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This content will become publicly available on February 26, 2017

Title: Model-centric distribution automation: Capacity, reliability, and efficiency

A series of analyses along with field validations that evaluate efficiency, reliability, and capacity improvements of model-centric distribution automation are presented. With model-centric distribution automation, the same model is used from design to real-time control calculations. A 14-feeder system with 7 substations is considered. The analyses involve hourly time-varying loads and annual load growth factors. Phase balancing and capacitor redesign modifications are used to better prepare the system for distribution automation, where the designs are performed considering time-varying loads. Coordinated control of load tap changing transformers, line regulators, and switched capacitor banks is considered. In evaluating distribution automation versus traditional system design and operation, quasi-steady-state power flow analysis is used. In evaluating distribution automation performance for substation transformer failures, reconfiguration for restoration analysis is performed. In evaluating distribution automation for storm conditions, Monte Carlo simulations coupled with reconfiguration for restoration calculations are used. As a result, the evaluations demonstrate that model-centric distribution automation has positive effects on system efficiency, capacity, and reliability.
 [1] ;  [2] ;  [3] ;  [3] ;  [4] ;  [5] ;  [5] ;  [6] ;  [6]
  1. Abdullah Gul Univ., Kayseri (Turkey)
  2. Ajou Univ., Suwon (Korea)
  3. Electrical Distribution Design, Inc., Blacksburg, VA (United States)
  4. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
  5. Orange and Rockland Electric Utility Inc., Spring Valley, NY (United States)
  6. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1532-5008; YN0100000
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Electric Power Components and Systems
Additional Journal Information:
Journal Volume: 44; Journal Issue: 5; Journal ID: ISSN 1532-5008
Taylor & Francis
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
25 ENERGY STORAGE distribution automation; Monte Carlo simulation; coordinated control; power system reliability; power system efficiency; power system capacity