Zonal Partitioning of Deregulated Power Systems Considering Fuzzy-Random Load Model

Market zonal partitioning is one of the most efficient methods for congestion management which is used in many power markets around the world. To improve the market performance it is very important to recognize the best zones of the system. While congestion is due to deficiencies in transmission network or generation, load pattern of the system is the most important variable which has impact on occurrence of congestion and therefore it has a large influence on zonal partitioning. This paper uses a fuzzy-random load model to take into account both probabilistic and possibilistic uncertainties of long-term predicted load in market studies. Based on this load model, the proposed approach finds mostly congested transmission lines using Fuzzy Monte Carlo Simulation (FMCS). Finally, using a clustering feature which depends just on the network parameters, Fuzzy C-mean (FCM) clustering algorithm is used to divide the network into the best partitions. Such a partitioning algorithm is useful especially in the stage of transmission expansion planning to study the market with any possible future plan. The work is illustrated using 30-bus IEEE test system. (