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Kriging assisted evolutionary computation in power system optimization

Kriging assisted evolutionary computation in power system optimization
Kriging assisted evolutionary computation in power system optimization
Optimal Power Flow (OPF) is an important decision support tool for system operator to achieve reliable and economic processesin power system operation, control, market and planning. It provides system operators with optimal decisions and necessary control changes to achieve desired operational objectives, such as economically dispatching active power generations, minimizing active power losses, while satisfying load demand and specified constraints. However, OPF has proven to be the very difficult optimization problem due to its nonlinearity, nonconvexity and high constraints, while the current optimization algorithms cannot satisfy both computational speed and accurate solution simultaneously. Although the traditional method is widely applied to solve the practical OPF problem due to its computation efficiency, such technique is a compromise between computation efficiency and solution quality, producing local optimum and resulting in a large amount of unnecessary operatorial cost.

Alternative to the traditional method, the evolutionary computation method has attracted a lot of attentions in academic research because of its capability of finding the global optimum, however, the impractical computation time has limited its application especially in real-time applications. In order to improve the computation performance of evolutionary computation while saving the operational cost, the Kriging surrogate method has been found as a potential technique and not been applied to power system optimization in the past.

To achieve the aim, the work focuses on three topics related to the computational efficiency and solution quality: 1) the effective strategy used to alleviate the computation burden associated to Kriging, 2) the balance between exploitation and exploration during the optimization process; 3) the effective and efficient strategy used to manage the Kriging in evolutionary computation. The effectiveness and the performance of proposed algorithms are verified on the complex multimodal optimization functions and benchmark power systems with results compared with the algorithms reported in literature.
University of Southampton
Deng, Zhida
e059baea-8a79-4e08-ad14-fa30d4feba68
Deng, Zhida
e059baea-8a79-4e08-ad14-fa30d4feba68
Chen, George
3de45a9c-6c9a-4bcb-90c3-d7e26be21819

Deng, Zhida (2020) Kriging assisted evolutionary computation in power system optimization. University of Southampton, Doctoral Thesis, 236pp.

Record type: Thesis (Doctoral)

Abstract

Optimal Power Flow (OPF) is an important decision support tool for system operator to achieve reliable and economic processesin power system operation, control, market and planning. It provides system operators with optimal decisions and necessary control changes to achieve desired operational objectives, such as economically dispatching active power generations, minimizing active power losses, while satisfying load demand and specified constraints. However, OPF has proven to be the very difficult optimization problem due to its nonlinearity, nonconvexity and high constraints, while the current optimization algorithms cannot satisfy both computational speed and accurate solution simultaneously. Although the traditional method is widely applied to solve the practical OPF problem due to its computation efficiency, such technique is a compromise between computation efficiency and solution quality, producing local optimum and resulting in a large amount of unnecessary operatorial cost.

Alternative to the traditional method, the evolutionary computation method has attracted a lot of attentions in academic research because of its capability of finding the global optimum, however, the impractical computation time has limited its application especially in real-time applications. In order to improve the computation performance of evolutionary computation while saving the operational cost, the Kriging surrogate method has been found as a potential technique and not been applied to power system optimization in the past.

To achieve the aim, the work focuses on three topics related to the computational efficiency and solution quality: 1) the effective strategy used to alleviate the computation burden associated to Kriging, 2) the balance between exploitation and exploration during the optimization process; 3) the effective and efficient strategy used to manage the Kriging in evolutionary computation. The effectiveness and the performance of proposed algorithms are verified on the complex multimodal optimization functions and benchmark power systems with results compared with the algorithms reported in literature.

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Published date: June 2020

Identifiers

Local EPrints ID: 447733
URI: http://eprints.soton.ac.uk/id/eprint/447733
PURE UUID: a8e2ff73-8c1a-4963-be6c-f5a9344812f2
ORCID for Zhida Deng: ORCID iD orcid.org/0000-0002-8448-1934

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Date deposited: 19 Mar 2021 17:30
Last modified: 20 Mar 2021 02:49

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Contributors

Author: Zhida Deng ORCID iD
Thesis advisor: George Chen

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