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A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles

Author

Listed:
  • Feng Qi

    (School of Electrical Engineering, Zhejiang University, No. 38 Zheda Rd., Hangzhou 310027, China)

  • Fushuan Wen

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Xunyuan Liu

    (School of Electrical Engineering, Zhejiang University, No. 38 Zheda Rd., Hangzhou 310027, China)

  • Md. Abdus Salam

    (Department of Electrical and Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei)

Abstract

Renewable energy generation and electric vehicles (EVs) have attracted much attention in the past decade due to increasingly serious environmental problems as well as less and less fossil energy reserves. Moreover, the forms of energy utilization are changing with the development of information technology and energy technology. The term “energy hub” has been introduced to represent an entity with the capability of energy production, conversion and storage. A residential quarter energy-hub-optimization model including a concentrating solar power (CSP) unit is proposed in this work, with solar energy and electricity as its inputs to supply thermal and electrical demands, and the operating objective is to minimize the involved operation costs. The optimization model is a mixed integer linear programming (MILP) problem. Demand side management (DSM) is next implemented by modeling shiftable electrical loads such as EVs and washers, as well as flexible thermal loads such as hot water. Finally, the developed optimization model is solved with the commercial CPLEX solver based on the YALMIP/MATLAB toolbox, and sample examples are provided for demonstrating the features of the proposed method.

Suggested Citation

  • Feng Qi & Fushuan Wen & Xunyuan Liu & Md. Abdus Salam, 2017. "A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles," Energies, MDPI, vol. 10(8), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1159-:d:107266
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    Cited by:

    1. Zuoyu Liu & Weimin Zheng & Feng Qi & Lei Wang & Bo Zou & Fushuan Wen & You Xue, 2018. "Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading," Energies, MDPI, vol. 11(6), pages 1-19, June.
    2. Ruifeng Shi & Shaopeng Li & Changhao Sun & Kwang Y. Lee, 2018. "Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-22, August.
    3. 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.
    4. Changsen Feng & Fushuan Wen & Lijun Zhang & Chenbo Xu & Md. Abdus Salam & Shi You, 2018. "Decentralized Energy Management of Networked Microgrid Based on Alternating-Direction Multiplier Method," Energies, MDPI, vol. 11(10), pages 1-18, September.
    5. Sultan, Ali J. & Hughes, Kevin J. & Ingham, Derek B. & Ma, Lin & Pourkashanian, Mohamed, 2020. "Techno-economic competitiveness of 50 MW concentrating solar power plants for electricity generation under Kuwait climatic conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    6. Sultan, Ali J. & Ingham, Derek B. & Hughes, Kevin J. & Ma, Lin & Pourkashanian, Mohamed, 2021. "Optimization and performance enhancement of concentrating solar power in a hot and arid desert environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    7. Antonio Pepiciello & Alfredo Vaccaro & Mario Mañana, 2019. "Robust Optimization of Energy Hubs Operation Based on Extended Affine Arithmetic," Energies, MDPI, vol. 12(12), pages 1-15, June.
    8. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    9. Kamran Taghizad-Tavana & As’ad Alizadeh & Mohsen Ghanbari-Ghalehjoughi & Sayyad Nojavan, 2023. "A Comprehensive Review of Electric Vehicles in Energy Systems: Integration with Renewable Energy Sources, Charging Levels, Different Types, and Standards," Energies, MDPI, vol. 16(2), pages 1-23, January.
    10. Lin, Haiyang & Liu, Yiling & Sun, Qie & Xiong, Rui & Li, Hailong & Wennersten, Ronald, 2018. "The impact of electric vehicle penetration and charging patterns on the management of energy hub – A multi-agent system simulation," Applied Energy, Elsevier, vol. 230(C), pages 189-206.
    11. Qi Wang & Ping Chang & Runqing Bai & Wenfei Liu & Jianfeng Dai & Yi Tang, 2019. "Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station," Energies, MDPI, vol. 12(18), pages 1-16, September.
    12. Pierpaolo Garavaso & Fabio Bignucolo & Jacopo Vivian & Giulia Alessio & Michele De Carli, 2021. "Optimal Planning and Operation of a Residential Energy Community under Shared Electricity Incentives," Energies, MDPI, vol. 14(8), pages 1-24, April.
    13. Yining Zhang & Yubin He & Mingyu Yan & Chuangxin Guo & Yi Ding, 2018. "Linearized Stochastic Scheduling of Interconnected Energy Hubs Considering Integrated Demand Response and Wind Uncertainty," Energies, MDPI, vol. 11(9), pages 1-23, September.
    14. Alaa Farah & Hamdy Hassan & Alaaeldin M. Abdelshafy & Abdelfatah M. Mohamed, 2020. "Optimal Scheduling of Hybrid Multi-Carrier System Feeding Electrical/Thermal Load Based on Particle Swarm Algorithm," Sustainability, MDPI, vol. 12(11), pages 1-21, June.

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