Research Papers

An Approach to Uncertainty Identification in the Valuation of Transactive Energy Systems

[+] Author and Article Information
Arun Veeramany

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99354
e-mail: arun.veeramany@pnnl.gov

James T. Woodward

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99354
e-mail: Jim.Woodward@pnnl.gov

Donald J. Hammerstrom

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99354
e-mail: donald.hammerstrom@pnnl.gov

1Corresponding author.

Manuscript received May 22, 2017; final manuscript received February 9, 2018; published online April 30, 2018. Assoc. Editor: Athanasios Pantelous.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes.

ASME J. Risk Uncertainty Part B 4(4), 041005 (Apr 30, 2018) (7 pages) Paper No: RISK-17-1064; doi: 10.1115/1.4039465 History: Received May 22, 2017; Revised February 09, 2018

Valuation of transactive energy (TE) systems should be supported by a structured and systematic approach to uncertainty identification, assessment, and treatment in the interest of risk-informed decision making. The proposed approach, a variation of fault tree analysis, is anticipated to support valuation analysts in analyzing conventional and transactive system scenarios. This approach allows for expanding the entire tree up to the level of minute details or collapsing them to a level sufficient enough to get an overview of the problem. Quantification scheme for the described approach lends itself for valuation. The method complements value exchange analysis, simulation, and field demonstration studies. The practicality of the proposed approach is demonstrated through uncertainty assessment of the smart grid interoperability panel peak heat day scenario.

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Fig. 1

Basic valuation process

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Fig. 2

Deductive reasoning supports uncertainty identification and impact measure evaluation

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Fig. 3

Top level impact measures

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Fig. 4

Difference between forecasted demand and actual demand that needs to be managed

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Fig. 5

Options to reduce demand during peak heat day

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Fig. 6

Options to curtail demand during peak heat day

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Fig. 7

Options to increase capacity during peak demand

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Fig. 8

An expanded view of the solutions tree for uncertainty identification



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