Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it mus...Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.展开更多
为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy...为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy optimization)的分布式混合储能系统HESS(hybrid energy storage system)充、放电优化控制策略。在考虑HCSY-MG系统并网电流与分布式HESS特性的条件下,确定影响并网电流的主要系统变量,以及HESS接入系统的最佳拓扑结构。然后结合串联系统的特点,将分布式HESS的充、放电问题转换为深度强化学习的Markov决策过程。同时针对PPO算法中熵损失权重难以确定的问题,提出1种改进的PPO算法,兼顾智能体的收敛性和探索性。最后以某新能源发电基地的典型运行数据为算例,验证所提控制策略的可行性和有效性。展开更多
阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优...阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优化(proximal policy optimization,PPO)算法求解CIES低碳优化调度问题。该方法基于低碳优化调度模型搭建强化学习交互环境,利用设备状态参数及运行参数定义智能体的状态、动作空间及奖励函数,再通过离线训练获取可生成最优策略的智能体。算例分析结果表明,采用PPO算法得到的CIES低碳优化调度方法能够充分发挥阶梯式碳交易机制减少碳排放量和提高能源利用率方面的优势。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62103349)the Henan Province Science and Technology Research Project(Grant No.232102210104).
文摘Edge computing has transformed smart grids by lowering latency,reducing network congestion,and enabling real-time decision-making.Nevertheless,devising an optimal task-offloading strategy remains challenging,as it must jointly minimise energy consumption and response time under fluctuating workloads and volatile network conditions.We cast the offloading problem as aMarkov Decision Process(MDP)and solve it with Deep Reinforcement Learning(DRL).Specifically,we present a three-tier architecture—end devices,edge nodes,and a cloud server—and enhance Proximal Policy Optimization(PPO)to learn adaptive,energy-aware policies.A Convolutional Neural Network(CNN)extracts high-level features from system states,enabling the agent to respond continually to changing conditions.Extensive simulations show that the proposed method reduces task latency and energy consumption far more than several baseline algorithms,thereby improving overall system performance.These results demonstrate the effectiveness and robustness of the framework for real-time task offloading in dynamic smart-grid environments.
文摘为平抑微源半桥变流器串联星型结构微电网HCSY-MG(half-bridge converter series Y-connection micro-grids)并网系统中微源出力的波动,保证各相直流侧电压之和相等,与并网电流三相平衡,提出1种基于改进近端策略优化PPO(proximal policy optimization)的分布式混合储能系统HESS(hybrid energy storage system)充、放电优化控制策略。在考虑HCSY-MG系统并网电流与分布式HESS特性的条件下,确定影响并网电流的主要系统变量,以及HESS接入系统的最佳拓扑结构。然后结合串联系统的特点,将分布式HESS的充、放电问题转换为深度强化学习的Markov决策过程。同时针对PPO算法中熵损失权重难以确定的问题,提出1种改进的PPO算法,兼顾智能体的收敛性和探索性。最后以某新能源发电基地的典型运行数据为算例,验证所提控制策略的可行性和有效性。
