A multi-scale control method for wind, solar, thermal and storage microgrid systems in oil production

Abstract

The present invention discloses a multi-scale control method for a wind, solar, thermal, and storage microgrid system in oil production, comprising the following steps: S1. Establishing multi-scale data collection points for data collection; S2. Predicting future changes in wind and solar power generation, solar thermal energy supply, and oil extraction load; S3. Constructing a master-slave game model with the wind, solar, thermal, and storage supply system as the master and the oil extraction load as the slave; S4. Solving the master-slave game model using a particle swarm optimization algorithm; S5. Generating control instructions based on the solution of S4. The present invention employs the aforementioned multi-scale control method for a wind, solar, thermal, and storage microgrid system in oil production. By tapping multi-scale potential and applying intelligent algorithms, it can improve energy utilization, reduce costs, and ensure the stable operation of extraction equipment.

Description

Technical Field

[0001] The present invention relates to the field of energy scheduling technology, and in particular to a multi-scale control method for a wind, solar, thermal storage microgrid system in oil production. Background Art

[0002] In today's global pursuit of sustainable energy development, the oil extraction industry faces an urgent need for energy transformation. As a model for the utilization of clean, renewable energy, wind, solar, thermal, and energy storage smart microgrid systems are gradually being introduced into the oil extraction industry. However, their practical application faces numerous challenges. Given that oil fields are key areas of energy production and consumption, promoting the use of new energy to replace traditional energy sources in oil field extraction has far-reaching significance for the sustainable development of the entire energy industry.

[0003] Wind power generation is highly dependent on wind speed. Instantaneous, diurnal, and seasonal variations in wind speed can cause significant fluctuations in power generation. For example, in a coastal oil production area, wind turbines can operate at full capacity during strong daytime sea breezes, but wind speeds drop sharply at night, potentially causing power generation to drop to extremely low levels. Photovoltaic power generation is also constrained by factors such as light intensity, cloud cover, and the angle of the sun. Power generation varies significantly between sunny and cloudy days, and between summer and winter. While solar thermal systems are relatively stable, thermal energy output is limited when sunlight is insufficient, and the capacity and efficiency of thermal storage devices require careful consideration. Energy storage systems are key to balancing supply and demand, but their charge-discharge characteristics, capacity fading, and cost are important considerations. Different types of energy storage technologies, such as lithium batteries, lead-acid batteries, and thermal storage, are suitable for different scenarios and require appropriate configuration.

[0004] Oil production equipment is even more diverse. Pumping units, water injection pumps, and compressors each have distinct operating power curves and operating hours. Pumping units have relatively stable power requirements during the pumping process, but experience a large inrush current at startup. Water injection pumps, on the other hand, experience phased power changes depending on the required injection pressure. Furthermore, different stages of oil production—such as exploration, initial production, peak production, and decline—have distinct requirements for energy availability, stability, and quality. Traditional control methods, often based on experience or simple timing controls, are unable to adapt to this complex and volatile energy supply and demand landscape.

[0005] On the one hand, it's difficult to fully tap the potential of wind, solar, thermal, and energy storage components, leading to frequent energy waste. For example, during peak wind and solar power generation periods, excess electricity isn't properly stored or utilized due to a lack of effective coordination. On the other hand, it's impossible to guarantee the stable operation of key oil drilling equipment. Power outages and undervoltage conditions frequently occur, impacting drilling efficiency, increasing equipment maintenance costs, and even potentially posing safety risks. Furthermore, traditional regulation lacks foresight when responding to long-term environmental changes and adjustments to oil drilling plans, making it impossible to proactively plan for energy system optimization and upgrades. Therefore, a new, multi-scale, integrated regulation approach is urgently needed to break this impasse. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-scale control method for wind, solar, thermal and storage microgrid systems in oil production, which solves the problems of difficult balance between supply and demand of oil mechanical production energy and lack of foresight in traditional control. By using intelligent algorithms, it can cope with energy fluctuations and ensure the stable operation of mechanical production equipment.

[0007] To achieve the above objectives, the present invention provides a multi-scale control method for a wind-solar-thermal-storage microgrid system in oil production, comprising the following steps:

[0008] S1. Set up multi-scale data collection points for data collection;

[0009] S2. Predict the changes in wind and solar power generation, solar thermal power supply, and oil extraction load in the future;

[0010] S3. Construct a master-slave game model with the wind, solar, thermal and storage supply system as the master and the oil drilling load as the slave.

[0011] S4, using particle swarm optimization algorithm to solve the master-slave game model;

[0012] S5. Generate control instructions based on the solution result of S4.

