Probabilistic load flow method considering large-scale wind power integration

The increasing penetration of wind power brings great uncertainties into power systems,which poses challenges to system planning and operation.This paper proposes a novel probabilistic load flow(PLF)method based on clustering technique to handle large fluctuations from large-scale wind power integration.The traditional cumulant method(CM)for PLF is based on the linearization of load flow equations around the operation point,therefore resulting in significant errors when input random variables have large fluctuations.In the proposed method,the samples of wind power and loads are first generated by the inverse Nataf transformation and then clustered using an improved K-means algorithm to obtain input variable samples with small variances in each cluster.With such pre-processing,the cumulant method can be applied within each cluster to calculate cumulants of output random variables with improved accuracy.The results obtained in each cluster are combined according to the law of total probability to calculate the final cumulants of output random variables for the whole samples.The proposed method is validated on modified IEEE 9-bus and 118-bus test achieve a better performance with the consideration of both traditional CM,2 m+1 point estimate method(PEM),Monte Carlo simulation(MCS)and Latin hypercube sampling(LHS)based MCS,the proposed method can achieve a better performance with the consideration of bothcomputational efficiency and accuracy.

Large-scale OPF Based on Voltage Grading and Network Partition

In this paper,a model of a large-scale optimal power flow(OPF)under voltage grading and network partition and its algorithm is presented.Based on the principles of open loop operations,the node injecting current method is used to divide the large-scale power grid into voltage grading and district dividing structures.The power network is further divided into a high-voltage main network and several subnets according to voltage levels of 220 kV.The subnets are connected by means of boundary nodes,and the partition model is solved using the improved approximate Newton direction method,which achieves complete dynamic decoupling simply by exchanging boundary variables between the main network and the subnets.A largescale power grid thus is decomposed into many subnets,making the solution of the problem simpler and faster while helping to protect the information of individual subnets.The system is tested for correctness and effectiveness of the proposed model,and the results obtained are matched in real-time.Finally,the algorithm is seen to have good convergence while improving calculation speed.

Cross-seam Hybrid MTDC System for Integration and Delivery of Large-scale Renewable Energy

To better utilize the diversity of renewable energies in the U. S., this paper proposes a cross-seam hybrid multi-terminal high-voltage direct current(MTDC) system for the integration of different types of renewable energies in the U. S.Based on a developed station-hybrid converter design, the proposed hybrid MTDC system further investigates the connection methods of renewable energies and develops novel flexible power flow control strategies for realizing uninterrupted integration of renewable energies. In addition, the frequency response control of the hybrid MTDC system is proposed by utilizing the coordination between the converters in the hybrid MTDC system.The feasibility of the hybrid MTDC system and the performance of its corresponding control strategies are conducted in the PSCAD/EMTDC simulation. The simulation results indicate that the proposed hybrid MTDC system could realize the uninterrupted integration of renewable energies and flexible power transmission to both coasts of U.S.