Provision of an Optimal Strategy to Forecast the Prices Set by the Electricity Market in the Competitive Iranian Energy Market in Fall

Document Type : Original Article

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad university, Tehran, Iran

2 Department of Mathematics, Science and Research Branch, Islamic Azad university, Tehran, Iran

3 Department of Industrial Engineering, Mazandaran University of Science and Technology Branch, Babol, Iran

Abstract

Given the complexities of the electricity market, various factors, such as uncertainties, the ways upon which the markets are set, how the debts are settled, the market structure and regulations, production prices, constraints governing the units and networks, etc. are influential in determining the optimal pricing strategies. Various methods and models have been presented to resolve the pricing issue in the competitive electricity industry. The most prominent of which include pricing methods are based on the prediction of competitors’ behavior; also pricing methods based on the forecasts of market price, methods based on the game theory and lastly, pricing methods based on the intelligent algorithms. Therefore, this study was conducted to provide an optimal strategy in order to forecast the electricity market price set in the competitive Iranian electricity market (based on the data collected). In this paper, the proposed method uses a compound network based on the neural networks. The analyzed data include the amount of the consumed energy as well as temperature (if applicable) and the price set for the past days and weeks. The self-organizing map (SOM) network was used for the input clustering based on the similar days. A number of multilayer perceptron (MLP) neural networks were used to combine the extracted data consisting of the energy levels, the price set, and temperature (if possible). The results showed improvements in the performance of the smart systems based on the neural networks in predicting the electricity prices.

