Static reliability probability index acquiring method for medium-term power grid planning

Abstract

The invention provides a static reliability probability index acquiring method for medium-term power grid planning, comprising the following steps: carrying out determinate fault analysis on preliminary planning design schemes, and taking the schemes meeting the requirements of N-1 standard as preselected planning design schemes; selecting one preselected scheme, and inputting a component reliability parameter; analyzing the state of the preselected scheme; calculating the static reliability index, determining a reference value for evaluating the static reliability of the power grid, judging that the reliability meets the requirements if the reliability index value is higher than the reference value, or correcting the scheme; repeating the process for all the preselected planning schemes, and selecting the planning schemes meeting the reliability requirements; and forming a preselected planning scheme set based on all the planning schemes meeting the reliability requirements, carrying out comprehensive evaluation through a multi-attribute decision-making approach, and selecting a recommended planning scheme based on the result of comprehensive evaluation. The time for system recovery is shortened, and the disturbance of adjustment measures to the system is reduced.

Description

technical field [0001] The invention relates to a method for obtaining a reliability probability index, in particular to a method for obtaining a static reliability probability index for medium-term power grid planning. Background technique [0002] As the number and types of power grid components continue to increase, the degree of automation of power systems continues to increase, and users' requirements for power quality increase, the planning, operation, and maintenance of power systems face great challenges. The reliability evaluation of the power generation and transmission system is based on the consideration of the random fault characteristics of the power generation and transmission equipment, and comprehensively assesses whether the power generation system has sufficient power generation capacity and whether the main transmission network has sufficient power transmission capacity to provide the load point with qualified power. Quantitative probability evaluation. ...

AI Technical Summary

Problems solved by technology

The linear programming model based on DC power flow has a large model error because it completely ignores the influence of reactive power flow and node voltage

The nonlinear programming model based on AC power flow truly reflects the actual constraints of system operation, but it is time-consuming and complicated to program

In terms of calculation efficiency, the calculation time of linear programming and nonlinear programming models is closely related to the scale of the system. In large-scale power systems, even if component failures only cause individual line overload or local node voltage limit, these two algorithms must System-wide optimization adjustments to restore security in localized areas are time consuming and inefficient

In order to overcome these shortcomings, some scholars have proposed a heuristic nearby load shedding model. This model uses power flow tracking to search for a load shedding node set that can effectively alleviate the system failure situation in a certain area near the faulty component, avoiding the need for load shedding in the entire system. Carry out global optimization to obtain the optimal load reduction amount, but this method is not direct and fast enough in terms of shortening the system recovery time and reducing the disturbance of the adjustment measures to the system

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