随着可再生能源在有源配电网中的渗透比例逐年增加,其带来的随机性、间歇性对已有调度策略产生了重大挑战。文章提出了一种基于多智能体深度强化学习的有源配电网经济调度策略,构建多区域能源自治框架,每个新能源自治区域对应一个智能体...随着可再生能源在有源配电网中的渗透比例逐年增加,其带来的随机性、间歇性对已有调度策略产生了重大挑战。文章提出了一种基于多智能体深度强化学习的有源配电网经济调度策略,构建多区域能源自治框架,每个新能源自治区域对应一个智能体,应用多智能体深度强化学习(multi-agent deep reinforcement learning,MADRL)算法解决各区域的协同经济调度问题,并对包含风机、储能设备的有源配电网进行区域建模,设定经济优化目标及运行约束条件,在多智能体深度确定性策略梯度(multi-agent deep deterministic policygradient,MADDPG)算法基础上,采用BiGRU(bidirectional gated recurrent unit)代替全连接层,进行新能源的出力预测,有效降低新能源波动性带来的影响,以改进的IEEE33测试系统进行算例分析,验证了所提策略的有效性和对比同类算法的优越性。展开更多
As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncert...As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling,especially for small-scale microgrids.Instead of deploying stochastic models to cope with such challenges,this paper presents a retroactive approach to real-time energy scheduling,which is prediction-independent and computationally efficient.Extensive case studies were conducted using 3-year-long real-life system data,and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11%on average,which suggests better performance than model predictive control with the cost difference at 30%compared to the perfect dispatch.展开更多
文摘随着可再生能源在有源配电网中的渗透比例逐年增加,其带来的随机性、间歇性对已有调度策略产生了重大挑战。文章提出了一种基于多智能体深度强化学习的有源配电网经济调度策略,构建多区域能源自治框架,每个新能源自治区域对应一个智能体,应用多智能体深度强化学习(multi-agent deep reinforcement learning,MADRL)算法解决各区域的协同经济调度问题,并对包含风机、储能设备的有源配电网进行区域建模,设定经济优化目标及运行约束条件,在多智能体深度确定性策略梯度(multi-agent deep deterministic policygradient,MADDPG)算法基础上,采用BiGRU(bidirectional gated recurrent unit)代替全连接层,进行新能源的出力预测,有效降低新能源波动性带来的影响,以改进的IEEE33测试系统进行算例分析,验证了所提策略的有效性和对比同类算法的优越性。
基金partially supported by Hong Kong RGC Theme-based Research Scheme(No.T23-407/13N and No.T23-701/14N)SUSTech Faculty Startup Funding(No.Y01236135 and No.Y01236235).
文摘As an emerging paradigm in distributed power systems,microgrids provide promising solutions to local renewable energy generation and load demand satisfaction.However,the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling,especially for small-scale microgrids.Instead of deploying stochastic models to cope with such challenges,this paper presents a retroactive approach to real-time energy scheduling,which is prediction-independent and computationally efficient.Extensive case studies were conducted using 3-year-long real-life system data,and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11%on average,which suggests better performance than model predictive control with the cost difference at 30%compared to the perfect dispatch.