[0013] Preferably, in S1, a high-precision sensor is used to collect the real-time power of wind power generation , real-time power of photovoltaic power generation , Real-time energy supply power of solar thermal system , energy storage system charge state , charging power , discharge power and real-time load power of various oil drilling equipment ;in, Indicates time, with a collection frequency of seconds , hourly level Japanese level .

[0014] Preferably, in S2, the autoregressive moving average model ARIMA and the long short-term memory network LSTM are used to combine historical meteorological data, historical energy production data and historical machine load data to predict the wind power generation power in the future period. , photovoltaic power generation , solar thermal energy power and oil drilling load Changes, forecasting future periods are divided into short-term, medium-term and long-term.

[0015] Preferably, in S3, the main objective function is to maximize the energy supply benefits, then the wind, solar, heat and storage supply system is The energy supply benefit at the moment is calculated as follows:

[0016]

[0017] ;

[0018] in, 、 and They are The unit electricity selling price of wind power, photovoltaic power and solar thermal power at the moment, for Unit charging and discharging cost of energy storage at any moment;

[0019] The objective function is to minimize the sum of energy procurement cost and production loss cost, so the oil extraction load is The sum of energy procurement cost and production loss cost at the moment is calculated as:

[0020] ;

[0021] in, For the Congfang From the moment The amount of electricity purchased from the energy components, is the purchase price of the corresponding energy, for Time machine mining equipment idle cost, for The load demand of the machine at each moment;

[0022] Energy supply and demand balance constraint, expressed as The total energy generated by the energy supply system at any given moment is equal to the total energy consumed by the oil extraction load, that is:

[0023] ;

[0024] The upper and lower limits of operating capacity and power of each component are as follows:

[0025] ;

[0026] in, Represents different energy components, and Components exist Minimum and maximum power operating limits at all times.

[0027] Preferably, in S4, the particle swarm optimization algorithm PSO is used to solve the master-slave game model, and the particle swarm size is set , inertia weight , learning factor 、 , and the maximum number of iterations ;

[0028] Particle velocity update formula:

[0029] ;

[0030] in, represents the particle number, n Represents the dimension, represents the number of iterations, For the The particle in In the iteration n The speed of the dimension, and For the In the iteration n Two random numbers of dimension, For the The particle in In the iteration n The individual optimal position of the dimension, For the group In the iteration n Optimal position of dimension;

[0031] Particle position update formula:

[0032] ;

[0033] in, For the The particle in In the iteration n Dimensional location.

[0034] Preferably, in S5, a control instruction is generated according to the solution result of the particle swarm optimization algorithm in S4:

[0035] set up 、 They are The instantaneous fluctuation value of wind power generation and photovoltaic power generation at the moment or Exceeding the threshold When the wind, solar, heat and storage supply systems are quickly charged and discharged within the second response time to balance the power;

[0036] During hourly regulation, the operating parameters of the solar thermal system are adjusted in advance and the energy storage charging and discharging plan is optimized based on short-term forecast results; during seasonal regulation, the energy storage capacity planning is adjusted in advance and the maintenance cycle of wind and solar equipment is optimized based on long-term forecasts.

[0037] Preferably, after S5, it also includes the establishment of a multi-dimensional evaluation indicator system, including full life cycle cost, carbon emission reduction, energy utilization efficiency and the number of oil machine extraction operation interruptions; regularly evaluate the control effect, and adjust the prediction model parameters, master-slave game model price and cost parameters and particle swarm optimization algorithm parameters based on the evaluation results and the feedback mechanism.

[0038] Therefore, the present invention adopts the above-mentioned multi-scale control method of the wind-solar-thermal storage microgrid system in oil production, and the beneficial effects are as follows:

[0039] (1) The present invention uses precise multi-scale integrated control to fully consider the characteristics of various energy components of wind, solar, thermal and storage and the load requirements of oil extraction, optimize energy generation, storage and distribution, avoid energy waste, significantly improve energy utilization efficiency, reduce dependence on traditional fossil energy, and reduce energy costs.

[0040] (2) The present invention is based on the optimization strategy of master-slave game theory and swarm intelligence algorithm. On the premise of meeting the energy demand of oil extraction, it minimizes the system operation cost, including energy production cost, equipment maintenance cost and energy storage cost, and maximizes the efficiency of oil extraction, thereby improving the overall economic benefits and enhancing the competitiveness of enterprises.

[0041] (3) The present invention can effectively cope with the intermittent and volatile nature of wind and solar energy. Through reasonable charge and discharge control of energy storage units and coordinated complementarity of multiple energy sources, it ensures that oil extraction equipment can obtain stable and reliable energy supply under different working conditions and time scales, reduces the risk of production interruption caused by unstable energy supply, improves production continuity and stability, and ensures the smooth progress of oil extraction operations.

[0042] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is an overall flow chart of an embodiment of a multi-scale control method for a wind-solar-thermal storage microgrid system in oil production according to the present invention.

