Finding the location and size of distributed generation in radial distribution systems using geolocation-aware heuristic approach based algorithms

With the rapidly growing concerns about the environment, especially with the growing demand on power, the use of renewable energy has attracted significant attention in recent years. The integration of these units in existing power grids, which rely on large centralized units, has emerged significant challenges, according to their smaller sizes and distributed installation. One of the main challenges in the integration of these units faces is the size of the unit being installed in the grid and the location in which is must be connected. An optimal location and size can significantly improve the overall quality of the power being provided to the customers over the power grid. For this purpose, many of the recent studies have employed heuristic optimization algorithms to optimize the placement of these units. However, the use of the busbar number to select the busbar that the DG unit is to be connect to can impose significant limitation to the performance of that optimizer, according to the absence of the required relation between the value and its location. Accordingly, a new optimization approach is proposed in this study, in which the optimizer selects the busbar based on its geolocation, which provides the required relation between the values and their positioning in the search space. The proposed method has been evaluated using three optimization algorithms, the Particle Swarm Optimizer (PSO), the Artificial Bee Colony (ABC) and the Sine-Cosine Algorithm (SCA). The evaluation results show significant improvement in the performance of these optimizers when using the proposed geolocation-aware approach, which has led to a significant improvement in the quality of power being provided by the grid. Placing the DG units based on the locations and sizes selected by the proposed method has been able to maintain the voltages in the acceptable range, while minimizing the total losses in the power grid.