skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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.

Authors:
ORCiD logo [1]
  1. Stanford Univ., CA (United States). Dept. of Management Science and Engineering
Publication Date:
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)
OSTI Identifier:
1324468
Grant/Contract Number:
SC0005171
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energy Economics
Additional Journal Information:
Journal Volume: 59; Journal ID: ISSN 0140-9883
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; Electricity; Investment; Optimisation; Variability; Renewables

Citation Formats

Merrick, James H. On representation of temporal variability in electricity capacity planning models. United States: N. p., 2016. Web. doi:10.1016/j.eneco.2016.08.001.
Merrick, James H. On representation of temporal variability in electricity capacity planning models. United States. doi:10.1016/j.eneco.2016.08.001.
Merrick, James H. 2016. "On representation of temporal variability in electricity capacity planning models". United States. doi:10.1016/j.eneco.2016.08.001. https://www.osti.gov/servlets/purl/1324468.
@article{osti_1324468,
title = {On representation of temporal variability in electricity capacity planning models},
author = {Merrick, James H.},
abstractNote = {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.},
doi = {10.1016/j.eneco.2016.08.001},
journal = {Energy Economics},
number = ,
volume = 59,
place = {United States},
year = 2016,
month = 8
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share:
  • This study proposes an integrated model for capacity planning in electricity generation. It utilizes a multiple-criteria linear programming to incorporate cost and environmental objectives into the planning. To treat the uncertainties embedded in definition of model parameters, the concept of decision-maker degree of optimism will be used. Optimization of the model provides different planning scenarios. To determine the best compromise plan, a post-optimization assessment based on fuzzy set theory concepts is developed. The proposed methodology is employed for a medium-term capacity planning in Canada's electricity generation sector. The results approve a major capacity growth for natural gas facilities accompanied bymore » retirement of most coal-burning facilities.« less
  • The preliminary results are presented of a study aimed at quantifying the variability in precipitation data and its role in the modeling of acidic deposition processes. The character of this variability for various regions of New York State is assessed over a 30-year period, with emphasis on the Adirondack region. Spatial and temporal means as well as coefficients of variation are presented. Of the five regions where precipitation data are investigated, the Adirondacks have the greatest overall variability, around 23 percent, while Long Island has the least, about 17 percent. A proportionality factor, based on the coefficient of variation, ismore » suggested to account for the precipitation variability in achieving targeted wet deposition threshold values.« less
  • Two numerical techniques are proposed to construct a polynomial chaos (PC) representation of an arbitrary second-order random vector. In the first approach, a PC representation is constructed by matching a target joint probability density function (pdf) based on sequential conditioning (a sequence of conditional probability relations) in conjunction with the Rosenblatt transformation. In the second approach, the PC representation is obtained by having recourse to the Rosenblatt transformation and simultaneously matching a set of target marginal pdfs and target Spearman's rank correlation coefficient (SRCC) matrix. Both techniques are applied to model an experimental spatio-temporal data set, exhibiting strong non-stationary andmore » non-Gaussian features. The data consists of a set of oceanographic temperature records obtained from a shallow-water acoustics transmission experiment. The measurement data, observed over a finite denumerable subset of the indexing set of the random process, is treated as a collection of observed samples of a second-order random vector that can be treated as a finite-dimensional approximation of the original random field. A set of properly ordered conditional pdfs, that uniquely characterizes the target joint pdf, in the first approach and a set of target marginal pdfs and a target SRCC matrix, in the second approach, are estimated from available experimental data. Digital realizations sampled from the constructed PC representations based on both schemes capture the observed statistical characteristics of the experimental data with sufficient accuracy. The relative advantages and disadvantages of the two proposed techniques are also highlighted.« less