A Stochastic Model for Medium-Term Distribution System Planning Considering CO2 Emissions

This paper presents a new two-stage stochastic mixed-integer linear programming model for the medium-term expansion planning of power distribution systems considering the uncertainty associated with renewable energy sources, demand, and energy prices at substations. Investment decisions comprise the installation of both 1) classical alternatives such as conductors, capacitor banks, and voltage regulators, and 2) modern alternatives such as renewable distributed generation and energy storage units. Moreover, unlike conventional planning models, the proposed approach includes a voltage-dependent load representation. The proposed model aims to find a planning strategy that minimizes the investment and operating costs while meeting network operational constraints and CO2 emissions requirements. Tests are carried out with a 69-node distribution system and the results demonstrate the effectiveness and applicability of this model as an effective means of promoting an efficient, sustainable, and environmentally-friendly network.