Comparative analysis of deterministic and probabilistic methods for the integration of distributed generation in power systems

• Published in: Energy reports 2020-02, Vol.6 (3), p.88-104
• Main Authors: Beltrán, Juan Carlos, Aristizábal, Andrés Julián, López, Alejandra, Castaneda, Mónica, Zapata, Sebastián, Ivanova, Yulia
• Format: Article
• Language: English
• Subjects: Deterministic methods; Distributed generation; Probabilistic methods; Solar power; Wind power
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2019.10.025

In this article, a comparative analysis is made between three statistical methods (Taguchi's Orthogonal Array Testing method, Monte Carlo and Two-Point method) by integrating the uncertainty of primary sources of renewable generation in systems of electric power. The modeling of the Institute of Electrical and Electronics Engineers test system of 13 nodes is made by integrating the distributed generation with two different sources: wind and photovoltaic. For the simulation of wind power generation, the wind speed data is from El Cabo de la Vela in the Guajira department in Colombia and for the simulation of solar power generation, the solar radiation data is from Bogota city in Colombia. Once the system of 13 nodes is modeled and incorporated to the variability of primary resource and the load in each case; the load flow can be made by using the Matpower tool in Matlab for each one of the statistical methods proposed. The voltage, power generated, and power demanded data is recovered for each method to create comparison charts, establish the advantages, and disadvantages of each one in the analysis of the distribution of power systems with distributed generation. The main results are: the Taguchi's Orthogonal Array Testing method improves its behavior if the number of levels is increased for each variable; more iterations in the Montecarlo method produce a greater precision of the probabilities; and the two-point method is a combination between the benefits of the deterministic and the probabilistic.