Simulation of demand growth scenarios in the Colombian electricity market: An integration of system dynamics and dynamic systems: An integration of system dynamics and dynamic systems

Modeling and simulation of electricity markets have increasingly involved the use of a system dynamics (SD) approach. Accordingly, the resulting dynamic hypothesis and the stock-flow structures are represented and simulated using softwares such as Stella, Powersim, iThink, or Vensim. However, SD models can be exploited even more, of which the investigation of signals in the time domain or the sensitivity analysis is just a small part of the study. Since SD models are mathematical objects, they deserve an analytical or numerical study using tools provided by the dynamic systems (DS) methodology. Therefore, this paper not only studies the dynamic hypothesis or the stock-flow structure of an electricity market model in the classical form, but also uses its inner mathematical object to provide a deeper insight into the system. Using MATLAB/Simulink® the system is evaluated from a different approach not yet reported in the literature. The combination of the SD and DS methodologies can open the door to a new and alternative method of analysis for electricity market models and even for any SD model. In fact, this paper demonstrates that with this methodologies combination, more detailed analysis strategies and novel insights of the SD models can be developed, which can be easily exploited by policy makers to suggest improvements in regulations or market structures. Moreover, considering that the energy market is evolving, and a series of macro and microstructural changes are impacting demand, we consider as an example a simplified version of the Colombian electricity market to report a detailed description of its dynamics under a broad range of growth rate of demand (GRD) scenarios. Our study, inspired by the bifurcation and control theory of DS, primarily shows that Colombia is in dire need of a new capacity before 2020 to avoid rationing events expected to occur in the upcoming years.