The potential for generating electricity with photovoltaic systems is high in Colombia given its geographical position in the tropic. Some departments in Colombia have low electricity coverage and high rates. In the department of Putumayo there is a low coverage rate and high energy costs, while the solar radiation potential is high. Due to the geographical differences of the Putumayo subregions, the radiation potential for electricity generation is unknown. In addition, in this department the energy tariffs are above the national average. The objective of this paper is to determine the effective potential for solar photovoltaic power generation in the Putumayo department with a detailed methodology considering the information of different remote database and meteorological stations and some technical conditions. It was found that the highest effective solar potential occurs in the Amazon region, and the lowest in the Andean region in the Putumayo. On the other hand, when evaluating electricity consumption and tariffs in the regions, it is concluded that consumption can be satisfied with photovoltaic systems by producing self-generating electricity and distributed generation.

The phenomena of climatic variability such as El Niño affect the expansion planning of electricity supply systems with hydroelectric power plants due to the uncertainty presented in the variables of rainfall patterns, temperature, wind, solar radiation changes, among others. The El Niño affects the electricity generation in Colombia, Venezuela and northwestern Brazil due to severe droughts that reduce water flows in rivers and water volume in dams. While in Peru, Paraguay, Bolivia, Uruguay, Argentina and southern Brazil, causes heavy rains that lead to an increase in reservoirs. Recent findings provide sufficient evidence on how climate change modifies the patterns of duration, frequency and intensity of El Niño and therefore will introduce additional uncertainties to the expansion planning of electricity generation systems in countries that uses predominantly hydroelectric power. The vulnerability of electricity supply systems with a significant participation of hydroelectric power plants in Colombia,

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

The optimal power flow is an important tool for power system planning and power system operation. It is used in a 24-hour period to find an economic dispatch of generating units considering network restrictions. The optimal power flow provides valuable information about the operation cost, the transmission flows, the generation and the congestion in the system. This information is used by generators, planners, operators and regulators in order to analyze and take decisions about the system at short and long term. The first one corresponds to the information for the operation. The second one corresponds to the information for the planning. This paper proposes a detailed optimal power flow formulation looking for a minimum cost of generation considering wind generation. Five solvers (CBC, CLP, CPLEX, Gurobi and GLPK.) have been used in order to compare differences between them. These solvers are commonly used to solve the multiperiod DC optimal power flow. An IEEE-24 test system is used to compare the solutions provided by the solvers. The findings reveal significant differences between the solvers when they are used to solve the IEEE-24 test system. Additionally, the computing time for each solver is reported. The solvers CPLEX and Gurobi show the lowest computational time to find a solution.