CN-122026509-A - Well group peak-to-peak interval pumping intelligent scheduling method for multifunctional micro-grid pumping unit

CN122026509ACN 122026509 ACN122026509 ACN 122026509ACN-122026509-A

Abstract

The invention discloses an intelligent scheduling method for well group staggered peak interval pumping of a multi-functional micro-grid oil pumping unit, which relates to the technical field of oil field production scheduling and comprises the following steps of pre-constructing a multi-functional micro-grid oil pumping unit well group production system model, wherein the multi-functional micro-grid oil pumping unit well group production system model comprises a wind-light output uncertainty model, an energy storage battery model, an oil pumping unit liquid yield and power model; and establishing a multi-objective optimization scheduling model, and simultaneously calibrating source end constraint, load end constraint and direct current bus power balance constraint by taking the lowest running cost, highest green electricity consumption rate and minimum well group load fluctuation variance of the multi-functional micro-grid as objective functions. According to the invention, through combining the NSGA-II algorithm with the entropy weight TOPSIS decision, a Pareto non-inferior solution set covering a multi-objective weighing relation can be generated efficiently, and scientific screening of an optimal solution can be realized based on objective weighting, so that the rationality and the superiority of a scheduling scheme are improved obviously.

Inventors

SONG JIAN
WANG JUN
Niu Huizhao
LI ZHAOBIN
TAN CHAOLUAN
Tan zhunan

Assignees

北京雅丹石油技术开发有限公司

Dates

Publication Date: 20260512
Application Date: 20251230

Claims (10)

