Forecasting Spikes in Electricity Prices

In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily regulated price. Consequently the occurrence of extreme movements in the spot price represents a major source of risk to retailers and the accurate forecasting of these extreme events or price spikes is an important aspect of effective risk management. Traditional approaches to modeling electricity prices are aimed primarily at predicting the trajectory of spot prices. By contrast, this paper focuses exclusively on the prediction of spikes in electricity prices. The time series of price spikes is treated as a realization of a discrete-time point process and a nonlinear variant of the autoregressive conditional hazard (ACH) model is used to model this process. The model is estimated using half-hourly data from the Australian electricity market for the sample period 1 March 2001 to 30 June 2007. The estimated model is then used to provide one-step-ahead forecasts of the probability of an extreme event for every half hour for the forecast period, 1 July 2007 to 30 September 2007, chosen to correspond to the duration of a typical forward contract. The forecasting performance of the model is then evaluated against a benchmark that is consistent with the assumptions of commonly-used electricity pricing models.Electricity Prices, Price Spikes, Autoregressive Conditional Duration, Autoregressive

Short-term electricity price forecasting with time series models: A review and evaluation

We investigate the forecasting power of different time series models for electricity spot prices. The models include different specifications of linear autoregressive time series with heteroscedastic noise and/or additional fundamental variables and non-linear regime-switching TAR-type models. The models are tested on a time series of hourly system prices and loads from the California power market. Data from the period July 5, 1999 - April 2, 2000 are used for calibration and from the period April 3 - December 3, 2000 for out-of-sample testing.Electricity price forecasting; Autoregression (AR) model; Threshold Autoregression (TAR) model; Electricity load;

Short-term electricity price point and probabilistic forecasts

Accurate short-term electricity price forecasts are essential to all electricity market participants. Generation companies adopt price forecasts to hedge generation shortage risks; load serving entities use price forecasts to purchase energy with low cost; and trading companies utilize price forecasts to arbitrage between markets. Currently, researches on point forecast mainly focus on exploring periodic patterns of electricity price in time domain. However, frequency domain enables us to identify more information within price data to facilitate forecast. Besides, price spike forecast has not been fully studied in the existing works. Therefore, we propose a short-term electricity price forecast framework that analyzes price data in frequency domain and consider price spike predictions. First, the variational mode decomposition is adopted to decompose price data into multiple band-limited modes. Then, the extended discrete Fourier transform is used to transform the decomposed price mode into frequency domain and perform normal price forecasts. In addition, we utilize the enhanced structure preserving oversampling and synthetic minority oversampling technique to oversample price spike cases to improve price spike forecast accuracy. In addition to point forecasts, market participants also need probabilistic forecasts to quantify prediction uncertainties. However, there are several shortcomings within current researches. Although wide prediction intervals satisfy reliability requirement, the over-width intervals incur market participants to derive conservative decisions. Besides, although electricity price data follow heteroscedasticity distribution, to reduce computation burden, many researchers assume that price data follow normal distribution. Therefore, to handle the above-mentioned deficiencies, we propose an optimal prediction interval method. 1) By considering both reliability and sharpness, we ensure the prediction interval has a narrow width without sacrificing reliability. 2) To avoid distribution assumptions, we utilize the quantile regression to estimate the bounds of prediction intervals. 3) Exploiting the versatile abilities, the extreme learning machine method is adopted to forecast prediction intervals. The effectiveness of proposed point and probabilistic forecast methods are justified by using actual price data from various electricity markets. Comparing with the predictions derived from other researches, numerical results show that our methods could provide accurate and stable forecast results under different market situations

A Survey on Data Mining Techniques Applied to Energy Time Series Forecasting

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.Ministerio de Economía y Competitividad TIN2014-55894-C2-RJunta de Andalucía P12- TIC-1728Universidad Pablo de Olavide APPB81309

Probabilistic forecasting of electricity prices using an augmented LMARX-model

In this paper, we study the performance of prediction intervals in situations applicable to electricity markets. In order to do so we first introduce an extension of the logistic mixture autoregressive with exogenous variables (LMARX) model, see (Wong, Li, 2001), where we allow for multiplicative seasonality and lagged mixture probabilities. The reason for using this model is the prevalence of spikes in electricity prices. This feature creates a quickly varying, and sometimes bimodal, forecast distribution. The model is fitted to the price data from the electricity market forecasting competition GEFCom2014. Additionally, we compare the outcomes of our presumably more accurate representation of reality, the LMARX model, with other widely utilized approaches that have been employed in the literature

Optimal Participation of Power Generating Companies in a Deregulated Electricity Market

The function of an electric utility is to make stable electric power available to consumers in an efficient manner. This would include power generation, transmission, distribution and retail sales. Since the early nineties however, many utilities have had to change from the vertically integrated structure to a deregulated system where the services were unbundled due to a rapid demand growth and need for better economic benefits. With the unbundling of services came competition which pushed innovation and led to the improvement of efficiency. In a deregulated power system, power generators submit offers to sell energy and operating reserve in the electricity market. The market can be described more as oligopolistic with a System Operator in-charge of the power grid, matching the offers to supply with the bid in demands to determine the market clearing price for each interval. This price is what is paid to all generators. Energy is sold in the day-ahead market where offers are submitted hours prior to when it is needed. The spot energy market caters to unforeseen rise in load demand and thus commands a higher price for electrical energy than the day-ahead market. A generating company can improve its profit by using an appropriate bidding strategy. This improvement is affected by the nature of bids from competitors and uncertainty in demand. In a sealed bid auction, bids are submitted simultaneously within a timeframe and are confidential, thus a generator has no information on rivals’ bids. There have been studies on methods used by generators to build optimal offers considering competition. However, many of these studies base estimations of rivals’ behaviour on analysis with sufficient bidding history data from the market. Historical data on bidding behaviour may not be readily available in practical systems. The work reported in this thesis explores ways a generator can make security-constrained offers in different markets considering incomplete market information. It also incorporates possible uncertainty in load forecasts. The research methodology used in this thesis is based on forecasting and optimization. Forecasts of market clearing price for each market interval are calculated and used in the objective function of profit maximization to get maximum benefit at the interval. Making these forecasts includes competition into the bid process. Results show that with information on historical data available, a generator can make adequate short-term analysis on market behaviour and thus optimize its benefits for the period. This thesis provides new insights into power generators’ approach in making optimal bids to maximize market benefits