Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market

Pape, Christian; Hagemann, Simon; Weber, Christoph

doi:10.1016/j.eneco.2015.12.013

Cited by 103 publications

(74 citation statements)

References 54 publications

(52 reference statements)

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“…DE [3], [6], [10], [13], [14], [42]. [3], [6], [14], [15], [32], [35], [42]. [3], [14], [16], [17], [18], [19], [20], [21], [22], [23], [34], [35], [41], [42], [43].…”

Section: Day-ahead (Da)unclassified

“…[3], [6], [14], [15], [32], [35], [42]. [3], [14], [16], [17], [18], [19], [20], [21], [22], [23], [34], [35], [41], [42], [43]. NL [10], [24], [32], [42].…”

Section: Day-ahead (Da)unclassified

“…Ref. [13] focuses on the DA market in Germany (DE) and investigates the impact of offshore wind, and in [14] a brief overview of the functioning of the different German markets is given. Ref.…”

Section: Context: the Theory Of Short-term Energy Pricing And Rewardimentioning

confidence: 99%

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Interactions between the design of short-term electricity markets in the CWE region and power system flexibility

et al. 2017

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Short-term electricity markets are generally defined as markets that take place from the day-ahead stage until physical generation and consumption. These markets include dayahead, intra-day, and real-time balancing markets. In Europe, the first two are managed by power exchanges, while the third consists of reserve procurement and imbalance settlement and is operated by the local transmission system operator. Short-term markets are important tools to deal with net demand variability in the system, in which the need for flexibility is expressed and its provision is valorized. Due to the ongoing integration of variable renewables in the generation mix, the system's variability is increasing as a result of the limited controllability and predictability of those resources. As such, these markets become increasingly important. The contribution of this article is a comprehensive upto-date discussion of the key design parameters and functioning of all three short-term markets, and their impact on the demand for and supply of flexibility. An understanding of the design and its implications is useful to policy-makers who are considering changes to facilitate the integration, availability, or valorization of flexibility, while also contributing to the decision-making of flexibility investors and operators. The geographical scope is the Central Western European region, including the Belgian, French, German, and Dutch market zones.

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“…DE [3], [6], [10], [13], [14], [42]. [3], [6], [14], [15], [32], [35], [42]. [3], [14], [16], [17], [18], [19], [20], [21], [22], [23], [34], [35], [41], [42], [43].…”

Section: Day-ahead (Da)unclassified

“…[3], [6], [14], [15], [32], [35], [42]. [3], [14], [16], [17], [18], [19], [20], [21], [22], [23], [34], [35], [41], [42], [43]. NL [10], [24], [32], [42].…”

Section: Day-ahead (Da)unclassified

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Interactions between the design of short-term electricity markets in the CWE region and power system flexibility

et al. 2017

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“…The capacities are adjusted to account for planned and unscheduled non-usability of conventional power plants. Based on historical non-usability data, availability factors for the major conventional power plants are calculated as quotient between unavailable and installed capacities [21]. The availabilities show yearly, weekly and daily cycles.…”

Section: Application Datamentioning

confidence: 99%

“…The expected non-availabilities are created by extrapolating those cycles from historical data (see Appendix, Figure 1). The yearly electricity production from must-run CHP is distributed over the year based on typical average heating-degree profile, since must-run CHP production is largely driven by heating demand, which in turn is temperature-dependent [21].…”

Section: Application Datamentioning

confidence: 99%

A Parsimonious Fundamental Model for Wholesale Electricity Markets - Analysis of the Plunge in German Futures Prices

Kallabis

Pape

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ABSTRACTThe German market has seen a plunge in wholesale electricity prices from 2007 until 2014, when base futures prices dropped by more than 40 percent. In this paper we determine the fundamental components of electricity futures prices and quantify their impact on the price drop as well as on operation margins. Our methodology is based on a parsimonious model in which the supply stack is approximated by piecewise linear functions. A fundamental futures price estimate can then be given by averaging up the hourly equilibrium prices over the futures contract's delivery period. It turns out that the parsimonious model is able to replicate electricity futures prices and discover non-linear dependencies in futures price formation. We quantify which of the factors fuel prices, emission prices, renewable feed-in, conventional generation capacities, and demand developments contributed most to the observed price slide.

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Reinforcement learning for the optimization of electric vehicle virtual power plants

Al‐Gabalawy

2021

Int Trans Electr Energ Syst

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Summary Integrating weather‐dependent renewable energy sources into the electricity system impose challenges on the power grid. Balancing services are needed, which can be provided by virtual power plants (VPP) that aggregate distributed energy resources (DER) to consume or produce electricity on demand. Electric vehicle (EV) fleets can use idle cars' batteries as combined storage to offer balancing services on smart electricity markets. However, there are risks associated with this business model extension. The fleet faces severe imbalance penalties if it cannot charge the offered amount of balancing energy due to the vehicles' unpredicted mobility demand. Ensuring the fleet can fulfill all market commitments risks denying profitable customer rentals. We study the design of a decision support system that estimates these risks, dynamically adjusts the composition of a VPP portfolio, and profitably places bids on multiple electricity markets simultaneously. Here we show that a reinforcement learning agent can optimize the VPP portfolio by learning from favorable market conditions and fleet demand uncertainties. In comparison to previous research, in which the bidding risks were unknown and fleets could only offer conservative amounts of balancing power to a single market, our proposed approach increases the amount of offered balancing power by 48% to 82% and achieves a charging cost reduction of the fleet by 25%. In experiments with real‐world carsharing data of 500 EVs, we found that mobility demand forecasting algorithms' accuracy is crucial for a successful bidding strategy. Moreover, we show that recent advancements in deep reinforcement learning decrease the convergence time and improve the results' robustness. Our results demonstrate how modern RL algorithms can be successfully used for fleet management, VPP optimization, and demand response in the smart grid. We anticipate that DER, such as EVs, will play an essential role in providing reliable backup power for the grid and formulate market design recommendations to allow easier access to these resources.

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Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market

Cited by 103 publications

References 54 publications

Interactions between the design of short-term electricity markets in the CWE region and power system flexibility

Interactions between the design of short-term electricity markets in the CWE region and power system flexibility

A Parsimonious Fundamental Model for Wholesale Electricity Markets - Analysis of the Plunge in German Futures Prices

Reinforcement learning for the optimization of electric vehicle virtual power plants

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