Modeling and Forecasting Day-Ahead Hourly Electricity Prices: A Review

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1 Proceedings of the International Conference on Business Management & Information Systems, 2012 Modeling and Forecasting Day-Ahead Hourly Electricity Prices: A Review Girish G.P. Department of Finance, IFHE University, IBS Hyderabad ABSTRACT Electricity markets around the world are being transformed from a highly Government regulated and controlled markets into deregulated markets. Today, electricity trading has transformed from primarily being a technical business, to one in which the product is treated in the same way as any other commodity. The main objective of deregulation is to introduce competition in the power industry and provide more choices to market participants in the way they trade electricity and ancillary services. In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools because price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities. A Generator/firm/Individual Power Producer (IPP) which is able to forecast prices correctly can adjust its own production schedule accordingly and hence maximize its profits. But one has to bear in mind that electricity is a very unique commodity that cannot be economically stored and the end user demand shows strong seasonality. This current study reviews literature pertaining to short term electricity price modeling and forecasting and gives direction for future researchers. Keywords: Electricity, Price, Market, Modeling, Forecasting Introduction Electricity markets around the world are being transformed from a highly Government regulated and controlled markets into deregulated markets. In recent years, traditional vertically integrated electric utility structure has been deregulated and replaced by a competitive market scheme in many countries world-wide [1]. The main objective of power system restructuring and deregulation is to introduce competition in the power industry and provide more choices to market participants in the way they trade electricity and ancillary services [2]. With deregulation and increased competition, power market participants are facing new challenges everyday. Today, electricity trading has indeed transformed from primarily being a technical business, to one in which the product is treated in the same way as any other commodity [3]. But one has to bear in mind that electricity is a very unique commodity that cannot be economically stored and the end user demand shows strong seasonality. Events like power plant outages, breakdown of electrical transformers, non availability of resources (ex: Non availability of coal for thermal power stations) or imperfect transmission grid reliability may have extreme effects on electricity prices. This makes electricity price forecasting critical for all power market participants. Electricity Price forecasting techniques in literature can be broadly divided into six classes: [4] Production-cost (or cost-based) models Equilibrium (or game theoretic) approaches [5] Fundamental (or structural) methods [6], [7] Quantitative (or stochastic, econometric, reduced-form) models

2 Modeling and Forecasting Day-Ahead Hourly Electricity Prices 67 Statistical (or technical analysis) approaches Artificial intelligence-based (or non-parametric) techniques [8], [9], [10] The deregulation and liberalization of electricity markets around the world has not only led to new challenges for market participants, but, has also created a new field of research. Liberalization and deregulation has propelled research in electricity price modeling and forecasting. The main objective of this study is to review literature pertaining to Short term Electricity Price Forecasting (i.e. for day ahead markets) Literature Review According to literature, forecasting of electricity prices can be categorized into: [11] a. Long-term price forecasting: The main objective is for investment profitability analysis and planning (especially for determining the future sites or fuel sources of power plants) b. Medium-term forecasting: These are generally preferred for balance sheet calculations, risk management and derivatives pricing. In many cases, they do not concentrate on the actual point forecasts but on the distributions of future prices over certain time periods c. Short-term price forecasting: This is of particular interest for participants of auction-type spot markets wherein participants are requested to express their bids in terms of prices and