Capacity Management in Smart Grids Using Greedy Randomized Adaptive Search Procedure and Tabu Search

Over time, distribution systems have progressed from small-scale to complex networks, requiring modernization adapt these increasing levels of active loads and devices. It is essential manage the capacity networks support all new technologies. This work, therefore, presents a method for evaluating impact optimal allocation sizing DGs load shedding response demand programs on improve reliability financial performance electric power systems. The proposed optimization tool uses Greedy Randomized Adaptive Search Procedure Tabu algorithms. combined DG simultaneously with shedding, indices, transference, possibility islanded operation significantly improves quality planning proposals obtained by developed method. results demonstrate efficiency robustness method, improving voltage profile up 2.02%, relieving network capacity, restoration capability reliability. Statistical analysis also carried out highlight methodology.