Afanasyev, Dmitriy and Fedorova, Elena and Popov, Viktor (2015): Fine structure of the pricedemand relationship in the electricity market: multiscale correlation analysis. Published in: Energy Economics , Vol. 51, (September 2015): pp. 215226.
This is the latest version of this item.

PDF
MPRA_paper_58827.pdf Download (757kB)  Preview 
Abstract
In this research we investigate the problems of dynamic relationship between electricity price and demand over different time scales for two largest price zones of the Russian wholesale electricity market. We use multiscale correlation analysis based on a modified method of timedependent intrinsic correlation and the complete ensemble empirical mode decomposition with adaptive noise for this purpose. Three hypotheses on the type and strength of correlations in the short, medium and longruns were tested. It is shown that price zones significantly differ in internal price–demand correlation structure over the comparable time scales, and not each of the theoretically formulated hypotheses is true for each of them. We can conclude that the answer to the question whether it is necessary to take into account the influence of demandside on electricity spot prices over different time scales, is significantly dependent on the structure of electricity generation and consumption on the corresponding market.
Item Type:  MPRA Paper 

Original Title:  Fine structure of the pricedemand relationship in the electricity market: multiscale correlation analysis 
English Title:  Fine structure of the pricedemand relationship in the electricity market: multiscale correlation analysis 
Language:  English 
Keywords:  electricity spot price, electricity demand, pricedemand correlation, empirical mode decomposition, timedependent intrinsic correlation, trend estimation 
Subjects:  C  Mathematical and Quantitative Methods > C0  General > C02  Mathematical Methods C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C65  Miscellaneous Mathematical Tools L  Industrial Organization > L1  Market Structure, Firm Strategy, and Market Performance > L11  Production, Pricing, and Market Structure ; Size Distribution of Firms L  Industrial Organization > L9  Industry Studies: Transportation and Utilities > L94  Electric Utilities 
Item ID:  66138 
Depositing User:  Mr. Dmitriy Afanasyev 
Date Deposited:  18. Aug 2015 05:34 
Last Modified:  18. Aug 2015 06:01 
References:  AlvarezRamirez, J., EscarelaPerez, R., 2010. Timedependent correlations in electricity markets. Energy Economics 32 (2), 269277. An, N., Zhao, W., Wang, J., Shang, D., Zhao, E., 2013. Using multioutput feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 49, 279288. Barlow, M., 2002. A diffusion model for electricity prices. Mathematical Finance 12, 287289. Carmon, R., Coulon, M., 2014. A survey of commodity markets and structural models for electricity prices. Quantitative energy finance: modeling, pricing, and hedging in energy and commodity markets. Springer New York. Cartea, A., Figueroa, M., 2005. Pricing in electricity markets: a mean reverting. Jump diffusion model with seasonality. Applied Mathematical Finance 12 (4), 313335. Cartea, A., Villaplana, P., 2008. Spot price modeling and the valuation of electricity forward contracts: The role of demand and capacity. Journal of Banking and Finance 32 (12), 25022519. Chen, N., Wu, Z., Huang, N., 2010. The timedependent intrinsic correlation based on the empirical mode decom position. Advances in Adaptive Data Analysis 2 (2), 223265. Colominas, M., Schlotthauer, G., Torres, M., Flandrin, P., 2012. Noiseassisted EMD methods in action. Advances in Adaptive Data Analysis 4 (4). Coulon, M., Howison, S., 2009. Stochastic behavior of the electricity bid stack: from fundamental drivers to power prices. Energy Markets 2. Crowley, P., 2012. How do you make a time series sing like a choir? Extracting embedded frequencies from economic and financial time series using empirical mode decomposition. Studies in Nonlinear Dynamics and Econometrics 16 (5). De Jong, C., 2006. The nature of power spikes: a regimeswitch approach. Studies in Nonlinear Dynamics and Econometrics 10 (3). Dong, Y., Wang, J., Jiang, H., Wu, J., 2011. Shortterm electricity price forecast based on the improved hybrid model. Energy Conversion and Management 52 (89), 29872995. Efron, B., 1979. Bootstrap methods: