Abstract
The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.
Highlights
In recent years, grid operators have been forced by new regulations and incentives imposed by grid regulators to improve the operating conditions of their electrical grids, such as power losses, voltage profiles, line loadability and harmonic distortion index [1]
Evolutionary algorithms have been used to face this problem [22,23] due to the satisfactory results given by this type of optimization techniques in non-convex mixed-integer nonlinear problems [26], which is the type of problem describing the location of Distributed Generators (DGs) in DS [27]
For Case 1, The parallel PBIL (PPBIL) algorithm presents a minimum reduction of active power losses equal to 38.62%, which is the same obtained by the parallel implementation of the Monte-Carlo (PMC), and just 1.69% lower than the best solution (GA)
Summary
Grid operators have been forced by new regulations and incentives imposed by grid regulators to improve the operating conditions of their electrical grids, such as power losses, voltage profiles, line loadability and harmonic distortion index [1]. Evolutionary algorithms have been used to face this problem [22,23] due to the satisfactory results given by this type of optimization techniques in non-convex mixed-integer nonlinear problems [26], which is the type of problem describing the location of DGs in DS [27] Such an approach becomes ineffective as the distribution systems grow because, as the solution space expands and the complexity of the problem increases, the computation time becomes longer and the possibility of falling into a local optima increases, which, in most cases, fails to provide a good solution for the system.
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