The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with s...The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing.This situation is expected to worsen with the proliferation of renewable energy and electric vehicles.In this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible topologies.In the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation transformers.This is known as the upper-layer PSD.The lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest.This optimum topology is tested by standard power flow to ascertain its feasibility.The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating.The proposed framework is tested on a 56-node test system.Results show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.展开更多
Accurate short-term prediction of overhead line(OHL)transmission ampacity can directly affect the efficiency of power system operation and planning.Any overcstiniation of the dynamic thermal line rating(DTLR)can lead ...Accurate short-term prediction of overhead line(OHL)transmission ampacity can directly affect the efficiency of power system operation and planning.Any overcstiniation of the dynamic thermal line rating(DTLR)can lead to the lifetime degradation and failure of OHLs,safety hazards,etc.This paper presents a secure yet sharp probabilistic model for the hour-ahead prediction of the DTLR.The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR.The model is based on an augmented deep learning architecture that makes use of a wide range of predictors,including historical climatology data and latent variables obtained during DTLR calculation.Furthermore,by introducing a customized cost function,the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing the deviations of the predicted DTLRs from the actual values.The proposed probabilistic DTLR is developed and verified using recorded experimental data.The simulation results validate the superiority of the proposed DTLR compared with the state-of-the-art prediction models using well-known evaluation metrics.展开更多
基金supported by the Universiti Sains Malaysia,Research University Team(RUTeam)Grant Scheme(No.1001/PELECT/8580011).
文摘The urban power grid(UPG)combines transmission and distribution networks.Past studies on UPG congestion mitigation have primarily focused on relieving local congestion while ignoring large-scale energy transfer with safety margins and load balancing.This situation is expected to worsen with the proliferation of renewable energy and electric vehicles.In this paper,a two-layer congestion mitigation framework is proposed,one which considers the congestion of the UPG with flexible topologies.In the upper-layer,the particle swarm optimization algorithm is employed to optimize the power supply distribution(PSD)of substation transformers.This is known as the upper-layer PSD.The lower-layer model recalculates the new PSD,known as the lower-layer PSD,based on the topology candidates.A candidate topology is at an optimum when the Euclidean distance mismatch between the upper-and lower-layer PSDs is the smallest.This optimum topology is tested by standard power flow to ascertain its feasibility.The optimum transitioning sequence between the initial and optimum topologies is also determined by the two-layer framework to minimize voltage deviation and line overloading of the UPG considering dynamic thermal rating.The proposed framework is tested on a 56-node test system.Results show that the proposed framework can significantly reduce congestion,maintain safety margins,and determine the optimum transitioning sequence.
文摘Accurate short-term prediction of overhead line(OHL)transmission ampacity can directly affect the efficiency of power system operation and planning.Any overcstiniation of the dynamic thermal line rating(DTLR)can lead to the lifetime degradation and failure of OHLs,safety hazards,etc.This paper presents a secure yet sharp probabilistic model for the hour-ahead prediction of the DTLR.The security of the proposed DTLR limits the frequency of DTLR prediction exceeding the actual DTLR.The model is based on an augmented deep learning architecture that makes use of a wide range of predictors,including historical climatology data and latent variables obtained during DTLR calculation.Furthermore,by introducing a customized cost function,the deep neural network is trained to consider the DTLR security based on the required probability of exceedance while minimizing the deviations of the predicted DTLRs from the actual values.The proposed probabilistic DTLR is developed and verified using recorded experimental data.The simulation results validate the superiority of the proposed DTLR compared with the state-of-the-art prediction models using well-known evaluation metrics.
