Multi-objective planning of distributed resources (PV and SVC) with NSGA-II for radial networks: application to the IEEE 33-bus test system

Abstract

The quality of electricity supply in distribution networks is critically dependent on minimizing active power losses and ensuring voltage stability. This study proposes a unified multi-objective optimization approach for the simultaneous placement and sizing of a photovoltaic (PV) source and a static var compensator (SVC) in radial networks. The non-dominated sorting genetic algorithm II (NSGA-II) is employed as the robust methodology to generate the Pareto optimal front, effectively exploring the trade-offs between two conflicting objectives: active loss minimization and voltage profile improvement. Unlike sequential or single-unit optimization strategies, this joint optimization framework is the key novelty, leveraging the specific physical interaction between PV active power injection and SVC-based dynamic reactive support to maximize overall network efficiency. Simulations are performed on the standard IEEE 33-bus test system. The results demonstrate that the optimal and coordinated integration of a 0.97 MW PV system at bus 14 and a 1.32 MVAr SVC at bus 30 yields superior electrical performance. Specifically, the system achieves a substantial active power loss reduction of 62.53% and decreases the voltage deviation index from 0.117 p.u. to a minimum of 0.0169 p.u., confirming the effectiveness of the proposed NSGA-II approach for comprehensive distributed resource planning.