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Bi-level mathematical model to optimize the unit commitment in energy systems considering the worst scenarios of failure of renewable resources
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Masoumeh Azadikhouy *1   , Hossein Karimianfard |
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Abstract: (701 Views) |
| This paper presents a bi-level mathematical model for optimizing the unit commitment of energy generation units in the presence of worst-case scenarios of renewable resource failures. The main innovation of this research is the integration of optimal load management, intelligent scheduling of electric vehicle charging and discharging, and precise modeling of renewable resource failures within a comprehensive framework. The high-level model is designed to reduce operational costs, manage flexible loads, and minimize pollution, while the low-level model examines the effects of resource failures and ensures system stability under critical conditions. To solve the problem, the Karush-Kuhn-Tucker (KKT) optimality conditions are employed as an efficient tool to reduce the complexity of the bi-level problem, and the model is transformed into a Mixed-Integer Linear Programming (MILP) problem. The proposed model is evaluated on the standard IEEE network, and simulation results demonstrate that this approach significantly outperforms existing models. Specifically, operational costs are reduced by up to 18.5%, system reliability in critical conditions is improved by up to 35%, and energy losses are reduced by 22%. These achievements highlight the model's capability to provide resilient and efficient solutions for optimal energy resource management under uncertainty scenarios. Overall, this research offers a novel framework for enhanced productivity in renewable energy systems, which can serve as an effective tool in the development of future sustainable and resilient systems. |
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| Keywords: Unit commitment, electric vehicles, energy storage systems, renewable energy resources, bi-level optimization. |
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Type of Study: Research |
Received: 2024/10/23 | Accepted: 2024/12/3 | Published: 2025/04/6
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1. [1] K. Doubleday, J. D. Lara, B.-M. Hodge, "Investigation of stochastic unit commitment to enable advanced flexibility measures for high shares of solar PV", Applied Energy, vol. 321, no. 119337, 2022. [ DOI:10.1016/j.apenergy.2022.119337] 2. [2] Alexandre Moreira, Bruno Fanzeres, Patricia Silva, Miguel Heleno, André Luís Marques Marcato, On the role of Battery Energy Storage Systems in the day-ahead Contingency-Constrained Unit Commitment problem under renewable penetration, Electric Power Systems Research, Volume 235, 110856, 2024. [ DOI:10.1016/j.epsr.2024.110856] 3. [3] Mostafa Esmaeili Shayan, Mario Petrollese, Seyed Hossein Rouhani, Saleh Mobayen, Anton Zhilenkov, Chun Lien Su, An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems, International Journal of Electrical Power & Energy Systems, Volume 160, 110087, 2024. [ DOI:10.1016/j.ijepes.2024.110087] 4. [4] Luis Montero, Antonio Bello, Javier Reneses, Analyzing the computational performance of balance constraints in the medium-term unit commitment problem: Tightness, compactness, and arduousness, International Journal of Electrical Power & Energy Systems, Volume 160, 110080, 2024. [ DOI:10.1016/j.ijepes.2024.110080] 5. [5] Sara Lumbreras, Diego Tejada, Daniel Elechiguerra, Explaining the solutions of the unit commitment with interpretable machine learning, International Journal of Electrical Power & Energy Systems, Volume 160, 110106, 2024. [ DOI:10.1016/j.ijepes.2024.110106] 6. [6] Jingwei Huang, Hui Qin, Keyan Shen, Yuqi Yang, Benjun Jia, Study on hierarchical model of hydroelectric unit commitment based on similarity schedule and quadratic optimization approach, Energy, Volume 305, 132229, 2024. [ DOI:10.1016/j.energy.2024.132229] 7. [7] Jiajie Ling, Liangyu Zhang, Guangchao Geng, Quanyuan Jiang, Feasible-enabled integer variable warm start strategy for security-constrained unit commitment, International Journal of Electrical Power & Energy Systems, Volume 160, 110137, 2024. [ DOI:10.1016/j.ijepes.2024.110137] 8. [8] Peijie Li, Jianming Su, Xiaoqing Bai, An objective feasibility pump method for optimal power flow with unit commitment variables, Electric Power Systems