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CO2 emission characteristics modeling and low-carbon scheduling of thermal power units under peak shaving conditions

•CO2 emission characteristic of thermal power units for peak shaving is modeled.•The carbon reduction rate considering renewable power accommodation is defined.•A low-carbon scheduling method based on carbon reduction rate is proposed.•Impact of renewable power on CO2 emissions of power systems is s...

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Bibliographic Details
Published in:Fuel (Guildford) 2024-10, Vol.373, p.132339, Article 132339
Main Authors: Yuan, Rongsheng, Liu, Ming, Chen, Weixiong, Yan, Junjie
Format: Article
Language:English
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Summary:•CO2 emission characteristic of thermal power units for peak shaving is modeled.•The carbon reduction rate considering renewable power accommodation is defined.•A low-carbon scheduling method based on carbon reduction rate is proposed.•Impact of renewable power on CO2 emissions of power systems is studied with a case. To accommodate the high penetration of renewable power, the operational flexibility of thermal power units is highly required by the power system, so thermal power units frequently operate under peak shaving conditions. The CO2 emission characteristics of a thermal power unit are significantly influenced by the load factor, and also by the installed capacity and design parameters of the thermal power unit. Therefore, detailed CO2 emission characteristics of thermal power units should be modeled and considered in the low-carbon scheduling of the power system. In this study, thermal power units are classified by live steam parameters, installed capacities, reheat stages, cooling modes, and operation modes, and the models of detailed CO2 emission characteristics are developed and validated. The turbine isentropic efficiency, boiler thermal efficiency, and auxiliary power consumption are comprehensively considered in the CO2 emission model. A new indicator is defined to evaluate the reduction of carbon emissions in a power system when a TPU accommodates per unit of renewable power generation, and an optimization method for low-carbon scheduling based on it is proposed. Finally, a reference case study is conducted. Results show that the total carbon emissions for a day of the proposed low-carbon scheduling method are 0.06 %−0.20 % less than the efficiency priority method, and 1.53 %−2.36 % less than the capacity weighting method.
ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2024.132339