Transmission Expansion Planning Based on Reinforcement Learning

被引：0
|
作者：
Wang Y. [1 ,2 ]
Hu S. [1 ,2 ]
Song Y. [1 ,2 ]
Jiang L. [3 ]
Shen L. [3 ]
机构：
[1] College of Electrical Engineering, Sichuan University, Chengdu
[2] Key Laboratory of Intelligent Electric Power Grid of Sichuan Province, Sichuan University, Chengdu
[3] State Grid Southwest Branch Corporation, Chengdu
来源：
Dianwang Jishu/Power System Technology | 2021年 / 45卷 / 07期
关键词：
Adaptive learning factor; Multi-step backtracking Q(λ); Reinforcement learning; Transmission expansion planning;
D O I：
10.13335/j.1000-3673.pst.2020.0831
中图分类号：
学科分类号：
摘要：
By applying the artificial intelligence to the traditional transmission expansion planning, a transmission expansion planning method using α-Q(λ) algorithm with adaptive learning factor is proposed based on reinforcement learning. With the help of the prepared database and the Monte Carlo method, the transmission expansion planning model is constructed by considering the reliability cost in the optimal object function.combining the characteristics of the transmission network, the multi-step backtracking α-Q(λ) algorithm with adaptive learning factor is designed. Then the mixed integer planning model is transformed into the agent and environment of in the α-Q(λ) algorithm to simulate the planning process of a power grid. The validity of the proposed method is verified by Garver-6 and IEEE 24-RTS system, and the comparison with other intelligent algorithms is shown. © 2021, Power System Technology Press. All right reserved.
引用
收藏
页码：2829 / 2838
页数：9
相关论文
共 27 条
  • [1] GARVER L L., Transmission network estimation using linear programming, IEEE Transactions on Power Apparatus and Systems, PAS-89, 7, pp. 1688-1697, (1970)
  • [2] MAJIDI-QADIKOLAI M, BALDICK R., Stochastic transmission capacity expansion planning with special scenario selection for integrating N-1 contingency analysis, IEEE Transactions on Power Systems, 31, 6, pp. 4901-4912, (2016)
  • [3] MAJIDI-QADIKOLAI M, BALDICK R., Integration of N-1 contingency analysis with systematic transmission capacity expansion planning: ERCOT case study, IEEE Transactions on Power Systems, 31, 3, pp. 2234-2245, (2016)
  • [4] CHOI J, EL-KEIB A A, TRAN T., A fuzzy branch and bound-based transmission system expansion planning for the highest satisfaction level of the decision maker, IEEE Transactions on Power Systems, 20, 1, pp. 476-484, (2005)
  • [5] DEHGHAN S, AMJADY N., Robust transmission and energy storage expansion planning in wind farm-integrated power systems considering transmission switching, IEEE Transactions on Sustainable Energy, 7, 2, pp. 765-774, (2016)
  • [6] WU Zhi, LIU Yafei, GU Wei, Et al., A modified decomposition method for multistage planning of energy storage, distributed generation and distribution network, Proceedings of the CSEE, 39, 16, pp. 4705-4715, (2019)
  • [7] CAMPONOGARA E, DE ALMEIDA K C, JUNIOR H R., Piecewise-linear approximations for a non-linear transmission expansion planning problem, IET Generation, Transmission & Distribution, 9, 12, pp. 1235-1244, (2015)
  • [8] YAN Taishan, YANG Di, ZHANG Libo, Et al., Transmission network planning model considering economy and security based on improved niche genetic algorithm, Water Resources and Power, 36, 7, pp. 206-210, (2018)
  • [9] LIU Zifa, YU Hanxiao, WANG Shuai, Et al., Transmission grid planning considering operation efficiency and wind curtailment loss, Power System Technology, 42, 3, pp. 827-834, (2018)
  • [10] KARIMI E, EBRAHIMI A., Inclusion of blackouts risk in probabilistic transmission expansion planning by a multi-objective framework, IEEE Transactions on Power Systems, 30, 5, pp. 2810-2817, (2015)
123