Security assessment and optimization of energy supply (neural networks approach)
DOI:
https://doi.org/10.12775/OeC.2015.016Keywords:
energy supply, security, neural networks, operating reserveAbstract
The question of energy supply continuity is essential from the perspective of the functioning of society and the economy today. The study describes modern methods of forecasting emergency situations using Artificial Intelligence (AI) tools, especially neural networks. It examines the structure of a properly functioning model in the areas of input data selection, network topology and learning algorithms, analyzes the functioning of an energy market built on the basis of a reserve market, and discusses the possibilities of economic optimization of such a model, including the question of safety.
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References
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Aganagic, M., Abdul-Rahman, K. H., & Waight, J. G. (1998). Spot pricing for generation and transmission of reserve in an extended Poolco model. IEEE Transactions on Power Systems, 13(3). http://dx.doi.org/10.1109/59.709110.
Aghaei, J., Shayanfar, H., & Amjady, N. (2009). Multi-objective market clearing of joint energy and reserves auctions ensuring power system security, Energy Conversion and Management, 50(4). http://dx.doi.org/10.1016/j.enconman.2008.12.027.
Alvey, T., Goodwin, D., Ma, X., Streiffert, D., & Sun, D. (1998). A security constrained bid clearing system for the New Zealand wholesale electricity market. IEEE Transactions on Power Systems, 13(2). http://dx.doi.org/10.1109/59.667349.
Baldick, R., Helman, U., Hobbs, B. F., & O?Neill, R. P. (2005). Design of efficient generation markets. Proceedings of the IEEE, 93(11). http://dx.doi.org/10.1109/JPROC.2005.857484.
CAISO, Scarcity Pricing Team (2008). California ISO Draft Proposal. Reserve Scarcity Pricing. February 6. Retrieved from https://www.caiso.com/Documents/DraftProposal_ReserveScarcityPricingDesign06-Feb-2008.pdf (01.12.2014).
Chen, W. H., Jiang, Q. Y., & Cao, Y. J. (2006). Low Voltage Risk Assessment in Power Systems Using Neural Network Ensemble. Advances in Neural Networks - ISNN 2006. Lecture Notes in Computer Science, 3972. http://dx.doi.org/10.1007/11760023_204.
Cheung, K. W., Shamsollahi, P., Sun, D., Milligan, J., & Potishnak, M. (2000). Energy and ancillary service dispatch for the interim ISO New England electricity market. IEEE Transactions on Power Systems, 15(3). http://dx.doi.org/10.1109/59.871720.
Gan, D., & Litvinov, E. (2003). Energy and reserve market designs with explicit consideration to lost opportunity cost. IEEE Transactions on Power Systems, 18(1). http://dx.doi.org/10.1109/MPER.2002.4311914.
Jasiński, T. (2014). Dane wejściowe sztucznych sieci neuronowych prognozujących krótkoterminowe zapotrzebowanie na energię. Logistyka, 6.
Jasiński, T. (2003). Przegląd architektur sztucznych sieci neuronowych wykorzystywanych w ekonomii do przewidywania szeregów czasowych. Studia i Prace Kolegium Zarządzania i Finansów, 35, Warszawa: SGH.
Jasiński, T. (2011). The usage of artificial neural networks on the energy market. In L. Kiełtyka (Ed.), IT Tools in Management and Education. Selected Problems. Częstochowa: Wydawnictwo Politechniki Częstochowskiej.
Jasiński, T., & Ścianowska, A. (2014). Analiza i modelowanie rynku energii w perspektywie długoterminowej. Systemy aukcji i przewidywanie popytu. Logistyka, 6.
Kim, M., & Joo, S. K. (2006), Feature Reduction Techniques for Power System Security Assessment. Advances in Natural Computation. Lecture Notes in Computer Science, 4221. http://dx.doi.org/10.1007/11881070_73.
Likover, R. (2014). Reserve Market Overview. Introduction to Wholesale Electricity Markets (WEM 101). Retrieved from http://www.iso-ne.com/support/training/courses/wem101/17_reserve_market_overview.pdf (01.12.2014).
Liu, S., & Liu, H. (2007). California ISO Issue Paper. Reserve Shortage Pricing. May 31. Retrieved from https://www.caiso.com/Documents/ReserveShortagePricingDesignIssuePaper31-May-2007.pdf (01.12.2014).
Liu, Y., Alaywan, Z., Rothleder, M., Liu, S., & Assadiam, M. (2000). A rational buyer?s algorithm used for ancillary service procurement. In Proceedings of the IEEE Power Engineering Society winter meeting 2000, 2.
Ma, X., Sun, D., & Cheung, K. (1999). Energy and reserve dispatch in a multi-zone electricity market. IEEE Transactions on Power Systems, 14(3). http://dx.doi.org/10.1109/59.780903.
Marsadek, M., & Mohamed, A. (2013). Risk based security assessment of power systems using generalized regression neural network with feature extraction. Journal of Central South University, 20. http://dx.doi.org/10.1007/s11771-013-1508-9.
Rashidi, M., & Rashidi, F. (2004). Design of an Adaptive Artificial Neural Network for Online Voltage Stability Assessment. Innovations in Applied Artificial Intelligence. Lecture Notes in Computer Science, 3029. http://dx.doi.org/10.1007/978-3-540-24677-0_108.
Samaan, N. (2007). Reliability Assessment of Composite Power Systems Using Genetic Algorithms. In G. Levitin (Ed.), Computational Intelligence in Reliability Engineering. Evolutionary Techniques in Reliability Analysis and Optimization. Springer. http://dx.doi.org/10.1007/978-3-540-37368-1_8.
Singh, H., & Papalexopoulos, A. (1999). Competitive procurement of ancillary services by an independent system operator. IEEE Transactions on Power Systems, 14(2). http://dx.doi.org/10.1109/59.761872.
Swetha, G. C., & Sudarshana Reddy, H. R. (2014). Voltage Stability Assessment in Power Network Using Artificial Neural Network. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(3).
Ukil, A. (2007). Intelligent Systems and Signal Processing in Power Engineering. Springer.
Wu, T., Rothleder, M., Alaywan, Z., & Papalexopoulos, A. D. (2004). Pricing energy and ancillary services in integrated market systems by an optimal power flow. IEEE Transactions on Power Systems, 19(1). http://dx.doi.org/10.1109/TPWRS.2003.820701.
