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Method for analyzing uncertain wind power AC-OPF problem via data driven robustness model

An AC-OPF, data-driven technology, applied to the AC network with the same frequency from different sources, can solve the problem that the optimization scheme is not optimal, and achieve the effect of superior security and stability

Inactive Publication Date: 2019-06-04
GUANGXI UNIV
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Robust optimization considers the impact of the disturbance boundary on the scheduling index, and has high computational efficiency, but its modeling idea is based on the worst-case optimization, which represents a conservative point of view, and the obtained optimization scheme is not the best. Excellent

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  • Method for analyzing uncertain wind power AC-OPF problem via data driven robustness model
  • Method for analyzing uncertain wind power AC-OPF problem via data driven robustness model
  • Method for analyzing uncertain wind power AC-OPF problem via data driven robustness model

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[0044] In order to understand the above-mentioned purpose, features and advantages of the present invention more clearly, the technical solutions of the present invention will be described in further non-limiting detail below in conjunction with the accompanying drawings and specific embodiments.

[0045] First, use the existing historical data and wind power fitting statistics to obtain a stricter wind power uncertainty set, and then convert the AC-OPF problem with uncertain wind power through second-order cone programming and dual norm theory for a linear programming problem.

[0046] Step 1: Set parameters historical data of wind power generation, ball box uncertain set (uncertain box set), confidence level, risk degree, iteration step size, number of iterations, radius parameters of spherical box uncertain set;

[0047]Step 2: Solve the dual problem of the given problem with the ellipsoidal uncertainty set and find the robust optimal solution;

[0048] Step 3: Calculate t...

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Abstract

The invention discloses a method for analyzing an uncertain wind power AC-OPF problem via a data driven robustness model. The method comprises the following steps that 1) historical data is introduced, and parameters including a ball-box uncertain set, the confidence level, the risk degree, the iteration step, the iteration frequency and a radius parameter of the box-ball uncertain set of wind power generation are set; 2) an ellipsoid uncertain set is used to solve a dual problem of the given problem and find a robustness optimal solution; 3) an uncertain area is calculated; 4) the uncertain area replaces the uncertain set, and a safety approximation of optimal trend of the AC power grid is calculated; 5) if the safety approximation is greater than or equivalent to the risk degree, a result is output, and the algorithm is ended, and otherwise, a next step is turned to; 6) a new radius parameter is set, the iteration counted number is updated, and the step 2) is returned to. The data driven robustness is used, so that the economic performance is higher on the premise that the operational safety and stability of the power system are ensured.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of power grid control, and in particular, relate to a method for analyzing uncertain wind power AC-OPF problems using a data-driven robust model. Background technique [0002] With the continuous development and utilization of renewable energy, new energy grid integration has become an inevitable trend in the development of today's power grid. Nowadays, new energy power generation, especially wind power generation, has been widely used, but the subsequent large-scale wind power grid connection has brought new challenges to the dispatching and operation of the power system. How to ensure safe and reliable power supply, Reasonably deploying wind power to make the power grid system operate to achieve the optimal power flow is an urgent problem that needs to be solved by power dispatching operators. The optimal power flow problem (Optimal power flow, OPF) is a large-scale, multi-constraine...

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Application Information

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IPC IPC(8): H02J3/06
Inventor 白晓清曹益奇苏向阳郑义秦征凤韦尚富陈衍杏
Owner GUANGXI UNIV
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