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Electric Distribution Network Management and Control pp 131–151Cite as

Achieving Efficiency and Fairness in Dynamic Demand Response

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Part of the book series: Power Systems ((POWSYS))

Abstract

This chapter discusses the feasibility of using customer coupon demand response in meshed secondary networks. Customers are rewarded by coupons to achieve the objective of optimal operation cost during peak periods. The interdependence of the locational marginal price and the demand is modeled by an artificial neural network. The effect of multiple load aggregators participating in customer coupon demand response is also investigated. Because load aggregators satisfy different proportions of the objective, a fairness function is defined that guarantees that aggregators are rewarded in correspondence with their participation towards the objective. Energy loss is also considered in the objective as it is an essential part of the electric distribution networks. A dynamic coupon mechanism is designed to cope with the changing nature of the demand. To validate the effectiveness of the method, simulations of the presented method have been performed on a real heavily-meshed distribution network in this chapter. The results show that customer coupon demand response significantly contributes to shaving the peak, therefore, bringing considerable economic savings and reduction of loss.

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References

  1. H. Mohsenian-Rad, Optimal demand bidding for time-shiftable loads. IEEE Trans. Power Syst. 30(2), 939–951 (2015)

    Article  Google Scholar 

  2. C. Chen, S. Kishore, Z. Wang, M. Alizadeh, A. Scaglione, How will demand response aggregators affect electricity markets?—A cournot game analysis, in Proc. of international symposium ISCCSP, May 2012

    Google Scholar 

  3. National Action Plan on Demand Response (FERC, Tech. Rep. Docket AD09-10, 2010 [Online]). Available: http://www.ferc.gov/legal/staffreports/06-17-10-demand-response.pdf

  4. C. Vivekananthan, Y. Mishra, G. Ledwich, F. Li, Demand response for residential appliances via customer reward scheme. IEEE Trans. Smart Grid 5(2), 809–820 (2014)

    Article  Google Scholar 

  5. A.H. Mohsenian-Rad, A. Leon-Garcia, Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans. Smart Grid 1(2), 120–133 (2010)

    Article  Google Scholar 

  6. A.H. Mohsenian-Rad, V.W.S. Wong, J. Jatskevich, R. Schober, A. Leon-Garcia, Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1(3), 320–331 (2010)

    Article  Google Scholar 

  7. Q. Hu, F. Li, Hardware design of smart home energy management system with dynamic price response. IEEE Trans. Smart Grid 4(4), 1878–1887 (2013)

    Article  Google Scholar 

  8. Time based rate programs (U. S. Dept. of Energy, Washington, D. C. [Online]). Available: http://www.smartgrid.gov/recovery_act/time_based_rate_programs.html

  9. Dynamic pricing and smart grid (Elevate Energy, Chicage, IL [Online]). Available: http://www.elevateenergy.org/home-savings/dynamic-pricing-smart-grid/

  10. Federal Energy Regulatory Commission, Assessment of Demand Response & Advanced Metering. Staff Report, 2006

    Google Scholar 

  11. B. Severin, J. Michael, R. Arthur, Dynamic Pricing, Advanced Metering and Demand Response in Electricity Markets (Univ. California Energy Institute, Berkeley, CA, 2002)

    Google Scholar 

  12. C. Kang, W. Jia, Transition of tariff structure and distribution pricing in China, in Proc. IEEE Power Eng. Soc. General Meeting (July 2011)

    Google Scholar 

  13. X. Zhang, Optimal scheduling of critical peak pricing considering wind commitment. IEEE Trans. Sustainable Energy 5(2), 637–645 (2014)

    Article  Google Scholar 

  14. H. Zhong, L. Xie, Q. Xia, Coupon incentive-based demand response: theory and case study. IEEE Trans. Power Syst. 28(2), 1266–1276 (2013)

    Article  Google Scholar 

  15. X. Fang, Q. Hu, F. Li, B. Wang, Y. Li, Coupon-based demand response considering wind power uncertainty: a strategic bidding model for load serving entities. IEEE Trans. Power Syst. PP(99), 1–13 (2015)

    Google Scholar 

  16. Locational marginal pricing (ISO New England, Holyoke, MA [Online]). Available: http://www.iso-ne.com/participate/support/faq/Imp

  17. H. Yuan, F. Li, Y. Wei, J. Zhu, Novel linearized power flow and linearized OPF models for active distribution networks with application in distribution LMP. IEEE Trans. Smart Grid (2016). https://doi.org/10.1109/TSG.2016.2594814

    Google Scholar 

  18. H. Yuan, F. Li, Y. Wei, LMP step pattern detection based on real-time data, in IEEE PES General Meeting, 1–5 (2013)

    Google Scholar 

  19. Y. Y. Hong, C. Y. Hsiao, Locational marginal price forecasting in deregulated electricity markets using artificial intelligence. Proc. Inst. Elect. Eng. Gen. Transm. Distrib. 149(5), 621–626 (2002)

