Wind, solar and storage capacity optimization configuration method and system based on pumping well group load

Abstract

The present invention relates to the field of integrated development of new energy and oil and gas, and discloses a method and system for optimizing the configuration of wind, solar and storage capacity based on the load of a pumping well group, which comprises: obtaining meteorological data and energy consumption data of the pumping unit throughout the year on the well site, substituting them into output models of wind turbines and photovoltaic panels for post-processing to obtain a first set of output data and energy consumption data; adding random fluctuations within a set range to the meteorological data and energy consumption data, and processing the changed meteorological data to obtain a second set of output data and energy consumption data; designing an artificial bee colony solution algorithm based on an established energy storage charge and discharge state model and a wind, solar and storage capacity optimization configuration model, substituting the two sets of output data and energy consumption data of the pumping unit into the artificial bee colony solution algorithm for solution to obtain two configuration schemes, and selecting the scheme with the highest total investment cost as the optimal scheme; and using a genetic algorithm to perform a secondary capacity optimization configuration solution, and comparing the solution with the optimal scheme solved by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated development of new energy and oil and gas, and in particular to a method and system for optimizing the configuration of wind, solar and storage capacity based on the load of a pumping well group. Background Art

[0002] Currently, due to the intermittent, fluctuating, and unstable nature of renewable energy generation, such as wind and solar power, the application of renewable energy in the oil and gas industry faces challenges such as insufficient absorption capacity, unstable energy supply, and poor production economics and reliability. The development of integrated power generation, grid, load, and storage systems aims to address this issue of renewable energy absorption capacity and enable the large-scale development of clean energy. This requires ensuring the system's renewable energy absorption capacity while also considering system economics, and the selection and sizing of energy production, energy conversion, and energy storage equipment within the microgrid must be carefully considered.

[0003] Renewable energy sources such as wind and solar power are characterized by intermittent, large output fluctuations and instability, which leads to the application of new energy in the oil and gas industry facing difficulties such as insufficient absorption capacity, unstable energy supply, and poor production economy and reliability. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide a method and system for optimizing the configuration of wind, solar and storage capacity based on the load of a group of pumping wells, which can solve problems such as insufficient absorption capacity of new energy and unstable functions.

[0005] To achieve the above-mentioned purpose, in the first aspect, the technical solution adopted by the present invention is: a method for optimizing the configuration of wind, solar and storage capacity based on the load of a group of pumping wells, which comprises: obtaining the meteorological data and pumping unit energy consumption data in hourly units throughout the year on the well site, substituting the meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain the output data of the wind turbine and photovoltaic panel for one year, normalizing the output data to obtain a first set of output data and energy consumption data; adding random fluctuations within a set range to the meteorological data and energy consumption data, substituting the changed meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain new output data and normalizing them to obtain a second set of output data. power data and energy consumption data; establish a wind, solar and storage capacity optimization configuration model, design an artificial bee colony solution algorithm based on the established energy storage charging and discharging state model and the wind, solar and storage capacity optimization configuration model, substitute the two sets of output data and pumping unit energy consumption data into the artificial bee colony solution algorithm for solution, and obtain two configuration schemes. After comparing the two configuration schemes, select the scheme with the highest total investment cost as the optimal scheme; input the two sets of output data and pumping unit energy consumption data into the genetic algorithm for secondary capacity optimization configuration solution, and obtain two configuration schemes. Select the scheme with the highest total investment cost, and compare it with the optimal scheme solved by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

[0006] Furthermore, meteorological data includes wind speed, light intensity and temperature; oil pump energy consumption data includes the rated power, cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine, the rated power of the photovoltaic panel, the rated power and charge and discharge efficiency of the battery, and the construction cost, operation and maintenance cost and service life of the wind turbine, photovoltaic panel and battery respectively.

[0007] Furthermore, a model for optimizing the configuration of wind, solar and storage capacity is established, including: setting objective functions and constraints;

[0008] Among them, the objective function is the total investment cost of the system;

[0009] The constraints include the new energy consumption rate constraint, the power balance constraint between the energy supply and energy consumption ends, the upper and lower limits of the configuration capacity of wind turbines, photovoltaic panels and batteries, the energy storage system charging and discharging power constraint and the energy storage system SOC constraint.

