Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal s...Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.展开更多
In volt/var control(VVC)for active distribution networks,it is essential to integrate traditional voltage regulation devices with modern smart photovoltaic inverters to prevent voltage violations.However,model-based m...In volt/var control(VVC)for active distribution networks,it is essential to integrate traditional voltage regulation devices with modern smart photovoltaic inverters to prevent voltage violations.However,model-based multi-device VVC methods rely on accurate system models for decision-making,which can be challenging due to the extensive modeling workload.To tackle the complexities of multi-device cooperation in VVC,this paper proposes a two-timescale VVC method based on reinforcement learning with hybrid action space,termed the hybrid action representation twin delayed deep deterministic policy gradient(HAR-TD3)method.This method simultaneously manages traditional discrete voltage regulation devices,which operate on a slower timescale,and smart continuous voltage regulation devices,which function on a faster timescale.To enable effective collaboration between the different action spaces of these devices,we propose a variational auto-encoder based hybrid action reconstruction network.This network captures the interdependencies of hybrid actions by embedding both discrete and continuous actions into the latent representation space and subsequently decoding them for action reconstruction.The proposed method is validated on IEEE 33-bus,69-bus,and 123-bus distribution networks.Numerical results indicate that the proposed method successfully coordinates discrete and continuous voltage regulation devices,achieving fewer voltage violations compared with stateof-the-art reinforcement learning methods.展开更多
This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-f...This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.展开更多
Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressin...Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.展开更多
In physical information theory elementary objects are represented as correlation structures with oscillator properties and characterized by action. The procedure makes it possible to describe the photons of positive a...In physical information theory elementary objects are represented as correlation structures with oscillator properties and characterized by action. The procedure makes it possible to describe the photons of positive and negative charges by positive and negative real action;gravitons are represented in equal amounts by positive and negative real, i.e., virtual action, and the components of the vacuum are characterized by deactivated virtual action. An analysis of the currents in the correlation structures of photons of static Maxwell fields with wave and particle properties, of the Maxwell vacuum and of the gravitons leads to a uniform three-dimensional representation of the structure of the action. Based on these results, a basic structure consisting of a system of oscillators is proposed, which describe the properties of charges and masses and interact with the photons of static Maxwell fields and with gravitons. All properties of the elemental components of nature can thus be traced back to a basic structure of action. It follows that nature can be derived from a uniform structure and this structure of action must therefore also be the basis of the origin of the cosmos.展开更多
Segal’s chronometric theory is based on a space-time D, which might be viewed as a Lie group with a causal structure defined by an invariant Lorentzian form on the Lie algebra u(2). Similarly, the space-time F is rea...Segal’s chronometric theory is based on a space-time D, which might be viewed as a Lie group with a causal structure defined by an invariant Lorentzian form on the Lie algebra u(2). Similarly, the space-time F is realized as the Lie group with a causal structure defined by an invariant Lorentzian form on u(1,1). Two Lie groups G, GF are introduced as representations of SU(2,2): they are related via conjugation by a certain matrix Win Gl(4). The linear-fractional action of G on D is well-known to be global, conformal, and it plays a crucial role in the analysis on space-time bundles carried out by Paneitz and Segal in the 1980’s. This analysis was based on the parallelizing group U(2). In the paper, singularities’ general (“geometric”) description of the linear-fractional conformal GF-action on F is given and specific examples are presented. The results call for the analysis of space-time bundles based on U(1,1) as the parallelizing group. Certain key stages of such an analysis are suggested.展开更多
基金This work was supported by the National Key R&D Program of China(2018AAA0101400)the National Natural Science Foundation of China(62173251,61921004,U1713209)the Natural Science Foundation of Jiangsu Province of China(BK20202006).
文摘Driven by the improvement of the smart grid,the active distribution network(ADN)has attracted much attention due to its characteristic of active management.By making full use of electricity price signals for optimal scheduling,the total cost of the ADN can be reduced.However,the optimal dayahead scheduling problem is challenging since the future electricity price is unknown.Moreover,in ADN,some schedulable variables are continuous while some schedulable variables are discrete,which increases the difficulty of determining the optimal scheduling scheme.In this paper,the day-ahead scheduling problem of the ADN is formulated as a Markov decision process(MDP)with continuous-discrete hybrid action space.Then,an algorithm based on multi-agent hybrid reinforcement learning(HRL)is proposed to obtain the optimal scheduling scheme.The proposed algorithm adopts the structure of centralized training and decentralized execution,and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables.The simulation experiment results demonstrate the effectiveness of the algorithm.
基金supported in part by the National Science and Technology Major Project(No.2022ZD0116900)the National Natural Science Foundation of China(No.52277118)the Natural Science Foundation of Tianjin(No.22JCZDJC00660).
