A Stochastic Investment Model for Renewable Generation in Distribution Systems

A model to obtain the optimal allocation and timing of renewable distributed generation under uncertainty is proposed as part of distribution expansion planning. The problem is formulated using a stochastic two-stage multiperiod mixed-integer linear programming (MILP) model, where investment decisions are done in the first stage and scenario-dependent operation variables are solved in the second stage. The model aims to minimize renewable distributed generation (photovoltaic and wind) investment costs, substation expansion investment cost, operation and maintenance costs, energy losses cost, and the cost of the power purchased from the transmission system. Active and reactive power flow equations are linearized and constraints include voltage limits, substation and feeders capacities, renewable generation limits, and investment constraints. The model is tested on a 34-bus system and conclusions are duly drawn.