quantities. In such markets buy (sell) orders are accepted in order of increasing (decreasing) prices until total demand (supply) is met. In a power market, the price of electricity is the most important signal to all market participants. Time series forecasting is the prediction of future events based on already known past events using an appropriate model. Accordingly, there are different models that can be used for time series forecasting. [12] Classify Electricity Price forecasting Models broadly into 3 categories: Game Theory Models Time Series Models Simulation Models According to [12], a lot of researchers and academicians around the world are engaged in developing tools and algorithms for load and price forecasting. They are of the opinion that, load forecasting has reached advanced stage of development and load forecasting algorithms having mean absolute percentage error (MAPE) below 3% are available as highlighted by [13] and [14]. However price-forecasting models and techniques, which are being applied by power market participants around the world, are still in their early stages of maturity. [12] investigated the state of price-forecasting methodologies by reviewing 47 papers published during 1997 to November 2006 based on the type of model used for forecasting, time horizon for prediction, input variables, output variables, analysis of results, data points used for analysis, preprocessing employed and the model architecture. They have observed that forecasting errors are still high from risk management perspective and the results obtained by different price forecasting methodologies are difficult to compare with each other. The authors are of the belief that most of the electricity markets are still at the stage of infancy, so, the researchers have had to make predictive analysis based on very small data set which is available with them. They also point out that very little work has been done in the direction of prediction of price spikes. [15] in their study forecasted day ahead hourly electricity price in the electricity markets of mainland Spain and California by making use of price forecasting tools based on time series analysis i.e. using dynamic regression and transfer function models. The analysis in the paper was based on setting up a hypothetical probability model to represent the data. The models were selected based on a careful

3 68 Business Management and Information Systems inspection of the main characteristics of the hourly price series {i.e. high frequency, non-constant mean and variance, multiple seasonality (corresponding to a daily and weekly periodicity, respectively), calendar effect (such as weekends and holidays), high volatility, high percentage of unusual prices (mainly in periods of high demand)}. It was found that the Price predictions obtained for the Spanish and Californian electricity markets were accurate enough to be used by producers and consumers to prepare their corresponding bidding strategies. Average errors in the Spanish market were around 5% and around 3% in the Californian market for the weeks under study. However, it had been observed that the Spanish market showed more volatility in general. (Reason being a higher proportion of outliers and a lesser degree of competition) This made the Spanish market less predictable compared to Californian electricity market. [16] Predicted next-day electricity prices of mainland Spain and Californian markets by making use of ARIMA model. Market clearing prices of the day-ahead pool of mainland Spain and the Californian pool (which are publicly available) was considered for their study. The authors found the following differences between the Spanish electricity market and the Californian electricity market: a) Spanish electricity market showed more volatility in general. Its ARIMA model needed data from the previous 5 hours and did not use differentiation to attain a stable mean. b) Whereas, Price predictions were found to be better for Californian electricity market before the electricity market collapsed. This could be due to the fact that the Californian market showed less volatility in the period considered. ARIMA model for Californian market needed data from the previous 2 hours and required differentiations. Till 2003, in technical literature, Artificial Neural Networks (ANN) techniques had been widely used for load forecasting and for electricity price prediction. The paper by [16] was one among those very few initial works, in the field of electricity price