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Optimal Configuration Method of Microgrid Capacity Based on Improved Hybrid Particle Swarm Optimization Algorithm

A capacity-optimized configuration and hybrid particle swarm technology, applied in circuit devices, calculations, calculation models, etc., can solve problems such as inability to jump out of local optimum, complex calculation process, slow convergence speed, etc., to maintain population excellence and configuration accuracy High, reduce the effect of the number of calculations

Active Publication Date: 2022-01-18
国家电网有限公司西南分部 +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method can scientifically and rationally allocate the optimal output point of each generator set under the total power of the given power, so that the power supply coal consumption of the optimized unit can be significantly reduced. However, the chaotic method needs to generate a solution through the mapping method, and the calculation process is relatively complicated. , resulting in a lot of unnecessary computational costs, which need to be simplified
Xiao Xin, Zeng Yujiao, Cao Hongbin and other inventors applied for the invention patent "multi-system joint optimization scheduling method and device", which uses chaotic particle swarm algorithm to solve the joint optimization scheduling model to obtain the entire gas-steam-electricity system. Gas distribution, steam and electricity production, and outsourced power transmission optimization schemes to generate the final fuel distribution of each energy conversion equipment. This method is only based on the basic chaotic particle swarm algorithm, and there is still the problem of not being able to jump out of local optimum
The traditional particle swarm algorithm has the disadvantage of falling into local optimum, and at the same time, the convergence speed is slow, and the anti-interference ability to the internal disturbance of the system is poor.

Method used

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  • Optimal Configuration Method of Microgrid Capacity Based on Improved Hybrid Particle Swarm Optimization Algorithm
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  • Optimal Configuration Method of Microgrid Capacity Based on Improved Hybrid Particle Swarm Optimization Algorithm

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Embodiment 1

[0165] Find the global optimum for the analysis algorithm of the present invention, the improved mixing and the ability to jump out of local optima, respectively, here using adaptive inertia weight particle swarm optimization, Particle Swarm Optimization of the basic invention, and natural selection based on Gaussian disturbance Particle Swarm and hybrid particle swarm optimization proposed by the invention substantially nine test functions.

[0166] Basic parameters are the same, the search speed limit v max And v = 4 min = -4, the lower limit of the inertia weight factor w respectively max And w = 0.9 min = 0.4, are learning factor c 1 = C 2 = 2, the number of iterations are M = 500, group size is N = 50, the particle dimensions were D = 10. Test functions are selected Rosenbrock, Acley, Schwefel, Weierstrass, Happycat, Elliptic, Rastring, Griewank, Salomon function PSO tested and compared the performance of other hybrid particle swarm optimization algorithm proposed by the pres...

Embodiment 2

[0178]The method of the present invention will be described as an example. When the micro-grid island is running, it is lost with the main network, and can only rely on internal distribution power supplies to meet load requirements. The wind turbine, solar photovoltaic plate and battery parameters are first configured here.

[0179] Distributed Power Supply Economic Parameters Table 3 is as follows:

[0180] Table 3 Distributed power economy parameter table

[0181]

[0182] The parameter settings in the running policy are shown in Table 4:

[0183] Table 4 Run Policy parameter setting table

[0184]

[0185] In the table, S rated Is the rated capacity of the battery; MT Is a miniature gas turbine rated power; P char It is the rating of the battery.

[0186] In this example, the actual climatic conditions and load requirements of a small island are design planning a microcouns. The total time is one year, and the unit interval is 1H, which uses different control strategies to ...

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Abstract

The invention relates to a micro-grid capacity optimization configuration method based on an improved hybrid particle swarm algorithm, comprising the following steps: 1) obtaining the power sequence and load sequence of wind power generators and solar photovoltaic cells; 2) taking the comprehensive investment cost, comprehensive reliability rate, The remaining energy rate and the renewable energy rate are the planning goals, and the optimal configuration model for the capacity of the island microgrid is established; 3) Based on step 1), the optimized hybrid particle swarm optimization algorithm is used to solve the optimal configuration model for the capacity of the island microgrid, and the optimal configuration plan is obtained . Compared with the prior art, the invention has high configuration precision, can make the power grid reliable, environment-friendly and fully utilize energy, and has practical significance for micro-grid planning.

Description

Technical field [0001] The present invention relates to a capacity planning microgrid technology, particularly, to a method for optimizing the configuration based on improved hybrid particle swarm optimization microgrid capacity. Background technique [0002] Particle swarm optimization (PSO) The basic idea comes from the bird population biologist Frank Heppner behavior model established, although it is still PSO algorithm is a stochastic iterative search algorithm, but compared to the genetic algorithm, it does not "cross" speaking "variation", but each individual as a particle in the search space flight, and flight speed is dynamically adjusted based on individual and collective experience, so it has the characteristics of simple rules. Currently, PSO its easy to implement, fast convergence and high precision characteristics are widely used in practical problems. [0003] By the existing literature and patent retrieval found that the existing literature, Zhou Yan, LIU Pei-yu, Z...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/38G06Q10/04G06Q50/06G06N3/00
CPCH02J3/383H02J3/386G06N3/006G06Q10/04G06Q50/06H02J3/388H02J2203/20Y02B10/10Y02E10/56Y02E10/76Y04S10/50
Inventor 周全魏明奎路亮江栗蔡绍荣柳璐程浩忠张程铭袁杨
Owner 国家电网有限公司西南分部