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Proposing an efficient wind forecasting agent using adaptive MFDFA

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
High penetration by distributed energy sources (DERs) such as wind turbines (WT) and various types of consumer have triggered a need for new approach to coordination and control strategy to meet the stochastic wind speed of the environment. Here, a Multi Agent System is used to deliver strengthened, distributed, self-governing energy management of a multiple micro-grid to adapt to changes in the environment. Prediction of wind speed is crucial for various aspects, such as control and planning of wind turbine operation and guaranteeing stable performance of multiple micro-grids. The main purpose of the proposed system is to account for wind variability in the energy management of a multiple micro-grid based on a hierarchical multi-factor system. In this study, the prediction is based on adaptive multifractal detrended fluctuation analysis (Adaptive MFDFA). A genetic algorithm is used to solve the optimization problem. Eventually, the proposed strategy is applied to a typical MG which consists of micro turbine (MT), wind turbine (WT) and energy storage system (ESS). Evaluation of the results show that the proposed strategy works well and can adapt the level of confidence interval in various situations.
Rocznik
Strony
152--162
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr.
Twórcy
  • Department of Electrical Engineering of Shiraz University, Shiraz, Iran
  • Department of Electrical Engineering of Shiraz University, Shiraz, Iran
Bibliografia
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  • [5] C.-x. Dou, B. Liu, Hierarchical hybrid control for improving comprehensive performance in smart power system, International Journal of Electrical Power & Energy Systems 43 (1) (2012) 595–606.
  • [6] T. Logenthiran, D. Srinivasan, A. M. Khambadkone, Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system, Electric Power Systems Research 81 (1) (2011) 138–148.
  • [7] Z. Jun, L. Junfeng, W. Jie, H. Ngan, A multi-agent solution to energy management in hybrid renewable energy generation system, Renewable Energy 36 (5) (2011) 1352–1363.
  • [8] D. Shao, Q. Wei, T. Nie, A multi-agent control strategy in microgrid island mode, in: Proceedings of 2011 6th International Forum on Strategic Technology, Vol. 1, IEEE, 2011, pp. 429–432.
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  • [10] W.-D. Zheng, J.-D. Cai, A multi-agent system for distributed energy resources control in microgrid, in: 2010 5th International Conference on Critical Infrastructure (CRIS), IEEE, 2010, pp. 1–5.
  • [11] J. Sarshar, S. S. Moosapour, M. Joorabian, Multi-objective energy management of a micro-grid considering uncertainty in wind power forecasting, Energy 139 (2017) 680–693.
  • [12] C. M. Colson, M. H. Nehrir, Algorithms for distributed decision-making for multi-agent microgrid power management, in: 2011 IEEE Power and Energy Society General Meeting, IEEE, 2011, pp. 1–8.
  • [13] G. Zheng, N. Li, Multi-agent based control system for multi-microgrids, in: 2010 International Conference on Computational Intelligence and Software Engineering, IEEE, 2010, pp. 1–4.
  • [14] A. Dimeas, N. Hatziargyriou, Multi-agent reinforcement learning for microgrids, in: IEEE PES General Meeting, IEEE, 2010, pp. 1–8.
  • [15] Y. Xu, W. Liu, Novel multiagent based load restoration algorithm for microgrids, IEEE Transactions on Smart Grid 2 (1) (2011) 152–161.
  • [16] M. Castañeda, L. Fernández, H. Sánchez, A. Cano, F. Jurado, Sizing methods for stand-alone hybrid systems based on renewable energies and hydrogen, in: 2012 16th IEEE Mediterranean Electrotechnical Conference, IEEE, 2012, pp. 832–835.
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  • [23] M. Mozaffarilegha, H. Namazi, M. Ahadi, S. Jafari, Complexity-based analysis of the difference in speech-evoked auditory brainstem responses (s-abrs) between binaural and monaural listening conditions, Fractals 26 (04) (2018) 1850052.
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  • [25] M. Das, S. K. Ghosh, Short-term prediction of land surface temperature using multifractal detrended fluctuation analysis, in: 2014 Annual IEEE India Conference (INDICON), IEEE, 2014, pp. 1–6.
  • [26] A. K. Maity, R. Pratihar, A. Mitra, S. Dey, V. Agrawal, S. Sanyal, A. Banerjee, R. Sengupta, D. Ghosh, Multifractal detrended fluctuation analysis of alpha and theta eeg rhythms with musical stimuli, Chaos, Solitons & Fractals 81 (2015) 52–67.
  • [27] M. Motevasel, A. R. Seifi, Expert energy management of a micro-grid considering wind energy uncertainty, Energy Conversion and Management 83 (2014) 58–72.
  • [28] F. Y. Eddy, H. Gooi, Multi-agent system for optimization of microgrids, in: 8th International Conference on Power Electronics-ECCE Asia, IEEE, 2011, pp. 2374–2381.
  • [29] S. Tegen, E. Lantz, M. Hand, B. Maples, A. Smith, P. Schwabe, Cost of wind energy review national renewable energy laboratory, Tech. rep., Technical Report (2011).
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  • [31] A. Zakariazadeh, S. Jadid, P. Siano, Smart microgrid energy and reserve scheduling with demand response using stochastic optimization, International Journal of Electrical Power & Energy Systems 63 (2014) 523–533.
  • [32] X. Wu, X. Wang, C. Qu, A hierarchical framework for generation scheduling of microgrids, IEEE Transactions on Power Delivery 29 (6) (2014) 2448–2457.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a0cbb1e7-a2bf-4f40-a945-bcd7f0951586
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