Optimal distributed generation allocation in radial distribution networks using a modified seagull optimization algorithm with elite reserve strategy

Abstract

Reconstructing radial distribution networks (RDNs) by integrating distributed generation (DG) is essential for enhancing the sustainability, reliability, and flexibility of power supply, thereby improving renewable energy utilization. This paper investigates the optimal distributed generation allocation (ODGA) problem, which involves both discrete (DG location) and continuous (DG capacity and power factor) decision variables and exhibits high non-convexity and computational complexity. To address these challenges, a single-objective modified seagull optimization algorithm (mSOA-SO) is proposed, incorporating an elite reserve strategy with access location guidance (ALG) and effective re-migration (ERM) into the base algorithm. Experimental results demonstrate that mSOA-SO achieves active power loss reductions of 93.96 %, 98.11 %, and 63.18 % on the IEEE 33-, 69-, and 119-node RDNs, respectively, by optimally integrating DGs with controllable power factors. To extend the method for practical multi-objective ODGA scenarios, this paper further develops a multi-objective modified seagull optimization algorithm (mSOA-MO) through the integration of an innovative multi-level performance evaluation (MLPE) strategy. Notably, mSOA-MO identifies superior DG schemes for both dual- and triple-objective ODGA problems, effectively reducing voltage deviation, active and reactive power loss in RDNs. Thus, the proposed mSOA-SO and mSOA-MO effectively identify advantageous DG allocation schemes, serving as robust techniques for the secure integration of renewable energy into RDNs. This study emphasizes the crucial role of intelligent algorithms in energy management and enhancing the environmental benefits of energy supply.