[0044] Figure 2 This is an overall framework diagram of an embodiment of a multi-scale control method for a wind-solar-thermal storage microgrid system in oil production according to the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0046] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0047] like Figure 1 、 Figure 2 As shown, a multi-scale control method for a wind-solar-thermal storage microgrid system in oil production includes the following steps:

[0048] S1. Set up multi-scale data collection points for data collection and use high-precision sensors to collect real-time wind power generation power. , which is used to reflect the power generation capacity of the wind turbine at that moment. Its value depends on factors such as wind speed and performance parameters of the wind turbine.

[0049] Wind power model:

[0050] The formula for calculating the real-time power of wind power generation is as follows:

[0051] ;

[0052] in, is the air density ( ), is the swept area of ​​the wind wheel ( ), by the rotor radius R Decide, , the larger the rotor radius, the larger the swept area and the stronger the ability to capture wind energy; Indicates the wind speed at the time ( ), is a key factor affecting wind power generation. The size and changes of wind speed directly determine how much wind energy the wind turbine can capture.

[0053] is the wind energy utilization coefficient, is the tip speed ratio and pitch angle Tip speed ratio ,in yes The angular velocity of the wind wheel at the moment ( / s ); pitch angle The pitch angle is the angle between the rotor blades and the airflow. Adjusting the pitch angle can change the wind energy utilization factor, thereby controlling wind turbine power generation. For example, when wind speeds are low, the pitch angle can be increased to improve the wind energy utilization factor; when wind speeds are high, the pitch angle can be decreased to protect the turbine.

[0054] Photovoltaic power generation real-time power , which is determined by factors such as light intensity, conversion efficiency of photovoltaic panels, and temperature, and reflects the power generation output of the photovoltaic array at a specific moment.

[0055] Photovoltaic model

[0056] The real-time power calculation formula for photovoltaic power generation is:

[0057] ;

[0058] in: Indicates the number of PV modules connected in series. The number of modules in series will affect the output voltage. The more modules connected in series, the higher the output voltage. However, the current of the modules in series must be consistent, otherwise the overall output power will be affected by the short-circuit effect.

[0059] Indicates the number of PV panels connected in parallel. Parallel connection can increase the output current, thereby improving the overall power generation. The more parallel connections there are, the greater the output current.

[0060] It represents the photocurrent (A) at a given moment, which is determined by the light intensity and the characteristics of the photovoltaic cell. The stronger the light intensity, the greater the photocurrent. Factors such as the material and temperature of the photovoltaic cell will also affect it.

[0061] Represents the reverse saturation current (A), which is related to the material and temperature of the photovoltaic cell. When the temperature rises, the reverse saturation current will increase. Generally, the reverse saturation current of silicon-based photovoltaic cells is small at room temperature.

[0062] Represents the charge of an electron and is a constant.

[0063] express The output voltage (V) of the photovoltaic cell at any moment is affected by factors such as light intensity, temperature, and load. When the light intensity changes or the load changes, the output voltage will change accordingly.

[0064] express The photovoltaic cell output current (A) at a given moment is the actual output current of the photovoltaic cell, which is related to factors such as photocurrent, reverse saturation current and battery internal resistance.

[0065] is the series resistance ( ), which is mainly composed of the resistance of the battery material itself, the contact resistance between the electrode and the battery, etc. The series resistance will consume part of the electrical energy and reduce the output power. Generally, it is hoped that the smaller the series resistance, the better.

[0066] It represents the diode ideality factor, and its value range is generally between 1 and 2. It reflects the physical properties of the photovoltaic cell. The value varies with photovoltaic cells made of different materials and manufacturing processes.

[0067] is the Boltzmann constant, a physical constant.

[0068] express The temperature of the photovoltaic cell at a given moment (K). An increase in temperature will reduce the open-circuit voltage of the photovoltaic cell, thereby affecting the output power. For example, in the hot summer, the temperature of the photovoltaic cell will increase and the power generation power may decrease.

[0069] Represents the parallel resistance ( ), which is mainly caused by leakage at the edge of the battery and defects in the battery body. The smaller the parallel resistance, the greater the leakage current and the lower the output power. High-quality photovoltaic cells usually have higher parallel resistance.

[0070] Real-time energy supply power of solar thermal system , which is related to the lighting conditions, the efficiency of the photothermal conversion device, and the heat storage and transmission conditions, and represents the rate at which the photothermal system provides thermal energy to oil recovery operations.

[0071] Photothermal model

[0072] Calculation formula for real-time energy supply power of solar thermal system:

[0073] ;

[0074] in, Indicates the collector lighting area ( ), the larger the lighting area, the more solar radiation energy can be absorbed. Generally, the lighting area of ​​the collector is determined according to actual needs and installation space.

