Monte Carlo Simulation Based Price Zone Partitioning Considering Market Uncertainty

The key for establishing simple and efficient zonal pricing scheme is to accurately partition transmission network in the presence of congestion. Unfortunately, in actual power market operation, operators usually establish price zones based on their experiences without mathematical analysis. In order to achieve accurate price zone partition, this paper proposes a Monte Carlo simulation based price zone partitioning method under market uncertainty. In this method, the lines that are most likely to get congested are determined first through a probability analysis while taking into account the market uncertainties in the generation bids, loads and bilateral transactions. Secondly the sensitivities of nodal power injections to the power flows on all congested lines are extracted as features of the nodal price, forming the cluster samples. Then a fuzzy c-means clustering algorithm for price zone partition is proposed by which the accurate and rational price zone partitioning is realized. Finally, taking IEEE 118-node system for example, the effectiveness and feasibility of this proposed method are verified