A Distributed Control Approach for Demand Response in Smart Grids

Authors

  • A. El Gharbi Faculty of Computing and Information Technology, Northern Border University, Saudi Arabia | Institut National des Sciences Appliquées et de Technologie, LISI, Carthage University, Tunisia
Volume: 12 | Issue: 1 | Pages: 8129-8135 | February 2022 | https://doi.org/10.48084/etasr.4634

Abstract

The smart grid is a new concept that has been developed during recent years to improve the intelligence and efficiency of electric power system management. Traditional electricity systems are combined and integrated with information technology, communication technology, and intelligent control technology in the smart grid. Demand Response (DR) refers to the changes in consumers' electricity consumption behavior in response to dynamic pricing or financial incentives. Based on the control manner, DR methods are classified as centralized or distributed. In distributed techniques, customers communicate with the other consumers and provide data to the power utility about the overall use. In this paper, we focus on the distributed approach of DR using the shifting method for a short-term horizon. To be more specific, three well-known solutions were studied: the Resource Allocation with Legitimate Claims, the Constrained Fair-Splitting Dispatch, and Real-Time Pricing. Finally, we compare the different techniques of DR distributed approaches based on the control mechanism.

Keywords:

Demand response, Distributed approach, Resource Allocation with Legitimate Claims, Constrained Fair-Splitting Dispatch Problem, Real-Time pricing

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References

M. E. Bendib and A. Mekias, "Solar Panel and Wireless Power Transmission System as a Smart Grid for Electric Vehicles," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5683–5688, Jun. 2020. DOI: https://doi.org/10.48084/etasr.3473

T. A. Trivedi, R. Jadeja, and P. Bhatt, "A Review on Direct Power Control for Applications to Grid Connected PWM Converters," Engineering, Technology & Applied Science Research, vol. 5, no. 4, pp. 841–849, Aug. 2015. DOI: https://doi.org/10.48084/etasr.544

J. B. V. Subrahmanyam, P. Alluvada, Bandana, K. Bhanupriya, and C. Shashidhar, "Renewable Energy Systems: Development and Perspectives of a Hybrid Solar-Wind System," Engineering, Technology & Applied Science Research, vol. 2, no. 1, pp. 177–181, Feb. 2012. DOI: https://doi.org/10.48084/etasr.104

X. Jiang and L. Wu, "A Residential Load Scheduling Based on Cost Efficiency and Consumer’s Preference for Demand Response in Smart Grid," Electric Power Systems Research, vol. 186, Sep. 2020, Art. no. 106410. DOI: https://doi.org/10.1016/j.epsr.2020.106410

X. Yan, Y. Ozturk, Z. Hu, and Y. Song, "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, vol. 96, pp. 411–419, Nov. 2018. DOI: https://doi.org/10.1016/j.rser.2018.08.003

P.-A. Langendahl, H. Roby, S. Potter, and M. Cook, "Smoothing peaks and troughs: Intermediary practices to promote demand side response in smart grids," Energy Research & Social Science, vol. 58, Dec. 2019, Art. no. 101277. DOI: https://doi.org/10.1016/j.erss.2019.101277

M. F. Anjos, L. Brotcorne, and J. A. Gomez-Herrera, "Optimal setting of time-and-level-of-use prices for an electricity supplier," Energy, vol. 225, Jun. 2021, Art. no. 120517. DOI: https://doi.org/10.1016/j.energy.2021.120517

G. Ruan, H. Zhong, J. Wang, Q. Xia, and C. Kang, "Neural-network-based Lagrange multiplier selection for distributed demand response in smart grid," Applied Energy, vol. 264, Apr. 2020, Art. no. 114636. DOI: https://doi.org/10.1016/j.apenergy.2020.114636

Z. X. Pi, X. H. Li, Y. M. Ding, M. Zhao, and Z. X. Liu, "Demand response scheduling algorithm of the economic energy consumption in buildings for considering comfortable working time and user target price," Energy and Buildings, vol. 250, Nov. 2021, Art. no. 111252. DOI: https://doi.org/10.1016/j.enbuild.2021.111252

