Online Area Load Modeling in Power Systems Using Enhanced Reinforcement Learning

Xiaoya Shang, Zhigang Li, Tianyao Ji, P. Z. Wu and Qinghua Wu
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Xiaoya Shang: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Zhigang Li: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
Tianyao Ji: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
P. Z. Wu: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences, Shenzhen 518055, China
Qinghua Wu: School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China

Energies, 2017, vol. 10, issue 11, 1-17

Abstract: The accuracy of load modeling directly influences power system operation and control. Previous modeling studies have mainly concentrated on the loads connected to a single boundary bus, without thoroughly considering the static load characteristics of the voltage. To remedy this oversight, this paper proposes an accurate modeling approach for area loads with multiple boundary buses and ZIP loads (a combination of constant-impedance, constant-current and constant-power loads) based on Ward equivalence. Furthermore, to satisfy the requirements for real-time monitoring, the model parameters are identified in an online manner using an enhanced reinforcement learning (ERL) algorithm. Parallel tables of value functions are implemented in the ERL algorithm to improve its tracking performance. Three simulation cases are addressed, the first involving a single ZIP load and the second and third involving area loads in the IEEE 57-bus system and in a real 1209-bus power system in China, respectively. The results demonstrate that the ERL algorithm outperforms an existing reinforcement learning algorithm and the improved least-squares method in terms of convergence and the ability to track both step-changing and time-varying loads. Additionally, the results obtained on test cases confirm that the proposed area load model is more accurate than a previously introduced model.

Keywords: load modeling; parameter tracking; reinforcement learning; Ward equivalence (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 (1)

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