Keywords


1.     Lin, L., Cunshan, Z., Vittayapadung, S., Xiangqian, S., and Mingdong, D., "Opportunities and challenges for biodiesel fuel", Applied Energy, Vol. 88, No. 4, (2011), 1020–1031. doi:10.1016/j.apenergy.2010.09.029
2.     Dhinesh, B., Maria Ambrose Raj, Y., Kalaiselvan, C., and KrishnaMoorthy, R., "A numerical and experimental assessment of a coated diesel engine powered by high-performance nano biofuel", Energy Conversion and Management, Vol. 171, (2018), 815–824. doi:10.1016/j.enconman.2018.06.039
3.     Vigneswaran, R., Annamalai, K., Dhinesh, B., and Krishnamoorthy, R., "Experimental investigation of unmodified diesel engine performance, combustion and emission with multipurpose additive along with water-in-diesel emulsion fuel", Energy Conversion and Management, Vol. 172, (2018), 370–380. doi:10.1016/j.enconman.2018.07.039
4.     Dhinesh, B., and Annamalai, M., "A study on performance, combustion and emission behaviour of diesel engine powered by novel nano nerium oleander biofuel", Journal of Cleaner Production, Vol. 196, (2018), 74–83. doi:10.1016/j.jclepro.2018.06.002
5.     Nomura, N., Inaba, A., Tonooka, Y., and Akai, M., "Life-cycle emission of oxidic gases from power-generation systems", Applied Energy, Vol. 68, No. 2, (2001), 215–227. doi:10.1016/S0306-2619(00)00046-5
6.     Sánchez de la Nieta, A., González, V., and Contreras, J., "Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming", Energies, Vol. 9, No. 12, (2016), 1069. doi:10.3390/en9121069
7.     Najafi, A., Falaghi, H., Contreras, J., and Ramezani, M., "Medium-term energy hub management subject to electricity price and wind uncertainty", Applied Energy, Vol. 168, (2016), 418–433. doi:10.1016/j.apenergy.2016.01.074
8.     Yang, P., Tang, G., and Nehorai, A., "A game-theoretic approach for optimal time-of-use electricity pricing", IEEE Transactions on Power Systems, Vol. 28, No. 2, (2013), 884–892. doi:10.1109/TPWRS.2012.2207134
9.     Yang, Z., Ce, L., and Lian, L., "Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods", Applied Energy, Vol. 190, (2017), 291–305. doi:10.1016/j.apenergy.2016.12.130
10.   Wang, D., Luo, H., Grunder, O., Lin, Y., and Guo, H., "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm", Applied Energy, Vol. 190, (2017), 390–407. doi:10.1016/j.apenergy.2016.12.134
11.   Abedinia, O., Amjady, N., Shafie-Khah, M., and Catalão, J. P. S., "Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method", Energy Conversion and Management, Vol. 105, (2015), 642–654. doi:10.1016/j.enconman.2015.08.025
12.   Lago, J., De Ridder, F., Vrancx, P., and De Schutter, B., "Forecasting day-ahead electricity prices in Europe: The importance of considering market integration", Applied Energy, Vol. 211, (2018), 890–903. doi:10.1016/j.apenergy.2017.11.098
13.   Weron, R., (, October 1)"Electricity price forecasting: A review of the state-of-the-art with a look into the future", International Journal of Forecasting, Vol. 30, No. 4, (2014), 1030–1081, Elsevier B.V., 1030–1081. doi:10.1016/j.ijforecast.2014.08.008
14.   Lago, J., De Ridder, F., and De Schutter, B., "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms", Applied Energy, Vol. 221, (2018), 386–405. doi:10.1016/j.apenergy.2018.02.069
15.   Wang, J., Liu, F., Song, Y., and Zhao, J., "A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system", Applied Soft Computing Journal, Vol. 48, (2016), 281–297. doi:10.1016/j.asoc.2016.07.011
16.   Bento, P. M. R., Pombo, J. A. N., Calado, M. R. A., and Mariano, S. J. P. S., "A bat optimized neural network and wavelet transform approach for short-term price forecasting", Applied Energy, Vol. 210, (2018), 88–97. doi:10.1016/j.apenergy.2017.10.058
17.   Ghasemi, A., Shayeghi, H., Moradzadeh, M., and Nooshyar, M., "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management", Applied Energy, Vol. 177, (2016), 40–59. doi:10.1016/j.apenergy.2016.05.083
18.   Mirakyan, A., Meyer-Renschhausen, M., and Koch, A., "Composite forecasting approach, application for next-day electricity price forecasting", Energy Economics, Vol. 66, (2017), 228–237. doi:10.1016/j.eneco.2017.06.020
19.   Tian, L., and Noore, A., "Short-term load forecasting using optimized neural network with genetic algorithm", In International Conference on Probabilistic Methods Applied to Power Systems, (2005). https://ieeexplore.ieee.org/abstract/document/1378676
20.   Ansari, M., and Amoli, M. T., "Optimal pricing strategy in Iran’s electricity market using probabilistic supply curve model", 28th International Electricity Conference, (2013). [In Persion]. http://psc-ir.com/cd/2013/papers/2404.pdf
21.   Panapakidis, I. P., and Dagoumas, A. S., "Day-ahead electricity price forecasting via the application of artificial neural network based models", Applied Energy, Vol. 172, (2016), 132–151. doi:10.1016/j.apenergy.2016.03.089
22.   Sandhu, H. S., Fang, L., and Guan, L., "Forecasting day-ahead price spikes for the Ontario electricity market", Electric Power Systems Research, Vol. 141, (2016), 450–459. doi:10.1016/j.epsr.2016.08.005
23.   Ortiz, M., Ukar, O., Azevedo, F., and Múgica, A., "Price forecasting and validation in the Spanish electricity market using forecasts as input data", International Journal of Electrical Power and Energy Systems, Vol. 77, (2016), 123–127. doi:10.1016/j.ijepes.2015.11.004
24.   Keles, D., Scelle, J., Paraschiv, F., and Fichtner, W., "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks", Applied Energy, Vol. 162, (2016), 218–230. doi:10.1016/j.apenergy.2015.09.087
25.   Singh, N., Mohanty, S. R., and Dev Shukla, R., "Short term electricity price forecast based on environmentally adapted generalized neuron", Energy, Vol. 125, (2017), 127–139. doi:10.1016/j.energy.2017.02.094
