Optimized operation strategy for energy storage charging piles based on multi-strategy hybrid improved Harris hawk algorithm.

In response to the issues arising from the disordered charging and discharging behavior of electric vehicle energy storage Charging piles, as well as the dynamic characteristics of electric vehicles, we have developed an ordered charging and discharging optimization scheduling strategy for energy storage Charging piles considering time-of-use electricity prices. The decision variables include the charging and discharging prices, states, and power of electric vehicles. We have constructed a mathematical model for electric vehicle charging and discharging scheduling with the optimization objectives of minimizing the charging and discharging costs of electric vehicles and maximizing the revenue of Charging piles. To address the challenges of multivariable, multi-objective, and high-dimensional optimization in the proposed model, we propose a Multi-strategy Hybrid Improved Harris Hawk Algorithm (MHIHHO). In addition, to validate the optimization performance of the proposed algorithm, CEC benchmark test functions are employed to assess the algorithm's optimization accuracy, convergence speed, stability, and significance. Finally, optimization-based scheduling simulations are performed considering power constraints for energy storage charging and discharging at different time intervals, as well as discharge loads. The proposed method reduces the peak-to-valley ratio of typical loads by 52.8 % compared to the original algorithm, effectively allocates charging piles to store electric power resources during off-peak periods, reduces user charging costs by 16.83 %-26.3 %, and increases Charging pile revenue.