[0075] express Total solar irradiance on the collector surface at the moment ( ), whose magnitude depends on factors such as solar radiation intensity, weather conditions (sunny, cloudy, etc.), geographical location (latitude, altitude, etc.), and time (season, different times of the day). For example, at noon, the solar altitude angle is large and the total solar irradiance is usually strong.

[0076] The thermal efficiency of a solar collector is a value between 0 and 1 that reflects the collector's ability to convert solar radiation into heat. This efficiency is affected by factors such as the collector type (flat plate, vacuum tube, etc.), material properties, optical performance, and heat loss. For example, a high-efficiency vacuum tube collector may have a thermal efficiency of 0.6-0.8, while an ordinary flat plate collector has a relatively low efficiency.

[0077] Energy storage system state of charge , its value range is between 0-1.

[0078] Energy storage model

[0079] State of Charge ( SOC ) Calculation formula:

[0080] ;

[0081] in, express The state of charge at the moment, with a value range of 0-1, reflects the ratio of the remaining power of the energy storage system to the total capacity at that moment. For example, it means that the remaining power of the energy storage system is half of the total capacity.

[0082] Indicates the last moment state of charge.

[0083] Indicates the time interval, which is used to describe the time difference between two adjacent state of charge calculations. Its size is determined by the data acquisition frequency and system control requirements. For example, in second-level control, Maybe 1 second.

[0084] Indicates the time interval The charge and discharge current (A) in the energy storage system is shown in Figure 2. A positive value indicates a charge current, and a negative value indicates a discharge current. The magnitude of the charge and discharge current depends on the working state of the energy storage system (charging, discharging, or stationary) and the requirements of the external load or power supply.

[0085] It represents the rated capacity (Ah) of the energy storage system, which is the maximum amount of electricity that the energy storage system can store. For example, a battery with a rated capacity of 100Ah can theoretically store 100 ampere-hours of electricity when fully charged.

[0086] Charging power and discharge power , that is, a positive value indicates charging and a negative value indicates discharging. The sizes of the two are affected by the performance of the energy storage device, the charging and discharging control strategy, and the energy balance requirements of the system.

[0087] The relationship formula between charging power and energy:

[0088] ;

[0089] in, for The charging energy (Wh or kWh) at a certain moment indicates the energy stored by the energy storage system through charging at that moment.

[0090] For the previous moment Charging energy;

[0091] is the charging efficiency, which is a value between 0 and 1, reflecting the energy loss caused by heat and other reasons during the charging process, such as This means that there is a 10% energy loss during the charging process. The charging efficiency is affected by factors such as the type of energy storage device (lithium battery, lead-acid battery, etc.), charging method (constant current charging, constant voltage charging, etc.) and ambient temperature.

[0092] The relationship formula between discharge power and energy:

[0093] ;

[0094] in, for The discharge energy (Wh or kWh) at a certain moment indicates the energy released by the energy storage system through discharge at that moment. The discharge efficiency is also a value between 0 and 1, reflecting the energy loss during the discharge process. The discharge efficiency is affected by factors such as the internal resistance of the energy storage device, the size of the discharge current, and temperature. For example, when discharging at a high current, the discharge efficiency may decrease.

[0095] Energy storage system cost calculation formula (unit charging and discharging cost):

[0096] ;

[0097] in,

[0098] for The unit charging and discharging cost of energy storage at each moment (yuan / kWh) is used in the master-slave game model to calculate the benefits of the wind, solar, thermal and energy storage supply system and the cost of oil extraction load.

[0099] It represents the initial investment cost (in RMB) of the energy storage system, including the one-time investment costs such as the purchase, installation, and commissioning of the energy storage equipment. For example, the cost of purchasing a lithium battery energy storage system and the materials and labor costs required for installation.

[0100] It represents the maintenance cost (in RMB) of the energy storage system during its service life, covering expenses such as daily inspection, upkeep, repair, and replacement of parts. The maintenance cost will gradually accumulate as the energy storage system is used for an extended period of time.

[0101] It represents the replacement cost (in RMB) of the energy storage system during its service life. When the energy storage equipment reaches the end of its service life or its performance is severely degraded and cannot meet demand, the cost of replacing the equipment needs to be paid, such as the cost of replacing the battery pack.

[0102] It represents the total charge and discharge capacity (kWh) of the energy storage system during its service life, reflecting the total amount of work done by the energy storage system throughout its entire life cycle. The larger the total charge and discharge capacity, the lower the unit charge and discharge cost.

[0103] Real-time load power of each oil drilling equipment It is used to reflect the actual power consumption of various equipment (such as pumping units, water injection pumps, compressors, etc.) at that moment in the oil production process. Its changes are related to factors such as the operating status of the equipment, production technology, and oil well operating conditions.