forecasting using time series analysis and methods, which was supported and funded by the Ministry of Science and Technology (Spain) and the European Union through a grant. If average mean errors are considered for both markets, the results obtained are pretty good, especially considering the complex nature of electricity price time series and in comparison with the results previously reported in the technical literature. Table 1. Studies Related to Electricity Price Forecasting S.I. No. Model Total Papers Paper No. 1 Autoregressive Models 2 [17] and [18] 2 ARMA Models 2 [15] and [19] 3 ARIMA Models 7 [16], [20], [21], [18], [22], [23] and [24] 4 Multiple Linear Regression Models 1 [25] 5 Dynamic Regression Models and Transfer Function 3 [26], [15] and [27] 6 GARCH Models 3 [28], [27] and [21] 7 Jump Diffusion Models 3 [29], [30] and [31] 8 Regime Switching Models 5 [32], [33], [34], [35] and [36] Why Electricity Price Forecasting? In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities. According to [4], if classical notion of volatility i.e. Standard deviation of returns is considered, and is calculated on the daily scale (i.e. for daily prices), then:

4 Modeling and Forecasting Day-Ahead Hourly Electricity Prices 69 Treasury bills and Notes have Volatility of less than 0.5% Stock indices have moderate volatility of about 1-1.5% Commodities like crude oil or natural gas have volatilities of 1.5-4% Very volatile stocks have volatilities not exceeding 4% Electricity exhibits extreme volatility up to 50%!!! [37] Highlight that Price curve of electricity market exhibits considerably richer structure when than load curve having the following characteristics: High frequency Non-constant mean and variance Multiple seasonality (i.e. daily, weekly, monthly, hourly) Calendar effect High level of volatility and High percentage of unusual price movements These characteristics of Electricity Price curve can be attributed to the following reasons which distinguish electricity from other commodities [38] Non-storable nature of electrical energy The requirement of maintaining constant balance between demand and supply Inelastic nature of demand over short time period Oligopolistic generation side Load and generation side uncertainties For both spot markets and long-term contracts in an electricity market, price forecasts are necessary so that a firm/company can develop bidding strategies or negotiation skills in order to maximize its own benefit/profit. A Generator/firm/Individual Power Producer (IPP) which is able to forecast spot prices correctly can adjust its own production schedule accordingly and hence maximize its profits. Since the Table 2. Price Forecasting Research in Different Electricity Markets S.I. No. Market Paper No. 1 PJM Electricity Market [39] and [40] 2 California Electricity Market [16] and [17] 3 New England Electricity Market [41] and [42] 4 Ontario Electricity Market [43] 5 Spanish Electricity Market [15] and [16] 6 Victoria Electricity Market, NEM [44] 7 Queensland Electricity Market [45] 8 UK Power Pool [46] and [47] 9 European Energy Exchange (Leipzig) [18] 10 Electricity Markets of China [48] 11 Korean Power Exchange [49] 12 Amsterdam Power Exchange [50] 13 Alberta s Power Market [51] 14 New Zealand Electricity Market [52] 15 Polish Power Exchange [28] 16 Ukrainian Electricity Market [53] 17 Turkey Electricity Market [54]

5 70 Business Management and Information Systems day-ahead spot market typically consists of 24 hourly auctions that take place simultaneously one day in advance, forecasting with lead times from a few hours to a few days is of prime importance in day-to-day market operations. The Road Ahead Short-term Electricity price forecasting (i.e. day ahead hourly electricity price forecasting) in case of organized power exchanges in developing nations (like India where power sector is getting de-regulated due to reforms such as Electricity Act 2003) is one of the direction for future research. There are very few studies related to short term electricity price forecasting for developing countries particularly India where electricity markets are getting deregulated. With Practical relevance and stakes for implications of price forecasting for a Generator/IPP/firm being high (i.e. next-day price forecasting is a crucial need for producers, consumers and energy service companies), accurate forecasting tools and techniques is valued by power market participants, especially in an emerging economy like India. One of the directions for