1. The intelligent scheduling method for well group peak-to-peak pumping of the multifunctional micro-grid pumping unit is characterized by comprising the following steps of: The method comprises the steps of pre-constructing a well group production system model of the multi-functional micro-grid oil pumping unit, wherein the well group production system model of the multi-functional micro-grid oil pumping unit comprises a wind-light output uncertainty model, an energy storage battery model and an oil pumping unit liquid production amount and power model; Establishing a multi-objective optimization scheduling model, and simultaneously calibrating source end constraint, load end constraint and direct current bus power balance constraint by taking the lowest running cost, highest green electricity consumption rate and minimum well group load fluctuation variance of the multi-functional micro-grid as objective functions; Solving the multi-objective optimization scheduling model by adopting an NSGA-II algorithm and entropy weight TOPSIS decision combined algorithm strategy to obtain an optimal peak-to-peak pumping scheduling scheme of the pumping unit well group; According to the optimal scheduling scheme, the charge and discharge of the energy storage battery, the power grid electricity purchasing and selling and the start and stop states of the well group of the oil pumping unit are coordinated and controlled, the method is used for realizing the intelligent scheduling of peak-to-peak extraction.
2. The intelligent scheduling method for well group peak-to-peak fault extraction of the multi-energy micro-grid pumping unit is characterized in that the wind-light output uncertainty model comprises a photovoltaic power model, a fan power model and a value-collecting uncertainty model based on a confidence domain and an uncertainty budget, wherein the photovoltaic power model is used for calibrating the influence of illumination intensity and battery surface temperature, the fan power model is used for calibrating the influence of wind speed, and the value-collecting uncertainty model is used for representing wind-light output as an interval variable based on a predicted value and controlling a fluctuation range through a binary state variable and the uncertainty budget.
3. The intelligent scheduling method for well group peak-to-peak shifting of the multi-energy micro-grid pumping unit according to claim 1 is characterized in that the energy storage battery model comprises a storage battery mathematical model and an improved energy storage battery charging and discharging strategy based on time-of-use electricity price, wherein the storage battery mathematical model is used for describing the relation between the state of charge and charging and discharging power and efficiency, and the energy storage battery charging and discharging strategy is used for respectively executing a peak-to-time optimization strategy, a usual balancing strategy and a valley Shi Chuneng strategy according to peak, flat-valley period electricity price differences and real-time state of charge of a storage battery.
4. The intelligent scheduling method for well group peak-to-peak pumping among multi-energy micro-grid pumping units according to claim 3, wherein the peak-to-peak optimization strategy comprises the step that the highest electricity price is obtained in the period of highest electricity price, and the primary aim of the system is to obtain the maximum economic benefit by selling electricity to a power grid if the net load of the system is the same as that of the system Indicating that the photovoltaic and wind power generation amounts are enough to cover or exceed the electricity demand of the pumping unit, the storage battery is discharged to the maximum extent within the allowable SOC range, and the residual electric energy is fed into the power grid, otherwise, if If a power utilization gap exists, the storage battery is preferably discharged to meet the requirement, and when the SOC of the storage battery is close to the preset lower discharge limit, the system can purchase electric energy from a power grid to ensure the power supply continuity; The balance strategy is characterized in that the SOC level of the storage battery is maintained in the normal period with moderate charge value when When the surplus renewable energy source is used for charging the storage battery preferentially, if the SOC of the storage battery is close to the upper charging limit, the surplus electric energy can be sold to the power grid, if To reduce transmission loss which may occur from power grid purchase, the priority scheduling battery is discharged to meet the pumping unit load; the valley Shi Chuneng strategy has the lowest electricity price in the valley period, and the strategy focuses on low-cost electricity purchasing and energy storage, and no matter what the stage is As long as the SOC of the storage battery does not reach the upper limit, the system considers the purchase of electric energy from the power grid, and the purchase power at the moment needs to meet the net load requirement, namely And the charging requirement of the storage battery, when the SOC of the storage battery approaches the upper charging limit and stops charging, the current net load difference of the system is balanced by electricity purchase and selling with the power grid.
5. The intelligent scheduling method for well group peak-to-peak pumping among the multi-functional micro-grid pumping units according to claim 1 is characterized in that the pumping unit liquid yield and power model comprises a liquid yield change model and a power model, wherein; The liquid yield change model is used for respectively establishing a liquid yield calculation formula and a liquid yield analysis solution at the well opening and closing stages based on the coordination of the liquid level height change and the liquid supply and discharge rate; The power model comprises an up-stroke power consumption model, a down-stroke reverse power generation power model and a total power model, wherein the up-stroke power consumption model is fitted by adopting multiple linear regression and trigonometric functions, and the down-stroke reverse power generation power model is built based on a motor regenerative braking principle.