[0104] in, Indicates time, 、 、 、 、 and The unit is watt (W) or kilowatt (kW), the collection frequency 、 and The unit is Hertz (Hz), which indicates the frequency of data collection for various parameters at the corresponding time scale, and is used to capture the changing characteristics of energy systems and machine loads at different time resolutions, among which the second level Used to capture instantaneous fluctuations, hourly To assist with short-term scheduling and day-level Can support medium-term planning.

[0105] S2. Use the autoregressive moving average model ARIMA and long short-term memory network LSTM to combine historical meteorological data, historical energy production data and historical machine-collected load data to build a forecast model and predict the future period. Wind power generation at the time , photovoltaic power generation , solar thermal energy power and oil drilling load Energy production, distribution, and storage strategies are planned in advance to cope with future changes in energy supply and demand.

[0106] The forecast period is divided into short-term (hourly, 1-24 hours), medium-term (daily, 1-7 days) and long-term (seasonal, 1-4 seasons). The forecast model is regularly updated and trained to adapt to environmental changes.

[0107] The autoregressive moving average model ARIMA includes stationary processing (difference), autoregressive part ( ) and the moving average part ( ):

[0108] Stationary processing (difference):

[0109] The ARIMA model first performs a difference operation on the non-stationary time series. Let the original time series be If the series is non-stationary, by differencing Convert the series into a stationary series.

[0110] in, is the lag operator, , is the difference order.

[0111] The autoregressive part ( ):

[0112] The stationary series (The differenced series) can be represented as a linear combination of past values. part( is the autoregressive order), the model can be written as:

[0113] ;

[0114] in, is the autoregressive coefficient, is a white noise sequence, which means Random disturbances at any moment.

[0115] Moving average part ( ):

[0116] for part( is the moving average order), stationary series It can be expressed as a linear combination of past white noise, namely:

[0117] ;

[0118] in, is the moving average coefficient.

[0119] Full ARIMA ( , , ) model integrates the difference, autoregression and moving average parts to predict time series. When predicting the changes in wind and solar power generation, solar thermal power supply and oil extraction load in the future, it will fit the historical data to produce 、 、 parameters and use these parameters and historical data to predict future values.

[0120] Long Short-Term Memory (LSTM) architecture

[0121] CellState:

[0122] The core of LSTM is the cell state , which is like an information conveyor belt that runs through the entire time series. The cell state can selectively forget or add information, and its initial value It is usually a given initial state that is continuously updated during time series processing.

[0123] ForgetGate:

[0124] The forget gate determines what information to discard from the cell state. It does this by sigmoid Function to achieve this, the formula is:

[0125] ;

[0126] in, is the weight matrix of the forget gate, is the bias, is the hidden state at the previous moment, is the input at the current moment, yes sigmoid Function, output Is a value between 0 and 1, used to control the cell state at the previous moment degree of forgetfulness.

[0127] Input Gate:

[0128] The input gate consists of two parts, a sigmoid The layer decides which values ​​to update, a tanh The layer creates a new vector of candidate values:

[0129] , used to control the degree of update;

[0130] , used to create candidate values.

[0131] Then, update the cell state to:

[0132] .

[0133] Output Gate:

[0134] The output gate determines the next hidden state First, through a sigmoid function:

[0135] ;

[0136] ;

[0137] Then, this hidden state It can be used for subsequent operations such as prediction. When predicting wind and solar power generation, the time series of historical data is processed time step by time, and the last hidden state is used for prediction.

[0138] S3. Construct a master-slave game model with the wind, solar, heat and storage supply system as the main party and the oil drilling load as the slave party. The main party's objective function is to maximize the energy supply benefits. Then the wind, solar, heat and storage supply system The energy supply income at the moment, in yuan, is calculated as follows:

[0139]

[0140] ;

[0141] in, 、 and They are The unit electricity sales price of wind power, photovoltaic power, and solar thermal power at that moment, in yuan / kilowatt-hour (yuan / kWh); for The unit charging and discharging cost of energy storage at each moment, expressed in yuan / kilowatt-hour (yuan / kWh), takes into account factors such as battery life degradation, charging and discharging efficiency, and operation and maintenance costs. This objective function aims to maximize the economic benefits of the supply system through reasonable energy allocation and pricing strategies.

[0142] The objective function is to minimize the sum of energy procurement cost and production loss cost, so the oil extraction load is The sum of energy procurement cost and production loss cost at the moment is calculated as:

[0143] ;

[0144] in, For the load of oil extraction machine From the moment The amount of electricity purchased from various energy components (wind power, photovoltaic arrays, solar thermal systems, and energy storage systems, etc.), in kilowatt-hours (kWh); is the purchase price of the corresponding energy, in Yuan / kilowatt-hour (Yuan / kWh); for The idle cost of the machine mining equipment at any time, in yuan / kilowatt-hour (yuan / kWh); for The objective function of this square is to minimize the energy procurement cost and the production loss cost caused by insufficient energy supply under the premise of meeting the oil extraction load demand.

