Electricity-water collaborative farm energy supply system operation optimization method

Abstract

The invention discloses an electricity-water collaborative farm energy supply system operation optimization method, which comprises the following steps of: respectively modeling a plurality of energy consumption links in a farm production process to obtain a farm equipment model of each energy consumption link; according to the layout of the farm energy supply system and the structures of the farm power distribution network and the farm water distribution network, constructing farm electricity-water coupling energy supply network models, including a farm power distribution network model and a farm water distribution network model; based on the farm equipment model of each energy consumption link and the farm electricity-water coupling energy supply network model, determining the carbon emission quota of a biogas generator set by using an annual reference value method, and establishing an electricity-water coupling energy supply system scheduling model by taking the lowest farm energy consumption cost and the lowest carbon emission cost of a farm energy supply system as an objective function; and solving the scheduling model of the electricity-water coupling energy supply system to obtain a low-carbon economic scheduling scheme of the farm energy supply system. According to the invention, the energy consumption cost and carbon emission of the farm can be effectively reduced, and low-carbon economical operation of the farm energy supply system is realized.

Description

Technical Field

[0001] The present invention relates to the field of farm production technology, and in particular to an operation optimization method for an electricity-water coordinated farm energy supply system. Background Art

[0002] With technological advancement and policy promotion, my country's animal husbandry is developing towards intensification, industrialization and electrification, and a number of large-scale integrated farms with dairy cattle breeding as the core industry have emerged.

[0003] A large amount of organic waste generated during agricultural production can be used to produce biogas through anaerobic fermentation, which can not only reduce the emission of greenhouse gases such as methane, but also provide a stable source of electricity. Therefore, farms are generally equipped with manure treatment and biogas power generation systems. At present, many scholars are conducting research on the operation optimization of farm energy supply systems around biogas power generation to help farms further reduce energy costs. Studies have shown that biogas power generation is an effective means for farms to achieve low-carbon economic operation. However, current research mainly focuses on the coordinated operation between biogas power generation facilities and other energy supply entities, and does not deeply consider the flexible interaction between biogas power generation and its own agricultural equipment. In addition, the production links of farms are complex, covering a variety of equipment such as dairy cow breeding, milking operations, manure collection, anaerobic fermentation, and sewage treatment. However, current research mainly conducts flexible scheduling research on a small part of the production links of the farm, and the research on the production links of the farm is not comprehensive enough. Therefore, the overall modeling and flexibility mining of the farm need to be further deepened.

[0004] In recent years, more and more scholars have begun to pay attention to the coordinated operation of power distribution systems and water distribution systems. By adjusting the water pumps and water storage facilities in the water distribution network, a benign interaction between the water network and the power grid can be achieved. In farm production, the consumption of electricity and water resources is closely related. The use of a large amount of water resources in the process of dairy cow feeding and milking, as well as the operation of the sewage treatment system, all rely on a stable power supply. At the same time, sewage treatment facilities and water-using equipment also consume a lot of electricity during operation, especially during the high temperatures in summer, when the energy consumption of these equipment increases significantly, becoming the main source of energy consumption on the farm. Therefore, the farm exhibits significant "electricity-water" coupling characteristics. However, current research on farm operation optimization mainly focuses on electricity, heat and natural gas, and has not yet studied the farm energy supply system from the perspective of "electricity-water" coupling.

[0005] In order to measure the low carbon nature of farms, carbon trading mechanisms are usually used to describe the carbon emissions of farms. However, since the current carbon trading mechanism is not yet mature, how to define the carbon emission quota of biogas power generation as a clean energy is still under study. However, current studies have not considered the carbon emission quota of biogas power generation, and a complete carbon trading mechanism that takes into account the participation of biogas power generation has not yet been established.

[0006] Therefore, a method for optimizing the operation of an electricity-water coordinated farm energy supply system is needed to solve the above technical problems. Summary of the invention

[0007] To this end, the present invention provides an electricity-water coordinated farm energy supply system operation optimization method to solve or at least alleviate the above problems.

[0008] According to one aspect of the present invention, there is provided an operation optimization method for an electricity-water coordinated farm energy supply system, which is executed in a computing device, wherein the farm energy supply system comprises a farm production layer, a farm distribution network, and a farm water distribution network, wherein the farm distribution network and the farm water distribution network have radial structures, and a plurality of farm equipment are deployed on the farm production layer, wherein the plurality of farm equipment comprises a biogas generator set, wherein the biogas generator set comprises at least one biogas generator, and the biogas generator set serves as a power source in the farm energy supply system, and the method comprises: respectively modeling a plurality of energy-consuming links in the farm production process to obtain a farm equipment model for each energy-consuming link, wherein the plurality of energy-consuming links comprise a dairy cow feeding and milking link, a biogas production energy supply link, and a sewage treatment link; according to the According to the layout of the farm energy supply system and the structures of the farm distribution network and the farm water distribution network, a farm electricity-water coupling energy supply network model is constructed, and the farm electricity-water coupling energy supply network model includes a farm distribution network model and a farm water distribution network model; based on the farm equipment models of each energy consumption link and the farm electricity-water coupling energy supply network model, the carbon emission quota of the biogas generator set is determined by using the annual benchmark value method, and the lowest farm energy consumption cost and the carbon emission cost of the farm energy supply system are used as the objective function to establish an electricity-water coupling energy supply system scheduling model; by solving the electricity-water coupling energy supply system scheduling model, a low-carbon economic scheduling plan of the farm energy supply system is obtained, so as to optimize the operation of the farm energy supply system based on the low-carbon economic scheduling plan.

[0009] Optionally, in the operation optimization method of the electricity-water coordinated farm energy supply system according to the present invention, the annual benchmark value method is used to determine the carbon emission quota of the biogas generator set, including: summing the annual emissions of the biogas generator set due to physical leakage of biogas, emissions caused by biogas combustion, carbon emissions of electricity consumed in biogas project operation, emissions caused by transporting manure, and emissions caused by manure storage to obtain the annual benchmark value of the biogas generator set; based on the annual benchmark value of the biogas generator set, the annual average power generation of the biogas generator set, the cooling method correction factor and the load correction factor, the carbon emission quota of the biogas generator set is determined.

[0010] Optionally, in the electricity-water coordinated farm energy supply system operation optimization method according to the present invention, the farm energy supply system includes multiple types of power sources, including grid electricity purchase, photovoltaic, wind power and biogas generator sets; taking the lowest farm energy cost and the carbon emission cost of the farm energy supply system as the objective function, an electricity-water coupled energy supply system scheduling model is established, including: determining the farm energy cost based on the grid electricity purchase cost, water purchase cost, photovoltaic operation and maintenance cost, wind power operation and maintenance cost, and biogas power generation operation and maintenance cost; quantifying the carbon emissions of the farm energy supply system based on a comprehensive carbon emission factor to determine the carbon emissions of the farm energy supply system; determining the carbon emission cost of the farm energy supply system based on the carbon emission quota of the farm energy supply system, the carbon emissions of the farm energy supply system and the carbon trading price; and establishing an electricity-water coupled energy supply system scheduling model with the lowest farm energy cost and the carbon emission cost of the farm energy supply system as the objective function.

[0011] Optionally, in the operation optimization method of the electricity-water coordinated farm energy supply system according to the present invention, the carbon emissions of the farm energy supply system are determined according to the following formula:

[0012]

[0013] in, It represents the proportion of the output of the zth type of power source in the total output of all types of power sources in the period t; represents the output of the zth type power supply during period t; θ t represents the comprehensive carbon emission factor in period t; α z Indicates the carbon emission coefficient of the zth type of power source; represents the carbon emissions of the farm energy supply system in period t;

[0014] The carbon emission cost of the farm energy supply system is determined according to the following formula:

[0015]

[0016] in, represents the carbon emission quota of the farm energy supply system; F 2 represents the carbon emission cost of the farm energy supply system; ξ z represents the carbon emission quota of the zth type of power source in period t; Represents the carbon trading price.

[0017] Optionally, in the electricity-water coordinated farm energy supply system operation optimization method according to the present invention, the farm energy consumption cost includes the grid electricity purchase cost, water purchase cost, photovoltaic operation and maintenance cost, wind power operation and maintenance cost, and biogas power generation operation and maintenance cost.

[0018] Optionally, in the operation optimization method of the electricity-water coordinated farm energy supply system according to the present invention, multiple energy-consuming links in the actual production process of the farm are modeled separately to obtain farm equipment models of each energy-consuming link, including: each farm equipment used in multiple energy-consuming links in the actual production process of the farm is modeled separately to obtain a model of each farm equipment; among which, the farm equipment used in the dairy cow feeding and milking links includes a cow house wet curtain cooling system, a dairy cow drinking system, and a rotary milking machine, and the cow house wet curtain cooling system includes a fan and a sprinkler; the farm equipment used in the biogas production energy supply link includes an anaerobic fermentation tank, a gas storage cabinet, and a biogas generator; the farm equipment used in the sewage treatment link includes a water pump, an aerator, a reverse osmosis equipment, and a reservoir.

