The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high co...The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.展开更多
为提高多无人船编队系统的导航能力,提出了一种基于注意力机制的多智能体深度确定性策略梯度(ATMADDPG:Attention Mechanism based Multi-Agent Deep Deterministic Policy Gradient)算法。该算法在训练阶段,通过大量试验训练出最佳策略...为提高多无人船编队系统的导航能力,提出了一种基于注意力机制的多智能体深度确定性策略梯度(ATMADDPG:Attention Mechanism based Multi-Agent Deep Deterministic Policy Gradient)算法。该算法在训练阶段,通过大量试验训练出最佳策略,并在实验阶段直接使用训练出的最佳策略得到最佳编队路径。仿真实验将4艘相同的“百川号”无人船作为实验对象。实验结果表明,基于ATMADDPG算法的队形保持策略能实现稳定的多无人船编队导航,并在一定程度上满足队形保持的要求。相较于多智能体深度确定性策略梯度(MADDPG:Multi-Agent Depth Deterministic Policy Gradient)算法,所提出的ATMADDPG算法在收敛速度、队形保持能力和对环境变化的适应性等方面表现出更优越的性能,综合导航效率可提高约80%,具有较大的应用潜力。展开更多
在无人机(UAV)集群攻击地面目标时,UAV集群将分为两个编队:主攻目标的打击型UAV集群和牵制敌方的辅助型UAV集群。当辅助型UAV集群选择激进进攻或保存实力这两种动作策略时,任务场景类似于公共物品博弈,此时合作者的收益小于背叛者。基于...在无人机(UAV)集群攻击地面目标时,UAV集群将分为两个编队:主攻目标的打击型UAV集群和牵制敌方的辅助型UAV集群。当辅助型UAV集群选择激进进攻或保存实力这两种动作策略时,任务场景类似于公共物品博弈,此时合作者的收益小于背叛者。基于此,提出一种基于深度强化学习的UAV集群协同作战决策方法。首先,通过建立基于公共物品博弈的UAV集群作战模型,模拟智能化UAV集群在合作中个体与集体间的利益冲突问题;其次,利用多智能体深度确定性策略梯度(MADDPG)算法求解辅助UAV集群最合理的作战决策,从而以最小的损耗代价实现集群胜利。在不同数量UAV情况下进行训练并展开实验,实验结果表明,与IDQN(Independent Deep QNetwork)和ID3QN(Imitative Dueling Double Deep Q-Network)这两种算法的训练效果相比,所提算法的收敛性最好,且在4架辅助型UAV情况下胜率可达100%,在其他UAV数情况下也明显优于对比算法。展开更多
In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each...In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.展开更多
Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite commun...Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.展开更多
基金supported by The National Key R&D Program of China(2020YFB0905900):Research on artificial intelligence application of power internet of things.
文摘The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.
文摘在无人机(UAV)集群攻击地面目标时,UAV集群将分为两个编队:主攻目标的打击型UAV集群和牵制敌方的辅助型UAV集群。当辅助型UAV集群选择激进进攻或保存实力这两种动作策略时,任务场景类似于公共物品博弈,此时合作者的收益小于背叛者。基于此,提出一种基于深度强化学习的UAV集群协同作战决策方法。首先,通过建立基于公共物品博弈的UAV集群作战模型,模拟智能化UAV集群在合作中个体与集体间的利益冲突问题;其次,利用多智能体深度确定性策略梯度(MADDPG)算法求解辅助UAV集群最合理的作战决策,从而以最小的损耗代价实现集群胜利。在不同数量UAV情况下进行训练并展开实验,实验结果表明,与IDQN(Independent Deep QNetwork)和ID3QN(Imitative Dueling Double Deep Q-Network)这两种算法的训练效果相比,所提算法的收敛性最好,且在4架辅助型UAV情况下胜率可达100%,在其他UAV数情况下也明显优于对比算法。
基金This work was supported in part by the US Department of Energy(DOE),Office of Electricity and Office of Energy Efficiency and Renewable Energy under contract DE-AC05-00OR22725in part by CURENT,an Engineering Research Center funded by US National Science Foundation(NSF)and DOE under NSF award EEC-1041877in part by NSF award ECCS-1809458.
文摘In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.
基金supported in part by the National Natural Science Foundation of China under Grants 62341104,62201085,62325108,and 62341131.
文摘Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.
