A planning model construction method for integrated energy microgrid

Abstract

The present invention discloses a method for constructing a planning model for an integrated energy microgrid, comprising the following steps: obtaining basic parameters; establishing a planning model for the integrated energy microgrid that takes into account energy suppliers and agricultural production through multi-objective optimal planning theory; substituting the basic parameters into a first model, solving the first model with the goal of minimizing the autonomous cost of the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when the autonomous cost of each device is minimized; substituting the basic parameters into a second model, solving the second model with the goal of maximizing carbon dioxide emission reduction in the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when the carbon emission reduction is maximized. It can be seen that the present invention achieves more reasonable production and consumption complementarity between agricultural production and energy suppliers by fully utilizing the large amount of biomass energy generated in agricultural production as the production power of energy suppliers, thereby reducing the autonomous cost and carbon emissions of the system.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a method, device and computing equipment for constructing a planning model for an integrated energy microgrid. Background Art

[0002] Energy suppliers can be understood as renewable energy suppliers who provide energy to energy consumers. Energy consumers can be understood as users who purchase and use energy from energy suppliers. Energy consumers include those in the agricultural sector, animal husbandry, and residential sectors.

[0003] In recent years, weather conditions have impacted agricultural production and energy generation. Energy suppliers have addressed the issue of insufficient agricultural power by providing electricity to agriculture. Crop straw and livestock manure from agricultural systems (i.e., consumers) can serve as raw materials for energy production. This allows integrated energy microgrids, encompassing both agricultural systems and energy suppliers, to function as both a "source" and a "load." On the one hand, energy suppliers input various forms of energy, such as electricity, gas, and heat, into agricultural systems, which in turn produce agricultural biomass energy. Biomass energy, for example, includes crop straw (such as rice straw) and livestock manure. On the other hand, biomass energy can be used as raw material for the production of energy equipment. For example, biomass energy can power combined heat and power (CHP) units, and the output heat and electricity can sustain the operation of agricultural systems. In this way, the traditional one-way energy flow model is transformed into a circular energy flow model.

[0004] With the development of scale and intensification in rural areas in my country, the rural animal husbandry industry has developed rapidly. A large amount of livestock manure has caused serious environmental pollution. In addition, the rural energy structure is still dominated by fossil energy, which is difficult to support the construction and rapid development of rural revitalization under the "carbon peak and carbon neutrality" energy strategy. Statistics show that my country produces 3.818 billion tons of livestock and poultry manure annually, and its biogas potential is 122.647 billion m3. 3 , equivalent to 69.078 billion m3 of natural gas 3 , which can replace 93.3 million tons of coal and reduce CO2 emissions by 464.6 million tons. The biogas potential of livestock and poultry manure in my country is as high as 96.377 billion to 287.609 billion m 3 , the energy supply potential is huge.

[0005] Therefore, it is necessary to conduct planning research on integrated energy microgrids that include agricultural systems and energy suppliers. Summary of the Invention

[0006] To this end, the present invention provides a method for constructing a planning model for an integrated energy microgrid, in an effort to solve or at least alleviate the above problems.

[0007] According to one aspect of the present invention, a method for constructing a planning model for an integrated energy microgrid is provided, which is suitable for execution in a computing device. The integrated energy microgrid includes energy suppliers and energy consumers, the energy consumers include crop farming, animal husbandry, and residents, and the crop farming and animal husbandry generate biomass energy. The method comprises: obtaining basic parameters; establishing a planning model for the integrated energy microgrid that takes into account energy suppliers and agricultural production through multi-objective optimal planning theory, the model comprising a first model and a second model, the first model comprising a first objective function and a first constraint, and the second model comprising a second objective function and a second constraint; substituting the basic parameters into the first model, solving the first model with the goal of minimizing the autonomous cost of the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when the autonomous cost of each device is minimized; substituting the basic parameters into the second model, solving the second model with the goal of maximizing carbon dioxide emission reduction of the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when the carbon emission reduction is maximized;

[0008] Among them, the first objective function includes: minF1=C inv +C opt +C DR -C BT , where F1 represents the autonomous cost of the integrated energy microgrid, C inv Indicates the initial investment cost of the system, C opt represents the system operation and maintenance cost, C DR represents the energy supplier's demand response cost to energy consumers, C BT represents the biomass energy processing cost; wherein the second objective function includes: Where F2 represents the carbon emission reduction of the integrated energy microgrid, Δt represents the time interval, T represents the cycle, and E C02 Indicates the amount of carbon emissions reduced after processing crop straw and livestock manure. represents the biogas carbon emission coefficient, Indicates the biogas produced by the biogas unit at each moment.

