The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial...The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.展开更多
自动电压控制(Automatic Voltage Control,AVC)系统通过调度数据网双平面接入调度系统主站,支撑区域电网电压/无功控制的安全与稳定运行。文章介绍了AVC系统基本原理,及接入调度数据网基本组成,对AVC子站接入新一代调度系统主站及接入...自动电压控制(Automatic Voltage Control,AVC)系统通过调度数据网双平面接入调度系统主站,支撑区域电网电压/无功控制的安全与稳定运行。文章介绍了AVC系统基本原理,及接入调度数据网基本组成,对AVC子站接入新一代调度系统主站及接入调度数据网双平面策略优化进行了研究,并在抽蓄电站对该策略AVC子站指令模式切换与双主模式切换进行了现场试验,对提升区域电网电压/无功控制能力,强化调度系统安全与稳定建设具有重要意义。展开更多
针对高比例可再生能源并网的电力系统中多源数据协同效率低、风光波动性突出等问题,提出一种基于云-边协同多能互补优化平台,融合逻辑回归多源数据静态融合模型(Multi-source Data Fusion Model Based on Logistic Regression,LR-MDFM)...针对高比例可再生能源并网的电力系统中多源数据协同效率低、风光波动性突出等问题,提出一种基于云-边协同多能互补优化平台,融合逻辑回归多源数据静态融合模型(Multi-source Data Fusion Model Based on Logistic Regression,LR-MDFM)与自适应提升多源数据动态融合模型(Multi-source Data Fusion Model Based on Adaptive Boosting,Adaboost-MDFM),结合分层长短时记忆(Long Short Term Memory,LSTM)网络时序特征提取技术,构建4层云-边协同架构,实现秒级边缘特征提取与云端分钟级全局调度。通过深度强化学习实现动态优化,解决风光波动与水风光协同运行调度滞后性挑战,为“双碳”目标下新型电力系统智能调控提供关键技术支撑。展开更多
基金supported by China Southern Power Grid Technology Project under Grant 03600KK52220019(GDKJXM20220253).
文摘The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.
文摘自动电压控制(Automatic Voltage Control,AVC)系统通过调度数据网双平面接入调度系统主站,支撑区域电网电压/无功控制的安全与稳定运行。文章介绍了AVC系统基本原理,及接入调度数据网基本组成,对AVC子站接入新一代调度系统主站及接入调度数据网双平面策略优化进行了研究,并在抽蓄电站对该策略AVC子站指令模式切换与双主模式切换进行了现场试验,对提升区域电网电压/无功控制能力,强化调度系统安全与稳定建设具有重要意义。
文摘针对高比例可再生能源并网的电力系统中多源数据协同效率低、风光波动性突出等问题,提出一种基于云-边协同多能互补优化平台,融合逻辑回归多源数据静态融合模型(Multi-source Data Fusion Model Based on Logistic Regression,LR-MDFM)与自适应提升多源数据动态融合模型(Multi-source Data Fusion Model Based on Adaptive Boosting,Adaboost-MDFM),结合分层长短时记忆(Long Short Term Memory,LSTM)网络时序特征提取技术,构建4层云-边协同架构,实现秒级边缘特征提取与云端分钟级全局调度。通过深度强化学习实现动态优化,解决风光波动与水风光协同运行调度滞后性挑战,为“双碳”目标下新型电力系统智能调控提供关键技术支撑。
