Electric utility resource planning using Continuous-Discrete Modular Simulation and Optimization (CoDiMoSO)

Juan Pablo Sáenz, Nurcin Celik, Shihab Asfour, Young Jun Son

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Electric utility resource planning traditionally focuses on conventional energy supplies such as coal, natural gas, and oil. Nowadays, planning of renewable energy generation as well as its side necessity of storage capacities have become equally important due to the increasing growth in energy demand, insufficiency of natural resources, and newly established policies for low carbon footprint. In this study, we propose to develop a comprehensive simulation based decision making framework to determine the best possible combination of resource investments for electric power generation and storage capacities. The proposed tool involves a combined continuous-discrete modular modeling approach for processes of different nature that exist within this complex system, and will help the utility companies conduct resource planning via employed multiobjective optimization techniques in a realistic simulation environment. The distributed power system considered here has four major components including (1) energy generation via a solar farm, a wind farm, and a fossil fuel power station, (2) storage via compressed air energy storage system, and batteries, (3) transmission via a bus and two main substations, and (4) demand of industrial, commercial, residential and transportation sectors. The proposed approach has been successfully demonstrated for the electric utility resource planning at a scale of the state of Florida.

Original languageEnglish (US)
Pages (from-to)671-694
Number of pages24
JournalComputers and Industrial Engineering
Volume63
Issue number3
DOIs
StatePublished - Nov 1 2012

Keywords

  • Continuous-discrete simulation modeling
  • Distributed energy generation and storage
  • Electric utility capacity planning
  • Integrated decision making

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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