Situation Awareness and Sensitivity Analysis for Absorption of Grid-connected Renewable Energy Power Generation Integrating Robust Optimization and Radial Basis Function Neural Network

The significance of situation awareness(SA) in power systems has increased to enhance the utilization of gridconnected renewable energy power generation(REPG). This paper proposes a real-time calculation architecture based on the integration of robust optimization(RO) and artificial intelligence. First, the time-series simulation of the REPG consumption capacity is carried out under the current grid operating conditions. RO is employed in this simulation, given the randomness of the REPG output and the grid load. Then, the radial basis function neural network(RBFNN) is trained with the results under different parameters using the artificial fish swarm algorithm(AFSA), enabling the neural network(NN) to be the replacement for the time-series simulation model. The trained NN can quickly perceive the REPG absorption situation within the predefined grid structure and period. Moreover, the Sobol' method is adopted to conduct the global sensitivity analysis for different parameters based on the input-output samples obtained by the trained NN. Finally, the simulation experiments based on the modified IEEE 14-bus system prove the real-time performance and accuracy of the proposed SA architecture.

Graph Neural Network Based Column Generation for Energy Management in Networked Microgrid

In this paper,we apply a model predictive control based scheme to the energy management of networked microgrid,which is reformulated based on column generation.Although column generation is effective in alleviating the computational intractability of large-scale optimization problems,it still suffers from slow convergence issues,which hinders the direct real-time online implementation.To this end,we propose a graph neural network based framework to accelerate the convergence of the column generation model.The acceleration is achieved by selecting promising columns according to certain stabilization method of the dual variables that can be customized according to the characteristics of the microgrid.Moreover,a rigorous energy management method based on the graph neural network accelerated column generation model is developed,which is able to guarantee the optimality and feasibility of the dispatch results.The computational efficiency of the method is also very high,which is promising for real-time implementation.We conduct case studies to demonstrate the effectiveness of the proposed energy management method.

Optimal Dispatch for Flexible Uncertainty Sets in Multi-energy Systems: An IGDT Based Two-stage Decision Framework

The operational stability and economy of multi-energy systems(MES)are threatened by various uncertainties,such as variable renewable energy power,energy demands,and weather conditions.Most of the existing methods for the dispatch decisions of MES are based on the prescribed probability distribution or uncertainty sets of random variables,which have many disadvantages,such as potential infeasibility and over-conservatism.In this paper,we propose a novel dispatch model for MES that integrates dispatch decision making,uncertainty set selection,and operational cost control into a unified framework.First,the deterministic dispatch model of MES is introduced,in which the physical characteristics of district heating systems and buildings are fully considered.Then,a novel decision framework that combines the two-stage dispatch strategy and info-gap decision theory(IGDT)is proposed for MES,where the uncertainty set is flexible and can be optimized based on the operational cost budget.Finally,a revised algorithm,based on the column-and-constraint generation method,is proposed for the model.Case studies are performed on MES that includes a 33-bus distribution system and a heating network modified from a real 51-node network located in Jinlin Province,China.The results verify the effectiveness of the proposed method.

Status and Progress on an HTS Strand with Quasi-isotropic Critical Current

The coated conductors(CCs),also known as the second-generation high-temperature superconducting(2G HTS)tape,has attractive potential applications in transmissions at low magnetic field/liquid nitrogen temperatures and high-field applications at low temperatures due to their excellent critical current density and mechanical performance.Its critical current density has strong anisotropy,particularly at 77 K.Because the single tape has limited capacity of carrying current,the cable or strand making of multi-tapes,stacked in parallel,is necessary.Within recent years,several cables,such as the Roebel cable,CORC cable,TSTC cable,TS cable,and the HTSCroCo cable,were proposed.This paper presents a status and progress on a type of HTS strand made from REBCO tapes.Its quasi-isotropic critical current is experimentally confirmed for liquid nitrogen temperature,mechanical characteristics,AC losses,thermal stability,and quench characterizations are also researched.Finally,the feasible process for the production of practical length of such strand is described by processing 10 m long sample,which is promising for applications of high fields or power transmission.

Synchrophasor Measurement Method Based on Cascaded Infinite Impulse Response and Dual Finite Impulse Response Filters

High-precision synchronized measurement data with short measurement latency are required for the applications of phasor measurement units(PMUs).This paper proposes a synchrophasor measurement method based on cascaded infinite impulse response(IIR)and dual finite impulse response(FIR)filters,meeting the M-class and P-class requirements in the IEC/IEEE 60255-118-1 standard.A low-group-delay IIR filter is designed to remove out-of-band interference components.Two FIR filters with different center frequencies are designed to filter out the fundamental negative frequency component and obtain synchrophasor estimates.The ratio of the amplitudes of the synchrophasor is used to calculate the frequency according to the one-to-one correspondence between the ratio of the amplitude frequency response of the FIR filters and the frequency.To shorten the response time introduced by IIR filter,a step identification and processing method based on the rate of change of frequency(RoCoF)is proposed and analyzed.The synchrophasor is accurately compensated based on the frequency and the frequency response of the IIR and FIR filters,achieving high-precision synchrophasor and frequency estimates with short measurement latency.Simulation and experiment tests demonstrate that the proposed method is superior to existing methods and can provide synchronized measurement data for M-class PMU applications with short measurement latency.