[0009] Optionally, the first objective function includes:

[0010]

[0011] Where M i Indicates the capacity of the device. represents the annual fixed maintenance cost per unit capacity of equipment i, μ represents the annual discount rate of the equipment, y represents the project life in years, represents the annual fixed maintenance cost per unit capacity of equipment i, Y represents the subsidy amount for the jth type of crop straw or livestock and poultry, jRepresents the total amount of the jth type of crop straw or livestock and poultry.

[0012] Optionally, the agricultural system includes planting and animal husbandry, and the first constraint condition includes one or more of energy power balance constraint, energy production equipment output constraint, energy storage constraint, energy storage equipment charging and discharging energy power constraint, energy consumer load compensation cost constraint, biogas output rate constraint, material entering the biogas unit constraint, biogas unit thermal balance constraint, planting energy consumption constraint, animal husbandry energy consumption constraint and load constraints of each energy source.

[0013] Optionally, the energy power balance constraint includes an electric power balance constraint, a thermal power balance constraint, and a gas power balance constraint;

[0014] Electric power balance constraints include:

[0015] Thermal power balance constraints include:

[0016] Gas power balance constraints include:

[0017] Where, represents the output power of distributed wind turbines, Indicates the output power of distributed photovoltaic generator sets, Indicates the output power of the cogeneration unit. Indicates the discharge power of the energy storage, It is represented by the electricity load of various users at time t, Indicates the electric power required by the electric boiler. Indicates the charging power of the energy storage device, Indicates the thermal power output of the cogeneration unit, Indicates the output power of the electric boiler. Indicates the heat release power of the thermal energy storage device, Indicates the thermal power supplied to the biogas unit, It is represented by the heat load of various users in the integrated energy microgrid at time t, Indicates the thermal energy storage charging power, Indicates the biogas power generated by the biogas unit. Indicates the deflation power of gas energy storage equipment, Indicates the thermal power required by the cogeneration unit, It is represented by the gas load of various users in the integrated energy microgrid at time t, Indicates the charging power of the gas energy storage device.

[0018] Optionally, the energy production equipment output constraint includes a cogeneration system output constraint and an electric boiler output constraint; the cogeneration system output constraint includes:

[0019]

[0020] Electric boiler output constraints include:

[0021] Where u t A 0-1 variable indicating whether the cogeneration unit is on or off at the moment. Respectively represent the upper and lower limits of the electrical output power of the cogeneration unit, represents the electrical power of the cogeneration unit, Respectively represent the upper and lower limits of the thermal output power of the cogeneration unit, represents the thermal power of the cogeneration unit, Indicates the minimum input of the electric boiler, Indicates the heat release power of the electric boiler. Indicates the maximum input of the electric boiler.

[0022] Optionally, the energy storage constraints include:

[0023]

[0024] Where M ES,min 、M ES,max They represent the maximum and minimum configuration capacity of the electric energy storage, Represents the capacity of the electric energy storage device, M HS,min 、M HS,max Indicates the maximum and minimum configuration capacity of gas energy storage, Represents the capacity of thermal energy storage equipment, M QS,min 、M QS,max They represent the maximum and minimum configuration capacities of gas energy storage, Indicates the capacity of gas energy storage equipment.

[0025] Optionally, the energy storage device charging and discharging energy power constraints include:

[0026]

[0027] Where, Variables representing the charge / heat / gas status of electric energy storage, thermal energy storage, and gas energy storage, respectively, are the discharge / heat / gas state variables of electric energy storage, thermal energy storage and gas energy storage, η ES ,η HS ,η QS Represent the ratio of power to capacity of electrical energy storage, thermal energy storage and gas energy storage respectively, M ES,max Indicates the maximum configuration capacity of electric energy storage, M HS,max Indicates the maximum configuration capacity of gas energy storage, M QS,maxIndicates the maximum configuration capacity of gas energy storage, P t ES,ch Indicates the charging power of the energy storage device, Indicates the charging power of thermal energy storage equipment, Indicates the charging power of the gas energy storage device, Indicates the discharge power of the electric energy storage device, Indicates the heat release power of the thermal energy storage device, Indicates the deflation power of gas energy storage equipment.

[0028] Optionally, the energy consumer load compensation cost constraints include:

[0029]

[0030] Where C DR represents the cost of industrial load demand response compensation, δ cut , δ mov , δ re denote the compensation cost coefficients for DR reduction, transfer and replacement load, They represent DR reduction, transfer and replacement loads respectively, and δ1, δ2 and δ3 represent the proportion of industrial loads that can be reduced, transferred and replaced respectively.

[0031] Optionally, the biogas production rate constraint includes:

[0032]

[0033] Constraints on materials entering a biogas production facility include:

[0034] Where, Indicates the biogas production rate per hour, V CH4 represents the daily biogas production, γ V Indicates the volumetric methane production rate, V r Indicates the volume of the biogas unit, E o represents the biochemical methane potential of methane, S o Indicates the total volatile solid concentration of the material, H RT represents the hydraulic retention time, μ m represents the intermediate parameter, K represents the power parameter of the biogas unit, Indicates the internal temperature of the biogas unit, m j Indicates the material entering the biogas unit, α j Indicates the material biogas conversion rate.

