Open Access
E3S Web Conf.
Volume 69, 2018
International Conference Green Energy and Smart Grids (GESG 2018)
Article Number 01015
Number of page(s) 6
Section Properties, Regimes and Development of Renewable Energy Sources
Published online 27 November 2018
  1. N. I. Voropai, A. Z. Gamm, A. M. Glazunova, P. V. Etingov, I. N. Kolosok, E. S. Korkina, V. G. Kurbatsky, D. N. Sidorov, V. A. Spiryaev, N. V. Tomin, R. A. Zaika, and B. Bat-Undraal, Application of Meta-Heuristic Optimization Algorithms in Electric Power Systems, pp. 564–615. IGI Global, 2013. [Google Scholar]
  2. “System Operator of Northern Ireland.” Accessed: 2016-01-07. [Google Scholar]
  3. D. Bunn and E. Farmer, “Comparative models for electrical load forecasting,” Int. J. Forecast, vol. 2, pp. 501–505, 1985. [Google Scholar]
  4. L. Soares and M. Medeiros, “Modeling and forecasting short-term electricity load: a comparison of methods with an application to Brazilian data,” Int. J. Forecast, vol. 24, pp. 630–644, 2008. [CrossRef] [Google Scholar]
  5. V. Kalkhambkar, R. Kumar, and R. Bhakar, “Energy loss minimization through peak shaving using energy storage,” Perspectives in Science, pp. –, 2016. In press. [PubMed] [Google Scholar]
  6. D. D. Sharma, S. Singh, and J. Lin, “Multi-agent based distributed control of distributed energy storages using load data,” Journal of Energy Storage, vol. 5, pp. 134–145, 2016. [CrossRef] [Google Scholar]
  7. R. Barzin, J. J. Chen, B. R. Young, and M. M. Farid, “Peak load shifting with energy storage and price-based control system,” Energy, vol. 92, Part 3, pp. 505–514, 2015. Sustainable Development of Energy, Water and Environment Systems. [CrossRef] [Google Scholar]
  8. Y. Gu, J. Xu, D. Chen, Z. Wang, and Q. Li, “Overall review of peak shaving for coal-fired power units in China,” Renewable and Sustainable Energy Reviews, vol. 54, pp. 723–731, 2016. [CrossRef] [Google Scholar]
  9. B. Zakeri and S. Syri, “Electrical energy storage systems: A comparative life cycle cost analysis,” Renewable and Sustainable Energy Reviews, vol. 42, pp. 569–596, 2015. [Google Scholar]
  10. G. Graditi, M. Ippolito, E. Telaretti, and G. Zizzo, “Technical and economic assessment of distributed electrochemical storages for load shifting applications: An Italian case study,” Renewable and Sustainable Energy Reviews, vol. 57, pp. 515–523, 2016. [CrossRef] [Google Scholar]
  11. D. Parra, S. A. Norman, G. S. Walker, and M. Gillott, “Optimum community energy storage system for demand load shifting,” Applied Energy, vol. 174, pp. 130–143, 2016. [CrossRef] [Google Scholar]
  12. X. Han, T. Ji, Z. Zhao, and H. Zhang, “Economic evaluation of batteries planning in energy storage power stations for load shifting,” Renewable Energy, vol. 78, pp. 643–647, 2015. [CrossRef] [Google Scholar]
  13. S. Pazouki and M.-R. Haghifam, “Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty,” International Journal of Electrical Power & Energy Systems, vol. 80, pp. 219–239, 2016. [CrossRef] [Google Scholar]
  14. N. Tomin, A. Zhukov, D. Sidorov, V. Kurbatsky, D. Panasetsky, and V. Spiryaev, “Random forest-based model for preventing large-scale emergencies in power systems,” International Journal of Artificial Intelligence, vol. 13, pp. 211–228, 2015. [Google Scholar]
  15. D. Sidorov, Integral dynamical models: singularities, signals, and control, vol. 87 of World Scientific Series on Nonlinear Science Series A. Singapore: World Scientific, 2015. [Google Scholar]
  16. L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001. [Google Scholar]
  17. J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Annals of statistics, pp. 1189–1232, 2001. [Google Scholar]
  18. A. Smola and V. Vapnik, “Support vector regression machines,” Advances in neural information processing systems, vol. 9, pp. 155–161, 1997. [Google Scholar]
  19. I. Muftahov, A. Tynda, and D. Sidorov, “Numeric Solution of Volterra Integral Equations of the First Kind with Discontinuous Kernels,” Journal of Computational and Applied Mathematics (Elsevier), vol. 313, pp. 119–128, 2017. [CrossRef] [Google Scholar]
