Multi-objective sustainable DER allocation in active distribution networks based on Slime Mould Algorithm

Abstract

The rapid increase in deployment of Distributed Energy Resources (DERs) is transforming traditional passive distribution networks into active and intelligent power systems capable of meeting the objectives of the sustainable energy transition. However, identifying appropriate locations and capacities for DER installation is a difficult multi-objective optimization problem since technical, operational and environmental aspects are to be satisfied simultaneously. This paper is focused on presenting a sustainability-oriented optimization framework for optimal DER placement and sizing in the IEEE 33-bus radial distribution system based on Slime Mould Algorithm (SMA). The developed framework takes into account several performance objectives including; minimizing the total active power loss, reducing the reactive power loss, improving the voltage stability, reducing the carbon emissions and increasing the penetration of renewable energy. A detailed mathematical model for multi-objective problem is developed under constraints on power balance, voltage regulation, feeder current and DER capacity. Back ward forward sweep load-flow calculation is performed to determine network performance during the optimization process. Besides, a sensitivity analysis is presented to demonstrate the effect of DER penetration, variations in the load and selection of weights of objective-function on network performance. The robustness and efficiency of the proposed approach is further verified using Friedman ranking and Wilcoxon signed rank statistical tests. The proposed SMA-based allocation reduces active power loss from 202.67 kW to 58.73 kW (71.0%) and reactive power loss from 135.14 kVAr to 42.85 kVAr (68.3%). The minimum bus voltage improves from 0.9131 p.u. to 0.988 p.u., while the voltage stability index increases from 0.6678 to 0.9548. Annual carbon emissions decrease from 1,455,819 kg CO₂/year to 842,600 kg CO₂/year (42.1%), accompanied by an increase in renewable energy penetration to 92.35%. The proposed SMA also demonstrates superior convergence, computational efficiency, and robustness compared with PSO, DE, GA, and WOA. These results establish SMA as an effective optimization tool for sustainable DER planning and the development of resilient active distribution networks.