Integrating the charging scheduling model and constraints into the scheduling optimization process and conducting a comprehensive economic evaluation of the charging station, could achieve the optimal scheduling strategy of charging piles .
Although a large number of demonstration projects of the integrated charging station have been constructed and their technical feasibility has been validated, the capacity configuration of both PV and BESS are usually determined by empirical methods .
Previous studies always assume the charging demand of EVs as a constant power profile , or employ simplistic rules to assign the power of charging piles, such as assuming that EVs would be charged at maximum power upon arrival at the charging piles .
Utilizing the proposed stochastic simulation method of EV behaviors, the integrated charging station would accommodate approximately 29604 EVs each year, and the total annual electricity demand is about 755.20 MWh. Table 4. The parameters of charging piles and EVs. Fig. 5. The expected time interval distribution for EV arrivals.
The storage energy St of BESS at time t is equal to the sum of the storage energy St-1 at the previous time t -1 and the net charging power during the time period Δ t, as shown in Eq. (21). (21) S t = {(P t P t) Δ, = 1 1 + (P t P t) Δ, ≥ 2
The maximum power in the optimal scheduling strategy is about 150.61 kW, which occurs at 10:00–13:00. The electricity generated by the PV system is abundant in this time period, and the operating cost of the charging piles is cheap.
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