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微网广义储能系统协调控制策略及容量优化配置方法研究
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摘要
微网可将各类分布式电源、储能装置以及负荷进行有效集成,以实现对局部区域灵活、可靠、经济地供电。微网可靠运行的关键是对其内部各单元进行合理规划设计和协调控制,以达到自我控制、保护和管理的目的。储能系统作为微网的重要组成部分,可通过电能交互达到调节功率分配、维持电能供需平衡、改善电能质量等目的。本文将储能系统的概念扩展至需求侧可控负荷领域,围绕微网广义储能系统协调控制策略及容量优化配置方法开展研究,主要工作如下:
     1、建立了微网系统的稳定性仿真模型,涵盖风机系统、光伏系统、蓄电池/超级电容器储能系统、家居型温控负荷、电力电子变换装置等,为后续各种控制策略及储能系统容量规划的研究奠定了模型基础。
     2、针对微网内间歇性分布式电源并网所引起的并网点电压波动与闪变问题,提出了一种双层电压协调控制策略,通过设定电压阀值实现自动电压控制与蓄电池储能控制的协调工作,可在抑制并网点电压波动的同时,减少蓄电池储能系统的充放电次数及容量需求,具有灵活高效的特点。
     3、针对孤立微网频率调节问题,分别从混合储能系统与家居型温控负荷的角度提出了新的微网频率控制策略:
     1)针对蓄电池与超级电容器构成的混合储能系统,提出了一种基于滞环的频率控制策略(策略I)。该控制策略在传统混合储能滤波控制的基础上,通过引入频率滞环以协调频率控制精度与蓄电池充放电次数间的关系,在保证频率控制精度的前提下,可减小蓄电池的小电流充放电次数。
     2)在策略I基础上,提出频率滞环控制策略II,在保障微网频率控制效果的前提下,综合考虑了蓄电池使用寿命和超级电容器容量两种约束,对两种储能装置进行协调控制,可同时提高混合储能的利用率和使用寿命。
     3)提出了一种适用于家居型温控负荷的变参与度需求侧分散控制策略,通过引入与频率变化幅值成正比的用户参与度,在保障用户用能舒适度、提高频率调节能力的同时,减小了对储能装置的容量需求。与需求侧集中控制策略相比,该控制策略的控制效果和经济性更优。
     4、针对孤立微网储能系统的容量规划问题,提出了一种基于概率模型的混合储能系统容量规划方法。将概率分析与时域仿真有机结合,依据面积准则规划算法来求解孤立微网调频所需混合储能系统容量的最优概率分布,为孤立微网储能系统的容量优化提供了一种可用工具。
Microgrid (MG) is an efficient structure to integrate distributed generators (DGs),energy storage systems (ESS) and loads, which can supply flexible, reliable andeconomic power to local customers. The key point to the reliable operation of MG isto achieve self-control, protection and management by reasonable planning andcoordination control of its internal units. As an important part of MG, ESS can adjustthe power distribution, maintain power balance and improve the power quality. In thisdissertation, ESS is extended to include the load demand response in MG. And, mainworks of this dissertation focus on its coordination control and capacity optimizationas follows:
     1. Various DG models suitable for stability simulation of MG are developed,including wind turbine, solar array, battery, supercapacitor, family-friendlycontrollable loads and power electronic converters. These models will be used in thefollowing studies.
     2. A double-layer voltage control strategy is developed to mitigate the voltagefluctuations and flickers at the gird-connected point (GCP) in MGs. It coordinates theautomatic voltage control and the battery energy storage control (BESC) by setting avoltage switching threshold so as to mitigate the voltage fluctuations and flickers atthe GCP and reduce the charging/discharging cycles of battery energy storage system(BESS) at the same time.
     3. According to the frequency issue of autonomous MG (AMG), novel frequencyregulation strategies are developed from the views of hybrid energy storage system(HESS) and family-friendly controllable loads.
     1) A frequency regulation strategy based on a frequency hysteresis loop isdeveloped to the battery-supercapacitor HESS (abbr. strategy I). Hysteresis loop isintroduced to the control strategy, and frequency regulation accuracy and BESS’scharging/discharging cycles are coordinated so as to avoid some small, meaninglesscharging/discharging actions.
     2) Based on strategy I, a new frequency regulation strategy (abbr. strategy II) isdeveloped. Priorities of BESS and supercapacitor are optimized in the strategy.Utilization ratio and service life of the battery-supercapacitor HESS can also beimproved while maintain the frequency control accuracy.
     3) A novel decentralized demand control (DDC) strategy for family-friendlycontrollable loads is proposed to regulate the AMG’s frequency in coordination with ESS, in which a variable participation degree proportional to the frequency deviationis introduced. DDC strategy not only can guarantee the customer comforts andimprove the capability of frequency regulation, but also can reduce the ESS’s capacity.Compared with the centralized based demand-side control methods, DDC strategy cansignificantly reduce the investment cost and improve the frequency control efforts.
     4. A probabilistic capacity optimization method is developed to determine theoptimal capacity of HESS in AMG. It combines the probability analysis andtime-domain simulation to generate probability distributions of HESS. Optimalobjective is obtained using a new area planning algorithm.
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