[0145] Constraints:

[0146] Energy supply and demand balance constraint, expressed as Total energy generated by the energy supply system at any moment Equal to the total energy consumed by oil extraction load ,Right now:

[0147] ;

[0148] = ;

[0149] This constraint ensures the stable operation of the energy system and avoids energy shortages or surpluses.

[0150] The upper and lower limits of operating capacity and power of each component are as follows:

[0151] ;

[0152] in, Represents different energy components, and Components exist The minimum and maximum power operating limits at any given moment, expressed in watts (W) or kilowatts (kW). These limits are based on equipment nameplate parameters (model, rated power, input voltage, rated current, number of phases, etc.) and safe operating specifications to ensure that all components operate within a safe and stable range, preventing equipment damage and energy waste.

[0153] S4. Use particle swarm optimization algorithm PSO to solve the master-slave game model and set the particle swarm size , inertia weight , learning factor 、 , and the maximum number of iterations .

[0154] In a simple system, Choose 30-50, and 80-150 in complex systems, based on the complexity of the problem to balance the algorithm's search range and computational efficiency.

[0155] Using a linear decreasing strategy, the initial value is 0.9, the final value is 0.4. Inertia weight It is used to control the influence of the particle's previous velocity on the current velocity. A larger inertia weight is conducive to global search, while a smaller inertia weight is conducive to local search. Through a linear decrease method, the algorithm can widely explore the solution space in the early stage, and conduct a fine search in the better area in the later stage to find the global optimal solution.

[0156] Learning Factor 、 ,in Focuses on individual learning, with a value range of 1.5-2.0; It focuses on group learning, with a value range of 2.0-2.5. The learning factor is used to adjust the step size of the particle's learning towards its own historical optimal position and the group's historical optimal position. By reasonably setting the learning factor, the individual exploration and group collaboration capabilities of the particles can be balanced, the convergence speed of the algorithm can be accelerated, and at the same time, it can avoid falling into the local optimal solution.

[0157] Maximum number of iterations The number of iterations is determined based on real-time requirements and the difficulty of problem convergence. For short-term control (e.g., seconds or hours), 30-50 iterations are used to ensure rapid response. For long-term control (e.g., seasonality or long-term planning), 80-150 iterations are used to ensure the algorithm can fully search for optimal solutions while taking into account the limitations of computing resources and time costs.

[0158] Particle position vector represents a set of possible energy distribution and load response strategies, the speed vector Formula for updating position and particle velocity:

[0159] ;

[0160] in, represents the particle number, n Representation dimension (corresponding to different energy distribution or load response variables), represents the number of iterations, For the The particle in In the iteration n The speed of the dimension, is the inertia weight, and For the In the iteration n Two random numbers of the dimension, ranging from 0 to 1, are used to increase the randomness of the search; For the The particle in In the iteration n The individual optimal position of the dimension, For the group In the iteration n Optimal position of dimension;

[0161] The particle velocity update formula updates the particle velocity by comprehensively considering the particle's current velocity, individual experience, and group experience, guiding the particle to move to a better position in the solution space.

[0162] Particle position update formula:

[0163] ;

[0164] in, For the The particle in In the iteration n The new position of the particle in the next iteration is obtained by adding the updated velocity to the current position, which represents a possible energy distribution and load response strategy.

[0165] S5. Generate control instructions based on the results of the particle swarm optimization algorithm in S4:

[0166] set up 、 They are The instantaneous fluctuation value of wind power generation and photovoltaic power generation at the moment, in watts (W) or kilowatts (kW); or Exceeding the threshold When the wind, solar, heat and storage supply systems are quickly charged and discharged within the second response time to balance the power;

[0167] Among them, the second-level control threshold Determine by reverse calculation based on the voltage and frequency fluctuation range allowed by oil drilling equipment. For equipment that is sensitive to voltage fluctuations, Set to 5% of the rated power (or other appropriate proportion determined by the characteristics of the equipment), it means that when the instantaneous fluctuation of wind and solar power generation exceeds this threshold, the energy storage system needs to quickly charge and discharge within a response time of seconds (such as 0.5 seconds) to balance the power and maintain the stable operation of the mining equipment.

[0168] During hourly regulation, based on short-term forecast results, the operating parameters of the solar thermal system are adjusted and the energy storage charging and discharging plan is optimized hourly response time (e.g., 1 hour in advance); during seasonal regulation, based on long-term forecasts, the energy storage capacity planning is adjusted and the maintenance cycle of wind and solar equipment is optimized seasonal response time (e.g., 1 month in advance).

