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Multi objective unit commitment with voltage stability and PV uncertainty

Author

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  • Furukakoi, Masahiro
  • Adewuyi, Oludamilare Bode
  • Matayoshi, Hidehito
  • Howlader, Abdul Motin
  • Senjyu, Tomonobu

Abstract

This paper proposes a novel multipurpose operation planning method for minimizing the prediction error of photovoltaic power generator outputs (PV); towards reducing the operating cost and improving voltage stability of power systems. The operation schedule (coordination) of demand response (DR) program and storage system are taken into account as the main parameters for achieving an improved voltage stability and reduction of PV output prediction error. In this approach, the stochastic programming algorithm is introduced for incorporating the uncertainty of PV output and the utility demand response for consumer side management. This is achieved by using the multi-objective genetic algorithm (MOGA) for multipurpose operation plan. The MATLAB optimization toolbox and neural network toolbox were applied in this research study. An IEEE-6 bus system is used to demonstrate the effectiveness of the proposed solution in power systems operation. The approach led to $25003.39(=$99594.53-$74591.14) reduction in the system operating cost, compared to the conventional approach. The simulation results also show that by using the proposed algorithm, the capacity of installed PV generators was increased and the voltage stability was improved at the same time. This accounted for the reduction in the effective operating cost and the improved operating condition of the power system.

Suggested Citation

  • Furukakoi, Masahiro & Adewuyi, Oludamilare Bode & Matayoshi, Hidehito & Howlader, Abdul Motin & Senjyu, Tomonobu, 2018. "Multi objective unit commitment with voltage stability and PV uncertainty," Applied Energy, Elsevier, vol. 228(C), pages 618-623.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:618-623
    DOI: 10.1016/j.apenergy.2018.06.074
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    References listed on IDEAS

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    1. Quan, Hao & Srinivasan, Dipti & Khambadkone, Ashwin M. & Khosravi, Abbas, 2015. "A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources," Applied Energy, Elsevier, vol. 152(C), pages 71-82.
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    2. Jinhwa Jeong & Dongkyu Lee & Young Tae Chae, 2023. "A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service," Energies, MDPI, vol. 16(22), pages 1-21, November.
    3. Zepter, Jan Martin & Weibezahn, Jens, 2019. "Unit commitment under imperfect foresight – The impact of stochastic photovoltaic generation," Applied Energy, Elsevier, vol. 243(C), pages 336-349.
    4. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
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    7. Mohammad Masih Sediqi & Mohammed Elsayed Lotfy & Abdul Matin Ibrahimi & Tomonobu Senjyu & Narayanan. K, 2019. "Stochastic Unit Commitment and Optimal Power Trading Incorporating PV Uncertainty," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    8. Lai, Qiupin & Liu, Chengxi & Sun, Kai, 2021. "Vulnerability assessment for voltage stability based on solvability regions of decoupled power flow equations," Applied Energy, Elsevier, vol. 304(C).
    9. Veerasamy, Veerapandiyan & Abdul Wahab, Noor Izzri & Ramachandran, Rajeswari & Othman, Mohammad Lutfi & Hizam, Hashim & Devendran, Vidhya Sagar & Irudayaraj, Andrew Xavier Raj & Vinayagam, Arangarajan, 2021. "Recurrent network based power flow solution for voltage stability assessment and improvement with distributed energy sources," Applied Energy, Elsevier, vol. 302(C).
    10. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    11. Kim, Jangkyum & Oh, Hyeontaek & Choi, Jun Kyun, 2022. "Learning based cost optimal energy management model for campus microgrid systems," Applied Energy, Elsevier, vol. 311(C).
    12. Thomas Carrière & Rodrigo Amaro e Silva & Fuqiang Zhuang & Yves-Marie Saint-Drenan & Philippe Blanc, 2021. "A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors," Energies, MDPI, vol. 14(16), pages 1-19, August.
    13. Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.
    14. Adewuyi, Oludamilare Bode & Lotfy, Mohammed E. & Akinloye, Benjamin Olabisi & Rashid Howlader, Harun Or & Senjyu, Tomonobu & Narayanan, Krishna, 2019. "Security-constrained optimal utility-scale solar PV investment planning for weak grids: Short reviews and techno-economic analysis," Applied Energy, Elsevier, vol. 245(C), pages 16-30.
    15. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    16. Isuru, Mohasha & Hotz, Matthias & Gooi, H.B. & Utschick, Wolfgang, 2020. "Network-constrained thermal unit commitment fortexhybrid AC/DC transmission grids under wind power uncertainty," Applied Energy, Elsevier, vol. 258(C).
    17. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
    18. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    19. 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.
    20. Badakhshan, Sobhan & Hajibandeh, Neda & Shafie-khah, Miadreza & Catalão, João.P.S., 2019. "Impact of solar energy on the integrated operation of electricity-gas grids," Energy, Elsevier, vol. 183(C), pages 844-853.

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