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Author Topic: TechReport not correctly shown in Word
scotto
Newbie
Posts: 4
Post TechReport not correctly shown in Word
on: May 5, 2017, 12:13

Here is what it looks like in Word

Otterson, S., H. Madsen, P. Pinson, and T. Jónsson. 2011. Scenario generation: A review. . DTU Informatics, Building 305, Kgs. Lyngby, Denmark.

It's missing the report number and institution, and there is an extra period and blank spot.

Here is the Bibtex:

@TechReport{Otterson11scenGenReview,
author = {Scott Otterson and Henrik Madsen and Pierre Pinson and Tryggvi J{\'o}nsson},
title = {Scenario generation: A review},
institution = {Technical University of Denmark (DTU)},
year = {2011},
number = {IMM-Technical Report-2011-08},
location = {Kgs. Lyngby, Denmark},
url = {http://orbit.dtu.dk/en/publications/scenario-generation-a-review%28b7979f7a-cb76-46eb-bbf4-58719104a276%29.html},
abstract = {Wind power, while abundant and benign, is only available when the wind blows. If wind forecasts
were perfectly accurate, power producers could schedule in advance the exact amount of conventional
energy needed to make up for predicted low wind production. But forecasts are not perfectly accurate,
as is illustrated in Fig. 1.1, which shows a wind power time series, along with its forecasted value and
estimated error intervals. The blue contours cover the range of potential outcomes, which generally
increase with look-ahead time; the red line is the conditional expectation for each look-ahead time;
and the black line is the power that was actually measured. The error distribution also depends upon
the predicted wind power, and for this reason it is well known that forecasting via classical linear time
series analysis [2, 31] is inadequate.
Because of forecast uncertainty, power system operators must limit the risk of underproduction by
scheduling additional conventional reserve capacity, which may or may not be used. Conversely, they
may also need to waste wind power when inflexible conventional production has been scheduled and
wind power becomes available at an unpredicted time. Optimally scheduling conventional capacity is
crucial, as too many scheduling errors in a mixed conventional/wind system can cause more greenhouse
gas emissions than would be emitted by a system using conventional sources alone [41, 52].
The risk of over or under scheduling conventional production can be optimally balanced with
stochastic programming, in which future demand and production are simulated based on each of their
point forecasts and a randomly generated realization of their errors. A scheduling decision made at
one point in time affects subsequent scheduling options, so each decision must be made considering the
error probabilities at several decision points into the future; probabilistic forecasts like the one shown
in Fig. 1.1 are not adequate stochastic programming inputs, as they provide statistically independent
error distributions at each forecast look-ahead time. Therefore, the stochastic programming algorithm
needs a set of likely time series ? scenarios ? which capture temporal error dependence. A set of
scenarios corresponding to the probabilistic forecast in Fig. 1.1 is shown in 1.2.
Fig. 1.3. illustrates scenario generation in the reserve allocation process. A model of forecast
errors is estimated using historical demand and wind (and/or power) forecasts. Then, when it is
time to schedule reserve allocations, the errors of the latest forecasts are simulated by driving the
error models with random perturbations. The result is a set of likely scenarios, which a stochastic
programming algorithm uses to determine the final allocation. For each of a number of scenarios,
conventional sources are virtually scheduled to minimize the economic cost of simulated consumption
when given simulated wind production; the actual reserve scheduling is derived from the simulation
results. Depending upon the allocation method, the computational burden may be decreased by first
reducing the size of the set of scenarios. See [27] for an example with scenario reduction and [44] for
an example without it.
In this review, we discuss the problem of scenario generation. We begin with desirable scenario
performance attributes, and then summarize scenario generation and reduction algorithms which we
believe are relevant to wind power system integration.},
file = {Otterson11scenGenReview.pdf:Otterson11scenGenReview.pdf:PDF},
groups = {Ensemble, PointDerived, Test, doReadNonWPV_1},
owner = {sotterson},
publisher = {DTU Informatics, Building 305},
series = {IMM-Technical Report-2011-08},
timestamp = {2013.04.12},
}

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