含可再生能源与电动汽车的微网系统调度优化研究

发布时间：2018-08-02 17:21

【摘要】：电动汽车产业的蓬勃发展为电网的发展带来了机遇与挑战,电动汽车在减少化石燃料燃烧,节能减排方面具有明显优势,然而大量无序接入电网的电动汽车会对电网的稳定运行带来巨大影响如电压变化、电能质量变差、谐波污染等;另一方面电动汽车的电池具有储能功能,通过先进合理的控制策略管理电动汽车的充放电行为可以减少电动汽车接入对电网的影响甚至提高电网运行的经济性和稳定性。本文利用鲁棒优化算法对含可再生能源和电动汽车的微网系统调度策略进行研究,通过控制电动汽车以及可再生能源在微网中协调互动在保证电网稳定运行的前提下,达到提高经济性的目的。本文创新点可以总结如下:1.将鲁棒优化的方法应用于电动汽车在含可再生能源的微网系统有序充电策略中,在调度模型中以预测区间来描述可再生能源出力以及电动汽车充电功率的不确定性,使调度策略对于不确定变量在其区间内变化时具有较好的鲁棒性。2.针对鲁棒优化理论的“过度保守”导致模型经济性较差的问题,引入“鲁棒系数”的概念对前文所提传统鲁棒优化进行改进,使得调度策略在经济性和鲁棒性中寻找折中,为决策者提供调度理论依据。并将改进的鲁棒优化应用于微网的电动汽车与可再生能源协同调度中,利用电动汽车的V2G功能,使得电动汽车在可再生能源丰富的时段充电而在可再生能源匮乏时段将电能回馈给电网,达到增强微网经济性的同时保证微网的稳定性。3.针对鲁棒优化算法在微网协同优化模型应用中面临的模型复杂度增加问题,本文使用基于电动汽车到达时间的分类调度算法,将有相似充/放电行为的电动汽车进行分组统一调度,使得算法的复杂度不随电动汽车数量的增加而增加,提高了算法求解效率。4.鲁棒优化理论应用于含有电动汽车的V2G网络的研究还处于初级阶段,本文对如何利用鲁棒优化解决V2G网络能量调度问题进行探索,提出V2G应用的三种场景,并针对每种场景问题的构成提出相对应的鲁棒优化解决方法。为学者运用鲁棒优化解决V2G能量调度问题提供参考。
[Abstract]:The vigorous development of electric vehicle industry brings opportunities and challenges to the development of power grid. Electric vehicles have obvious advantages in reducing fossil fuel burning, energy saving and emission reduction. However, a large number of unordered electric vehicles connected to the power grid will have a great impact on the stable operation of the power grid, such as voltage changes, power quality deterioration, harmonic pollution, etc. On the other hand, the batteries of electric vehicles have the function of energy storage. The charging and discharging behavior of electric vehicles can be managed by advanced and reasonable control strategy, which can reduce the influence of electric vehicle access on the power grid and even improve the economy and stability of power grid operation. In this paper, the robust optimization algorithm is used to study the scheduling strategy of microgrid system with renewable energy and electric vehicle. By controlling the electric vehicle and the renewable energy in the microgrid, the coordination and interaction of the electric vehicle and the renewable energy in the microgrid can ensure the stable operation of the power grid. To achieve the purpose of improving economy. The innovations of this article can be summarized as follows: 1. The robust optimization method is applied to the ordered charging strategy of electric vehicles in microgrid systems with renewable energy. The prediction interval is used to describe the uncertainty of the renewable energy output and the charging power of electric vehicles in the scheduling model. The scheduling policy is robust to the uncertain variables changing in its interval. 2. In view of the problem that the model economy is poor due to the "excessive conservatism" of the robust optimization theory, the concept of "robust coefficient" is introduced to improve the traditional robust optimization mentioned in the previous paper, so that the scheduling strategy can find a compromise between economy and robustness. It provides a theoretical basis for decision makers. The improved robust optimization is applied to the cooperative scheduling of micro grid electric vehicle and renewable energy, and the V2G function of electric vehicle is utilized. The electric vehicle is charged in the renewable energy rich period, and the electric energy is fed back to the power network during the renewable energy shortage period, which can enhance the economic efficiency of the microgrid and ensure the stability of the microgrid. 3. Aiming at the problem of increasing the complexity of robust optimization algorithm in the application of microgrid cooperative optimization model, this paper uses a classification scheduling algorithm based on the arrival time of electric vehicles. In order to improve the efficiency of the algorithm, the complexity of the algorithm does not increase with the increase of the number of electric vehicles. The application of robust optimization theory to V2G networks with electric vehicles is still in its infancy. This paper explores how to solve the energy scheduling problem of V2G networks by robust optimization, and proposes three scenarios for V2G applications. A corresponding robust optimization solution is proposed for each scenario problem. It provides a reference for scholars to solve V2G energy scheduling problem by robust optimization.
【学位授予单位】：华北电力大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM73

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