[0010] Furthermore, the objective function is the total investment cost of the system:

[0011]

[0012] Where, is the total investment cost, They are the total investment cost of photovoltaic power generation, the total investment cost of wind turbines and the total investment cost of batteries, in yuan.

[0013] Furthermore, the new energy consumption rate is constrained as follows: the ratio of total load power consumption to the total output of wind turbines and photovoltaic panels is ≥80%.

[0014] Furthermore, the power balance constraint is: 0≤wind turbine output+photovoltaic panel output+battery discharge-battery charge-load power consumption≤0.15*load power consumption.

[0015] Furthermore, the established energy storage charging and discharging state model is:

[0016] State of charge model:

[0017]

[0018] Discharge state model:

[0019]

[0020] Where, is the power loss coefficient; For charging efficiency; is the discharge efficiency; is the time interval; are the battery charging power and discharging power respectively; is the battery power factor; The battery charging current; is the battery discharge current; are the battery charging voltage and discharging voltage respectively; is the rated capacity of the battery; is the remaining capacity state of the battery at time t+1.

[0021] In the second aspect, the technical solution adopted by the present invention is: a wind, solar and storage capacity optimization configuration system based on the load of a group of pumping wells, which includes: a first data processing module, which obtains the meteorological data and pumping unit energy consumption data in hours on the well site throughout the year, substitutes the meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain the output data of the wind turbine and photovoltaic panel for one year, normalizes the output data to obtain a first set of output data and energy consumption data; a second data processing module, which adds random fluctuations within a set range to the meteorological data and energy consumption data, substitutes the changed meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain new output data and normalizes it to obtain a second set of output data The artificial bee solving module establishes a wind, solar and storage capacity optimization configuration model, designs an artificial bee colony solving algorithm based on the established energy storage charging and discharging state model and the wind, solar and storage capacity optimization configuration model, substitutes the two sets of output data and pumping unit energy consumption data into the artificial bee colony solving algorithm for solution, and obtains two configuration schemes. After comparing the two configuration schemes, the scheme with the highest total investment cost is selected as the optimal scheme; the comparison output module inputs the two sets of output data and pumping unit energy consumption data into the genetic algorithm for secondary capacity optimization configuration solution, and obtains two configuration schemes. The scheme with the highest total investment cost is selected and compared with the optimal scheme solved by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

[0022] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0023] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0024] The present invention has the following advantages due to the adoption of the above technical solution:

[0025] 1. The present invention can transform abandoned wells in medium and deep layers into open heat exchange wells. There is no need to drill a reinjection well during geothermal development, which can meet the technical requirement of "taking heat without taking water" and has higher heat exchange efficiency than buried pipe heat exchange wells.

[0026] 2. This invention is equally applicable to idle geothermal wells and abandoned oil and gas wells. Highly thermally conductive and insulating cement is used to seal the perforated sections of the oil and gas wells. The split-well heat exchange device connects to the heat exchange medium output port, forming a production heat exchange channel. This creates a recharge channel with the annulus between the abandoned well casings and connects to the heat exchange medium injection port. This creates a closed circulation loop below the surface, enabling open-well heat exchange.

[0027] 3. The heat exchange string of the present invention can be made of insulated pipes, or 80% of the upper portion near the wellhead can be made of insulated pipes, and the lower 20% can be made of steel pipes or PE pipes, which can achieve better heat exchange efficiency. The temperature measuring optical cables outside the heat exchange string can be divided into internal temperature optical cables and external temperature optical cables, which can monitor the temperatures inside and outside the heat exchange device respectively. The heat exchange electric pump unit is arranged in the insulated pump chamber pipe and is connected to a delivery device, including a continuous pipe and cable downhole connection tool. The lower end of the downhole connection tool is connected to the electric pump unit and is fixed and sealed by an expansion packer. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a method for optimizing wind, solar, and storage capacity configuration based on the load of a pumping well group in an embodiment of the present invention;

