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In this paper, a new method is proposed for network loss and voltage deviation minimization in distribution networks. The proposed method makes use of multi-objective meta-heuristics (MOMH) that evaluates a set of the Pareto solutions systematically. The power networks become more deregulated and competitive due to the power liberalization. Hence, the cost minimization has been recognized as one of the most important factors in distribution systems. To compete with other players, saving the cost is of main concern in distribution companies This paper focuses on network reconfigurations and capacitor control to minimize the two objective functions of the network loss and voltage deviation minimization. The formulation may be expressed as a combinatorial optimization problem. It needs to use MOMH to evaluate a set of the Pareto solutions for the two objective functions. As MOMH, this paper uses SPEA2 to solve the minimization problem. The proposed method is successfully applied to the 69-node distribution system.

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In this paper, a data mining technique is proposed for distribution network voltage regulation. The proposed method efficiently extracts rules for dealing with three-phase distribution network loss minimization. Distribution companies are concerned with the network operation to make a profit under competitive environment. From a standpoint of power quality, the mathematical formulation of distribution network voltage control is hard to solve due to the complexity. In that sense, distribution operators do not understand the problem features well. This paper presents a regression-tree-based method that clarifies the nonlinear relationship between control variables and the network loss in a distribution network. A regression tree is constructed to find out rules in a distribution network voltage regulation. The proposed method is successfully applied to the IEEE 34-node system.