6. The intelligent scheduling method for well group peak-to-peak pumping among multi-functional micro-net pumping units according to claim 1, wherein the objective function comprises: calibrating an operation cost target, and minimizing the difference value between the photovoltaic unit, the fan operation cost, the energy storage battery maintenance cost and the electricity purchasing and selling cost and the income of the power grid, wherein the daily operation cost is expressed as: ; In the formula, The daily running cost of the system is set; The output power of the energy storage battery at the moment t; And The power purchased from the power grid at the moment t and the power sold to the power grid are respectively; 、 And The operation cost and the maintenance cost of the energy storage battery of the photovoltaic unit and the wind turbine unit are respectively; And The electricity price of electricity purchase and electricity selling from the power grid at the moment T is respectively, and T is the scheduling period; Calibrating a green electricity consumption rate target, and maximizing the ratio of the total wind-solar electric quantity consumed by a well group of the oil pumping unit to the total wind-solar power generation amount, wherein the ratio is expressed as follows: ; Wherein R is green electricity consumption rate; the number of the pumping units in the pumping unit well group; the electric quantity consumed by a single well i at the time t; calibrating a load fluctuation variance target, and minimizing the variance of the well group output power and the output average value in a dispatching period, wherein the variance is expressed as: ; In the formula, The total output curve variance of the pumping unit; the total output of the pumping unit is t time period; The average value of the total output of the pumping unit.
7. The intelligent scheduling method for well group peak-to-peak shifting of the multi-functional micro-grid pumping unit according to claim 6, wherein the source end constraint comprises photovoltaic and fan output upper and lower limit constraint, grid interaction power upper and lower limit constraint, storage battery state of charge constraint and charge and discharge power upper and lower limit constraint; wherein, photovoltaic unit power upper and lower limit constraint: ; In the formula, And Respectively the minimum value and the maximum value of the output power of the photovoltaic unit; The power upper and lower limit constraint of the wind turbine generator is as follows: ; In the formula, And Respectively the minimum value and the maximum value of the output power of the wind turbine generator; wherein, the upper and lower limit constraint of the interactive power of the power grid: ; In the formula, Is that The interactive power of the power grid at any moment; And Respectively the minimum value and the maximum value of the power grid interaction power; Wherein, battery state of charge, SOC, constraints: When the storage battery is in a charged state: ; when the storage battery is in a discharging state: ; the upper and lower limit constraint of the remaining capacity SOC of the battery is expressed as: ; In the formula, And Respectively the minimum value and the maximum value of the residual electric quantity SOC of the storage battery; The upper and lower limit constraints of the charge and discharge power of the battery can be expressed as: ; ; In the formula, And The maximum values of the charging power and the discharging power of the storage battery are respectively; And And the binary marks are respectively used for charging and discharging the storage battery at the moment t.
8. The intelligent scheduling method for well group peak-to-peak shifting of the multi-functional micro-grid oil pumping unit according to claim 1, wherein the load end constraint comprises a single well capacity lower limit constraint, a single well and well group total daily yield constraint, a gathering pipeline flow constraint, a single well daily operation time constraint, a well group operation state constraint, a pump efficiency constraint and an oil pumping unit power upper limit constraint and lower limit constraint; Wherein, single well productivity lower limit constraint, expressed as: ; In the formula, The liquid yield of the oil well i at the time t is obtained; The productivity of the single well i is the minimum; wherein, the daily total yield constraint of a single well is expressed as: ; In the formula, The working state of the oil well i at the moment t, Daily production minimum for single well i; wherein, the total daily production constraint of the well group is expressed as: ; In the formula, Is the minimum value of daily output of the well group of the oil pumping unit; wherein, the gathering and delivery pipeline flow constraint is expressed as: ; In the formula, And The minimum and maximum flow permitted by the gathering pipeline respectively; wherein, the single well day run time constraint is expressed as: ; In the formula, And Respectively a minimum value and a maximum value of Shan Jingri open-hole time lengths; wherein, well group operating state constraint is expressed as: ; In the formula, And The minimum value and the maximum value of the well group well opening quantity in each period are respectively; Wherein, pumping unit pump efficiency constraint, express as: ; In the formula, The pump efficiency of the ith pumping unit; wherein, the upper and lower limit constraint of the pumping unit power: ; In the formula, And Respectively the minimum value and the maximum value of the output force of the ith pumping unit.
9. The intelligent scheduling method for well group peak-to-peak pumping among the multi-functional micro-grid pumping units according to claim 1, wherein the power balance constraint of the direct current bus comprises the following steps: When no pumping unit is in the reverse power generation state at the moment t, the power balance of the direct current bus is expressed as: ; When the pumping unit is in the reverse power generation at the moment t, the method is expressed as: ; In the formula, And Respectively represents the number of pumping units in the phase of reverse power generation and power consumption, and 。
10. The intelligent scheduling method for well group peak-to-peak shifting of the multi-functional micro-net pumping unit according to claim 1, wherein the algorithm strategy combining NSGA-II algorithm and entropy weight TOPSIS decision comprises the following steps: Generating a Pareto non-inferior solution set by adopting an NSGA-II algorithm, expressing a scheduling scheme by binary codes, and realizing population evolution through selection, intersection, variation and elite selection operation; And evaluating the Pareto non-inferior solution set by adopting an entropy weight TOPSIS decision, and screening out a comprehensive optimal scheduling scheme by constructing an evaluation matrix, carrying out standardization processing, calculating entropy weight and relative proximity.

