Stochastic multi-objective optimal placement and sizing of distributed generation in multi-microgrid systems using enhanced modified spider wasp optimization

The incorporation of Distributed Generation (DG) units into Multi-Microgrid (MMG) systems has significantly enhanced the operational flexibility, reliability and economic efficiency of modern smart grids. The nonlinear, constrained and stochastic nature of the optimization problem, in addition to the multi-objective nature, makes the determination of the optimum location and size of a DG unit a challenging task. In this paper, Enhanced Modified Spider Wasp Optimization (EMSWO) algorithm is proposed for the stochastic placement and sizing of DGs in interconnected MMGs. In order to promote population diversity, fasten convergence speed, avoid premature convergence and meet the network operation constraints, chaotic population initialization, adaptive exploration–exploitation strategy, elite preservation, repair-based constraint handling mechanism are proposed for the proposed EMSWO. The proposed stochastic planning framework aims at minimizing the Total Present Worth (TPW) and Levelized Cost of Energy (LCOE) while simultaneously meeting power balance, voltage deviation, DG penetration, and generation capacity constraints. Using Monte Carlo simulation, uncertainties in load demand, renewable generation and electricity prices are captured, and a scenario reduction technique is used to maintain the statistical properties in a lower dimensional space with less computational effort. The proposed EMSWO is tested on a benchmark MMG system for different operating scenarios. The simulation results show that the obtained TPW is reduced from 434.25 million USD to 404.52 million USD and LCOE is reduced to 0.174 USD/kWh which is better than the conventional Spider Wasp Optimization (SWO), Grey Wolf Optimization (GWO), Cyclonic Convergent Particle Swarm Optimization (CCPSO) and Particle Swarm Optimization (PSO). In addition, the convergence and statistical results validate that the proposed EMSWO results in faster convergence, more stable solutions, better voltage profile and lower operating cost, which proves its effectiveness as a solid optimization framework for stochastic DG planning in interconnected MMG networks.