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Towards long-period operational reliability of independent microgrid: A risk-aware energy scheduling and stochastic optimization method

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  • Liu, Yixin
  • Shi, Haoqi
  • Guo, Li
  • Xu, Tao
  • Zhao, Bo
  • Wang, Chengshan

Abstract

Independent microgrids (MGs) consisting of diesel generator (DG), photovoltaic (PV), and energy storage system (ESS) are becoming a cost effective solution for the power supply in remote areas. However, besides PV intermittence, limited reserve and time-consuming replenishment of diesel fuel in remote areas make it challenging to guarantee long-period reliable power supply. In this paper, a risk-aware energy scheduling and stochastic optimization method is proposed to enhance long-period operational reliability of independent MGs. The possible extreme scenarios in the future are considered in an energy scheduling optimization model (ESOM). Based on energy forecast results of PV and loads for the next 7 days, ESOM maximizes the reliable power supply probability by optimizing energy scheduling strategies and reserve requirement for future operational risk simultaneously. Subsequently, a day-ahead stochastic optimization model is established to determine optimal power scheduling strategies of DG, PV, ESS, and flexible loads. The conditional value at risk (CVaR) is used to address the operation risk caused by uncertainties of PV and loads. Compared with traditional day-ahead optimization methods by numerous simulations, the proposed method has less expected load losses and PV curtailment, as well as less total supply-demand deviation. The resistance for future operational risks of independent MGs is therefore significantly enhanced.

Suggested Citation

  • Liu, Yixin & Shi, Haoqi & Guo, Li & Xu, Tao & Zhao, Bo & Wang, Chengshan, 2022. "Towards long-period operational reliability of independent microgrid: A risk-aware energy scheduling and stochastic optimization method," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s036054422201194x
    DOI: 10.1016/j.energy.2022.124291
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    References listed on IDEAS

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    1. Zhang, Yan & Fu, Lijun & Zhu, Wanlu & Bao, Xianqiang & Liu, Cang, 2018. "Robust model predictive control for optimal energy management of island microgrids with uncertainties," Energy, Elsevier, vol. 164(C), pages 1229-1241.
    2. Obara, Shin'ya & Hamanaka, Ryo & El-Sayed, Abeer Galal, 2019. "Design methods for microgrids to address seasonal energy availability – A case study of proposed Showa Antarctic Station retrofits," Applied Energy, Elsevier, vol. 236(C), pages 711-727.
    3. Shang, Yuwei & Wu, Wenchuan & Guo, Jianbo & Ma, Zhao & Sheng, Wanxing & Lv, Zhe & Fu, Chenran, 2020. "Stochastic dispatch of energy storage in microgrids: An augmented reinforcement learning approach," Applied Energy, Elsevier, vol. 261(C).
    4. Wang, Han & Han, Shuang & Liu, Yongqian & Yan, Jie & Li, Li, 2019. "Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system," Applied Energy, Elsevier, vol. 237(C), pages 1-10.
    5. Hossain, Md Alamgir & Pota, Hemanshu Roy & Squartini, Stefano & Abdou, Ahmed Fathi, 2019. "Modified PSO algorithm for real-time energy management in grid-connected microgrids," Renewable Energy, Elsevier, vol. 136(C), pages 746-757.
    6. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
    7. Ravindra, Kumudhini & Iyer, Parameshwar P., 2014. "Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis," Energy, Elsevier, vol. 76(C), pages 32-41.
    8. Skalyga, Mikhail & Wu, Qiuwei & Zhang, Menglin, 2021. "Uncertainty-fully-aware coordinated dispatch of integrated electricity and heat system," Energy, Elsevier, vol. 224(C).
    9. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    10. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    11. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
    12. Nelson, James & Johnson, Nathan G. & Fahy, Kelsey & Hansen, Timothy A., 2020. "Statistical development of microgrid resilience during islanding operations," Applied Energy, Elsevier, vol. 279(C).
    13. Tabar, Vahid Sohrabi & Jirdehi, Mehdi Ahmadi & Hemmati, Reza, 2017. "Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option," Energy, Elsevier, vol. 118(C), pages 827-839.
    14. Liu, Yixin & Guo, Li & Wang, Chengshan, 2018. "A robust operation-based scheduling optimization for smart distribution networks with multi-microgrids," Applied Energy, Elsevier, vol. 228(C), pages 130-140.
    15. Roslan, M.F. & Hannan, M.A. & Jern Ker, Pin & Begum, R.A. & Indra Mahlia, TM & Dong, Z.Y., 2021. "Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction," Applied Energy, Elsevier, vol. 292(C).
    Full references (including those not matched with items on IDEAS)

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    3. Yan, Sizhe & Wang, Weiqing & Li, Xiaozhu & Zhao, Yi, 2022. "Research on a cross-regional robust trading strategy based on multiple market mechanisms," Energy, Elsevier, vol. 261(PB).

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