Wind and light storage capacity optimal configuration method and system based on rod-pumped well group load

Abstract

The invention relates to the field of new energy and oil gas fusion development, and discloses a wind and light storage capacity optimal configuration method and system based on rod-pumped well group load, and the method comprises the steps: obtaining annual meteorological data on a well site and energy consumption data of a pumping unit, substituting the data into a fan and photovoltaic panel output model, and carrying out the processing to obtain a first set of output data and energy consumption data; increasing random fluctuation within a set range for the meteorological data and the energy consumption data, and processing the changed meteorological data to obtain a second set of output data and energy consumption data; an artificial bee colony solving algorithm is designed based on the established energy storage charging and discharging state model and the wind and light storage capacity optimization configuration model, two sets of output data and pumping unit energy consumption data are substituted into the artificial bee colony solving algorithm for solving to obtain two configuration schemes, and a scheme with high total investment cost is selected as an optimal scheme; and carrying out secondary capacity optimization configuration solving by adopting a genetic algorithm, and comparing with an optimal scheme solved by an artificial bee colony algorithm to obtain an 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 particularly to a method and system for optimizing the capacity configuration of a wind-solar-storage system based on the load of a pumping unit well group. Background Art

[0002] Currently, due to the characteristics of intermittent power generation, large output fluctuations, and instability of new energy sources such as wind and solar, the application of new energy in the oil and gas industry faces problems such as insufficient consumption capacity, unstable energy supply, and poor economic efficiency and reliability of production. The construction of source-network-load-storage integration is mainly to solve the problem of new energy consumption capacity, so as to enable the large-scale development of the utilization of clean energy. It is necessary to select and size the energy production equipment, energy conversion equipment, and energy storage equipment in the microgrid while ensuring the renewable energy consumption capacity of the system and taking into account the economy of the system.

[0003] New energy sources such as wind and solar have characteristics such as intermittent power generation, large output fluctuations, and instability, resulting in problems such as insufficient consumption capacity, unstable energy supply, and poor economic efficiency and reliability of production in the application of new energy in the oil and gas industry. Summary of the Invention

[0004] In view of the above problems, the object of the present invention is to provide a method and system for optimizing the capacity configuration of a wind-solar-storage system based on the load of a pumping unit well group, which can solve problems such as insufficient new energy consumption capacity and unstable function.

[0005] To achieve the above object, in a first aspect, the technical solution adopted by the present invention is: a method for optimizing the capacity configuration of a wind-solar-storage system based on the load of a pumping unit well group, which includes: obtaining the annual hourly meteorological data and the energy consumption data of the pumping unit on the well site, substituting the meteorological data into the fan output model and the photovoltaic panel output model to obtain the output data of the fan and the photovoltaic panel for one year, normalizing the output data to obtain the first set of output data and energy consumption data; adding random fluctuations within a set range to the meteorological data and the energy consumption data, substituting the changed meteorological data into the fan output model and the photovoltaic panel output model to obtain new output data and normalizing it to obtain the second set of output data and energy consumption data; establishing a wind-solar-storage capacity optimization configuration model, designing an artificial bee colony solution algorithm based on the established energy storage charge and discharge state model and the wind-solar-storage capacity optimization configuration model, substituting the obtained two sets of output data and the energy consumption data of the pumping unit into the artificial bee colony solution algorithm for solution to obtain two configuration schemes, comparing the two configuration schemes and selecting the scheme with a higher total investment cost as the optimal scheme; inputting the obtained two sets of output data and the energy consumption data of the pumping unit into the genetic algorithm for secondary capacity optimization configuration solution to obtain two configuration schemes, selecting the scheme with a higher total investment cost, and comparing it with the optimal scheme obtained by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

[0006] Further, the meteorological data includes wind speed, light intensity, and temperature; the energy consumption data of the pumping unit 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-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] Further, an optimization configuration model for the capacity of wind, light, and storage is established, including: setting the objective function and constraint conditions; Among them, the objective function is the total investment cost of the system; The constraint conditions include the new energy consumption rate constraint, the power balance constraint between the energy supply side and the energy consumption side, the upper and lower limit constraints on the configured capacity of the wind turbine, photovoltaic panel, and battery, the charge-discharge power constraint of the energy storage system, and the SOC constraint of the energy storage system.

