Load flow balancing and transient stability analysis in renewable integrated power grids

Due to random patterns of demand response from the consumer side and the unreliable nature of renewable energy resources, load flow balancing and transient stability become challenging issues in power systems. They are even more challenging in cases of multiple interval three phase (L-L-L) faults (MITPFs), which arise in power systems due to power quality disturbances. The intent of this work is to examine the influence of MITPFs on renewable energy resources (RERs) for load flow balancing and transient stability analysis. Wind turbine power dispatchability and uncertainty have a significant impact on load flow balancing and transient stability, especially in cases of occurrence of MITPFs. Probabilistic modeling is performed in this paper to formulate the complexity of randomness for load flow balancing through a smart node and transient stability analysis through a unified power flow controller. The proposed probabilistic algorithm is based on the deviations between generation and demand response patterns due to an MITPF. An autocorrelation expansion is applied to approximate the randomness of probabilistic variables between the forecast generation and actual response pattern. Future contingencies can be predicted before disruptive changes arise due to the occurrence of an MITPF using the above probabilistic analysis. Simulation results show that the proposed algorithm outperforms existing alternatives and can achieve near optimal performance for a wide range of load variations and power quality disturbances in renewable-integrated power grids.