  20. D. Sidorov, “Volterra Equations of the First Kind with Discontinuous Kernels in the Theory of Evolving Systems Control,” Studia Informatica Universalis. Paris: Hermann Publ., vol. 9, no. 3, pp. 135–146, 2011. [Google Scholar]
  21. D. N. Sidorov, “On parametric families of solutions of Volterra integral equations of the first kind with a piecewise smooth kernel,” Differential Equations, vol. 49, no. 2, pp. 210–216, 2013. [CrossRef] [Google Scholar]
  22. D. N. Sidorov, “Solution to Systems of Volterra Integral Equations of the First Kind with Piecewise Continuous Kernels,” Russian Mathematics (Transl. from Izvestia VUZov), vol. 57, no. 1, pp. 62–72, 2013. [CrossRef] [Google Scholar]
  23. E. V. Markova and D. N. Sidorov, “Volterra Integral Equation of the First Kind with Discontinuous Kernels in the Theory of Evolving Dynamical Systems Modeling,” Izvestia Irkutskogo gos. univ. Matematika, no. 2, pp. 31–45, 2012. [Google Scholar]
  24. N. A. Sidorov and D. N. Sidorov, “On the solvability of a class of Volterra operator equations of the first kind with piecewise continuous kernels,” Mathematical Notes, vol. 96, no. 5, pp. 811–826, 2014. [CrossRef] [Google Scholar]
  25. E. V. Markova and D. N. Sidorov, “On one integral Volterra model of developing dynamical systems,” Automation and Remote Control, vol. 75, no. 3, pp. 413–421, 2014. [CrossRef] [Google Scholar]
  26. L. V. Kantorovich and V. I. Zhiyanov, “Single-commodity dynamic model of the economy allowing for changes in asset structure in the presence of technical progress,” Dokl. Akad. Nauk USSR, vol. 211, no. 6, pp. 1280—1283, 1973. [Google Scholar]
  27. R. M. Solow, Mathematical Methods in the Social Sciences, ch. Investment and Technical Progress, pp. 89–104. Stanford, California: Stanford University Press, 1960. [Google Scholar]
  28. V. M. Glushkov, V. V. Ivanov, and V. M. Janenko, Developing Systems Modeling. Moscow: Nauka, 1983. [Google Scholar]
  29. P. K. Kythy and P. Puri, Computational Methods for Linear Integral Equations. Boston: Birkhauser, 2002. [CrossRef] [Google Scholar]
  30. I. R. Muftahov, D. N. Sidorov, and N. A. Sidorov, “Lavrentiev regularization of integral equations of the first kind in the space of continuous functions,” Izvestia Irkutskogo gos. univ. Matematika, no. 15, pp. 62–77, 2016. [Google Scholar]
  31. “Eirgrid Group. System Information of Ireland’s Power System.” Accessed: 2016-02-05. [Google Scholar]
  32. A. Zhukov, D. Sidorov, and A. Foley, “Random forest-based approach for concept drift handling,” arXiv: Artificial Intelligence (cs.AI), vol. 1602.04435 [cs.AI], pp. 1–8, 2016. [Google Scholar]
  33. Yang Wei, Wu YuLin, and Liu ShuHong, “An optimization method on runner blades in bulb turbine based on CFD analysis,” Science China Technological Sciences, vol. 54, no. 2, pp. 338—344. [Google Scholar]
  34. M. Kezunovic, S. Meliopoulos, V. Venkatasubramanian, V. Vittal. Application of Time-Synchronized Measurements in Power System Transmission Networks (Springer, NY, 2014). [Google Scholar]
  35. A. Mokeev, Digital Substation. URL: [Google Scholar]
  36. P. Ilyshin, Energoexpert, 1, 58-62 (2015). [Google Scholar]
  37. A. Mokeev, Proceedings IEEE International Conference SIBCON (2017). [Google Scholar]
  38. A. Mokeev, V. Bovykin, A. Miklashevich, D. Ulyanov. Proceedings International Conference Actual trends in development of Power System Relay Protection and Automation (2015). [Google Scholar]
  39. A. Gamm, A. Glazunova, Yu. Grishin, I. Kolosok, E. Korkina, Electrical Technology Russia, 6, 2-9 (2009). [Google Scholar]
  40. P. Ilyshin, P. Chusovitin, Relay Protection and Automation, 4, 16-22 (2014). [Google Scholar]
  41. V. Narovlyanskii, V. Kurmak, Power Plant, 3, 48-51 (2012). [Google Scholar]
  42. V. Narovlyanskii, Modern Methods and Means of Prevention of Asynchronous Operation of the Power Grid (Energoatomizdat, Moscow, 2004). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.