Generator start-stop scheduling rapid optimization method and device based on particle swarm optimization

CN119362929APending Publication Date: 2025-01-24ECONOMIC & TECHNOLOGICAL RESEARCH INSTITUTE STATE GRID INNER MONGOLIA EASTERN ELECTRIC POWER CO LTD +3
0 Cites 0 Cited by

Patent Information

Application Number
CN202411255763.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-09-09
Publication Date
2025-01-24

AI Technical Summary

Technical Problem

The traditional generator start-stop scheduling method is difficult to effectively adapt to the rapid changes in new energy and loads. It has a long calculation cycle and is inefficient, and the importance of reducing the number of unit start-up and shutdowns is not fully considered in improving generator operation efficiency and extending unit life.

Method used

A rapid optimization method for generator start-stop scheduling based on particle swarm algorithm is used to establish a generator start-stop scheduling optimization model. Through the combination of linear simplification and particle swarm algorithm, the unit combination results are determined and the generator start-stop plan is optimized to improve efficiency.

Benefits of technology

This method can quickly respond to market changes, reduce calculation time, improve solution efficiency, ensure economy and system stability, and is suitable for the needs of modern power systems.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN119362929A_ABST
    Figure CN119362929A_ABST
Patent Text Reader

Abstract

The invention discloses a generator start-stop scheduling rapid optimization method and device based on a particle swarm algorithm. The method comprises the following steps: establishing a power system security constraint condition and a priority target constraint function of a generator start-stop scheduling optimization model, and constructing the generator start-stop scheduling optimization model; performing linear simplification on the established generator start-stop scheduling optimization model, and constructing a simplified generator start-stop scheduling optimization model; and based on a particle swarm algorithm, according to input system parameters of a generator start-stop scheduling to-be-optimized system and the simplified generator start-stop scheduling optimization model, a unit commitment result is determined, and the system parameters comprise network parameters, load parameters and generator parameters.
Need to check novelty before this filing date? Find Prior Art