A day-ahead unit commitment optimization method for ramping demand in uncertain scenarios

Through a day-ahead unit combination optimization method for uncertain scenarios, combined with thermal power unit and energy storage scheduling models, Latin hypercube sampling is used to generate renewable energy output error prediction curves. Taking into account the power support of inter-provincial interconnection lines, the output of flexible resources within the power system is optimized, solving the problem of insufficient peak-shaving capacity of the power system, achieving cost reduction and stable system operation.

CN119765405BActive Publication Date: 2025-09-23TIANJIN UNIV +2
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Patent Information

Application Number
CN202411947435.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-27
Publication Date
2025-09-23
Estimated Expiration
2044-12-27

AI Technical Summary

Technical Problem

Existing technologies, when dealing with the uncertainty of renewable energy output, result in insufficient peak-shaving capacity of the power system, and existing methods have problems of poor economic efficiency or conservative results.

Method used

A day-ahead unit commitment optimization method for uncertain scenarios is adopted. Through the thermal power unit scheduling model, energy storage scheduling model and renewable energy output scenario set, combined with Latin hypercube sampling, a renewable energy output error prediction curve is generated. Taking into account the power support of inter-provincial interconnection lines, the output of flexible resources within the system is optimized.

Benefits of technology

While ensuring the stable operation of the system, it reduces power supply costs, optimizes the output of thermal power units and energy storage resources within the system, and solves the problem of insufficient peak-shaving capacity of the power system.

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Abstract

The present invention discloses a day-ahead unit combination method for ramping demand in uncertain scenarios of a power system. The day-ahead unit combination method is based on a power system, and the power system includes a thermal power unit scheduling model, an energy storage scheduling model, and a renewable energy output scenario set. Step 101: Establish a day-ahead economic scheduling model according to the output load of the thermal power unit scheduling model and the energy storage scheduling model. Step 102: Calculate the maximum regulation capacity of the thermal power units in the power system at any time t of the day-ahead economic scheduling model through a linear programming method. Step 103: Sample the renewable energy output scenario set through hypercube to generate a renewable energy output error prediction curve. Step 104: Determine whether the maximum regulation capacity of the thermal power units in the power system meets the ramping demand power in the renewable energy output error prediction curve. Step 105: Regulate the maximum regulation capacity of the thermal power units in the power system. The present invention can reduce the power supply cost of the system while ensuring stable operation of the system, and has good engineering application value.
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