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Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation

Author

Listed:
  • Danish Mahmood

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

  • Nadeem Javaid

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

  • Sheraz Ahmed

    (COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
    Institute of Management Sciences (IMS), Peshawar 25000, Pakistan)

  • Imran Ahmed

    (Institute of Management Sciences (IMS), Peshawar 25000, Pakistan)

  • Iftikhar Azim Niaz

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

  • Wadood Abdul

    (Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Sanaa Ghouzali

    (Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

Abstract

Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at “low-priced” hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,“User Comfort Level (UCL)”isformulatedonthebasisofuserpreferencesoncostsaving,toleranceindelayregardinguse of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.

Suggested Citation

  • Danish Mahmood & Nadeem Javaid & Sheraz Ahmed & Imran Ahmed & Iftikhar Azim Niaz & Wadood Abdul & Sanaa Ghouzali, 2017. "Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation," Energies, MDPI, vol. 10(3), pages 1-25, March.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:335-:d:92658
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    References listed on IDEAS

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    1. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    2. Pusnik, Matevz & Al-Mansour, Fouad & Sucic, Boris & Gubina, A.F., 2016. "Gap analysis of industrial energy management systems in Slovenia," Energy, Elsevier, vol. 108(C), pages 41-49.
    3. Tadahiro Taniguchi & Koki Kawasaki & Yoshiro Fukui & Tomohiro Takata & Shiro Yano, 2015. "Automated Linear Function Submission-Based Double Auction as Bottom-up Real-Time Pricing in a Regional Prosumers’ Electricity Network," Energies, MDPI, vol. 8(7), pages 1-26, July.
    4. Luay N. Dwaikat & Kherun N. Ali, 2016. "Measuring the Actual Energy Cost Performance of Green Buildings: A Test of the Earned Value Management Approach," Energies, MDPI, vol. 9(3), pages 1-20, March.
    5. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    6. Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie & Russell Haggar & Mike Baker, 2016. "Experimental Study of 6LoPLC for Home Energy Management Systems," Energies, MDPI, vol. 9(12), pages 1-19, December.
    7. Whei-Min Lin & Chia-Sheng Tu & Ming-Tang Tsai, 2015. "Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization," Energies, MDPI, vol. 9(1), pages 1-16, December.
    8. Mulder, Grietus & Six, Daan & Claessens, Bert & Broes, Thijs & Omar, Noshin & Mierlo, Joeri Van, 2013. "The dimensioning of PV-battery systems depending on the incentive and selling price conditions," Applied Energy, Elsevier, vol. 111(C), pages 1126-1135.
    9. Zdenek Bradac & Vaclav Kaczmarczyk & Petr Fiedler, 2014. "Optimal Scheduling of Domestic Appliances via MILP," Energies, MDPI, vol. 8(1), pages 1-16, December.
    10. Graditi, G. & Ippolito, M.G. & Telaretti, E. & Zizzo, G., 2016. "Technical and economical assessment of distributed electrochemical storages for load shifting applications: An Italian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 515-523.
    11. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    12. Brian L. Thomas & Diane J. Cook, 2016. "Activity-Aware Energy-Efficient Automation of Smart Buildings," Energies, MDPI, vol. 9(8), pages 1-17, August.
    13. Gabriele Lobaccaro & Salvatore Carlucci & Erica Löfström, 2016. "A Review of Systems and Technologies for Smart Homes and Smart Grids," Energies, MDPI, vol. 9(5), pages 1-33, May.
    14. Tiago D. P. Mendes & Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Smart Home Communication Technologies and Applications: Wireless Protocol Assessment for Home Area Network Resources," Energies, MDPI, vol. 8(7), pages 1-33, July.
    15. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "Residential demand response scheme based on adaptive consumption level pricing," Energy, Elsevier, vol. 113(C), pages 301-308.
    16. Bingtuan Gao & Wenhu Zhang & Yi Tang & Mingjin Hu & Mingcheng Zhu & Huiyu Zhan, 2014. "Game-Theoretic Energy Management for Residential Users with Dischargeable Plug-in Electric Vehicles," Energies, MDPI, vol. 7(11), pages 1-20, November.
    17. Nikoleta Andreadou & Miguel Olariaga Guardiola & Gianluca Fulli, 2016. "Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications," Energies, MDPI, vol. 9(5), pages 1-35, May.
    18. Hye Yeon Kim & Hae Jin Kang, 2016. "A Study on Development of a Cost Optimal and Energy Saving Building Model: Focused on Industrial Building," Energies, MDPI, vol. 9(3), pages 1-19, March.
    19. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    20. Danish Mahmood & Nadeem Javaid & Nabil Alrajeh & Zahoor Ali Khan & Umar Qasim & Imran Ahmed & Manzoor Ilahi, 2016. "Realistic Scheduling Mechanism for Smart Homes," Energies, MDPI, vol. 9(3), pages 1-28, March.
    21. Jaffe, Adam B. & Stavins, Robert N., 1994. "The energy-efficiency gap What does it mean?," Energy Policy, Elsevier, vol. 22(10), pages 804-810, October.
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