The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllabl...The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.展开更多
为了有效地扩大基站无线覆盖范围,吸收更多的用户和话务量,降低建设成本,提高收益,实现科学的基站选址,提出了一种适应于分时长期演进(time division long term evolution,TD-LTE)网络的高效的、智能的4G无线网络规划方法,通过综合考虑4...为了有效地扩大基站无线覆盖范围,吸收更多的用户和话务量,降低建设成本,提高收益,实现科学的基站选址,提出了一种适应于分时长期演进(time division long term evolution,TD-LTE)网络的高效的、智能的4G无线网络规划方法,通过综合考虑4G网络的同频干扰、正交频分复用(OFDM)、小区边缘速率、参考信号强度(RSRP)和基站站址密度等,建立一个以建设成本、覆盖率和容量为目标的多目标组合优化规划模型;并采用加入局部搜索的遗传算法进行智能求解。仿真结果表明该模型不但能够求出以最少的成本建设最大覆盖的网络方案,而且能够求出每个建设基站的天线类型、天线挂高和小区类型;同时加入局部搜索后的算法速度得到明显的提高。展开更多
基金supported in part by Science and Technology Project of State Grid Corporation of China(No.5400-202319829A-4-1-KJ).
文摘The integration of large-scale-distributed new energy resources has led to heightened source‒load uncertainty.As energy prosumers,microgrids urgently require enhanced real-time regulation capabilities over controllable resources amid uncertain environments,rendering real-time and rapid decision-making a critical issue.This paper proposes a tailored twin delayed deep deterministic policy gradient(TD3)reinforcement learning algorithm that explicitly accounts for source‒load uncertainty.First,following an expert experience-based methodology,Gaussian process regression was implemented using the radial basis function covariance with historical source and load data.The parameters were adaptively adjusted by maximum likelihood estimation to generate the expected curves of demand and wind‒solar power generation,along with their 95%confidence regions,which were treated as representative uncertainty scenarios.Second,the traditional scheduling model was transformed into a deep reinforcement learning(DRL)environment through a Markov process.To minimize the total operational cost of the microgrid,the tailored TD3 algorithm was applied to formulate rapid intraday scheduling decisions.Finally,simulations were conducted using real historical data from an actual region in Zhejiang province,China,to verify the efficacy of the proposed method.The results demonstrate the potential of the algorithm for achieving economic scheduling for microgrids.
文摘为了有效地扩大基站无线覆盖范围,吸收更多的用户和话务量,降低建设成本,提高收益,实现科学的基站选址,提出了一种适应于分时长期演进(time division long term evolution,TD-LTE)网络的高效的、智能的4G无线网络规划方法,通过综合考虑4G网络的同频干扰、正交频分复用(OFDM)、小区边缘速率、参考信号强度(RSRP)和基站站址密度等,建立一个以建设成本、覆盖率和容量为目标的多目标组合优化规划模型;并采用加入局部搜索的遗传算法进行智能求解。仿真结果表明该模型不但能够求出以最少的成本建设最大覆盖的网络方案,而且能够求出每个建设基站的天线类型、天线挂高和小区类型;同时加入局部搜索后的算法速度得到明显的提高。