[0019] Optionally, in the operation optimization method of the electricity-water coordinated farm energy supply system according to the present invention, the farm distribution network includes a plurality of distribution network nodes and a plurality of branches; the farm distribution network model is expressed as follows:

[0020]

[0021] Among them, P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch ij during period t respectively; l ij,t represents the square of the current in branch ij during period t; r ij 、x ij They represent the resistance and reactance of branch ij during period t respectively; u i,t Represents the square of the voltage of the distribution network node i during period t; Ω b I represents the set of distribution network nodes of the farm distribution network; ij,max ,I ij,min Respectively represent the upper and lower limits of the current of branch ij; U i,max , U i,min They represent the upper and lower limits of the voltage at the distribution network node i respectively;

[0022] The farm water distribution network includes multiple water distribution network nodes and multiple pipelines, wherein the multiple pipelines include at least one common pipeline not connected to a water pump and at least one water pump pipeline connected to a water pump; the farm water distribution network model includes flow balance constraints of water distribution network nodes and water head constraints of pipelines, and the farm water distribution network model is expressed as follows:

[0023]

[0024] in, It represents the flow rate provided by the water source at the node m of the water distribution network during period t; represents the flow rate required by the water load at the node m of the water distribution network during period t; q mn, t represents the flow rate of pipeline mn during period t; and Respectively represent the head changes of ordinary pipelines and pump pipelines during period t; r mn Indicates the friction coefficient of the pipeline mn; Indicates the static head of the pump; A mn Indicates the internal resistance coefficient of the water pump; B mn Indicates the head index of the pump; ω mn,t Indicates the speed ratio of the water pump; Indicates the power of the water pump; Indicates the water extraction efficiency of the pump.

[0025] Optionally, in the operation optimization method of the electricity-water coordinated farm energy supply system according to the present invention, the electricity-water coupled energy supply system scheduling model also includes electricity-water coupling constraints, abandoned solar and wind energy constraints, and abandoned biogas constraints; wherein the abandoned solar and wind energy constraints are used to indicate that the abandoned solar and wind energy in each time period should not exceed the photovoltaic power generation and wind power generation in that time period, and the abandoned biogas constraints are used to indicate that the abandoned biogas in each time period should not exceed the maximum gas transmission capacity of the gas pipeline in that time period;

[0026] The electrical-water coupling constraint condition is expressed as follows:

[0027]

[0028] Where P j,t represents the active power at node j in the distribution network during period t; P pump,t , P trough,t Respectively represent the active power of the pipeline water pump and the water tank water pump in time period t; P rest,t Represents the power of the remaining loads except the water pump at the distribution network node j: Ω pump Represents the set of pipeline water pump nodes; Ω trough Represents a collection of water tank inlet pump nodes.

[0029] Optionally, in the electricity-water coordinated farm energy supply system operation optimization method according to the present invention, it also includes: linearizing the farm distribution network model and the farm water distribution network model.

[0030] Optionally, in the operation optimization method of the electricity-water coordinated farm energy supply system according to the present invention, the cowshed wet curtain cooling system model is expressed as follows:

[0031]

[0032] Among them, P t fas represents the operating power of the wet curtain cooling system of the cowshed during period t, μ fan is a 0-1 variable, indicating the working status of the fan during period t; Pt fan represents the operating power of the nth fan in period t; μ spr is a 0-1 variable, indicating the working status of the sprinkler during period t; Represents the operating power of the nth sprinkler in period t; N fan Indicates the total number of fans included in the wet curtain cooling system of the cowshed; N spr Indicates the total number of sprinklers included in the wet curtain cooling system of the cowshed; W t fas Indicates the water consumption of the wet curtain cooling system in the cowshed during period t; represents the water consumption of the nth sprinkler during period t; T air Indicates the ambient temperature; Indicates the critical temperature when the fan is turned on; Indicates the critical temperature at which the sprinkler opens;

[0033] The cow drinking water system model is used to describe the relationship between the amount of water cows drink and the daily average temperature, which is expressed as follows:

[0034]

[0035] Among them, W t dr N represents the amount of water the cow drinks during period t. dairy represents the number of cows in the farm, represents the daily average temperature, d 1 ,d 2 It represents the fitting coefficient of the water consumption of dairy cows and the daily average temperature, and T is the decision cycle;

[0036] The rotary milking machine model is expressed as follows:

[0037]

[0038]

[0039] in, It represents the power of the rotary milking machine of type D during period t; are 0-1 variables, representing the three working gears of the d-type rotary milking machine in period t; is the working gear constraint condition of the D-type rotary milking machine, indicating that the D-type rotary milking machine can only be in one working gear during the t period; Respectively represent the power corresponding to the three working gears; It represents the number of milking heads of the D-type rotary milking machine in period t; They represent the number of teats milked per unit time corresponding to the three working gears; represents the number of lactating cows that need to be milked; D represents the total number of types of rotary milking machines; the milking operation in the dairy farm is carried out in three shifts. Indicates the three starting periods of milking operation; T I 、T II 、T III Indicates the three end periods of the milking operation; is the milking task constraint, which means that within the decision cycle T, the sum of the number of teats of various rotary milking machines must not be less than the number of lactating cows that need to complete the milking task; represents the amount of water consumed by the D-type rotary milking machine during period t; w cl It indicates the water consumption for washing and disinfecting a dairy cow;

[0040] The anaerobic fermentation tank model is used to describe the relationship between biogas production and fermentation environment temperature, which is expressed as follows:

[0041]

[0042] in, is the microbial growth rate; 1 ,φ 2 represents microbial fermentation parameters; T t bio Indicates the temperature inside the anaerobic fermentation tank; is the biogas production per unit volume of material at time t; is the primary biogas production at time t, in m 3 ; B h is the total methane production obtained after full fermentation of the materials used; c VS V is the volatile solid concentration of the material; bio is the volume of biomass; Z HRT K is the hydraulic retention period, which refers to the time it takes for a batch of materials to enter the anaerobic fermentation tank and be completely discharged; d Indicates microbial fermentation parameters;

[0043] The anaerobic fermentation tank model is expressed as follows:

[0044]

[0045] in, Indicates the gas storage capacity of the gas storage tank during period t; They represent the air intake and air output of the gas storage cabinet during period t respectively; They represent the air intake efficiency and air outlet efficiency of the gas holder respectively; It represents the amount of biogas used for power generation by biogas generator during period t; G represents the amount of biogas burned by the torch during period t; trans,max Indicates the upper limit of gas transmission volume of gas pipeline; G bios,max,G bios ,min represent the upper and lower limits of the gas storage tank capacity respectively;

[0046] The output model of biogas generator is expressed as follows:

[0047]

[0048] P bio,min ≤P t bio ≤P bio,max

[0049]

[0050] Among them, P t bio Indicates the power generated by the biogas generator during period t; LHV bio is the calorific value of biogas; η g Indicates the power generation efficiency of the biogas generator; represents the amount of biogas after purification during period t; η c is the efficiency of biogas purification; P bio,max Indicates the upper and lower limits of the output of the biogas generator; r DE It represents the upper limit of regulation of biogas generator during period t;

[0051] The water pump model is expressed as follows:

[0052]

[0053] Among them, P t pump represents the power of the water pump during period t; Indicates the flow rate of the water pump during period t; H t pump represents the pump head during time period t; ρ and g are the density and gravitational acceleration of the sewage respectively; q pump,min ,q pump,max Respectively represent the upper and lower flow limits of the water pump;

[0054] The aerator model is expressed as follows:

[0055]

[0056] Among them, P t aer It represents the power of the aerator during period t; represents the basic power of the aerator in the non-aerated state; a represents the power factor of the aerator; k represents the fitting equation coefficient; Fl g Indicates the amount of aeration; Q gIndicates aeration rate; N indicates the impeller speed of the aerator; D indicates the impeller diameter of the aerator; is a 0-1 variable, indicating the working status of the aerator;

[0057] The start and stop constraints of the aerator are:

[0058]

[0059] in, It is a 0-1 variable. When it is 1, it means that the aerator changes from the stopped state to the running state; It is a 0-1 variable. When it is 1, it means that the aerator changes from the running state to the stopped state; N op Indicates the maximum number of starts and stops of the aerator;

[0060] The reverse osmosis equipment model is expressed as follows:

[0061] P th,t =k th q th,t

[0062] Among them, P th,t and q th,t are the power and water flow of the reverse osmosis equipment respectively; k th is the power consumption coefficient of the reverse osmosis equipment;

[0063] The reservoir model is expressed as follows:

[0064]

[0065] Among them, V t pool,s represents the water storage capacity of type s reservoir in period t; represents the water inflow of type s reservoir during period t; V represents the water output of the s-type reservoir during period t; pool,s,min 、V pool,s,max They represent the upper and lower limits of the capacity of the s-type reservoir respectively; q in,s,max ,q out,s,max They represent the upper limit of water inflow and water outflow of s type of reservoir respectively; the set s∈{ga,fi,se,th,st,dr 1 ,dr 2 ,…,dr 10} indicates the type of water storage tank, which respectively represents the sewage collection tank, primary treatment tank, secondary treatment tank, tertiary treatment tank, clean water buffer tank and No. 1-10 cowshed drinking pool.

