Optimal Control of a Stochastic Power System -- Algorithms and Mathematical Analysis
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910311327465472 |
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| author | Wang, Zhen Xi, Kaihua Cheng, Aijie Lin, Hai Xiang van Schuppen, Jan H. |
| author_facet | Wang, Zhen Xi, Kaihua Cheng, Aijie Lin, Hai Xiang van Schuppen, Jan H. |
| contents | The considered optimal control problem of a stochastic power system, is to select the set of power supply vectors which infimizes the probability that the phase-angle differences of any power flow of the network, endangers the transient stability of the power system by leaving a critical subset. The set of control laws is restricted to be a periodically recomputed set of fixed power supply vectors based on predictions of power demand for the next short horizon. Neither state feedback nor output feedback is used. The associated control objective function is Lipschitz continuous, nondifferentiable, and nonconvex. The results of the paper include that a minimum exists in the value range of the control objective function. Furthermore, it includes a two-step procedure to compute an approximate minimizer based on two key methods: (1) a projected generalized subgradient method for computing an initial vector, and (2) a steepest descent method for approximating a local minimizer. Finally, it includes two convergence theorems that an approximation sequence converges to a local minimum. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16879 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimal Control of a Stochastic Power System -- Algorithms and Mathematical Analysis Wang, Zhen Xi, Kaihua Cheng, Aijie Lin, Hai Xiang van Schuppen, Jan H. Optimization and Control Systems and Control 93E20, 90C30, and 90C26 The considered optimal control problem of a stochastic power system, is to select the set of power supply vectors which infimizes the probability that the phase-angle differences of any power flow of the network, endangers the transient stability of the power system by leaving a critical subset. The set of control laws is restricted to be a periodically recomputed set of fixed power supply vectors based on predictions of power demand for the next short horizon. Neither state feedback nor output feedback is used. The associated control objective function is Lipschitz continuous, nondifferentiable, and nonconvex. The results of the paper include that a minimum exists in the value range of the control objective function. Furthermore, it includes a two-step procedure to compute an approximate minimizer based on two key methods: (1) a projected generalized subgradient method for computing an initial vector, and (2) a steepest descent method for approximating a local minimizer. Finally, it includes two convergence theorems that an approximation sequence converges to a local minimum. |
| title | Optimal Control of a Stochastic Power System -- Algorithms and Mathematical Analysis |
| topic | Optimization and Control Systems and Control 93E20, 90C30, and 90C26 |
| url | https://arxiv.org/abs/2401.16879 |