Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty

This study proposes a stochastic dynamic multi-objective model for integration of distributed generations in distribution networks. The proposed model optimises three objectives, namely technical constraint dissatisfaction, costs and environmental emissions and simultaneously determines the optimal location, size and timing of investment for both distributed generation (DG) units and network components. The uncertainties of electric load, electricity price and wind power generations are taken into account using scenario modelling. A scenario reduction technique is used to reduce the computational burden of the model. The Pareto optimal solutions of the problem are found using a binary particle swarm optimisation (PSO) algorithm and finally a fuzzy satisfying method is applied to select the optimal solution considering the desires of the planner. The effectiveness of the proposed model is demonstrated by applying it to a realistic 201-node distribution network.