Journal Article

FZJ-2019-00198

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A maximum entropy approach to the estimation of spatially and sectorally disaggregated electricity load curves

Többen, J. (Corresponding author) ; Schröder, T.FZJ*

2018
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.apenergy.2018.04.126

Abstract: Usually, disaggregated electricity load curves are estimated by using Top-Down or Bottom-Up approaches. The former requires estimating weightings for downscaling aggregated information, while the latter requires extrapolating micro-level information. In both cases, estimation would ideally be based on as much regional and sector specific information as possible, in order to obtain a realistic representation of the magnitude and temporal pattern of a regional sector’s electricity consumption. Typically, such attempts are significantly hampered by issues of limited and possibly inconsistent data, differing levels of detail, and mismatching data classifications.This paper proposes a novel nonlinear programming model based on the maximum entropy approach. The model allows for electricity load curve estimation at arbitrary spatial, sectoral and temporal resolution, from partial and possibly inconsistent information. The proposed model integrates and systematically utilizes data usually used by either Top-Down or Bottom-Up approaches. In a case study using German data it is shown that the model combines the strength of both and, at the same time, overcomes the challenges specific to Top-Down or Bottom-up estimation.

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Contributing Institute(s):

1. Systemforschung und Technologische Entwicklung (IEK-STE)

Research Program(s):

1. 153 - Assessment of Energy Systems – Addressing Issues of Energy Efficiency and Energy Security (POF3-153) (POF3-153)

Appears in the scientific report 2018

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Engineering, Computing and Technology ; Ebsco Academic Search ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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