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Effects of mixed electronic loads on the electrical energy systems considering different loading conditions with focus on power quality and billing issues

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  • da Silva, Roberto Perillo Barbosa
  • Quadros, Rodolfo
  • Shaker, Hamid Reza
  • da Silva, Luiz Carlos Pereira

Abstract

•The conditions under which electronic loads cause power quality issues are presented.•Total demand distortion has been calculated considering different variables.•Different variables considered: short-circuit and voltage levels, transformers, and cables.•The conditions which cause violation of the limits set by IEEE 519 have been presented.•The billing proposals under nonsinusoidal conditions are reviewed and discussed.

Suggested Citation

  • da Silva, Roberto Perillo Barbosa & Quadros, Rodolfo & Shaker, Hamid Reza & da Silva, Luiz Carlos Pereira, 2020. "Effects of mixed electronic loads on the electrical energy systems considering different loading conditions with focus on power quality and billing issues," Applied Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920310709
    DOI: 10.1016/j.apenergy.2020.115558
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    References listed on IDEAS

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    1. Öhrlund, Isak & Schultzberg, Mårten & Bartusch, Cajsa, 2019. "Identifying and estimating the effects of a mandatory billing demand charge," Applied Energy, Elsevier, vol. 237(C), pages 885-895.
    2. Sun, Yuanyuan & Xie, Xiangmin & Wang, Qingyan & Zhang, Linghan & Li, Yahui & Jin, Zongshuai, 2020. "A bottom-up approach to evaluate the harmonics and power of home appliances in residential areas," Applied Energy, Elsevier, vol. 259(C).
    3. Ahmad, Ali & Kashif, Syed Abdul Rahman & Saqib, Muhammad Asghar & Ashraf, Arslan & Shami, Umar Tabrez, 2019. "Tariff for reactive energy consumption in household appliances," Energy, Elsevier, vol. 186(C).
    4. Wang, Shouxiang & Chen, Haiwen, 2019. "A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network," Applied Energy, Elsevier, vol. 235(C), pages 1126-1140.
    5. Roberto Perillo Barbosa da Silva & Rodolfo Quadros & Hamid Reza Shaker & Luiz Carlos Pereira da Silva, 2019. "Analysis of the Electrical Quantities Measured by Revenue Meters Under Different Voltage Distortions and the Influences on the Electrical Energy Billing," Energies, MDPI, vol. 12(24), pages 1-18, December.
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    Cited by:

    1. Hari Prasad Devarapalli & Venkata Samba Sesha Siva Sarma Dhanikonda & Sitarama Brahmam Gunturi, 2021. "Demand-Side Management for Improvement of the Power Quality in Smart Homes Using Non-Intrusive Identification of Appliance Usage Patterns with the True Power Factor," Energies, MDPI, vol. 14(16), pages 1-19, August.
    2. Mayurkumar Rajkumar Balwani & Karthik Thirumala & Vivek Mohan & Siqi Bu & Mini Shaji Thomas, 2021. "Development of a Smart Meter for Power Quality-Based Tariff Implementation in a Smart Grid," Energies, MDPI, vol. 14(19), pages 1-21, September.
    3. Xie, Xiangmin & Chen, Daolian, 2022. "Data-driven dynamic harmonic model for modern household appliances," Applied Energy, Elsevier, vol. 312(C).
    4. Budhavarapu, Jayaprakash & Thirumala, Karthik & Mohan, Vivek & Bu, Siqi & Sahoo, Manoranjan, 2022. "Tariff structure for regulation of reactive power and harmonics in prosumer-enabled low voltage distribution networks," Energy Economics, Elsevier, vol. 114(C).

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