Mitigating production uncertainties of renewables by adjustable load scheduling

Zofia Lukszo; T. Gerres

doi:10.1109/ICNSC.2017.8000152

Mitigating production uncertainties of renewables by adjustable load scheduling

Energy and Industry

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

Abstract

An increasing share of renewable energy sources (RES), such as wind and solar generation, is traded on the electricity markets. The volatility and forecast uncertainty of RES production cause market imbalances and hinder the transition towards emission-free electricity generation. Flexible load scheduling in the form of charging electric vehicles (EVs) offers an opportunity to counterbalance RES variability and forecast uncertainty and thereby enables a higher share of renewables. A stochastic linear multistage optimisation approach is presented to explore the benefits of combining RES production and flexible EV charging in the form of a hybrid aggregator trading on the liberalised electricity market. Model results show that the hybrid aggregator is able to minimise its imbalance requirements, but uses RES production for charging only if such is financially optimal. Further research is required to explore the market impact of the hybrid aggregator and test its feasibility.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017
Publisher	IEEE
Pages	555-560
Number of pages	6
ISBN (Electronic)	9781509044283
DOIs	https://doi.org/10.1109/ICNSC.2017.8000152
Publication status	Published - 2017
Event	14th IEEE International Conference on Networking, Sensing and Control, ICNSC 2017 - Calabria, Italy Duration: 16 May 2017 → 18 May 2017 Conference number: 14

Conference

Conference	14th IEEE International Conference on Networking, Sensing and Control, ICNSC 2017
Abbreviated title	ICNSC 2017
Country/Territory	Italy
City	Calabria
Period	16/05/17 → 18/05/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Controlled EV charging
Load scheduling
RES market trading
RES uncertainty
Stochastic optimisation

Access to Document

10.1109/ICNSC.2017.8000152

Cite this

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title = "Mitigating production uncertainties of renewables by adjustable load scheduling",

abstract = "An increasing share of renewable energy sources (RES), such as wind and solar generation, is traded on the electricity markets. The volatility and forecast uncertainty of RES production cause market imbalances and hinder the transition towards emission-free electricity generation. Flexible load scheduling in the form of charging electric vehicles (EVs) offers an opportunity to counterbalance RES variability and forecast uncertainty and thereby enables a higher share of renewables. A stochastic linear multistage optimisation approach is presented to explore the benefits of combining RES production and flexible EV charging in the form of a hybrid aggregator trading on the liberalised electricity market. Model results show that the hybrid aggregator is able to minimise its imbalance requirements, but uses RES production for charging only if such is financially optimal. Further research is required to explore the market impact of the hybrid aggregator and test its feasibility.",

keywords = "Controlled EV charging, Load scheduling, RES market trading, RES uncertainty, Stochastic optimisation",

author = "Zofia Lukszo and T. Gerres",

year = "2017",

doi = "10.1109/ICNSC.2017.8000152",

language = "English",

pages = "555--560",

booktitle = "Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017",

publisher = "IEEE",

address = "United States",

note = "14th IEEE International Conference on Networking, Sensing and Control, ICNSC 2017, ICNSC 2017 ; Conference date: 16-05-2017 Through 18-05-2017",

}

Lukszo, Z & Gerres, T 2017, Mitigating production uncertainties of renewables by adjustable load scheduling. in Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017., 8000152, IEEE, pp. 555-560, 14th IEEE International Conference on Networking, Sensing and Control, ICNSC 2017, Calabria, Italy, 16/05/17. https://doi.org/10.1109/ICNSC.2017.8000152

Mitigating production uncertainties of renewables by adjustable load scheduling. / Lukszo, Zofia; Gerres, T.
Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017. IEEE, 2017. p. 555-560 8000152.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Mitigating production uncertainties of renewables by adjustable load scheduling

AU - Lukszo, Zofia

AU - Gerres, T.

N1 - Conference code: 14

PY - 2017

Y1 - 2017

N2 - An increasing share of renewable energy sources (RES), such as wind and solar generation, is traded on the electricity markets. The volatility and forecast uncertainty of RES production cause market imbalances and hinder the transition towards emission-free electricity generation. Flexible load scheduling in the form of charging electric vehicles (EVs) offers an opportunity to counterbalance RES variability and forecast uncertainty and thereby enables a higher share of renewables. A stochastic linear multistage optimisation approach is presented to explore the benefits of combining RES production and flexible EV charging in the form of a hybrid aggregator trading on the liberalised electricity market. Model results show that the hybrid aggregator is able to minimise its imbalance requirements, but uses RES production for charging only if such is financially optimal. Further research is required to explore the market impact of the hybrid aggregator and test its feasibility.

AB - An increasing share of renewable energy sources (RES), such as wind and solar generation, is traded on the electricity markets. The volatility and forecast uncertainty of RES production cause market imbalances and hinder the transition towards emission-free electricity generation. Flexible load scheduling in the form of charging electric vehicles (EVs) offers an opportunity to counterbalance RES variability and forecast uncertainty and thereby enables a higher share of renewables. A stochastic linear multistage optimisation approach is presented to explore the benefits of combining RES production and flexible EV charging in the form of a hybrid aggregator trading on the liberalised electricity market. Model results show that the hybrid aggregator is able to minimise its imbalance requirements, but uses RES production for charging only if such is financially optimal. Further research is required to explore the market impact of the hybrid aggregator and test its feasibility.

KW - Controlled EV charging

KW - Load scheduling

KW - RES market trading

KW - RES uncertainty

KW - Stochastic optimisation

UR - https://www.scopus.com/pages/publications/85028509135

U2 - 10.1109/ICNSC.2017.8000152

DO - 10.1109/ICNSC.2017.8000152

M3 - Conference contribution

AN - SCOPUS:85028509135

SP - 555

EP - 560

BT - Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017

PB - IEEE

T2 - 14th IEEE International Conference on Networking, Sensing and Control, ICNSC 2017

Y2 - 16 May 2017 through 18 May 2017

ER -

Mitigating production uncertainties of renewables by adjustable load scheduling

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Cite this