A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack

  • Asif Khan
  • Nadeem JavaidEmail author
  • Adnan Ahmad
  • Mariam Akbar
  • Zahoor Ali Khan
  • Manzoor Ilahi
Original Research


Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolution, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.


Demand side management Smart grid Meta-heuristic algorithms Home energy management Knapsack 


  1. Ahmad A, Khan A, Javaid N, Hussain HM, Abdul W, Almogren A, Azim Niaz I (2017) An optimized home energy management system with integrated renewable energy and storage resources. Energies 10(4):549CrossRefGoogle Scholar
  2. Arafa M, Sallam EA, Fahmy MM (2014) An enhanced differential evolution optimization algorithm. In Digital Information and Communication Technology and it’s Applications (DICTAP), 2014 fourth international conference on. IEEE, pp 216–225Google Scholar
  3. Azar AG, Jacobsen RH (2016) Appliance scheduling optimization for demand response. Int J Adv Intell Syst 9(1 & 2):50–64Google Scholar
  4. Chih M (2015) Self-adaptive check and repair operator-based particle swarm optimization for the multidimensional knapsack problem. Appl Soft Comput 26:378–389CrossRefGoogle Scholar
  5. Del Valle Y, Venayagamoorthy GK, Mohagheghi S, Hernandez JC, Harley RG (2008) Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans Evol Comput 12(2):171–195CrossRefGoogle Scholar
  6. Derakhshan G, Shayanfar HA, Kazemi A (2016) The optimization of demand response programs in smart grids. Energy Policy 94:295–306CrossRefGoogle Scholar
  7. Framework, N. I. S. T. (2010) Roadmap for smart grid interoperability standards. National Institute of Standards and Technology [online]. Accessed 1 Dec 2017
  8. Gao J, Xiao Y, Liu J, Liang W, Chen CP (2012) A survey of communication/networking in smart grids. Future Gen Comput Syst 28(2):391–404CrossRefGoogle Scholar
  9. Gentile U, Marrone S, Mazzocca N, Nardone R (2016) Cost-energy modelling and profiling of smart domestic grids. Int J Grid Util Comput 7(4):257–271CrossRefGoogle Scholar
  10. GuNtzer MM, Jungnickel D (2000) Approximate minimization algorithms for the 0/1 knapsack and subset-sum problem. Oper Res Lett 26(2):55–66MathSciNetCrossRefzbMATHGoogle Scholar
  11. Haider HT, See OH, Elmenreich W (2016) A review of residential demand response of smart grid. Renew Sustain Energy Rev 59:166–178CrossRefGoogle Scholar
  12. Hossain MS, Madlool NA, Rahim NA, Selvaraj J, Pandey AK, Khan AF (2016) Role of smart grid in renewable energy: an overview. Renew Sustain Energy Rev 60:1168–1184CrossRefGoogle Scholar
  13. Iwayemi A, Yi P, Dong X, Zhou C (2011) Knowing when to act: an optimal stopping method for smart grid demand response. IEEE Netw 25(5):44–49CrossRefGoogle Scholar
  14. Javaid N, Hussain SM, Ullah I, Noor MA, Abdul W, Almogren A, Alamri A (2017) Demand side management in nearly zero energy buildings using heuristic optimizations. Energies 10(8):1131CrossRefGoogle Scholar
  15. Javaid N, Naseem M, Rasheed MB, Mahmood D, Khan SA, Alrajeh N, Iqbal Z (2017) A new heuristically optimized home energy management controller for smart grid. Sustain Cities Soc 34:211–227CrossRefGoogle Scholar
  16. Joy J, Rajeev S, Narayanan V (2016) Particle swarm optimization for resource constrained-project scheduling problem with varying resource levels. Procedia Technol 25:948–954CrossRefGoogle Scholar
  17. Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer US, pp 760–766Google Scholar
  18. Khameis A, Rashed S, Abou-Elnour A, Tarique M (2015) Zigbee based optimal scheduling system for home appliances in the United Arab Emirates. Netw Protoc Alg 7(2):60–80Google Scholar
  19. Khan A, Javaid N, Ahmed A, Kazmi S, Hussain HM, Khan ZA (2017) Efficient utilization of HEM controller using heuristic optimization techniques. In: International conference on emerging internetworking, data & web technologies. Springer, Cham, pp 573–584Google Scholar
