PROJECT TITLE :

A Stochastic Investment Model for Renewable Generation in Distribution Systems

ABSTRACT:

A model to obtain the optimal allocation and timing of renewable distributed generation under uncertainty is proposed as part of distribution expansion designing. The problem is formulated using a stochastic 2-stage multiperiod mixed-integer linear programming (MILP) model, where investment choices are worn out the primary stage and situation-dependent operation variables are solved in the second stage. The model aims to minimize renewable distributed generation (photovoltaic and wind) investment costs, substation enlargement investment value, operation and maintenance prices, energy losses value, and the cost of the power purchased from the transmission system. Active and reactive power flow equations are linearized and constraints embrace voltage limits, substation and feeders capacities, renewable generation limits, and investment constraints. The model is tested on a thirty four-bus system and conclusions are duly drawn.