Advertisement

Scheduling Operations of Smart Appliances Using Demand Response

  • Nilima R. DasEmail author
  • S. C. Rai
  • Ajit K. Nayak
Chapter
  • 36 Downloads
Part of the Modeling and Optimization in Science and Technologies book series (MOST, volume 17)

Abstract

Nowadays, many countries are concerned about the environmental problems which are mainly caused due to insensible and careless use of energy. The ignorance of the people towards energy consumption is greatly affecting the environment. The contribution of electric sector in polluting the environment is the highest among all other sectors that work on energy. High demands of electricity during peak hours result in increased production of electricity using fossil fuel based plants which increases the level of CO2 in the atmosphere. However improved performance of the grid system in reducing the peak loads and availability of electricity can reduce the green house gas emission which is considered as the main reason of climate change. The work in this chapter is mainly focused on optimal load scheduling for energy cost minimization and peak load reduction. The proposed model uses Time of Use (TOU) pricing tariff in the optimization process. The optimization problem has been solved with multiple optimization techniques including Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and a hybrid algorithm formed by combining GA and PSO. The proposed scheme has many applications like peak load reduction and energy cost minimization which can benefit consumers and utilities.

Keywords

Smart home DSM Demand response TOU pricing GA PSO 

References

  1. 1.
    Kothari DP, Nagrath IJ(2006) Modern power systems analysis, 3rd edn. McGraw-HillGoogle Scholar
  2. 2.
    Gellings CW, Chamberlin JH (1993) Demand side management: concepts and methods, 2nd edn. PennWell Books, TulsaGoogle Scholar
  3. 3.
    Masters GM (2004) Renewable and efficient electric power systems. Wiley, HobokenCrossRefGoogle Scholar
  4. 4.
    Samadi P, Mohsenian-Rad H, Schober R, Wong WS (2012) Advanced demand side management for the future smart grid using mechanism design. IEEE Trans Smart Grid 3:1170–1180CrossRefGoogle Scholar
  5. 5.
    Faruqui A, George S (2005) Quantifying customer response to dynamic pricing. Electr J 18:53–63CrossRefGoogle Scholar
  6. 6.
    Kumar S, Sodha NS, Wadhwa K(2013) Dynamic tariff structures for demand side management and demand response. An approach paper from IndiaGoogle Scholar
  7. 7.
    Demand response: an introduction (2006) South West Energy Efficiency Project. Rockey Mountain Institute, ColoradoGoogle Scholar
  8. 8.
    Albadi MH, El-Saadany EF (2007) Demand response in electricity markets: an overview. In: IEEE conferenceGoogle Scholar
  9. 9.
    Mohsenian-Rad AH, Wong VWS, Jatskevich J, Schober R, Leon-Garcia A (2010) Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1:320–331CrossRefGoogle Scholar
  10. 10.
    Farhadi P, Taheri B (2017) Smart meter tariff pricing using load demand response model. In: 5th international Istanbul smart grid and cities congress and fair (ICSG)Google Scholar
  11. 11.
    Sianaki OA, Hussain O, Dillon T, Tabesh AR(2010) Intelligent decision support system for including consumers’ preferences in residential energy consumption in smart grid. In: IEE second international conference on computational intelligence, modelling and simulationGoogle Scholar
  12. 12.
    Lujano-Rojasa JM, Monteiro C, Dufo-Lopez R, Bernal-Agustin J (2012) Optimum residential load management strategy for real time pricing (RTP) demand response programs. Energy Policy 45:671–679CrossRefGoogle Scholar
  13. 13.
    Federal Energy Regulatory Commission (FERC) (2003), White paper: wholesale power market platformGoogle Scholar
  14. 14.
    AEMC (Australian Energy Market Commission) (2015) National electricity market. http://www.aemc.gov.au/Australias-Energy-Market/Markets-Overview/National-electricity-market
  15. 15.
    Smart demand response market by end user—residential, commercial, and industrial and global industry analysis, size, share, growth, trends and forecast 2014–2025 Industry Report (2016)Google Scholar
  16. 16.
    Smart demand response market analysis by application (Residential, commercial, industrial) and segment forecasts to 2022, 2016 Base Year for Estimate, Report ID: 978-1-68038-694-3 (2015)Google Scholar
  17. 17.
    Global automated demand response management systems market for the forecast period 2018–2023, segmented by geography—growth, trends and forecast (2018)Google Scholar
  18. 18.
    Demand response system market: global industry analysis and forecast 2016–2024, report, industry: IT and Telecommunication (2018)Google Scholar
  19. 19.
    Lund H (2015) Renewable energy systems: a smart energy systems approach to the choice and modeling of 100% renewable solutions. Academic PressGoogle Scholar
