Aiming at the problem of imbalance between the power purchasing cost and benefit due to policy restrictions that high energy-consuming enterprises

such as carbon fibre chemical plants

cannot have their own power plants

a novel capacity allocation and optimization strategy for photovoltaic

energy storage

and charging piles in low-carbon parks considering photovoltaic and pure electric heavy trucks is proposed. Firstly

in order to simulate the uncertainty of the travel characteristics of pure electric heavy trucks

simulate daily mileage and charging start-up moments of pure electric heavy trucks using Monte Carlo sampling method

and the charging capacity model of pure electric heavy trucks is established.Secondly

the method of self-organizing map(SOM)neural network clustering is used to scientifically divide the peak and valley hours

and the fuzzy demand response mechanism is used as a basis for the Logistic function.Then

non-dominated sorted genetic algorithm are used to optimize the time-of-use pricing and the charging quantity of pure electric heavy trucks.Finally

a system capacity allocation planning model aiming for minimizing investment costs of upper parks equipment

and a low-carbon optimal dispatching model are aiming for minimizing the operating costs of lower tier systems established so as to create a green power supply mode of "photovoltaic power station-charging pile-pure electric heavy truck".The results show that the proposed strategy can reduce carbon emissions and economic costs

and realize the synergetic development of green low-carbon power supply and economic benefits.