The power grid is a fusion of technologies in energy systems, and how to adjust and control the output power of each generator to balance the load of the grid is a crucial issue. As a platform, the smart grid is for t...The power grid is a fusion of technologies in energy systems, and how to adjust and control the output power of each generator to balance the load of the grid is a crucial issue. As a platform, the smart grid is for the convenience of the implementation of adaptive control generators using advanced technologies. In this paper, we are introducing a new approach, the Central Lower Configuration Table, which optimizes dispatch of the generating capacity in a smart grid power system. The dispatch strategy of each generator in the grid is presented in the configuration table, and the scenario consists of two-level agents. A central agent optimizes dispatch calculation to get the configuration table, and a lower agent controls generators according to the tasks of the central level and the work states during generation. The central level is major optimization and adjustment. We used machine learning to predict the power load and address the best optimize cost function to deal with a different control strategy. We designed the items of the cost function, such as operations, maintenances and the effects on the environment. Then, according to the total cost, we got a new second-rank-sort table. As a result, we can resolve generator’s task based on the table, which can also be updated on-line based on the environmental situation. The signs of the driving generator’s controller include active power and system’s f. The lower control level agent carries out the generator control to track f along with the best optimized cost function. Our approach makes optimized dispatch algorithm more convenient to realize, and the numerical simulation indicates the strategy of machine learning forecast of optimized power dispatch is effective.展开更多
针对新能源出力的强随机性、间歇性影响配电网功率平衡问题,提出了一种融合多步贪婪策略改进的深度双Q网络(double deep Q network,DDQN)算法和一致性算法的双层功率分配策略,该方法在源荷波动情况下可自适应调整配电网各机组出力,保证...针对新能源出力的强随机性、间歇性影响配电网功率平衡问题,提出了一种融合多步贪婪策略改进的深度双Q网络(double deep Q network,DDQN)算法和一致性算法的双层功率分配策略,该方法在源荷波动情况下可自适应调整配电网各机组出力,保证功率调节的快速性和经济性。首先,基于“资源集群”的划分提出了分层分布式功率分配框架,将智能配电网功率分配问题分解为协调调度层和自治层功率优化分配模型进行求解。然后,协调调度层采用多步贪婪策略改进的DDQN算法来实现“资源集群”间的功率分配,自治层提出以成本微增量为一致性状态变量的功率动态分配方法。最后,典型智能配电网算例仿真结果表明,所提的双层功率分配策略能够在新能源波动情况下解决功率的优化分配问题;与多种方法相比,所提方法具有较快的收敛速度和较低的调节成本。展开更多
随着能源革命的深入推进,传统电力调度模式难以适应新形势下的电网运行需求。文章探讨了一种基于无线通信技术的自动化电力调度系统设计方案。该系统由数据采集、通信网络、智能调度以及用户交互4个功能模块组成,综合运用5G、远距离无线...随着能源革命的深入推进,传统电力调度模式难以适应新形势下的电网运行需求。文章探讨了一种基于无线通信技术的自动化电力调度系统设计方案。该系统由数据采集、通信网络、智能调度以及用户交互4个功能模块组成,综合运用5G、远距离无线电(Long Range Radio,LoRa)、窄带物联网(Narrow Band Internet of Things,NB-IoT)等无线通信技术,结合深度学习、强化学习等人工智能算法,实现了电力系统的实时感知、智能决策以及高效执行。实验结果表明,与传统系统相比,该系统在数据采集准确率、通信效率、调度决策的经济性和安全性等方面均具有显著优势,研究成果对于推动能源电力系统的数字化、网络化、智能化发展具有重要意义。展开更多
文摘The power grid is a fusion of technologies in energy systems, and how to adjust and control the output power of each generator to balance the load of the grid is a crucial issue. As a platform, the smart grid is for the convenience of the implementation of adaptive control generators using advanced technologies. In this paper, we are introducing a new approach, the Central Lower Configuration Table, which optimizes dispatch of the generating capacity in a smart grid power system. The dispatch strategy of each generator in the grid is presented in the configuration table, and the scenario consists of two-level agents. A central agent optimizes dispatch calculation to get the configuration table, and a lower agent controls generators according to the tasks of the central level and the work states during generation. The central level is major optimization and adjustment. We used machine learning to predict the power load and address the best optimize cost function to deal with a different control strategy. We designed the items of the cost function, such as operations, maintenances and the effects on the environment. Then, according to the total cost, we got a new second-rank-sort table. As a result, we can resolve generator’s task based on the table, which can also be updated on-line based on the environmental situation. The signs of the driving generator’s controller include active power and system’s f. The lower control level agent carries out the generator control to track f along with the best optimized cost function. Our approach makes optimized dispatch algorithm more convenient to realize, and the numerical simulation indicates the strategy of machine learning forecast of optimized power dispatch is effective.
文摘针对新能源出力的强随机性、间歇性影响配电网功率平衡问题,提出了一种融合多步贪婪策略改进的深度双Q网络(double deep Q network,DDQN)算法和一致性算法的双层功率分配策略,该方法在源荷波动情况下可自适应调整配电网各机组出力,保证功率调节的快速性和经济性。首先,基于“资源集群”的划分提出了分层分布式功率分配框架,将智能配电网功率分配问题分解为协调调度层和自治层功率优化分配模型进行求解。然后,协调调度层采用多步贪婪策略改进的DDQN算法来实现“资源集群”间的功率分配,自治层提出以成本微增量为一致性状态变量的功率动态分配方法。最后,典型智能配电网算例仿真结果表明,所提的双层功率分配策略能够在新能源波动情况下解决功率的优化分配问题;与多种方法相比,所提方法具有较快的收敛速度和较低的调节成本。
文摘随着能源革命的深入推进,传统电力调度模式难以适应新形势下的电网运行需求。文章探讨了一种基于无线通信技术的自动化电力调度系统设计方案。该系统由数据采集、通信网络、智能调度以及用户交互4个功能模块组成,综合运用5G、远距离无线电(Long Range Radio,LoRa)、窄带物联网(Narrow Band Internet of Things,NB-IoT)等无线通信技术,结合深度学习、强化学习等人工智能算法,实现了电力系统的实时感知、智能决策以及高效执行。实验结果表明,与传统系统相比,该系统在数据采集准确率、通信效率、调度决策的经济性和安全性等方面均具有显著优势,研究成果对于推动能源电力系统的数字化、网络化、智能化发展具有重要意义。
