TY - JOUR
T1 - Multi-Objective Stochastic Techno-Economic-Environmental Optimization of Distribution Networks with G2V and V2G Systems
AU - Ahmadi, Seyed Ehsan
AU - Kazemi-Razi, S. Mahdi
AU - Marzband, Mousa
AU - Ikpehai, Augustine
AU - Abusorrah, Abdullah
N1 - Funding information: This work was supported from DTENetwork+funded by EPSRC grant reference EP/S032053/1. The authors would like to thanks Mr. Alex S. Daramola and Mr. Nnamdi Anthony Iwoba for their assistance and contribution during data collection, simulation, and analysis the corresponding results.
PY - 2023/5/1
Y1 - 2023/5/1
N2 - Plug-in electric vehicles (PEVs) are one of the most promising technologies for decarbonizing the transportation sector towards the global Net-zero target. However, charging/discharging of PEVs impacts the electricity network's stability, increases the operating costs, and affects the voltage profile. This paper proposes a flexible multi-objective optimization approach to evaluate and deploy vehicle-to-grid and grid-to-vehicle technologies considering techno-economical and environmental factors. Furthermore, life cycle of PEV batteries, charging/discharging pattern, and driving behaviours of the PEV owners are considered. The simulations are run over a modified IEEE 69-bus radial distribution test system to minimize two objective functions including the operating costs and CO
2 emissions using the heuristic-based Firefly Algorithm in a stochastic optimization framework considering renewable generations, load consumption, and charging/discharging timing of PEVs as the uncertain parameters. The results demonstrate significant reductions in the operating costs and CO
2 emissions, and the voltage profile of the network is improved properly. Besides, by implementing the discharging facility of PEVs in the network, the PEV owners save a considerable amount in operating costs.
AB - Plug-in electric vehicles (PEVs) are one of the most promising technologies for decarbonizing the transportation sector towards the global Net-zero target. However, charging/discharging of PEVs impacts the electricity network's stability, increases the operating costs, and affects the voltage profile. This paper proposes a flexible multi-objective optimization approach to evaluate and deploy vehicle-to-grid and grid-to-vehicle technologies considering techno-economical and environmental factors. Furthermore, life cycle of PEV batteries, charging/discharging pattern, and driving behaviours of the PEV owners are considered. The simulations are run over a modified IEEE 69-bus radial distribution test system to minimize two objective functions including the operating costs and CO
2 emissions using the heuristic-based Firefly Algorithm in a stochastic optimization framework considering renewable generations, load consumption, and charging/discharging timing of PEVs as the uncertain parameters. The results demonstrate significant reductions in the operating costs and CO
2 emissions, and the voltage profile of the network is improved properly. Besides, by implementing the discharging facility of PEVs in the network, the PEV owners save a considerable amount in operating costs.
KW - CO emission
KW - Firefly algorithm
KW - Multi-objective optimization
KW - Plug-in electric vehicle
UR - http://www.scopus.com/inward/record.url?scp=85147911152&partnerID=8YFLogxK
U2 - 10.1016/j.epsr.2023.109195
DO - 10.1016/j.epsr.2023.109195
M3 - Article
SN - 0378-7796
VL - 218
JO - Electric Power Systems Research
JF - Electric Power Systems Research
M1 - 109195
ER -