[0169] Finally, a multi-dimensional evaluation indicator system is established, including the full life cycle cost (including investment costs, operation and maintenance costs, and energy purchase costs, etc.), carbon emission reductions, energy utilization efficiency, and the number of oil drilling operation interruptions; the regulation effect is evaluated regularly, and based on the evaluation results and using the feedback mechanism, the prediction model parameters, the master-slave game model price and cost parameters, and the particle swarm optimization algorithm parameters are adjusted.

[0170] Example 1: Initial application of small inland oil production sites

[0171] Data Collection: Adaptive sensors were installed on key mining equipment, including wind turbines, 500kW photovoltaic arrays, solar thermal collectors, 500kWh lithium-ion battery energy storage systems, as well as pumping units and water injection pumps. Data was transmitted using Industrial Ethernet. Second-level data acquisition was used to monitor transient fluctuations in wind and photovoltaic power, while hourly data acquisition aided in analyzing mining load patterns. For example, at noon on a sunny day, the photovoltaic power generation reached 400kW, the wind power generation reached 50kW, the pumping unit load reached 200kW, and the water injection pump load reached 150kW.

[0172] Forecasting: The site collected meteorological and mechanical load data from the past three years and used an ARIMA model combined with an LSTM network to predict energy supply and demand for the next 24 hours. After training, the average error in wind and solar power generation forecasts for the next six hours was kept within 10%, and the error in mechanical load forecasts was within 8%. The forecast predicted that wind speeds would increase at night, photovoltaic power generation would drop to zero, and mechanical load would remain around 300 kW.

[0173] The master-slave game model was constructed based on the local energy market, with wind power prices set at 0.45 yuan / kWh, photovoltaic power at 0.55 yuan / kWh, and energy storage charging and discharging costs at 0.2 yuan / kWh. The idle cost of mining equipment was estimated at 1,000 yuan / hour based on crude oil prices and downtime losses. Constraints on the rated parameters of each component were considered, such as a maximum wind turbine power of 100kW, a maximum photovoltaic array power of 500kW, and a maximum energy storage charging power of 100kW.

[0174] Swarm intelligence algorithm solution: select particle swarm optimization algorithm, set the number of particles to 50, the initial inertia weight to 0.9, and the learning factor , with a maximum number of iterations of 100. After 30 iterations, the optimal energy allocation scheme during the day is obtained as 60% photovoltaic power supply, 20% wind power, and energy storage to supplement the remaining demand, while wind power and energy storage are the main power supply at night.

[0175] Control Execution: The central control system controls various devices in real time based on the solution. For example, during the day, the tilt of the photovoltaic panels is adjusted to increase power generation efficiency by 10%, and the energy storage system is controlled to charge and discharge according to schedule. At night, when wind speeds increase and wind power exceeds 80kW, the energy storage system is charged; when the drilling load increases, the energy storage is discharged in a timely manner to replenish the system. On the day of implementation, energy utilization efficiency increased by 12% compared to traditional control methods, and oil drilling operations were not interrupted.

[0176] Example 2: Seasonal Control of Medium-Sized Coastal Oil Production Platforms

[0177] Data collection: Sensors continuously collected data and found that the average photovoltaic power generation power in winter daytime dropped to 200kW, the wind power generation power fluctuation increased, and the mechanical mining load increased slightly due to process adjustments.

[0178] Forecasting: Update weather and load data, retrain the forecasting model, and predict energy supply and demand for the next month. It was found that the available hours for wind power at night in winter are shortened, and the peak hours for wind power generation are extended.

[0179] Adjustment of the master-slave game model: Based on the changes in winter energy costs, the electricity sales price is adjusted, with wind power reduced to 0.4 yuan / kWh and photovoltaic power reduced to 0.5 yuan / kWh. Taking into account the low-temperature performance of the battery, the energy storage charging and discharging cost rises to 0.25 yuan / kWh.

[0180] Swarm intelligence algorithm re-solved: Particle swarm optimization algorithm parameter adjustment, particle number 50, inertia weight 0.8, learning factor Both are set to 1.5, and the maximum number of iterations is 200. The winter strategy is re-solved, giving priority to protecting key mining equipment during the day, with less energy storage and more charging, and increasing the proportion of solar thermal energy supply for wellhead insulation.

[0181] Regulation and implementation: According to the new strategy, during the winter, the number of power outages of machine mining equipment decreased by 70% compared with the same period in previous years, the stability of energy supply was greatly improved, carbon emissions were reduced by 25%, and crude oil extraction efficiency remained stable.

[0182] Example 3: Long-term expansion and upgrade planning for a large oil production base

[0183] After three years of operation, the mining scale has expanded by 50%, and the original energy system needs to be expanded.

