Cooperative renewable energy management with distributed generation

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

Cooperative renewable energy management with distributed generation. / Leithon, Johann; Werner, Stefan; Koivunen, Visa.

2018 26th European Signal Processing Conference, EUSIPCO 2018. Vuosikerta 2018-September IEEE, 2018. s. 191-195 8553316 (European Signal Processing Conference).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

Leithon, J, Werner, S & Koivunen, V 2018, Cooperative renewable energy management with distributed generation. julkaisussa 2018 26th European Signal Processing Conference, EUSIPCO 2018. Vuosikerta. 2018-September, 8553316, European Signal Processing Conference, IEEE, Sivut 191-195, Rome, Italia, 03/09/2018. https://doi.org/10.23919/EUSIPCO.2018.8553316

APA

Leithon, J., Werner, S., & Koivunen, V. (2018). Cooperative renewable energy management with distributed generation. teoksessa 2018 26th European Signal Processing Conference, EUSIPCO 2018 (Vuosikerta 2018-September, Sivut 191-195). [8553316] (European Signal Processing Conference). IEEE. https://doi.org/10.23919/EUSIPCO.2018.8553316

Vancouver

Leithon J, Werner S, Koivunen V. Cooperative renewable energy management with distributed generation. julkaisussa 2018 26th European Signal Processing Conference, EUSIPCO 2018. Vuosikerta 2018-September. IEEE. 2018. s. 191-195. 8553316. (European Signal Processing Conference). https://doi.org/10.23919/EUSIPCO.2018.8553316

Author

Leithon, Johann ; Werner, Stefan ; Koivunen, Visa. / Cooperative renewable energy management with distributed generation. 2018 26th European Signal Processing Conference, EUSIPCO 2018. Vuosikerta 2018-September IEEE, 2018. Sivut 191-195 (European Signal Processing Conference).

Bibtex - Lataa

@inproceedings{2352f2530908446490f96ac8ba5c4c3c,
title = "Cooperative renewable energy management with distributed generation",
abstract = "We propose an energy cost minimization strategy for cooperating households equipped with renewable energy generation and storage capabilities. The participating households minimize their collective energy expenditure by sharing renewable energy through the grid. We assume location and time dependent electricity prices, as well as parametrized transfer fees. We then formulate an optimization problem to minimize the energy cost incurred by the participating households over any specified planning horizon. The proposed strategy serves as a performance benchmark for online energy management algorithms, and can be implemented in real time by incorporating adequate forecasting techniques. We solve the optimization problem through relaxation, and use simulations to illustrate the characteristics of the solution. These simulations show that energy sharing takes place when there are differences in the load/generation and price profiles across participants. We also show that no energy sharing takes place when the load is above the local generation at all times.",
keywords = "Non-convex optimization, Renewable energy optimization, Storage management",
author = "Johann Leithon and Stefan Werner and Visa Koivunen",
year = "2018",
month = "11",
day = "29",
doi = "10.23919/EUSIPCO.2018.8553316",
language = "English",
isbn = "978-1-5386-3736-4",
volume = "2018-September",
series = "European Signal Processing Conference",
publisher = "IEEE",
pages = "191--195",
booktitle = "2018 26th European Signal Processing Conference, EUSIPCO 2018",
address = "United States",

}

RIS - Lataa

TY - GEN

T1 - Cooperative renewable energy management with distributed generation

AU - Leithon, Johann

AU - Werner, Stefan

AU - Koivunen, Visa

PY - 2018/11/29

Y1 - 2018/11/29

N2 - We propose an energy cost minimization strategy for cooperating households equipped with renewable energy generation and storage capabilities. The participating households minimize their collective energy expenditure by sharing renewable energy through the grid. We assume location and time dependent electricity prices, as well as parametrized transfer fees. We then formulate an optimization problem to minimize the energy cost incurred by the participating households over any specified planning horizon. The proposed strategy serves as a performance benchmark for online energy management algorithms, and can be implemented in real time by incorporating adequate forecasting techniques. We solve the optimization problem through relaxation, and use simulations to illustrate the characteristics of the solution. These simulations show that energy sharing takes place when there are differences in the load/generation and price profiles across participants. We also show that no energy sharing takes place when the load is above the local generation at all times.

AB - We propose an energy cost minimization strategy for cooperating households equipped with renewable energy generation and storage capabilities. The participating households minimize their collective energy expenditure by sharing renewable energy through the grid. We assume location and time dependent electricity prices, as well as parametrized transfer fees. We then formulate an optimization problem to minimize the energy cost incurred by the participating households over any specified planning horizon. The proposed strategy serves as a performance benchmark for online energy management algorithms, and can be implemented in real time by incorporating adequate forecasting techniques. We solve the optimization problem through relaxation, and use simulations to illustrate the characteristics of the solution. These simulations show that energy sharing takes place when there are differences in the load/generation and price profiles across participants. We also show that no energy sharing takes place when the load is above the local generation at all times.

KW - Non-convex optimization

KW - Renewable energy optimization

KW - Storage management

UR - http://www.scopus.com/inward/record.url?scp=85059810926&partnerID=8YFLogxK

U2 - 10.23919/EUSIPCO.2018.8553316

DO - 10.23919/EUSIPCO.2018.8553316

M3 - Conference contribution

SN - 978-1-5386-3736-4

VL - 2018-September

T3 - European Signal Processing Conference

SP - 191

EP - 195

BT - 2018 26th European Signal Processing Conference, EUSIPCO 2018

PB - IEEE

ER -

ID: 31344385