Method for optimizing electric power system tide base on part automatic differential technology

An automatic differential and power system technology, applied in information technology support systems, system integration technology, electrical components, etc., can solve problems such as insufficient flexibility and slow calculation speed, so as to avoid repeated calculations, increase calculation speed, and improve convenience and the effect of flexibility

Inactive Publication Date: 2009-04-15
ZHEJIANG UNIV
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Problems solved by technology

[0012] The purpose of the present invention is to overcome the disadvantages of the insufficient flexibility of the power flow optimization method based on manual programming and the slow calculation speed of the power flow optimization method based on automatic differentiation, which is different from the existing methods that use automatic differentiation technology to obtain the objective function and constraint conditions. "Full automatic differentiation" of comparable matrix and / or Hessian matrix, providing a power system power flow optimization method based on partial automatic differentiation technique

Method used

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  • Method for optimizing electric power system tide base on part automatic differential technology
  • Method for optimizing electric power system tide base on part automatic differential technology
  • Method for optimizing electric power system tide base on part automatic differential technology

Examples

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Embodiment 1

[0045] consider as figure 2 In the example power system shown, one embodiment of the present invention is used to optimize the power flow of the system, and each step is described as follows:

[0046] Step 1: Calculate the grid admittance matrix (results omitted), and set the optimization variable of the power flow optimization problem as x, which includes [P G , Q G , V e , V f , X c ], where P G and Q G are the active output and reactive output of the generator respectively, V e and V f are the real and imaginary parts of the voltage at each node, X c Customize the control variables of the model for other users in the system.

[0047] The objective function is set as the minimum fuel cost of system power generation (1), where α is the economic coefficient of each generator.

[0048] f ( x ) = Σ ( α i 2 ...

Embodiment 2

[0085] This embodiment uses the same objective function, constraint conditions and numerical optimization algorithm as in Embodiment 1, considers multiple groups of test power systems as shown in Table 2, and uses the power flow optimization method based on partial automatic differential technology to optimize it, and at the same time for the convenience For comparison, compare the calculation efficiency of the existing manual programming and all automatic differentiation methods with this embodiment, and the results are shown in Table 3.

[0086] Table 2 Summary of Test System

[0087]

[0088] Table 3 Comparison of calculation efficiency of power flow optimization methods

[0089]

[0090] According to the data in Table 3, the CPU time of all automatic differentiation is about 2-3 times of manual programming, while the partial automatic differentiation method is only 1.2-1.3 times of manual programming. Therefore, the power flow optimization method based on the partia...

Embodiment 3

[0092] This embodiment adopts the same objective function, constraint conditions and numerical optimization algorithm as in Embodiment 1. On the basis of the CASE2383 test power system shown in Embodiment 2, a high-voltage direct current transmission system (HVDC) and a static var compensator are respectively added. (SVC) and thyristor controlled series compensator (TCSC). These new power electronic devices mentioned above will be treated as user-defined models in this embodiment, as shown in Example 1, the control variable x c Add it to the optimization variable x, and realize the integration of the original algorithm to the user-defined model through the self-defined constraints (3) and (7). Table 4 shows three groups of power flow optimization calculation examples including the above user-defined models, and gives the percentage of CPU time spent on processing user-defined models.

[0093] Table 4. Power flow optimization results considering user-defined models

[0094] ...

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Abstract

The invention discloses a tidal current optimization method of an electric power system based on partial automatic differentiation technique. Compared with the existing tidal current optimization methods based on the automatic differentiation technique, the algorithm fully utilizes the unchangeable characteristic of most elements in a Jacobian matrix and / or a Hessian matrix of an objective function and a constraint function during iteration, and is added with a function of identifying invariable elements in the Jacobian matrix and the Hessian matrix, and stores the invariable elements in a list before the first iteration; in each iteration of a numerical optimization algorithm, variable elements in the Jacobian matrix and / or the Hessian matrix can be computed only by the automatic differentiation technique. The tidal current optimization method based on the partial automatic differentiation technique can greatly reduce the burden on software developers and maintainers, improve the maintainability and the flexibility of a tidal current optimization application program, efficiently support customized models, and satisfy the analysis, running and scheduling requirements of the modern power system on the premise that the computational efficiency is not reduced substantially.

Description

technical field [0001] The invention belongs to the technical field of power system operation, analysis and scheduling, and in particular relates to a power system power flow optimization method based on partial automatic differential technology. Background technique [0002] In recent years, with the continuous deepening of power market reform and the continuous diversification of grid-connected power equipment types, faster and more flexible requirements have been put forward for power system power flow optimization methods. Newton's method widely used in the field of power flow optimization [1] , successive quadratic programming [2] , interior point method [3] In numerical optimization methods such as , it is necessary to calculate the Jacobian matrix and / or Hessian matrix of the objective function and constraints. In order to obtain the above matrix, developers have to manually derive and implement these derivative calculation formulas. This manual programming method...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02J3/00G06Q50/00G06Q50/06
CPCY04S10/545Y02E40/76
Inventor 江全元耿光超
Owner ZHEJIANG UNIV
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