[0008] Further, the objective function is the total investment cost of the system:

[0009] In the formula, is the total investment cost, are the total investment cost of the photovoltaic system, the total investment cost of the wind turbine, and the total investment cost of the battery respectively, with the unit of yuan.

[0010] Further, the new energy consumption rate constraint is: the ratio of the total electricity consumption of the load to the total output of the wind turbine and photovoltaic panel ≥ 80%.

[0011] Further, the power balance constraint is: 0 ≤ wind turbine output + photovoltaic panel output + battery discharge - battery charge - load electricity consumption ≤ 0.15 * load electricity consumption.

[0012] Further, the established energy storage charge-discharge state model is: Charge state model:

[0013] Discharge state model:

[0014] In the formula, is the power loss coefficient; is the charge efficiency; is the discharge efficiency; is the time interval; are the battery charge power and discharge power respectively; is the battery power factor; is the battery charge current; is the battery discharge current; are the battery charge voltage and discharge voltage respectively; is the rated capacity of the battery; is the remaining capacity state of the storage battery at time t+1.

[0015] In a second aspect, the technical solution adopted by the present invention is: a wind-solar-storage capacity optimization configuration system based on the load of a pumping unit well group, which includes: a first data processing module, which acquires the annual hourly meteorological data and the energy consumption data of the pumping unit on the well site, substitutes the meteorological data into the fan output model and the photovoltaic panel output model to obtain the output data of the fan and the photovoltaic panel for one year, and normalizes the output data to obtain the 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 the energy consumption data, substitutes the changed meteorological data into the fan output model and the photovoltaic panel output model to obtain new output data and normalizes it to obtain the second set of output data and energy consumption data; an artificial bee solving module, which establishes a wind-solar-storage capacity optimization configuration model, designs an artificial bee colony solving algorithm based on the established energy storage charge and discharge state model and the wind-solar-storage capacity optimization configuration model, substitutes the obtained two sets of output data and the energy consumption data of the pumping unit into the artificial bee colony solving algorithm for solution, obtains two configuration schemes, and selects the scheme with the higher total investment cost as the optimal scheme; a comparison output module, which inputs the obtained two sets of output data and the energy consumption data of the pumping unit into the genetic algorithm for secondary capacity optimization configuration solution, obtains two configuration schemes, selects the scheme with the higher total investment cost, and compares it with the optimal scheme solved by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration scheme.

[0016] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to execute any of the above methods.

[0017] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, which includes: 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.

[0018] Due to the above technical solutions adopted by the present invention, it has the following advantages: 1. The present invention can realize the transformation of medium-deep abandoned wells into open-type co-well heat exchange wells, and there is no need to drill a re-injection well during geothermal development, which can meet the technical requirement of "extracting heat without extracting water", and the heat exchange efficiency is higher than that of the buried tube co-well heat exchange well.

[0019] 2. The present invention is also applicable to idle geothermal wells and abandoned oil and gas wells. High thermal conductivity and heat-insulating cement is selected to plug the perforated sections of the oil and gas wells. The open-type co-well heat exchange device is connected to the heat exchange medium output hole to form a mining heat exchange channel; an annular space between the device and the abandoned well casing forms a reinjection channel and is connected to the heat exchange medium injection hole. Therefore, a closed circulation loop is formed on the ground and underground, so that the function of open-type co-well heat exchange can be realized.

