Recent trends of the most used metaheuristic techniques for distribution network reconfiguration

Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topi

Comparison between time-constrained and time-unconstrained optimization for power losses minimization in Smart Grids using genetic algorithms

The power losses reduction is one of the main targets for any electrical energy distribution company. This paper studies the applicability of a control system based on a Genetic Algorithm (GA) on a portion of the actual Italian electric distribution network located in Rome and surroundings, managed by the ACEA Distribuzione S.p.A. The joint optimization of both power factor correction (PFC) and distributed feeder reconfiguration (DFR) is faced. The PFC is performed tuning the phases of the distributed generators (DGs) and the output voltage of the Thyristor Voltage Regulator (TVR). The DFR is performed by opening and closing the available breakers according to a graph based algorithm that is able to find all the possible radial configurations of the network. The joint PFC and the DFR optimization problem are faced by solving a suitable optimization problem, defining the fitness function that drives the GA. In order to have the opportunity to study a realistic future scenario, the actual network has been modified by introducing a few extra distributed generators. Aiming to validate the applicability of the proposed algorithm to an operative scenario, two different tests have been performed. The first one, referred to as time-unconstrained optimization, represents an ideal scenario where there are no constraints on time available for optimization. The second one, referred to as time-constrained optimization, represents a real scenario where the optimization must be completed within a time slot of one hour. Both tests have been performed by feeding the developed simulation tool with real data concerning dissipated and generated active and reactive power values. The comparison between results obtained in the two tests campaigns furnishes the opportunity to evaluate the effectiveness of the proposed control algorithm in real time, relying on the computational performances of an entry-level workstation. The obtained results encourage the use of derivative free methods in a real-time control scenario, showing that the performances achieved by the time-constrained optimization procedures are very close in terms of objective function values to the ones obtained by the time-unconstrained procedure