Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage

Scott, Ian J.; Carvalho, Pedro M.S.; Botterud, Audun; Silva, Carlos A.

doi:10.1016/j.apenergy.2019.113603

Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage

Journal Article · Fri Nov 01 04:00:00 EDT 2019 · Applied Energy

DOI:https://doi.org/10.1016/j.apenergy.2019.113603· OSTI ID:1818516

Scott, Ian J.; Carvalho, Pedro M.S.; Botterud, Audun; Silva, Carlos A.

Decision makers rely on models to make important regulatory, policy, and investment decisions. For power systems, these models must capture (i) the future challenges introduced by the integration of large quantities of variable renewable energy sources and (ii) the role that energy storage technologies should play. In this paper, we explore several different approaches to selecting representative days for generation expansion planning models, focusing on capturing these dynamics. Further, we propose a new methodology for adjusting the outputs of clustering algorithms that provides three advantages: the targeted level of net demand is captured, the underlying net demand shapes that define ramping challenges are accurately represented, and the relationship between annual energy and peak demand is captured. This weighting methodology reduces the magnitude of the error in the representative day based generation expansion planning models estimation of costs by 61% on average. The results also demonstrate the importance of carefully performing the clustering of representative days for both system costs and technology mix. In most cases improvements to the total cost of different representative day based expansion plans are realised where conventional generation capacity is substituted for energy storage. Based on the energy storage technology selected we conclude this capacity is being used to address ramping challenges as opposed to shifting renewable generation from off to on peak periods, reinforcing the importance of capturing detailed intraday dynamics in the representative day selection process.

Research Organization:: Argonne National Laboratory (ANL)

Sponsoring Organization:: Fundacao para a Ciencia ea Tecnologia of Portugal

DOE Contract Number:: AC02-06CH11357

OSTI ID:: 1818516

Journal Information:: Applied Energy, Journal Name: Applied Energy Vol. 253; ISSN 0306-2619

Country of Publication:: United States

Language:: English

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Clustering
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Generation expansion planning
Optimization
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Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage

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