Proposing an Efficient Wind Forecasting Agent Using Adaptive MFDFA
Abstract
High penetration by distributed energy sources (DERs) such as wind turbines (WT) and various types of consumer havetriggered 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 multiplemicro-grid to adapt to changes in the environment. Prediction of wind speed is crucial for various aspects, such as controland planning of wind turbine operation and guaranteeing stable performance of multiple micro-grids. The main purpose of theproposed system is to account for wind variability in the energy management of a multiple micro-grid based on a hierarchicalmulti-factor system. In this study, the prediction is based on adaptive multifractal detrended fluctuation analysis (AdaptiveMFDFA). A genetic algorithm is used to solve the optimization problem. Eventually, the proposed strategy is applied to atypical MG which consists of micro turbine (MT), wind turbine (WT) and energy storage system (ESS). Evaluation of theresults show that the proposed strategy works well and can adapt the level of confidence interval in various situations.References
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of ann wind power estimation into unit commitment considering
the forecasting uncertainty, IEEE Transactions on Industry Applications
43 (6) (2007) 1441–1448.
[2] A. Hajizadeh, M. A. Golkar, Intelligent power management strategy of
hybrid distributed generation system, International Journal of Electrical
Power & Energy Systems 29 (10) (2007) 783–795.
[3] Z. Jiang, L. Gao, R. A. Dougal, Flexible multiobjective control of power
converter in active hybrid fuel cell/battery power sources, IEEE Transactions
on Power Electronics 20 (1) (2005) 244–253.
[4] Z. Jiang, L. Gao, R. A. Dougal, Adaptive control strategy for active
power sharing in hybrid fuel cell/battery power sources, IEEE Transactions
on Energy Conversion 22 (2) (2007) 507–515.
[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.
[9] N. Cai, X. Xu, J. Mitra, A hierarchical multi-agent control scheme for a
black start-capable microgrid, in: 2011 IEEE Power and Energy Society
General Meeting, IEEE, 2011, pp. 1–7.
[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.
[17] J. Hu, J. Zhu, G. Platt, Smart grid-the next generation electricity grid
with power flow optimization and high power quality, in: Journal of international
Conference on Electrical Machines and Systems, Vol. 1,
Journal of International Conference on Electrical Machines and Systems,
2012, pp. 425–433.
[18] D. E. Olivares, C. A. Cañizares, M. Kazerani, A centralized optimal
energy management system for microgrids, in: 2011 IEEE Power and
Energy Society General Meeting, IEEE, 2011, pp. 1–6.
[19] T. Logenthiran, D. Srinivasan, A. M. Khambadkone, H. N. Aung, Multiagent
system for real-time operation of a microgrid in real-time digital
simulator, IEEE Transactions on smart grid 3 (2) (2012) 925–933.
[20] V.-H. Bui, A. Hussain, H.-M. Kim, A multiagent-based hierarchical energy
management strategy for multi-microgrids considering adjustable
power and demand response, IEEE Transactions on Smart Grid 9 (2)
(2016) 1323–1333.
[21] L. Soder, Simulation of wind speed forecast errors for operation planning
of multiarea power systems, in: 2004 International Conference
on Probabilistic Methods Applied to Power Systems, IEEE, 2004, pp.
723–728.
[22] G. G.Wu, Z. Dou, Wind pattern recognition in neural fuzzy wind turbine
control system, in: NAFIPS/IFIS/NASA’94. Proceedings of the First International
Joint Conference of The North American Fuzzy Information
Processing Society Biannual Conference. The Industrial Fuzzy Control
and Intellige, IEEE, 1994, pp. 381–385.
[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.
[24] D. Chakrabarti, C. Faloutsos, F4: large-scale automated forecasting
using fractals, in: Proceedings of the eleventh international conference
on Information and knowledge management, ACM, 2002, pp. 2–9.
[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).
[30] L. Fingersh, M. Hand, A. Laxson, Wind turbine design cost and scaling
model, Tech. rep., National Renewable Energy Lab.(NREL), Golden,
CO (United States) (2006).
[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.
Published
2019-07-09
How to Cite
FARJAH, Ebrahim.
Proposing an Efficient Wind Forecasting Agent Using Adaptive MFDFA.
Journal of Power Technologies, [S.l.], v. 99, n. 2, p. 152–162, july 2019.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1511>. Date accessed: 21 nov. 2024.
Issue
Section
Electrical Engineering
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