Optimization of staggered peak intermittent pumping operation scheduling of pumping unit well clusters under wind, solar and energy storage microgrid with improved adaptive GAPSO hybrid algorithm

Using manual experience-developed intermittent pumping systems for clustered wells in low-permeability reservoirs in the middle and late stages of production often leads to problems such as high operating costs. After the wind and photovoltaic power generation is introduced into the intermittent pumping system of pumping well clusters, the system faces deficiencies such as low consumption capacity of green power. For this reason, with the objectives of the lowest daily operating cost (including the operation and maintenance cost of the source-side equipment, the power purchase cost and the power sale revenue generated from the interaction with the grid) and the highest green power consumption rate of the intermittent pumping system, an optimization model for the staggered peak intermittent pumping operation and scheduling of pumping well clusters under Wind, Solar and Energy Storage microgrids was established by taking into account the power constraints of the devices at the source side and the production constraints of the wells at the load side. The improved adaptive GAPSO hybrid algorithm is used to solve the model. Compared with the results of the traditional optimization algorithm, the daily operation cost of the scheduling scheme solved by the improved algorithm is reduced from RMB 170 to RMB 150, the green power consumption rate is increased from 83 % to 86 %, and the daily production of the well cluster is also increased by 4.86 %. The case study shows that the proposed model can effectively improve the system's green power consumption capacity by adjusting the charging and discharging power of the storage battery and the power purchased and sold from the grid, and minimizing the system's daily operating cost while ensuring that the pumping well cluster meets the production demand. All simulations are performed in MATLAB by a computer with a 13th Gen Intel(R) Core(TM) i5-13500H CPU at 2.60 GHz with 16 GB of RAM .