[0066] According to one aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for execution by the at least one processor, and the program instructions include instructions for executing the method for optimizing the operation of an electricity-water coordinated farm energy supply system as described above.

[0067] According to one aspect of the present invention, a computer program product is provided, comprising a computer program / instruction, wherein the computer program / instruction implements the method as described above when executed by a processor.

[0068] According to one aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the above-mentioned method for optimizing the operation of the electricity-water coordinated farm energy supply system.

[0069] According to the technical solution of the present invention, a method for optimizing the operation of an electricity-water coordinated farm energy supply system is provided. First, each energy-consuming link in the actual production process of the farm is modeled, and a farm electricity-water coupled energy supply network model is constructed. Then, an electricity-water coupled energy supply system scheduling model is constructed. This model comprehensively considers each energy-consuming link in farm production, and can provide a low-carbon economic scheduling plan for the farm energy supply system from the perspective of electricity-water coupling. In addition, the biogas generator set is included in the carbon trading mechanism, and the operation mode of each equipment in the farm energy supply system can be adjusted with economy and low carbon as the goal. In this way, the farm energy cost and carbon emissions can be effectively reduced, and the low-carbon economic operation of the farm energy supply system can be achieved.

[0070] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to achieve the above and related purposes, the present invention describes certain illustrative aspects in conjunction with the following description and the accompanying drawings, which indicate various ways in which the principles disclosed in the present invention can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. By reading the following detailed description in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present disclosure will become more apparent. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0072] Figure 1 It shows a structural schematic diagram of a farm energy supply system 100 provided according to an embodiment of the present invention;

[0073] Figure 2A schematic diagram of a computing device 200 provided according to an embodiment of the present invention is shown;

[0074] Figure 3 A schematic flow chart of an electricity-water coordinated farm energy supply system operation optimization method 300 provided according to an embodiment of the present invention is shown;

[0075] Figure 4 A schematic diagram of the farm electricity-water coupling relationship according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0076] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0077] The existing technology for optimizing the operation of farm energy supply systems does not take into account the farm production links comprehensively. It mainly studies the farm operation optimization plans around electricity, heat and natural gas, but has not yet studied the operation optimization plans of farm energy supply systems from the perspective of "electricity-water" coupling. In addition, a complete carbon trading mechanism that takes into account the participation of biogas power generation has not been established.

[0078] In response to the above technical problems, an embodiment of the present invention proposes an operation optimization method for an electricity-water coordinated farm energy supply system. First, each energy consumption link in the actual production process of the farm is modeled, and a farm electricity-water coupled energy supply network model is constructed. Then, an electricity-water coupled energy supply system scheduling model is constructed, which comprehensively considers each energy consumption link in farm production, and can provide a low-carbon economic scheduling plan for the farm energy supply system from the perspective of electricity-water coupling. In addition, the biogas generator set is included in the carbon trading mechanism, and the operation mode of each equipment in the farm energy supply system can be adjusted with economy and low carbon as the goals. In this way, the farm energy cost and carbon emissions can be effectively reduced, and the low-carbon economic operation of the farm energy supply system can be achieved.

[0079] Figure 1 A schematic structural diagram of a farm energy supply system 100 provided according to an embodiment of the present invention is shown.

[0080] like Figure 1 As shown, the farm energy supply system 100 includes a farm production layer, a farm distribution network, and a farm water distribution network. The farm distribution network and the farm water distribution network have radial structures.

[0081] In some embodiments, Figure 1As shown, the upper layer of the farm energy supply system 100 is the farm distribution network, which is a single-circuit radial network with a voltage level of 10kV. The farm distribution network includes multiple distribution network nodes and multiple branches. Specifically, the farm distribution network includes 22 distribution network nodes and 21 branches, such as key distribution network nodes such as cowshed, rotary milking machine, sewage treatment system, etc. The power of the farm distribution network comes from power purchase from the power grid (connected to the No. 22 distribution network node), biogas power generation (connected to the No. 1 distribution network node), photovoltaic (connected to the No. 13 distribution network node) and wind power (connected to the No. 3 distribution network node).

[0082] The lower layer of the farm energy supply system 100 is the farm water distribution network, which includes multiple water distribution network nodes and multiple pipelines. Specifically, the farm water distribution network includes 14 water distribution network nodes and 13 pipelines, of which water distribution network nodes 2-11 supply water to cowsheds 1-10 respectively, and water distribution network nodes 12-14 supply water to rotary milking machines 1-3 respectively. In order to prevent the water pressure at the end of the farm water distribution network from being too low, 4 water pumps can be installed in the farm water distribution network, and the 4 water pumps are respectively connected to pipelines I, III, VIII and XI of the farm water distribution network.

[0083] The middle layer of the farm energy supply system 100 is the farm production layer, which is used for farm production. The farm production layer can deploy multiple farm equipment, including, for example, a biogas generator set, an anaerobic fermentation tank (with an integrated gas storage cabinet on the top of the tank), a flare, a primary sewage treatment area, a secondary sewage treatment area, a tertiary sewage treatment area, a buffer pool (a clear water buffer pool), multiple milking parlors (with a rotary milking machine), and multiple cow sheds. The biogas generator set includes at least one biogas generator.

[0084] It should be noted that in the embodiments of the present invention, based on the actual production process of the farm, the main energy-consuming links of the farm can be summarized into three parts: dairy cow feeding and milking link, biogas production energy supply link, and farm sewage treatment link. That is, the farm production process includes multiple energy-consuming links, and the multiple energy-consuming links include dairy cow feeding and milking link, biogas production energy supply link, and sewage treatment link. Among them, each energy-consuming link in the farm production process requires the use of one or more farm equipment.

[0085] In some embodiments, farm equipment used in dairy cow feeding and milking includes a cowshed wet curtain cooling system, a dairy cow drinking system, and a rotary milking machine. Farm equipment used in biogas production and energy supply includes anaerobic fermentation tanks, gas storage cabinets, and biogas generators. Farm equipment used in farm sewage treatment includes water pumps, aerators, reverse osmosis equipment, and water storage tanks.

[0086] In an embodiment of the present invention, the computing device may be configured to execute the electricity-water coordinated farm energy supply system operation optimization method 300. By executing the electricity-water coordinated farm energy supply system operation optimization method 300, the optimal behavior strategy of each load in the new power system market can be determined. The electricity-water coordinated farm energy supply system operation optimization method 300 of the present invention will be described below.

[0087] A computing device 200 provided by an embodiment of the present invention is introduced below.

[0088] Figure 2 FIG. 2 is a schematic diagram of a computing device 200 provided according to an embodiment of the present invention. Figure 2 As shown, in a basic configuration, the computing device 200 includes at least one processing unit 202 and a system memory 204. According to one aspect, depending on the configuration and type of the computing device, the processing unit 202 can be implemented as a processor. The system memory 204 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, the system memory 204 includes an operating system 205.

[0089] According to one aspect, operating system 205 is suitable for controlling the operation of computing device 200, for example. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 2 The basic configuration is shown in FIG. 2 by those components within the dashed lines. According to one aspect, the computing device 200 has additional features or functionality. For example, according to one aspect, the computing device 200 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 2 2 is illustrated by a removable storage device 209 and a non-removable storage device 210.

[0090] As stated above, according to one aspect, a program module 203 is stored in the system memory 204. According to one aspect, the program module 203 may include one or more application programs, and the present invention does not limit the type of application program, for example, the application program may include: email and contact application programs, word processing application programs, spreadsheet application programs, database application programs, slide show application programs, drawing or computer-aided application programs, web browser application programs, etc.

[0091] In an embodiment according to the present invention, the program module 203 includes a plurality of program instructions for executing the method 300 for optimizing the operation of the electricity-water coordinated farm energy supply system of the present invention.

[0092] According to one aspect, the examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 2 Each or many components shown in can be integrated into a system on a chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") into a chip substrate as a single integrated circuit. When operated via SOC, the functions described in the present invention can be operated via a dedicated logic integrated with other components of the computing device 200 on a single integrated circuit (chip). Other technologies capable of performing logical operations (such as AND, OR, and NOT) can also be used to practice embodiments of the present invention, including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0093] According to one aspect, the computing device 200 may also have one or more input devices 212, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 214 may also be included, such as a display, a speaker, a printer, etc. The aforementioned devices are examples and other devices may also be used. The computing device 200 may include one or more communication connections 216 that allow communication with other computing devices 218. Examples of suitable communication connections 216 include, but are not limited to: RF transmitters, receivers and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0094] The term computer-readable medium as used in the present invention includes computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 204, removable storage device 209, and non-removable storage device 210 are all examples of computer storage media (i.e., memory storage). Computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computing device 200. According to one aspect, any such computer storage medium may be part of computing device 200. Computer storage media do not include carrier waves or other propagated data signals.