[0035] Optionally, the heat balance constraints of the biogas unit include:

[0036]

[0037] Where, ρ b 、C b Represent the density and specific heat capacity of the material, T r represents the integral operation, Indicates the heat input to the biogas unit, Indicates the amount of heat dissipated by the biogas unit to the external environment. Indicates the internal temperature of the biogas unit. represents the ambient temperature, U represents the total heat transfer coefficient, and A represents the total heat dissipation area of the biogas unit.

[0038] Optionally, the energy consumption constraints for the planting industry include greenhouse energy consumption constraints, irrigation energy consumption constraints, and crop straw collection quantity constraints; greenhouse energy consumption constraints include:

[0039]

[0040] Irrigation energy constraints include:

[0041]

[0042] The crop straw collection constraints include:

[0043]

[0044] Where, ρ c Indicates the air density, V c Indicates the volume of the greenhouse, C c represents the specific heat capacity of indoor air, Indicates the heat load power required to supply the greenhouse, m c Indicates indoor air quality, represents the indoor temperature of the building at time t, Indicates the ambient temperature, represents heat loss, ξ c represents the building heat loss coefficient, The electrical load power of the water pump representing the irrigation energy, represents the irrigation water flow rate, ρ g represents the density of irrigation water, g represents the acceleration of gravity, Z represents the geometric head, represents the irrigation water flow rate, h s 、 They represent the standard humidity of the air and the humidity of the air during period t, respectively. Represent the fitting coefficients, Y 秸秆 represents the total amount of crop straw that can be collected in the area, Y j represents the yield of the jth crop, λ j represents the grass-to-grain ratio of the jth crop, η j represents the collectability coefficient of the j-th crop.

[0045] Optionally, the animal husbandry energy consumption constraint includes an animal husbandry energy consumption constraint and a livestock and poultry manure collection amount constraint; the animal husbandry energy consumption constraint includes: Constraints on the amount of livestock and poultry manure collected include: Where, Indicates the electric load power of the farm, η a Indicates the electricity consumption of livestock and poultry per unit area, S j Indicates the breeding area, represents the number of livestock and poultry j at time t, Y 畜禽 Represents the total production of livestock and poultry manure, N j represents the output of livestock and poultry of category j, O j represents the j-th livestock and poultry feeding cycle, ω j It represents the excretion coefficient of feces and urine of the jth type of livestock and poultry.

[0046] Optionally, the load constraints of each energy source include:

[0047]

[0048] Where, L e 、L h 、L g They represent the electric load, heat load and gas load in the integrated energy microgrid respectively, β represents the distribution coefficient of electric energy to electric boilers, φ1 and φ2 represent the electric and heat distribution coefficients of biogas to cogeneration units respectively, Respectively represent the conversion efficiency of electric energy and thermal energy of the cogeneration unit, η EB Indicates the heat production efficiency of the electric boiler, P W Represents the output of distributed wind turbines, P VT Represents the output of distributed photovoltaic generators, P ES , Q HS , G QS Represent the output power of electric energy storage equipment, thermal energy storage equipment and gas energy storage equipment respectively, G BIO Indicates the output of the biogas unit.

[0049] Optionally, biomass energy includes crop straw and livestock manure, and the second constraint includes a biomass energy carbon emission constraint, which includes:

[0050] E C02 =E 秸秆 +E 畜禽

[0051]

[0052] E 畜禽 =EF 畜禽 ×AP j ×10 -7

[0053]

[0054] Where, E C02 Indicates that E 秸秆 represents the total methane emission from rice fields, E 畜禽 Indicates methane emissions from livestock and poultry manure, EF 秸秆 represents the straw methane emission factor, AD j represents the sown area of the jth crop, EF represents the coefficient of converting methane and nitrous oxide into carbon dioxide equivalent. 畜禽 represents the methane emission factor for livestock and poultry manure management, AP j represents the number of the jth type of livestock and poultry, VS j represents the daily volatile solid excretion of animal species j, B oi represents the maximum methane production capacity of manure of livestock species j.

[0055] Optionally, the model is solved by a multi-objective genetic algorithm.

