考虑储能功率控制的微电网优化运行研究

发布时间：2018-09-19 08:34

【摘要】：随着我们国家经济的高速发展,对于能源的需求量与日俱增,但同时传统的化石能源煤、石油等储量却日益减少,此时如何高效合理的开发并利用绿色清洁能源变得越来越重要。微电网作为太阳能、风能等绿色清洁能源的有效接入方式,越来越受到全世界的关注。微电网的优化运行能够有效提高资源的利用率,减少各分布式电源的发电成本和NOx、SO2、CO2等污染气体的排放,对系统的经济性、环保性、可靠运行都有着重要的意义。所以,对微电网进行优化运行研究有很大的实用价值和理论意义。目前,微电网的优化运行方法中,最为广泛使用的是根据短期的负荷预测和光伏发电、风力发电等不可控分布式电源的出力预测,而制定的日前经济调度计划。但是,由于负荷预测和不可控分布式电源的出力预测存在误差,从而使得以此制定的发电计划往往与实际值存在一定的偏差。本文以光伏、风机、微型燃气轮机、燃料电池、柴油发电机、蓄电池和超级电容器组成的混合储能微电网为研究对象,构建了微电网优化运行的目标函数,以及功率平衡等约束条件,并运用粒子群算法进行求解。针对粒子群算法易早熟的缺点,本文基于一维正态云构建云变异算子,改进了粒子群算法。针对预测误差对日前经济调度的影响,本文运用t location-scale分布函数对预测误差进行量化估计,并完成净负荷功率公式的修正,运用蓄电池和超级电容器混合储能装置来平衡系统中由于预测误差而引起的功率波动。为了避免储能装置过度充放电以及循环充放电损害其使用寿命,产生额外的经济成本,本文将云模型控制理论应用到储能装置的能量管理中,通过构建三维云模型控制器,对蓄电池和超级电容器的充放电功率进行控制。为了对比研究,本文同时构建了蓄电池单独储能的二维云模型控制器。最后通过仿真分析,验证了采用混合储能方式能够有效降低蓄电池循环充放电的次数,有效保护了储能装置的使用寿命。
[Abstract]:With the rapid development of our national economy, the demand for energy is increasing day by day, but the reserves of traditional fossil energy such as coal and oil are decreasing day by day. At this time, how to develop and utilize green and clean energy efficiently and rationally is becoming more and more important. The optimal operation of micro-grid can effectively improve the utilization rate of resources, reduce the generation cost of distributed generators and the emission of NOx, SO2, CO2 and other pollutants. It is of great significance to the economy, environmental protection and reliable operation of the system. At present, the most widely used method for optimizing the operation of micro-grid is based on short-term load forecasting and the output forecasting of uncontrollable distributed generators such as photovoltaic power generation and wind power generation. In this paper, a hybrid energy storage microgrid consisting of photovoltaic, fan, micro gas turbine, fuel cell, diesel generator, storage battery and super capacitor is studied, and the objective function of optimal operation and power balance of the microgrid are constructed. In order to overcome the shortcoming of the precocity of the particle swarm algorithm, this paper constructs a cloud mutation operator based on one-dimensional normal cloud and improves the particle swarm optimization algorithm. In order to avoid excessive charge and discharge and cyclic charge and discharge of energy storage devices damaging their service life and generating additional economic costs, this paper applies cloud model control theory to energy storage. In the energy management of the device, a three-dimensional cloud model controller is constructed to control the charging and discharging power of the battery and the supercapacitor. In order to compare and study, a two-dimensional cloud model controller is constructed to store the energy separately. Finally, the simulation results show that the hybrid energy storage method can effectively reduce the storage battery. The number of cycle charging and discharging effectively protects the life of the energy storage device.
【学位授予单位】：南京师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM732

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