Ensuring Data Privacy in AC Optimal Power Flow with a Distributed Co-Simulation Framework
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916311244734464 |
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| author | Dai, Xinliang Kocher, Alexander Kovačević, Jovana Dindar, Burak Jiang, Yuning Jones, Colin N. Çakmak, Hüseyin Hagenmeyer, Veit |
| author_facet | Dai, Xinliang Kocher, Alexander Kovačević, Jovana Dindar, Burak Jiang, Yuning Jones, Colin N. Çakmak, Hüseyin Hagenmeyer, Veit |
| contents | During the energy transition, the significance of collaborative management among institutions is rising, confronting challenges posed by data privacy concerns. Prevailing research on distributed approaches, as an alternative to centralized management, often lacks numerical convergence guarantees or is limited to single-machine numerical simulation. To address this, we present a distributed approach for solving AC Optimal Power Flow (OPF) problems within a geographically distributed environment. This involves integrating the energy system Co-Simulation (eCoSim) module in the eASiMOV framework with the convergence-guaranteed distributed optimization algorithm, i.e., the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). Comprehensive evaluations across multiple system scenarios reveal a marginal performance slowdown compared to the centralized approach and the distributed approach executed on single machines -- a justified trade-off for enhanced data privacy. This investigation serves as empirical validation of the successful execution of distributed AC OPF within a geographically distributed environment, highlighting potential directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ensuring Data Privacy in AC Optimal Power Flow with a Distributed Co-Simulation Framework Dai, Xinliang Kocher, Alexander Kovačević, Jovana Dindar, Burak Jiang, Yuning Jones, Colin N. Çakmak, Hüseyin Hagenmeyer, Veit Distributed, Parallel, and Cluster Computing During the energy transition, the significance of collaborative management among institutions is rising, confronting challenges posed by data privacy concerns. Prevailing research on distributed approaches, as an alternative to centralized management, often lacks numerical convergence guarantees or is limited to single-machine numerical simulation. To address this, we present a distributed approach for solving AC Optimal Power Flow (OPF) problems within a geographically distributed environment. This involves integrating the energy system Co-Simulation (eCoSim) module in the eASiMOV framework with the convergence-guaranteed distributed optimization algorithm, i.e., the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). Comprehensive evaluations across multiple system scenarios reveal a marginal performance slowdown compared to the centralized approach and the distributed approach executed on single machines -- a justified trade-off for enhanced data privacy. This investigation serves as empirical validation of the successful execution of distributed AC OPF within a geographically distributed environment, highlighting potential directions for future research. |
| title | Ensuring Data Privacy in AC Optimal Power Flow with a Distributed Co-Simulation Framework |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2402.01001 |