文摘In volt/var control(VVC)for active distribution networks,it is essential to integrate traditional voltage regulation devices with modern smart photovoltaic inverters to prevent voltage violations.However,model-based multi-device VVC methods rely on accurate system models for decision-making,which can be challenging due to the extensive modeling workload.To tackle the complexities of multi-device cooperation in VVC,this paper proposes a two-timescale VVC method based on reinforcement learning with hybrid action space,termed the hybrid action representation twin delayed deep deterministic policy gradient(HAR-TD3)method.This method simultaneously manages traditional discrete voltage regulation devices,which operate on a slower timescale,and smart continuous voltage regulation devices,which function on a faster timescale.To enable effective collaboration between the different action spaces of these devices,we propose a variational auto-encoder based hybrid action reconstruction network.This network captures the interdependencies of hybrid actions by embedding both discrete and continuous actions into the latent representation space and subsequently decoding them for action reconstruction.The proposed method is validated on IEEE 33-bus,69-bus,and 123-bus distribution networks.Numerical results indicate that the proposed method successfully coordinates discrete and continuous voltage regulation devices,achieving fewer voltage violations compared with stateof-the-art reinforcement learning methods.
基金supported by the National Natural Science Foundation of China(No.62002016)the Science and Technology Development Fund,Macao S.A.R.(No.0137/2019/A3)+1 种基金the Beijing Natural Science Foundation(No.9204028)the Guangdong Basic and Applied Basic Research Foundation(No.2019A1515111165)。
文摘This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household appliances.Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation.However,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions.Thus,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)algorithm.The DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous actions.The MDRL algorithm learns optimal strategy by trialand-error interactions with the environment.However,unsafe actions,which violate system constraints,can give rise to great cost.To handle such problem,a safe-MDRL algorithm is further proposed.Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management.The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset.Moreover,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.
基金the Collaborative Innovation Project of Shanghai,China for the financial support。
文摘Unmanned Aerial Vehicle(UAV)plays a prominent role in various fields,and autonomous navigation is a crucial component of UAV intelligence.Deep Reinforcement Learning(DRL)has expanded the research avenues for addressing challenges in autonomous navigation.Nonetheless,challenges persist,including getting stuck in local optima,consuming excessive computations during action space exploration,and neglecting deterministic experience.This paper proposes a noise-driven enhancement strategy.In accordance with the overall learning phases,a global noise control method is designed,while a differentiated local noise control method is developed by analyzing the exploration demands of four typical situations encountered by UAV during navigation.Both methods are integrated into a dual-model for noise control to regulate action space exploration.Furthermore,noise dual experience replay buffers are designed to optimize the rational utilization of both deterministic and noisy experience.In uncertain environments,based on the Twin Delay Deep Deterministic Policy Gradient(TD3)algorithm with Long Short-Term Memory(LSTM)network and Priority Experience Replay(PER),a Noise-Driven Enhancement Priority Memory TD3(NDE-PMTD3)is developed.We established a simulation environment to compare different algorithms,and the performance of the algorithms is analyzed in various scenarios.The training results indicate that the proposed algorithm accelerates the convergence speed and enhances the convergence stability.In test experiments,the proposed algorithm successfully and efficiently performs autonomous navigation tasks in diverse environments,demonstrating superior generalization results.
文摘In physical information theory elementary objects are represented as correlation structures with oscillator properties and characterized by action. The procedure makes it possible to describe the photons of positive and negative charges by positive and negative real action;gravitons are represented in equal amounts by positive and negative real, i.e., virtual action, and the components of the vacuum are characterized by deactivated virtual action. An analysis of the currents in the correlation structures of photons of static Maxwell fields with wave and particle properties, of the Maxwell vacuum and of the gravitons leads to a uniform three-dimensional representation of the structure of the action. Based on these results, a basic structure consisting of a system of oscillators is proposed, which describe the properties of charges and masses and interact with the photons of static Maxwell fields and with gravitons. All properties of the elemental components of nature can thus be traced back to a basic structure of action. It follows that nature can be derived from a uniform structure and this structure of action must therefore also be the basis of the origin of the cosmos.
文摘Segal’s chronometric theory is based on a space-time D, which might be viewed as a Lie group with a causal structure defined by an invariant Lorentzian form on the Lie algebra u(2). Similarly, the space-time F is realized as the Lie group with a causal structure defined by an invariant Lorentzian form on u(1,1). Two Lie groups G, GF are introduced as representations of SU(2,2): they are related via conjugation by a certain matrix Win Gl(4). The linear-fractional action of G on D is well-known to be global, conformal, and it plays a crucial role in the analysis on space-time bundles carried out by Paneitz and Segal in the 1980’s. This analysis was based on the parallelizing group U(2). In the paper, singularities’ general (“geometric”) description of the linear-fractional conformal GF-action on F is given and specific examples are presented. The results call for the analysis of space-time bundles based on U(1,1) as the parallelizing group. Certain key stages of such an analysis are suggested.
