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2023 | 14 | nr 2 | 99--110
Tytuł artykułu

Agents and Multi-agent Systems in the Management of Electric Energy Systems

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Electric energy systems need constant modernization and updating to solve such problems as distributed management, self-sealing, improving the quality of electricity, demand manage- ment, and integration of renewable energy systems. Currently, energy systems need advanced and intelligent technologies to perform various system-level tasks. The purpose of this re- search is to analyze the existing control systems of electric energy systems, as well as to consider the possibility of using multi-agent systems to control electric energy systems. To achieve the aim of the research, the following scientific approaches were implemented: method of direct research, experimental method, questioning, comparative method, analysis method, and method of observation. The primary value of the research is in the novelty of the work and the fact, that functional components in multi-agent systems act as independent agents, which can interact with each other through a message communication system. This provides a simple connection between the components, which can benefit complex systems designed for an intelligent network. The intelligent network provides an efficient energy management system, and the modernization of the existing power system using a multi-agent system pro- vides solutions to many problems. The best implementation of a multi-agent system can be achieved through the employment of fast and protected communication protocols. The au- thors of the research have conducted research and presented key statistical data on electricity usage in Kazakhstan over the past few years. The practical significance of the research is determined by the applied results, and their scientific significance, which is conditioned upon the use of deep, modern mathematical results and the development of an optimal control system. This research is a part of a universal model and optimal system of emergency quick response, conducting a quick preliminary prognosis as well as ensuring more lasting planning in electricity consumption.(original abstract)
Rocznik
Tom
14
Numer
Strony
99--110
Opis fizyczny
Twórcy
  • L.N. Gumilyov Eurasian National University, Kazakhstan
  • L.N. Gumilyov Eurasian National University, Kazakhstan
Bibliografia
  • Abushnaf J., Rassau A. and Gornisiewicz W. (2015), Impact of dynamic energy pricing schemes on a novel multi-user home energy management system, Electric Power Systems Research, Vol. 125, pp. 124-132
  • Akpojedje F.O., Ogujor E.A. and Folorunso O. (2018), A comprehensive review of optimal demand side management and its influence on enhancing distribution network congestion management, International Journal of Research in Engineering and Technology, No. 16, Vol. 2, pp. 107-113
  • Alsaif A.K. (2017), Challenges and benefits of integrating the renewable energy technologies into the AC power system grid, American Journal of Engineering Research, No. 4, Vol. 6, pp. 95-100
  • Amanbek Y., Kalakova A., Zhakiyeva S., Kayisli K., Zhakiyev N. and Friedrich D. (2022), Distribution locational marginal price based transactive energy management in distribution systems with smart prosumers - A multi-agent approach, Energies, No. 7, Vol. 15, Article number: 2704
  • Amini M., Frye J., Ili'c M.D. and Karabasoglu O. (2015), Smart residential energy scheduling utilizing two stage mixed integer linear programming, Proceedings of the 2015 North American Power Symposium (NAPS), No. 6, Vol. 4, pp. 1-6
  • Arcos-Aviles D., Pascual J., Marroyo L., Sanchis P. and Guinjoan F. (2017), Fuzzy logic-based energy management system design for residential grid-connected microgrids, IEEE Transactions on Smart Grid, Vol. 5, pp. 1-14
  • Avor D. and Janjic A. (2016), Application of demand side management techniques in successive optimization, Communications in Dependability and Quality Management an International Journal, No. 4, Vol. 19, pp. 40-51
  • Bellifemine F., Poggi A. and Rimassa G. (1999), JADE - A FIPA-compliant agent framework, Proceedings of the PAAM, Vol. 99, pp. 33-34
  • Bellifemine F.L., Caire G. and Greenwood D. (2007), Developing multi-agent systems with JADE, Hoboken, John Wiley & Sons
  • Bitimanova S.S. and Abdildaeva A.A. (2020), Algorithm for optimal control of electric power systems, Bulletin of Toraigyrov University, Vol. 1, pp. 78-89
  • Briones A.G., Chamoso P. and Barriuso A. (2016), Review of the main security problems with multi-agent systems used in e-commerce applications, Advances in Distributed Computing and Artificial Intelligence Journal, Vol. 5, pp. 55-61
  • Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan(2021), https://stat.gov.kz/, [data of access 05.12.2022]
  • Chen X., Wang Q. and Srebric J. (2017), Occupant feedback-based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation, Applied Energy, Vol. 164, pp. 341- 351
  • Cheyer A. and Martin D. (2001), The open agent architecture, Autonomous Agents Multi-Agent Systems, Vol. 4, pp. 143-148
  • Di Somma M., Graditi G., Heydarian-Forushani E., Shafie-khah M. and Siano P. (2018), Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects, Renewable Energy, Vol. 116, pp. 272-287
  • Gelig A.H., Leonov G.A. and Yakubovich V.A. (1978), Stability of nonlinear systems with a nonunique equilibrium state, Mosñow, Nauka