G. Le Ray and P. Pinson, "The ethical smart grid: Enabling a fruitful and long-lasting relationship between utilities and customers," Energy Policy, vol. 140, May 2020, Art. no. 111258. DOI: https://doi.org/10.1016/j.enpol.2020.111258

D. Wang, S. Parkinson, W. Miao, H. Jia, C. Crawford, and N. Djilali, "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, vol. 96, pp. 104–114, Aug. 2012. DOI: https://doi.org/10.1016/j.apenergy.2011.12.005

A. S. Farsangi, S. Hadayeghparast, M. Mehdinejad, and H. Shayanfar, "A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs," Energy, vol. 160, pp. 257–274, Oct. 2018. DOI: https://doi.org/10.1016/j.energy.2018.06.136

M. Fleschutz, M. Bohlayer, M. Braun, G. Henze, and M. D. Murphy, "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices," Applied Energy, vol. 295, Aug. 2021, Art. no. 117040. DOI: https://doi.org/10.1016/j.apenergy.2021.117040

B. Li, H. Wang, and Z. Tan, "Capacity optimization of hybrid energy storage system for flexible islanded microgrid based on real-time price-based demand response," International Journal of Electrical Power & Energy Systems, vol. 136, Mar. 2022, Art. no. 107581. DOI: https://doi.org/10.1016/j.ijepes.2021.107581

M. H. Imani, P. Niknejad, and M. R. Barzegaran, "Implementing Time-of-Use Demand Response Program in microgrid considering energy storage unit participation and different capacities of installed wind power," Electric Power Systems Research, vol. 175, Oct. 2019, Art. no. 105916. DOI: https://doi.org/10.1016/j.epsr.2019.105916

Y. Zhang, M. M. Islam, Z. Sun, S. Yang, C. Dagli, and H. Xiong, "Optimal sizing and planning of onsite generation system for manufacturing in Critical Peaking Pricing demand response program," International Journal of Production Economics, vol. 206, pp. 261–267, Dec. 2018. DOI: https://doi.org/10.1016/j.ijpe.2018.10.011

R. Alasseri, T. J. Rao, and K. J. Sreekanth, "Institution of incentive-based demand response programs and prospective policy assessments for a subsidized electricity market," Renewable and Sustainable Energy Reviews, vol. 117, Jan. 2020, Art. no. 109490. DOI: https://doi.org/10.1016/j.rser.2019.109490

E. Shahryari, H. Shayeghi, B. Mohammadi-Ivatloo, and M. Moradzadeh, "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, vol. 155, pp. 205–214, Jul. 2018. DOI: https://doi.org/10.1016/j.energy.2018.04.170

P. Siano, "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, vol. 30, pp. 461–478, Feb. 2014. DOI: https://doi.org/10.1016/j.rser.2013.10.022

R. Deng, Z. Yang, M.-Y. Chow, and J. Chen, "A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches," IEEE Transactions on Industrial Informatics, vol. 11, no. 3, pp. 570–582, Jun. 2015. DOI: https://doi.org/10.1109/TII.2015.2414719

J. Pitt, J. Schaumeier, D. Busquets, and S. Macbeth, "Self-Organising Common-Pool Resource Allocation and Canons of Distributive Justice," in Sixth International Conference on Self-Adaptive and Self-Organizing Systems, Lyon, France, Sep. 2012, pp. 119–128. DOI: https://doi.org/10.1109/SASO.2012.31

A. D. Dominguez-Garcia, S. T. Cady, and C. N. Hadjicostis, "Decentralized optimal dispatch of distributed energy resources," in 51st IEEE Conference on Decision and Control, Maui, HI, USA, Dec. 2012, pp. 3688–3693. DOI: https://doi.org/10.1109/CDC.2012.6426665

P. Samadi, A.-H. Mohsenian-Rad, R. Schober, V. W. S. Wong, and J. Jatskevich, "Optimal Real-Time Pricing Algorithm Based on Utility Maximization for Smart Grid," in First IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA, Oct. 2010, pp. 415–420. DOI: https://doi.org/10.1109/SMARTGRID.2010.5622077

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How to Cite

[1]
A. El Gharbi, “A Distributed Control Approach for Demand Response in Smart Grids”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 8129–8135, Feb. 2022.

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