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Monte Carlo based probabilistic partitioning of price zone with scale-space hierarchical clustering

Abstract It is important for establishing a simple and efficient zonal pricing scheme to accurately partition price zone in the presence of congestion. In the existed partition methods, price zones are usually established based on the operators’ experience, considering the determinate locations of congested lines. However, in actual power system operation, the congestion conditions of the transmission network vary with many uncertain market factors. In order to consider the market uncertainties in price zone partition caused by the generation bids, loads and bilateral transactions, this paper proposes a Monte Carlo based probabilistic partitioning method of price zone with scale-space hierarchical clustering. In this method, the lines that are most likely to get congested are determined firstly through a Monte Carlo based 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 power flows on all the congested lines are extracted as features of nodal price, forming the cluster samples. Then, by means of the scale-space theory simulating human visual system, a scale-space based hierarchical clustering method for price zone partition is proposed. Through this method, an accurate and rational price zone partition is realised without any prior zone partitioning information. Finally, taking IEEE 118-node system for example, the effectiveness and feasibility of this proposed method are verified.