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 language | English (US) |
|---|---|
| Title of host publication | Procedia Computer Science |
| Publisher | Elsevier |
| Pages | 2543-2552 |
| Number of pages | 10 |
| Volume | 51 |
| Edition | 1 |
| DOIs | |
| State | Published - 2015 |
| Event | International Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands Duration: Apr 21 2002 → Apr 24 2002 |
Other
| Other | International Conference on Computational Science, ICCS 2002 |
|---|---|
| Country | Netherlands |
| City | Amsterdam |
| Period | 4/21/02 → 4/24/02 |
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Keywords
- Dynamic data driven
- Microgrid operation
- Multi-objective optimization
- Scale identification
ASJC Scopus subject areas
- Computer Science(all)
Cite this
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 proceeding › Conference contribution
}
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 -