  20. 20.
    Stevens L, Wilcox M, Leopold A, Taylor C, Waters L(2016) Poor people’s energy outlook 2016, 1st edn. Practical Action Publishing Ltd., Rugby, WarwickshireGoogle Scholar
  21. 21.
    Schnitzer D, Lounsbury DS, Carvallo JP, Deshmukh R, Apt J, Kammen DM (2014) Microgrids for rural electrification: a critical review of best practices based on seven case studies, Technical report. University California Berkeley & Carnegie Mellon University, BerkeleyGoogle Scholar
  22. 22.
    Jung J, Villaran M (2017) Optimal planning and design of hybrid renewable energy systems for microgrids. Renew Sustain Energy Rev 75:180–191CrossRefGoogle Scholar
  23. 23.
    Bigerna S, Bollino CA, Micheli S (2016) Socio-economic acceptability for smart grid development: a comprehensive review. J Clean Prod 131(9):399–409CrossRefGoogle Scholar
  24. 24.
    Hsu YY, Su CC (1991) Dispatch of direct load control using dynamic programming. IEEE Trans Power Syst 6:1056–1061CrossRefGoogle Scholar
  25. 25.
    Kurucz CN, Brandt D, Sim S (1996) A linear programming model for reducing system peak through customer load control programs. IEEE Trans Power Syst 11:1817–1824CrossRefGoogle Scholar
  26. 26.
    Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z(2011) An integer linear programming and game theory based optimization for demand-side management in smart grid. In: IEEE international workshop on smart grid communications and networksGoogle Scholar
  27. 27.
    Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z (2012) An integer linear programming based optimization for home demand-side management in smart grid. IEEE PES innovative smart grid technologies (ISGT)Google Scholar
  28. 28.
    Nguyen HK, Song JB, Han Z (2012) Demand side management to reduce peak-to-average ratio using game theory in smart grid. IEEE INFOCOM workshop on communications and control for sustainable energy systems: green networking and smart grids, pp 91–96Google Scholar
  29. 29.
    Bu S, Yu FR (2013) A game-theoretical scheme in the smart grid with demand-side management: towards a smart cyber-physical power infrastructure. IEEE Trans 1:22–32Google Scholar
  30. 30.
    Yi Liu, Yuen C, Huang S, Hassan NU, Wang X, Xie S (2014) Peak-to-average ratio constrained demand-side management with consumer’s preference in residential smart grid. IEEE J Sel Top Sig Process 8:1084–1097CrossRefGoogle Scholar
  31. 31.
    Fadlullah ZM, Quan DM, Kato N, Stojmenovic I (2014) GTES: an optimized game -theoretic demand -side management scheme for smart grid. IEEE Syst JGoogle Scholar
  32. 32.
    Soliman HM, Leon-Garcia A (2014) Game-theoretic demand-side management with storage devices for the future smart grid. IEEE Trans Smart Grid 5:1475–1485CrossRefGoogle Scholar
  33. 33.
    Chen H, Li Y, Louie RHY, Vucetic B (2014) Autonomous demand-side management based on energy consumption scheduling and instantaneous load billing: an aggregative game approach. IEEE Trans Smart Grid 5:1744–1754CrossRefGoogle Scholar
  34. 34.
    Raj CA, Aravind E, Sundaram BR, Vasudevan SK (2015) Smart meter based on real time pricing. Smart Grid Technol 21:120–124Google Scholar
  35. 35.
    Zhao C, Dong S, Li F, Song Y (2015) Optimal home energy management system with mixed types of loads. CSEE J Power Energy Syst 1:29–36CrossRefGoogle Scholar
  36. 36.
    Barbatoa A, Caponea A, Chenb L, Martignonb F, Paris S (2015) A distributed demand-side management framework for the smart grid. Elsevier Comput Commun 57:13–24CrossRefGoogle Scholar
  37. 37.
    Logenthiran T, Srinivasan D, Khambadkone AM (2011) Multi agent system for energy resource scheduling of integrated microgrids in a distributed system. Electr Power Syst Res 81:138–148CrossRefGoogle Scholar
  38. 38.
    Huang Y, Wang L, Wu Q (2014) A hybrid PSO-DE algorithm for smart home energy management. ICSI 2014, Part II, LNCS 8795:292–300Google Scholar
  39. 39.
    Venayagamoorthy GK (2010) A dynamic optimization method for a smart grid. In: Conference: power and energy society general meeting. IEEEGoogle Scholar
  40. 40.
    Labeeuw W, Deconinck G (2013) Residential electrical load model based on mixture model clustering and Markov Models. IEEE Trans Industr Inf 9:1561–1568CrossRefGoogle Scholar
  41. 41.
    Prinsloo G, Mammoli A, Dobson R (2017) Customer domain supply and load coordination: a case for smart villages and transactive control in rural off-grid microgrids. Elsevier Energy 135(1):1–12Google Scholar
  42. 42.
    Goutam D, Krishnendranath M (2017) A literature review on dynamic pricing of electricity. J Oper Res Soc 68:1131–1145CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Engineering and TechnologySiksha ‘O’ Anusandhan (Deemed to be University)BhubaneswarIndia
  2. 2.Department of ITSilicon Institute of TechnologyBhubaneswarIndia

Personalised recommendations