  20. Kumaraguruparan N, Sivaramakrishnan H, Sapatnekar SS (2012) Residential task scheduling under dynamic pricing using the multiple knapsack method. In: Innovative smart grid technologies (ISGT), 2012 IEEE PES. IEEE, pp 1–6Google Scholar
  21. Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3(3):1244–1252CrossRefGoogle Scholar
  22. Manzoor A, Javaid N, Ullah I, Abdul W, Almogren A, Alamri A (2017) An intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes. Energies 10(9):1258CrossRefGoogle Scholar
  23. Mhanna S, Chapman AC, Verbic G (2016) A fast distributed algorithm for large-scale demand response aggregation. IEEE Trans Smart Grid 7(4):2094–2107CrossRefGoogle Scholar
  24. Moon S, Lee JW (2016) Multi-residential demand response scheduling with multi-class appliances in smart grid. In: IEEE transactions on smart gridGoogle Scholar
  25. Morales DX, Besanger Y, Sami S, Bel CA (2017) Assessment of the impact of intelligent DSM methods in the Galapagos Islands toward a Smart Grid. Electr Power Syst Res 146:308–320CrossRefGoogle Scholar
  26. Muralitharan K, Sakthivel R, Shi Y (2016) Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing 177:110–119CrossRefGoogle Scholar
  27. Muratori M, Rizzoni G (2016) Residential demand response: dynamic energy management and time-varying electricity pricing. IEEE Trans Power Syst 31(2):1108–1117CrossRefGoogle Scholar
  28. Ogunjuyigbe ASO, Ayodele TR, Akinola OA (2017) User satisfaction-induced demand side load management in residential buildings with user budget constraint. Appl Energy 187:352–366CrossRefGoogle Scholar
  29. Ozkan HA (2016) Appliance based control for home power management systems. Energy 114:693–707CrossRefGoogle Scholar
  30. Rahim S, Javaid N, Ahmad A, Khan SA, Khan ZA, Alrajeh N, Qasim U (2016) Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build 129:452–470CrossRefGoogle Scholar
  31. Rasheed MB, Javaid N, Ahmad A, Khan ZA, Qasim U, Alrajeh N (2015) An efficient power scheduling scheme for residential load management in smart homes. Appl Sci 5(4):1134–1163CrossRefGoogle Scholar
  32. Rasheed MB, Javaid N, Ahmad A, Awais M, Khan ZA, Qasim U, Alrajeh N (2016) Priority and delay constrained demand side management in real?time price environment with renewable energy source. Int J Energy Res 40(14):2002–2021CrossRefGoogle Scholar
  33. Rastegar M, Fotuhi-Firuzabad M, Zareipour H (2016) Home energy management incorporating operational priority of appliances. Int J Electr Power Energy Syst 74:286–292CrossRefGoogle Scholar
  34. Shirazi E, Jadid S (2015) Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build 93:40–49CrossRefGoogle Scholar
  35. Storn R, Price K (1997) Differential evolution? A simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefzbMATHGoogle Scholar
  36. Tuballa ML, Abundo ML (2016) A review of the development of Smart Grid technologies. Renew Sustain Energy Rev 59:710–725CrossRefGoogle Scholar
  37. Vardakas JS, Zorba N, Verikoukis CV (2015) A survey on demand response programs in smart grids: pricing methods and optimization algorithms. IEEE Commun Surv Tutor 17(1):152–178CrossRefGoogle Scholar
  38. Vardakas JS, Zorba N, Verikoukis CV (2015) Performance evaluation of power demand scheduling scenarios in a smart grid environment. Appl Energy 142:164–178CrossRefGoogle Scholar
  39. Vardakas JS, Zorba N, Verikoukis CV (2016) Power demand control scenarios for smart grid applications with finite number of appliances. Appl Energy 162:83–98CrossRefGoogle Scholar
  40. Yi P, Dong X, Iwayemi A, Zhou C, Li S (2013) Real-time opportunistic scheduling for residential demand response. IEEE Trans Smart Grid 4(1):227–234CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.Higher Colleges of TechnologyFujairahUAE

Personalised recommendations