Based Eq. , to reduce the charging cost for users and charging piles, an effective charging and discharging load scheduling strategy is implemented by setting the charging and discharging power range for energy storage charging piles during different time periods based on peak and off-peak electricity prices in a certain region.
Fig. 11 Before and after optimization of charging pile discharge load. The MHIHHO algorithm optimizes the charging pile's discharge power and discharge time, as well as the energy storage's charging and discharging rates and times, to maximize the charging pile's revenue and minimize the user's charging costs.
Combining Figs. 10 and 11, it can be observed that, based on the cooperative effect of energy storage, in order to further reduce the discharge load of charging piles during peak hours, the optimized scheduling scheme transfers most of the controllable discharge load to the early morning period, thereby further reducing users' charging costs.
In the charging and discharging process of the charging piles in the community, due to the inability to precisely control the charging time periods for users and charging piles, this paper divides a day into 48 time slots, with the control system utilizing a minimum charging and discharging control time of 30 min.
To optimize grid operations, concerning energy storage charging piles connected to the grid, the charging load of energy storage is shifted to nighttime to fill in the valley of the grid's baseline load. During peak electricity consumption periods, priority is given to using stored energy for electric vehicle charging.
Based on the flat power load curve in residential areas, the storage charging and discharging plan of energy storage charging piles is solved through the Harris hawk optimization algorithm based on multi-strategy improvement.