[0020] 3. For the heat exchange pipe string of the present invention, heat-insulating pipes can be selected, or 80% of the overall length near the wellhead in the upper part can adopt heat-insulating pipes, and 20% in the lower part can adopt steel pipes or PE pipes, which can achieve better heat exchange efficiency. The temperature measurement optical cable outside the heat exchange pipe string can be divided into an internal temperature optical cable and an external temperature optical cable, which can respectively monitor the temperatures inside and outside the heat exchange device. The heat exchange electric pump unit is arranged in the heat-insulating pump chamber pipe and is connected with a delivery device, including a coiled tubing downhole connection tool. The lower end of the downhole connection tool is connected to the electric pump unit and is fixedly sealed through an expandable packer. Description of the Drawings

[0021] Figure 1 is a flowchart of a method for optimizing the capacity of wind power, photovoltaics and energy storage based on the load of a pumping unit well group in an embodiment of the present invention; Figure 2 is a comparison diagram of the system operating power during the model solving process of the present invention. Detailed Embodiments

[0022] The construction of source-grid-load-storage integration is mainly to solve the problem of the consumption capacity of new energy and enable the large-scale development of the utilization of clean energy. It is necessary to consider the economy of the system on the premise of ensuring the renewable energy consumption capacity of the system, and carry out type selection and capacity determination design for 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 capacity of wind power, photovoltaics and energy storage based on the load of a pumping unit well group, which includes: by establishing a wind turbine and photovoltaic output model, calculating wind power and photovoltaic output data by using meteorological information and equipment parameters; establishing a capacity optimization configuration model, taking the total investment cost of the system as the objective function, setting power balance constraints, new energy consumption rate constraints, upper and lower limits of capacity configuration constraints, charge and discharge power constraints of the energy storage system, and energy storage SOC (State of Charge) constraints; considering the uncertainty of wind power, photovoltaic output and load energy consumption, setting two sets of output and energy consumption data, and respectively optimizing and solving the optimization configuration model established by combining two sets of wind power, photovoltaic output data and load energy consumption data with an hourly time limit in one year by using the traditional artificial bee colony algorithm, and making a comparison with the genetic algorithm to prove the effectiveness and rationality of the present invention.

[0023] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0024] 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 otherwise clearly specified in the context, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0025] In one embodiment of the present invention, a method for optimizing the configuration of the capacity of a wind-solar-storage system based on the load of a group of pumping wells is provided. In this embodiment, as Figure 1 shown, the method includes the following steps: 1) Obtain the meteorological data including wind speed, light intensity and temperature in hours throughout the year on the well site and the energy consumption data of the pumping unit. Substitute the meteorological data into the fan output model and the photovoltaic panel output model to obtain the output data of the fan and the photovoltaic panel for one year, and normalize the output data to obtain the first set of output data and energy consumption data.

[0026] 2) Considering the uncertainty of wind-solar output and the energy consumption of the pumping unit, artificially add random fluctuations within a set range (±30%) to the meteorological data and the energy consumption data. Substitute the changed meteorological data into the fan output model and the photovoltaic panel output model to obtain new output data and normalize it to obtain the second set of output data and energy consumption data.

[0027] 3) Establish a wind-solar-storage capacity optimization configuration model, design an artificial bee colony optimization algorithm based on the established energy storage charge-discharge state model and the wind-solar-storage capacity optimization configuration model. Substitute the two sets of output data and the energy consumption data of the pumping unit obtained into the artificial bee colony optimization algorithm for solution to obtain two configuration schemes, and compare the two configuration schemes to select the scheme with the higher total investment cost as the optimal scheme.

[0028] 4) Substitute the two sets of output data and energy consumption data obtained into the genetic algorithm for secondary capacity optimization configuration solution to obtain two configuration schemes, select the scheme with the higher total investment cost, and compare it with the optimal scheme obtained by the artificial bee colony algorithm to verify that the scheme obtained by the artificial bee colony algorithm has the lowest total investment cost and the highest new energy consumption rate, that is, prove the effectiveness and rationality of the established capacity optimization configuration model and the proposed artificial bee colony algorithm, and obtain the optimal capacity optimization configuration scheme.

[0029] In the above step 1), the meteorological data includes wind speed, light intensity, and temperature; the energy consumption data of the pumping unit includes the rated power, cut-in wind speed, rated wind speed, and cut-out wind speed of the fan, the rated power of the photovoltaic panel, the rated power and charge-discharge efficiency of the battery, and the construction cost, operation and maintenance cost, and service life of the fan, photovoltaic panel, and battery respectively.

