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Scheduling Distributed Energy Resource Operation and Daily Power Consumption for a Smart Building to Optimize Economic and Environmental Parameters

Author

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  • Zahra Pooranian

    (Department of Mathematics, University of Padua, Padua 35131, Italy)

  • Jemal H. Abawajy

    (School of Information Technology, Deakin University, Geelong, VIC 3125, Australia)

  • Vinod P

    (Department of Mathematics, University of Padua, Padua 35131, Italy)

  • Mauro Conti

    (Department of Mathematics, University of Padua, Padua 35131, Italy)

Abstract

In this paper, we address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences. As the household appliances contribute significantly to the energy consumption of the smart houses, it is possible to decrease electricity cost in buildings by scheduling the operation of domestic appliances. In this paper, we propose an optimization model for jointly minimizing electricity costs and CO 2 emissions by considering consumer preferences in smart buildings that are equipped with distributed energy resources (DERs). Both controllable and uncontrollable tasks and DER operations are scheduled according to the real-time price of electricity and a peak demand charge to reduce the peak demand on the grid. We formulate the daily energy consumption scheduling problem in multiple smart homes from economic and environmental perspectives and exploit a mixed integer linear programming technique to solve it. We validated the proposed approach through extensive experimental analysis. The results of the experiment show that the proposed approach can decrease both CO 2 emissions and the daily energy cost.

Suggested Citation

  • Zahra Pooranian & Jemal H. Abawajy & Vinod P & Mauro Conti, 2018. "Scheduling Distributed Energy Resource Operation and Daily Power Consumption for a Smart Building to Optimize Economic and Environmental Parameters," Energies, MDPI, vol. 11(6), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1348-:d:148995
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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Rastegar, Mohammad & Fotuhi-Firuzabad, Mahmud & Aminifar, Farrokh, 2012. "Load commitment in a smart home," Applied Energy, Elsevier, vol. 96(C), pages 45-54.
    3. Soares, Ana & Antunes, Carlos Henggeler & Oliveira, Carlos & Gomes, Álvaro, 2014. "A multi-objective genetic approach to domestic load scheduling in an energy management system," Energy, Elsevier, vol. 77(C), pages 144-152.
    4. Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
    5. Soares, Ana & Gomes, Álvaro & Antunes, Carlos Henggeler, 2014. "Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 490-503.
    6. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    7. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    8. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
    9. Wang, Zhu & Wang, Lingfeng & Dounis, Anastasios I. & Yang, Rui, 2012. "Multi-agent control system with information fusion based comfort model for smart buildings," Applied Energy, Elsevier, vol. 99(C), pages 247-254.
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    5. Wadim Strielkowski & Elena Volkova & Luidmila Pushkareva & Dalia Streimikiene, 2019. "Innovative Policies for Energy Efficiency and the Use of Renewables in Households," Energies, MDPI, vol. 12(7), pages 1-17, April.
    6. Saman Nikkhah & Adib Allahham & Janusz W. Bialek & Sara L. Walker & Damian Giaouris & Simira Papadopoulou, 2021. "Active Participation of Buildings in the Energy Networks: Dynamic/Operational Models and Control Challenges," Energies, MDPI, vol. 14(21), pages 1-28, November.
    7. Backe, Stian & Zwickl-Bernhard, Sebastian & Schwabeneder, Daniel & Auer, Hans & Korpås, Magnus & Tomasgard, Asgeir, 2022. "Impact of energy communities on the European electricity and heating system decarbonization pathway: Comparing local and global flexibility responses," Applied Energy, Elsevier, vol. 323(C).
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    10. Mohammad Shakeri & Jagadeesh Pasupuleti & Nowshad Amin & Md. Rokonuzzaman & Foo Wah Low & Chong Tak Yaw & Nilofar Asim & Nurul Asma Samsudin & Sieh Kiong Tiong & Chong Kok Hen & Chin Wei Lai, 2020. "An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid," Energies, MDPI, vol. 13(13), pages 1-15, June.
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    13. Thomas Märzinger & Doris Österreicher, 2019. "Supporting the Smart Readiness Indicator—A Methodology to Integrate A Quantitative Assessment of the Load Shifting Potential of Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-22, May.
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