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Title: Managing Microgrids Using Grid Services.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the IEEE 2nd International Conference on System of Systems Engineering held April 16-18, 2007 in San Antonio, TX.
Country of Publication:
United States

Citation Formats

Phillips, Laurence R. Managing Microgrids Using Grid Services.. United States: N. p., 2007. Web.
Phillips, Laurence R. Managing Microgrids Using Grid Services.. United States.
Phillips, Laurence R. Tue . "Managing Microgrids Using Grid Services.". United States. doi:.
title = {Managing Microgrids Using Grid Services.},
author = {Phillips, Laurence R.},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 01 00:00:00 EDT 2007},
month = {Tue May 01 00:00:00 EDT 2007}

Other availability
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  • Abstract not provided.
  • In this Project we collaborated with two DOE National Laboratories, Pacific Northwest National Lab (PNNL) and Lawrence Berkeley National Lab (LBL). Dr. Hammerstrom of PNNL initially supported our project and was on the graduate committee of one of the Ph.D. students (graduated in 2014) who was supported by this project. He is also a committee member of a current graduate student of the PI who was supported by this project in the last two years (August 2014-July 2016). The graduate student is now supported be the Electrical and Computer Engineering (ECE) Department at Montana State University (MSU). Dr. Chris Marneymore » of LBL provided actual load data, and the software WEBOPT developed at LBL for microgrid (MG) design for our project. NEC-Labs America, a private industry, also supported our project, providing expert support and modest financial support. We also used the software “HOMER,” originally developed at the National Renewable Energy Laboratory (NREL) and the most recent version made available to us by HOMER Energy, Inc., for MG (hybrid energy system) unit sizing. We compared the findings from WebOpt and HOMER and designed appropriately sized hybrid systems for our case studies. The objective of the project was to investigate real-time power management strategies for MGs using intelligent control, considering maximum feasible energy sustainability, reliability and efficiency while, minimizing cost and undesired environmental impact (emissions). Through analytic and simulation studies, we evaluated the suitability of several heuristic and artificial-intelligence (AI)-based optimization techniques that had potential for real-time MG power management, including genetic algorithms (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and multi-agent systems (MAS), which is based on the negotiation of smart software-based agents. We found that PSO and MAS, in particular, distributed MAS, were more efficient and better suited for our work. We investigated the following: • Intelligent load control - demand response (DR) - for frequency stabilization in islanded MGs (partially supported by PNNL). • The impact of high penetration of solar photovoltaic (PV)-generated power at the distribution level (partially supported by PNNL). • The application of AI approaches to renewable (wind, PV) power forecasting (proposed by the reviewers of our proposal). • Application of AI approaches and DR for real-time MG power management (partially supported by NEC Labs-America) • Application of DR in dealing with the variability of wind power • Real-time MG power management using DR and storage (partially supported by NEC Labs-America) • Application of DR in enhancing the performance of load-frequency controller • MAS-based whole-sale and retail power market design for smart grid A« less
  • The negative aspects of having a nuclear criticality accident at a fuel fabrication facility have substantially increased in recent years. Although ensuring that the facility is designed and operating in a critically safe manner is a high management priority, practices of managing the risk of a criticality accident have not significantly changed. The method of evaluating risk based on quantitative analysis can enable management to adapt to the increased consequences of a nuclear criticality accident. Additional controls may be placed on high-risk areas within a facility to ensure safe operation of the plant. Areas where controls are in place thatmore » impede the productivity of the facility and have negligible impact on criticality safety may be removed or replaced. Management can also streamline the safety analysis efforts applied to facility upgrades by demonstrating that proposed design changes do not compromise criticality safety. Future expansion of quantitative analysis techniques will also allow risk-based management decisions on industrial, radiological, and environmental safety.« less