[0029] Figure 2 It is a comparison chart of system operating power during the model solving process of the present invention. DETAILED DESCRIPTION

[0030] The construction of integrated power generation, grid, load, and storage systems is primarily aimed at addressing the issue of renewable energy absorption capacity and enabling the large-scale development of clean energy utilization. It is necessary to consider the economic efficiency of the system while ensuring the system's renewable energy absorption capacity, and to select and design the energy production equipment, energy conversion equipment, and energy storage equipment in the microgrid. Therefore, the present invention proposes a method and system for optimizing the configuration of wind, solar, and storage capacity based on the load of a pumping well group. The method comprises: establishing wind turbine and photovoltaic output models, and calculating wind and solar output data using meteorological information and equipment parameters; establishing a capacity optimization configuration model, using the system's total investment cost as the objective function, and setting power balance constraints, new energy absorption rate constraints, upper and lower capacity configuration constraints, energy storage system charge and discharge power constraints, and energy storage SOC (remaining power) constraints. Considering the uncertainty of wind and solar output and load energy consumption, two types of output and energy consumption data are set. The optimization configuration model established by combining the two types of wind and solar output data and load energy consumption data, measured in hours over a year, is optimized and solved using a traditional artificial bee colony algorithm, and compared using a genetic algorithm to demonstrate the effectiveness and rationality of the present invention.

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0032] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0033] In one embodiment of the present invention, a method for optimizing wind, solar and storage capacity based on the load of a pumping well group is provided. Figure 1 As shown, the method includes the following steps:

[0034] 1) Obtain hourly meteorological data on wind speed, light intensity, and temperature throughout the year, as well as pumping unit energy consumption data, at the well site. Substitute this meteorological data into the wind turbine output model and photovoltaic panel output model to obtain one year's wind turbine and photovoltaic panel output data. Normalize this output data to obtain the first set of output and energy consumption data.

[0035] 2) Considering the uncertainty of wind and solar power output and oil pumping unit energy consumption, random fluctuations within a set range (±30%) were artificially added to the meteorological data and energy consumption data. The changed meteorological data were substituted into the wind turbine output model and photovoltaic panel output model to obtain new output data, which were then normalized to obtain the second set of output data and energy consumption data.

[0036] 3) Establish a wind, solar and storage capacity optimization configuration model. Design an artificial bee colony solution algorithm based on the established energy storage charging and discharging state model and the wind, solar and storage capacity optimization configuration model. Substitute the two sets of output data and pumping unit energy consumption data into the artificial bee colony solution algorithm for solution, and obtain two configuration schemes. After comparing the two configuration schemes, select the one with the higher total investment cost as the optimal scheme.

[0037] 4) The two sets of output data and energy consumption data obtained are brought into the genetic algorithm for secondary capacity optimization configuration solution, and two configuration schemes are obtained. The scheme with the highest total investment cost is selected and compared with the optimal scheme solved by the artificial bee colony algorithm. It is verified that the scheme solved by the artificial bee colony algorithm has the lowest total investment cost and the highest new energy consumption rate. In other words, the effectiveness and rationality of the established capacity optimization configuration model and the proposed artificial bee colony algorithm are proved, and the optimal capacity optimization configuration scheme is obtained.

[0038] In step 1) above, meteorological data includes wind speed, light intensity, and temperature; pumping unit energy consumption data includes the wind turbine's rated power, cut-in wind speed, rated wind speed, and cut-out wind speed; the photovoltaic panel's rated power; the battery's rated power and charge / discharge efficiency; and the construction cost, operation and maintenance cost, and service life of the wind turbine, photovoltaic panel, and battery, respectively.

[0039] In this embodiment, a wind turbine output model and a photovoltaic output model are established:

[0040]

[0041] in, is the actual output of the fan at time t, in kW; is the rated power of the fan, in kW; is the natural wind speed at time t, in m·s -1 ; and They are cut-in wind speed, cut-out wind speed and rated wind speed, in m·s -1 .

