An intelligent microgrid optimal scheduling method for improving a particle swarm algorithm
A technology for improving particle swarm and optimizing scheduling, applied in computing, computing models, data processing applications, etc., to reduce operating costs and maximize economic benefits
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment Construction
[0018] The present invention will be further described below in conjunction with accompanying drawing.
[0019] Such as figure 1 and figure 2 Shown, the inventive method has is:
[0020] Step 1: Set the basic parameters of the annealing mutation algorithm, including the population size N, the maximum number of iterations it, and the initial value C of the two learning factors 1s 、C 2s and the termination value C 1e 、C 2e , the initial value ω of the inertia weight s and the termination value ω e , the mutation probability P m Wait.
[0021] Step 2: Initialize the individuals in the population according to the upper and lower limits of the output of each distributed power source in the microgrid.
[0022] Step 3: Calculate the fitness value of each particle according to the established microgrid operation objective function, and record the individual optimal and global optimal values.
[0023] Step 3: Introduce the simulated annealing algorithm. In each iteration, a ...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com