配电系统中电动汽车与风电的协同调度算法研究

发布时间：2018-11-21 17:18

【摘要】：风力发电作为可再生能源的主力,正在受到越来越多国家的关注。风电功率输出具有波动性和随机性,大规模风电的接入将会给电网的调度与控制带来难度,有时为保证电网的安全运行不免会有“弃风”行为。电动汽车的发展近年来受到很多国家的高度重视,规模化发展会对电网负荷特性产生影响。电动汽车作为分散式的大容量储能设备,如果对其充放电行为进行优化调度,通过电动汽车、风电以及常规负荷之间的协调,不仅可以起到增加风电消纳的作用,而且还可以拉平电网的等效负荷,提高电网运行的安全性与经济性。可以预见,未来将有大量的电动汽车接入到电网。在这种情况下如果依旧采用传统的直接调度方式可能会存在“维数灾”的问题。另外一种可行思路,即调度策略通过电价来体现,电动汽车用户基于电价激励自动响应调度。基于上述背景,本文提出了一种计及风电接入和车主互动意愿的两实时电价优化调度策略,两实时电价具体包括：日前电价和实时电价。论文主要对电动汽车日前电价以及电动汽车实时电价的计算方法进行了研究,具体研究内容包括：(1)电动汽车充电站负荷建模。电动汽车充电站的负荷建模是实现整个调度策略的重要环节,本文采用概率分布的方式对影响电动汽车充电站负荷的参数进行描述,并且以小区充电站为例,采用蒙特卡罗法对充电站的充电负荷进行了模拟。(2)电动汽车日前电价计算方法。首先,建立了考虑机组组合和电动汽车V2G直接成本的电动汽车日前电价计算模型；然后,将优化模型等效为机组组合模型,采用自适应遗传算法进行求解。(3)电动汽车实时电价计算方法。由于电动汽车的充电功率由充电桩控制,并且该装置不同于发电机(无转子惯性效应),所以具有较快的功率调节速度。因此,本文考虑由电动汽车和发电机组共同承担补偿风电功率预测误差的任务。首先,建立了以电力公司运营经济性最优为目标的数学模型,并且将电动汽车用户的互动意愿率纳入考虑；然后,采用优化算法逐个时段修正电动汽车的充电功率以及各发电机组的有功出力；最后,通过对日前电价进行修正形成实时电价的方式,间接地向电动汽车用户支付相应的服务费用。(4)仿真算例。本文搭建了风电装机容量占总装机容量7.69%情况下的IEEE-30节点区域电网仿真模型,通过算例仿真验证所提出的电动汽车与风电的协同调度策略是否能够降低风电功率波动对电力系统的影响,以及提高电网运行的经济性。
[Abstract]:Wind power generation as the main renewable energy, is attracting more and more countries' attention. Wind power output is fluctuating and random. The access of large-scale wind power will bring difficulty to the dispatching and control of power network, and sometimes "abandon wind" behavior will occur in order to ensure the safe operation of power grid. In recent years, the development of electric vehicles has been attached great importance to by many countries, and the large-scale development will have an impact on the load characteristics of power grid. As a distributed large capacity energy storage equipment, if the charge and discharge behavior of electric vehicle is optimized, through the coordination of electric vehicle, wind power and conventional load, it can not only increase the wind power consumption, It can also level the equivalent load of the power network and improve the security and economy of the network operation. It can be predicted that a large number of electric vehicles will be connected to the power grid in the future. In this case, if the traditional direct scheduling method is still adopted, there may be the problem of "dimension disaster". Another feasible idea is that the dispatching strategy is reflected by the electricity price, and the electric vehicle user automatically responds to the dispatch based on the electricity price incentive. Based on the above background, this paper proposes an optimal scheduling strategy for two real time electricity prices, which includes: day price and real time price, taking into account wind power access and owner's interactive desire. This paper mainly studies the calculation method of electric vehicle price before day and real time price of electric vehicle. The specific research contents include: (1) load modeling of electric vehicle charging station. The load modeling of electric vehicle charging station is an important link to realize the whole dispatching strategy. In this paper, the parameters that affect the load of charging station of electric vehicle are described by probability distribution, and the charging station in residential area is taken as an example. Monte-Carlo method is used to simulate the charging load of charging station. (2) the method of calculating electric vehicle price before day. Firstly, the pre-day electric price calculation model of electric vehicle considering the direct cost of unit combination and electric vehicle V2G is established. Then, the optimization model is equivalent to the unit combination model and solved by adaptive genetic algorithm. (3) the real-time electric vehicle price calculation method. Because the charging power of electric vehicle is controlled by charging pile, and the device is different from generator (no rotor inertia effect), it has faster power regulation speed. Therefore, the task of compensating wind power prediction error by electric vehicle and generator is considered in this paper. Firstly, a mathematical model aiming at optimal operation economy of electric power company is established, and the interactive willingness rate of electric vehicle users is taken into account. Then, the optimal algorithm is used to correct the charging power of the electric vehicle and the active power output of each generator unit. Finally, by modifying the electricity price before the day to form the real-time electricity price, the corresponding service cost is indirectly paid to the electric vehicle users. (4) A simulation example. In this paper, the simulation model of IEEE-30 node area power network with 7.69% of total installed capacity of wind power plant is built. The simulation results show that the proposed cooperative scheduling strategy of electric vehicle and wind power can reduce the influence of wind power fluctuation on power system and improve the economy of power grid operation.
【学位授予单位】：东南大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM73

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