The planning problem of electrical power distribution networks, stated as a mixed nonlinear integer optimization problem, is solved using the ant colony system algorithm (ACS). The behavior of real ants has inspired the development of the ACS algorithm, an improved version of the ant system (AS) algorithm, which reproduces the technique used by ants to construct their food recollection routes from their nest, and where a set of artificial ants cooperate to find the best solution through the interchange of the information contained in the pheromone deposits of the different trajectories. This metaheuristic approach has proven to be very robust when applied to global optimization problems of a combinatorial nature, such as the traveling salesman and the quadratic assignment problem, and is favorably compared to other solution approaches such as genetic algorithms (GAs) and simulated annealing techniques. In this work, the ACS methodology is coupled with a conventional distribution system load-flow algorithm and adapted to solve the primary distribution system planning problem. The application of the proposed methodology to two real cases is presented: a 34.5-kV system with 23 nodes from the oil industry and a more complex 10-kV electrical distribution system with 201 nodes that feeds an urban area. The performance of the proposed approach outstands positively when compared to GAs, obtaining improved results with significant reductions in the solution time. The technique is shown as a flexible and powerful tool for the distribution system planning engineers.

Linear network modeling and phasor measurement units (PMUs) simplify the traditional system state estimation (SSE) problem. The existing multiphase SSE-PMU-based models are linear including earthing resistances as a fixed and invariable parameter. However, earthing resistances strongly depend on moisture and temperature changes over time. Thus, under unbalanced operation time-varying Neutral-Earth Voltages (NEV) could be higher than admisible touch and step voltages in urban areas. Earthing resistances can be now monitored using specialized meters and therefore duly incorporated as measured and state variables in a multigrounded SSE problem. Thus, the SSE problem becomes non-linear and the standard linear solution approach is no longer suitable. This fact has been overlooked in the literature. To fulfill the research gap, a new multigrounded SSE-PMU-based formulation is presented. As a key contribution, the normal-equation structure used in linear SSE approaches was extended to a non-linear one in order to allow the estimation of grounding resistances, neutral-to-earth voltages, and neutral currents. The proposal was applied in a 2-bus example for illustration purposes and successfully applied and compared with existing methods under large-scale conditions.