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Real time economic dispatch considering renewable energy resources

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  • Reddy, S. Surender
  • Bijwe, P.R.

Abstract

In practice, the real time economic dispatch is performed in every 5–15 min intervals with the static snapshot forecast data. During the period between two consecutive schedules, generators participate in managing power imbalance, based on participation factors from previous economic dispatch. In modern power system with considerable renewable energy resources that have high variability, this conventional approach may not adequately accommodate the economic implication of the said variability. This paper proposes the evaluation of ‘best-fit’ participation factors by considering the minute-to-minute variability of solar, wind and load demand, for a scheduling period. The voltage, reactive power limit and line flow constraints are included for all minute-to-minute sub-intervals. Since ‘best-fit’ participation factors are evaluated only once, i.e., at the start of scheduling interval, the dimensionality of optimization problem remains the same as that of conventional approach. The proposed approach is suggested for sequential as well as dynamic variants. The proposed real time economic dispatch approaches are tested on IEEE 30 bus and 118 bus test systems considering variability in renewable energy sources and load demand.

Suggested Citation

  • Reddy, S. Surender & Bijwe, P.R., 2015. "Real time economic dispatch considering renewable energy resources," Renewable Energy, Elsevier, vol. 83(C), pages 1215-1226.
  • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:1215-1226
    DOI: 10.1016/j.renene.2015.06.011
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    References listed on IDEAS

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    Cited by:

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    3. Hui Zhou & Jian Ding & Yinlong Hu & Zisong Ye & Shang Shi & Yonghui Sun & Qiyu Zhang, 2022. "Economic Dispatch of Power Retailers: A Bi-Level Programming Approach via Market Clearing Price," Energies, MDPI, vol. 15(19), pages 1-17, September.
    4. Hao Chen & Chi Kong Chyong & Jia-Ning Kang & Yi-Ming Wei, 2018. "Economic dispatch in the electricity sector in China: potential benefits and challenges ahead," Working Papers EPRG 1819, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    5. Nghitevelekwa, K. & Bansal, R.C., 2018. "A review of generation dispatch with large-scale photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 615-624.
    6. Wang, Kun & He, Ya-Ling & Qiu, Yu & Zhang, Yuwen, 2016. "A novel integrated simulation approach couples MCRT and Gebhart methods to simulate solar radiation transfer in a solar power tower system with a cavity receiver," Renewable Energy, Elsevier, vol. 89(C), pages 93-107.
    7. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    8. Nicu Bizon & Phatiphat Thounthong, 2020. "Energy Efficiency and Fuel Economy of a Fuel Cell/Renewable Energy Sources Hybrid Power System with the Load-Following Control of the Fueling Regulators," Mathematics, MDPI, vol. 8(2), pages 1-22, January.
    9. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    10. Henao, Felipe & Rodriguez, Yeny & Viteri, Juan Pablo & Dyner, Isaac, 2019. "Optimising the insertion of renewables in the Colombian power sector," Renewable Energy, Elsevier, vol. 132(C), pages 81-92.
    11. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    12. Pan, Jeng-Shyang & Hu, Pei & Chu, Shu-Chuan, 2021. "Binary fish migration optimization for solving unit commitment," Energy, Elsevier, vol. 226(C).
    13. Kasaeian, Alibakhsh & Barghamadi, Hossein & Pourfayaz, Fathollah, 2017. "Performance comparison between the geometry models of multi-channel absorbers in solar volumetric receivers," Renewable Energy, Elsevier, vol. 105(C), pages 1-12.
    14. Kumbuso Joshua Nyoni & Anesu Maronga & Paul Gerard Tuohy & Agabu Shane, 2021. "Hydro–Connected Floating PV Renewable Energy System and Onshore Wind Potential in Zambia," Energies, MDPI, vol. 14(17), pages 1-42, August.
    15. Lin, Zhenjia & Chen, Haoyong & Wu, Qiuwei & Li, Weiwei & Li, Mengshi & Ji, Tianyao, 2020. "Mean-tracking model based stochastic economic dispatch for power systems with high penetration of wind power," Energy, Elsevier, vol. 193(C).
    16. Erfan Mohagheghi & Aouss Gabash & Pu Li, 2017. "A Framework for Real-Time Optimal Power Flow under Wind Energy Penetration," Energies, MDPI, vol. 10(4), pages 1-28, April.
    17. Wei, Yi-Ming & Chen, Hao & Chyong, Chi Kong & Kang, Jia-Ning & Liao, Hua & Tang, Bao-Jun, 2018. "Economic dispatch savings in the coal-fired power sector: An empirical study of China," Energy Economics, Elsevier, vol. 74(C), pages 330-342.
    18. Mo, Hua-Dong & Li, Yan-Fu & Zio, Enrico, 2016. "A system-of-systems framework for the reliability analysis of distributed generation systems accounting for the impact of degraded communication networks," Applied Energy, Elsevier, vol. 183(C), pages 805-822.
    19. Henao, Felipe & Dyner, Isaac, 2020. "Renewables in the optimal expansion of colombian power considering the Hidroituango crisis," Renewable Energy, Elsevier, vol. 158(C), pages 612-627.

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