https://www.lef.wiwi.uni-due.de/fileadmin/fileupload/BWL-LEF/Seminarreihe/RWeron14_Essen.pdf

Electricity price forecasting: A review of the state-of-the-art with a look into the future

A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.

Preface. Acknowledgments. 1 Complex Electricity Markets. 1.1 Liberalization. 1.2 The Marketplace. 1.2.1 Power Pools and Power Exchanges. 1.2.2 Nodal and Zonal Pricing. 1.2.3 Market Structure. 1.2.4 Traded Products. 1.3 Europe. 1.3.1 The England and Wales Electricity Market. 1.3.2 The Nordic Market. 1.3.3 Price Setting at Nord Pool. 1.3.4 Continental Europe 13. 1.4 North America. 1.4.1 PJM Interconnection. 1.4.2 California and the Electricity Crisis. 1.4.3 Alberta and Ontario. 1.5 Australia and New Zealand. 1.6 Summary. 1.7 Further Reading. 2 Stylized Facts of Electricity Loads and Prices. 2.1 Introduction. 2.2 Price Spikes. 2.2.1 Case Study: The June 1998 Cinergy Price Spike. 2.2.2 When Supply Meets Demand. 2.2.3 What is Causing the Spikes?. 2.2.4 The Definition. 2.3 Seasonality. 2.3.1 Measuring Serial Correlation. 2.3.2 Spectral Analysis and the Periodogram. 2.3.3 Case Study: Seasonal Behavior of Electricity Prices and Loads. 2.4 Seasonal Decomposition. 2.4.1 Differencing. 2.4.2 Mean or Median Week. 2.4.3 Moving Average Technique. 2.4.4 Annual Seasonality and Spectral Decomposition. 2.4.5 Rolling Volatility Technique. 2.4.6 Case Study: Rolling Volatility in Practice. 2.4.7 Wavelet Decomposition. 2.4.8 Case Study: Wavelet Filtering of Nord Pool Hourly System Prices. 2.5 Mean Reversion. 2.5.1 R/S Analysis. 2.5.2 Detrended Fluctuation Analysis. 2.5.3 Periodogram Regression. 2.5.4 Average Wavelet Coefficient. 2.5.5 Case Study: Anti-persistence of Electricity Prices. 2.6 Distributions of Electricity Prices. 2.6.1 Stable Distributions. 2.6.2 Hyperbolic Distributions. 2.6.3 Case Study: Distribution of EEX Spot Prices. 2.6.4 Further Empirical Evidence and Possible Applications. 2.7 Summary. 2.8 Further Reading. 3 Modeling and Forecasting Electricity Loads. 3.1 Introduction. 3.2 Factors Affecting Load Patterns. 3.2.1 Case Study: Dealing with Missing Values and Outliers. 3.2.2 Time Factors. 3.2.3 Weather Conditions. 3.2.4 Case Study: California Weather vs Load. 3.2.5 Other Factors. 3.3 Overview of Artificial Intelligence-Based Methods. 3.4 Statistical Methods. 3.4.1 Similar-Day Method. 3.4.2 Exponential Smoothing. 3.4.3 Regression Methods. 3.4.4 Autoregressive Model. 3.4.5 Autoregressive Moving Average Model. 3.4.6 ARMA Model Identification. 3.4.7 Case Study: Modeling Daily Loads in California. 3.4.8 Autoregressive Integrated Moving Average Model. 3.4.9 Time Series Models with Exogenous Variables. 3.4.10 Case Study: Modeling Daily Loads in California with Exogenous Variables. 3.5 Summary. 3.6 Further Reading. 4 Modeling and Forecasting Electricity Prices. 4.1 Introduction. 4.2 Overview of Modeling Approaches. 4.3 Statistical Methods and Price Forecasting. 4.3.1 Exogenous Factors. 4.3.2 Spike Preprocessing. 4.3.3 How to Assess the Quality of Price Forecasts. 4.3.4 ARMA-type Models. 4.3.5 Time Series Models with Exogenous Variables. 4.3.6 Autoregressive GARCH Models. 4.3.7 Case Study: Forecasting Hourly CalPX Spot Prices with Linear Models. 4.3.8 Case Study: Is Spike Preprocessing Advantageous?. 4.3.9 Regime-Switching Models. 4.3.10 Calibration of Regime-Switching Models. 4.3.11 Case Study: Forecasting Hourly CalPX Spot Prices with Regime-Switching Models. 4.3.12 Interval Forecasts. 4.4 Quantitative Models and Derivatives Valuation. 4.4.1 Jump-Diffusion Models. 4.4.2 Calibration of Jump-Diffusion Models. 4.4.3 Case Study: A Mean-Reverting Jump-Diffusion Model for Nord Pool Spot Prices. 4.4.4 Hybrid Models. 4.4.5 Case Study: Regime-Switching Models for Nord Pool Spot Prices. 4.4.6 Hedging and the Use of Derivatives. 4.4.7 Derivatives Pricing and the Market Price of Risk. 4.4.8 Case Study: Asian-Style Electricity Options. 4.5 Summary. 4.6 Further Reading. Bibliography. Index.

Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach

The impact of renewable energies on EEX day-ahead electricity prices

/pdf/modeling-and-forecasting-electricity-loads-and-prices-3ep61ivb7l.pdf

Modeling and Forecasting Electricity Loads and Prices

In this paper we present a mean-reverting jump diffusion model for the electricity spot price. We obtain a closed-form solution for forward contracts and calibrate it to market data from England and Wales. Finally, based on the calibrated forward curve we present months, quarters, and seasons-ahead forward surfaces.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID592262_code349872.pdf?abstractid=592262&mirid=5

Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality

This paper presents a mean‐reverting jump diffusion model for the electricity spot price and derives the corresponding forward price in closed‐form. Based on historical spot data and forward data from England and Wales the model is calibrated and months, quarters, and seasons–ahead forward surfaces are presented.

/pdf/pricing-in-electricity-markets-a-mean-reverting-jump-3edwtn14en.pdf

We present a spot price model for wholesale electricity prices which incorporates forward looking information that is available to all market players. We focus on information that measures the extent to which the capacity of the England and Wales generation park will be constrained over the next 52 weeks. We propose a measure of 'tight market conditions', based on capacity constraints, which identifies the weeks of the year when price spikes are more likely to occur. We show that the incorporation of this type of forward looking information, not uncommon in the electricity markets, improves the modeling of spikes (timing and magnitude) and the different speeds of mean reversion.

/pdf/modelling-electricity-prices-with-forward-looking-capacity-508p3vfepd.pdf

Modelling Electricity Prices with Forward Looking Capacity Constraints

We present a spot price model for wholesale electricity prices which incorporates forward looking information that is available to all market players. We focus on information that measures the extent to which the capacity of the England and Wales generation park will be constrained over the next 52 weeks. We propose a measure of ‘tight market conditions’, based on capacity constraints, which identifies the weeks of the year when price spikes are more likely to occur. We show that the incorporation of this type of forward looking information, not uncommon in electricity markets, improves the modelling of spikes (timing and magnitude) and the different speeds of mean reversion.

/pdf/modelling-electricity-prices-with-forward-looking-capacity-4o6nsb3lwq.pdf

In this article we price a multiple-interruptible contract for the electricity market in England and Wales under a mean-reverting jump-difiusion model with seasonality. We do so by combining forward contracts with a swing option which can be exercised a pre-specifled number of times. We price this swing option by means of an extension of the Least-Squares Monte Carlo methodology for American options. We additionally compute the lower and upper bounds for this contract. For the computation of the lower bound we provide a semianalytical formula which reduces greatly the required computational time.

Marcelo G. Figueroa

Papers

Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality

Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality

Modelling Electricity Prices with Forward Looking Capacity Constraints

Modelling Electricity Prices with Forward Looking Capacity Constraints

Pricing Multiple Interruptible-Swing Contracts