计及网络安全约束和需求侧低碳资源的机组组合研究

发布时间：2018-04-11 13:57

本文选题：机组组合 + 需求侧低碳资源　；参考：《北京交通大学》2016年硕士论文

【摘要】：机组组合问题是电力系统规划和运行领域的重要课题,对系统安全稳定、可靠经济运行具有重要意义。在现代大规模电力系统中,发电电源的结构逐步趋向于多元化,出现了多种类型电源并存的局面。机组组合是实现在电力系统短期运行中,对各种发电资源和能源结构性优化的核心环节,它使电力系统能够应对未来负荷的不确定性和可再生能源发电的随机性,满足系统的调峰、调频和备用需求,为电力系统的经济调度和安全校核提供基础。随着环境问题的日益严峻,为了兼顾环境问题,减少含碳、含硫和含氮等气体的排放,电力系统中低碳资源的渗透率逐渐增大,特别是近年来电动汽车和分布式光伏发展迅速,将会出现电动汽车和分布式光伏发电在需求侧大规模并网的状况,这势必会影响到机组组合问题。因此,机组组合模型必然要在综合考虑多种需求侧低碳资源的基础上做出相应改进,使其适应电力系统多元化结构的发展。本文以数学优化理论为依据和指导,一方面建立了综合考虑电动汽车、分布式光伏发电和需求响应等需求侧低碳资源的新型机组组合模型,同时在模型中计及网络安全约束,并对机组组合模型的求解算法进行了研究：另一方面,研究了以经济效益和环境效益为目标的模糊双目标机组组合优化问题。首先,结合本文所考虑的多种需求侧资源的特性,对传统机组组合模型的目标函数和常规约束条件进行了修改,同时添加了网络安全约束和需求侧资源的渗透率约束。在经济效益目标和环境效益目标的基础上,根据模糊解法建立了可计及目标相对优先级的模糊双目标机组组合优化模型。其次,考虑到需求侧资源以及系统负荷都存在不确定性,本文将可信性理论引入到机组组合模型中,对系统功率平衡约束和旋转备用约束进行模糊机会处理,建立了模糊机会约束机组组合数学模型。接着,根据粒子群优化算法和混合蛙跳算法在复杂函数寻优上易于陷入局部最优值的缺点,分别对PSO和SFLA进行一定程度改进的基础上,引入协同进化机制,将PSO与SFLA结合起来,两个种群采取不同的进化方式,以提高种群的多样性和增强算法的全局搜索能力和收敛速度,提出了一种基于协同进化机制的粒子群和混合蛙跳融合算法(CPSO-SFLA)。最后,分别以10机系统、IEEE 6节点3机系统和IEEE 118节点54机系统进行了算例仿真,通过仿真验证了本文模型和算法的有效性。
[Abstract]:The unit combination problem is an important subject in the field of power system planning and operation, which is of great significance to the safe and stable, reliable and economic operation of the system.In modern large-scale power system, the structure of power supply tends to be diversified gradually, and many types of power supply coexist.The unit combination is the key link to realize the structural optimization of various generation resources and energy in the short-term operation of power system. It enables the power system to cope with the uncertainty of future load and the randomness of renewable energy generation.It meets the demand of peak shaving, frequency modulation and backup, and provides the foundation for economic dispatch and safety check of power system.With the increasingly serious environmental problems, in order to take into account environmental problems and reduce emissions of carbon, sulfur and nitrogen, the permeability of low carbon resources in power systems is gradually increasing, especially the rapid development of electric vehicles and distributed photovoltaic in recent years.There will be a large-scale grid connection of electric vehicles and distributed photovoltaic power generation on the demand side, which will inevitably affect the unit combination problem.Therefore, the unit commitment model must be improved on the basis of comprehensive consideration of a variety of demand side low-carbon resources, so as to adapt to the development of power system diversification structure.Based on the theory of mathematical optimization, on the one hand, a new type of unit combination model considering the demand side low carbon resources, such as electric vehicle, distributed photovoltaic generation and demand response, is established, and the network security constraints are taken into account in the model.On the other hand, the fuzzy dual-objective unit combination optimization problem with economic and environmental benefits as the goal is studied.Firstly, combined with the characteristics of various demand-side resources considered in this paper, the objective function and conventional constraints of the traditional unit commitment model are modified, and the network security constraints and the demand-side resource permeability constraints are added at the same time.Based on the objective of economic benefit and environmental benefit, a fuzzy dual-objective unit combination optimization model considering the relative priority of target is established by fuzzy method.Secondly, considering the uncertainty of demand side resources and system load, the credibility theory is introduced into the unit model to deal with the system power balance constraints and rotational standby constraints.The mathematical model of fuzzy chance constrained unit is established.Then, based on the shortcomings of particle swarm optimization and hybrid leapfrog algorithm, which are easy to fall into local optimal value on complex function optimization, the co-evolution mechanism is introduced to combine PSO and SFLA on the basis of improving PSO and SFLA to some extent, respectively.In order to improve the diversity of population and enhance the global search ability and convergence speed of the two populations, a new algorithm based on coevolutionary mechanism, particle swarm optimization and hybrid leapfrog fusion algorithm, is proposed in this paper.At last, the simulation examples of 10-machine system with IEEE 6-bus 3-machine system and IEEE 118-bus 54-machine system are given, and the validity of the proposed model and algorithm is verified by simulation.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM73

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