When Supply Meets Demand: The Case of Hourly Spot Electricity Prices

Boogert, Alexander; Dupont, Dominique

doi:10.1109/tpwrs.2008.920731

Cited by 64 publications

(40 citation statements)

References 20 publications

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“…It is commonly assumed that the spot price S t is a function of the demand D t and the installed capacity C t in terms of the demand-supply ratio (DSR) r SD,t =1−D t /C t , i.e., S t =f(r SD,t ). Boogert and Dupont (2008) studied the Dutch electricity market structure and showed that, as expected, the spot price is a decreasing function of DSR. A drawback for using this approach is that estimates of available capacity are not published for the Canadian markets.…”

Section: Resultssupporting

confidence: 53%

Time-dependent correlations in electricity markets

Alvarez-Ramı́rez

Escarela‐Perez

2010

Energy Economics

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Section: Resultssupporting

confidence: 53%

Time-dependent correlations in electricity markets

Alvarez-Ramı́rez

Escarela‐Perez

2010

Energy Economics

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“…Some authors have given special attention to such effects by directly modeling the behaviour of the 'reserve margin' C t − D t (or the percentage reserve margin 1 − D t /C t ), emphasizing the advantage of capturing both demand and capacity movements in a single variable. Boogert and Dupont [16] analyse the relationship between margin and spot price as well as margin and spike probability, and suggest a nonparametric approach. Cartea et al [35] advocate using forward-looking margin information as an indicator of when a spike is likely to occur, and defining a separate price regime when a threshold level of margin is reached.…”

Section: Price Relationship With Capacity or Marginmentioning

confidence: 99%

A Survey of Commodity Markets and Structural Models for Electricity Prices

Carmona

Coulon

2013

Quantitative Energy Finance

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Abstract. The goal of this survey is to review the major idiosyncrasies of the commodity markets and the methods which have been proposed to handle them in spot and forward price models. We devote special attention to the most idiosyncratic of all: electricity markets. Following a discussion of traded instruments, market features, historical perspectives, recent developments and various modeling approaches, we focus on the important role of other energy prices and fundamental factors in setting the power price. In doing so, we present a detailed analysis of the structural approach for electricity, arguing for its merits over traditional reduced-form models. Building on several recent articles, we advocate a broad and flexible structural framework for spot prices, incorporating demand, capacity and fuel prices in several ways, while calculating closed-form forward prices throughout.

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“…• Fixed price thresholds where all prices exceeding some subjectively chosen price level (e.g., 100 EUR/MWh) are classified as spikes (Boogert and Dupont, 2008;Lapuerta and Moselle, 2001). Graphical techniques, like the sample mean excess function, might be used for the selection of a more optimal cutoff level (Fanone et al, 2012); however, this procedure can hardly be automated.…”

Section: Identification Of Spikesmentioning

confidence: 99%

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

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An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatment of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

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When Supply Meets Demand: The Case of Hourly Spot Electricity Prices

Cited by 64 publications

References 20 publications

Time-dependent correlations in electricity markets

Time-dependent correlations in electricity markets

A Survey of Commodity Markets and Structural Models for Electricity Prices

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

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