Description

Well group peak-to-peak interval pumping intelligent scheduling method for multifunctional micro-grid pumping unit Technical Field The invention relates to the technical field of oilfield production scheduling, in particular to an intelligent scheduling method for well group peak-to-peak interval pumping of a multifunctional micro-grid pumping unit. Background With the development of oil fields, low-permeability oil reservoirs become the main force for increasing the production of crude oil, but the oil reservoirs generally have the problem of insufficient liquid supply capacity in the middle and later stages of production. If a 24-hour continuous well opening system is adopted, pump efficiency is easy to be reduced, equipment is easy to damage and electric energy is easy to waste, so that a pumping system combining pumping and stopping is commonly adopted for the pumping well. However, the current intermittent pumping system mostly depends on manual experience to make, and has a plurality of defects that firstly, the dynamic liquid supply rule of an oil well is difficult to be matched accurately, the optimal balance of energy consumption and yield cannot be realized, the operation cost is high, and secondly, the well mouth and the pipeline can be blocked by freezing due to long-time stop in winter or at night, so that the safety risk and the maintenance cost are brought. In addition, with the strategic pushing of double carbon, new energy sources such as wind and light are accelerated to be fused into an oilfield power supply system, but the double randomness of source and load causes the new problems of insufficient green electricity consumption, impact on the stability of a micro-grid and the like. In the existing research, partial scholars build a related model around the optimization of the intermittent pumping system, but do not fully consider the cooperative scheduling of the wind-light storage multi-energy micro-grid, partial research relates to the fusion of wind-light and the pumping unit, but do not consider the peak clipping and valley filling effects of an energy storage battery or the reverse power generation phenomenon of the pumping unit, so that the model has deviation from actual production. Therefore, an intelligent scheduling method for well group peak-to-peak pumping of the multi-functional micro-grid pumping unit, which can achieve the economical efficiency, the stability and the green electricity consumption capability, is needed. Disclosure of Invention Aiming at the problems in the related art, the invention provides an intelligent scheduling method for well group peak-to-peak shifting of a multi-functional micro-grid oil pumping unit, which aims to overcome the technical problems in the prior art. The technical scheme of the invention is realized as follows: an intelligent scheduling method for well group peak-to-peak pumping of a multifunctional micro-grid oil pumping unit comprises the following steps: The method comprises the steps of pre-constructing a well group production system model of the multi-functional micro-grid oil pumping unit, wherein the well group production system model of the multi-functional micro-grid oil pumping unit comprises a wind-light output uncertainty model, an energy storage battery model and an oil pumping unit liquid production amount and power model; Establishing a multi-objective optimization scheduling model, and simultaneously calibrating source end constraint, load end constraint and direct current bus power balance constraint by taking the lowest running cost, highest green electricity consumption rate and minimum well group load fluctuation variance of the multi-functional micro-grid as objective functions; Solving the multi-objective optimization scheduling model by adopting an NSGA-II algorithm and entropy weight TOPSIS decision combined algorithm strategy to obtain an optimal peak-to-peak pumping scheduling scheme of the pumping unit well group; According to the optimal scheduling scheme, the charge and discharge of the energy storage battery, the power grid electricity purchasing and selling and the start and stop states of the well group of the oil pumping unit are coordinated and controlled, the method is used for realizing the intelligent scheduling of peak-to-peak extraction. The wind-light output uncertainty model comprises a photovoltaic power model, a fan power model and a value-collecting uncertainty model based on a confidence domain and an uncertainty budget, wherein the photovoltaic power model is used for calibrating the influence of illumination intensity and battery surface temperature, the fan power model is used for calibrating the influence of wind speed, and the value-collecting uncertainty model is used for representing wind-light output as an interval variable based on a predicted value and controlling a fluctuation range through a binary state variable and the uncertainty budget. The energy storage battery model compr