Day-before-day multi-time scale optimization scheduling method for wind-light-storage alliance system

By constructing a day-ahead and intraday multi-timescale optimization scheduling method for the wind-solar-storage alliance system, typical wind and solar power output scenarios are generated and multi-objective stochastic programming is performed. This solves the problems of power system decision-making errors and operational instability caused by wind and solar power output fluctuations, and improves the economic benefits and stability of the system.

CN122092371APending Publication Date: 2026-05-26GUIZHOU ELECTRIC POWER TRADING CENT CO LTD
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Patent Information

Application Number
CN202610039286.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-13
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing research has failed to effectively combine the day-day and intraday stochastic fluctuation characteristics of wind and solar power output, resulting in decision-making errors and operational instability in the power system when absorbing new energy sources, and lacking a multi-timescale collaborative optimization framework.

Method used

A day-ahead and intraday multi-timescale optimization scheduling method for the wind-solar-storage alliance system is constructed. Typical wind and solar power output scenarios are generated through Latin hypercube sampling and Kantorovich distance. A multi-objective stochastic programming model is established to maximize the economic benefits of the wind-solar-storage alliance and minimize the fluctuation of the grid-connected residual load. The model is then corrected in real time during the intraday rolling optimization.

Benefits of technology

It significantly improves the economic benefits and grid stability of the wind-solar-storage alliance system, reduces decision-making errors caused by forecast deviations, achieves a balance between planning and flexibility, and ensures maximum benefits and stable grid-connected power when facing uncontrollable forecast errors.

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Abstract

The invention relates to the technical field of power system scheduling optimization, and discloses a wind-light-storage alliance system day-ahead-day multi-time scale optimization scheduling method, which comprises the following steps of: constructing a wind-light output scene generation and reduction model considering uncertainty, generating an initial scene set of wind and light output based on a Latin hypercube sampling method, and generating a wind-light output scene set; reducing the initial scene set by a scene reduction method based on a Kantorovich distance to obtain a typical wind and light output scene; constructing a day-ahead optimization scheduling model, establishing a multi-target stochastic programming model based on a typical wind and light output scene, and solving to obtain an optimization output plan of each unit in each day-ahead time period; and constructing an intra-day rolling optimization scheduling model, constructing an intra-day rolling optimization model based on a multi-target stochastic programming model, and performing real-time feedback and correction on the optimization output plan to obtain a final scheduling instruction of each time period in the day, wherein the constraint condition of the intra-day rolling optimization model is the same as that of the day-ahead optimization model. The method has the advantages of being capable of ensuring that the total revenue of the system is maximized and the grid-connected power is stable in the face of uncontrollable prediction errors.
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