A robust optimization scheduling method and system for microgrids across multiple time scales

By using a multi-objective optimization model and a multi-timescale rolling optimization method, combined with an improved particle swarm optimization algorithm, the problems of single objective and poor robustness in microgrid dispatch schemes are solved, achieving a synergistic optimization effect of safety, economy and environmental protection.

CN122137014APending Publication Date: 2026-06-02CHINA ENERGY CONSTR ENERGY STORAGE TECH (WUHAN) CO LTD
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA ENERGY CONSTR ENERGY STORAGE TECH (WUHAN) CO LTD
Filing Date
2026-03-05
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing microgrid optimization scheduling schemes suffer from problems such as single objective, poor robustness, and disconnect between the model and the physical characteristics of the equipment, making scheduling plans difficult to execute or costly to execute.

Method used

A multi-objective optimization model is adopted, combining a multi-timescale rolling optimization method and an improved multi-objective particle swarm optimization algorithm to establish a multi-objective optimization model based on system safety risks, operating costs and environmental impacts. The scheduling plan is formulated through the multi-timescale rolling optimization method, and the local controller is used for rapid compensation.

Benefits of technology

It achieves coordinated optimization of the safety, economy, and environmental protection of microgrids, improves the robustness and engineering practicality of scheduling plans, and reduces the difficulty of deployment and the threshold for use.

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Abstract

This invention relates to a robust optimization scheduling method and system for microgrids across multiple time scales. The method includes: establishing a multi-objective optimization model based on system security risks, operating costs, and environmental impacts; solving the multi-objective optimization model using a multi-objective optimization algorithm to obtain a target scheduling plan; and executing the target scheduling plan based on a multi-time-scale rolling optimization method: within a first time scale, determining the start / stop status and power setpoints of each distributed power source; within a second time scale, rolling corrections to the power setpoints and performing risk preventative corrections; and within a third time scale, rapidly compensating for system deviations through a local controller based on the real-time operating status of the system. This invention, through the combination of multi-objective optimization and multi-scale scheduling, achieves collaborative optimization of multiple objectives, upgrading from passive response to risk warning and preventative correction, and significantly reducing deployment difficulty and usage barriers.
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