[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Social Network Membership
Linkedin
Researchgate
..
Indexing Databases
..
DOI
کلیک کنید
..
ِDOR
..
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 7, Issue 1 (9-2018) ::
ieijqp 2018, 7(1): 84-92 Back to browse issues page
Market clearing price prediction using improved neural network with genetic algorithm in Iranian day ahead market for competitiveness clustering’s
Bakhtiar Ostadi * 1, Omid Motamedi1 , Ali Husseinzadeh Kashan1 , Mohamad Reza Amin Naseri1
1- Tarbiat Modares University
Abstract:   (4378 Views)
In The deregulation in power market is lead to competition among market participant to increase efficiency. In electricity market generation is the best candidate for iterance in competition to improve productivity and efficiency in resource allocation and offer lowest price by highest quality will be yielded. In the pool-based electricity market, every Genco submits a bidding price in ten step offer to the Independent System Operator (ISO) for every hour of the next day. The ISO uses the bidding price and forecasting demand to determine the MCP. The resulting spot price series exhibit strong seasonality at the annual, weekly and daily levels, as well as mean reversion, very high volatility and abrupt, short-lived and generally unanticipated extreme price changes known as spikes or jumps. So in this article we cluster time horizon in three cluster then we applied improved neural network by genetic algorithm for all clusters. In compare of normal neural network, results of our model are more better by 95% Accuracy.
Keywords: neural network, genetic algorithm, competitive times Clustering, market clearing price prediction.
Full-Text [PDF 905 kb]   (1141 Downloads)    
Type of Study: Research |
Received: 2018/01/17 | Accepted: 2018/05/12 | Published: 2018/08/25
References
1. Lora AT, Santos JMR, Exposito AG, Ramos JLM, Santos JCR. Electricity market price forecasting based on weighted nearest neighbors techniques. Power Systems, IEEE Transactions on. 2007;22(3):1294-301.
2. Bigdeli N, Afshar K, Fotuhi-Firuzabad M. Bidding strategy in pay-as-bid markets based on supplier-market interaction analysis. Energy Conversion and Management. 2010;51(12):2419-3
3. Aggarwal SK, Saini LM, Kumar A. Electricity price forecasting in deregulated markets: A review and evaluation. International Journal of Electrical Power & Energy Systems. 2009;31(1):13-22.
4. Kwon RH, Frances D. Optimization-based bidding in day-ahead electricity auction markets: A review of models for power producers. Handbook of Networks in Power Systems I: Springer; 2012. p. 41-59
5. F. Gao, X. Guan, X. –R. Cao, A. Papalexopoulos, “Forecasting Power Market Clearing Price and Quantity Using a Neural Network Method” IEEE PES Winter Meeting, pp. 2183-2188, 2000.
6. Y: Y. Hong, C: -Y. Hsiao “Locational Marginal Price Forecasting in Deregulated Electricity Markets Using Artificial Intelligence”, IEE Proc. Gener. Transm. Distrib., Vol. 149,No.5., pp. 621- 626, 2002.
7. A. Wang, B. Ramsay, “Prediction of System Marginal Price in the UK Power Pool”, Int. Conf. on Neural Networks and Systems, Vol. 1, pp. 2116- 2120, 1997.
8. M. P. Moghaddam, M. K. Sheikh-El-Eslami, S. Jadid, “A Price Guideline for Generation Expansion Planning in Competitive Electricity Markets” IEEE Conf., pp. 1-5, 2005
9. Z. Hu,Y. Yu, Z. Wang, W. Sun, D. Gan, Z. Han, “Price Forecasting Using an Integrated Approach” IEEE Int. Conf. on Electric Utility, April 2004, Hong Kong.
10. D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning representations by back propagating errors,” Nature, Vol. 323, No. 1, pp. 533-536, 1986.
11. J. P. S. Catal˜ao, S.J.P.S. Mariano, V. M. F. Mendes and L. A. F. M. Ferreira, “Short-term electricity price forecasting in a competitive market: A neural network approach,” Electric Power Systems Research, Vol.77, No. 10, pp. 1297-1304, 2007.
12. V. Vahidinasab, S. Jadid and A. Kazemi, “Day-ahead price forecasting in restructured power system using artificial neural networks”. Electric Power Systems Research, Vol. 78, No. 8, pp. 1332-1342, 2008
13. Trippi, R.R. and E. Turban, Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real World Performance. 1992: McGraw-Hill, Inc.
14. Uritskaya OY, Uritsky VM. Predictability of price movements in deregulated electricity markets. Energy Economics. 2015;49:72-81.
15. Regime-switching models for electricity spot prices Introducing heteroskedastic base regime dynamics and shifted spike distributions


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

ostadi B, motamedi O, Husseinzadeh Kashan A, Amin Naseri M R. Market clearing price prediction using improved neural network with genetic algorithm in Iranian day ahead market for competitiveness clustering’s. ieijqp 2018; 7 (1) :84-92
URL: http://ieijqp.ir/article-1-498-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 7, Issue 1 (9-2018) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
Persian site map - English site map - Created in 0.07 seconds with 39 queries by YEKTAWEB 4645