Electric power unit commitment scheduling using a dynamically evolving mixed integer program
Name
MIT-EL-73-007-13433824.pdf
Size
7.42 MB
Format
Adobe PDF
Checksum (MD5)
20d3b272381ade04dab8b89b284081d0
Author(s)
Gruhl, Jim
Date Issued
1973
Publisher
MIT Energy Lab
Series/Report no.
MIT-EL
73-007
Abstract
A quasi-optimal technique ('quasi' in that the
technique discards unreasonable optimums), realized by a
dynamically evolving mixed integer program, is used to
develop regional electric power unit commitment schedules
for a one week time span. This sophisticated, yet
computationally feasible, method is used to develop the hourly
bulk dispatch schedules required to meet electric power
demands at a given reliability level while controlling the
associated dollar costs and environmental impacts.
The electric power system considered is a power
exchange pool of closely coupled generation facilities
supplying a region approximately the size of New England.
Associated with a tradeoff between a given cost of
production and the relevant ecological factors, an optimum
generation schedule is formulated which considers fossil,
nuclear, hydroelectric, gas turbine and pumped storage
generation facilities; power demands, reliabilities,
operating constraints, startup and shutdown factors,
geographic considerations, as well as various contracts
such as interregional power exchanges, interruptible loads,
gas contracts and nuclear fuel optimum batch utilization.
A prerequisite of the model was that it be flexible
enough for use in the evaluation of the optimum system
performance associated with hypothesized expansion patterns.
Another requirement was that the effects of changed
scheduling factors could be predicted, and if necessary
corrected with a minimal computational effort.
A discussion of other existing and potential solution
techniques is included, with an example of the proposed
solution technique used as a scheduler. Although the
inputs are precisely defined, this paper does not deal with
the explicit fabrication of inputs to the model, such as e.g.
river flow prediction or load forecasting. Rather, it is
meant as a method of incorporating those inputs into the
optimum operation scheduling process.
Description
Prepared in association with Electric Power Systems Engineering Laboratory and Dept. of Civil Engineering, M.I.T.
Subjects
Production scheduling
Integer programming
Electric power systems -- Mathematical models
Persistent DSpace Link
