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Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid

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  • Howlader, Harun Or Rashid
  • Matayoshi, Hidehito
  • Senjyu, Tomonobu

Abstract

This paper deals with an optimal battery energy storage capacity for the smart grid operation. Distributed renewable generator and conventional thermal generator are considered as the power generation sources for the smart grid. Usually, a battery energy storage system (BESS) is used to satisfy the transmission constraints but installation cost of battery energy storage is very high. Sometimes, it is not possible to install a large capacity of the BESS. On the other hand, the competition of the electricity market has been increased due to the deregulation and liberalization of the power market. Therefore, the power companies are required to reduce the generation cost in order to maximize the profit. In this paper, a thermal units commitment program considers the demand response system to satisfy the transmission constraints. The BESS capacity can be reduced by the demand response system. The electric vehicle (EV) and heat pump (HP) in the smart house are considered as the controllable loads of the demand side. The effectiveness of the proposed method is validated by extensive simulation results which ensure the reduction of BESS capacity and power generation cost, and satisfy the transmission constraints.

Suggested Citation

  • Howlader, Harun Or Rashid & Matayoshi, Hidehito & Senjyu, Tomonobu, 2016. "Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid," Renewable Energy, Elsevier, vol. 99(C), pages 107-117.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:107-117
    DOI: 10.1016/j.renene.2016.06.050
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    References listed on IDEAS

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    Cited by:

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    3. K. Selvakumar & K. Vijayakumar & C. S. Boopathi, 2017. "Demand Response Unit Commitment Problem Solution for Maximizing Generating Companies’ Profit," Energies, MDPI, vol. 10(10), pages 1-18, September.
    4. Eng Tseng Lau & Kok Keong Chai & Yue Chen & Jonathan Loo, 2018. "Efficient Economic and Resilience-Based Optimization for Disaster Recovery Management of Critical Infrastructures," Energies, MDPI, vol. 11(12), pages 1-20, December.
    5. Anatoly P. Dzyuba & Irina A. Solovyeva & Aleksandr V. Semikolenov, 2022. "Prospects of introducing microgrids in Russian industry," Journal of New Economy, Ural State University of Economics, vol. 23(2), pages 80-101, July.
    6. Haque, A.N.M.M. & Ibn Saif, A.U.N. & Nguyen, P.H. & Torbaghan, S.S., 2016. "Exploration of dispatch model integrating wind generators and electric vehicles," Applied Energy, Elsevier, vol. 183(C), pages 1441-1451.
    7. Gonocruz, Ruth Anne Tanlioco & Yoshida, Yoshikuni & Ozawa, Akito & Aguirre, Rodolfo A. & Maguindayao, Edward Joseph H., 2023. "Impacts of agrivoltaics in rural electrification and decarbonization in the Philippines," Applied Energy, Elsevier, vol. 350(C).
    8. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.
    9. Wang, Zhimin & Gu, Chenghong & Li, Furong, 2018. "Flexible operation of shared energy storage at households to facilitate PV penetration," Renewable Energy, Elsevier, vol. 116(PA), pages 438-446.
    10. Tohid Harighi & Ramazan Bayindir & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Eklas Hossain, 2018. "An Overview of Energy Scenarios, Storage Systems and the Infrastructure for Vehicle-to-Grid Technology," Energies, MDPI, vol. 11(8), pages 1-18, August.
    11. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    12. Kyritsis, A. & Voglitsis, D. & Papanikolaou, N. & Tselepis, S. & Christodoulou, C. & Gonos, I. & Kalogirou, S.A., 2017. "Evolution of PV systems in Greece and review of applicable solutions for higher penetration levels," Renewable Energy, Elsevier, vol. 109(C), pages 487-499.
    13. Lu, Qing & Zhang, Yufeng, 2022. "A multi-objective optimization model considering users' satisfaction and multi-type demand response in dynamic electricity price," Energy, Elsevier, vol. 240(C).
    14. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.

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