[0030] In this embodiment, a fan output model and a photovoltaic output model are established:

[0031] Among them, is the actual output of the fan at time t, in kW; is the rated power of the fan, in kW; is the natural incoming wind speed at time t, in m·s -1 ; and are the cut-in wind speed, cut-out wind speed, and rated wind speed rates respectively, in m·s -1 .

[0032] In the formula, is the output power of the photovoltaic power generation system at the operating point, in kW; is the number of photovoltaic panels; is the rated output power of the photovoltaic array under standard conditions, in kW; is the actual solar irradiance at the operating point, in kW·m -2 ; is the solar irradiance under standard conditions, in kW·m -2 , taking 1000 W·m -2 ; is the power temperature coefficient, with a value of -0.0043 / °C; is the operating point temperature, in °C; is the temperature under standard conditions, taking 25°C.

[0033] In this embodiment, the equipment model is selected and the parameters are obtained, as shown in Table 1.

[0034]

[0035] In the above step 3), the established energy storage charge and discharge state model is: Charge state model:

[0036] Discharge state model:

[0037] In the formula, is the power loss coefficient, with an average value of 0.0002; is the charging efficiency; is the discharging efficiency; is the time interval; are the charging power and discharging power of the battery, in kW respectively; is the power factor of the battery; is the charging current of the battery; is the discharging current of the battery, in A; are the charging voltage and discharging voltage of the battery respectively, is the rated capacity of the battery, in kWh; is the remaining capacity state of the battery at the (t + 1)th moment.

[0038] In step 3) above, an optimal configuration model for the capacities of wind power, photovoltaic power and energy storage is established, including: setting the objective function and constraint conditions; Among them, the objective function is the total investment cost of the system; The constraint conditions include the new energy consumption rate constraint, the power balance constraint between the energy supply end and the energy consumption end, the upper and lower limit constraints on the configured capacities of the wind turbines, photovoltaic panels and batteries, the charge and discharge power constraint of the energy storage system, and the SOC constraint of the energy storage system.

[0039] Specifically, in this embodiment, the objective function is the total investment cost of the system:

[0040] In the formula, is the total investment cost, are the total investment cost of photovoltaic power, the total investment cost of wind power and the total investment cost of the battery respectively, with the unit of yuan. By optimizing the configured capacities of the wind turbines, photovoltaic panels and batteries, the upfront investment cost is minimized, the load demand of the pumping unit is ensured, and at the same time, the new energy consumption rate is not less than 80%.

[0041] Among them, the total investment cost of photovoltaic power in 3.1.1) is:

[0042] In the formula: is the discount rate, is the annual depreciation number of the photovoltaic device, are the investment cost per unit capacity of photovoltaic power and the operation and maintenance cost of the photovoltaic device respectively, in yuan; is the total capacity of the photovoltaic panels, in kW; is the operation and maintenance cost coefficient, represents the photovoltaic operation power at the tth moment of the ith day, in kW; is the time step, taking 1 h.

[0043] 3.1.2) The investment cost of the fan is:

[0044] Where: is the discounted annual number of years of the fan device, are the investment cost per unit capacity and the operation and maintenance cost of the fan, in yuan; is the total capacity of the fan, in kW; is the operation and maintenance cost coefficient of the fan, represents the operating power of the fan at time t on the i-th day, in kW.

[0045] 3.1.3) The investment cost of the battery is:

[0046] Where: is the discounted annual number of years of the battery device, are the investment cost per unit capacity and the operation and maintenance cost of the battery, in yuan; is the total capacity of the battery, in kW; is the operation and maintenance cost coefficient of the energy storage device, represents the operating power of the battery at time t on the i-th day, in kW.

[0047] In this embodiment, the constraint conditions are: 3.2.1) The power balance constraint is: 0 ≤ fan output + PV panel output + battery discharge - battery charge - load power consumption ≤ 0.15 * load power consumption.

[0048] Specifically, the power balance constraint:

[0049] Where: are the PV output and the fan output at time t, in kW; is the battery charging power and the battery discharging power at time t, in kW; is the power of the pumping unit well group at time t, in kW.

