Deep adaptive dynamic programming based integration algorithm for generation control and optimization of islanded active distribution network

• Published in: Kongzhi lilun yu yingyong Vol. 35; no. 2; p. 169
• Main Authors: Yin, Lin-fei, Yu, Tao, Zhang, Ze-yu, Zhang, Xiao-shun
• Format: Journal Article
• Language: Chinese
• Published: Guangzhou South China University of Technology 01.02.2018
• Subjects: Adaptive algorithms; Adaptive control; Algorithms; Artificial neural networks; Biomass energy production; Computer simulation; Control methods; Distribution management; Dynamic programming; Dynamic stability; Electric power grids; Energy storage; Frequency stability; Hybrid electric vehicles; Machine learning; Neural networks; Plug & play; Robustness; Wind power
• ISSN: 1000-8152
• DOI: 10.7641/CTA.2017.70239

With the development of many distributed generations (DGs) (e.g. wind power and photovoltaic power, biomass energy, energy storage device and plug in hybrid electric vehicle (PHEV)), the traditional methods for generation control of isolated island power grids cannot meet the requirements of frequency stability. This paper proposes deep adaptive dynamic programming (DADP) algorithm to solve this problem. Replacing neural network (NN) in adaptive dynamic programming (ADP) algorithm by the deep neural network (DNN) in the field of machine learning (ML), and adding deep model forecast neural network in, the proposed DADP algorithm is designed. Generation commands, which are achieved by the algorithms of both generation control and generation command dispatch in the traditional way, are obtained by the proposed DADP algorithm. Finally, to verify the robustness of the proposed DADP algorithm, many simulations for isolated island micro-gird are simulated, for instance, the normal isolated island situation, plug and