[0095] According to one aspect, communication media is implemented by computer readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in a manner that encodes information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0096] In an embodiment according to the present invention, the computing device 200 is configured to execute the electricity-water coordinated farm energy supply system operation optimization method 300. The computing device 200 includes one or more processors and one or more readable storage media storing program instructions. When the program instructions are configured to be executed by one or more processors, the computing device executes the electricity-water coordinated farm energy supply system operation optimization method 300 in the embodiment of the present invention.

[0097] In some embodiments, the computing device 200 executing the electricity-water coordinated farm energy supply system operation optimization method 300 in the embodiment of the present invention may be a terminal or a server.

[0098] The following is a detailed description of the operation optimization method 300 of the electricity-water coordinated farm energy supply system in an embodiment of the present invention.

[0099] Figure 3 FIG. 3 is a flow chart showing an operation optimization method 300 for an electricity-water coordinated farm energy supply system according to an embodiment of the present invention. Figure 3 As shown, the electricity-water coordinated farm energy supply system operation optimization method 300 includes the following steps 310-340.

[0100] Step 310, the computing device 200 can model multiple energy-consuming links in the farm production process (actual production process) respectively to obtain a farm equipment model for each energy-consuming link, wherein the multiple energy-consuming links specifically include dairy cow breeding and milking links, biogas production and energy supply links, and sewage treatment links.

[0101] Step 320, the computing device 200 can construct a farm electricity-water coupled energy supply network model according to the layout of the farm energy supply system and the structure (actual structure) of the farm distribution network and the farm water distribution network, wherein the farm electricity-water coupled energy supply network model includes a farm distribution network model and a farm water distribution network model.

[0102] Step 330, the computing device 200 can consider the full-cycle carbon emissions of the biogas generator set based on the farm equipment model of each energy consumption link established in the aforementioned step 310, and the farm electricity-water coupled energy supply network model established in step 320 (integrating the farm equipment model constraints and the network model constraints), and use the annual benchmark value method to determine the carbon emission quota of the biogas generator set, and establish an electricity-water coupled energy supply system scheduling model (that is, an operation optimization model) with the lowest farm energy cost and the carbon emission cost of the farm energy supply system as the objective function.

[0103] It should be noted that the present invention measures the carbon emissions of farms through a carbon trading mechanism. The current status of biogas power generation in the carbon trading market is underestimated for two reasons: first, the current carbon trading mechanism has not yet included biogas power generation in quota management; second, the carbon emission reduction benefits of biogas power generation are difficult to measure. To this end, in an embodiment of the present invention, a carbon trading mechanism that takes biogas power generation into consideration is proposed, and biogas power generation units are included in the carbon trading mechanism. In order to ensure that the method of the present invention matches my country's current carbon trading mechanism, the annual benchmark value method can be used to calculate the carbon emission quota of biogas power generation units.

[0104] In an embodiment of the present invention, the farm energy supply system may include multiple types of power sources, and the biogas generator set may be used as one of the multiple types of power sources of the farm energy supply system. That is, in addition to the biogas generator set, the farm energy supply system also includes other types of power sources (such as grid-purchased electricity, photovoltaic power, and wind power).

[0105] In some embodiments, the multiple power sources of the farm energy supply system include power purchase from the power grid, photovoltaic power, wind power, and biogas generator sets. The farm energy cost may include power purchase from the power grid, water purchase cost, photovoltaic operation and maintenance cost, wind power operation and maintenance cost, and biogas power generation operation and maintenance cost. That is, in step 330, the farm energy cost may be determined based on the power purchase from the power grid, water purchase cost, photovoltaic operation and maintenance cost, wind power operation and maintenance cost, and biogas power generation operation and maintenance cost.

[0106] Step 340, the computing device 200 can obtain a low-carbon economic scheduling plan for the farm energy supply system by solving the electricity-water coupled energy supply system scheduling model, and then optimize the operation of the farm energy supply system based on the low-carbon economic scheduling plan.

[0107] Therefore, the electricity-water coupling energy supply system scheduling model constructed according to the embodiment of the present invention comprehensively considers all energy consumption links in farm production, can provide a low-carbon economic scheduling plan for the farm energy supply system from the perspective of electricity-water coupling, and incorporates biogas generator sets into the carbon trading mechanism. It can adjust the operation mode of each equipment in the farm energy supply system with economy and low carbon as the goals. In this way, it can effectively reduce the farm energy consumption cost and carbon emissions, and realize the low-carbon economic operation of the farm energy supply system.

[0108] It should be understood that the low-carbon economic scheduling plan for the farm energy supply system obtained by solving the electricity-water coupled energy supply system scheduling model constructed in the embodiment of the present invention can be used to optimize the operation mode of farm equipment, farm distribution network, and farm water distribution network in each energy-consuming link in the farm energy supply system.

[0109] In the embodiment of the present invention, based on the actual production process of the farm, the main energy-consuming links of the farm can be summarized into three parts: dairy cow feeding and milking link, biogas production energy supply link, and farm sewage treatment link. That is, the farm production process includes multiple energy-consuming links, and the multiple energy-consuming links include dairy cow feeding and milking link, biogas production energy supply link, and sewage treatment link. Among them, each energy-consuming link in the farm production process requires one or more farm equipment.

[0110] In some embodiments, farm equipment used in dairy cow feeding and milking includes a cowshed wet curtain cooling system, a dairy cow drinking system, and a rotary milking machine. Farm equipment used in biogas production and energy supply includes anaerobic fermentation tanks, gas storage cabinets, and biogas generators. Farm equipment used in farm sewage treatment includes water pumps, aerators, reverse osmosis equipment, and water storage tanks.

[0111] It should be noted that for the dairy cow breeding and milking process, in addition to feeding the dairy cows, it is also necessary to control the environmental conditioning equipment to maintain the cowshed environment in the best milk production state for the dairy cows, and collect milk through a rotary milking machine to complete the milk production operation. In the biogas production and energy supply link, the excrement of dairy cows on the farm can be transported to an integrated anaerobic fermentation tank, and the generated biogas is purified and supplied to the gas turbine for power generation. In the farm sewage treatment link, sewage such as milk parlor wastewater can be collected into the wastewater pool. The wastewater meets the discharge standard after three-level treatment. The treated clean water is stored in the clean water buffer pool, part of which is used for farmland irrigation, and the rest is discharged.

[0112] In some embodiments, in step 310, each farm equipment used in multiple energy-consuming links in the actual production process of the farm can be modeled separately to obtain a model (mathematical model) of each farm equipment.

[0113] According to some embodiments of the present invention, in the process of raising cows and milking, energy-using scenarios include cooling of cowshed, drinking water for cows and milking operations. These three energy-using scenarios can respectively use the cowshed wet curtain cooling system, the cow drinking water system and the rotary milking machine to complete the production operations. In other words, the farm equipment (energy-using equipment) used in the process of raising cows and milking includes the cowshed wet curtain cooling system, the cow drinking water system and the rotary milking machine. Accordingly, by modeling each farm equipment used in the process of raising cows and milking, a cowshed wet curtain cooling system model, a cow drinking water system model and a rotary milking machine model can be obtained.

[0114] It should be noted that when the ambient temperature is too high, dairy cows will experience heat stress, which will lead to a significant decrease in milk production. Therefore, the cowshed wet curtain cooling system must be used to control the environment of the cowshed to help the cows cool down and prevent heat stress. In some embodiments, the cowshed wet curtain cooling system includes a fan and a sprinkler. When the ambient temperature exceeds 16°C, the fan is turned on. When it exceeds 25°C, the fan and sprinkler must be turned on at the same time. Depending on the ambient temperature, the cowshed wet curtain cooling system can adopt three working modes: fan on and sprinkler off, fan and sprinkler off, and fan and sprinkler on. Due to the large number of dairy cows in large-scale farms and the high breeding density, in order to meet the cooling needs of dairy cows, the cowshed wet curtain cooling system needs to be equipped with a large number of fans and sprinklers. Therefore, the cowshed wet curtain cooling system is the main electrical equipment in the breeding process of dairy farms. The cowshed wet curtain cooling system model can be expressed as follows:

[0115]

[0116] Where P t fas represents the operating power of the wet curtain cooling system of the cowshed during period t, μ fan is a 0-1 variable, indicating the working status of the fan during period t; Pt fan represents the operating power of the nth fan in period t; μ spr is a 0-1 variable, indicating the working status of the sprinkler during period t; Represents the operating power of the nth sprinkler in period t; N fan Indicates the total number of fans included in the wet curtain cooling system of the cowshed; N spr Indicates the total number of sprinklers included in the wet curtain cooling system of the cowshed; W t fas Indicates the water consumption of the wet curtain cooling system in the cowshed during period t; represents the water consumption of the nth sprinkler during period t; T air Indicates the ambient temperature; Indicates the critical temperature when the fan is turned on; Indicates the critical temperature at which the sprinkler opens.

