Hybrid AC/DC distribution networks are promising candidates for future applications due to their rapid advancement in power electronics technology.They use interface converters(IFCs)to link DC and AC distribution netw...Hybrid AC/DC distribution networks are promising candidates for future applications due to their rapid advancement in power electronics technology.They use interface converters(IFCs)to link DC and AC distribution networks.However,the networks possess drawbacks with AC voltage and frequency offsets when transferring from grid-tied to islanding modes.To address these problems,this paper proposes a simple but effective strategy based on the reverse droop method.Initially,the power balance equation of the distribution system is derived,which reveals that the cause of voltage and frequency offsets is the mismatch between the IFC output power and the rated load power.Then,the reverse droop control is introduced into the IFC controller.By using a voltage-active power/frequency-reactive power(U-P/f-Q)reverse droop loop,the IFC output power enables adaptive tracking of the rated load power.Therefore,the AC voltage offset and frequency offset are suppressed during the transfer process of operational modes.In addition,the universal parameter design method is discussed based on the stability limitations of the control system and the voltage quality requirements of AC critical loads.Finally,simulation and experimental results clearly validate the proposed control strategy and parameter design method.展开更多
The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy base...The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.展开更多
基金This work was supported by the National Key R&D Program of China(2018YFB0904700).
文摘Hybrid AC/DC distribution networks are promising candidates for future applications due to their rapid advancement in power electronics technology.They use interface converters(IFCs)to link DC and AC distribution networks.However,the networks possess drawbacks with AC voltage and frequency offsets when transferring from grid-tied to islanding modes.To address these problems,this paper proposes a simple but effective strategy based on the reverse droop method.Initially,the power balance equation of the distribution system is derived,which reveals that the cause of voltage and frequency offsets is the mismatch between the IFC output power and the rated load power.Then,the reverse droop control is introduced into the IFC controller.By using a voltage-active power/frequency-reactive power(U-P/f-Q)reverse droop loop,the IFC output power enables adaptive tracking of the rated load power.Therefore,the AC voltage offset and frequency offset are suppressed during the transfer process of operational modes.In addition,the universal parameter design method is discussed based on the stability limitations of the control system and the voltage quality requirements of AC critical loads.Finally,simulation and experimental results clearly validate the proposed control strategy and parameter design method.
基金supported by the National Natural Science Foundation of China(No.52077146)Sichuan Science and Technology Program(No.2023NSFSC1945)。
文摘The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.
