Electricity Price Volatility and the Marginal Cost of Congestion: An Empirical Study of Peak Hours on the NYISO Market, 2001-2004

Hadsell, Lester; Shawky, Hany A.

doi:10.5547/issn0195-6574-ej-vol27-no2-9

Cited by 59 publications

(34 citation statements)

References 29 publications

(47 reference statements)

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“…We compute the standard deviations by using a moving window of 25 days (about one trading month) for all factors described in the data section. This methodology is in line with Hadsell and Shawky (2006) and Oberndorfer (2008), and has more formal support than "de-meaning" the mean equation (as in Bologna and Cavallo (2002) for instance).…”

Section: Introducing Exogenous Variablesmentioning

confidence: 64%

Options introduction and volatility in the EU ETS

Chevallier

Pen

Sévi³

2011

Resource and Energy Economics

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Section: Introducing Exogenous Variablesmentioning

confidence: 64%

Options introduction and volatility in the EU ETS

Chevallier

Pen

Sévi³

2011

Resource and Energy Economics

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“…A large body of economic literature deals with the structure and evolution of energy markets [19,20,21], market failures, and arbitrage opportunities for securities traders (e.g. [22,23]). …”

Section: Wholesale Electricity Marketsmentioning

confidence: 99%

Cutting the electric bill for internet-scale systems

QureshiAsfandyar¹,

WeberRick²,

BalakrishnanHari³

et al. 2009

SIGCOMM Comput. Commun. Rev.

317

239

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Energy expenses are becoming an increasingly important fraction of data center operating costs. At the same time, the energy expense per unit of computation can vary significantly between two different locations. In this paper, we characterize the variation due to fluctuating electricity prices and argue that existing distributed systems should be able to exploit this variation for significant economic gains. Electricity prices exhibit both temporal and geographic variation, due to regional demand differences, transmission inefficiencies, and generation diversity. Starting with historical electricity prices, for twenty nine locations in the US, and network traffic data collected on Akamai's CDN, we use simulation to quantify the possible economic gains for a realistic workload. Our results imply that existing systems may be able to save millions of dollars a year in electricity costs, by being cognizant of locational computation cost differences.

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“…They document important differences among the regional electricity markets not only with respect to wholesale price volatility and seasonal variations, but also with respect to asymmetric properties and persistence of volatility. Hadsell and Shawky (2006) examine the volatility characteristics of the NYISO day-ahead and real time electricity markets for peak hours from January 2001 to June 2004. They use GARCH to study the differences in volatility across zones and find that price volatility is higher but less persistent in the real time market than in the day-ahead market.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis

Erdoğdu

2016

Energy Economics

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This paper uses high frequency spot price data from fourteen wholesale electricity markets in Europe to analyze asymmetric volatility in European day-ahead power markets with Exponential GARCH (E-GARCH) and TARCH models. Our data set ranges from 1992 to 2015 and consists of approximately 926 thousand observations. As such, this paper constitutes the most extensive and comprehensive work conducted so far on European power markets, to the best of our knowledge. Unlike most of the literature that treats price as a continuous Volatility has decreased in many European countries in post-2008 period. Besides, we find magnitude effect is usually larger than the leverage effect, meaning that the absolute value of price change is relatively more important than the sign of the change (whether it is an increase or a decrease) to explain volatility in European day-ahead power markets.Moreover, the results imply there isn't a uniform inverse leverage effect in electricity prices; that is, price increases are more destabilizing in some European markets (e.g.

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Electricity Price Volatility and the Marginal Cost of Congestion: An Empirical Study of Peak Hours on the NYISO Market, 2001-2004

Cited by 59 publications

References 29 publications

Options introduction and volatility in the EU ETS

Options introduction and volatility in the EU ETS

Cutting the electric bill for internet-scale systems

Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis

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