[0042] Where, is the output power of the photovoltaic power generation system at the working point, kW; is the number of photovoltaic panels; is the rated output power of the photovoltaic array under standard conditions, kW; is the actual solar irradiance at the working point, kW·m -2 ; is the solar irradiance under standard conditions, kW·m -2 , take 1000W·m -2 ; is the power temperature coefficient, which is -0.0043 / ℃; is the operating point temperature, °C; The temperature under standard conditions is 25°C.

[0043] In this embodiment, the device model is selected and the parameters are obtained, as shown in Table 1.

[0044]

[0045] In the above step 3), the energy storage charge and discharge state model established is:

[0046] State of charge model:

[0047]

[0048] Discharge state model:

[0049]

[0050] Where, is the power loss coefficient, with an average value of 0.0002; is charging efficiency; is the discharge efficiency; is the time interval; are the battery charging power and discharging power, kW; is the battery power factor; The battery charging current; is the battery discharge current, A; are the battery charging voltage and discharging voltage, respectively. is the rated capacity of the battery, kWh; is the remaining capacity of the battery at time t+1.

[0051] In step 3) above, a wind, solar and storage capacity optimization configuration model is established, including: setting the objective function and constraint conditions;

[0052] Among them, the objective function is the total investment cost of the system;

[0053] The constraints include the new energy consumption rate constraint, the power balance constraint between the energy supply and energy consumption ends, the upper and lower limits of the configuration capacity of wind turbines, photovoltaic panels and batteries, the energy storage system charging and discharging power constraint and the energy storage system SOC constraint.

[0054] Specifically, in this embodiment, the objective function is the total investment cost of the system:

[0055]

[0056] Where, is the total investment cost, The total investment costs for photovoltaics, wind turbines, and batteries are expressed in RMB. By optimizing the configuration and capacity of wind turbines, photovoltaic panels, and batteries, the initial investment costs are minimized, the load requirements of the oil pumping units are met, and the renewable energy consumption rate is maintained at no less than 80%.

[0057] Among them, 3.1.1) the total investment cost of photovoltaic is:

[0058]

[0059] Where: is the discount rate, is the discounted number of years for the photovoltaic installation, are the investment cost per unit capacity of photovoltaic power plants and the operation and maintenance cost of photovoltaic devices, in yuan; is the total capacity of photovoltaic panels, kW; is the operation and maintenance cost coefficient, represents the PV operating power at time t on day i, kW; is the time step, which is 1h.

[0060] 3.1.2) The investment cost of the fan is:

[0061]

[0062] Where: is the discounted number of years for the wind turbine installation, They are the unit capacity investment cost and operation and maintenance cost of wind turbine, RMB; is the total capacity of the fan, kW; is the wind turbine operation and maintenance cost coefficient, Represents the wind turbine operating power at time t on day i, kW.

[0063] 3.1.3) The investment cost of batteries is:

[0064]

[0065] Where: is the discounted years of the battery installation, They are the investment cost and operation and maintenance cost per unit capacity of the battery, in yuan; is the total capacity of the battery, kW; is the operation and maintenance cost coefficient of the energy storage device, Represents the battery operating power at time t on day i, kW.

[0066] In this embodiment, the constraints are:

[0067] 3.2.1) The power balance constraint is: 0 ≤ wind turbine output + photovoltaic panel output + battery discharge - battery charge - load power consumption ≤ 0.15 * load power consumption.

[0068] Specifically, the power balance constraint is:

[0069]

[0070] Where: are the photovoltaic output and wind turbine output at time t, kW; is the battery charging power and discharging power at time t, kW; is the power of the pumping well group at time t, kW.

[0071] 3.2.2) The new energy consumption rate constraint is: the ratio of total load power consumption to the total output of wind turbines and photovoltaic panels ≥ 80%.

[0072] Specifically, the constraints on new energy consumption rate are:

[0073]

[0074] Where: is the new energy consumption rate, They are the total output of wind turbines, total output of photovoltaic power and total energy consumption of the pumping well group (i.e. total load power consumption), kW.

