What Caused the Drop in European Electricity Prices? A Factor Decomposition Analysis

Hirth, Lion

doi:10.5547/01956574.39.1.lhir

Cited by 90 publications

(56 citation statements)

References 20 publications

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“…Wind is not constrained to daylight hours, and can be complementarity with PV, with higher outputs often in winter, but in some locations wind may peak inconveniently in late night hours (Hoste et al, 2011, figs 5, 6). As peak output in both cases is a multiple of average output, wide-area penetration is ultimately limited by the extent of excess renewable generation (Hirth, 2018). This paper develops techniques to handle all three characteristics, while an appendix discusses other low-carbon options like Carbon Capture and Storage (CCS) that avoid these limitations.…”

Section: Relevant Technology Characteristicsmentioning

confidence: 99%

“…13 Hirth (2018) notes that 40% of the drop in German spot prices between 2008 and 2015 was caused by the increase in renewable energy. In addition to the curtailment modeled in this paper, wholesale prices can fall because of the merit order effect, which, with very diverse generating plant, can be pronounced (Hirth, 2018;Sensfuß et al, 2008). The merit order effect is modeled in section 4.1.…”

Section: Sequencing Investmentmentioning

confidence: 99%

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Evaluating the case for supporting renewable electricity

Newbery

2018

Energy Policy

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Renewable electricity, particularly solar PV and wind, creates external benefits of learningby-doing that drive down costs and reduce CO 2 emissions. The Global Apollo Programme called for collective action to develop renewable energy. This paper sets out a method for assessing whether a trajectory of investment that involves initial subsidies is justified by the subsequent learning-by-doing spillovers and if so, computes the maximum justifiable additional subsidy to provide, taking account of the special features of renewable electricitygeographically dispersed and variable quality resource base and local saturation. Given current costs and learning rates, accelerating the current rate of investment appears globally socially beneficial for solar PV in most but not all cases, less so for onshore wind. The optimal trajectory appears to involve a gradually decreasing rate of growth of installed capacity. 1 The case for supporting renewables The Global Apollo Programme called for collective action with "one aim only-to develop renewable energy supplies that are cheaper than those from fossil fuels.. .. These price trends help to create a prima facie case in favour of focussing heavily on solar energy." (King et al., 2015, p15). The case for support is primarily to compensate for the otherwise unremunerated learning spill-overs arising from cumulative production. Each additional installation adds to the cumulative production, which figure 1 1 persuasively suggests is the prime driver of cost reductions * This paper was prompted by Neuhoff (2008), who was pessimistic about the social profitability of PV when its cost was much higher, but noted that increasing current investment might relax constraints on future investment rates, which conferred an additional and potentially large extra benefit. I am indebted to insightful comments from Rutger-Jan Lange, and very careful checking of the paper and formulae to Linden Ralph and Bowei Guo, as well as to very helpful reviewers. 1 Source: Delphi234-Own work, CC0, https://commons.wikimedia.org/w/index.php?curid=33955173. The straight green line predicts that modules decrease in price by 20% for every doubling of cumulative shipped modules. The other line (with squares) shows worldwide module shipments vs. average module price. The data 1 of solar modules (Fraunhofer, 2016; Rubin et al., 2015a). Renewable electricity technologies, particularly wind and solar PV (hereafter just PV), have been heavily subsidized for many years. Both PV and wind are finally at the point of becoming commercially viable without subsidies in some locations, but new installations continue to enjoy significant, if now much lower, support in many jurisdictions. This paper asks whether past and continued support for such technologies is justified, and, more fundamentally, how to determine the appropriate level of support now and in the future for emerging low-carbon technologies with learning spillovers. While it is easy to present qualitative arguments for such support, the practical question is to qua...

