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

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1114 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

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。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘宝勇
	林依桐
	唐亮
	史洁
	张昕雪

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）

Abstract：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 algorithm Ⅱ (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.

Key words： demand response alumina multi-objective optimization normal boundary intersection (NBI) method renewable energy nondominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ)

收稿日期: 2024-08-28

基金资助:济南市“高校20条”项目（2021GXRC113）

作者简介: 刘宝勇（1975—）,男,高级工程师,研究方向为新能源场站优化运行、太阳能风能资源测量与评估。

引用本文:

刘宝勇, 林依桐, 唐亮, 史洁, 张昕雪. 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[J]. 电气技术, 2025, 26(2): 26-34. LIU Baoyong, LIN Yitong, TANG Liang, SHI Jie, ZHANG Xinxue. Research on multi-objective optimal scheduling method of green power and low carbon aluminum based synergy considering electric energy substitution and demand response. Electrical Engineering, 2025, 26(2): 26-34.

链接本文:

https://dqjs.cesmedia.cn/CN/Y2025/V26/I2/26

[1] PARASCHIV S, PARASCHIV L S.Trends of carbon dioxide (CO₂) emissions from fossil fuels combustion (coal, gas and oil) in the EU member states from 1960 to 2018[J]. Energy Reports, 2020, 6: 237-242.
[2] IEA. Industry: tracking report2022[EB/OL].[202412]. https://www.iea.org/topics/industry.
[3] IPCC. Climate change2023: synthesis report[EB/OL].[202412]. https://www.ipcc.ch/report.
[4] TSOUMALIS G I, BAMPOS Z N, CHATZIS G V, et al.Overview of natural gas boiler optimization tech- nologies and potential applications on gas load balancing services[J]. Energies, 2022, 15(22): 8461.
[5] 梁珩, 黄耕, 侯宾, 等. 工业用户连续参与需求响应的用户基线负荷精准计算方法[J]. 中国电力, 2024, 57(3): 34-42.
[6] 王长浩, 高红均, 周文毅, 等. 考虑准线需求响应的高比例新能源电力系统调度优化[J]. 电网技术, 2024, 48(11): 4427-4435.
[7] 袁坤龙, 张少康, 常冉, 等. 阶梯式碳交易机制下计及电-气-热综合能源系统需求响应优化运行[J]. 电气技术, 2024, 25(1): 8-16.
[8] 王昀, 谢海鹏, 孙啸天, 等. 计及激励型综合需求响应的电-热综合能源系统日前经济调度[J]. 电工技术学报, 2021, 36(9): 1926-1934.
[9] 徐成司, 董树锋, 华一波, 等. 基于改进一致性算法的工业园区分布式综合需求响应[J]. 电工技术学报, 2022, 37(20): 5175-5187.
[10] 李智轩, 李嘉丰, 叶晓华, 等. 基于Copula函数与多目标进化算法的负荷区间预测[J]. 电气技术, 2024, 25(6): 24-30.
[11] 边晓燕, 史越奇, 裴传逊, 等. 计及经济性和可靠性因素的区域综合能源系统双层协同优化配置[J]. 电工技术学报, 2021, 36(21): 4529-4543.
[12] 温春雪, 赵天赐, 于赓, 等. 基于改进粒子群算法的储能优化配置[J]. 电气技术, 2022, 23(10): 1-9, 58.
[13] CAPONE M, GUELPA E, VERDA V.Multi-objective optimization of district energy systems with demand response[J]. Energy, 2021, 227: 120472.
[14] 马光宇. 钢铁联合企业电力系统分析与优化研究[D]. 沈阳: 东北大学, 2014.
[15] 张钦, 王锡凡, 王建学, 等. 电力市场下需求响应研究综述[J]. 电力系统自动化, 2008, 32(3): 97-106.
[16] 刘澧庆, 吴宁, 张焕亨, 等. 微电网经济型二次频率和电压控制的多目标优化模型及仿真验证[J]. 电网技术, 2019, 43(2): 521-529.