TY - GEN
T1 - Coordinated Optimization of EVs and Multiple Buildings with Renewable Energy
AU - Liu, Fengxia
AU - Xu, Zhanbo
AU - Liu, Kun
AU - Wu, Jiang
AU - Guan, Xiaohong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - This paper focuses on the jointly optimization of a micro-grid comprising electric vehicles (EVs) and buildings equipped with photovoltaic (PV) on the roof. The optimization problem of EVs and multiple buildings with renewable energy is formulated as a stochastic mixed integer linear programming (MILP) problem, in which bidirectional integration of EVs, building's demand response (DR) flexibility caused by outside air temperature (OAT) and thermostat setpoint temperature adjustment, and the uncertainty in PV power generation are fully taking into account. Scenario based method is adopted to deal with the randomness of PV generation and stochastic capacity requirements of EVs. The performance of jointly optimization is discussed based on case studies. Numerical results show that the aggregated EVs modeled as virtual storage system could not only significantly reduce the system cost and add building DR flexibility, but also could compensate the intermittency of renewable energy generation through peak shaving and valley filling.
AB - This paper focuses on the jointly optimization of a micro-grid comprising electric vehicles (EVs) and buildings equipped with photovoltaic (PV) on the roof. The optimization problem of EVs and multiple buildings with renewable energy is formulated as a stochastic mixed integer linear programming (MILP) problem, in which bidirectional integration of EVs, building's demand response (DR) flexibility caused by outside air temperature (OAT) and thermostat setpoint temperature adjustment, and the uncertainty in PV power generation are fully taking into account. Scenario based method is adopted to deal with the randomness of PV generation and stochastic capacity requirements of EVs. The performance of jointly optimization is discussed based on case studies. Numerical results show that the aggregated EVs modeled as virtual storage system could not only significantly reduce the system cost and add building DR flexibility, but also could compensate the intermittency of renewable energy generation through peak shaving and valley filling.
KW - Electric vehicles (EVs)
KW - stochastic mixed integer programming
KW - system optimization
UR - https://www.scopus.com/pages/publications/85094160763
U2 - 10.1109/CASE48305.2020.9217047
DO - 10.1109/CASE48305.2020.9217047
M3 - 会议稿件
AN - SCOPUS:85094160763
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1491
EP - 1496
BT - 2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020
PB - IEEE Computer Society
T2 - 16th IEEE International Conference on Automation Science and Engineering, CASE 2020
Y2 - 20 August 2020 through 21 August 2020
ER -