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Section: Relevant Technology Characteristicsmentioning

confidence: 99%

Section: Sequencing Investmentmentioning

confidence: 99%

Evaluating the case for supporting renewable electricity

Newbery

2018

Energy Policy

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“…For example, authors of this article have used European electricity models to analyse the impact of renewable energy transitions on various aspects of transmission networks, including congestion management [7], country-specific grid issues [8], European long-term transmission scenarios [9], merchant interconnectors [10] and unscheduled flows under market splitting [11]. Other analyses have focused on wholesale prices [12], the changing dispatch of thermal power plants [13], energy storage requirements [14], interactions of investments in transmission, storage and thermal plants [15], system effects of electric vehicles [16], or optimal configurations of 100% renewable electricity sectors and their respective transition pathways [17].…”

Section: Introductionmentioning

confidence: 99%

Open Power System Data – Frictionless data for electricity system modelling

et al. 2019

Self Cite

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The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. This paper describes the Open Power System Data platform that aims at realising the efficiency and quality gains of centralised data provision by collecting, checking, processing, aggregating, documenting and publishing data required by most modellers. We conclude that the platform can provide substantial benefits to energy system analysis by raising efficiency of data pre-processing, providing a method for making data pre-processing for energy system modelling traceable, flexible and reproducible and improving the quality of original data published by data providers.

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“…they represent regions with among the highest shares of VRE, and (2) as single-state ISOs, they are relatively easier to model than multi-state markets.The simple fundamental model uses a merit-order supply curve based on individual generator capacity and marginal costs along with hourly observed demand and VRE production to estimate hourly prices. Following generally similar approaches used to explain the decrease in wholesale prices in Germany[56][57][58] 6 as well as in the Midwest and Mid-Atlantic[52], we isolate the impact of each individual factor on the decline in wholesale prices by holding all factors from 2016 fixed except for one that is changed from its 2016 value to its 2008 value. For example, we estimate the impact of growing amounts of renewable energy by changing the renewable electricity supply from its 2016 value to its 2008 value, while keeping other factors constant at their 2016 levels.…”

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confidence: 99%

Impacts of variable renewable energy on wholesale markets and generating assets in the United States: A review of expectations and evidence

Mills

Levin

Wiser

et al. 2020

Renewable and Sustainable Energy Reviews

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We synthesize available literature, data, and analysis on the degree to which growth in variable renewable energy (VRE) has impacted or might in the future impact bulk power system assets, pricing, and costs in the United States. Most studies of future scenarios indicate that VRE reduces wholesale energy prices and capacity factors of thermal generators. Traditional baseload generators are more exposed to these changing market conditions than low-capital cost and more flexible intermediate and peak-load generators. From analysis of historical data we find that VRE is already influencing the bulk power market through changes in temporal and geographic patterns areas with higher levels of VRE. The most significant observed impacts have concentrated in areas with significant VRE and/or nuclear generation along with limited transmission, with negative pricing also often occurring during periods with lower system-wide load. So far, however, VRE, has had a relatively modest impact on historical average annual wholesale prices across entire market regions, at least in comparison to other drivers. The reduction of natural gas prices is the primary contributor to the decline in wholesale prices since 2008. Similarly, VRE impacts on thermal plant retirements have been limited and there is little relationship between the location of recent retirements and VRE penetration levels. Although impacts on wholesale prices have been modest so far, impacts of VRE on the electricity market will be more significant under higher VRE penetrations. KEYWORDSVariable renewable energy; electricity markets; electricity prices; generator profitability 3 ABBREVIATIONS AS Ancillary services CAISO California Independent System Operator CCGT Combined cycle gas turbine CREZ Competitive Renewable Energy Zones CT Combustion turbine DOE

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What Caused the Drop in European Electricity Prices? A Factor Decomposition Analysis

Cited by 90 publications

References 20 publications

Evaluating the case for supporting renewable electricity

Evaluating the case for supporting renewable electricity

Open Power System Data – Frictionless data for electricity system modelling

Impacts of variable renewable energy on wholesale markets and generating assets in the United States: A review of expectations and evidence

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