    Google Scholar 

  20. B.R. Szkuta, L.A. Sanabria, T.S. Dillon, Electricity price short-term forecasting using artificial neural networks. IEEE Trans. Power Syst. 14(3), 851–857 (1999)

    Article  Google Scholar 

  21. P. Mandal, A.K. Srivastava, J.W. Park, An effort to optimize similar days parameters for ANN-based electricity price forecasting. IEEE Trans. Ind. Appl. 45(5), 1888–1896 (2009)

    Article  Google Scholar 

  22. P. Mandal, T. Senjyu, N. Urasaki, T. Funabashi, A.K. Srivastava, A novel approach to forecast electricity price for PJM using neural network and similar days method. IEEE Trans. Power Syst. 22(4), 2058–2065 (2007)

    Article  Google Scholar 

  23. Demand response programs details (Con Edison, New York, NY [Online]). Available: http://www.coned.com/energyefficiency/demand_response_program_details.asp

  24. Z. Baharlouei, M. Hashemi, H. Narimani, H. Mohsenian-Rad, Achieving optimality and fairness in autonomous demand response: benchmarks and billing mechanisms. IEEE Trans. Smart Grid 4(2), 968–975 (2013)

    Article  Google Scholar 

  25. L. Yu, D. Czarkowski, F. de León, Optimal distributed voltage regulation for secondary networks with DGs. IEEE Trans. Smart Grid 3(2), 959–967 (2012)

    Article  Google Scholar 

  26. P. Chen, R. Salcedo, Q. Zhu, F. de León, D. Czarkowski, Z. Jiang, V. Spitsa, Z. Zabar, R.E. Uosef, Analysis of voltage profile problems due to the penetration of distributed generation in low-voltage secondary distribution networks. IEEE Trans. Power Del. 27(4), 2020–2028 (2012)

    Article  Google Scholar 

  27. M. Mohri, A. Rostamizadeh, A. Talwalker, Foundations of machine learning (MA, USA, 2012), pp. 63–84

    Google Scholar 

  28. Energy market (PJM, Valley Forge, PA [Online]). Available: http://www.pjm.com/markets-and-operations/energy.aspx

  29. Electric power monthly (U. S. Energy Info. Admin., Washington, D. C. [Online]). Available: http://www.eia.com.gov/electricity/monthly/epm_table_grapher.cfm?t=epmt_5_6_a

  30. N. G. Mankiw, Principles of economics, 6th edn. (OH, USA, 2012), pp. 66–154

    Google Scholar 

  31. D. S. Kirschen, G. Strbac, P. Cumperayot, D. de Paiva Mendes, Factoring the elasticity of demand in electricity prices. IEEE Trans. Power Syst. 15(2), 612–617 (2000)

    Google Scholar 

  32. M.G. Lijesen, The real-time price elasticity of electricity. Energy Econ. 29(2), 249–258 (2007)

    Article  Google Scholar 

  33. P.R. Thimmapuram, J. Kim, Consumers’ price elasticity of demand modeling with economic effects on electricity markets using an agent-based model. IEEE Trans. Smart Grid 4(1), 390–397 (2013)

    Article  Google Scholar 

  34. S. Boyd, L. Vandenberghe, Convex optimization (United Kingdom, 2009), pp. 215–273

    Google Scholar 

  35. Nodal Protocol of ERCOT. [Online]. Available: http://www.ercot.com/mktrules/nprotocols/current

  36. Z. Li, S. Wang, X. Zheng, F. de León, T. Hong, Dynamic Demand Response using Customer Coupons Considering Multiple Load Aggregators to Simultaneously Achieve Efficiency and Fairness. IEEE Trans. Smart Grid. PP(99), 1–10 (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan, China

    Zhechao Li & Xuejun Zheng

Authors
  1. Zhechao Li
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  2. Xuejun Zheng
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Correspondence to Zhechao Li .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Murdoch University, Perth, Western Autralia, Australia

    Ali Arefi

  2. School of Engineering and Information Technology, Murdoch University, Perth, Western Autralia, Australia

    Farhad Shahnia

  3. Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Queensland, Australia

    Gerard Ledwich

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Li, Z., Zheng, X. (2018). Achieving Efficiency and Fairness in Dynamic Demand Response. In: Arefi, A., Shahnia, F., Ledwich, G. (eds) Electric Distribution Network Management and Control. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-10-7001-3_6

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  • DOI: https://doi.org/10.1007/978-981-10-7001-3_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7000-6

  • Online ISBN: 978-981-10-7001-3

  • eBook Packages: EnergyEnergy (R0)

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