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Power Capacity Expansion Planning Considering Endogenous Technology Cost Learning

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Title: Power Capacity Expansion Planning Considering Endogenous Technology Cost Learning
Authors: Heuberger, C
Rubin, ES
Staffell, I
Shah, N
Mac Dowell, N
Item Type: Journal Article
Abstract: We present an power systems optimisation model for national-scale power supply capacity expansion considering endogenous technology cost reduction (ESO-XEL). The mixed-integer linear program minimises total system cost while complying with operational constraints, carbon emission targets, and ancillary service requirements. A data clustering technique and the relaxation of integer scheduling constraints is evaluated and applied to decrease the model solution time. Two cost learning curves for the different power technologies are derived: one assuming local learning effects, the other accounting for global knowledge spill-over. A piece-wise linear formulation allows the integration of the exponential learning curves into the ESO-XEL model. The model is applied to the UK power system in the time frame of 2015 to 2050. The consideration of cost learning effects moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. In addition, the maximum capacity build rate parameter influences the share of power generation significantly; the possibility of rapid capacity build-up is more important for total system cost reduction by 2050 than accounting for technology cost reduction.
Issue Date: 8-Aug-2017
Date of Acceptance: 18-Jul-2017
URI: http://hdl.handle.net/10044/1/50150
DOI: https://dx.doi.org/10.1016/j.apenergy.2017.07.075
ISSN: 0306-2619
Publisher: Elsevier
Start Page: 831
End Page: 845
Journal / Book Title: Applied Energy
Volume: 204
Copyright Statement: © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
IEAGHG t/a IEA Environmental Projects Ltd
Funder's Grant Number: EP/M001369/1
Keywords: 09 Engineering
14 Economics
Publication Status: Published
Appears in Collections:Centre for Environmental Policy
Chemical Engineering
Faculty of Natural Sciences