集中供热管网动态系统的分布式预测优化策略研究

发布时间：2018-11-06 15:10

【摘要】：集中供热节能环保,是城市建设发展的基础设施之一,目前供暖的主要形式,集中供热系统的好坏直接影响着人民生活质量的高低。而供热系统又是一个复杂多变量的大系统,这无疑加大了运行控制的难度。随着人们环保意识的增强和供热需求的提高,供热运行优化策略研究便提上日程,以更好的解决热力失调问题,提高供热品质。与此同时,为了提高企业效率和社会经济效益,采用现代化的技术手段,实现供热运行的综合化监控、管理、优化势在必行。本文依据能量守恒定律和供热设备机理,推导出供热运行各组成部分物理模型,并采用最小二乘法结合实际响应曲线的方法对机理模型进行参数辨识,最终建立了供热质调节、量调节过程模型,实现了供热系统的动态建模。运行优化作为集中供热的热点问题在控制领域一直备受关注,合理可行的优化策略才能最大限度的发挥集中供热节能降耗的优点。本文在MATLAB环境下对供热运行过程的优化进行了探索,通过几种控制策略的仿真比较,结果显示分布式预测控制策略更有效。但耦合强弱不同时,基于纳什的分布式预测控制最优解并不一定收敛于帕累托最优解,所以,本文在合作博弈论的基础上提出了分布式预测控制策略的改进算法,使得在不同耦合状况下,改进策略最优解收敛于集中控制最优解。MATLAB仿真结果表明,改进算法在迭代跟踪过程中表现出良好的控制性能。结合以上策略研究,提出了供热子系统分布式控制,全系统分散控制的供热运行调节方案,在降低集中控制在线实施计算量的同时,易于满足热用户自主调节的需求。为提高供热系统的自动化水平,增强优化策略的可操作性,最大限度的节能降耗,本文最后开发了供热监控优化系统软件。系统从界面设计、系统功能设计以及数据库设计三方面着手,实现了供热管网的实时监控、历史查询、系统优化等功能,为供热运行系统的综合化操作和智能化管理奠定了基础。
[Abstract]:Energy saving and environmental protection of central heating is one of the basic facilities of urban construction and development. At present, the main form of heating, the quality of central heating system directly affects the quality of life of the people. The heating system is a complex multivariable large system, which undoubtedly increases the difficulty of operation control. With the enhancement of people's awareness of environmental protection and the improvement of heating demand, the research on optimization strategy of heating operation is put on the agenda to better solve the problem of thermal imbalance and improve the quality of heating. At the same time, in order to improve the efficiency and social economic benefit of enterprises, it is imperative to adopt modern technical means to realize the comprehensive monitoring, management and optimization of heating operation. According to the law of conservation of energy and the mechanism of heating equipment, the physical model of each component of heating operation is deduced in this paper, and the parameters of the model are identified by the method of least square method combined with the actual response curve, and finally the regulation of heating quality is established. The dynamic modeling of heating system is realized by the model of quantity regulation process. Operation optimization as a hot issue of central heating has been concerned in the field of control, reasonable and feasible optimization strategy can maximize the advantages of central heating energy saving and consumption reduction. In this paper, the optimization of heating operation process in MATLAB environment is explored. The simulation results show that the distributed predictive control strategy is more effective than other control strategies. However, with different coupling, the optimal solution of distributed predictive control based on Nash does not necessarily converge to Pareto optimal solution. Therefore, an improved algorithm of distributed predictive control strategy based on cooperative game theory is proposed in this paper. The optimal solution of the improved strategy converges to the optimal solution of centralized control under different coupling conditions. The MATLAB simulation results show that the improved algorithm performs well in the iterative tracking process. Based on the above strategy research, a scheme of distributed control and decentralized control for heating subsystem is proposed, which can reduce the calculation amount of centralized control on line and meet the demand of heat users' self-regulation easily at the same time. In order to improve the automation level of the heating system, enhance the maneuverability of the optimization strategy, and maximize the energy saving and consumption reduction, the software of the heating monitoring and optimization system is developed in this paper. The system starts from the interface design, the system function design and the database design, realizes the heating network real-time monitoring, the history inquiry, the system optimization and so on function. It lays a foundation for the integrated operation and intelligent management of the heating operation system.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TU995.3

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