%0 Journal Article %T 结合新能源实现火力发电低碳高效运行方法探析
Analysis on Low-Carbon and Efficient Operation Methods of Thermal Power Generation Combined with New Energy %A 李慧云 %J Advances in Energy and Power Engineering %P 161-171 %@ 2328-0506 %D 2025 %I Hans Publishing %R 10.12677/aepe.2025.133017 %X 为了响应国家低碳号召，传统的电力系统急需降低化石能源消耗和减少污染气体的排放从而转型成低碳高效的电力系统。在系统负荷需求不变的情况下，增加对新能源的消纳可以实现电力系统的低碳运行。本文采用拉丁超立方方法结合K-means聚类改进算法得到了风光的综合预测出力情况，为电力系统对风光调度提供数据支撑。为实现电力系统的低碳高效运行，本文提出多目标低碳优化调度模型，考虑碳排放量和发电资源消耗量最少，采用6台不同性能和参数的火电机组进行调度，并用粒子群优化算法求解。实验结果表明，在满足日负荷需求的条件下，在电力系统中引入新能源，可以有效地减少系统的碳排放和节省发电资源消耗，有助于实现低碳高效的电力系统。
In response to the national call for a low-carbon power system, the traditional power system urgently needs to reduce fossil energy consumption and pollutant emissions to transform into a low-carbon and high-efficiency power system. Under the condition of unchanged system load demand, increasing the consumption of new energy can realize the low-carbon operation of the power system. In this paper, the Latin hypercube method combined with the K-means clustering improvement algorithm is used to complete the comprehensive prediction of the wind power output, which provides data support for the power system to dispatch the wind power. In order to realize the low-carbon and high-efficiency operation of the power system, this paper proposes a multi-objective low-carbon optimization scheduling model, which takes into account the minimum carbon emission and power generation resource consumption, and uses six thermal power units with different performances and parameters for scheduling, and solves the problem with particle swarm optimization algorithm. The experimental results show that under the condition of meeting daily load demand, introducing new energy into the power system can effectively reduce carbon emission and save the consumption of power generation resources, which helps to realize a low-carbon and high-efficiency power system. %K 综合预测， %K 多目标优化， %K 低碳调度， %K 粒子群优化算法
Integrated Forecasting %K Multi-Objective Optimization %K Low-Carbon Dispatch %K Particle Swarm Optimization Algorithm %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=118103