Search results

1 – 10 of 631
Content available
Article
Publication date: 30 November 2004

Shi Yong Yoo

This paper is concerned with the effects of weather uncertainty on the electricity future curve. Following the approach used by Lucia and Schwartz (2002), the behavior of…

Abstract

This paper is concerned with the effects of weather uncertainty on the electricity future curve. Following the approach used by Lucia and Schwartz (2002), the behavior of the underlying spot price is assumed to consist of two components ‘ a totally predictable deterministic component that accounts for regularities in the evolution of prices and a stochastic component that accounts for the behavior of residuals from the deterministic part. The weather uncertainty is modeled consistently with seasonal outlook probabilities from the CPC (Climate Prediction Center) outlook. For a given realization of temperature, the electricity load can be predicted very accurately by a time series model using temperature and other explanatory variables. Furthermore, if temperature and electricity load are known, the spot price can be predicted as well using the regime switching model with time-varying transition probabilities. The electricity future price can be calculated for the given seasonal probabilities from the CPC outlook. Then the electricity future price can be obtained as the arithmetic average of the one-day electricity future price. The future price reflects clearly the response of the spot price to different weather patterns. As the summer gets warmer, the high price regime is more likely to be realized, and as a result, the future price increases.

To view the access options for this content please click here
Book part
Publication date: 27 August 2014

Kevin Jones

This chapter focuses on the common occurrence of wholesale electricity prices that fall below the cost of production. This “negative pricing” in effect represents payment…

Abstract

This chapter focuses on the common occurrence of wholesale electricity prices that fall below the cost of production. This “negative pricing” in effect represents payment to high-volume consumers for taking excess power off the grid, thus relieving overload. Occurrences of negative pricing have been observed since the wholesale electricity markets have been operating, and occur during periods of low demand, while generators are being kept in reserve for rapid engagement when demand increases (it is expensive and time-consuming to shut down generators and then restart them, so they are often kept in “spooling mode”). In such situations power production may temporarily exceed demand, potentially overloading the system. When the federal government began subsidizing the construction of wind generation projects, with regulations in place requiring transmission grids to accept all of the electricity produced by the wind generators, negative pricing became more frequent.

Details

Research in Finance
Type: Book
ISBN: 978-1-78190-759-7

To view the access options for this content please click here
Article
Publication date: 19 August 2021

Hendrik Kohrs, Benjamin Rainer Auer and Frank Schuhmacher

In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to…

Abstract

Purpose

In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to dimensionality problems, i.e. ill-defined models with too many parameters, which require an adequate remedy. This study addresses this issue.

Design/methodology/approach

In an application for the German/Austrian market, this study derives variable importance scores from a random forest algorithm, feeds the identified variables into a support vector machine and compares the resulting forecasting technique to other approaches (such as dynamic factor models, penalized regressions or Bayesian shrinkage) that are commonly used to resolve dimensionality problems.

Findings

This study develops full importance profiles stating which hours of which past days have the highest predictive power for specific hours in the future. Using the profile information in the forecasting setup leads to very promising results compared to the alternatives. Furthermore, the importance profiles provide a possible explanation why some forecasting methods are more accurate for certain hours of the day than others. They also help to explain why simple forecast combination schemes tend to outperform the full battery of models considered in the comprehensive comparative study.

Originality/value

With the information contained in the variable importance scores and the results of the extensive model comparison, this study essentially provides guidelines for variable and model selection in future electricity market research.

Details

International Journal of Energy Sector Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-6220

Keywords

To view the access options for this content please click here
Book part
Publication date: 1 May 2012

Kevin Jones

Midwest Independent Transmission System Operator, Inc. (MISO) is a nonprofit regional transmission organization (RTO) that oversees electricity production and transmission…

Abstract

Midwest Independent Transmission System Operator, Inc. (MISO) is a nonprofit regional transmission organization (RTO) that oversees electricity production and transmission across 13 states and 1 Canadian province. MISO also operates an electronic exchange for buying and selling electricity for each of its five regional hubs.

MISO oversees two types of markets. The forward market, which is referred to as the day-ahead (DA) market, allows market participants to place demand bids and supply offers on electricity to be delivered at a specified hour the following day. The equilibrium price, known as the locational marginal price (LMP), is determined by MISO after receiving sale offers and purchase bids from market participants. MISO also coordinates a spot market, which is known as the real-time (RT) market. Traders in the RT market must submit bids and offers by 30minutes prior to the hour for which the trade will be executed. After receiving purchase and sale offers for a given hour in the RT market, MISO then determines the LMP for that particular hour.

The existence of the DA and RT markets allows producers and retailers to hedge against the large fluctuations that are common in electricity prices. Hedge ratios on the MISO exchange are estimated using various techniques. No hedge ratio technique examined consistently outperforms the unhedged portfolio in terms of variance reduction. Consequently, none of the hedge ratio methods in this study meet the general interpretation of FASB guidelines for a highly effective hedge.

Details

Research in Finance
Type: Book
ISBN: 978-1-78052-752-9

To view the access options for this content please click here
Article
Publication date: 1 June 2005

Steen Koekebakker and Fridthjof Ollmar

The forward curve dynamics in the Nordic electricity market is examined. Six years of price data on futures and forward contracts traded in the Nordic electricity market…

Abstract

The forward curve dynamics in the Nordic electricity market is examined. Six years of price data on futures and forward contracts traded in the Nordic electricity market are analysed. For the forward price function of electricity, we specify a multi‐factor term structure models in a Heath‐Jarrow‐Morton framework. Principal component analysis is used to reveal the volatility structure in the market. A two‐factor model explains 75 per cent of the price variation in our data, compared to approximately 95 per cent in most other markets. Further investigations show that correlation between short‐ and long‐term forward prices is lower than in other markets. We briefly discuss possible reasons why these special properties occur, and some consequences for hedging exposures in this market.

Details

Managerial Finance, vol. 31 no. 6
Type: Research Article
ISSN: 0307-4358

Keywords

To view the access options for this content please click here
Article
Publication date: 20 November 2009

Sanjeev Kumar Aggarwal, L.M. Saini and Ashwani Kumar

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a…

Abstract

Purpose

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

Design/methodology/approach

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

Findings

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

Originality/value

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various price‐forecasting techniques, across different electricity markets, is presented.

Details

International Journal of Energy Sector Management, vol. 3 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

To view the access options for this content please click here
Article
Publication date: 25 September 2020

Christof Naumzik and Stefan Feuerriegel

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…

Abstract

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.

Details

International Journal of Energy Sector Management, vol. 15 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

To view the access options for this content please click here

Abstract

Details

Financial Derivatives: A Blessing or a Curse?
Type: Book
ISBN: 978-1-78973-245-0

To view the access options for this content please click here
Article
Publication date: 12 July 2019

Ikhlaas Gurrib

The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to…

Abstract

Purpose

The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market.

Design/methodology/approach

Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices.

Findings

Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model.

Research limitations/implications

Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil.

Originality/value

As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.

Details

Studies in Economics and Finance, vol. 36 no. 3
Type: Research Article
ISSN: 1086-7376

Keywords

To view the access options for this content please click here
Article
Publication date: 1 January 1975

Knight's Industrial Law Reports goes into a new style and format as Managerial Law This issue of KILR is restyled Managerial Law and it now appears on a continuous…

Abstract

Knight's Industrial Law Reports goes into a new style and format as Managerial Law This issue of KILR is restyled Managerial Law and it now appears on a continuous updating basis rather than as a monthly routine affair.

Details

Managerial Law, vol. 18 no. 1
Type: Research Article
ISSN: 0309-0558

1 – 10 of 631