Optimal distributed generation allocation in unbalanced radial distribution networks via empirical discrete metaheuristic and steepest descent method

Loss minimization and voltage improvement through distributed generation (DG) planning is a well-established problem. However, a careful review of the literature shows that there is still room for the development of efficient algorithms for this purpose. In special, hybridization between optimization techniques is suitable for this complex problem, as it allows taking advantage of the positive features of different approaches. In this work, a novel empirical discrete metaheuristic (EDM) is presented and merged with the steepest descent method to solve the DG allocation problem. The allocation is broken into two subproblems: sitting the DGs and sizing them. The EDM deals with the first subproblem, while the second one is solved by the steepest descent method in an interchangeable optimization structure. The EDM tackles some key limitations of metaheuristic family methods. Relatively, it shows: low results variability in different executions; low initial conditions dependency; and few number parameters to tune. All simulations are performed in a communication scheme using the softwares Matlab and OpenDSS. The obtained results with IEEE 34-bus and IEEE 123-bus distribution test systems were compared to the literature and other metaheuristics, attesting the quality of the proposed approach.