An insight into the impact of solar and wind powers' probability distributions on distribution network investments

Abstract

With the introduction of renewable generators, the investment challenges have also increased recently because of the associated stochastic behaviors. Their impacts in terms of the investment related to the distribution network could be different depending on the probability distribution of the corresponding renewable generators, historical-data modeling, and network structure. Therefore, the impacts of the probability distributions of wind power plants (WPPs) and solar power plants (SPPs) are analyzed thoroughly for different case studies by using a convolution-based distribution network planning (DNP) model. The following six cases are considered: the 1) integration of only WPPs considering one scenario of load, wind, and solar powers, 2) integration of only WPPs considering four scenarios, 3) integration of only SPPs considering one scenario, 4) integration of only SPPs considering four scenarios, 5) integration of both WPPs and SPPs considering one scenario, and 6) integration of both WPPs and SPPs considering four scenarios. The results show that considering the four scenarios is more suitable for a risk-averse approach planning, as the chance constraints are formulated separately for all the scenarios. However, the probability distribution of a different generation technology exerts a significant impact on the investment results of DNP. © 2020 Elsevier B.V., All rights reserved.