26.   Itaba, S., and Mori, H., "A Fuzzy-Preconditioned GRBFN Model for Electricity Price Forecasting", Procedia Computer Science, Vol. 114, (2017), 441–448. doi:10.1016/j.procs.2017.09.010
27.   Tang, L., Yu, L., He, K., “A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting”. Applied Energy; Vol. 128, 1–14 (2014). https://doi.org/10.1016/j.apenergy.2014.04.021
28.   Chai, J., Zhang, Z. Y., Wang, S. Y., Lai, K. K., and Liu, J., "Aviation fuel demand development in China", Energy Economics, Vol. 46, (2014), 224–235. doi:10.1016/j.eneco.2014.09.007
29.   Diongue, A. K., Guégan, D., and Vignal, B., "Forecasting electricity spot market prices with a k-factor GIGARCH process", Applied Energy, Vol. 86, No. 4, (2009), 505–510. doi:10.1016/j.apenergy.2008.07.005
30.   Girish, G. P., "Spot electricity price forecasting in Indian electricity market using autoregressive-GARCH models", Energy Strategy Reviews, Vols 11–12, (2016), 52–57. doi:10.1016/j.esr.2016.06.005
31.   Zhang, J., and Tan, Z., "Day-ahead electricity price forecasting using WT, CLSSVM and EGARCH model", International Journal of Electrical Power and Energy Systems, Vol. 45, No. 1, (2013), 362–368. doi:10.1016/j.ijepes.2012.09.007
32.   Yan, X., and Chowdhury, N. A., "Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach", International Journal of Electrical Power and Energy Systems, Vol. 53, No. 1, (2013), 20–26. doi:10.1016/j.ijepes.2013.04.006
33.   Zhu, B., Chevallier, J., Zhu, B., and Chevallier, J., "Carbon Price Forecasting with a Hybrid ARIMA and Least Squares Support Vector Machines Methodology", Pricing and Forecasting Carbon Markets, (2017), 87–107. doi:10.1007/978-3-319-57618-3_6
34.   He, K., Yu, L., and Tang, L., "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology", Energy, Vol. 91, (2015), 601–609. doi:10.1016/j.energy.2015.08.021
35.   Qiu, X., Suganthan, P. N., and Amaratunga, G. A. J., "Short-term Electricity Price Forecasting with Empirical Mode Decomposition based Ensemble Kernel Machines", Procedia Computer Science, Vol. 108, (2017), 1308–1317. doi:10.1016/j.procs.2017.05.055
36.   Mirzayian Dezfuli, I., and Nikukar, J., "Assessment of electricity price forecast methods in the energy market", 4th International Science and Engineering Conference, (2017). [In Persion]. https://www.civilica.com/Paper-ICESCON04-ICESCON04_148.html
37.   Vahidi, A., and Tofighi, M. H., "Simultaneous forecast of price and demand in a smart electricity distribution network", 2nd International Conference on Modern Scientific Research in Computer Science and Engineering, (2017). [In Persion]. https://www.civilica.com/Paper-COMCONF02-COMCONF02_222.html
38.   Nasiri Ghusheh Bolagh, M., "Short-term electricity price forecast using times series in a neural fuzzy method" 3rd Regional Control, Electronics and Artificial Intelligence Conference, (2016). [In Persion]. https://www.civilica.com/Paper-CEAI03-CEAI03_006.html
39.   Shayeghi, H., and Ghasemi, A., "Daily electricity price forecast using an enhanced neural network based on wavelet transform and gravitational search chaotic method, ", Journal of Electrical Engineering, Tabriz University, (2016). [In Persion]. https://www.civilica.com/Paper-JR_TJEE-JR_TJEE-45-4_010.html
40.   Meng, L., Hossain, M. U., Farzana, S., and Thengolose, A. L., "Estimation and Prediction of Residential Building Energy Consumption in Rural Areas of Chongqing", International Journal of Engineering, Transactions C: Aspects, Vol. 26, No. 9, (2013), 955–962. doi:10.5829/idosi.ije.2013.26.09c.03
41.   Neshat, N., "An Approach of Artificial Neural Networks Modeling Based on Fuzzy Regression for Forecasting Purposes", International Journal of Engineering, Transactions B: Applications, Vol. 28, No. 11, (2015), 1651–1655. doi:10.5829/idosi.ije.2015.28.11b.13
42.   Ahmadi, E., Abooie, M. H., Jasemi, M., and Mehrjardi, Y. Z., "A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis", International Journal of Engineering, Transactions C: Aspects, Vol. 29, No. 12, (2016), 1717–1725. doi:10.5829/idosi.ije.2016.29.12c.10
43.   Neshat, N., Amin-Naseri, M. R., and Ganjavi, H. S., "A Game Theoretic Approach for Sustainable Power Systems Planning in Transition", International Journal of Engineering, Transactions C: Aspects, Vol. 30, No. 3, (2017), 394–403. doi:10.5829/idosi.ije.2017.30.03c.09
44.   Khedmati, M., Seifi, F., and Azizi, M. J., "Time series forecasting of bitcoin price based on autoregressive integrated moving average and machine learning approaches", International Journal of Engineering, Transactions A: Basics, Vol. 33, No. 7, (2020), 1293–1303. doi:10.5829/ije.2020.33.07a.16
45.   Kavoosi Davoodi, S. M., Najafi, S. E., Hosseinzadeh Lotfi, F., and Mohammadiyan bisheh, H., "An accurate analysis of the parameters affecting consumption and price fluctuations of Electricity in the Iranian market during Summer", Scientia Iranica, (2020). doi:10.24200/sci.2020.52550.2771
46.   Bagheri, F., Ziaratban, M., and Tarokh, M. J., "Predicting Behaviors of Insurance Costumers by Using the Genetic Algorithm", Journal of Mathematics and Computer Science, Vol. 14, (2014), 54–70
47.   Kohani, S., and Zong, P., "A Genetic Algorithm for Designing Triplet LEO Satellite Constellation with Three Adjacent Satellites", International Journal of Aeronautical and Space Sciences, Vol. 20, No. 2, (2019), 537–552. doi:10.1007/s42405-019-00149-6
48.           Purohit, I., and Purohit, P., "Inter-comparability of solar radiation databases in Indian context", Renewable and Sustainable Energy Reviews, Vol. 50, (2015), 735–747, Elsevier Ltd, 735–747. doi:10.1016/j.rser.2015.05.020