Application of Deep Sleep Optimizer for strategic placement and sizing of distributed generation in power distribution systems

Strategic siting and sizing of renewable-dominated distributed generators (DGs) is pivotal to curbing technical losses and safeguarding voltage quality in modern distribution networks. This paper presents a fully scripted, direct Python-PowerFactory framework that eliminates the file-exchange overhead of conventional MATLAB co-simulation and embeds the recently conceived Deep-Sleep Optimizer(DSO) a meta-heuristic that alternates between “deep-sleep” global exploration and “wake” local exploitation. The proposed workflow is evaluated on the IEEE-33 radial feeder and rigorously benchmarked against the Genetic Algorithm (GA) and Teaching-Learning-Based Optimisation (TLBO). The multi-objective problem minimises real-power losses and maximises the minimum voltage-stability index subject to generator, voltage, and thermal constraints. For fair comparison all solvers use identical population sizes and iteration budgets. DSO reaches steady convergence in fewer than 20 iterations, whereas TLBO and GA require roughly 40 and 70 iterations, respectively. In the single-DG case DSO and TLBO each halve active losses (to ≈ 91 kW , − 56 % ) and raise the weakest node to 0.983 pu, outperforming GA’s 0.978 pu. With two DGs, DSO yields the lowest loss ( 55 . 9 kW , − 73 % ) using only 2 . 72 MW of capacity and completes in 528 s one third of TLBO’s runtime. The most demanding three-DG scenario underscores DSO’s superiority: losses plummet from 210 kW to 28 . 9 kW ( − 86 % ), the weakest-bus voltage climbs to 0.998 pu, no bus exceeds 1.013 pu, and the installed capacity ( 3 . 84 MW ) is 30% lower than GA’s requirement while runtime ( 666 s ) is 81 % shorter than TLBO’s. DSO also trims reactive losses by 81 % and produces the flattest voltage profile (standard deviation < 0 . 005 pu) without breaching the 0.95-1.05 pu statutory band. Because both DSO and TLBO are parameter-free, the framework removes the need for heuristic tuning, making it attractive for day-to-day planning. Overall, the results establish DSO as a high-performance, low-maintenance optimisation engine that unlocks deeper loss cuts and tighter voltage regulation than established methods at a computational cost compatible with routine distribution-planning studies. Future work will extend the methodology to time-series optimisation with stochastic photovoltaic and load profiles, meshed feeders, and full techno-economic assessment. • Deep Sleep Optimizer (DSO), a novel metaheuristic for enhancing DG placement and sizing in distribution grids. • DSO outperforms GA and TLBO in convergence speed, computational efficiency, and power loss minimization. • IEEE-33 bus simulations confirm DSO’s effectiveness in improving voltage stability and reducing power losses. • Multi-objective optimization in DSO enhances power loss reduction and voltage stability. • DSO demonstrates robust performance and has the potential for scalability in smart grid DG integration.