冷热电联供优化方法研究与系统实现

发布时间：2018-05-30 15:27

本文选题：冷热电联供 + 联供系统优化　；参考：《电子科技大学》2014年硕士论文

【摘要】：冷热电联供系统是一种建立能源梯级利用的基础之上,它是将能源一次燃烧后的余热等多余能量加以回收利用,形成制冷、供热及发电一体的综合能源供应系统,具有能源利用效率高、环境污染少,生产可控可调等优点,具有广阔的发展前景。各发达国家都在积极发展以冷热电联供系统为代表的分布式能源系统,逐步提高能源的利用效率,缓解能源危机。我国也在大力开展冷热电联供系统应用,在全国范围内建立了数个试点工程。长期以来,冷热电联供系统的优化,大多数都是热力学为基础,进行能源生产端的设备调节和流程优化,无法兼顾消费端的实时消费需求,其优化过程有相当大的局限。本文以信息化为手段,同时考虑客户的消费需求和能源生产的过程优化,并把对能源生产实际效益有重大影响的运营费用考虑在内,建立一个应用于冷热电联供的能源生产优化运营管理系统,为能源企业的生产运营提供参考。本文首先对冷热电联供系统的生产端和消费端进行分析,提出了利用客户的实时消费需求进行能源生产端的反馈调节的优化方法,同时利用多目标优化方法对能源生产端的生产流程进行优化,并最终建立了优化系统的整体架构和各个模块的设计与实现。本文在对生产流程进行详细分析的基础之上,提出了基于负荷预测的冷热电联供优化模型。该模型首先对用户端的历史负荷数据进行分析,找到影响负荷的相关因素,并建立未来一段时间的负荷预测,将负荷预测值送至能源生产端,按照这个负荷值和相关的目标函数对冷热电联供系统的运行策略进行优化。该优化模型主要由两大功能模块:负荷预测和系统运营优化。负荷预测是对用户消费的历史数据进行分析,并考虑到影响用户消费的其它因素,建立模型,对用户未来一个周期的消费数据进行预测。系统运营优化是根据负荷预测得到的数据对能源生产机组进行优化,满足用户的消费需求,并设定以最小运营费用和最小污染物排放量为优化目标,利用Pareto优化方法,得到系统运行的最优解集,按照最优解集里的解对机组设备进行调整。实验证明该优化方法降低了生产成本,提高了环境效益。
[Abstract]:The combined cooling and heat supply system is a kind of integrated energy supply system based on the energy cascade utilization. It is a comprehensive energy supply system which reclaims the excess energy such as the residual heat after the primary combustion of energy and forms an integrated system of refrigeration, heat supply and power generation. It has many advantages, such as high energy efficiency, less environmental pollution, controllable and adjustable production. All the developed countries are actively developing distributed energy systems represented by the combined cooling and heat supply system, gradually improving the efficiency of energy use and alleviating the energy crisis. China is also vigorously developing the application of the combined cooling and heat supply system, and several pilot projects have been established throughout the country. For a long time, most of the optimization of the cooling and heat supply system is based on thermodynamics. The equipment adjustment and process optimization of the energy production end can not take into account the real-time consumption demand of the consumer side, and the optimization process is quite limited. This paper takes informatization as a means, takes into account the customer's consumption demand and the optimization of the energy production process, and takes into account the operating expenses which have a significant impact on the actual benefits of energy production. An energy production optimization operation management system applied to combined cooling and heat supply is established, which provides a reference for the production and operation of energy enterprises. In this paper, firstly, the production and consumer end of the CCHP system is analyzed, and the optimization method of feedback regulation of the energy production terminal is put forward, which is based on the real-time consumption demand of the customer. At the same time, the multi-objective optimization method is used to optimize the production process of the energy production end, and finally, the overall structure of the optimization system and the design and implementation of each module are established. Based on the detailed analysis of the production process, a combined cooling and heat supply optimization model based on load forecasting is proposed in this paper. The model firstly analyzes the historical load data of the user side, finds out the relevant factors that affect the load, and establishes the load forecasting for a period of time in the future, and sends the load forecast value to the energy production end. According to the load value and the related objective function, the operation strategy of the combined cooling and heat supply system is optimized. The optimization model consists of two functional modules: load forecasting and system operation optimization. Load forecasting is to analyze the historical data of consumer consumption and to establish a model to predict the consumption data in a future cycle taking into account other factors that affect the consumption of users. System operation optimization is to optimize the energy production units according to the data from load forecasting to meet the consumer's consumption demand, and set the minimum operating cost and the minimum pollutant emissions as the optimization goal, using the Pareto optimization method. The optimal solution set is obtained and the unit equipment is adjusted according to the solution in the optimal solution set. The experimental results show that the optimization method reduces the production cost and improves the environmental benefit.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM611.3

【参考文献】