To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs base...To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.展开更多
Aiming at issues of life loss(LL)and overall energy efficiency(OEE)for battery energy storage system(BESS)in smoothing wind power fluctuations,a dynamic grouping control strategy of BESS for remaining useful life(RUL)...Aiming at issues of life loss(LL)and overall energy efficiency(OEE)for battery energy storage system(BESS)in smoothing wind power fluctuations,a dynamic grouping control strategy of BESS for remaining useful life(RUL)extension and OEE improvement is proposed.First,grid-connected power signals are obtained.Second,a model to optimize capacity allocation for three battery groups(BGs)in BESS is established considering LL and OEE,and it is solved by the designed improved beetle swarm antennae search algorithm.Then,a dynamic grouping method is proposed to dynamically adjust the grouping state of battery units(BUs)during operation to keep good sustainable dispatchability.Then,a double-layer power allocation approach coordinated with multi-principle is designed to reduce LL and improve OEE,and also keeps consistency of state of charge for BUs simultaneously.The upper layer achieves power allocation from BESS into the three BGs and power allocation method for each BG is determined.The lower layer,considering PCS efficiency under different working conditions,finishes power allocation from each BG into BUs inside it.Subsequently,an RUL evaluation model based on the swing door trend algorithm is built to shorten required calculation time.Finally,the proposed control strategy is simulated and results compared with other strategies demonstrate the proposed strategy acquires the longest RUL and highest OEE with smoothing wind power fluctuations effectively,which verifies its correctness and validity.展开更多
With the rapid increase of distributed photovoltaic(PV) power integrating into the distribution network(DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV p...With the rapid increase of distributed photovoltaic(PV) power integrating into the distribution network(DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demandside needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling(PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment.Thus, a demand-side management strategy based on model predictive control(MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally,the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.展开更多
基金This study was supported by the National Key Research and Development Program of China(No.2018YFE0122200)National Natural Science Foundation of China(No.52077078)Fundamental Research Funds for the Central Universities(No.2020MS090).
文摘To address the significant lifecycle degradation and inadequate state of charge(SOC)balance of electric vehicles(EVs)when mitigating wind power fluctuations,a dynamic grouping control strategy is proposed for EVs based on an improved k-means algorithm.First,a swing door trending(SDT)algorithm based on compression result feedback was designed to extract the feature data points of wind power.The gating coefficient of the SDT was adjusted based on the compression ratio and deviation,enabling the acquisition of grid-connected wind power signals through linear interpolation.Second,a novel algorithm called IDOA-KM is proposed,which utilizes the Improved Dingo Optimization Algorithm(IDOA)to optimize the clustering centers of the k-means algorithm,aiming to address its dependence and sensitivity on the initial centers.The EVs were categorized into priority charging,standby,and priority discharging groups using the IDOA-KM.Finally,an two-layer power distribution scheme for EVs was devised.The upper layer determines the charging/discharging sequences of the three EV groups and their corresponding power signals.The lower layer allocates power signals to each EV based on the maximum charging/discharging power or SOC equalization principles.The simulation results demonstrate the effectiveness of the proposed control strategy in accurately tracking grid power signals,smoothing wind power fluctuations,mitigating EV degradation,and enhancing the SOC balance.
基金supported by the National Key Research and Development Program of China(No.2018YFE0122200)the National Natural Science Foundation of China(No.52077078)the Fundamental Research Funds for the Central Universities(No.2020MS090).
文摘Aiming at issues of life loss(LL)and overall energy efficiency(OEE)for battery energy storage system(BESS)in smoothing wind power fluctuations,a dynamic grouping control strategy of BESS for remaining useful life(RUL)extension and OEE improvement is proposed.First,grid-connected power signals are obtained.Second,a model to optimize capacity allocation for three battery groups(BGs)in BESS is established considering LL and OEE,and it is solved by the designed improved beetle swarm antennae search algorithm.Then,a dynamic grouping method is proposed to dynamically adjust the grouping state of battery units(BUs)during operation to keep good sustainable dispatchability.Then,a double-layer power allocation approach coordinated with multi-principle is designed to reduce LL and improve OEE,and also keeps consistency of state of charge for BUs simultaneously.The upper layer achieves power allocation from BESS into the three BGs and power allocation method for each BG is determined.The lower layer,considering PCS efficiency under different working conditions,finishes power allocation from each BG into BUs inside it.Subsequently,an RUL evaluation model based on the swing door trend algorithm is built to shorten required calculation time.Finally,the proposed control strategy is simulated and results compared with other strategies demonstrate the proposed strategy acquires the longest RUL and highest OEE with smoothing wind power fluctuations effectively,which verifies its correctness and validity.
基金supported by the National Natural Science Foundation of China (No. U2066601)。
文摘With the rapid increase of distributed photovoltaic(PV) power integrating into the distribution network(DN), the critical issues such as PV power curtailment and low equipment utilization rate have been caused by PV power fluctuations. DN has less controllable equipment to manage the PV power fluctuation. To smooth the power fluctuations and further improve the utilization of PV, the regulation ability from the demandside needs to be excavated. This study presents a continuous control method of the feeder load power in a DN based on the voltage regulation to respond to the rapid fluctuation of the PV power output. PV power fluctuations will be directly reflected in the point of common coupling(PCC), and the power fluctuation rate of PCCs is an important standard of PV curtailment.Thus, a demand-side management strategy based on model predictive control(MPC) to mitigate the PCC power fluctuation is proposed. In pre-scheduling, the intraday optimization model is established to solve the reference power of PCC. In real-time control, the pre-scheduling results and MPC are used for the rolling optimization to control the feeder load demand. Finally,the data from the field measurements in Guangzhou, China are used to verify the effectiveness of the proposed strategy in smoothing fluctuations of the distributed PV power.
