智能光伏微网的能量优化管理方法研究
发布时间：2018-07-20 12:00
【摘要】：进入21世纪以来,具有能量自治管理与控制能力的光伏微网作为可再生能源利用的主要形式之一,得到了迅速发展。但由于光伏微网的电源单一,出力波动大,且配置的储能容量一般也比较有限,较小的电源或负荷波动都会对其优化运行调度带来较大影响。因此,必须针对光伏微网自身的特点,建立适用于光伏微网安全稳定运行的能量优化调度模型,并研究相应的求解方法进行仿真分析,具有重要的理论和工程意义。本文首先介绍了包含分布式电源的微网结构,并从微网优化调度策略、优化调度模型及其模型求解算法这三个方面阐述关于微网能量优化调度的研究历史与研究现状,在此基础上,重点研究了考虑需求需求侧管理(demand side managment,DSM)的多目标优化调度问题、考虑预测误差的随机优化调度问题和多时间尺度的光伏微网随机优化调度问题。主要的研究工作为以下3个部分:(1)在考虑需求侧管理的光伏微网多目标优化调度问题中。首先对典型光伏微网的运行状态进行了简要分析,并针对考虑电动汽车参与光伏微网能量优化调度与不考虑电动汽车参与时对系统运行经济性的影响,分别建立了不包含电动汽车充电的、及包含电动汽车充电的优化调度模型,并综合考虑功率平衡、储能系统荷电状态、负荷可转移的时间范围、电动汽车的充电时间等约束条件,提出了基于非支配排序遗传算法(NSGA-Ⅱ)的优化求解方法。仿真结果分析表明了该方法的可行性和有效性,且考虑需求侧管理和电动汽车充电对于提高系统运行经济性效果显著。(2)在考虑预测误差的随机优化调度问题中,针对光伏发电功率及负荷功率的预测精准度对系统安全经济运行的影响,采用概率密度分布函数对系统运行中的不确定因素进行模拟,综合考虑实时电价、实时旋转备用电价、系统旋转备用约束等约束条件,然后提出了一种基于机会约束规划理论的并网型光伏微电网优化调度模型。其中引入了随机变量和机会约束,不易直接求解,但应用蒙特卡罗模拟与遗传算法相结合的混合算法可将机会约束规划模型转化为确定性问题进行求解,为含不确定性因素的光伏微网系统经济调度问题提供了一个有效方法。(3)在多时间尺度的光伏微网随机优化调度问题中,考虑到由于可再生能源及负荷的预测误差而造成的日前调度和实时调度的偏差,提出了一种考虑需求侧管理的多时间尺度的光伏微网随机优化调度模型,以减轻由光伏和负荷侧的预测误差引起的功率波动。在超短期日前光伏和负荷功率预测的基础上,基于机会约束规划理论建立日前的经济调度模型,并在实时调度时采用实时的功率调整策略对日前的调度方案进行修改。仿真结果分析表明通过此种多时间尺度的调度方法可以有效补偿系统实时运行的功率波动,保证系统运行的安全可靠性,提高了方案的可行性。
[Abstract]:Since the beginning of the 21st century, photovoltaic microgrids, which have the ability to manage and control energy autonomously, have been developed rapidly as one of the main forms of renewable energy utilization. However, because of the single power supply of the photovoltaic microgrid, the output force fluctuates greatly, and the energy storage capacity of the configuration is generally limited, the smaller power supply or load fluctuation will have a great impact on the optimal operation and scheduling of the photovoltaic microgrid. Therefore, according to the characteristics of photovoltaic microgrid, it is necessary to establish an energy optimal scheduling model suitable for the safe and stable operation of photovoltaic microgrid, and to study the corresponding solution method for simulation and analysis, which has important theoretical and engineering significance. This paper first introduces the microgrid structure including distributed power supply, and expatiates the history and research status of microgrid energy optimal scheduling from three aspects: microgrid optimal scheduling strategy, optimal scheduling model and its model solving algorithm. On this basis, the multi-objective optimal scheduling problem with demand-side management (demand side), stochastic optimal scheduling problem with prediction error and stochastic scheduling problem with multiple time scales for photovoltaic microgrid are studied. The main research work is as follows: (1) in the multi-objective scheduling problem of photovoltaic microgrid considering demand-side management. Firstly, the operation state of typical photovoltaic microgrid is briefly analyzed, and the effect of considering the energy optimization of photovoltaic microgrid and not considering the participation of electric vehicle on the operation economy of the system is discussed. The optimal scheduling models without electric vehicle charging and with electric vehicle charging are established, and the power balance, the charging state of energy storage system and the time range of load transfer are considered synthetically. Based on the charging time constraints of electric vehicles, an optimization method based on the non-dominated sorting genetic algorithm (NSGA- 鈪，
本文编号：2133447
[Abstract]:Since the beginning of the 21st century, photovoltaic microgrids, which have the ability to manage and control energy autonomously, have been developed rapidly as one of the main forms of renewable energy utilization. However, because of the single power supply of the photovoltaic microgrid, the output force fluctuates greatly, and the energy storage capacity of the configuration is generally limited, the smaller power supply or load fluctuation will have a great impact on the optimal operation and scheduling of the photovoltaic microgrid. Therefore, according to the characteristics of photovoltaic microgrid, it is necessary to establish an energy optimal scheduling model suitable for the safe and stable operation of photovoltaic microgrid, and to study the corresponding solution method for simulation and analysis, which has important theoretical and engineering significance. This paper first introduces the microgrid structure including distributed power supply, and expatiates the history and research status of microgrid energy optimal scheduling from three aspects: microgrid optimal scheduling strategy, optimal scheduling model and its model solving algorithm. On this basis, the multi-objective optimal scheduling problem with demand-side management (demand side), stochastic optimal scheduling problem with prediction error and stochastic scheduling problem with multiple time scales for photovoltaic microgrid are studied. The main research work is as follows: (1) in the multi-objective scheduling problem of photovoltaic microgrid considering demand-side management. Firstly, the operation state of typical photovoltaic microgrid is briefly analyzed, and the effect of considering the energy optimization of photovoltaic microgrid and not considering the participation of electric vehicle on the operation economy of the system is discussed. The optimal scheduling models without electric vehicle charging and with electric vehicle charging are established, and the power balance, the charging state of energy storage system and the time range of load transfer are considered synthetically. Based on the charging time constraints of electric vehicles, an optimization method based on the non-dominated sorting genetic algorithm (NSGA- 鈪，
本文编号：2133447
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