[0117] The daily drinking water of dairy cows is the main source of water for farms, accounting for about 80% of the total water consumption of farms. The amount of water that dairy cows drink is affected by temperature. Generally, the higher the daily average temperature, the more water the cows drink. The energy consumption model of the dairy cow drinking system is used to describe the specific relationship between the amount of water that dairy cows drink and the daily average temperature, which can be expressed as the following formula:

[0118]

[0119] Where W t dr N represents the amount of water the cow drinks during period t. dairy represents the number of cows on the farm; represents the daily average temperature; d 1 ,d 2 It represents the fitting coefficient of the cow's water consumption and the daily average temperature; T is the decision cycle, which can be 24 hours.

[0120] In some embodiments, d 1 and d 2 They can be 0.836 and 27.03 respectively.

[0121] In order to improve milking efficiency, the farm can use a rotary milking machine for milking, which is an important energy-consuming equipment on the farm. The rotary milking machine mainly includes a driving device and a rotating device. By adjusting the driving device, the rotary milking machine can be started, stopped, accelerated, and decelerated to adjust the milking speed at any time. Therefore, the rotary milking machine can participate in the operation of the farm energy supply system as a flexible resource. The rotary milking machine model is expressed as follows:

[0122]

[0123] In the formula, It represents the power of the rotary milking machine of type D during period t; are 0-1 variables, representing the three working gears of the D-type rotary milking machine in period t; Formula (8) is the working gear constraint condition of the D-type rotary milking machine, indicating that the D-type rotary milking machine can only be in one working gear in period t; Respectively represent the power corresponding to the three working gears; It represents the number of milking heads of the D-type rotary milking machine in period t; They represent the number of teats milked per unit time corresponding to the three working gears; represents the number of lactating cows that need to be milked; D represents the total number of types of rotary milking machines; the milking operation in the dairy farm is carried out in three shifts. Indicates the three starting periods of milking operation; T I 、T II 、T III represents the three end periods of the milking operation; Formula (9) is the constraint condition of the milking task, which means that within the decision cycle T, the sum of the number of milking heads of various rotary milking machines must not be less than the number of lactating cows that need to complete the milking operation.

[0124] After the rotary milking machine completes the milking operation, it is necessary to rinse the cow's udder and clean and disinfect the rotary milking machine. The water consumption can be expressed as:

[0125]

[0126] In the formula, represents the amount of water consumed by the D-type rotary milking parlor during period t; w cl Indicates the water consumption for washing and disinfecting a dairy cow.

[0127] According to some embodiments of the present invention, in the biogas production and energy supply link, the excrement (materials) of dairy cows in the farm can be collected centrally and transported to an integrated anaerobic fermentation tank for anaerobic fermentation. The biogas produced by anaerobic fermentation is stored in a gas storage tank on the tank top. The biogas in the gas storage tank can be used for power generation after purification and impurity removal, wherein power generation based on the biogas can be generated through a biogas generator. In other words, the farm equipment used in the biogas production and energy supply link includes anaerobic fermentation tanks, gas storage tanks, and biogas generators. Accordingly, each farm equipment used in the biogas production and energy supply link is modeled separately, and an anaerobic fermentation tank model, a gas storage tank model, and a biogas generator model (power generation output model) can be obtained.

[0128] In some embodiments, the computing device 200 can establish an anaerobic fermentation tank model based on the Monod equation, and the anaerobic fermentation tank model can be expressed as the following equations (11)-(13). It should be noted that the anaerobic fermentation tank model is used to describe the relationship between biogas production and fermentation environment temperature. Currently, large anaerobic fermentation tanks are generally equipped with constant temperature devices, which can stably maintain the environment temperature in the tank at the optimal biogas production temperature. Therefore, the anaerobic fermentation model established based on this equation can be approximately regarded as a steady state.

[0129]

[0130] In the formula, is the microbial growth rate; 1 ,φ 2 represents microbial fermentation parameters; T t bio Indicates the temperature inside the anaerobic fermentation tank; is the biogas production per unit volume of material at time t; is the primary biogas production at time t, in m 3 ; B h is the total methane production obtained after full fermentation of the materials used; c VS V is the volatile solid concentration of the material; bio is the volume of biomass; Z HRT K is the hydraulic retention period, which refers to the time it takes for a batch of materials to enter the anaerobic fermentation tank and be completely discharged; d Represents microbial fermentation parameters.

[0131] In some embodiments, the biogas produced in the anaerobic fermentation tank can be stored in a gas storage cabinet, and the biogas in the gas storage cabinet is purified and pressurized before being supplied to the biogas generator for power generation. By controlling the gas output of the gas storage cabinet, the output of the biogas generator can be flexibly adjusted. If the biogas generator cannot consume the biogas provided by the gas storage cabinet in time, causing the gas storage capacity in the gas storage cabinet to reach the upper limit, in order to ensure the safety of the equipment, the excess biogas must be discharged to the torch for combustion in time. The anaerobic fermentation tank model can be expressed as follows:

[0132]

[0133] In the formula, Indicates the gas storage capacity of the gas storage tank during period t; They represent the air intake and air output of the gas storage cabinet during period t respectively; They represent the air intake efficiency and air outlet efficiency of the gas holder respectively; It represents the amount of biogas used for power generation by biogas generator during period t; G represents the amount of biogas burned by the torch during period t; trans,max Indicates the upper limit of gas transmission volume of gas pipeline; G bios ,max , Gbios,min They respectively represent the upper and lower limits of the gas storage tank capacity.

[0134] In some embodiments, the biogas in the gas storage cabinet can enter the biogas generator to generate electricity after being purified, cold-dried, and pressurized, wherein the biogas can be purified by biogas purification equipment. The output model of the biogas generator can be expressed as follows:

[0135]

[0136] P bio,min ≤P t bio ≤P bio,max (18)

[0137]

[0138] Where P t bio Indicates the power generated by the biogas generator during period t; LHV bio is the calorific value of biogas; η g Indicates the power generation efficiency of the biogas generator; represents the amount of biogas after purification during period t; η c is the efficiency of biogas purification (i.e., the efficiency of biogas purification equipment); P bio,max Indicates the upper and lower limits of the output of the biogas generator; r DE It indicates the upper limit of regulation of biogas generator in period t.

[0139] According to some embodiments of the present invention, the energy consumption scenarios of farm sewage treatment include sewage collection, primary treatment, secondary treatment, tertiary treatment, clean water cache, etc. Among them, the primary, secondary and tertiary treatment of sewage involves a large number of water treatment equipment, which is the core energy consumption scenario of farm sewage treatment. The present invention assumes that all the sewage generated by the farm enters the sewage collection pool, and then enters the primary treatment area through a water pump (sewage drainage pump), and is transported to the secondary treatment area after dosing and preliminary sedimentation in the primary treatment area, and then transported to the tertiary treatment area after flotation treatment in the secondary treatment area. After completing the biochemical treatment and reverse osmosis in the tertiary treatment area, the sewage treatment is completed. The clean water obtained after the treatment can enter the clean water cache pool through the drainage pump. Part of the water in the clean water cache pool can be used for farmland irrigation, and the rest of the water can be discharged into the surface runoff.

[0140] In addition, the farm sewage treatment link is also equipped with several water storage tanks, including cattle house drinking pools, sewage collection pools, primary treatment pools, secondary treatment pools, tertiary treatment pools and clean water buffer pools, etc. Within the capacity of the water storage tank, the water pump power can be adjusted by adjusting the flow of the water pump, providing a certain degree of flexibility for the farm energy supply system.

[0141] The farm equipment (energy-consuming equipment) used in the farm sewage treatment link includes water pumps, aerators for the secondary sewage treatment link, reverse osmosis equipment for the tertiary sewage treatment link, and water storage tanks. Accordingly, each farm equipment used in the biogas production energy supply link is modeled separately to obtain a water pump model, an aerator model, a reverse osmosis equipment model, and a water storage tank model.

[0142] It should be noted that the water pump is an important energy-consuming equipment on the farm and is also a coupling hub for electric energy and water energy. In the farm sewage treatment process, the water pump operates in a constant speed mode, the flow is adjusted by a valve, and the adjustable flow range is small. Therefore, the present invention assumes that the water pump head in the farm sewage treatment process is a constant value, and the operating constraints of the water pump (that is, the water pump model) are:

[0143]

[0144] Where P t pump represents the power of the water pump during period t; represents the flow rate of the water pump during period t; represents the pump head during time period t; ρ and g are the density and gravitational acceleration of the sewage respectively; q pump,min ,q pump,max They respectively represent the upper and lower flow limits of the water pump.

