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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.01331 |
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| _version_ | 1866908346533019648 |
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| author | Al-Shafei, Ahmed Amjady, Nima Zareipour, Hamidreza Cao, Yankai |
| author_facet | Al-Shafei, Ahmed Amjady, Nima Zareipour, Hamidreza Cao, Yankai |
| contents | This work introduces the category of Power System Transition Planning optimization problem. It aims to shift power systems to emissions-free networks efficiently. Unlike comparable work, the framework presented here broadly applies to the industry's decision-making process. It defines a field-appropriate functional boundary focused on the economic efficiency of power systems. Namely, while imposing a wide range of planning factors in the decision space, the model maintains the structure and depth of conventional power system planning under uncertainty, which leads to a large-scale multistage stochastic programming formulation that encounters intractability in real-life cases. Thus, the framework simultaneously invokes high-performance computing defaultism. In this comprehensive exposition, we present a guideline model, comparing its scope to existing formulations, supported by a fully detailed example problem, showcasing the analytical value of the solution gained in a small test case. Then, the framework's viability for realistic applications is demonstrated by solving an extensive test case based on a realistic planning construct consistent with Alberta's power system practices for long-term planning studies. The framework resorts to Stochastic Dual Dynamic Programming as a decomposition method to achieve tractability, leveraging High-Performance Computing and parallel computation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization Al-Shafei, Ahmed Amjady, Nima Zareipour, Hamidreza Cao, Yankai Systems and Control This work introduces the category of Power System Transition Planning optimization problem. It aims to shift power systems to emissions-free networks efficiently. Unlike comparable work, the framework presented here broadly applies to the industry's decision-making process. It defines a field-appropriate functional boundary focused on the economic efficiency of power systems. Namely, while imposing a wide range of planning factors in the decision space, the model maintains the structure and depth of conventional power system planning under uncertainty, which leads to a large-scale multistage stochastic programming formulation that encounters intractability in real-life cases. Thus, the framework simultaneously invokes high-performance computing defaultism. In this comprehensive exposition, we present a guideline model, comparing its scope to existing formulations, supported by a fully detailed example problem, showcasing the analytical value of the solution gained in a small test case. Then, the framework's viability for realistic applications is demonstrated by solving an extensive test case based on a realistic planning construct consistent with Alberta's power system practices for long-term planning studies. The framework resorts to Stochastic Dual Dynamic Programming as a decomposition method to achieve tractability, leveraging High-Performance Computing and parallel computation. |
| title | Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2505.01331 |