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

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Abstract

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.

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Authors and Affiliations

  1. COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan

    Asif Khan, Nadeem Javaid, Adnan Ahmad, Mariam Akbar & Manzoor Ilahi

  2. Higher Colleges of Technology, Fujairah Campus, 4114, Fujairah, UAE

    Zahoor Ali Khan

Authors
  1. Asif Khan
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  2. Nadeem Javaid
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  3. Adnan Ahmad
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  4. Mariam Akbar
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  5. Zahoor Ali Khan
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  6. Manzoor Ilahi
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Correspondence to Nadeem Javaid.

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Khan, A., Javaid, N., Ahmad, A. et al. A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. J Ambient Intell Human Comput 10, 1655–1678 (2019). https://doi.org/10.1007/s12652-018-0761-z

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  • DOI: https://doi.org/10.1007/s12652-018-0761-z

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