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Power grid load recovery robustness optimization method based on information gap decision theory

A technology of load recovery and decision theory, applied in the field of power grid, can solve the problem of difficulty in obtaining distribution characteristics, and achieve the effect of simple model, good stability and robustness, and guaranteeing safety.

Active Publication Date: 2017-07-28
NANJING UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] Aiming at the uncertainty of load restoration, some scholars proposed a load restoration strategy based on real-time monitoring of power grid parameters in the process of load restoration based on wide-area measurement system and dynamic adjustment. This method reduces the influence of load uncertainty by reducing the observation step size, and has not yet Fundamentally solve the problem of load uncertainty
Some scholars have proposed to use fuzzy models to describe the uncertainty of load restoration, and to formulate dynamic load combination restoration schemes considering load fluctuation and complementarity. However, although load fluctuation and fuzzy models can describe load uncertainty, they cannot Accurate distribution characteristics are difficult to obtain during load recovery

Method used

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  • Power grid load recovery robustness optimization method based on information gap decision theory
  • Power grid load recovery robustness optimization method based on information gap decision theory
  • Power grid load recovery robustness optimization method based on information gap decision theory

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Embodiment 1

[0122] Taking the IEEE10-machine 39-node system as an example, the power grid topology is as follows: image 3 As shown, unit 30 is a hydroelectric unit with self-starting capability, and the rest are thermal power units without self-starting capability. Assume that at the current time step, apart from the self-starting units, units 37, 38, and 39 have recovered, image 3 The bold solid line in the center is the restored path. The small system that has been restored will provide utility power for unit 33, and its recovery path is: 26-27-17-16-19-33, such as image 3 Shown by the dotted line.

[0123] (1) Robust optimization results of load restoration based on information gap decision theory

[0124] When considering the uncertainty of load restoration, by changing the deviation factor δ, determining different expected goals, and solving the load restoration robust model based on IGDT theory, the corresponding maximum fluctuation range of uncertain parameters α and the corres...

Embodiment 2

[0130] Taking the acceptable minimum recovery amount of load recovery as 50% of the optimal solution of the deterministic model as an example, the stability of the model of the present invention is solved by using the artificial bee colony algorithm to analyze the calculation results of repeated operations 20 times. The relevant parameters are set as follows: the population size N=20, the maximum mining times of nectar sources Limit=5, and the maximum iteration times MCN=200. Calculated as Image 6 shown.

[0131] from Image 6 It can be seen that the fluctuation degree of the solution result of the artificial bee colony algorithm is small, and the up and down fluctuations are all within 5%. Therefore, using the artificial bee colony algorithm to solve the model of the present invention has better stability.

[0132] It can be seen from the results of the calculation example that the present invention does not need to know the uncertainty distribution of the load, the model ...

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Abstract

The invention discloses a power grid load recovery robustness optimization method based on an information gap decision theory. A determinacy load recovery optimization model is converted into a robustness optimization model, and an artificial bee colony algorithm is adopted for carrying out solving, so a load recovery scheme related to indeterminacy is acquired. The method comprises steps of 1, establishing a determinacy load recovery optimization model in a power grid recovery process; 2, adopting the artificial bee colony algorithm to solve a determinacy model to obtain an optimal solution of the determinacy load recovery optimization model; 3, according to an optimal solution of an original model, determining the acceptable smallest recovery quantity of the load recovery; 4, based on the information gap decision theory, establishing a robustness optimization model considering indeterminacy of the load recovery; and 5, adopting the information gap decision theory to solve the robustness model so as to obtain a load recovery scheme capable of achieving an expected recovery target. According to the invention, load fluctuations in a certain range can be borne by the obtained load recovery scheme; safety of a power grid recovery process can be ensured; and the method has certain theoretical and engineering value.

Description

technical field [0001] The invention belongs to the technical field of power grids, in particular to a robust optimization method for power grid load restoration based on information gap decision theory. Background technique [0002] Load restoration is an important task in the restoration process of power grid after power failure. It is not only the purpose of power grid restoration but also an important means to ensure the stability of the restoration process. Scholars at home and abroad have carried out a lot of research work on load restoration optimization. However, in most studies, the amount of load restoration is assumed to be a definite value, that is, the estimated restored load is equal to the actual restored load. However, in the actual power grid restoration, the uncertainty of the load is inevitable. Uncertain load recovery schemes considering load may affect the safety of the recovery process when implemented. For this reason, it is necessary to consider the ...

Claims

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Application Information

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IPC IPC(8): H02J3/00G06Q10/04G06Q50/06G06N3/00
CPCG06N3/006G06Q10/04G06Q50/06H02J3/00H02J2203/20
Inventor 陈晞吕友杰谢云云黄琳雁李凯嵘宋雯雯蔡胜陈佳欣卜京殷明慧姚娟邹云蔡晨晓张俊芳
Owner NANJING UNIV OF SCI & TECH
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