future research is developing a Time Series Econometric forecasting model that would most accurately forecast day-ahead hourly electricity prices in Indian Electricity markets and also investigate and compare the developed model with all other forecasting models given in literature. Acknowledgements I would like to thank Dr. Ajaya Panda, Dr. Subrata Sarkar, Dr. Badri Narayan Rath and Dr. Ban Sharma for all their valuable comments and feedback References [1] Li, Liu, Mattson & Lawarree, Day-ahead electricity price forecasting in a grid environment, IEEE Transactions on Power Systems, Vol. 2, 2007, pp [2] Amjady and Daraeepour, Design of input vector for day-ahead price forecasting of electricity markets, Expert Systems with Applications, Vol. 36, 2009, pp [3] Pilipovic, Energy Risk: Valuing and Managing Energy Derivatives, McGraw-Hill, [4] Weron, Modeling and forecasting electricity loads and prices: A statistical approach, Wiley, 2006, Chichester. [5] Ventosa, Baillo, Ramos and Rivier, Electricity market modeling trends, Energy Policy, Vol. 33, Issue 7, 2005, pp [6] Dueholm and Ravn, Modelling of short term electricity prices, hydro inflow and water values in the Norwegian hydro system, Proceedings of the 6th IAEE European Conference, 2004, Zurich. [7] Vahvilainen and Pyykkonen, Stochastic factor model for electricity spot price the case of the Nordic market, Energy Economics, Vol. 27, Issue 2, 2005, pp [8] Gonzalez, San Roque, and Garcıa-Gonzalez, Modeling and forecasting electricity prices with Input/Output Hidden Markov Models, IEEE Transactions on Power Systems, Vol. 20, Issue 1, 2005, pp [9] Rodriguez and Anders, Energy price forecasting in the Ontario competitive power system market, IEEE Trans Power Systems, Vol. 19, Issue 3, 2004, pp [10] Shahidehpour, Yamin, and Li, Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management, Wiley, [11] Misiorek, Trueck and Weron, Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non- Linear Time Series Models, Nonlinear Analysis of Electricity Prices, Vol. 10, Issue 3, [11] Aggarwal, Saini and Kumar, Electricity price forecasting in deregulated markets: A review and evaluation, Electrical Power and Energy Systems, Vol. 31, 2009, pp

6 Modeling and Forecasting Day-Ahead Hourly Electricity Prices 71 [12] Abdel-Aal, Modeling and forecasting electric daily peak loads using abductive networks, Electric Power Energy Systems, Vol. 28, 2006, pp [13] Mandal, Senjyu, Urasaki and Funabashi, A neural network based several hour- ahead electric load forecasting using similar days approach, Electric Power Energy Systems, Vol. 28, 2006, pp [14] Nogales, Contreras, Conejo and Espınola, Forecasting next-day electricity prices by time series models, IEEE Trans. Power Systems, Vol. 17, 2002, pp [15] Contreras, Espınola, Nogales and Conejo, ARIMA models to predict next-day electricity prices, IEEE Trans. Power Systems, Vol. 18, Issue 3, 2003, pp [16] Weron and Misiorek, Forecasting spot electricity prices with time series models, Proceedings of the European Electricity Market, EEM-05 Conference, Lodz, 2005, pp [17] Cuaresma, Hlouskova, Kossmeier and Obersteiner, Forecasting electricity spot prices using linear univariate time-series models, Applied Energy, Vol. 77, 2004, pp [18] Carnero, Koopman and Ooms, Periodic heteroskedastic Reg ARFIMA models for daily electricity spot prices, Tinbergen Institute Discussion Paper, 2003, TI, /4. [19] Zhou, Yan, Ni and Li, An ARIMA approach to forecasting electricity price with accuracy improvement by predicted errors, Proceedings of the IEEE Power Engineering Society General Meeting, 2004, pp [20] Garcia, Contreras, Akkeren, and Garcia, A GARCH forecasting model to predict day-ahead electricity prices, IEEE Transactions on Power Systems, Vol. 20, Issue 2, 2005, pp [21] Conejo, Contreras, Espınola and Plazas, Forecasting electricity prices for a day-ahead pool-based electric energy market, International Journal of Forecasting, Vol. 21, Issue 3, 2005, pp [22] Bowden and Payne, Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA EGARCH models, Energy Economics, Vol. 30, 2008, pp [23] Gianfreda & Grossi (2011), Forecasting Italian Electricity Zonal Prices with Exogenous Variables, Working paper, University of Verona [24] Schmutz and Elkuch, Electricity price forecasting: Application and experience in the European power markets, Proceedings of the 6th IAEE European Conference, 2004, Zurich. [25] Lora, Santos, Santos, Exposito, and Ramos, A comparison of two techniques for next-day electricity price