Quantifying power system flexibility for the energy transition in Colombia

González-Dumar, Antonio; Arango-Aramburo, Santiago; Correa-Posada, Carlos M.

doi:10.1016/j.ijepes.2023.109614

Cited by 9 publications

(1 citation statement)

References 26 publications

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“…Depending on the country, most of these generation systems are composed of mixed matrices that include renewable and fossil energy sources [28]. In Colombia, the electricity generation matrix is composed of about 67% renewable sources (primarily hydraulic plants), and the remaining 33% is based on fossil fuels (coal, natural gas, and diesel), which implies that a third of the power consumption has a carbon footprint [29]. Considering the above, the C g CO 2 ,k coefficient seeks to emulate the equivalent emissions caused by distribution networks [22].…”

Section: Objective Functionsmentioning

confidence: 99%

Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses

Montoya,

Grisales-Noreña,

Gil-González

2024

Sustainability

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The techno–environmental analysis of distributed energy resources in electrical distribution networks is a complex optimization task due to the non-convexities of its nonlinear programming formulation. This research employs convex optimization to address this issue while minimizing the expected carbon dioxide emissions and daily energy losses of a distribution grid via the optimal dispatch of battery energy storage units (BESUs) and renewable energy units (REUs). The exact non-convex model is approximated via semi-definite programming in the complex variable domain. The optimal Pareto front is constructed using a weighting-based optimization approach. Numerical results using an IEEE 69-bus grid confirm the effectiveness of our proposal when considering unitary and variable power factor operation for the BESUs and the REUs. All numerical simulations were carried out using MATLAB software (version 2022b), a convex disciplined tool (CVX), and the semi-definite programming solvers SEDEUMI and SDPT3.

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Section: Objective Functionsmentioning

confidence: 99%