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Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm

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  • Wang, Wenxiao
  • Li, Chaoshun
  • Liao, Xiang
  • Qin, Hui

Abstract

Wind power and photovoltaic power, two types of renewable energy (RE), have made large inroads into the power system. In this paper, we study a unit commitment (UC) problem that considers the uncertainty in RE and pumped hydro-energy storage (PHES). To improve the optimisation performance for this problem, we propose a novel heuristic algorithm called the Binary Artificial Sheep Algorithm (BASA) that is based on the social behaviour of sheep flock. To evaluate the effect of the uncertainty of RE, a scenario evaluation method is defined to assess quantitatively the stability and economy of the UC results with respect to different levels of RE forecasting errors. In addition, we investigate and analyse the effect of PHES on the UC problem. Three UC test systems with different RE and PHES combinations are used to verify the feasibility and effectiveness of the proposed BASA as well as its performance. The proposed BASA performed better than traditional fundamental metaheuristics in solving UC problems. Our results also demonstrated that the equivalent load fluctuation and operating costs of the thermal units will increase significantly with an increase in RE power forecast error, but the PHES can effectively counterbalance this adverse effect.

Suggested Citation

  • Wang, Wenxiao & Li, Chaoshun & Liao, Xiang & Qin, Hui, 2017. "Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm," Applied Energy, Elsevier, vol. 187(C), pages 612-626.
    • Handle: RePEc:eee:appene:v:187:y:2017:i:c:p:612-626
    DOI: 10.1016/j.apenergy.2016.11.085
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    References listed on IDEAS

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    1. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2016. "Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study," Energy, Elsevier, vol. 103(C), pages 735-745.
    2. Nazari, M.E. & Ardehali, M.M. & Jafari, S., 2010. "Pumped-storage unit commitment with considerations for energy demand, economics, and environmental constraints," Energy, Elsevier, vol. 35(10), pages 4092-4101.
    3. Ji, Bin & Yuan, Xiaohui & Chen, Zhihuan & Tian, Hao, 2014. "Improved gravitational search algorithm for unit commitment considering uncertainty of wind power," Energy, Elsevier, vol. 67(C), pages 52-62.
    4. 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.
    5. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    6. Tuohy, A. & O'Malley, M., 2011. "Pumped storage in systems with very high wind penetration," Energy Policy, Elsevier, vol. 39(4), pages 1965-1974, April.
    7. Foley, A.M. & Leahy, P.G. & Li, K. & McKeogh, E.J. & Morrison, A.P., 2015. "A long-term analysis of pumped hydro storage to firm wind power," Applied Energy, Elsevier, vol. 137(C), pages 638-648.
    8. PAPAVASILIOU, Anthony & OREN, Shmuel & ROUNTREE, Barry, 2015. "Applying high performance computing to transmissions-consstrained stochastic unit commitment for renewable energy integration," LIDAM Reprints CORE 2679, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Koltsaklis, Nikolaos E. & Georgiadis, Michael C., 2015. "A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints," Applied Energy, Elsevier, vol. 158(C), pages 310-331.
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