[0050] 3.2.2) The new energy consumption rate constraint is: the ratio of the total power consumption of the load to the total output of the fan and PV panel ≥ 80%.

[0051] Specifically, the new energy consumption rate constraint:

[0052] Where: is the new energy consumption rate, are the total output of the fan, the total output of the PV panel and the total energy consumption of the pumping unit well group (i.e., the total power consumption of the load), in kW.

[0053] 3.2.3) Wind turbine capacity constraint: The upper and lower limits of the wind turbine capacity are expressed as:

[0054] where, is the lower bound of the installed capacity of the wind turbine; is the actual installed capacity of the wind turbine is the upper bound of the installed capacity of the wind turbine.

[0055] 3.2.4) Photovoltaic panel capacity constraint: The upper and lower limits of the photovoltaic capacity are expressed as:

[0056] where, is the lower bound of the installed capacity of the photovoltaic panel; is the actual installed capacity of the photovoltaic panel; is the upper bound of the installed capacity of the photovoltaic panel.

[0057] 3.2.5) Battery capacity constraint: The upper and lower limits of the installed battery capacity are expressed as:

[0058] where: is the lower bound of the installed battery capacity; is the actual installed battery capacity; is the upper bound of the installed battery capacity.

[0059] 3.2.6) Energy storage charge and discharge power constraint:

[0060] where: are the charging power and discharging power of the energy storage system at time t respectively, is the state variable of the battery charge and discharge at time t, 1 for charging and 0 for discharging; is the upper limit of the energy storage charge and discharge power, is the fixed proportional coefficient of the energy storage power upper limit and capacity.

[0061] 3.2.7) Energy storage SOC (State of charge) constraint: The energy storage capacity needs to consider the charge and discharge power constraints of the battery itself. 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 over-discharging, upper and lower limits of charge and discharge are set for the battery, and its expression is:

[0062] Wherein: is the remaining capacity coefficient of the battery at time t, are the upper and lower limit coefficients of the remaining capacity of the battery, taking 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, taking 1 h is the upper limit of the energy storage system capacity.

[0063] In the above step 3), the number of seeds of the artificial bee colony solution algorithm is 50, and the number of iterations is 100 times. Based on the Python + Pycharm platform, the normalized output data of the fan and photovoltaic and the load data are used for solution.

[0064] Among them, in this embodiment, the genetic algorithm solution process is designed, which is the same as the number of seeds, the number of iterations, the input data, etc. of the designed artificial bee colony solution algorithm, and a secondary solution is carried out for comparison.

[0065] In the above step 2), specifically, considering the volatility of the wind and light output and the load, random fluctuations are artificially added to the wind speed, light intensity, temperature and load data, and the fluctuation range is controlled within ±30% to obtain new normalized data, which is substituted into the same model and algorithm as in step 1) for solution to obtain the second set of solution results. It can be seen that as Figure 2 shown, the artificial bee colony algorithm is superior to the genetic algorithm, and the solution result considering the volatility is more applicable to actual production than the result without considering it. The capacity configuration scheme is obtained.

[0066] In an embodiment of the present invention, a wind-solar-storage capacity optimization configuration system based on the load of a pumping well group is provided, which includes: The first data processing module obtains the annual hourly meteorological data and the energy consumption data of the pumping unit on the well site, substitutes the meteorological data into the fan output model and the photovoltaic panel output model to obtain the output data of the fan and the photovoltaic panel for one year, 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 the energy consumption data, substitutes the changed meteorological data into the fan output model and the photovoltaic panel output model to obtain new output data and normalizes it to obtain the second set of output data and energy consumption data; The artificial bee solution module establishes a wind-solar-storage capacity optimization configuration model, designs an artificial bee colony solution algorithm based on the established energy storage charge and discharge state model and the wind-solar-storage capacity optimization configuration model, substitutes the obtained two sets of output data and the energy consumption data of the pumping unit into the artificial bee colony solution algorithm for solution, obtains two configuration schemes, and compares the two configuration schemes to select the scheme with the higher total investment cost as the optimal scheme; The comparison output module inputs the two sets of output data and the energy consumption data of the pumping unit into the genetic algorithm for secondary capacity optimization configuration solution, obtaining two configuration schemes. Select the scheme with the higher 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.

