高寒地区综合智慧供热平台建设及应用分析
Construction and application analysis of Hailar's comprehensive intelligent heating platform
张亮 1白烨 2佟吉日木图 1蔡浩飞 2郭鑫 1于微 2吴燕玲3
作者信息
- 1. 呼伦贝尔安泰热电有限责任公司海拉尔热电厂,内蒙古 呼伦贝尔 021000
- 2. 中国华能集团清洁能源技术研究院有限公司,北京 102209
- 3. 浙江大学,浙江 杭州 310027
- 折叠
摘要
为了推进高寒地区供热系统的智能化与低碳化,围绕海拉尔智慧供热平台的构建与实施,本文研究分析了智慧供热在极端寒冷环境中面临的挑战与潜在机遇,并对海拉尔智慧供热平台进行了实际运行效果的评估.通过数据分析方法探讨了平台在不同温度条件下的性能,重点关注供回水温度的精准控制、能耗的优化以及在热网波动和负荷变化下的稳定运行.研究结果表明,该平台在-30～-10℃的不同温度工况下均展现出卓越的运行效率和稳定性,尤其在极端温度下,热源调度模拟与实测值高度吻合,并表现出较低的误差.研究突显了智慧供热平台在提升高寒地区供热系统效率、降低能耗方面的显著作用,并为供热行业的智能化、低碳化发展提供了宝贵的实践经验和策略指导.
Abstract
To promote the intelligence and decarbonization of the heating system in high-cold regions,around the construction and implementation of the Hailar intelligent heating platform,this paper researches and analyzes the challenges and potential opportunities of intelligent heating in the extreme cold environment,and evaluates the actual operation effect of the Hailar intelligent heating platform.Using data analysis methods,the platform's performance under various temperature conditions is investigated,with a particular emphasis on the precise control of supply and return water temperatures,optimization of energy consumption,and stable operation amid fluctuations in the heating network and load changes.The results indicate that the platform demonstrates exceptional operational efficiency and stability across different temperature scenarios ranging from-30～-10℃,especially under extreme temperatures,where the simulation of heat source scheduling closely aligns with actual measurements,exhibiting low error margins.This research highlights the significant role of the intelligent heating platform in enhancing the efficiency of heating systems in high-cold regions,reducing energy consumption,and providing valuable practical experience and strategic guidance for the intelligent and low-carbon development of the heating industry.
关键词
高寒地区/智慧供热/节能降耗/综合管理平台Key words
high-cold region/intelligent heating/energy saving and consumption reduction/integrated management platform引用本文复制引用
出版年
2024
NETL
NSTL
万方数据