A Stochastic Programming Model for the Planning of Distribution Systems Considering Renewable Distributed Generation and CO2 Emissions

• Main Author: De Lima, Tayenne Dias [UNESP]
• Publication Date: 2019
• Other Authors: Tabares, Alejandra [UNESP], Arias, Nataly Bañol, Franco, John F. [UNESP]
• Format: Conference object
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1109/ISGT-LA.2019.8895395; http://hdl.handle.net/11449/221406
• topic: Electrical distribution systems; expansion planning; renewable distributed generation; stochastic programming; uncertainties

The presence of renewable distributed generation (DG) in electrical distribution systems (EDSs) has been increased in recent years, bringing technical, economical, and environmental benefits. However, the stochastic nature of renewable DG units increases the complexity of the planning and operation of EDSs. Hence, advanced planning models that take into account the uncertain nature of the renewable DG units, as well as their benefits in reducing emissions, are required. This work proposes a two-stage stochastic programming model for the expansion planning of EDSs that considers the uncertainties associated with the renewable DG units, the demand, and the energy price. The objective function minimizes the net present value of investments and operation costs, as well as the cost of CO2 emissions. The proposed model was implemented in the AMPL modeling language and solved via the commercial solver CPLEX. Tests with a 24-node system illustrate the efficiency of the proposed model.