Stochastic bi-level modelling and optimization of dynamic distribution networks with DG and EV integration

Abstract

This study proposes a two-level multi-objective particle swarm optimization (MPSO) framework, enhanced by a novel mutation mechanism, to optimize energy management in stochastic dynamic distribution network reconfiguration (DDNR). The hierarchical model addresses real-time decision-making under uncertainty by minimizing power losses at Level 1 through optimal switching configurations, and simultaneously reducing operating costs and Energy Not Supplied (ENS) at Level 2 by leveraging distributed generation (DG) and electric vehicles (EV) with the Eliminating Zone method to manage uncertainties in demand and market prices. The three objectives—losses, costs, and ENS—are integrated into a non-dominated solution set to balance trade-offs. Simulation on a 95-node test network shows that the proposed MPSO outperforms conventional methods (PSO, SFLA, GWO), achieving a 25% reduction in static distribution network reconfiguration losses (from 540 kW to 449.51 kW), a 21% reduction in losses (from 39,695.45 kWh to 32,823.36 kWh), and a 35% decrease in ENS under dynamic reconfiguration. These quantitative results demonstrate the effectiveness of the proposed approach in enhancing energy efficiency, reducing costs, and improving reliability, supporting the development of sustainable and resilient smart grids.