[0145] The aerator is the main energy-consuming equipment in the secondary sewage treatment environment. It is used to inject bubbles into the sewage to increase the oxygen content of the sewage and promote the oxidation process. The aerator model (the operating power model of the aerator) is shown in the following formula:

[0146]

[0147] Where P t aer It represents the power of the aerator during period t; represents the basic power of the aerator in the non-aerated state; a represents the power factor of the aerator; k represents the fitting equation coefficient; Fl g Indicates the amount of aeration; Q g Indicates aeration rate; N indicates the impeller speed of the aerator; D indicates the impeller diameter of the aerator; It is a 0-1 variable, indicating the working status of the aerator.

[0148] Short-term shutdown of the aerator (within 2 hours) can reduce system energy consumption without affecting the sewage treatment effect. Therefore, the aerator can be regarded as a load that can be reduced to participate in the regulation of the farm energy supply system. The start and stop of the aerator follows the following constraints (start and stop constraints):

[0149]

[0150] In the formula, It is a 0-1 variable. When it is 1, it means that the aerator changes from the stopped state to the running state; It is a 0-1 variable. When it is 1, it means that the aerator changes from the running state to the stopped state; N op Indicates the maximum number of starts and stops of the aerator.

[0151] Reverse osmosis equipment is the main energy-consuming equipment in the tertiary sewage treatment process. The reverse osmosis equipment model (reverse osmosis equipment energy consumption model) can be expressed as follows:

[0152] P th,t =k th q th,t (twenty three)

[0153] Where P th,t and q th,t are the power and water flow of the reverse osmosis equipment respectively; k th is the power consumption coefficient of the reverse osmosis equipment.

[0154] For the water storage tank in the farm sewage treatment link, taking the cowshed drinking pool as an example, the traditional control strategy is to replenish water in real time as the water volume of the drinking pool is consumed, and control the drinking pool at a fixed water level. Under the flexible control strategy, the water inlet pump is controlled to run at high power during the low electricity price period, so that the water inflow of the cowshed drinking pool is higher than the water consumption, so that the water level of the drinking pool is higher than the fixed water level. During the high electricity price period, the water inlet pump runs at low power or stops running. Under the condition of ensuring that the drinking pool is not lower than the minimum water level, load transfer is realized, achieving a virtual energy storage effect. The water storage tank model can be expressed as follows:

[0155]

[0156] Where V t pool,s represents the water storage capacity of type s reservoir in period t; represents the water inflow of type s reservoir during period t; V represents the water output of the s-type reservoir during period t; pool,s,min 、V pool,s,max They represent the upper and lower limits of the capacity of the s-type reservoir respectively; q in,s,max ,q out,s,max They represent the upper limit of water inflow and water outflow of s type of reservoir respectively; the set s∈{ga,fi,se,th,st,dr 1 ,dr 2 ,…,dr 10} indicates the type of water storage tank, which respectively represents the sewage collection tank, primary treatment tank, secondary treatment tank, tertiary treatment tank, clean water buffer tank and No. 1-10 cowshed drinking pool.

[0157] According to the above embodiments, it can be understood that in step 310, each farm equipment used in multiple energy-consuming links in the actual production process of the farm is modeled separately, which can specifically include: each farm equipment used in the dairy cow feeding and milking links (cowshed wet curtain cooling system, dairy cow drinking water system, rotary milking machine) is modeled separately to obtain a cowshed wet curtain cooling system model, a dairy cow drinking water system model, and a rotary milking machine model; each farm equipment used in the biogas production energy supply link (anaerobic fermentation tank, gas storage cabinet, biogas generator) is modeled separately to obtain an anaerobic fermentation tank model, a gas storage cabinet model, and a biogas generator model; and each farm equipment used in the sewage treatment link (water pump, aerator, reverse osmosis equipment, water reservoir) is modeled separately to obtain a water pump model, an aerator model, a reverse osmosis equipment model, and a water reservoir model.

[0158] Furthermore, in step 320, a farm electricity-water coupled energy supply network model (including a farm distribution network model and a farm water distribution network model) can be constructed according to the actual layout of the farm energy supply system and the actual structure of the farm distribution network and the farm water distribution network.

[0159] In some embodiments, the farm distribution network includes multiple distribution network nodes and multiple branches. The computing device 200 can construct a farm distribution network model based on the DistFlow model. The farm distribution network model can be expressed as follows:

[0160]

[0161] Where P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch ij during period t respectively; l ij,t represents the square of the current in branch ij during period t; r ij 、x ij They represent the resistance and reactance of branch ij during period t respectively; u i,t Represents the square of the voltage of the distribution network node i during period t; Ω b I represents the set of distribution network nodes of the farm distribution network; ij,max ,I ij,min Respectively represent the upper and lower limits of the current of branch ij; U i,max , U i,min They represent the upper and lower limits of the voltage at the distribution network node i respectively.

[0162] In some embodiments, the farm water distribution network includes multiple water distribution network nodes and multiple pipelines, and the multiple pipelines include at least one ordinary pipeline not connected to a water pump and at least one water pump pipeline connected to a water pump. The farm water distribution network model constructed by the present invention includes water distribution network node flow balance constraints and pipeline head constraints. In the pipeline constraints, the pipelines are divided into ordinary pipelines and water pump pipelines according to whether a water pump is installed. Specifically, the farm water distribution network model can be expressed as the following formula:

[0163]

[0164] In the formula, It represents the flow rate provided by the water source at the node m of the water distribution network during period t; represents the flow rate required by the water load at the node m of the water distribution network during period t; q mn,t represents the flow rate of pipeline mn during period t; and Respectively represent the head changes of ordinary pipelines and pump pipelines during period t; r mn Indicates the friction coefficient of the pipeline mn; Indicates the static head of the pump; A mn Indicates the internal resistance coefficient of the water pump; B mn Indicates the head index of the pump; ω mn,t Indicates the speed ratio of the water pump; Indicates the power of the water pump; Indicates the water extraction efficiency of the pump.

[0165] In addition, the farm water distribution network model must also meet the following constraints (including water distribution network node head constraints, water pump relative speed constraints, and pipeline flow constraints):

[0166]

[0167] In the formula, They represent the upper and lower limits of the water head at the node m of the water distribution network respectively; Respectively represent the upper and lower limits of the relative speed of the water pump; They represent the upper and lower limits of the pipeline flow respectively.

[0168] After constructing the farm distribution network model and the farm water distribution network model, the farm distribution network model and the farm water distribution network model can be linearized.

[0169] Among them, the farm distribution network model has nonlinear terms in equation (25) (4), which is transformed into the second-order cone to obtain the following equation (30).

[0170]

[0171] There are nonlinear terms in equations 2, 3, and 4 of equation (28) in the farm water distribution network model. The pipe head equation is piecewise linearized to obtain the following equation (31).

[0172]

[0173] In the formula, K represents the number of segmentation points, and the pipeline head equation is divided into K-1 segments on the flow axis through K segmentation points; represents the linear coefficient, and the third equation in equation (31) represents the flow rate of pipeline mn during period t can be uniquely represented by a number of consecutive breakpoints q k Linear representation, at the same time, due to It's about q mn,t function, so several consecutive breakpoints can be used Linear combination of Approximate representation To ensure that each time period uses only one linear segment to approximate the original nonlinear function, The following constraints must be met:

[0174]

[0175] In the formula, is a 0-1 variable, indicating whether to use the Kth linear segment to approximate the nonlinear function. Indicates that the Kth linear segment is used. If These formulas ensure that only the corresponding When is 1, It can take positive values, thus achieving linearization on the selected segment.

[0176] The same method is used to perform piecewise linearization on the pump head equation and power equation, converting the nonlinear model into a mixed integer linear model, as shown in Equation (33).

[0177]

[0178] Where: X and Y represent the number of segmentation points. Through X and Y segmentation points, the pump head equation is divided into X-1 and Y-1 segments on the flow axis and speed axis respectively; λ mn,t,x,y represents the linear coefficient, and the first and second equations in equation (33) represent the flow rate of the water pump mn during period t; and speed Can be uniquely represented by several consecutive breakpoints and Linear representation, due to About and function, so several consecutive breakpoints can be used Linear combination of Approximate representation Similarly, can be Approximate expression. Similar to formula (32), λ mn,t,x,y The following constraints must also be met:

[0179]

[0180] In the formula, and It is a 0-1 variable.

[0181] In some embodiments, in step 330, the farm energy cost F 1 Divided into the power purchase cost of the power grid C g , water purchase cost C w , Photovoltaic operation and maintenance cost C pv , Wind power operation and maintenance cost C wind , Biogas power generation operation and maintenance cost C bio .

[0182] The electric-water coupled energy supply system scheduling model constructed by the present invention can be expressed as the following equations (35)-(37). The following equation (35) takes the farm energy cost and the carbon emission cost of the farm energy supply system as the minimum objective function. The farm energy cost F can be calculated by the following equations (36)-(37): 1 .