forecasting, Lecture Notes in Computer Science, 2002, pp [26] Karakatsani and Bunn, Forecasting electricity prices: The impact of fundamentals and time-varying coefficients, International Journal of Forecasting, Vol. 24, Issue 4, 2008, pp [27] Mugele, Rachev and Trueck, Stable modeling of different European power markets, Investment Management and Financial Innovations, [28] Johnson and Barz, Selecting stochastic processes for modeling electricity prices, In Energy Modelling and the Management of Uncertainty, Risk Books, London, [29] Skantze and Illic, The Joint Dynamics of Electricity Spot and Forward Markets: Implications of Formulating Dynamic Hedging Strategies, Energy Laboratory Report No. MIT-EL , [30] Knittel and Roberts, An empirical examination of restructured electricity prices, Energy Economics, Vol. 27, Issue 5, 2005, pp [31] Ethier and Mount, Estimating the volatility of spot prices in restructured electricity markets and the implications for option values, Cornell University Working Paper, [32] Huisman and De Jong, Option formulas for mean-reverting power prices with spikes, Energy Power Risk Management, Vol. 7, 2003, pp [33] Huisman and Mahieu, Regime jumps in electricity prices, Energy Economics, Vol. 25, 2003, pp [34] Weron, Bierbrauer, and Trueck, Modeling electricity prices: Jump diffusion and regime switching, Physica, 2004, pp

7 72 Business Management and Information Systems [35] Haldrup and Nielsen, A regime switching long memory model for electricity prices, Working Paper, Aarhus University, [36] Karakatsani and Bunn, Modelling stochastic volatility in high frequency spot electricity prices, London Business School Working Paper, [37] Bunn, Forecasting loads and prices in competitive power markets, Proceedings of IEEE, Vol. 88, Issue 2, 2000, pp [38] Haiteng and Niimura, Short-term electricity price modeling and forecasting using wavelets and multivariate time series, IEEE power systems conference and exposition PES, No. 1, 2004, pp [39] Bastian, Zhu, Banunaryanan and Mukherji, Forecasting energy prices in a competitive market, IEEE Computer Application Power, 1999, pp [40] Zhang and Luh, Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method, IEEE Trans Power Systems, Vol. 20, Issue 1, 2005, pp [41] Guo and Luh, Improving market clearing price prediction by using a committee machine of neural networks, IEEE Trans Power Systems, Vol. 19, Issue 4, 2004, pp [42] Rodriguez and Anders, Energy price forecasting in the Ontario competitive power system market, IEEE Transactions on Power Systems, Vol. 19, Issue 1, 2004, pp [43] Szkuta, Sanabria and Dillon, Electricity price short-term forecasting using artificial neural networks, IEEE Trans Power Systems, Vol. 14, Issue 3, 1999, pp [44] Zhao, Dong, Li and Wong, A general method for electricity market price spike analysis, IEEE power engineering society general meeting, IEEE, Vol. 1, 2005, pp [45] Wang and Ramsay, A neural network based estimator for electricity spot pricing with particular reference to weekend and public holidays, Neurocomputing, Vol. 23, 1998, pp [46] Yao, Song, Zhang and Cheng, Prediction of system marginal price by wavelet transform and neural network, Electric Mach Power Systems, Vol. 28, [47] Hu, Yu, Wang, Sun, Gan and Han, Price forecasting using integrated approach, Proceedings of IEEE international conference on electric utility deregulation, restructuring and power technologies, Hong Kong, 2004, pp [48] Zhou, Yan, Ni, Li and Nie, Electricity price forecasting with confidence interval estimation through an extended ARIMA approach, IEEE Proc. Generation Trans Distribution, Vol. 153, Issue 2, 2006, pp [49] Culot, Goffin, Lawford, Menten and Smeers, An Affine Jump diffusion Model for Electricity, working paper, Catholic University of Leuven, [50] Serletis and Shahmoradi, Measuring and Testing Natural Gas and Electricity Markets Volatility: Evidence from Alberta s Deregulated Markets, Studies in Nonlinear Dynamics & Econometrics, Nonlinear Analysis Of Electricity Prices, Vol. 10, Issue 3, [51] Guthrie and Videbeck, Electricity Spot Price Dynamics: Beyond Financial Models, Available at SSRN: [52] Frunze, Modeling spot prices in Ukrainian wholesale electricity market, Economics Education and Research Consortium, National University - Kyiv-Mohyla Academy, [53] Ozmen, Miray, Bayrak and Wilhelm, Electricity Price Modelling for Turkey, Institute of Applied Mathematics, Middle East Technical University, 2011, Ankara.

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