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This content will become publicly available on August 23, 2017

Title: On representation of temporal variability in electricity capacity planning models

This study systematically investigates how to represent intra-annual temporal variability in models of optimum electricity capacity investment. Inappropriate aggregation of temporal resolution can introduce substantial error into model outputs and associated economic insight. The mechanisms underlying the introduction of this error are shown. How many representative periods are needed to fully capture the variability is then investigated. For a sample dataset, a scenario-robust aggregation of hourly (8760) resolution is possible in the order of 10 representative hours when electricity demand is the only source of variability. The inclusion of wind and solar supply variability increases the resolution of the robust aggregation to the order of 1000. A similar scale of expansion is shown for representative days and weeks. These concepts can be applied to any such temporal dataset, providing, at the least, a benchmark that any other aggregation method can aim to emulate. Finally, how prior information about peak pricing hours can potentially reduce resolution further is also discussed.
  1. Stanford Univ., CA (United States). Dept. of Management Science and Engineering
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Energy Economics
Additional Journal Information:
Journal Volume: 59; Journal ID: ISSN 0140-9883
Research Org:
Stanford Univ., CA (United States). Stanford University Energy Modeling Forum
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
29 ENERGY PLANNING, POLICY, AND ECONOMY Electricity; Investment; Optimisation; Variability; Renewables