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

Journal Article · Fri Feb 26 00:00:00 EST 2016 · Electric Power Components and Systems

DOI:https://doi.org/10.1080/15325008.2015.1121416· OSTI ID:1245384

Onen, Ahmet ^[1]; Jung, Jaesung ^[2]; Dilek, Murat ^[3]; Cheng, Danling ^[3]; Broadwater, Robert P. ^[4]; Scirbona, Charlie ^[5]; Cocks, George ^[5]; Hamilton, Stephanie ^[6]; Wang, Xiaoyu ^[6]

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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)

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.

• Research Organization: Brookhaven National Lab. (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: SC00112704
• OSTI ID: 1245384
• Report Number(s): BNL-111918-2016-JA; YN0100000
• Journal Information: Electric Power Components and Systems, Vol. 44, Issue 5; ISSN 1532-5008
• Publisher: Taylor & Francis
• Country of Publication: United States
• Language: English

Related Subjects

25 ENERGY STORAGE
distribution automation
Monte Carlo simulation
coordinated control
power system reliability
power system efficiency
power system capacity