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Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment

Author

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  • Yeongenn Kwon

    (School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea)

  • Taeyoung Kim

    (School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea)

  • Keon Baek

    (School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea)

  • Jinho Kim

    (School of Integrated Technology, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea)

Abstract

A Time-of-Use (TOU)-tariff scheme, helps residential customers to adjust their energy consumption voluntarily and reduce energy cost. The TOU tariff provides flexibility in demand, alleviate volatility caused by an increase in renewable energy in the power system. However, the uncertainty in the customer’s behavior, causes difficulty in predicting changes in residential demand patterns through the TOU tariff. In this study, the dissatisfaction model for each time slot is set as the energy consumption data of the customer. Based on the actual customer’s consumption pattern, the user sets up a model of dissatisfaction that enables aggressive energy cost reduction. In the proposed Home Energy Management System (HEMS) model, the efficient use of jointly invested offsite photovoltaic (PV) power generation is also considered. The optimal HEMS scheduling result considering the dissatisfaction, cost, and PV curtailment was obtained. The findings of this study indicate, that incentives are required above a certain EV battery capacity to induce EV charging for minimizing PV curtailment.

Suggested Citation

  • Yeongenn Kwon & Taeyoung Kim & Keon Baek & Jinho Kim, 2020. "Multi-Objective Optimization of Home Appliances and Electric Vehicle Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment," Energies, MDPI, vol. 13(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2852-:d:366906
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    References listed on IDEAS

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    Cited by:

    1. Aya Amer & Khaled Shaban & Ahmed Gaouda & Ahmed Massoud, 2021. "Home Energy Management System Embedded with a Multi-Objective Demand Response Optimization Model to Benefit Customers and Operators," Energies, MDPI, vol. 14(2), pages 1-19, January.
    2. Héricles Eduardo Oliveira Farias & Camilo Alberto Sepulveda Rangel & Leonardo Weber Stringini & Luciane Neves Canha & Daniel Pegoraro Bertineti & Wagner da Silva Brignol & Zeno Iensen Nadal, 2021. "Combined Framework with Heuristic Programming and Rule-Based Strategies for Scheduling and Real Time Operation in Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(5), pages 1-27, March.
    3. Aya Amer & Khaled Shaban & Ahmed Massoud, 2022. "Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes," Energies, MDPI, vol. 15(21), pages 1-20, November.
    4. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Tingting Hou & Rengcun Fang & Jinrui Tang & Ganheng Ge & Dongjun Yang & Jianchao Liu & Wei Zhang, 2021. "A Novel Short-Term Residential Electric Load Forecasting Method Based on Adaptive Load Aggregation and Deep Learning Algorithms," Energies, MDPI, vol. 14(22), pages 1-21, November.

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