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Research Papers: Energy Conversion/Systems

An Optimization Framework for Decision Making in Large, Collaborative Energy Supply Systems

[+] Author and Article Information
Bryony DuPont, Scott Proper, Christopher Hoyle

School of Mechanical, Industrial, and
Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331

Ridwan Azam, Eduardo Cotilla-Sanchez

School of Electrical Engineering and Computer Science,
Oregon State University,
Corvallis, OR 97331

Joseph Piacenza

Mechanical Engineering,
California State University,
Fullerton, CA 92834

Danylo Oryshchyn, Stephen E. Zitney, Stephen Bossart

National Energy Technology Laboratory,
Morgantown, WV 26507

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received October 25, 2015; final manuscript received January 13, 2016; published online February 22, 2016. Assoc. Editor: Kau-Fui Wong.This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Energy Resour. Technol 138(5), 051601 (Feb 22, 2016) (8 pages) Paper No: JERT-15-1399; doi: 10.1115/1.4032521 History: Received October 25, 2015; Revised January 13, 2016

As demand for electricity in the U.S. continues to increase, it is necessary to explore the means through which the modern power supply system can accommodate both increasing affluence (which is accompanied by increased per-capita consumption) and the continually growing global population. Though there has been a great deal of research into the theoretical optimization of large-scale power systems, research into the use of an existing power system as a foundation for this growth has yet to be fully explored. Current successful and robust power generation systems that have significant renewable energy penetration—despite not having been optimized a priori—can be used to inform the advancement of modern power systems to accommodate the increasing demand for electricity. This work explores how an accurate and state-of-the-art computational model of a large, regional energy system can be employed as part of an overarching power systems optimization scheme that looks to inform the decision making process for next generation power supply systems. Research scenarios that explore an introductory multi-objective power flow analysis for a case study involving a regional portion of a large grid will be explored, along with a discussion of future research directions.

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References

Office of the National Coordinator for Smart Grid Interoperability, 2012, NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0, National Institute of Standards and Technology, Gaithersburg, MD.
U.S. Energy Information Administration, 2014, “ Annual Energy Outlook 2014 With Projections to 2040,” U.S. Department of Energy, Washington, DC, www.eia.gov/forecasts/aeo
Happ, H. , 1973, “ The Operation and Control of Large Interconnected Power Systems,” IEEE Trans. Circuit Theory, 20(3), pp. 212–222. [CrossRef]
Pan European Grid Advanced Simulation and State Estimation (PEGASE), 2011, “ Algorithmic Requirements for Simulation of Large Network Extreme Scenarios,” http://www.fp7-pegase.com/pdf/D4.1.pdf
Cotilla-Sanchez, E. , Hines, P. , Barrows, C. , Blumsack, S. , and Patel, M. , 2013, “ Multi-Attribute Partitioning of Power Networks Based on Electrical Distance,” IEEE Trans. Power Syst., 28(4), pp. 4978–4987. [CrossRef]
Mavris, D. N. , and Griendling, K. , 2011, “ Relational Oriented Systems Engineering and Technology Tradeoff Analysis (ROSETTA) Environment,” IEEE 6th International Conference on Systems Engineering (SoSE), Albuquerque, NM, June 27–30, pp. 49–54.
ReVelle, J. B. , Moran, J. W. , and Cox, C. A. , 1996, The QFD Handbook, Wiley, Hoboken, NJ.
Cano-Andrade, S. , von Spakovsky, M. R. , Fuentes, A. , Lo Prete, C. , and Mili, L. , 2015, “ Upper Level of a Sustainability Assessment Framework for Power System Planning,” ASME J. Energy Resour. Technol., 137(4), p. 041601. [CrossRef]
Facci, A. L. , Andreassi, L. , Martini, F. , and Ubertini, S. , 2014, “ Comparing Energy and Cost Optimization in Distributed Energy Systems Management,” ASME J. Energy Resour. Technol., 136(3), p. 32001. [CrossRef]
Franco, A. , and Vazquez, A. , 2006, “ A Thermodynamic Based Approach for the Multicriteria Assessment of Energy Conversion Systems,” ASME J. Energy Resour. Technol., 128(4), pp. 346–351. [CrossRef]
Widiyanto, A. , Kato, S. , and Maruyama, N. , 2004, “ Optimizing Selection of Appropriate Power Generation Systems in Indonesia by Using Distance Based Approach Method,” ASME J. Energy Resour. Technol., 126(1), pp. 63–71. [CrossRef]
The Mathworks, Inc., 2011, MATLAB Version 7.13.0.564, Natick, MA.
Zimmerman, R. D. , and Murillo-Sanchez, C. E. , 2011, MATPOWER 4.1 User's Manual, Power Systems Engineering Research Center, Tempe, AZ, pp. 1–116.
Glover, J. D. , Sarma, M. S. , and Overbye, T. J. , 2012, Power System Analysis and Design, Cengage Learning, Samford, CT.
Zimmerman, R. D. , Murillo-Sanchez, C. E. , and Thomas, R. J. , 2011, “ MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education,” IEEE Trans. Power Syst., 26(1), pp. 12–19. [CrossRef]
Phoenix Integration, 2011, ModelCenter Basics, Phoenix Integration Inc., Blacksburg, VA.
WECC, 2014, Western Electricity Coordinating Council, Salt Lake City, UT, https://www.wecc.biz/Pages/About.aspx
NERC, 2013, “ North American Regional Reliability Councils and Interconnections,” North American Reliability Corporation, Atlanta, GA, http://upload.wikimedia.org/wikipedia/commons/thumb/f/f4/NERC-map-en.svg/2000px-NERC-map-en.svg.png
PowerWorld, 2008, “ Power System Equivalents' Online Training,” PowerWorld Corp., Champaign, IL, http://www.powerworld.com/files/TrainingI14Equivalents.pdf
Energy Visuals, 2014, “ Transmission Atlas WECC Data,” Energy Visuals, Inc., Champaign, IL.

Figures

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Fig. 1

Two-stage optimization framework

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Fig. 2

Western interconnection, including OR/WA subset [18]

Grahic Jump Location
Fig. 4

The nominal load distribution at each of the 200 buses with loads in the SubOR/WA data set. Some loads are negative, indicating an input of power at that bus. The leftmost ten loads (boxed) were chosen as control variables and are shown in more detail in Fig. 5.

Grahic Jump Location
Fig. 5

The best solution found for the SubOR/WA data, compared to the nominal load. On average, 9.7% of the load was shed at each bus.

Grahic Jump Location
Fig. 6

The nominal load distribution at each of the 1761 buses with loads in the OR/WA data set. As with the SubOR/WA data set, some loads are negative, and the leftmost ten loads (boxed) were chosen as control variables and may be seen in more detail in Fig. 7.

Grahic Jump Location
Fig. 7

The best solution found for the OR/WA data set, compared to the nominal load. On average, 10.3% of the load was shed at each bus.

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