/approach This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, this paper implements a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis. Findings This study disentangles the respective relevance of each predictor. This study shows that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior. Research limitations/implications The performance gain from including more predictors might be larger than from a better model. Future research should place attention on expanding the data basis in electricity price forecasting. Practical implications When developing pricing models, practitioners can achieve reasonable performance with a simple model (e.g. seasonal-autoregressive moving-average) that is built upon a wide range of predictors. Originality/value The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.

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“…In Reference [22] two methodologies were used to identify the influence of wind integration in the Netherlands market. In the first methodology, an average hourly EMP was considered every day, considering an analytical regression for the withdrawal production with wind power.…”

Section: Introductionmentioning

confidence: 99%

Influence of Wind Power on Intraday Electricity Spot Market: A Comparative Study Based on Real Data

et al. 2018

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Overtime, in the electricity sector, there has been a technological transfer to renewable electricity generation. With this change, processes, in the economic and availability terms, are expected to improve. In this new paradigm, society demands electricity without an impact on the environment and with the lowest possible cost. The wind power (WP) integration appears in this evolution process by achieving important technological advances, supporting in 2017 a growth of 44% of new projects in Europe, higher than any other renewable technology. However, the renewable energy sources (RES) integration in the electricity networks still presents technical difficulties and challenges, leading to challenges in the electricity markets (EMs). Therefore, this work evaluates the importance of WP and its influence on the Iberian Electricity Market (MIBEL), at the level of the intraday electricity spot market (IESM). This is an innovative study because literature usually focuses on day-ahead WP impact and this study focuses on intraday markets, which are closer to the consumption periods. The goal was to make an analysis on the impacts when betting on WP sources, in order to improve the market interaction with WP integration, considering as criteria the consumer satisfaction, in terms of lower electricity prices and WP availability. For this study, the market bids registered by the Iberian Electricity Market Operator (OMIE), from 2015 to 2017, ran over a new market simulator, specially developed for this proposal, considering a virtual market condition, but not considering the bids made by WP producers. The comparison of the results allowed the evaluation of the WP influence on the EM quantitative, which is noteworthy.

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The impact of wind power on day-ahead electricity prices in the Netherlands

Cited by 17 publications

References 1 publication

Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria

Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria

Forecasting electricity prices with machine learning: predictor sensitivity

Influence of Wind Power on Intraday Electricity Spot Market: A Comparative Study Based on Real Data

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