This paper presents a distribution planning on a geographical network, using improved K-means clustering algorithm and is compared with the conventional Euclidean distance based K-means clustering algorithm. The distribution planning includes optimal placement of substation, minimization of expansion cost, optimization of network parameters such as network topology, routing of single/multiple feeders, and reduction in network power losses. The improved K-means clustering is an iterative weighting factor based optimization algorithm which locates the substation optimally and improves the voltage drop at each node. For feeder routing shortest path based algorithm is proposed and the modified load flow method is used to calculate the active and reactive power losses in the network. Simulation is performed on 54 nodes based geographical network with load points and the results obtained show significant power loss minimization as compared to the conventional K-means clustering algorithm.

This paper presents a marketing mechanism based on the Pay-As-Bid (PAB) method for reactive power ancillary services in the deregulated electricity market. Security, reliability and the location is major concern for Independent System Operator (ISO). So a modified Optimal Power Flow (OPF) optimization method is proposed in this paper to provide the system security. Firstly, the reactive power solution is obtained by solving a modified OPF model which maximizes system loadability subject to transmission security constraints imposed by thermal limits, voltage limits and stability limits. This modified OPF model is used for ensuring systemsecurity as well as for contingency analysis. Secondly, the Expected Payment Function (EPF) of generators is used to develop a bidding framework while Total Payment Function (TPF) based OPF is used to clear the PAB market. For the simulation and analysis purposes, a 24 bus RTS network is used in normal condition as well as in worst contingency state. The system security is preserved even in the worst contingency state. 

This paper presents a distribution network reconfiguration based on bacterial foraging optimization algorithm (BFOA) along with backward-forward sweep (BFS) load flow method and geographical information system (GIS). Distribution network reconfiguration (DNR) is a complex, non-linear, combinatorial, and non-differentiable constrained optimization process aimed at finding the radial structure that minimized network power loss while satisfying all operating constraints. BFOA is used to obtain the optimal switching configuration which results in a minimum loss, BFS is used to optimize the deviation in node voltages, and GIS is used for planning and easy analysis purposes. Simulation is performed on the 33-bus system and results are compared with the other approaches. The obtained results show that the proposed approach is better in terms of efficiency and having good convergence criteria.