Power dispatching automation method based on MALPSO algorithm and virtual private cloud technology

CN120256085APending Publication Date: 2025-07-04XIAN THERMAL POWER RES INST CO LTD +1
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Patent Information

Application Number
CN202510175545.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-02-18
Publication Date
2025-07-04

AI Technical Summary

Technical Problem

Traditional particle swarm optimization algorithm (PSO) has problems such as premature convergence, sensitivity to parameter settings and insufficient flexibility in power scheduling automation systems, resulting in low utilization of virtual resources in the cloud and high network security risks, making it difficult to meet the needs of large-scale data processing and security.

Method used

Using MALPSO algorithm and virtual private cloud technology, we build a virtual private network, configure a virtual router and firewall, combine particle swarm optimization algorithm to optimize resource allocation and load balancing, provide security and reliability, adjust cloud environment resource allocation through adaptive characteristics, and use virtual private cloud technology backup and disaster recovery solutions.

Benefits of technology

It realizes efficient resource utilization, load balancing, security improvement and rapid response of the power scheduling system, reduces operating costs, improves the intelligence level and data security of the system, and supports flexible configuration of multiple business scenarios.

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Abstract

The invention discloses a power dispatching automation method based on an MALPSO algorithm and a virtual private cloud technology. The method comprises the following steps: establishing an objective function and a constraint condition according to a specific demand of a power plant side and a task dispatching optimization structure model of a power cloud platform environment; writing an adaptive function of a particle swarm according to a target function, updating pBest and cgBest according to latest particle swarm information, and updating a nonlinear inertia weight according to a logic function; repeatedly calculating until the objective function converges to obtain a maximum value, and guiding resource allocation by using the particle information of the iteration; and the physical machine executes the task according to a set plan, recovers resources after the task is finished, facilitates next task scheduling, outputs a result and finishes the task scheduling. According to the method, big data can be more effectively integrated and analyzed, the intelligent analysis capability of the electric power system is improved, remarkable technical innovation and economic benefits are brought to the electric power industry, and the method is an important tool for promoting intelligent transformation of the electric power system.
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