计及大气污染物时空扩散的电-气能流分散协同优化

doi:10.19595/j.cnki.1000-6753.tces.181564

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Abstract To resolve the low efficiency of traditional atmospheric emission amount control in reducing air pollution of power generation activities, this paper proposed a distributed optimizing algorithm to find the optimal energy flow in the integrated power and gas system with consideration of the spatiotemporal diffusion of atmospheric pollutants as well as the difference of pollution tolerance between regions. Meanwhile, a method describing the direction of pipeline airflow in natural gas networks without integer variables was also proposed. The diffusion process of atmospheric pollutants was depicted by the Gaussian puff model, with which an evaluation index of air pollution of power generation activities and several additional environmental constraints were established. The Weymouth airflow equation with absolute value was replaced by several equivalent constraints with only square terms of variables, so that the model can be convexified by a penalty convex-concave procedure. Finally, the generalized Benders decomposition was employed to break the problem into a main-problem of power network and a sub-problem of natural gas network which can be calculated independently, so that synergetic optimization can be performed with a little information exchanged between the two networks. Simulation verified the effectiveness of the proposed model in reducing air pollution, as well as the accuracy and superiority of the proposed airflow direction handling method.

Key words： Space-time diffusion of atmospheric pollutants integrated power and gas system pipeline airflow direction generalized Benders decomposition

Received: 26 September 2018 Published: 11 December 2019

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TM732

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	Shi Shouyuan
	Qu Kaiping
	Yu Tao
	Liu Qianjin
	Wang Wenrui

Cite this article:

Shi Shouyuan,Qu Kaiping,Yu Tao等. A Distributed Algorithm for Optimal Power-Gas Flow Considering Space-Time Diffusion of Atmospheric Pollutants[J]. Transactions of China Electrotechnical Society, 2019, 34(23): 4957-4971.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.181564 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I23/4957

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