A Kriging Model Based Optimization of Active Distribution Networks Considering Loss Reduction and Voltage Profile Improvement

Dan Wang, Qing’e Hu, Jia Tang, Hongjie Jia, Yun Li, Shuang Gao and Menghua Fan
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Dan Wang: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Qing’e Hu: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Jia Tang: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Hongjie Jia: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Yun Li: State Grid Beijing Electric Power Company, Xicheng District, Beijing 100031, China
Shuang Gao: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Menghua Fan: State Grid Energy Research Institute, Changping District, Beijing 102249, China

Energies, 2017, vol. 10, issue 12, 1-19

Abstract: Optimal operation of the active distribution networks (ADN) is essential to keep its safety, reliability and economy. With the integration of multiple controllable resources, the distribution networks are facing more challenges in which the optimization strategy is the key. This paper establishes the optimal operation model of the ADN considering a diversity of controllable resources including energy storage devices, distributed generators, voltage regulators and switchable capacitor banks. The objective functions contain reducing the power losses and improving the voltage profiles. To solve the optimization problem, the Kriging model based Improved Surrogate Optimization-Mixed-Integer (ISO-MI) algorithm is proposed in this paper. The Kriging model is applied to approximate the complicated distribution networks, which speeds up the solving process. Finally, the accuracy of the Kriging model is validated and the efficiency among the proposed method, genetic algorithm (GA) and particle swarm optimization (PSO) is compared in an unbalanced IEEE-123 nodes test feeder. The results demonstrate that the proposed method has better performance than GA and PSO.

Keywords: optimal operation; active distribution network; power loss reduction; voltage profile improvement; Kriging model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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