[0184] Data collection: Review historical operating data and comprehensively analyze energy supply and demand bottlenecks in each season and time period.

[0185] Forecast: Use big data analysis to predict energy demand in the next three years, estimate the peak load growth rate, combine geological exploration planning, mining progress expectations and other factors, and comprehensively consider the long-term change trend of wind and solar resources.

[0186] Therefore, the present invention adopts the above-mentioned multi-scale control method of the wind, solar, thermal storage microgrid system in oil production, and relies on precise potential tapping of energy components and intelligent optimization and allocation to effectively improve energy utilization efficiency, stably ensure oil mechanical production operations, and reduce costs.

[0187] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multi-scale control method for a wind, solar, thermal storage microgrid system in oil production, characterized in that: The following steps are involved: S1. Set up multi-scale data collection points for data collection; S2. Predict the changes in wind and solar power generation, solar thermal power supply, and oil extraction load in the future; S3. Construct a master-slave game model with the wind, solar, thermal and storage supply system as the master and the oil drilling load as the slave. S4, using particle swarm optimization algorithm to solve the master-slave game model; S5. Generate control instructions based on the solution result of S4; In S1, high-precision sensors are used to collect real-time wind power generation power. , real-time power of photovoltaic power generation , Real-time energy supply power of solar thermal system , energy storage system charge state , charging power , discharge power and real-time load power of various oil drilling equipment ;in, Indicates time, with a collection frequency of seconds , hourly level Japanese level ; In S3, the main objective function is to maximize the energy supply benefits, so the wind, solar, heat and storage supply system is The energy supply benefit at the moment is calculated as follows: ； in, 、 and They are The unit electricity selling price of wind power, photovoltaic power and solar thermal power at the moment, for Unit charging and discharging cost of energy storage at any moment; The objective function is to minimize the sum of energy procurement cost and production loss cost, so the oil extraction load is The sum of energy procurement cost and production loss cost at the moment is calculated as: ； in, For the Congfang From the moment The amount of electricity purchased from the energy components, is the purchase price of the corresponding energy, for Time machine mining equipment idle cost, for The load demand of the machine at each moment; Energy supply and demand balance constraint, expressed as The total energy generated by the energy supply system at any given moment is equal to the total energy consumed by the oil extraction load, that is: ； The upper and lower limits of operating capacity and power of each component are as follows: ； in, Represents different energy components, and Components exist Minimum and maximum power operating limits at all times.

2. The multi-scale control method of a wind-solar-thermal storage microgrid system in oil production according to claim 1 is characterized in that: In S2, the autoregressive moving average model ARIMA and the long short-term memory network LSTM are used to combine historical meteorological data, historical energy production data, and historical machine load data to predict wind power generation in the future period. , photovoltaic power generation , solar thermal energy power and oil drilling load Changes, forecasting future periods are divided into short-term, medium-term and long-term.

3. The multi-scale control method of a wind-solar-thermal storage microgrid system in oil production according to claim 2 is characterized in that: In S4, the particle swarm optimization algorithm PSO is used to solve the master-slave game model and set the particle swarm size. , inertia weight , learning factor 、 , and the maximum number of iterations ; Particle velocity update formula: ； in, represents the particle number, Represents the dimension, represents the number of iterations, For the The particle in In the iteration The speed of the dimension, and For the In the iteration Two random numbers of dimension, For the The particle in In the iteration The individual optimal position of the dimension, For the group In the iteration Optimal position of dimension; Particle position update formula: ； in, For the The particle in In the iteration Dimensional location.

4. The multi-scale control method of a wind-solar-thermal storage microgrid system in oil production according to claim 3 is characterized in that: In S5, control instructions are generated based on the results of the particle swarm optimization algorithm in S4: set up 、 They are The instantaneous fluctuation value of wind power generation and photovoltaic power generation at the moment or Exceeding the threshold When the wind, solar, heat and storage supply systems are quickly charged and discharged within the second response time to balance the power; During hourly regulation, based on short-term forecast results, the operating parameters of the solar thermal system are adjusted in advance and the energy storage charging and discharging plan is optimized; In seasonal regulation, based on long-term forecasts, energy storage capacity planning is adjusted in advance and the maintenance cycle of wind and solar equipment is optimized.

5. The multi-scale control method of a wind-solar-thermal storage microgrid system in oil production according to claim 4 is characterized in that: S5 also includes the establishment of a multi-dimensional evaluation indicator system, including full life cycle costs, carbon emission reductions, energy utilization efficiency and the number of interruptions in oil drilling operations; regular evaluation of the control effects, and adjustment of the prediction model parameters, the master-slave game model price and cost parameters and the particle swarm optimization algorithm parameters based on the evaluation results and the feedback mechanism.

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