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Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

Author

Listed:
  • Sheraz Aslam

    (COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Zafar Iqbal

    (PMAS Arid Agriculture University, Rawalpindi 46000, Pakistan)

  • Nadeem Javaid

    (COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Zahoor Ali Khan

    (CIS, Higher Colleges of Technology, Fujairah 4114, UAE)

  • Khursheed Aurangzeb

    (College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
    COMSATS Institute of Information Technology, Attock 43600, Pakistan)

  • Syed Irtaza Haider

    (College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia)

Abstract

The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.

Suggested Citation

  • Sheraz Aslam & Zafar Iqbal & Nadeem Javaid & Zahoor Ali Khan & Khursheed Aurangzeb & Syed Irtaza Haider, 2017. "Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes," Energies, MDPI, vol. 10(12), pages 1-25, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2065-:d:121765
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    References listed on IDEAS

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    1. Nadeem Javaid & Sakeena Javaid & Wadood Abdul & Imran Ahmed & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid," Energies, MDPI, vol. 10(3), pages 1-27, March.
    2. Lior, Noam, 2010. "Sustainable energy development: The present (2009) situation and possible paths to the future," Energy, Elsevier, vol. 35(10), pages 3976-3994.
    3. Zdenek Bradac & Vaclav Kaczmarczyk & Petr Fiedler, 2014. "Optimal Scheduling of Domestic Appliances via MILP," Energies, MDPI, vol. 8(1), pages 1-16, December.
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