[0075] 3.2.3) Fan capacity constraints:

[0076] The upper and lower limit constraints of fan capacity are expressed as:

[0077]

[0078] Where, is the lower bound of the installed capacity of the wind turbine; The actual installed capacity of the wind turbine The upper limit of the fan installation capacity.

[0079] 3.2.4) Photovoltaic panel capacity constraints:

[0080] The upper and lower limit constraints of photovoltaic capacity are expressed as:

[0081]

[0082] Where, is the lower bound for the installed capacity of photovoltaic panels; The actual installed capacity of photovoltaic panels; The upper bound of the installed capacity of photovoltaic panels.

[0083] 3.2.5) Battery capacity constraints:

[0084] The upper and lower limit constraints of battery installation capacity are expressed as:

[0085]

[0086] Where: The lower bound of the battery installation capacity; The actual installed capacity of the battery; The upper limit of the battery installation capacity.

[0087] 3.2.6) Energy storage charging and discharging power constraints:

[0088]

[0089] Where: are the charging power and discharging power of the energy storage system at time t, is the battery charge and discharge state variable at time t, charging is 1 and discharging is 0; is the upper limit of the energy storage system’s charging and discharging power. It is a fixed proportional coefficient between the upper limit of energy storage power and capacity.

[0090] 3.2.7) Energy Storage SOC (State of Charge) Constraints:

[0091] The energy storage capacity needs to take into account the battery's own charge and discharge power constraints. In order to extend the life of the energy storage battery, ensure the performance and stability of the system, and prevent irreversible damage to the battery caused by overcharging and discharging, upper and lower limits of charge and discharge are set for the battery. The expression is:

[0092]

[0093] Where: is the remaining capacity coefficient of the battery at time t, They are the upper and lower limit coefficients of the remaining battery capacity, which are 0.1 and 0.9 respectively. is the initial battery charge value, is the charge and discharge efficiency of the battery, is the time step of the charge and discharge process, which is 1h The upper limit of energy storage system capacity.

[0094] In step 3) above, the number of seeds of the artificial bee colony solution algorithm is 50, the number of iterations is 100, and the solution is performed based on the Python+Pycharm platform using the normalized output data of wind turbines and photovoltaics and load data.

[0095] Among them, the genetic algorithm solution process in this embodiment is designed with the same number of seeds, number of iterations, input data, etc. as those of the artificial bee colony solution algorithm, and a secondary solution is performed for comparison.

[0096] In the above step 2), specifically, considering the volatility of wind and solar power output and load, random fluctuations are artificially added to the wind speed, light intensity, temperature and load data, and the fluctuation range is controlled within ±30%. New normalized data is obtained and substituted into the same model and algorithm as step 1) to obtain the second set of solution results. It can be seen that, if Figure 2 As shown in the figure, the artificial bee colony algorithm outperforms the genetic algorithm, and the solution considering volatility is more applicable to actual production than the solution without considering volatility. The capacity configuration solution is obtained.

[0097] In one embodiment of the present invention, a system for optimizing wind, solar, and storage capacity based on the load of a pumping well group is provided, comprising:

[0098] The first data processing module obtains hourly meteorological data and pumping unit energy consumption data for the entire year at the well site, substitutes the meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain one year's wind turbine and photovoltaic panel output data, and normalizes the output data to obtain the first set of output data and energy consumption data;

[0099] The second data processing module adds random fluctuations within a set range to the meteorological data and energy consumption data, substitutes the changed meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain new output data, and normalizes the new data to obtain the second set of output data and energy consumption data;

[0100] The artificial bee solving module establishes a model for optimizing the configuration of wind, solar, and storage capacity. Based on the established energy storage charge and discharge state model and the optimized configuration model for wind, solar, and storage capacity, an artificial bee swarm solving algorithm is designed. The two sets of output data and pumping unit energy consumption data are substituted into the artificial bee swarm solving algorithm for solution. Two configuration schemes are obtained, and the one with the highest total investment cost is selected as the optimal one after comparison.