[0067] In the above embodiments, the meteorological data includes wind speed, light intensity, and temperature; the energy consumption data of the pumping unit includes the rated power of the fan, cut-in wind speed, rated wind speed, and cut-out wind speed, the rated power of the photovoltaic panel, the rated power of the battery, charge and discharge efficiency, and the construction cost, operation and maintenance cost, and service life of the fan, photovoltaic panel, and battery respectively.

[0068] In the above embodiments, establishing a capacity optimization configuration model for wind-solar energy storage includes: setting the objective function and constraint conditions; Among them, the objective function is the total investment cost of the system; The constraint conditions include new energy consumption rate constraint, power balance constraint between the energy supply side and the energy consumption side, upper and lower limit constraints on the configured capacities of the fan, photovoltaic panel, and battery, charge and discharge power constraint of the energy storage system, and SOC constraint of the energy storage system.

[0069] In this embodiment, the objective function is the total investment cost of the system:

[0070] In the formula, is the total investment cost, are the total investment cost of the photovoltaic system, the total investment cost of the fan, and the total investment cost of the battery respectively, with the unit of yuan.

[0071] In this embodiment, the new energy consumption rate constraint is: the ratio of the total electricity consumption of the load to the total output of the fan and photovoltaic panel ≥ 80%.

[0072] In this embodiment, the power balance constraint is: 0 ≤ fan output + photovoltaic panel output + battery discharge - battery charge - load power consumption ≤ 0.15 * load power consumption.

[0073] In the above embodiments, the established energy storage charge and discharge state model is: Charge state model:

[0074] Discharge state model:

[0075] In the formula, is the power loss coefficient; is the charge efficiency; is the discharge efficiency; is the time interval; are the charging power and discharging power of the storage battery, respectively; is the power factor of the storage battery; is the charging current of the storage battery; is the discharging current of the storage battery; are the charging voltage and discharging voltage of the storage battery, respectively; is the rated capacity of the storage battery; is the remaining capacity state of the storage battery at the moment of t + 1.

[0076] The system provided in this embodiment is used to execute the above method embodiments. For the specific process and detailed content, please refer to the above embodiments and will not be elaborated here.

[0077] In an embodiment of the present invention, a computing device is provided. The computing device may be a terminal, and it may include: a processor, a communications interface, a memory, a display screen, and an input device. Among them, the processor, the communications interface, and the memory complete mutual communication through a communication bus. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. When the computer program is executed by the processor, the methods in the above embodiments are implemented; the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communications interface is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen may be a liquid crystal display screen or an electronic ink display screen. The input device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computing device, or an external keyboard, touchpad, or mouse, etc. The processor can call the logical instructions in the memory.

[0078] In addition, when the logic instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a 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 causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0079] In an embodiment of the present invention, there is provided a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is capable of executing the methods provided in the above-mentioned method embodiments.

[0080] In an embodiment of the present invention, there is provided a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores server instructions. The computer instructions cause the computer to execute the methods provided in the above-mentioned embodiments.

[0081] For the computer-readable storage medium provided in the above-mentioned embodiment, its implementation principle and technical effects are similar to those of the above-mentioned method embodiment, and will not be elaborated here.