  • Gonzalez-Briones A., Chamoso P., De La Prieta F., Demazeau Y. and Corchado J.M. (2018), Agreement technologies for energy optimization at home, Sensors, Vol. 18, Article number: 1633
  • Harper C. and Davis L. (2008), Evolutionary computation at American air liquid, In: Evolutionary Computation in Practice, Studies in Computational Intelligence, pp. 34-36, Berlin: Springer
  • Himoff J., Skobelev P. and Wooldridge M. (2005), Magenta technology: Multi-agent systems for industrial logistics, Proceedings of the 4th International Conference on Autonomous Agents and Multiagent Systems, Vol. 1, pp. 60-66
  • Hussain H.M., Javaid N., Iqbal S., Hasan Q.U., Aurangzeb K. and Alhussein M. (2018), An efficient demand side management system with a new optimized home energy management controller in smart grid, Journal of Energies, Vol. 6, pp. 2-28
  • Jacobson S.H. and Ycesan E. (2008), Development and implementation of an information-computing system for the study of the dynamic stability of electric power systems, Computational Technologies, No. 8, Vol. 4, pp. 59-68
  • Jiang Q., Xue M. and Geng G. (2013), Energy management of microgrid Ingrid-connected and stand-alone modes, IEEE Transactions on Power Apparatus and Systems, No. 3, Vol. 28, pp. 3380-3389
  • Kalman R.E. (1963), Lyapunov functions for the problem of Lur'e in automatic control, Proceedings of the National Academy of Science of USA, Vol. 2, pp. 201- 205
  • Khan H., Bashir Q. and Hashmi M.U. (2018), Scheduling based energy optimization technique in multiprocessor embedded systems, https://ieeexplore.ieeeorg/abstract/document/8338643, [date of access 1412.2022]
  • Khan M.A., Javaid N., Mahmood A., Khan Z.A. and Alrajeh N. (2015), A generic demand-side management model for smart grid, International Journal of Energy Research, Vol. 39, pp. 954-964
  • Koval V., Lysenko V., Kiktev N., Pylypenko Yu., Samkov O., Osinskiy O. and Popov I. (2022), Automated monitoring of time synchronisation devices and digital processing of vector measurements of dynamic characteristics of smart grid power systems, Machinery & Energetics, No. 13, Vol. 2, pp. 73-82, doi: 10.31548/machenergy.13(2).2022.73-82
  • Kron G. (1959), Tensors for circuits, New York, Dover Publications
  • Leturc C. and Bonnet G. (2022), Reasoning about manipulation in multi-agent systems, Journal of Applied Non-Classical Logics, No. 2-3, Vol. 32, pp. 89-155
  • McArthur S.D.J., Davidson E.M., Catterson V.M., Dimeas N.D., Hatziargyriou F. and Ponci T. (2007), Multi-agent systems for power engineering applications - Part 2: Technologies, standards and tools for building multi-agent systems, IEEE Transactions on Power Systems, No. 4, Vol. 22, pp. 1753-1759
  • Palma-Behnke R., Benavides C., Lanas F., Severino B., Reyes L. and Llanos J. (2013), A microgrid energy management system based on the rolling horizon strategy, IEEE Transactions on Smart Grid, No. 2, Vol. 4, pp. 996-1006
  • Rahbar K., Xu J. and Zhang R. (2015), Real-time energy storage management for renewable integration in microgrid: An off-line optimization approach, IEEE Transactions on Smart Grid, No. 1, Vol. 6, pp. 124- 134
  • Rajeswari N. and Janet J. (2018), Load scheduling using fuzzy logic in a home energy management system, International Journal of Engineering and Technology, No. 5, Vol. 10, pp. 1263-1272, doi: 10.21817/ijet/ 2018/v10i5/181005013
  • Rasheed M.B., Javaid N., Awais M., Khan Z.A., Qasim U. and Alrajeh, N. (2016), Real time informationbased energy management using customer preferences and dynamic pricing in smart homes, Energies, No. 7, Vol. 9, pp. 542
  • Schild K. and Bussmann S. (2007), Self-organization in manufacturing operations, Communications of the ACM, No. 12, Vol. 50, pp. 74-79
  • Su S., Tang T. and Roberts C. (2015), A cooperative train control model for energy saving, IEEE Transactions on Intelligent Transportation Systems, Vol. 16, pp. 622-631
  • Villarrubia G., De Paz J.F., Bajo J. and Corchado J.M (2014), Ambient agents: Embedded agents for remote control and monitoring using the PANGEA platform, Sensors, Vol. 14, pp. 13955-13979
  • Wouters C., Fraga E.S. and James A.M. (2015), An energy integrated, multi-microgrid, MILP (mixedinteger linear programming) approach for residential distributed energy system planning - A South Australian case-study, Energy, Vol. 85, pp. 30-44
  • Xu B. and Xiang Y. (2022), Optimal operation of regional integrated energy system based on multi-agent deep deterministic policy gradient algorithm, Energy Reports, Vol. 8, pp. 932-939
  • Yan C., Fang H. and Chao H. (2018), Energy-aware leader-follower tracking control for electric-powered multi-agent systems, Control Engineering Practice, Vol. 79, pp. 209-218
  • Yasinska A. (2021), Accounting procedures digital transformation for business processes improvement, Economics, Entrepreneurship, Management, No. 8, Vol 2, pp. 44-50. doi: 10.23939/eem2021.02.044
  • Zamora R. and Srivastava, A.K. (2010), Controls for microgrids with storage: Review, challenges, and research needs, Renewable and Sustainable Energy Reviews, No. 7, Vol. 14, pp. 2009-2018
  • Zato C., Villarrubia G., Sanchez A., Bajo J. and Corchado J.M. (2013), PANGEA: A new platform for developing virtual organizations of agents, International Journal of Artificial Intelligence, Vol. 11, Article number: 931102110
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171669249

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