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Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate

Author

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  • Esmaeily, Ali
  • Ahmadi, Abdollah
  • Raeisi, Fatima
  • Ahmadi, Mohammad Reza
  • Esmaeel Nezhad, Ali
  • Janghorbani, Mohammadreza

Abstract

A new optimization framework based on MILP model is introduced in the paper for the problem of stochastic self-scheduling of hydrothermal units known as HTSS Problem implemented in a joint energy and reserve electricity market with day-ahead mechanism. The proposed MILP framework includes some practical constraints such as the cost due to valve-loading effect, the limit due to DRR and also multi-POZs, which have been less investigated in electricity market models. For the sake of more accuracy, for hydro generating units’ model, multi performance curves are also used. The problem proposed in this paper is formulated using a model on the basis of a stochastic optimization technique while the objective function is maximizing the expected profit utilizing MILP technique. The suggested stochastic self-scheduling model employs the price forecast error in order to take into account the uncertainty due to price. Besides, LMCS is combined with roulette wheel mechanism so that the scenarios corresponding to the non-spinning reserve price and spinning reserve price as well as the energy price at each hour of the scheduling are generated. Finally, the IEEE 118-bus power system is used to indicate the performance and the efficiency of the suggested technique.

Suggested Citation

  • Esmaeily, Ali & Ahmadi, Abdollah & Raeisi, Fatima & Ahmadi, Mohammad Reza & Esmaeel Nezhad, Ali & Janghorbani, Mohammadreza, 2017. "Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate," Energy, Elsevier, vol. 122(C), pages 182-193.
    • Handle: RePEc:eee:energy:v:122:y:2017:i:c:p:182-193
    DOI: 10.1016/j.energy.2017.01.089
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    Cited by:

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    2. Jiefeng Duan & Zhiqiang Jiang, 2022. "Joint Scheduling Optimization of a Short-Term Hydrothermal Power System Based on an Elite Collaborative Search Algorithm," Energies, MDPI, vol. 15(13), pages 1-18, June.
    3. Tae-Woo Kim & Yenjae Chang & Dae-Wook Kim & Man-Keun Kim, 2020. "Preventive Maintenance and Forced Outages in Power Plants in Korea," Energies, MDPI, vol. 13(14), pages 1-12, July.
    4. Liu, Shuangquan & Xie, Mengfei, 2020. "Modeling the daily generation schedules in under-developed electricity markets with high-share renewables: A case study of Yunnan in China," Energy, Elsevier, vol. 201(C).
    5. Dong, Weijie & He, Guoqing & Cui, Quansheng & Sun, Wenwen & Hu, Zhenlong & Ahli raad, Erfan, 2022. "Self-scheduling of a novel hybrid GTSOFC unit in day-ahead energy and spinning reserve markets within ancillary services using a novel energy storage," Energy, Elsevier, vol. 239(PE).
    6. Zhong, Zhiming & Fan, Neng & Wu, Lei, 2023. "A hybrid robust-stochastic optimization approach for day-ahead scheduling of cascaded hydroelectric system in restructured electricity market," European Journal of Operational Research, Elsevier, vol. 306(2), pages 909-926.
    7. Zhongkai Feng & Wenjing Niu & Sen Wang & Chuntian Cheng & Zhenguo Song, 2019. "Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones," Energies, MDPI, vol. 12(11), pages 1-17, June.
    8. Razavi, Seyed-Ehsan & Esmaeel Nezhad, Ali & Mavalizadeh, Hani & Raeisi, Fatima & Ahmadi, Abdollah, 2018. "Robust hydrothermal unit commitment: A mixed-integer linear framework," Energy, Elsevier, vol. 165(PB), pages 593-602.
    9. Hassan Shokouhandeh & Mehrdad Ahmadi Kamarposhti & Ilhami Colak & Kei Eguchi, 2021. "Unit Commitment for Power Generation Systems Based on Prices in Smart Grid Environment Considering Uncertainty," Sustainability, MDPI, vol. 13(18), pages 1-13, September.
    10. Iram Parvez & Jianjian Shen & Ishitaq Hassan & Nannan Zhang, 2021. "Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant," Energies, MDPI, vol. 14(2), pages 1-28, January.

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