2014
DOI: 10.1002/er.3235
|View full text |Cite
|
Sign up to set email alerts
|

Simulation of disaggregated load profiles and development of a proxy microgrid for modelling purposes

Abstract: SUMMARYThe deployment of small-scale renewable energy technologies affects the electricity grid depending on the local resource potential as well as on the regional composition of consumers. Spatially explicit renewable energy supply data and spatially disaggregated load profiles of consumers are usually not available to modellers. These data are, however, necessary to better account for the particularities of electricity systems with high levels of distributed renewable production. We present a methodology to… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 23 publications
0
3
0
Order By: Relevance
“…Spatial modeling (or spatial statistics, spatial econometrics) combines statistics and geometry to create statistical spatial models of material flows, allowing researchers to make predictions for future scenarios and support spatial policy decisions (Dijst et al, 2018;Li & Kwan, 2018;Zhang et al, 2013). (Keirstead & Sivakumar, 2012) simulate urban resource demands using activitybased modeling, and (Zeyringer et al, 2015) estimate energy load profiles using spatial information on housing types and number of inhabitants. Other researchers have considered the interactions between an urban metabolism and the spatial distribution of land use and cover types (Huang et al, 2006;Huang & Chen, 2009;Idrus et al, 2008;Krausmann et al, 2003;Lee et al, 2009;Marull et al, 2010).…”
Section: Tocmentioning
confidence: 99%
“…Spatial modeling (or spatial statistics, spatial econometrics) combines statistics and geometry to create statistical spatial models of material flows, allowing researchers to make predictions for future scenarios and support spatial policy decisions (Dijst et al, 2018;Li & Kwan, 2018;Zhang et al, 2013). (Keirstead & Sivakumar, 2012) simulate urban resource demands using activitybased modeling, and (Zeyringer et al, 2015) estimate energy load profiles using spatial information on housing types and number of inhabitants. Other researchers have considered the interactions between an urban metabolism and the spatial distribution of land use and cover types (Huang et al, 2006;Huang & Chen, 2009;Idrus et al, 2008;Krausmann et al, 2003;Lee et al, 2009;Marull et al, 2010).…”
Section: Tocmentioning
confidence: 99%
“…IOA, LCA, and MFA frequently emphasize illustrated disaggregated and intersectoral resource flows, yet the spatial drivers and underlying causes of such flows are less discussed or ignored. However, spatially aggregated models are made possible with these static models coupling with intersectional resource flows and spatial data sets [27] or through simulation [28]. Nevertheless, data availability and quality, assumptions, or spatial averaging for these models may lead to different results with estimated uncertainty, as the interactions among the constituting elements are not spatially and temporally stationary [11,29].…”
Section: Introductionmentioning
confidence: 99%
“…Since the renewable energy resources have been increasingly used in the islanding and grid-connected modes, the various structures of microgrids including ac/dc/ hybrid microgrids have played a key role in creating an independent grid and enhancing the power quality and sharing of an autonomous power grid. [1][2][3][4][5] In order to increase noticeably the accurate performance of the microgrid systems, many microgrid-based control strategies have been proposed recently. [6][7][8] In Xia et al, 9 a P dc − v 2 dc droop control technique has been designed for controlling the common bus voltage and providing a suitable power sharing in a hybrid microgrid.…”
Section: Introductionmentioning
confidence: 99%