冷热电联供型微电网多目标动态优化调度

发布时间：2018-03-01 17:19

本文关键词： 冷热电联供微电网优化调度需求侧响应　出处：《杭州电子科技大学》2017年硕士论文　论文类型：学位论文

【摘要】：冷热电联供型微电网系统利用冷热电联供机组的能源阶梯高效利用的特点,并整合了其他分布式发电单元,是一种理想的微电网组成形式。它具有运行模式多样、能源利用率高、控制灵活、供电可靠性高以及环境污染小等特点,受到各国的广泛重视,已经成为第二代能源技术的重点研究对象。本文主要对并网冷热电联供型微电网日前多目标优化调度问题进行了研究。本文首先对组成冷热电联供型微电网系统的单元模型进行了详细的介绍与分析,随后以系统的运行成本和污染物排放治理费用最低为目标,选取系统中的可控设备如微型燃气轮机、蓄电池、蓄热槽的出力作为优化变量,建立了基于Pareto支配关系的冷热电联供型微电网多目标优化调度数学模型。对系统中的模型以及运行过程中的约束条件进行了分类,并根据不同约束种类给出了不同的解决方案。针对多目标遗传算法和粒子群算法的优缺点,提出基于优化粒子引导的多目标粒子群优化算法。最后,以某一居民小区日常能源供应为例,分别给出了夏季和冬季典型日优化调度问题的Pareto前沿。分析了系统运行成本最低和污染物排放治理费用最低时系统的调度运行策略。并与夏季典型日下以“以热定电”、“以电定热”方式以及分时电价模式下系统的调度策略进行了对比分析。研究表明使用本文的冷热电联供型微电网多目标动态优化调度方法,不仅可根据不同目标的侧重灵活选取调度方案,而且还可进一步降低系统的运行成本和环境成本。考虑到用户侧可平移负荷的响应,可先对负荷进行优化。本文制定了与电价成反比例关系的目标负荷,并建立了消毒柜、洗衣机、热水器三种可平移负荷的数学模型。本文提出了一种“以商定量,以余定度”的方法确定系统的平移策略,使平移后的负荷曲线和制定的目标负荷尽可能相似。在平移后负荷数据的基础上,对冷热电联供型微电网进行了多目标日前优化调度,结果表明系统的运行费用得到了进一步降低。
[Abstract]:Based on the characteristics of high efficiency utilization of the energy ladder of the combined cooling and heat generating unit and integrating other distributed generation units, the combined cooling and heat supply microgrid system is an ideal microgrid form. It has a variety of operation modes. The characteristics of high energy efficiency, flexible control, high reliability of power supply and small environmental pollution have received extensive attention from various countries. It has become the focus of the second generation energy technology. This paper mainly studies the multi-objective optimal dispatching problem of the combined cold and heat combined microgrid. Firstly, this paper studies the composition of the cold and heat combined micro-grid system. The unit model is introduced and analyzed in detail, Then taking the lowest operating cost and pollutant discharge treatment cost of the system as the target, the output of controllable equipment such as micro gas turbine, storage battery and heat storage tank is selected as the optimization variable. The mathematical model of multi-objective optimal dispatching for the combined cooling and heat power microgrid based on Pareto dominating relationship is established. The system models and the constraints in the operation process are classified. According to different kinds of constraints, different solutions are given. Aiming at the advantages and disadvantages of multi-objective genetic algorithm and particle swarm optimization, a multi-objective particle swarm optimization algorithm based on particle guidance optimization is proposed. Take the daily energy supply in a residential district as an example. In this paper, the Pareto frontier of the typical daily optimal scheduling problem in summer and winter is given, respectively. The scheduling strategies of the system with the lowest operating cost and the lowest pollutant emission treatment cost are analyzed. The scheduling strategy of the system under the mode of fixed electricity, fixed heat and time-sharing electricity price is compared and analyzed. The research shows that the multi-objective dynamic optimal dispatching method of the micro-grid based on combined cooling and heat supply is used in this paper. Not only can the scheduling scheme be flexibly selected according to different objectives, but also the operating cost and environmental cost of the system can be further reduced, taking into account the response of the translatable load on the user side. First, the load can be optimized. In this paper, the target load which is inversely proportional to the electricity price is established, and the mathematical model of three transportable loads of disinfector, washing machine and water heater is established. The translation strategy of the system is determined by the method of residual degree, so that the load curve after translation is similar to the target load as much as possible. On the basis of load data after translation, the multi-objective pre-day optimal dispatching is carried out for the micro-grid with combined cooling and heat supply. The results show that the operating cost of the system has been further reduced.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73

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