Multi-objective distribution planning approach for optimal network investment with EV charging control

The paper proposes a hybrid methodology based on a Gaussian local search and a genetic algorithms to address the multi-objective and multistage optimal distribution expansion planning problem. The methodology is applied to solve the problem of optimal network investment under the new possibilities enabled by the smart grid, namely the new observability and controllability investments that will be available to manage electric vehicles charging in the future. A multi-objective optimization problem is solved for a low-voltage network feeder and a medium-voltage network feeder with a high EV penetration level scenario. The Pareto surface solution is then projected onto the two investment types considered: demand control investments and traditional network asset investments. The projected surface is then analysed to discuss the merit of demand control w.r.t. postponing tradition asset investments.