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Two-Stage Stochastic Model Using Benders' Decomposition for Large-Scale Energy Resource Management in Smart Grids
The ever-increasing penetration level of renewable energy and electric vehicles threatens the operation of the power grid. Dealing with uncertainty in smart grids is critical in order to mitigate possible issues. This paper proposes a two-stage stochastic model for a large-scale energy resource scheduling problem of aggregators in a smart grid. The idea is to address the challenges brought by the variability of demand, renewable energy, electric vehicles, and market price variations while minimizing the total operation cost. Benders’ decomposition approach is implemented to improve the tractability of the original model and its computational burden. A realistic case study is presented using a real distribution network in Portugal with high penetration of renewable energy and electric vehicles. The results show the effectiveness of the proposed approach when compared with a deterministic model. They also reveal that demand response and storage systems can mitigate the uncertainty. ; This work has received funding from the EU's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 and by NSF (US National Science Foundation) grant: IPP #1312260. Bruno Canizes is supported by FCT Funds through SFRH/BD/110678/2015 PhD scholarship and M. Ali Fotouhi Ghazvini is supported by FCT Funds through SFRH/BD/94688/2013 PhD scholarship.
Two-Stage Stochastic Model Using Benders' Decomposition for Large-Scale Energy Resource Management in Smart Grids
The ever-increasing penetration level of renewable energy and electric vehicles threatens the operation of the power grid. Dealing with uncertainty in smart grids is critical in order to mitigate possible issues. This paper proposes a two-stage stochastic model for a large-scale energy resource scheduling problem of aggregators in a smart grid. The idea is to address the challenges brought by the variability of demand, renewable energy, electric vehicles, and market price variations while minimizing the total operation cost. Benders’ decomposition approach is implemented to improve the tractability of the original model and its computational burden. A realistic case study is presented using a real distribution network in Portugal with high penetration of renewable energy and electric vehicles. The results show the effectiveness of the proposed approach when compared with a deterministic model. They also reveal that demand response and storage systems can mitigate the uncertainty. ; This work has received funding from the EU's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 and by NSF (US National Science Foundation) grant: IPP #1312260. Bruno Canizes is supported by FCT Funds through SFRH/BD/110678/2015 PhD scholarship and M. Ali Fotouhi Ghazvini is supported by FCT Funds through SFRH/BD/94688/2013 PhD scholarship.
Two-Stage Stochastic Model Using Benders' Decomposition for Large-Scale Energy Resource Management in Smart Grids
Joao Soares (Autor:in) / Bruno Canizes (Autor:in) / Mohammad Ali Fotouhi Ghazvini (Autor:in) / Zita Vale (Autor:in) / Ganesh Kumar Venayagamoorthy (Autor:in)
05.07.2017
oai:zenodo.org:1066926
IEEE Transactions on Industry Applications 53(6) 5905-5914
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
690
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