Abstract
At the plant level, a smart grid unifies power grids, consumers and generating facilities in a single automated system which enables real-time tracking and monitoring the regimes of all participants. It responds automatically to the changes of all parameters in the power grid and maintains no-break power with maximum benefits and lower human involvement. The most effective control can be attained in smart grids by using intelligent agents. Their functionalities should be based on intelligent control algorithms with predictive models featuring high-precision treatment of process knowledge and adaptive learning. This chapter offers a concept of developing an intelligent multi-agent system that maintains stability of Russia’s Smart Grid incorporating an active analytical network (AAN) and new algorithms to determine the degree of the system’s stability by using Gramians.
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Bakhtadze N, Lototsky V, Maximov E, Pavlov B (2007) Associative search models in industrial systems. In: Proceedings of IFAC workshop on intelligent manufacturing systems, Alicante, Spain, pp 156–161
Bakhtadze N, Yadikin I, Kulba V, Lototsky V, Maximov E (2008) Intelligent power generation systems control. In: Proceedings of 9th IFAC Workshop on Intelligent Manufacturing Systems, Szczecin, Poland, pp 24–32
Bakhtadze N, Kulba V, Yadykin I, Lototsky V, Maximov E (2011a) Identification methods based on associative search procedure. Control Cybern 2(3):6–18
Bakhtadze N, Kulba V, Yadykin I, Lototsky V, Maximov E (2011b) Identification methods based on associative search procedure. Manage Prod Eng Rev 2(3):6–18
Bricker S, Gonen T, Rubin L (2001) Substation automation technologies and advantages. IEEE Comput Appl Power 14(3):31–37
Buse DP, Wu QH (2007) IP network-based multi-agent systems for industrial automation information management, condition monitoring and control of power systems. Springer, London, 178 pp
Catterson Victoria, Davidson Euan, Mcarthur Stephen (2012) Practical applications of multi-agent systems in electric power systems. Eur Trans Electr Power 22(2):235–252
Comacho EF, Bordons C (1998) Model predictive control. Springer, New York
Communications Networks and Systems in Substations (2005) Document IEC 61850
FIPA SL Content Language Specification (2002)
Foundation for Intelligent Physical Agents (FIPA) (2002) Agent management specification
Foundation for Intelligent Physical Agents (FIPA) (2002) FIPA ACL message structure specification
Hunt E (1989) Cognitive science: definition, status and questions. Annu Rev Psychol 40:603–629
IEC (2005) Energy Management System Application Program Interface (EMS-API)—Part 301: Common Information Model (CIM) base, 2005, Document IEC 61970-301
Nagata T, Nakayama H, Utatani M, Sasaki H (2002) A multi-agent approach to power system normal state operations. IEEE Power Eng Soc Summer Meet 3:1582–1586
Preiss O, Wegmann A (2001) Towards a composition model problem based on IEC61850. In: Proceedings of the 4th ICSE Workshop on component-based software engineering, Toronto, Canada. http://www.sei.cmu.edu/pacc/CBSE4_papers/PreissWegmann-CBSE4-4.pdf
Samigulina GA, Chebeiko SV (2003) Technology of elimination errors the energy estimations of artificial immune systems of the forecasting plague. In: Proceedings on the 6th International conference on computational intelligence and natural computation, Cary, North Carolina (USA), pp 1693–1696
Sukhanov OA, Novitsky DA, Yadykin I (2011) Method of steady-state stability analysis in large electrical power systems. In: Proceedings of 17th power systems computation conference 2011, PSCC’2011, Stockholm. Curran Associates, Inc., Stockholm, vol 2
Vishwanathan V, Mccalley J, Honava V (2001) A multi agent system infrastructure and negotiation framework for electric power systems. In: Proceedings of 2001 IEEE Porto power tech conference, Porto, Portugal
National Grid Company. http://www.nationalgrid.com/uk/library/documents/sys_03/default.asp
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Yadykin, I.B., Maximov, E.M. (2016). Knowledge-Based Models for Smart Grid. In: Różewski, P., Novikov, D., Bakhtadze, N., Zaikin, O. (eds) New Frontiers in Information and Production Systems Modelling and Analysis. Intelligent Systems Reference Library, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-319-23338-3_9
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