基于置信间隙决策的输电网络不确定性最优传输切换研究

doi:10.19595/j.cnki.1000-6753.tces.221375

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Abstract Optimal Transmission Switching （OTS） is a control mechanism that improves the transmission network's flexibility by changing the network's topology. OTS is widely used in transmission networks. OTS schemes are usually determined in the day-ahead scheduling stage. However, there are often uncertain wind power and load demands in the transmission network, which makes OTS schemes unable to achieve the desired effect. In order to solve these problems, this paper proposes to apply the Confidence Gap decision theory （CGDT） to find the OTS scheme under the condition of uncertainty and improve its robustness of OTS scheme.
Firstly, the Gaussian mixture model and normal distribution are used to fit the probability distribution function of wind power output and load demand prediction errors, and the uncertainty is incorporated into the OTS model of the transmission network. Secondly, CGDT based on the robust drive is introduced to solve the uncertainty, and a robust optimization method based on CGDT is proposed to solve the uncertainty OTS scheme. A mixed integer nonlinear programming model based on CGDT for OTS robust optimization of the transmission network is constructed to minimize the total generation cost as the optimization goal. Thirdly, the chance constraints in the model are transformed into equivalent deterministic constraints according to the uncertainty theory. Finally, this paper uses the improved IEEE-24 node power network to analyze the proposed CGDT-based OTS robust optimization model. In the proposed OTS robust optimization method, the influence of uncertainty parameters on OTS optimization results is maximized by maximizing the confidence level to provide a robust OTS scheme.
According to the OTS solution, the transmission network topology is changed. The operation results show that the proportion of the power generation of coal-fired units in the total power output of the power network is reduced from 84.96% to 60.13%. The power output of generating units that meet the load demand is transferred from thermal power units to gas units with high generation efficiency and wind turbines with low generation cost, which improves the power generation structure of the system and reduces the total power generation cost by 3.9%. In wind power and load demand robust range using the Monte Carlo method in extracting 1 000 groups of random wind power and load demand scenario, the total cost by probability distribution results show that the approximate probability distribution is normal, the corresponding scenarios of the total cost is less than the set value. Thus the solving scheme of OTS has good adaptability. Under the same operation condition, CGDT, IGDT, and multi-scene methods are used to obtain the corresponding OTS scheme. One thousand scenarios were randomly selected within the fluctuation range of wind power and load demand prediction errors to solve the total power generation cost corresponding to different scenarios. The statistical results show that the CGDT method has a relatively short computation time, its corresponding maximum, mean, and median values are optimal, and the OTS scheme is more robust. In addition, this method can give a robust fluctuation interval of uncertain parameters, which is of great significance to decision makers and has good scalability in practical applications.
The following conclusions can be drawn from the simulation analysis: （1） Compared with LSTM and GRU, the SRU in the proposed model not only significantly reduces the computational cost, but also effectively overcomes the gradient disappearance problem. Therefore, it is appropriate to apply SRU to the multi-turbine local wind speed forecast. （2） The proposed model only needs historical wind speed and direction measurements. In this sense, it is more practical than physical-based forecasting models, which depends on atmospheric motion equation. （3） The proposed model extracts informative features by firstly learning spatial correlations using CNN and subsequently learning temporal correlations using SRUs. Compared with traditional deep learning models, it formulates wind data as RGB images.

Key words： Uncertainty wind power generation load demand optimal transmission switching （OTS） confidence gap decision theory （CGDT）

Received: 16 July 2022

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TM734

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	Wang Dongfeng
	Chen Jiangli
	Huang Yu
	Sun Heyu
	Yang Mingye

Cite this article:

Wang Dongfeng,Chen Jiangli,Huang Yu等. Research on the Uncertain Optimal Transmission Switching in Transmission Networks Based on Confidence Gap Decision[J]. Transactions of China Electrotechnical Society, 0, (): 49-49.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.221375 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/49

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