Optimal Allocation and Sizing of Distributed Generation Using Interval Power Flow

Modern distribution systems and microgrids must deal with high levels of uncertainty in their planning and operation. These uncertainties are mainly due to variations in loads and distributed generation (DG) introduced by new technologies. This scenario brings new challenges to planners and system operators that need new tools to perform more assertive analyses of the grid state. This paper presents an optimization methodology capable of considering uncertainties in the optimal allocation and sizing problem of DG in distribution networks. The proposed methodology uses an interval power flow (IPF) that adds uncertainties to the combinatorial optimization problem in charge of sizing and allocating DG units in the network. Two metaheuristics were implemented for comparative purposes, namely, symbiotic organism search (SOS) and particle swarm optimization (PSO). The proposed methodology was implemented in Python((R)) function consists of minimizing technical losses and regulating network voltage levels. The results obtained from the proposed IPF on the tested networks are compatible with those obtained by the PPF, thus evidencing the robustness and applicability of the proposed method. For the solution of the optimization problem, the SOS metaheuristic proved to be robust, since it was able to find the best solutions (lowest losses) while keeping voltage levels within the predetermined range. On the other hand, the PSO metaheuristic showed less satisfactory results, since for all test systems, the solutions found were of lower quality than the ones found by the SOS.

Automated summary

Modern distribution systems and microgrids face uncertainties caused by variations in loads and distributed generation (DG), requiring new tools for more accurate grid state analysis. This paper presents an optimization methodology that addresses uncertainties in the optimal allocation and sizing of DG in distribution networks. The proposal utilizes interval power flow (IPF) to incorporate uncertainties into the combinatorial optimization problem, and implements symbiotic organism search (SOS) and particle swarm optimization (PSO) as metaheuristics for comparison. The results show that the SOS metaheuristic performs better in terms of finding optimal solutions and regulating voltage levels compared to the PSO metaheuristic.