another look at the jackknife. Annals of Statistics 7, 126. Fanone, E., Gamba, A., Prokopczuk, M., 2013. The case of negative dayahead electricity prices. Energy Economics 35, 2234. Flandrin, P., Goncalves, P., Rilling, G., 2004. Detrending and denoising with empirical mode decomposition. EUSIPCO. Fuss, R., Mahringer, S., Prokopczuk, M., 2013. Electricity derivatives pricing with forwardlooking information. University of St.Gallen, School of Finance Research Paper (2013/17). Geman, H., Nguyen, V., 2005. Soybean inventory and forward curve dynamics. Journal of Banking and Finance 51 (7), 10761091. Ghelardoni, L., Ghio, A., Anguita, D., 2013. Energy load forecasting using empirical mode decomposition and support vector regression. IEEE Transactions on Smart Grid 4 (1), 549556. Haldrup, N., Nielsen, F., Nielsen, M., 2010. A vector autoregressive model for electricity prices subject to long memory and regime switching. Energy Economics 32, 10441058. Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C., Liu, H., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454 (1971), 903995. Hurst, H., 1951. The longterm storage capacity of reservoirs. Transactions of the American society of civil engineers 116, 770799. Ismail, Z., 2013. A new approach to peak load forecasting based on EMD and ANFIS. Indian Journal of Science and Technology 6 (12), 56005606. Janczura, J., Truck, S., Weron, R., Wolff, R., 2013. Identifying spikes and seasonal components in electricity spot price data: A guide to robust modelings. Energy Economics 38, 96100. Kosater, P., Mosler, K., 2006. Can markov regimeswitching models improve powerprice forecasts? Evidence from german daily power prices. Applied Energy 83 (9), 943958. Kydland, F., Prescott, E., 1990. Business cycles, real facts and a monetary myth. Federal Reserve Bank of Minneapolis Quarterly Review 14, 318. Mhamdi, F., JadaneSadane, M., Poggi, J.M., 2010. Empirical mode decomposition for trend extraction: application to electrical data. 19th International Conference on Computational Statistics. Moghtader, A., Borgnat, P., Flandrin, P., 2011. Trend filtering: empirical mode decomposition versus l1 and HodrickPrescott. Advances in Adaptive Data Analysis 3 (1 and 2), 4161. Oladosu, G., 2009. Identifying the oil pricemacroeconomy relationship: An empirical mode decomposition analysis of US data. Energy Police 37. Papadimitriou, S., Sun, J., Yu, P., 2006. Local correlation tracking in time series. ICDM, 456465. Peters, E., 1991. Chaos and order in the capital markets: a new view of cycles, prices, and market volatility. New York: Wiley. Rilling, G., Flandrin, P., Gonçalvès, P., 2003. On empirical mode decomposition and its algorithms. IEEEEURASIP Workshop on Nonlinear Signal and Image Processing. Skantze, P., Gubina, A., Ilic, M., 2000. Bidbased stochastic model for electricity prices: the impact of fundamental drivers on market dynamics. Tech. rep., Energy Laboratory Publications MIT EL 00004, Massachusetts Institute of Technology. URL http://web.mit.edu/energylab/www/pubs/el00004.pdf. Torres, M., Colominas, M., Schlotthauer, G., Flandrin, P., 2011. A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Trück, S., Weron, R., Wolff, R., 2007. Outlier treatment and robust approaches for modeling electricity spot prices. Proceedings of the 56th Session of the ISI. URL http://mpra.ub.unimuenchen.de/4711/1/MPRA_paper_4711.pdf. Uritskaya, O. Y., Serletis, A., 2008. Quantifying multiscale inefficiency in electricity markets. Energy Economics 30 (6), 31093117. Wu, Z., Huang, N., 2009. Ensemble empirical mode decomposition: A noiseassisted data analysis method. Advances in Adaptive Data Analysis 1 (1), 141. Yule, G., 1926. Why do we sometimes get nonsense correlations between time series?  A study in sampling and the nature of time series. Journal of the Royal Statistical Society 89 (1), 164. Zachmann, G., 2013. A stochastic fuel switching model for electricity prices. Energy Economics 35, 513. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/66138 
Available Versions of this Item

Fine structure of the pricedemand relationship in the electricity market: multiscale correlation analysis. (deposited 25. Sep 2014 18:27)
 Fine structure of the pricedemand relationship in the electricity market: multiscale correlation analysis. (deposited 18. Aug 2015 05:34) [Currently Displayed]