Privacy-Preserving Computation for Large-Scale Security-Constrained Optimal Power Flow Problem in Smart Grid
In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/19016b0c7a074717a174577b10797c91 |
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| Sumario: | In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency conditions. However, due to the large problem size and real-time requirement, a fast and robust technique is required to solve this problem. Moreover, due to privacy concerns, it is important that the data remains confidential and processed on local computers. Therefore, a fully privacy-preserving algorithm is proposed which performs computation directly over the encrypted SCOPF problem. The SCOPF is decomposed into smaller subproblems corresponding to individual pre-contingency and post-contingency cases using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. Both algorithms are presented for solving the SCOPF problem in a privacy-preserving and distributed manner. Security analysis shows that our algorithm can preserve both system confidentiality and data privacy. Performance evaluations validate the correctness and effectiveness of the proposed algorithm. |
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