A Mid-Term DSO Market for Capacity Limits: How to Estimate Opportunity Costs of Aggregators?

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Abstract

A large number of mechanisms are proposed to manage potential problems in distribution networks caused by the participation of distributed energy resources (DERs) in the wholesale markets. In this paper, we first introduce a practical and straightforward mechanism, based on capacity limits, which avoids conflicts between the transmission system operator and the distribution system operators (DSOs). Using a large number of real electric vehicle (EV) commercial charging stations we
then show how an EV aggregator can forecast the opportunity cost incurred by offering a mid-term capacity limit service to the DSO. This cost is computed based on the estimated profit that the aggregator could gain in the day-ahead and real-time markets. The proposed methodology guarantees robustness against evolving EV uncertainty, both in terms of service delivery and driving requirements. It also allows the use of a variety of timeseries forecasting methods without forecasting electricity prices and EV scenarios. The results of our empirical analysis show the exponential increase of opportunity cost and the considerable increase of the prediction intervals as the capacity limit decreases. The produced offering curves can be used as an indication of the underutilization cost of DERs caused by the DSO’s limitations.
Original languageEnglish
JournalI E E E Transactions on Smart Grid
Number of pages12
ISSN1949-3053
DOIs
Publication statusAccepted/In press - 2019

Keywords

  • Aggregator
  • Capacity limit
  • DSO service
  • Electric vehicles
  • Offering curve
  • Opportunity Cost

Cite this

@article{090c8d4f4b2c4d7e8125718fe161f7d5,
title = "A Mid-Term DSO Market for Capacity Limits: How to Estimate Opportunity Costs of Aggregators?",
abstract = "A large number of mechanisms are proposed to manage potential problems in distribution networks caused by the participation of distributed energy resources (DERs) in the wholesale markets. In this paper, we first introduce a practical and straightforward mechanism, based on capacity limits, which avoids conflicts between the transmission system operator and the distribution system operators (DSOs). Using a large number of real electric vehicle (EV) commercial charging stations wethen show how an EV aggregator can forecast the opportunity cost incurred by offering a mid-term capacity limit service to the DSO. This cost is computed based on the estimated profit that the aggregator could gain in the day-ahead and real-time markets. The proposed methodology guarantees robustness against evolving EV uncertainty, both in terms of service delivery and driving requirements. It also allows the use of a variety of timeseries forecasting methods without forecasting electricity prices and EV scenarios. The results of our empirical analysis show the exponential increase of opportunity cost and the considerable increase of the prediction intervals as the capacity limit decreases. The produced offering curves can be used as an indication of the underutilization cost of DERs caused by the DSO’s limitations.",
keywords = "Aggregator, Capacity limit, DSO service, Electric vehicles, Offering curve, Opportunity Cost",
author = "Charalampos Ziras and Jalal Kazempour and Kara, {Emre Can} and Bindner, {Henrik W.} and Pierre Pinson and Sila Kiliccote",
year = "2019",
doi = "10.1109/TSG.2019.2921402",
language = "English",
journal = "I E E E Transactions on Smart Grid",
issn = "1949-3053",
publisher = "Institute of Electrical and Electronics Engineers",

}

A Mid-Term DSO Market for Capacity Limits: How to Estimate Opportunity Costs of Aggregators? / Ziras, Charalampos; Kazempour, Jalal; Kara, Emre Can; Bindner, Henrik W.; Pinson, Pierre; Kiliccote, Sila.

In: I E E E Transactions on Smart Grid, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - A Mid-Term DSO Market for Capacity Limits: How to Estimate Opportunity Costs of Aggregators?

AU - Ziras, Charalampos

AU - Kazempour, Jalal

AU - Kara, Emre Can

AU - Bindner, Henrik W.

AU - Pinson, Pierre

AU - Kiliccote, Sila

PY - 2019

Y1 - 2019

N2 - A large number of mechanisms are proposed to manage potential problems in distribution networks caused by the participation of distributed energy resources (DERs) in the wholesale markets. In this paper, we first introduce a practical and straightforward mechanism, based on capacity limits, which avoids conflicts between the transmission system operator and the distribution system operators (DSOs). Using a large number of real electric vehicle (EV) commercial charging stations wethen show how an EV aggregator can forecast the opportunity cost incurred by offering a mid-term capacity limit service to the DSO. This cost is computed based on the estimated profit that the aggregator could gain in the day-ahead and real-time markets. The proposed methodology guarantees robustness against evolving EV uncertainty, both in terms of service delivery and driving requirements. It also allows the use of a variety of timeseries forecasting methods without forecasting electricity prices and EV scenarios. The results of our empirical analysis show the exponential increase of opportunity cost and the considerable increase of the prediction intervals as the capacity limit decreases. The produced offering curves can be used as an indication of the underutilization cost of DERs caused by the DSO’s limitations.

AB - A large number of mechanisms are proposed to manage potential problems in distribution networks caused by the participation of distributed energy resources (DERs) in the wholesale markets. In this paper, we first introduce a practical and straightforward mechanism, based on capacity limits, which avoids conflicts between the transmission system operator and the distribution system operators (DSOs). Using a large number of real electric vehicle (EV) commercial charging stations wethen show how an EV aggregator can forecast the opportunity cost incurred by offering a mid-term capacity limit service to the DSO. This cost is computed based on the estimated profit that the aggregator could gain in the day-ahead and real-time markets. The proposed methodology guarantees robustness against evolving EV uncertainty, both in terms of service delivery and driving requirements. It also allows the use of a variety of timeseries forecasting methods without forecasting electricity prices and EV scenarios. The results of our empirical analysis show the exponential increase of opportunity cost and the considerable increase of the prediction intervals as the capacity limit decreases. The produced offering curves can be used as an indication of the underutilization cost of DERs caused by the DSO’s limitations.

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