基于电能替代和需求响应的氧化铝工业多目标优化调度方法  

Research on multi-objective optimal scheduling method of green power and low carbon aluminum based synergy considering electric energy substitution and demand response

作　　者：刘宝勇[1] 林依桐 唐亮[1] 史洁[2] 张昕雪 LIU Baoyong;LIN Yitong;TANG Liang;SHI Jie;ZHANG Xinxue(Shandong Electric Power Engineering Consulting Institute Co.,Ltd,Ji’nan 250013;School of Physics and Technology,University of Ji’nan,Ji’nan 250022)

机构地区：[1]山东电力工程咨询院有限公司,济南250013 [2]济南大学物理科学与技术学院,济南250022

出　　处：《电气技术》2025年第2期26-34,共9页Electrical Engineering

基　　金：济南市“高校20条”项目(2021GXRC113)。

摘　　要：随着可再生能源的发展和低碳需求的增加,氧化铝工业面临着电力消耗优化的挑战。在高比例可再生能源接入的背景下,本文以可再生能源实时功率调节作为需求响应优化对象,通过电能替代和需求响应策略,在最小化弃风弃光率的基础上,以满足系统经济性要求和保证产量为目标,建立多目标需求响应模型,并使用标准边界交叉(NBI)算法和第二代非支配排序遗传算法(NSGA-Ⅱ)分别进行求解。实际算例分析结果表明,NBI算法在降低电力成本和弃电率方面的表现优于NSGA-Ⅱ。在考虑成本时,NBI算法和NSGA-Ⅱ可分别使成本降低73%和70%;在考虑弃电率时,NBI算法和NSGA-Ⅱ可分别使弃电率降低16.65个百分点和15.65个百分点。With the growth of renewable energy and the increase of low carbon demand,alumina industry is facing the challenge of optimizing energy consumption.Targeting industrial production at high renewable energy ratios,it is optimized through electric energy substitution and demand response.In this paper,other green power real-time regulation is taken as the object of demand response,and a multi-objective demand response model of alumina production electricity consumption is established on the basis of guaranteeing that the rate of wind and light abandonment is minimized,and with the goal of satisfying the system economy and guaranteeing the output.The normal boundary intersection(NBI)method and nondominated sorting genetic algorithmⅡ(NSGA-Ⅱ)are used to optimize and solve the model.According to the analysis results of actual cases,the NBI algorithm performs better in reducing the electricity cost and the rate of power abandonment,with a cost reduction of 73%and a rate of power abandonment of 16.65 percentage points,compared to 70%and 15.65 percentage points,respectively,for NSGA-Ⅱ.

关 键 词：需求响应 氧化铝 多目标优化 标准边界交叉(NBI)算法 可再生能源 第二代非支配排序遗传算法(NSGA-Ⅱ) 

分 类 号：R73[医药卫生—肿瘤]