Modelling prices in competitive electricity markets
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it