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Modeling of photovoltaic power uncertainties for impact analysis on generation scheduling and cost of an urban micro grid

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  • Wen, Xin
  • Abbes, Dhaker
  • Francois, Bruno

Abstract

In electrical systems, the main objective is to satisfy the load demand at the least cost without having imbalance between generation and consumption. Thus, the uncertainty of photovoltaic (PV) power production must be considered in generation planning of a power system. In this paper, we develop a modeling method of this uncertainty to consider it into the generation scheduling. The optimal generation scheduling in an urban microgrid is made by taking in consideration the operating reserve provision under stochastic characteristics of PV power prediction. By considering a prescribed risk level of unbalancing, a dynamic programming algorithm sets the operational planning of conventional generators by solving a non-convex mixed-integer nonlinear programming model, so that the operational cost and available operating reserve can be calculated. Then, the effect of PV power uncertainty into the unit commitment is analyzed by considering PV forecast intervals with a 95 % confidence level. The unit commitment is then recalculated with new generator set points and the same criteria. Finally, variations of the targeted minimized costs and obtained OR is analyzed according to the uncertainty.

Suggested Citation

  • Wen, Xin & Abbes, Dhaker & Francois, Bruno, 2021. "Modeling of photovoltaic power uncertainties for impact analysis on generation scheduling and cost of an urban micro grid," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 116-128.
    • Handle: RePEc:eee:matcom:v:183:y:2021:i:c:p:116-128
    DOI: 10.1016/j.matcom.2020.02.023
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    References listed on IDEAS

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    1. Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
    2. Baziar, Aliasghar & Kavousi-Fard, Abdollah, 2013. "Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices," Renewable Energy, Elsevier, vol. 59(C), pages 158-166.
    3. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Integrating Renewables in Electricity Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9411-9, December.
    4. Erdinc, Ozan & Paterakis, Nikolaos G. & Catalão, João P.S., 2015. "Overview of insular power systems under increasing penetration of renewable energy sources: Opportunities and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 333-346.
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    Cited by:

    1. Zou, Bin & Peng, Jinqing & Yin, Rongxin & Li, Houpei & Li, Sihui & Yan, Jinyue & Yang, Hongxing, 2022. "Capacity configuration of distributed photovoltaic and battery system for office buildings considering uncertainties," Applied Energy, Elsevier, vol. 319(C).
    2. Capper, Timothy & Gorbatcheva, Anna & Mustafa, Mustafa A. & Bahloul, Mohamed & Schwidtal, Jan Marc & Chitchyan, Ruzanna & Andoni, Merlinda & Robu, Valentin & Montakhabi, Mehdi & Scott, Ian J. & Franci, 2022. "Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

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