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Co-optimization of energy and reserve in standalone micro-grid considering uncertainties

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  • Bahramara, Salah
  • Sheikhahmadi, Pouria
  • Golpîra, Hêmin

Abstract

The electrical energy systems suffer from several problems of operation including production of greenhouse gas emissions and low energy efficiency in fossil fuel-based power plants as well as high energy losses in transmission and distribution networks. Transition from the traditional centralized power generation into distributed power generation, via distributed energy resources, is introduced as a solution to deal with these problems. Micro-grid concept, as a cluster of distributed energy resources and local loads, is introduced to effectively realize distributed power generation. In standalone mode, micro-grid operator faces uncertainties which should be appropriately tackled into the operation problem formulation. For this purpose, a new decision making framework is proposed in this paper which guarantees optimal scheduling of distributed energy resources to simultaneously provide energy and reserve. The proposed modeling framework, which is visualized through a new risk-based stochastic approach, controls the risk of micro-grid operator in decision-making by Conditional Value at Risk method. Numerical results demonstrates the effectiveness of the proposed framework to model the operation problem of a standalone micro-grid under different uncertainties. Moreover, sensitivity analysis reveals that by increasing the percentage of invoked reserve the expected total cost of micro-grid operator increases to manage the uncertainties.

Suggested Citation

  • Bahramara, Salah & Sheikhahmadi, Pouria & Golpîra, Hêmin, 2019. "Co-optimization of energy and reserve in standalone micro-grid considering uncertainties," Energy, Elsevier, vol. 176(C), pages 792-804.
    • Handle: RePEc:eee:energy:v:176:y:2019:i:c:p:792-804
    DOI: 10.1016/j.energy.2019.04.057
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    Cited by:

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    3. To, Thanh & Heleno, Miguel & Valenzuela, Alan, 2022. "Risk-constrained multi-period investment model for Distributed Energy Resources considering technology costs and regulatory uncertainties," Applied Energy, Elsevier, vol. 319(C).
    4. Wu, Di & Ma, Xu & Huang, Sen & Fu, Tao & Balducci, Patrick, 2020. "Stochastic optimal sizing of distributed energy resources for a cost-effective and resilient Microgrid," Energy, Elsevier, vol. 198(C).
    5. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    6. Kang, Hyuna & Jung, Seunghoon & Kim, Hakpyeong & Jeoung, Jaewon & Hong, Taehoon, 2024. "Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    7. Nikzad, Mehdi & Samimi, Abouzar, 2021. "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems," Applied Energy, Elsevier, vol. 282(PA).

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