[0101] The comparison output module inputs the two sets of output data and pumping unit energy consumption data into the genetic algorithm for secondary capacity optimization configuration solution, and obtains two configuration schemes. The scheme with the highest total investment cost is selected and compared with the optimal scheme solved by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

[0102] In the above embodiment, the meteorological data includes wind speed, light intensity and temperature; the energy consumption data of the oil pump includes the rated power, cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine, the rated power of the photovoltaic panel, the rated power and charge and discharge efficiency of the battery, and the construction cost, operation and maintenance cost and service life of the wind turbine, photovoltaic panel and battery respectively.

[0103] In the above embodiment, establishing a wind, solar and storage capacity optimization configuration model includes: setting an objective function and constraint conditions;

[0104] Among them, the objective function is the total investment cost of the system;

[0105] The constraints include the new energy consumption rate constraint, the power balance constraint between the energy supply and energy consumption ends, the upper and lower limits of the configuration capacity of wind turbines, photovoltaic panels and batteries, the energy storage system charging and discharging power constraint and the energy storage system SOC constraint.

[0106] In this embodiment, the objective function is the total investment cost of the system:

[0107]

[0108] Where, is the total investment cost, They are the total investment cost of photovoltaic power generation, the total investment cost of wind turbines and the total investment cost of batteries, in yuan.

[0109] In this embodiment, the new energy consumption rate constraint is: the ratio of total load power consumption to the total output of wind turbines and photovoltaic panels is ≥80%.

[0110] In this embodiment, the power balance constraint is: 0≤wind turbine output+photovoltaic panel output+battery discharge-battery charge-load power consumption≤0.15*load power consumption.

[0111] In the above embodiment, the energy storage charge and discharge state model established is:

[0112] State of charge model:

[0113]

[0114] Discharge state model:

[0115]

[0116] Where, is the power loss coefficient; For charging efficiency; is the discharge efficiency; is the time interval; are the battery charging power and discharging power respectively; is the battery power factor; The battery charging current; is the battery discharge current; are the battery charging voltage and discharging voltage respectively; is the rated capacity of the battery; is the remaining capacity of the battery at time t+1.

[0117] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0118] A computing device provided in one embodiment of the present invention may be a terminal and may include: a processor, a communications interface, a memory, a display screen, and an input device. The processor, communications interface, and memory communicate with each other via a communications bus. The processor is configured to provide computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. When executed by the processor, the computer program implements the methods described in the aforementioned embodiments. The internal memory provides an environment for the operating system and computer program in the non-volatile storage medium to run. The communications interface is configured to communicate with an external terminal via wired or wireless communication, where wireless communication may be achieved via Wi-Fi, a network management service provider, NFC (near-field communication), or other technologies. The display screen may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen layer covering the display screen, or may be buttons, a trackball, or a touchpad provided on the computing device housing, or may be an external keyboard, touchpad, or mouse. The processor may invoke logic instructions stored in the memory.

[0119] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0120] In one embodiment of the present invention, a computer program product is provided, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the methods provided by the above-mentioned method embodiments.

[0121] In one embodiment of the present invention, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium stores server instructions. The computer instructions enable a computer to execute the methods provided in the above embodiments.

[0122] The above embodiment provides a computer-readable storage medium, whose implementation principle and technical effects are similar to those of the above method embodiment, and will not be repeated here.

[0123] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0126] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing wind, solar and storage capacity based on the load of a pumping well group, characterized in that: include: Obtain hourly meteorological data and pumping unit energy consumption data for the entire year at the well site. Substitute the meteorological data into the wind turbine output model and photovoltaic panel output model to obtain one year's wind turbine and photovoltaic panel output data. Normalize the output data to obtain the first set of output data and energy consumption data. Add random fluctuations within a set range to the meteorological data and energy consumption data, substitute the changed meteorological data into the wind turbine output model and photovoltaic panel output model to obtain new output data, and normalize them to obtain the second set of output data and energy consumption data; A wind, solar, and storage capacity optimization configuration model was established. An artificial bee colony solution algorithm was designed based on the established energy storage charge and discharge state model and the wind, solar, and storage capacity optimization configuration model. The two sets of output data and pumping unit energy consumption data were substituted into the artificial bee colony solution algorithm to solve the problem. Two configuration schemes were obtained. After comparing the two configuration schemes, the one with the highest total investment cost was selected as the optimal scheme. The two sets of output data and pumping unit energy consumption data were input into the genetic algorithm for secondary capacity optimization configuration solution, and two configuration schemes were obtained. The scheme with the highest total investment cost was selected and compared with the optimal scheme solved by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