[0082] The present invention is described with reference to the 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 flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0083] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes Figure 1 or boxes Figure 1 specified in one or more of the boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes Figure 1 or boxes Figure 1 specified in one or more of the boxes.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the configuration of the capacity of wind-solar energy storage based on the load of a group of pumping wells, characterized in that Including: Obtain the annual meteorological data in hours and the energy consumption data of the pumping unit on the well site. Substitute the meteorological data into the fan output model and the photovoltaic panel output model to obtain the output data of the fan and the photovoltaic panel for one year. 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 fan output model and the photovoltaic panel output model to obtain new output data and normalize it to obtain the second set of output data and energy consumption data; Establish an optimal configuration model for the capacity of wind, light, and energy storage. Based on the established energy storage charge and discharge state model and the optimal configuration model for the capacity of wind, light, and energy storage, design an artificial bee colony solution algorithm. Substitute the two sets of output data and the energy consumption data of the pumping unit obtained into the artificial bee colony solution algorithm for solution to obtain two configuration plans. Compare the two configuration plans and select the plan with the higher total investment cost as the optimal plan; Input the two sets of output data and the energy consumption data of the pumping unit obtained into the genetic algorithm for secondary capacity optimization configuration solution to obtain two configuration plans. Select the plan with the higher total investment cost and compare it with the optimal plan obtained by the artificial bee colony algorithm to obtain the optimal capacity optimization configuration plan.

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

3. The method for optimizing the configuration of the capacity of a wind-solar-storage system based on the load of a pumping unit well group according to claim 1, wherein Establish an optimal configuration model for the capacity of wind, light, and energy storage, including: setting the objective function 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 end and the energy consumption end, the upper and lower limit constraints on the configured capacities of the fan, photovoltaic panel, and battery, the energy storage system charge and discharge power constraint, and the energy storage system SOC constraint.

4. The method for optimizing the configuration of the capacity of a wind-solar-storage system based on the load of a group of pumping wells as claimed in claim 3, wherein The objective function is the total investment cost of the system: ； In the formula, is the total investment cost, are the total investment cost of photovoltaic, the total investment cost of wind turbine and the total investment cost of battery, respectively, with the unit of yuan.

5. The method for optimizing the configuration of the capacity of a wind-solar-storage system based on the load of a group of pumping wells according to claim 3, wherein The new energy consumption rate constraint is: the ratio of the total electricity consumption of the load to the total output of the fan and photovoltaic panel ≥ 80%.

6. The method for optimizing the capacity allocation of wind-solar-storage based on the load of pumping well groups according to claim 3, wherein The power balance constraint is: 0 ≤ fan output + photovoltaic panel output + battery discharge - battery charge - load power consumption ≤ 0.15 * load power consumption.

7. The method for optimizing the configuration of the capacity of a wind-solar-storage system based on the load of a group of pumping wells as claimed in claim 1, wherein The established energy storage charge and discharge state model is: Charge state model: ； Discharge state model: ； Wherein, is the power loss coefficient; is the charging efficiency; is the discharging efficiency; is the time interval; are the charging power and discharging power of the storage battery respectively; is the power factor of the storage battery; is the charging current of the storage battery; is the discharging current of the storage battery; are the charging voltage and discharging voltage of the storage battery respectively; is the rated capacity of the storage battery; is the remaining capacity state of the storage battery at the (t + 1)th moment.

8. A system for optimizing the capacity allocation of wind-solar-storage based on the load of pumping well groups, characterized in that, Including: The first data processing module obtains the annual meteorological data in hours and the energy consumption data of the pumping unit on the well site. Substitute the meteorological data into the fan output model and the photovoltaic panel output model to obtain the output data of the fan and the photovoltaic panel for one year. Normalize 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. Substitute the changed meteorological data into the fan output model and the photovoltaic panel output model to obtain new output data and normalize it to obtain the second set of output data and energy consumption data; Artificial bee solving module, which establishes an optimization configuration model for the capacities of wind power, photovoltaic power and energy storage. Based on the established energy storage charge and discharge state model and the optimization configuration model for the capacities of wind power, photovoltaic power and energy storage, an artificial bee colony solving algorithm is designed. The two sets of output data and the energy consumption data of the pumping unit are substituted into the artificial bee colony solving algorithm for solution, and two configuration schemes are obtained. By comparing the two configuration schemes, the scheme with the higher total investment cost is selected as the optimal scheme; Comparison and output module, which inputs the two sets of output data and the energy consumption data of the pumping unit into the genetic algorithm for secondary capacity optimization configuration solution, obtains two configuration schemes, selects the scheme with the higher total investment cost, and compares it 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 execute any of the methods described in claims 1 to 7.

10. A computing device, characterized in that, 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 methods described in claims 1 to 7.

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