A comprehensive optimization method for peak shaving performance of combined cycle steam heating units

Résumé fourni par la source

Introduction The rapid expansion of wind and photovoltaic power has intensified the demand for deep peak shaving in coal-fired power units. Conventional extraction steam heating units are constrained by thermal–electric coupling, limiting their ability to operate flexibly at low loads. To support low-carbon energy transformation, there is a pressing need to develop optimization methods that enhance both flexibility and economic efficiency of combined heat and power operations. Methods This study focuses on 300 MW and 600 MW extraction steam heating units. A thermodynamic simulation platform was established in EBSILON to evaluate multiple steam extraction schemes. A dual-objective optimization framework was developed, aiming to minimize power generation and coal consumption rates under peak shaving conditions. The framework integrates thermoelectric decoupling technologies, including thermal storage tanks, molten salt storage, and electric boilers, and applies a genetic algorithm to solve multi-scenario optimization problems in single- and dual-unit coordinated operations. Results Simulation results demonstrate that thermal storage tanks and molten salt storage improve system economy at shallow peak shaving depths, while electric boilers provide superior flexibility for deeper peak shaving despite higher costs. In dual-unit operation, designating the smaller 300 MW unit as the base-load unit and the larger 600 MW unit as the peak-shaving unit significantly enhances system performance. Under this configuration, net revenue increased by 67.3% compared with the reverse arrangement. Furthermore, equipping the peak-shaving unit with an electric boiler achieved deeper peak shaving without compromising heating demand, though excessive deployment of electric boilers reduced overall thermal economy. Discussion The proposed comprehensive optimization method enables practical balancing of technical feasibility and economic returns in combined heat and power systems. By coupling thermodynamic modeling with genetic algorithm-based optimization, the study provides quantitative evidence for selecting suitable unit configurations and decoupling technologies. These findings offer guidance for improving coal consumption efficiency, expanding renewable energy accommodation, and advancing the low-carbon transition of thermal power plants under high renewable penetration scenarios.

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