Evaluating R and D options under uncertainty. Volume 3. An electric-utility generation-expansion planning model. Final report

Technical Report · Sat Aug 01 04:00:00 EDT 1981

OSTI ID:6045451

Borison, A B; Judd, B R; Morris, P A; Walters, E C

This report describes an electric utility generation expansion model developed for use in research and development (R and D) planning under uncertainty. The model provides a framework for examining broad utility and R and D planning issues, rather than the specific generation expansion decisions of individual utilities. Unlike existing approaches, the model focuses directly on the demand, technological, and regulatory uncertainties and the long-term dynamics that affect the impact of R and D achievements. The model's somewhat aggregate approach to electric utility decision-making (to allow repeated application at low cost) can be modified, as needed, for more detailed utility planning. When fully implemented, the model can be applied to the analysis of issues such as technology adoption, reserve margin, unit size, reliability, storage and load management effects, lead time, and government regulation. The model inputs include demand, supply (generation technology characteristics), and external factors (regulatory constraints). The outputs are the optimal (minimum discounted expected cost) generation expansion plan, its cost, and other aspects of this plan. The model relies on three mathematical programming approaches: dynamic programming, iterative dynamic programming, and state-of-the-world decomposition. The state-of-the-world decomposition component separates the main problem into a set of individual scenario problems, each of which is solved with the iterative dynamic-programming component. The iterative dynamic-programming component, in turn, transforms each individual scenario problem into a series of even simpler problems, each of which is solved with the dynamic-programming component. Possible future extensions of the model involve increased operating detail, increased financial detail, explicit incorporation of storage and load management options, and more efficient treatment of closed-loop decision-making.

• Research Organization: Applied Decision Analysis, Inc., Menlo Park, CA (USA)
• OSTI ID: 6045451
• Report Number(s): EPRI-EA-1964(Vol.3); ON: DE81904237
• Country of Publication: United States
• Language: English

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COMPUTER CODES
COST
DECISION MAKING
DYNAMIC PROGRAMMING
ECONOMETRICS
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