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Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Author

Listed:
  • Nadeem Javaid

    (Department of Computer Science, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Sardar Mehboob Hussain

    (Department of Computer Science, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Ibrar Ullah

    (University of Engineering and Technology Peshawar, Bannu 28100, Pakistan
    Capital University of Science and Technology, Islamabad 44000, Pakistan)

  • Muhammad Asim Noor

    (Department of Computer Science, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Wadood Abdul

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Ahmad Almogren

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Atif Alamri

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

Abstract

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

Suggested Citation

  • Nadeem Javaid & Sardar Mehboob Hussain & Ibrar Ullah & Muhammad Asim Noor & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations," Energies, MDPI, vol. 10(8), pages 1-29, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1131-:d:106695
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    References listed on IDEAS

    as
    1. Muhammad Babar Rasheed & Nadeem Javaid & Muhammad Awais & Zahoor Ali Khan & Umar Qasim & Nabil Alrajeh & Zafar Iqbal & Qaisar Javaid, 2016. "Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes," Energies, MDPI, vol. 9(7), pages 1-30, July.
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    4. Lv, Tianguang & Ai, Qian, 2016. "Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources," Applied Energy, Elsevier, vol. 163(C), pages 408-422.
    5. 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.
    6. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
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    2. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
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    4. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Rick Cox & Shalika Walker & Joep van der Velden & Phuong Nguyen & Wim Zeiler, 2020. "Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids," Energies, MDPI, vol. 13(9), pages 1-27, May.
    6. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    7. Priyadharshini Ramu & Sivasankar Gangatharan & Sankar Rangasamy & Lucian Mihet-Popa, 2023. "Categorization of Loads in Educational Institutions to Effectively Manage Peak Demand and Minimize Energy Cost Using an Intelligent Load Management Technique," Sustainability, MDPI, vol. 15(16), pages 1-28, August.
    8. Bandeiras, F. & Gomes, M. & Coelho, P. & Fernandes, J., 2020. "Towards net zero energy in industrial and commercial buildings in Portugal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    9. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    10. Makhadmeh, Sharif Naser & Khader, Ahamad Tajudin & Al-Betar, Mohammed Azmi & Naim, Syibrah & Abasi, Ammar Kamal & Alyasseri, Zaid Abdi Alkareem, 2019. "Optimization methods for power scheduling problems in smart home: Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    11. Muhammad Azhar Hassan & Saad Ullah Khan & Muhammad Fahad Zia & Azka Sardar & Khawaja Khalid Mehmood & Fiaz Ahmad, 2023. "Demand-Side Management and Its Impact on the Growing Circular Debt of Pakistan’s Energy Sector," Energies, MDPI, vol. 16(15), pages 1-20, July.

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