2. The method for optimizing wind, solar and storage capacity configuration based on the load of a pumping well group according to claim 1, characterized in that: Meteorological data includes wind speed, light intensity and temperature; oil pump energy consumption data includes the rated power, cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine, the rated power of the photovoltaic panel, the rated power and charge and discharge efficiency of the battery, and the construction cost, operation and maintenance cost and service life of the wind turbine, photovoltaic panel and battery respectively.

3. The method for optimizing wind, solar and storage capacity configuration based on the load of a pumping well group according to claim 1, characterized in that: Establishing a wind, solar and storage capacity optimization configuration model, including: setting objective functions and constraints; Among them, the objective function is the total investment cost of the system; The constraints include the new energy consumption rate constraint, the power balance constraint between the energy supply and energy consumption ends, the upper and lower limits of the configuration capacity of wind turbines, photovoltaic panels and batteries, the energy storage system charging and discharging power constraint and the energy storage system SOC constraint.

4. The method for optimizing wind, solar and storage capacity configuration based on the load of a pumping well group according to claim 3, characterized in that: The objective function is the total investment cost of the system: ； Where, is the total investment cost, They are the total investment cost of photovoltaic power generation, the total investment cost of wind turbines and the total investment cost of batteries, in yuan.

5. The method for optimizing wind, solar and storage capacity configuration based on the load of a pumping well group according to claim 3, characterized in that: The constraint on the new energy consumption rate is: the ratio of total load power consumption to the total output of wind turbines and photovoltaic panels ≥ 80%.

6. The method for optimizing wind, solar and storage capacity configuration based on the load of a pumping well group according to claim 3, characterized in that: The power balance constraint is: 0 ≤ wind turbine output + photovoltaic panel output + battery discharge - battery charging - load power consumption ≤ 0.15 * load power consumption.

7. The method for optimizing wind, solar and storage capacity configuration based on the load of a pumping well group according to claim 1, characterized in that: The established energy storage charging and discharging state model is: State of charge model: ； Discharge state model: ； Where, is the power loss coefficient; is charging efficiency; is the discharge efficiency; is the time interval; are the battery charging power and discharging power respectively; is the battery power factor; The battery charging current; is the battery discharge current; are the battery charging voltage and discharging voltage respectively; is the rated capacity of the battery; is the remaining capacity of the battery at time t+1.

8. A wind, solar and storage capacity optimization configuration system based on the load of a group of pumping wells, characterized by: include: The first data processing module obtains hourly meteorological data and pumping unit energy consumption data for the entire year at the well site, substitutes the meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain one year's wind turbine and photovoltaic panel output data, and normalizes the output data to obtain the first set of output data and energy consumption data; The second data processing module adds random fluctuations within a set range to the meteorological data and energy consumption data, substitutes the changed meteorological data into the wind turbine output model and the photovoltaic panel output model to obtain new output data, and normalizes the new data to obtain the second set of output data and energy consumption data; The artificial bee solving module establishes a model for optimizing the configuration of wind, solar, and storage capacity. Based on the established energy storage charge and discharge state model and the optimized configuration model for wind, solar, and storage capacity, an artificial bee swarm solving algorithm is designed. The two sets of output data and pumping unit energy consumption data are substituted into the artificial bee swarm solving algorithm for solution. Two configuration schemes are obtained, and the one with the highest total investment cost is selected as the optimal one after comparison. The comparison output module inputs the two sets of output data and pumping unit energy consumption data into the genetic algorithm for secondary capacity optimization configuration solution, and obtains two configuration schemes. The scheme with the highest total investment cost is selected and compared with the optimal scheme solved by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

9. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 7 .

10. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods according to claims 1 to 7.

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