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Economic dispatch method of power system with wind farm based on improved radial movement algorithm

A technology for economic scheduling and power systems, applied in wind power generation, single-network parallel feeding arrangements, etc., can solve problems such as time-consuming, complex algorithms, and easy to fall into local optimal solutions

Active Publication Date: 2021-04-06
CHINA UNIV OF MINING & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

As the model becomes more reasonable and more complex, there are more factors to consider and more difficult constraints to deal with, especially dynamic economic dispatch (DED), which is an interactive, high-dimensional, nonlinear optimization problem. , it becomes more complicated after wind power access
[0004] To this end, relevant scholars have proposed various optimization algorithms, such as elite strategy non-dominated sorting genetic algorithm (NSGA-II), real coded genetic algorithm (RCGA), descent search particle swarm optimization algorithm (MPSO), evolutionary iterative particle swarm optimization algorithm (EIPSO) , Improved Bacterial Foraging Algorithm (IBFA), Improved Adaptive Multi-Objective Differential Evolution Algorithm (MAMODE), Improved Differential Algorithm (IMOEA / D-CH), etc. These algorithms can solve complex scheduling models to varying degrees, However, the common disadvantage is that it is easy to fall into the local optimal solution, and it is impossible to find the optimal scheduling scheme, and some algorithms are more complicated and time-consuming

Method used

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  • Economic dispatch method of power system with wind farm based on improved radial movement algorithm
  • Economic dispatch method of power system with wind farm based on improved radial movement algorithm
  • Economic dispatch method of power system with wind farm based on improved radial movement algorithm

Examples

Experimental program
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Effect test

Embodiment 1

[0110] Taking a 6-unit IEEE 30-node power system with two wind farms as an example, the model considers all items in formula (1), does not consider network loss and backup constraints, and the algorithm parameters take the value C 1 =0.7, C 2 =0.8, nop=50, N=200, calculate the unit output, total unit output (P / MW) and total consumption cost (C / USD·h -1 ), and compared with the results of QPSO and GABC algorithms proposed in related literature.

[0111] (1) First consider the case of no wind power, set nod=6, use IRMO to solve the model, the results are shown in Table 1.

[0112] Table 1 The optimization results of each algorithm under different loads without wind power model

[0113]

[0114]

[0115] It can be seen from Table 1 that under the load of 1200MW, the results obtained by QPSO, GABC algorithm and IRMO are 29555.72USD, 29147.00USD and 29109.64USD respectively. In contrast, the cost of IRMO is 445.39USD less than that of QPSO, and 37.36USD less than that of GA...

Embodiment 2

[0121] Taking the 10-unit system as an example, consider formulas (2), (3) and (6) in the model, consider network loss and backup constraints, dispatch period H=24h, and time interval is 1h. The total output power of the grid-connected wind farm is 100MW, a total of 50 wind turbines, and the system load forecast value P D , wind power prediction value w av See Table 3, and see Table 4 for the values ​​of algorithm-related parameters.

[0122] Table 3 Wind power and load forecast values ​​in each time period

[0123]

[0124] Table 4 Algorithm parameter value table

[0125]

[0126] (1) First consider the case of no wind power, and use IRMO to solve the DED model. The obtained unit output, network loss and cost in each period of 24h are shown in Table 5, and the total is represented by T. The total cost of 24h system consumption is 2476739USD, and the power borne by each unit is as follows: image 3 shown. It can be seen that this scheduling combination satisfies the...

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Abstract

The invention discloses a method based on the improved radial movement algorithm (IRMO) to solve the economic dispatching of the power system containing wind power, starting from the common consideration factors such as wind power overestimation cost, valve point effect, spinning reserve constraint and network loss, etc., and establishes a calculation method And the general economic scheduling model of wind power uncertainty, the model has generality; in order to solve this model, an improved radial movement algorithm (IRMO) is proposed. On the one hand, the algorithm randomly mutates some particles during the iteration process, Improve the diversity of the population, so that the algorithm can jump out of the local optimum; on the other hand, use the concave parabola inertia weight nonlinear decreasing strategy to further enhance the search accuracy in the middle and late stages of the algorithm, making it easier to find the global optimal solution; At the same time, taking into account the accuracy and accuracy, better results are obtained than other typical algorithms, thus providing decision makers with more economical and time-saving scheduling solutions.

Description

technical field [0001] The invention relates to a method for analyzing and solving a wind power grid-connected economic scheduling model by using an improved radial movement algorithm, and belongs to the field of wind power uncertainty analysis and new energy grid-connected scheduling. Background technique [0002] With the increasingly prominent environmental problems, wind energy has received more and more attention as a clean energy. However, wind power has strong randomness, and there are still large errors in the prediction of wind power at present. Therefore, the problem of economic dispatch of power system including wind power has become a research hotspot. [0003] In order to deal with the randomness of wind power, the establishment of models is mainly divided into two categories: uncertain models and deterministic models. The former usually uses fuzzy theory, probability model and scenario method, etc., and the latter usually considers the high and low cost of wi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/46H02J3/38
CPCY02E10/76
Inventor 韩丽张容畅
Owner CHINA UNIV OF MINING & TECH
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