TY - JOUR
T1 - Real-time multi-objective optimisation for electric vehicle charging management
AU - Das, Ridoy
AU - Wang, Yue
AU - Busawon, Krishna
AU - Putrus, Ghanim
AU - Neaimeh, Myriam
N1 - Funding information: This work is supported by The Alan Turing Institute Data-Centric Engineering Programme under a grant from the Lloyds Register Foundation (G0095), and an Innovate UK e4future grant (104227). The authors would like to thank Doshisha University for providing the bidirectional charger for the experiments.
PY - 2021/4/10
Y1 - 2021/4/10
N2 - The continuous increase in the uptake of electric vehicles and the interest to use electric vehicles to provide energy services require commercially viable business models for all involved stakeholders. It is, however, challenging to achieve the synergy among different stakeholders since their objectives are often conflicting. This work proposes a real-time multi-objective optimisation method where electric vehicle charging/discharging profile is scheduled in real-time to strike a balance among different objectives, namely electricity cost reduction, battery degradation minimisation and grid stress alleviation as well as meeting the electric vehicle user charging requirement by fulfilling the departure time. Dynamic programming is adopted due to its computational efficiency, which is suitable for real-time applications. The effectiveness of the proposed method is demonstrated using a residential case study where the house is equipped with an electric vehicle and a photovoltaic system, and is validated by experimental implementation. The results show that the proposed multi-objective optimisation algorithm achieves the set objectives to satisfy the stakeholders’ priorities and provides a profit for the electricity end-user that is double as compared to that achieved by a benchmark multi-objective algorithm. The results demonstrate the effectiveness of the proposed multi-objective method and its suitability for real-time charging/discharging scheduling.
AB - The continuous increase in the uptake of electric vehicles and the interest to use electric vehicles to provide energy services require commercially viable business models for all involved stakeholders. It is, however, challenging to achieve the synergy among different stakeholders since their objectives are often conflicting. This work proposes a real-time multi-objective optimisation method where electric vehicle charging/discharging profile is scheduled in real-time to strike a balance among different objectives, namely electricity cost reduction, battery degradation minimisation and grid stress alleviation as well as meeting the electric vehicle user charging requirement by fulfilling the departure time. Dynamic programming is adopted due to its computational efficiency, which is suitable for real-time applications. The effectiveness of the proposed method is demonstrated using a residential case study where the house is equipped with an electric vehicle and a photovoltaic system, and is validated by experimental implementation. The results show that the proposed multi-objective optimisation algorithm achieves the set objectives to satisfy the stakeholders’ priorities and provides a profit for the electricity end-user that is double as compared to that achieved by a benchmark multi-objective algorithm. The results demonstrate the effectiveness of the proposed multi-objective method and its suitability for real-time charging/discharging scheduling.
KW - Multi-objective optimization
KW - real-time optimization
KW - V2G
KW - electric vehicles
KW - renewable energy
KW - decentralized control
UR - http://www.scopus.com/inward/record.url?scp=85100242009&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2021.126066
DO - 10.1016/j.jclepro.2021.126066
M3 - Article
SN - 0959-6526
VL - 292
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 126066
ER -