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Energy Management Optimization and Voltage Evaluation for Residential and Commercial Areas

Author

Listed:
  • Hayder O. Alwan

    (Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23220, USA)

  • Hamidreza Sadeghian

    (Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23220, USA)

  • Sherif Abdelwahed

    (Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23220, USA)

Abstract

In most smart grids, load management techniques are required to handle multiple loads of several types. This paper studies decentralized demand-side management (DSM) in a grid with different types of appliances in two service areas: one with many residential households, and one bus with commercial customers. Each building runs an individual optimal DSM to reschedule the usage time of its flexible appliances to reduce its electric energy cost at a manageable sacrifice of inconvenience according to the forecasted time-varying electricity price. Using the developed model, we examined the effectiveness of decentralized DSM by comparing its performance on the operation status of the grid in terms of electricity cost saving, rooftop photovoltaic (PV) utilization efficiency, voltage fluctuation, power loss, voltage rises, and reverse power flows, which can easily be seen at the commercial load bus.

Suggested Citation

  • Hayder O. Alwan & Hamidreza Sadeghian & Sherif Abdelwahed, 2019. "Energy Management Optimization and Voltage Evaluation for Residential and Commercial Areas," Energies, MDPI, vol. 12(9), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1811-:d:230588
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    References listed on IDEAS

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    1. Esther, B. Priya & Kumar, K. Sathish, 2016. "A survey on residential Demand Side Management architecture, approaches, optimization models and methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 342-351.
    2. Iver Bakken Sperstad & Magnus Korpås, 2019. "Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties," Energies, MDPI, vol. 12(7), pages 1-24, March.
    3. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    4. Sadeghian, H.R. & Ardehali, M.M., 2016. "A novel approach for optimal economic dispatch scheduling of integrated combined heat and power systems for maximum economic profit and minimum environmental emissions based on Benders decomposition," Energy, Elsevier, vol. 102(C), pages 10-23.
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    Cited by:

    1. 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.
    2. Grace Muriithi & Sunetra Chowdhury, 2021. "Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach," Energies, MDPI, vol. 14(9), pages 1-24, May.

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