A dynamic data-driven approach for operation planning of microgrids

Xiaoran Shi, Haluk Damgacioglu, Nurcin Celik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data-driven application systems (DDDAS) approach is proposed for determining the realtime operation plan of an electric microgrid while considering its conflicting objectives. In particular, the proposed approach is equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scale identification procedures; and 3) a multiobjective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scale of the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study demonstrate the power of the proposed approach in real-time assessment and control of operation in microgrids.

Original languageEnglish (US)
Title of host publicationProcedia Computer Science
PublisherElsevier
Pages2543-2552
Number of pages10
Volume51
Edition1
DOIs
StatePublished - 2015
EventInternational Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands
Duration: Apr 21 2002Apr 24 2002

Other

OtherInternational Conference on Computational Science, ICCS 2002
CountryNetherlands
CityAmsterdam
Period4/21/024/24/02

Fingerprint

Planning
Distributed power generation
Multiobjective optimization
Solar radiation
Operating costs
Power generation
Electricity
Topology
Experiments
Temperature
Uncertainty

Keywords

  • Dynamic data driven
  • Microgrid operation
  • Multi-objective optimization
  • Scale identification

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Shi, X., Damgacioglu, H., & Celik, N. (2015). A dynamic data-driven approach for operation planning of microgrids. In Procedia Computer Science (1 ed., Vol. 51, pp. 2543-2552). Elsevier. https://doi.org/10.1016/j.procs.2015.05.362

A dynamic data-driven approach for operation planning of microgrids. / Shi, Xiaoran; Damgacioglu, Haluk; Celik, Nurcin.

Procedia Computer Science. Vol. 51 1. ed. Elsevier, 2015. p. 2543-2552.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shi, X, Damgacioglu, H & Celik, N 2015, A dynamic data-driven approach for operation planning of microgrids. in Procedia Computer Science. 1 edn, vol. 51, Elsevier, pp. 2543-2552, International Conference on Computational Science, ICCS 2002, Amsterdam, Netherlands, 4/21/02. https://doi.org/10.1016/j.procs.2015.05.362
Shi X, Damgacioglu H, Celik N. A dynamic data-driven approach for operation planning of microgrids. In Procedia Computer Science. 1 ed. Vol. 51. Elsevier. 2015. p. 2543-2552 https://doi.org/10.1016/j.procs.2015.05.362
Shi, Xiaoran ; Damgacioglu, Haluk ; Celik, Nurcin. / A dynamic data-driven approach for operation planning of microgrids. Procedia Computer Science. Vol. 51 1. ed. Elsevier, 2015. pp. 2543-2552
@inproceedings{b8b09dc779cc49cab62d7d5db39c839c,
title = "A dynamic data-driven approach for operation planning of microgrids",
abstract = "Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data-driven application systems (DDDAS) approach is proposed for determining the realtime operation plan of an electric microgrid while considering its conflicting objectives. In particular, the proposed approach is equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scale identification procedures; and 3) a multiobjective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scale of the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study demonstrate the power of the proposed approach in real-time assessment and control of operation in microgrids.",
keywords = "Dynamic data driven, Microgrid operation, Multi-objective optimization, Scale identification",
author = "Xiaoran Shi and Haluk Damgacioglu and Nurcin Celik",
year = "2015",
doi = "10.1016/j.procs.2015.05.362",
language = "English (US)",
volume = "51",
pages = "2543--2552",
booktitle = "Procedia Computer Science",
publisher = "Elsevier",
edition = "1",

}

TY - GEN

T1 - A dynamic data-driven approach for operation planning of microgrids

AU - Shi, Xiaoran

AU - Damgacioglu, Haluk

AU - Celik, Nurcin

PY - 2015

Y1 - 2015

N2 - Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data-driven application systems (DDDAS) approach is proposed for determining the realtime operation plan of an electric microgrid while considering its conflicting objectives. In particular, the proposed approach is equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scale identification procedures; and 3) a multiobjective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scale of the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study demonstrate the power of the proposed approach in real-time assessment and control of operation in microgrids.

AB - Distributed generation resources (DGs) and their utilization in large-scale power systems are attracting more and more utilities as they are becoming more qualitatively reliable and economically viable. However, uncertainties in power generation from DGs and fluctuations in load demand must be considered when determining the optimal operation plan for a microgrid. In this context, a novel dynamic data-driven application systems (DDDAS) approach is proposed for determining the realtime operation plan of an electric microgrid while considering its conflicting objectives. In particular, the proposed approach is equipped with three modules: 1) a database including the real-time microgrid topology data (i.e., power demand, market price for electricity, etc.) and the data for environmental factors (i.e., solar radiation, wind speed, temperature, etc.); 2) a simulation, in which operation of the microgrid is simulated with embedded rule-based scale identification procedures; and 3) a multiobjective optimization module which finds the near-optimal operation plan in terms of minimum operating cost and minimum emission using a particle-filtering based algorithm. The complexity of the optimization depends on the scale of the problem identified from the simulation module. The results obtained from the optimization module are sent back to the microgrid system to enhance its operation. The experiments conducted in this study demonstrate the power of the proposed approach in real-time assessment and control of operation in microgrids.

KW - Dynamic data driven

KW - Microgrid operation

KW - Multi-objective optimization

KW - Scale identification

UR - http://www.scopus.com/inward/record.url?scp=84939196774&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84939196774&partnerID=8YFLogxK

U2 - 10.1016/j.procs.2015.05.362

DO - 10.1016/j.procs.2015.05.362

M3 - Conference contribution

VL - 51

SP - 2543

EP - 2552

BT - Procedia Computer Science

PB - Elsevier

ER -