[0183] min F=F 1 +F 2 (35)

[0184]

[0185]

[0186] In the formula, F 2 represents the cost of carbon emissions; represents the power purchase price of the power grid during period t, a w 、a pv 、a wind and a bio They represent the water purchase price, unit photovoltaic maintenance cost, unit wind power maintenance cost and unit biogas power generation maintenance cost respectively. t pv and P t wind They represent the photovoltaic power generation and wind power generation during period t respectively; P t pvc and P t windc They represent the amount of abandoned solar power and abandoned wind power in period t respectively.

[0187] In some embodiments, in step 330, the specific method of determining the carbon emission quota of the biogas generator set using the annual benchmark value method is as follows: the annual emissions of the biogas generator set due to physical leakage of biogas, the emissions caused by biogas combustion, the carbon emissions of electricity consumed by biogas project operation, the emissions caused by transporting feces, and the emissions caused by feces storage are summed to obtain the annual benchmark value of the biogas generator set. Furthermore, the carbon emission quota of the biogas generator set can be determined based on the annual benchmark value of the biogas generator set, the annual average power generation of the biogas generator set, the cooling method correction factor, and the load correction factor.

[0188] Specifically, the specific method of determining the carbon emission quota of the biogas power generation unit using the annual benchmark value method is shown in the following formula (38). Among them, the annual benchmark value of the biogas power generation unit can be calculated according to the following formula (39).

[0189]

[0190] In the formula, ξ bio Indicates the carbon emission quota of the biogas generator set, in kgCO 2 / kWh; Represents the annual benchmark value of the biogas generator set, where: and They represent the emissions caused by physical leakage of biogas, emissions caused by biogas combustion, carbon emissions from electricity consumed in biogas project operation, emissions caused by transporting manure, and emissions caused by manure storage in year y respectively; It indicates the annual average power generation of biogas generator sets.

[0191] F I and F f They represent the cooling method correction factor and load correction factor respectively. If the cooling method is water cooling, F I =1; if the cooling method is air cooling, F I =1.05.

[0192] Load correction factor F f The calculation method is shown in the above formula (40), where F represents the output coefficient of the biogas generator set.

[0193] In some embodiments, a compensation coefficient may be added to the carbon emission coefficient of the biogas power generation set, and the carbon emission reduction benefit of the biogas power generation set is measured based on the compensation coefficient. The specific calculation method is as follows:

[0194]

[0195] In the formula, pf represents the comprehensive carbon emissions of biogas power generation units; bio Indicates the carbon emissions from complete combustion of biogas; and Respectively represent CO 2 and CH 4 The molar mass of m Indicates the mass fraction of methane in biogas.

[0196] C f represents the compensation coefficient, which is used to measure the carbon emission reduction benefit of the biogas power generation unit, that is, the contribution of biogas power generation to mitigating the greenhouse effect. The calculation method is shown in formula (43). bio It indicates the mass of biomass required by the biogas generator to produce unit electricity; represents the global warming potential of methane relative to carbon dioxide, which can be taken as 21 in the embodiment of the present invention; τ represents the time constant of natural decomposition of biomass; (1-e -1 / τ ) is used to describe the mass of methane volatilized into the atmosphere during the natural decomposition of a unit mass of biomass.

[0197] In some embodiments, the multiple power sources of the farm energy supply system include power purchase from the power grid, photovoltaic, wind power, and biogas generator sets. The farm energy cost may include power purchase cost from the power grid, water purchase cost, photovoltaic operation and maintenance cost, wind power operation and maintenance cost, and biogas power generation operation and maintenance cost. Based on this, in step 330, the farm energy cost may be determined based on the power purchase cost from the power grid, water purchase cost, photovoltaic operation and maintenance cost, wind power operation and maintenance cost, and biogas power generation operation and maintenance cost. In addition, the carbon emissions of the farm energy supply system may be quantified based on the comprehensive carbon emission factor to determine the carbon emissions of the farm energy supply system, and then the carbon emission cost of the farm energy supply system may be determined based on the carbon emission quota of the farm energy supply system, the carbon emissions of the farm energy supply system, and the carbon trading price. Furthermore, a scheduling model for an electric-water coupled energy supply system may be established with the lowest farm energy cost and the carbon emission cost of the farm energy supply system as the objective function.

[0198] Specifically, in order to measure the carbon emission intensity of the farm energy supply system in each time period, a comprehensive carbon emission factor can be used to quantify the carbon emissions of the farm energy supply system in each time period. The calculation method of the comprehensive carbon emission factor is shown in formulas (44)-(45), from which the carbon emissions of the farm energy supply system in each time period (t period) can be obtained: That is, the carbon emissions of the farm energy supply system can be determined according to the following formula:

[0199]

[0200] In the formula, P represents the proportion of the output of the zth type of power source in the total output of all types of power sources during the period t; t z Represents the output of the zth type of power source in time period t; power source type z includes power purchased from the power grid, photovoltaic, wind power and biogas generator sets. In the embodiment of the present invention, it is assumed that the power purchased from the power grid is all thermal power. t represents the comprehensive carbon emission factor in period t; α z Indicates the carbon emission coefficient of the zth type of power source; Represents the carbon emissions of the farm energy supply system in period t.

[0201] Furthermore, according to the carbon trading mechanism, the carbon emission cost of the farm energy supply system during operation (i.e., the carbon emission cost of the farm energy supply system) can be calculated based on the carbon emission quota of the farm energy supply system (in each time period), the carbon emissions of the farm energy supply system (in each time period), and the carbon trading price. It can be understood that when the carbon emissions of the farm energy supply system exceed the carbon emission quota of the farm energy supply system, the farm needs to purchase additional carbon emission quotas based on the carbon trading price in the carbon trading market; when the carbon emissions of the farm energy supply system are lower than the carbon emission quota of the farm energy supply system, the farm can sell excess carbon emission quotas based on the carbon trading price in the carbon trading market.

[0202] Carbon emission quotas for farm energy systems and the carbon emission cost of the farm energy system F 2 , can be determined by the following equations (47) and (48). That is, the carbon emission cost F of the farm energy supply system can be determined by the following equation: 2 :

[0203]

[0204] In the formula, represents the carbon emission quota of the farm energy supply system; F 2 represents the carbon emission cost of the farm energy supply system; ξ z represents the carbon emission quota of the zth type of power source in period t; Represents the carbon trading price.

[0205] In some embodiments, in addition to the above-mentioned farm equipment model constraints and network model constraints, the electricity-water coupled energy supply system scheduling model should also comply with the following constraints (including electricity-water coupling constraints, abandoned solar and wind energy constraints, and abandoned biogas energy constraints).

[0206] Figure 4 A schematic diagram of the farm electricity-water coupling relationship according to an embodiment of the present invention is shown.

[0207] Among them, the pipeline water pump connected to the farm water distribution network and the water tank inlet pump connected to the cowshed water tank are the farm electricity and water coupling hubs. The flow rate and speed of the water pump determine the operating power of the water pump, and the operating power of the water pump determines the load value of the corresponding distribution network node in the farm distribution network. Figure 4 As shown in the figure, four pipeline water pumps are connected to the 2nd, 6th, 11th and 13th distribution network nodes of the farm distribution network respectively, and each of the ten cowsheds is equipped with a set of trough water inlet pumps, which are connected to the 4th to 13th distribution network nodes of the farm distribution network respectively. The electricity-water coupling constraint condition is as follows:

[0208]

[0209] Where P j,t represents the active power at node j in the distribution network during period t; P pump,t , P trough,t Respectively represent the active power of the pipeline water pump and the water tank water pump in time period t; P rest,t Represents the power of the remaining loads except the water pump at the distribution network node j: Ω pump Represents the set of pipeline water pump nodes; Ω trough Represents a collection of water tank inlet pump nodes.

[0210] The constraints on the amount of abandoned solar power and wind power and the amount of abandoned biogas can be expressed as the following equations (50) and (51), respectively.

[0211]

[0212] Formula (50) indicates that the amount of abandoned solar power and wind power in period t should not exceed the photovoltaic power generation and wind power generation in that period. Formula (51) indicates that the amount of abandoned biogas in period t should not exceed the maximum gas transmission capacity of the gas pipeline in that period.

[0213] That is to say, the curtailment constraint condition is used to indicate that the curtailment amount of solar power and wind power in each period should not exceed the photovoltaic power generation and wind power generation in that period. The curtailment constraint condition is used to indicate that the curtailment amount of biogas in each period should not exceed the maximum gas transmission capacity of the gas pipeline in that period.

[0214] Based on this, the scheduling model of the electricity-water coupling energy supply system constructed according to the present invention must comply with the constraints of each farm equipment model, the farm distribution network model, the farm water distribution network model, the electricity-water coupling constraints, the abandoned solar and wind volume constraints, and the abandoned biogas volume constraints.

