Evaluating impact of new technologies on low voltage grids using probabilistic data enriched scenarios

R. Bernards; J. Morren; J.G. Slootweg

doi:10.1109/EEEIC.2016.7555570

Evaluating impact of new technologies on low voltage grids using probabilistic data enriched scenarios

R. Bernards, J. Morren, J.G. Slootweg

Electrical Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

4 Downloads (Pure)

Abstract

In this paper a scenario-based method for long term load forecasting is proposed that incorporates the uncertainty and diversity in adoption of future developments in the evaluation and planning process of low voltage grids and that is able to account for local differences in expected loading. The approach incorporates the influence of local parameters in the scenarios using logistic regression based on data gathered from different sources. The method can be used to assess the resilience of low voltage grids under various different scenarios and assists grid planners in making informed investment decisions under uncertainty. It is designed such that it is open for addition of extra parameters and data to increase its robustness against a changing environment.

Original language	English
Title of host publication	IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC2016), 7-10 June, 2016, Florence, Italy
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
DOIs	https://doi.org/10.1109/EEEIC.2016.7555570
Publication status	Published - 1 Sept 2016

Keywords

Logistic regression
Low voltage grid
Probabilistic methods
Scenario planning

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10.1109/EEEIC.2016.7555570

Cite this

Bernards, R., Morren, J., & Slootweg, J. G. (2016). Evaluating impact of new technologies on low voltage grids using probabilistic data enriched scenarios. In IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC2016), 7-10 June, 2016, Florence, Italy Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EEEIC.2016.7555570

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abstract = "In this paper a scenario-based method for long term load forecasting is proposed that incorporates the uncertainty and diversity in adoption of future developments in the evaluation and planning process of low voltage grids and that is able to account for local differences in expected loading. The approach incorporates the influence of local parameters in the scenarios using logistic regression based on data gathered from different sources. The method can be used to assess the resilience of low voltage grids under various different scenarios and assists grid planners in making informed investment decisions under uncertainty. It is designed such that it is open for addition of extra parameters and data to increase its robustness against a changing environment.",

keywords = "Logistic regression, Low voltage grid, Probabilistic methods, Scenario planning",

author = "R. Bernards and J. Morren and J.G. Slootweg",

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doi = "10.1109/EEEIC.2016.7555570",

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Bernards, R, Morren, J & Slootweg, JG 2016, Evaluating impact of new technologies on low voltage grids using probabilistic data enriched scenarios. in IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC2016), 7-10 June, 2016, Florence, Italy. Institute of Electrical and Electronics Engineers, Piscataway. https://doi.org/10.1109/EEEIC.2016.7555570

Evaluating impact of new technologies on low voltage grids using probabilistic data enriched scenarios. / Bernards, R.; Morren, J.; Slootweg, J.G.
IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC2016), 7-10 June, 2016, Florence, Italy. Piscataway: Institute of Electrical and Electronics Engineers, 2016.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Morren, J.

AU - Slootweg, J.G.

PY - 2016/9/1

Y1 - 2016/9/1

N2 - In this paper a scenario-based method for long term load forecasting is proposed that incorporates the uncertainty and diversity in adoption of future developments in the evaluation and planning process of low voltage grids and that is able to account for local differences in expected loading. The approach incorporates the influence of local parameters in the scenarios using logistic regression based on data gathered from different sources. The method can be used to assess the resilience of low voltage grids under various different scenarios and assists grid planners in making informed investment decisions under uncertainty. It is designed such that it is open for addition of extra parameters and data to increase its robustness against a changing environment.

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KW - Logistic regression

KW - Low voltage grid

KW - Probabilistic methods

KW - Scenario planning

U2 - 10.1109/EEEIC.2016.7555570

DO - 10.1109/EEEIC.2016.7555570

M3 - Conference contribution

BT - IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC2016), 7-10 June, 2016, Florence, Italy

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Evaluating impact of new technologies on low voltage grids using probabilistic data enriched scenarios

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Keywords

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