With the rapid expansion of the electric vehicle (EV) market, the frequency of
grid-connected charging has concentrated primarily during peak hours, notably
from 7:00 a.m. to 10:00 a.m. and 6:00 p.m. to 10:00 p.m., resulting in
substantial demand surges during both morning and evening periods. Such
uncoordinated charging patterns pose potential challenges to the stability and
economic efficiency of power systems. As vehicle-to-grid (V2G) technology
advances, facilitating bidirectional energy exchange between EVs and smart
grids, the need for optimized control of EV charging and discharging behaviors
has become critical to achieving effective peak shaving and valley filling in
the grid. This paper proposes a microgrid energy scheduling optimization
algorithm based on existing smart grid and EV charging control technologies. The
method establishes a multi-objective optimization model with EVs’ 24-h charging
and discharging power as decision variables and microgrid load rate, load
standard deviation, and total electricity cost as objective functions.
Considering regional distribution capacity limitations, vehicle behavior
patterns, and other boundary conditions, solved with the non-dominated sorting
genetic algorithm (NSGA-II) to obtain the local optimal pareto front
distribution in order to analyze the economic performance and stability of the
microgrid. This study uses field survey data for simulation calculations, taking
a microgrid in a residential area in Haiyang City, Shandong Province, as an
example. The results demonstrate that the algorithm can reduce the
peak-to-valley difference of the power grid by 46.02%, the charging peak load by
18.96%, and the total charging cost by 35.8% under the mode of 100 EVs charging
a total of 1000 kWh. The optimization model proposed in this paper, validated
through simulations, can effectively guide EVs in energy scheduling, balancing
the interests of both the power grid and EVs, and is suitable for the
distributed management of large-scale EVs.