面对能源短缺、环境污染、气候变化等人类共同的难题,安全高效、清洁低碳、灵活智能已成为能源电力转型发展的大趋势,而以数据深度利用为特征的智能化技术将是未来电力发展的核心领域。该文在前期研究的基础上,对智能发电系统的概念、...面对能源短缺、环境污染、气候变化等人类共同的难题,安全高效、清洁低碳、灵活智能已成为能源电力转型发展的大趋势,而以数据深度利用为特征的智能化技术将是未来电力发展的核心领域。该文在前期研究的基础上,对智能发电系统的概念、体系架构进行了进一步阐述,从数据应用的角度阐明了智能发电的五大数据化特征:泛在感知(数据获取)、信息融合(数据交互)、智能算法(数据监控)、智能管控(数据决策)、全生命周期管理(数据归档)。提出包括智能发电运行控制系统(intelligent control system,ICS)和智能发电公共服务系统(intelligent service system,ISS)的智能发电系统数据应用架构,在此基础上,给出了与2个系统相对应的数据应用功能。展开更多
通过研究各种分布式电源的发电特性,搭建了含风电、光伏发电、飞轮储能、小水电、微型燃气轮机与负荷的微电网负荷频率控制(Load Frequency Control,LFC)模型,其中小水电和微型燃气轮机为调频机组。将大型互联电网中的集中式自动发电控...通过研究各种分布式电源的发电特性,搭建了含风电、光伏发电、飞轮储能、小水电、微型燃气轮机与负荷的微电网负荷频率控制(Load Frequency Control,LFC)模型,其中小水电和微型燃气轮机为调频机组。将大型互联电网中的集中式自动发电控制(Automatic Generation Control,AGC)原理引入微电网,并结合基于平均报酬模型的多步R(λ)学习算法,提出了一种孤岛运行模式下基于强化学习的AGC控制器,以实现对微网的智能发电控制与频率调整。仿真试验分析表明,与PI控制、Q学习和Q(λ)学习相比,所提出的R(λ)控制器具有快速收敛特性和良好的动态性能以及较强的模型适应性。展开更多
Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable...Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy.Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties.The burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated methods.This paper reviews typical applications of ML and DDC at the level of monitoring,control,optimization,and fault detection of power generation systems,with a particular focus on uncovering how these methods can function in evaluating,counteracting,or withstanding the effects of the associated uncertainties.A holistic view is provided on the control techniques of smart power generation,from the regulation level to the planning level.The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility,maneuverability,flexibility,profitability,and safety(abbreviated as the“5-TYs”),respectively.Finally,an outlook on future research and applications is presented.展开更多
文摘面对能源短缺、环境污染、气候变化等人类共同的难题,安全高效、清洁低碳、灵活智能已成为能源电力转型发展的大趋势,而以数据深度利用为特征的智能化技术将是未来电力发展的核心领域。该文在前期研究的基础上,对智能发电系统的概念、体系架构进行了进一步阐述,从数据应用的角度阐明了智能发电的五大数据化特征:泛在感知(数据获取)、信息融合(数据交互)、智能算法(数据监控)、智能管控(数据决策)、全生命周期管理(数据归档)。提出包括智能发电运行控制系统(intelligent control system,ICS)和智能发电公共服务系统(intelligent service system,ISS)的智能发电系统数据应用架构,在此基础上,给出了与2个系统相对应的数据应用功能。
文摘有效协调小容量分布式发电(distributed generation,DG)和集中式可再生能源发电(collected renewable generation,CRG)是中国未来智能电网发展的重要特征。分散储能系统(distributed energy storage system,DESS)和集中储能系统(mass energy storage system,MESS)将在大容量CRG和小容量DG的安全、稳定接入大电网中发挥重大作用。文中在对智能电网兼容性问题进行深入分析的基础上,探讨了考虑电网供蓄特性的协同调度,提出了涵盖输配电网CRG-MESS供蓄配置以及微网DG-DESS供蓄配置的智能电网兼容性解决方案。
文摘通过研究各种分布式电源的发电特性,搭建了含风电、光伏发电、飞轮储能、小水电、微型燃气轮机与负荷的微电网负荷频率控制(Load Frequency Control,LFC)模型,其中小水电和微型燃气轮机为调频机组。将大型互联电网中的集中式自动发电控制(Automatic Generation Control,AGC)原理引入微电网,并结合基于平均报酬模型的多步R(λ)学习算法,提出了一种孤岛运行模式下基于强化学习的AGC控制器,以实现对微网的智能发电控制与频率调整。仿真试验分析表明,与PI控制、Q学习和Q(λ)学习相比,所提出的R(λ)控制器具有快速收敛特性和良好的动态性能以及较强的模型适应性。
文摘针对微电网孤岛运行模式下新能源发电强随机性导致的系统频率波动,提出了基于多智能体相关均衡强化学习(Correlated Equilibrium Q(λ),CEQ(λ))的微电网智能发电控制方法。在所搭建含有光伏发电、风力发电、小水电、微型燃气轮机和飞轮储能的微电网负荷频率控制(Load frequency Control,LFC)模型基础上,以频率偏差作为状态输入,提出了一种微电网孤岛运行模式下的CEQ(λ)智能发电控制器。仿真结果显示,与PI控制、单智能体R(λ)控制相比,CEQ(λ)控制器具有更好的在线学习能力,能显著增强孤岛微电网的鲁棒性和适应性,有效提高了频率的考核合格率。
文摘Due to growing concerns regarding climate change and environmental protection,smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy.Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties.The burgeoning era of machine learning(ML)and data-driven control(DDC)techniques promises an improved alternative to these outdated methods.This paper reviews typical applications of ML and DDC at the level of monitoring,control,optimization,and fault detection of power generation systems,with a particular focus on uncovering how these methods can function in evaluating,counteracting,or withstanding the effects of the associated uncertainties.A holistic view is provided on the control techniques of smart power generation,from the regulation level to the planning level.The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility,maneuverability,flexibility,profitability,and safety(abbreviated as the“5-TYs”),respectively.Finally,an outlook on future research and applications is presented.
