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Title: Design and Cosimulation of Hierarchical Architecture for Demand Response Control and Coordination

Journal Article · · IEEE Transactions on Industrial Informatics
 [1];  [2];  [3];  [3];  [4];  [2];  [1]
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  2. Univ. of Pittsburgh, PA (United States)
  3. Aalborg Univ. (Denmark)
  4. National Inst. of Scientific Research (INRS), Montreal, QC (Canada)

Demand response (DR) plays a key role for optimum asset utilization and to avoid or delay the need of new infrastructure investment. However, coordinated execution of multiple DRs is desired to maximize the DR benefits. In this paper, we propose a hierarchical DR architecture (HDRA) to control and coordinate the performance of various DR categories such that the operation of every DR category is backed-up by time delayed action of the others. A reliable, cost-effective communication infrastructure based on ZigBee, WiMAX, and fibers is designed to facilitate the HDRA execution. The performance of the proposed HDRA is demonstrated from the power system and communication perspectives in a cosimulation environment applied to a 0.4 kV/400 kVA real distribution network considering electric vehicles as a potential DR resource (DRR). The power simulation is performed employing a real time digital simulator whereas the communication simulation is performed using OMNeT++. Finally, the HDRA performance demonstrated the maximum utilization of available DR potential by facilitating simultaneous execution of multiple DRs and enabling participation of single DRR for multiple grid applications.

Research Organization:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Organization:
USDOE; INL Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
Report Number(s):
Journal Information:
IEEE Transactions on Industrial Informatics, Vol. 13, Issue 4; ISSN 1551-3203
IEEECopyright Statement
Country of Publication:
United States
Citation Metrics:
Cited by: 30 works
Citation information provided by
Web of Science

Cited By (2)

Modeling a Hybrid Microgrid Using Probabilistic Reconfiguration under System Uncertainties journal September 2017
A Flexible Responsive Load Economic Model for Industrial Demands journal March 2019