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Record W643821914

Modelling prices in competitive electricity markets

2004· book· en· W643821914 on OpenAlexaboutno aff
Derek W. Bunn

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsElectricity marketBiddingEconomicsSpot contractAutoregressive integrated moving averageMarket clearingElectricityEconomyFinancial economicsFutures contractMicroeconomicsComputer scienceTime seriesEngineering
DOInot available

Abstract

fetched live from OpenAlex

List of Contributors.Preface.1 Structural and Behavioural Foundations of Competitive Electricity Prices (Derek W. Bunn).PART I: PRICES AND STRATEGIC COMPETITION.2 Competitors' Response Representation for Market Simulation in the Spanish Daily Market (Efraim Centeno Hernaez, Julian Barquin Gil, Jose Ignacio de la Fuente Leon, Antonio Munoz San Roque, Mariano J. Ventosa Rodriguez, Javier Garcia Gonzalez, Alicia Mateo Gonzalez, and Agustin Martin Calmarza).2.1 Introduction.2.2 Hourly bidding-based Spanish electricity markets.2.3 A two-phase clustering procedure for the analysis of bid functions.2.4 Forecasting methods for residual demand functions using time series (ARIMA) models.2.5 Discovering electricity market states for forecasting the residual demand function using input output hidden Markov models.2.6 Conjectural variations approach for modelling electricity markets.2.7 Conclusions.Appendix: Nomenclature.References.3 Complementarity-Based Equilibrium Modeling for Electric Power Markets (Benjamin F. Hobbs and Udi Helman).3.1 Introduction.3.2 Definitions.3.3 A general complementarity-based model of energy commodity markets.3.4 A comparison of two approaches to modeling Cournot generators on a transmission network.3.5 A large-scale application: the North American Eastern Interconnection.3.6 Conclusion.Acknowledgments.References.4 Price Impact of Horizontal Mergers in the British Generation Market (John Bower).4.1 Introduction.4.2 England and Wales wholesale electricity market.4.3 Analysis.4.4 Price forecast.General references.Ofgem references.PART II: SPOT MARKET DYNAMICS.5 Testing for Weekly Seasonal Unit Roots in the Spanish Power Pool (Angel Le-on and Antonio Rubia).5.1 Introduction.5.2 Data.5.3 Testing for seasonal unit roots.5.4 Concluding remarks.Appendix A: Prewhitening procedure.Appendix B: Critical values of the HEGY test.Acknowledgements.References.6 Nonlinear Time Series Analysis of Alberta's Deregulated Electricity Market (Apostolos Serletis and Ioannis Andreadis).6.1 Introduction.6.2 A noise model.6.3 A multifractal formalism setting.6.4 On turbulent behavior.6.5 On nonlinearity.6.6 On chaos.6.7 Conclusion.Acknowledgments.References.7 Quantile-Based Probabilistic Models for Electricity Prices (Shi-Jie Deng and Wenjiang Jiang).7.1 Introduction.7.2 Quantile-based distributions and the modelling of marginal distributions of electricity price.7.3 Quantile-GARCH models and the modelling of time series of electricity price.7.4 Parameter Inference.7.5 Conclusion.Acknowledgements.References.8 Forecasting Time-Varying Covariance Matrices in the Intradaily Spot Market of Argentina (Angel Leon and Antonio Rubia).8.1 Introduction.8.2 VAR analysis for block bids.8.3 Modelling the conditional covariance matrix.8.4 Forecasting conditional covariance matrices.8.5 Concluding remarks.Acknowledgements.References.PART III: SPATIAL PRICE INTERACTIONS.9 Identifying Dynamic Interactions in Western US Spot Markets (Christine A. Jerko, James W. Mjelde and David A. Bessler).9.1 Introduction.9.2 Data.9.3 Methods.9.4 Results.9.5 Discussion.References.10 Transmission of Prices and Volatility in the Australian Electricity Spot Markets (Andrew C. Worthington and Helen Higgs).10.1 Introduction.10.2 Data and summary statistics.10.3 Multivariate GARCH model.10.4 Empirical results.10.5 Conclusion.References.PART IV: FORWARD PRICES.11 Forecasting Higher Moments of the Power Price Using Medium-Term Equilibrium Economics and the Value of Security of Supply (Chris Harris).11.1 Introduction.11.2 Construction of the moments of price.11.3 Worked example.11.4 Commentary.11.5 Conclusions.References.12 Modeling Electricity Forward Curve Dynamics in the Nordic Market (Nicolas Audet, Pirja Heiskanen, Jussi Keppo and Iivo Vehvilainen).12.1 Introduction.12.2 The model.12.3 Forward model in the Nordic market.12.4 Model usage examples.12.5 Conclusion.Appendix: Estimation of model parameters.Acknowledgments.References.13 The Forward Curve Dynamic and Market Transition Forecasts (Svetlana Borovkova).13.1 The term structure of commodity futures prices.13.2 Forecasting market transitions.13.3 Critical regions and bootstrap methods.13.4 Application to electricity and oil futures.13.5 Concluding remarks.References.PART V: FORECASTING AND RISK MANAGEMENT.14 Price Modelling for Profit at Risk Management (Jacob Lemming).14.1 Introduction.14.2 Electricity price modelling.14.3 A profit at risk risk management model.14.4 Modelling input parameters.14.5 Experimental results.14.6 Conclusions.References.15 ForecastingWeather Variable Densities for Weather Derivatives and Electricity Prices (James W. Taylor).15.1 Introduction.15.2 Weather ensemble predictions.15.3 Univariate time series modelling of weather variables.15.4 Empirical comparison of weather point forecasts.15.5 Empirical comparison of weather quantile forecasts.15.6 Summary of the analysis of temperature, wind speed and cloud cover.15.7 Forecasting the payoff density for a weather derivative.15.8 Electricity demand modelling.15.9 Concluding comments.References.Index.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0310.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.177
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations219
Published2004
Admission routes1
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Same topicElectric Power System OptimizationFrench-language works237,207