Research, Volume 236, 110928, 2024. [ DOI:10.1016/j.epsr.2024.110928] 9. [9] Zhang Zhi, Haiyu Huang, Wei Xiong, Yijia Zhou, Mingyu Yan, Shaolian Xia, Baofeng Jiang, Renbin Su, Xichen Tian, Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition, Energy Engineering, Volume 121, Issue 6, Pages 1557-1576, 2024. [ DOI:10.32604/ee.2024.047401] 10. [10] Robert Parker, Carleton Coffrin, Managing power balance and reserve feasibility in the AC unit commitment problem, Electric Power Systems Research, Volume 234, 110670, 2024. [ DOI:10.1016/j.epsr.2024.110670] 11. [11] Mao Liu, Xiangyu Kong, Chao Ma, Xuesong Zhou, Qingxiang Lin, Multi-stage fully adaptive distributionally robust unit commitment for power system based on mixed approximation rules, Applied Energy, Volume 376, Part A, 124051, 2024. [ DOI:10.1016/j.apenergy.2024.124051] 12. [12] Yu Gong, Tingxi Liu, Pan Liu, Xin Tong, Joint unit commitment model for hydro and hydrogen power to adapt to large-scale photovoltaic power, Energy Conversion and Management, Volume 317, 118794, 2024. [ DOI:10.1016/j.enconman.2024.118794] 13. [13] Ramin Sharikabadi, Amir Abdollahi, Masoud Rashidinejad, Mehdi Shafiee, Security constrained unit commitment in smart energy systems: A flexibility-driven approach considering false data injection attacks in electric vehicle parking lots, International Journal of Electrical Power & Energy Systems, Volume 161, 110180, 2024. [ DOI:10.1016/j.ijepes.2024.110180] 14. [14] Jiajie Ling, Quan Zhang, Guangchao Geng, Quanyuan Jiang, Hybrid quantum annealing decomposition framework for unit commitment, Electric Power Systems Research, Volume 238, 111121, 2025. [ DOI:10.1016/j.epsr.2024.111121] 15. [15] Jingfan Liu, Shijie Zhang, Stochastic two-stage multi-objective unit commitment of distributed resource energy systems considering uncertainties and unit failures, Reliability Engineering & System Safety, Volume 253, 110520, 2025. [ DOI:10.1016/j.ress.2024.110520] 16. [16] X. Zheng, J. Wang and M. Yue, "A Fast Quantum Algorithm for Searching the Quasi-Optimal Solutions of Unit Commitment," in IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4755-4758, March 2024. [ DOI:10.1109/TPWRS.2024.3350382] 17. [17] L. You, X. Jin and Y. Liu, "A Unit Commitment Model Considering the Flexibility Retrofit of Combined Heat and Power Units for Wind Integration," in IEEE Access, vol. 12, pp. 122199-122212, 2024. [ DOI:10.1109/ACCESS.2024.3452506] 18. [18] M. Qu, T. Ding, C. Mu, X. Zhang, K. Pan and M. Shahidehpour, "Linearization Method for Large-Scale Hydro-Thermal Security-Constrained Unit Commitment," in IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 1754-1766, April 2024. [ DOI:10.1109/TASE.2023.3241491] 19. [19] W. Li, T. Qian, X. Xie and W. Tang, "Piecewise Mixed Decision Rules Based Multi-Stage Distributionally Robust Unit Commitment for Integrated Electricity-Heat Systems," in IEEE Transactions on Power Systems, vol. 39, no. 5, pp. 6772-6775, Sept. 2024. [ DOI:10.1109/TPWRS.2024.3424151] 20. [20] H. Jokar, B Bahmani-Firouzi, H. Haes Alhelou, P. Siano, (2022). Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment. IEEE Access, vol.10, pp.123723-123735 [ DOI:10.1109/ACCESS.2022.3224458] 21. [1] K. Doubleday, J. D. Lara, B.-M. Hodge, "Investigation of stochastic unit commitment to enable advanced flexibility measures for high shares of solar PV", Applied Energy, vol. 321, no. 119337, 2022. [ DOI:10.1016/j.apenergy.2022.119337] 22. [2] Alexandre Moreira, Bruno Fanzeres, Patricia Silva, Miguel Heleno, André Luís Marques Marcato, On the role of Battery Energy Storage Systems in the day-ahead Contingency-Constrained Unit Commitment problem under renewable penetration, Electric Power Systems Research, Volume 235, 110856, 2024. [ DOI:10.1016/j.epsr.2024.110856] 23. [3] Mostafa Esmaeili Shayan, Mario Petrollese, Seyed Hossein Rouhani, Saleh Mobayen, Anton Zhilenkov, Chun Lien Su, An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems, International Journal of Electrical Power & Energy Systems, Volume 160, 110087, 2024. [ DOI:10.1016/j.ijepes.2024.110087] 24. [4] Luis Montero, Antonio Bello, Javier Reneses, Analyzing the computational performance of balance constraints in the medium-term unit commitment problem: Tightness, compactness, and arduousness, International Journal of Electrical Power & Energy Systems, Volume 160, 110080, 2024. [ DOI:10.1016/j.ijepes.2024.110080] 25. [5] Sara Lumbreras, Diego Tejada, Daniel Elechiguerra, Explaining the solutions of the unit commitment with