A Bi-level Multi-objective Optimization Model for the Planning, Design and Operation of Smart Grid Projects. Case Study: An Islanded Microgrid


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Authors

  • Ricardo Echeverri Martínez
  • Eduardo Caicedo Bravo
  • Wilfredo Alfonso Morales
  • Juan David Garcia-Racines

Abstract

The planning and operation of Smart Grid projects is an issue that has increased in complexity and requires further analysis. This is due to the increase of distributed generation sources, generation with renewable sources, storage systems, and a disarticulation of information between the different levels in the sector and the stakeholders. All these factors lead to the inherent difficulty of defining appropriate models that help decision making. This paper proposes a bi-level optimization model to solve the problem of planning and operation of microgrid projects, as these can be considered as an ideal small-scale prototype of the so-called Smart Grids. In this bi-level scheme, the problem of planning or design of the microgrid is formulated at the upper level, while the problem of power dispatch or operation of the units is described at the lower level. The proposed multilevel multi-objective decision model is inspired by the System of System (SoS) concept in order to integrate qualitative and quantitative decision-making tools. Likewise, Key Performance Indicators (KPIs) are used for the detailed and continuous monitoring of any project. The presented model is applied using the information of an electrically isolated microgrid on the Colombian Pacific coast.Keywords: Smart Grids, Bi-level optimization, Decision Making, Key Performance Indicators, QFD, Energy planning and management.JEL Classifications: C61, D70, L94, Q42.DOI: https://doi.org/10.32479/ijeep.9343

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Published

2020-05-16

How to Cite

Martínez, R. E., Caicedo Bravo, E., Alfonso Morales, W., & Garcia-Racines, J. D. (2020). A Bi-level Multi-objective Optimization Model for the Planning, Design and Operation of Smart Grid Projects. Case Study: An Islanded Microgrid. International Journal of Energy Economics and Policy, 10(4), 325–341. Retrieved from https://econjournals.com/index.php/ijeep/article/view/9343

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