[0215] It should also be noted that in the aforementioned embodiment, by linearizing the farm distribution network model and the farm water distribution network model, the electricity-water coupled energy supply system scheduling model can be converted into a mixed integer linear programming problem in order to solve the electricity-water coupled energy supply system scheduling model.

[0216] According to the electricity-water coordinated farm energy supply system operation optimization method 300 of the present invention, each energy consumption link in the actual production process of the farm is first modeled, and a farm electricity-water coupled energy supply network model is constructed, and then the electricity-water coupled energy supply system scheduling model is constructed, which comprehensively considers each energy consumption link in farm production, and can provide a low-carbon economic scheduling plan for the farm energy supply system from the perspective of electricity-water coupling, and incorporates the biogas generator set into the carbon trading mechanism, and can adjust the operation mode of each equipment in the farm energy supply system with economy and low carbon as the goal. In this way, the farm energy cost and carbon emissions can be effectively reduced, and the low-carbon economic operation of the farm energy supply system can be achieved.

[0217] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer readable instructions, data structures, program modules or other data. Communication media generally embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information transfer medium. Any combination of the above is also included within the scope of readable media.

[0218] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0219] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0220] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0221] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiments, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into a plurality of submodules.

[0222] Unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe common objects merely indicates that different instances of similar objects are involved and is not intended to imply that the objects so described must have a given order in time, space, order or in any other manner.

Claims

1. A method for optimizing the operation of an electricity-water coordinated farm energy supply system, executed in a computing device, wherein the farm energy supply system includes a farm production layer, a farm distribution network, and a farm water distribution network, wherein: The farm distribution network and the farm water distribution network are radially structured, a plurality of farm equipment are deployed on the farm production layer, the plurality of farm equipment include a biogas generator set, the biogas generator set includes at least one biogas generator, and the biogas generator set serves as a power source in the farm energy supply system, and the method includes: Modeling is performed for each of the multiple energy-consuming links in the farm production process to obtain farm equipment models for each energy-consuming link. The multiple energy-consuming links include dairy cow feeding and milking, biogas production and energy supply, and sewage treatment. According to the layout of the farm energy supply system and the structures of the farm distribution network and the farm water distribution network, a farm electricity-water coupling energy supply network model is constructed, and the farm electricity-water coupling energy supply network model includes a farm distribution network model and a farm water distribution network model; Based on the farm equipment model of each energy consumption link and the farm electricity-water coupling energy supply network model, the carbon emission quota of the biogas generator set is determined using the annual benchmark value method, and the lowest farm energy cost and the carbon emission cost of the farm energy supply system are used as the objective function to establish an electricity-water coupling energy supply system scheduling model; By solving the electricity-water coupled energy supply system scheduling model, a low-carbon economic scheduling plan for the farm energy supply system is obtained, so that the operation of the farm energy supply system can be optimized based on the low-carbon economic scheduling plan.

2. The method of claim 1, wherein: The annual benchmark method is used to determine the carbon emission quota of biogas power generation units, including: The annual emissions of the biogas generator set due to physical leakage of biogas, the emissions due to biogas combustion, the carbon emissions of electricity consumed in the operation of the biogas project, the emissions due to the transportation of feces, and the emissions due to the storage of feces are summed up to obtain the annual benchmark value of the biogas generator set; The carbon emission quota of the biogas power generation set is determined based on the annual benchmark value of the biogas power generation set, the annual average power generation of the biogas power generation set, the cooling method correction factor and the load correction factor.

3. The method according to claim 1 or 2, wherein: The farm energy supply system includes multiple types of power sources, including grid-purchased electricity, photovoltaic, wind power and biogas generators; Taking the lowest farm energy cost and carbon emission cost of the farm energy supply system as the objective function, a scheduling model of the electricity-water coupled energy supply system is established, including: Determine the farm energy cost based on the grid electricity purchase cost, water purchase cost, photovoltaic operation and maintenance cost, wind power operation and maintenance cost, and biogas power generation operation and maintenance cost; Quantifying the carbon emissions of the farm energy supply system based on a comprehensive carbon emission factor to determine the carbon emissions of the farm energy supply system; Determine the carbon emission cost of the farm energy supply system based on the carbon emission quota of the farm energy supply system, the carbon emissions of the farm energy supply system and the carbon trading price; Taking the lowest farm energy cost and the carbon emission cost of the farm energy supply system as the objective function, a scheduling model for the electricity-water coupled energy supply system is established.

4. The method of claim 3, wherein: The carbon emissions of the farm energy supply system are determined according to the following formula: in, P represents the proportion of the output of the zth type of power source in the total output of all types of power sources during the period t; t z represents the output of the zth type power supply during period t; θ t represents the comprehensive carbon emission factor in period t; α z Indicates the carbon emission coefficient of the zth type of power source; represents the carbon emissions of the farm energy supply system in period t; The carbon emission cost of the farm energy supply system is determined according to the following formula: in, represents the carbon emission quota of the farm energy supply system; F2 represents the carbon emission cost of the farm energy supply system; ξ z represents the carbon emission quota of the zth type of power source in period t; Represents the carbon trading price.

5. The method according to any one of claims 1 to 4, wherein: The farm energy costs include electricity purchase costs from the power grid, water purchase costs, photovoltaic operation and maintenance costs, wind power operation and maintenance costs, and biogas power generation operation and maintenance costs.

6. The method according to any one of claims 1 to 5, wherein: Modeling is performed for each of the multiple energy-consuming links in the actual production process of the farm, and farm equipment models for each energy-consuming link are obtained, including: Model each farm equipment used in multiple energy-consuming links in the actual production process of the farm, and obtain the model of each farm equipment; Among them, the farm equipment used in the dairy cow feeding and milking process includes a cowshed wet curtain cooling system, a dairy cow drinking water system, and a rotary milking machine. The cowshed wet curtain cooling system includes a fan and a sprinkler; Farm equipment used in the biogas production and energy supply phase includes anaerobic fermentation tanks, gas storage cabinets, and biogas generators; Farm equipment used in sewage treatment includes pumps, aerators, reverse osmosis equipment, and reservoirs.

7. The method according to any one of claims 1 to 6, wherein: The farm distribution network includes multiple distribution network nodes and multiple branches; the farm distribution network model is expressed as follows: Among them, P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch ij during period t respectively; l ij,t represents the square of the current in branch ij during period t; r ij 、x ij They represent the resistance and reactance of branch ij during period t respectively; u i,t Represents the square of the voltage of the distribution network node i during period t; Ω b I represents the set of distribution network nodes of the farm distribution network; ij,max ,I ij,min Respectively represent the upper and lower limits of the current of branch ij; U i,max , U i,min They represent the upper and lower limits of the voltage at the distribution network node i respectively; The farm water distribution network includes multiple water distribution network nodes and multiple pipelines, wherein the multiple pipelines include at least one common pipeline not connected to a water pump and at least one water pump pipeline connected to a water pump; the farm water distribution network model includes flow balance constraints of water distribution network nodes and water head constraints of pipelines, and the farm water distribution network model is expressed as follows: in, It represents the flow rate provided by the water source at the node m of the water distribution network during period t; represents the flow rate required by the water load at the node m of the water distribution network during period t; q mn,t represents the flow rate of pipeline mn during period t; and Respectively represent the head changes of ordinary pipelines and pump pipelines during period t; r mn Indicates the friction coefficient of the pipeline mn; Indicates the static head of the pump; A mn Indicates the internal resistance coefficient of the water pump; B mn Indicates the head index of the pump; ω mn,t Indicates the speed ratio of the water pump; Indicates the power of the water pump; Indicates the water extraction efficiency of the pump.

8. The method of claim 7, wherein: The electricity-water coupling energy supply system scheduling model also includes electricity-water coupling constraints, abandoned solar and wind power constraints, and abandoned biogas constraints; wherein the abandoned solar and wind power constraints are used to indicate that the abandoned solar and wind power in each time period should not exceed the photovoltaic power generation and wind power generation in that time period, and the abandoned biogas constraints are used to indicate that the abandoned biogas in each time period should not exceed the maximum gas transmission capacity of the gas pipeline in that time period; The electrical-water coupling constraint condition is expressed as follows: Where P j,t represents the active power at node j in the distribution network during period t; P pump,t , P troug h, t represent the active power of pipeline water pump and water tank inlet pump in time period t respectively; P rest,t Represents the power of the remaining loads except the water pump at the distribution network node j: Ω pump Represents the set of pipeline water pump nodes; Ω trough Represents a collection of water tank inlet pump nodes.

9. The method of claim 7, wherein: Also includes: The farm distribution network model and the farm water distribution network model are linearized.

10. A computing device comprising: at least one processor; and A memory storing program instructions, wherein the program instructions are configured to be processed by the at least one processor, and the program instructions include instructions for processing the method according to any one of claims 1 to 9.

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