interpretable machine learning, International Journal of Electrical Power & Energy Systems, Volume 160, 110106, 2024. [ DOI:10.1016/j.ijepes.2024.110106] 26. [6] Jingwei Huang, Hui Qin, Keyan Shen, Yuqi Yang, Benjun Jia, Study on hierarchical model of hydroelectric unit commitment based on similarity schedule and quadratic optimization approach, Energy, Volume 305, 132229, 2024. [ DOI:10.1016/j.energy.2024.132229] 27. [7] Jiajie Ling, Liangyu Zhang, Guangchao Geng, Quanyuan Jiang, Feasible-enabled integer variable warm start strategy for security-constrained unit commitment, International Journal of Electrical Power & Energy Systems, Volume 160, 110137, 2024. [ DOI:10.1016/j.ijepes.2024.110137] 28. [8] Peijie Li, Jianming Su, Xiaoqing Bai, An objective feasibility pump method for optimal power flow with unit commitment variables, Electric Power Systems Research, Volume 236, 110928, 2024. [ DOI:10.1016/j.epsr.2024.110928] 29. [9] Zhang Zhi, Haiyu Huang, Wei Xiong, Yijia Zhou, Mingyu Yan, Shaolian Xia, Baofeng Jiang, Renbin Su, Xichen Tian, Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition, Energy Engineering, Volume 121, Issue 6, Pages 1557-1576, 2024. [ DOI:10.32604/ee.2024.047401] 30. [10] Robert Parker, Carleton Coffrin, Managing power balance and reserve feasibility in the AC unit commitment problem, Electric Power Systems Research, Volume 234, 110670, 2024. [ DOI:10.1016/j.epsr.2024.110670] 31. [11] Mao Liu, Xiangyu Kong, Chao Ma, Xuesong Zhou, Qingxiang Lin, Multi-stage fully adaptive distributionally robust unit commitment for power system based on mixed approximation rules, Applied Energy, Volume 376, Part A, 124051, 2024. [ DOI:10.1016/j.apenergy.2024.124051] 32. [12] Yu Gong, Tingxi Liu, Pan Liu, Xin Tong, Joint unit commitment model for hydro and hydrogen power to adapt to large-scale photovoltaic power, Energy Conversion and Management, Volume 317, 118794, 2024. [ DOI:10.1016/j.enconman.2024.118794] 33. [13] Ramin Sharikabadi, Amir Abdollahi, Masoud Rashidinejad, Mehdi Shafiee, Security constrained unit commitment in smart energy systems: A flexibility-driven approach considering false data injection attacks in electric vehicle parking lots, International Journal of Electrical Power & Energy Systems, Volume 161, 110180, 2024. [ DOI:10.1016/j.ijepes.2024.110180] 34. [14] Jiajie Ling, Quan Zhang, Guangchao Geng, Quanyuan Jiang, Hybrid quantum annealing decomposition framework for unit commitment, Electric Power Systems Research, Volume 238, 111121, 2025. [ DOI:10.1016/j.epsr.2024.111121] 35. [15] Jingfan Liu, Shijie Zhang, Stochastic two-stage multi-objective unit commitment of distributed resource energy systems considering uncertainties and unit failures, Reliability Engineering & System Safety, Volume 253, 110520, 2025. [ DOI:10.1016/j.ress.2024.110520] 36. [16] X. Zheng, J. Wang and M. Yue, "A Fast Quantum Algorithm for Searching the Quasi-Optimal Solutions of Unit Commitment," in IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4755-4758, March 2024. [ DOI:10.1109/TPWRS.2024.3350382] 37. [17] L. You, X. Jin and Y. Liu, "A Unit Commitment Model Considering the Flexibility Retrofit of Combined Heat and Power Units for Wind Integration," in IEEE Access, vol. 12, pp. 122199-122212, 2024. [ DOI:10.1109/ACCESS.2024.3452506] 38. [18] M. Qu, T. Ding, C. Mu, X. Zhang, K. Pan and M. Shahidehpour, "Linearization Method for Large-Scale Hydro-Thermal Security-Constrained Unit Commitment," in IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 1754-1766, April 2024. [ DOI:10.1109/TASE.2023.3241491] 39. [19] W. Li, T. Qian, X. Xie and W. Tang, "Piecewise Mixed Decision Rules Based Multi-Stage Distributionally Robust Unit Commitment for Integrated Electricity-Heat Systems," in IEEE Transactions on Power Systems, vol. 39, no. 5, pp. 6772-6775, Sept. 2024. [ DOI:10.1109/TPWRS.2024.3424151] 40. [20] H. Jokar, B Bahmani-Firouzi, H. Haes Alhelou, P. Siano, (2022). Transmission and Distribution Substation Energy Management Considering Large-Scale Energy Storage, Demand Side Management and Security-Constrained Unit Commitment. IEEE Access, vol.10, pp.123723-123735 [ DOI:10.1109/ACCESS.2022.3224458] |
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Azadikhouy M, Karimianfard H. Bi-level mathematical model to optimize the unit commitment in energy systems considering the worst scenarios of failure of renewable resources. ieijqp 2024; 13 (2) URL: http://ieijqp.ir/article-1-1016-en.html
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