[0056] Optionally, the basic parameters include the rated output power of distributed wind turbines, wind speed cut-in, cut-out and rated speed of distributed wind turbines, real-time wind speed on a typical day in a certain area, rated output power of distributed photovoltaic generators, rated light radiation, rated temperature and temperature power coefficient of distributed photovoltaic generators, real-time light radiation and ambient temperature on a typical day in a certain area, biochemical methane potential of methane, total volatile solids concentration of materials, hydraulic retention time of materials, power parameters of biogas units, volume of biogas units, mass of materials entering the biogas units, biogas conversion rate of materials, density and specific heat capacity of materials entering the biogas units, and real-time light radiation and ambient temperature on a typical day in a certain area. The real-time temperature inside the biogas unit, the total heat transfer coefficient and total heat dissipation area of the biogas unit, the real-time temperature inside the greenhouse on a typical day in a certain area, the volume of the greenhouse, the internal gas density, the internal gas specific heat capacity, the internal gas mass and the heat loss coefficient of the greenhouse, the density of irrigation water, the gravity acceleration, the geometric head, the standard humidity and fitting coefficient of the air, the real-time air humidity on a typical day in a certain area, the total amount of crop straw that can be collected in a certain area, the total amount of livestock and poultry manure produced in a certain area, the heat production efficiency of the electric boiler, the conversion efficiency of the electric heat energy of the cogeneration unit, the electricity distribution coefficient of the electric boiler, the electricity heat distribution coefficient of the cogeneration unit, the straw methane emission factor, the livestock Methane emission factor of poultry manure, crop planting area, number of livestock and poultry, daily volatile solid excretion of livestock and poultry, maximum methane production capacity of livestock and poultry manure, proportion of load reduction, transfer and substitution participating in demand response in a certain region to total electricity load, compensation cost coefficient of load reduction, transfer and substitution of users participating in demand response, unit capacity investment cost of distributed wind turbines, distributed photovoltaic generators, biogas units, cogeneration units, electric boilers, and various energy storage equipment, annual fixed maintenance cost per unit capacity, annual discount rate, equipment life and subsidy amount for collecting straw or livestock and poultry, carbon emission coefficient of biogas combustion, typical daily load in a certain region, One or more of the following: heat load, gas load, a 0-1 variable indicating whether the cogeneration unit is on or off, the upper and lower limits of the electrical output power and the upper and lower limits of the thermal output power of the cogeneration unit, the upper and lower limits of the output power of the electric boiler, the upper and lower limits of the capacity configuration of the electric energy storage device, the upper and lower limits of the capacity configuration of the thermal energy storage device, the upper and lower limits of the capacity configuration of the gas energy storage device, a 0-1 variable indicating the charging / heat / gas status of the electric energy storage device, the thermal energy storage device, and the gas energy storage device, a 0-1 variable indicating the discharging / heat / gas status of the electric energy storage device, the thermal energy storage device, and the gas energy storage device, and the power to capacity ratio of the electric energy storage device, the thermal energy storage device, and the gas energy storage device.

[0057] According to one aspect of the present invention, there is provided a planning model construction device for an integrated energy microgrid, which is suitable for execution in a computing device. The integrated energy microgrid includes energy suppliers and energy consumers, and the energy consumers include crop farming, animal husbandry, and residents. The crop farming and animal husbandry produce biomass energy. The device includes: a parameter acquisition module, which is suitable for acquiring basic parameters; a model construction module, which is suitable for establishing a planning model for the integrated energy microgrid that takes into account energy suppliers and agricultural production through multi-objective optimal planning theory, the model including a first model and a second model, the first model including a first objective function and a first constraint, and the second model including a second objective function and a second constraint; a model solving module, which is suitable for substituting the basic parameters into the first model, solving the first model with the goal of minimizing the autonomous cost of the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when the autonomous cost of each device is minimized; and a model solving module, which is suitable for substituting the basic parameters into the second model, solving the second model with the goal of maximizing carbon dioxide emission reduction of the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when the carbon emission reduction is maximized.

[0058] Among them, the first objective function includes: minF1=C inv +C opt +C DR -C BT , where F1 represents the autonomous cost of the integrated energy microgrid, C inv Indicates the initial investment cost of the system, C opt represents the system operation and maintenance cost, C DR represents the energy supplier's demand response cost to energy consumers, C BT represents the biomass energy processing cost; wherein the second objective function includes: Where F2 represents the carbon emission reduction of the integrated energy microgrid, Δt represents the time interval, and E C02 Represents the cycle, represents the biogas carbon emission coefficient, Indicates the biogas produced by the biogas unit at each moment.

[0059] According to one aspect of the present invention, a computing device is provided, 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 described above.

[0060] 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 method described above.

[0061] According to the technical solution of the present invention, a planning model construction method for an integrated energy microgrid is proposed. By using the large amount of crop straw and livestock manure produced by the planting and animal husbandry industries as the production power (i.e., thermal power) of the biogas units in the energy supplier, it can not only provide thermal power for the energy producer, but also effectively reduce the carbon dioxide produced by crop straw and livestock manure in the natural state. In addition, since the biogas units in the energy supplier are provided with production power, the amount of coal purchased by the energy supplier is greatly reduced, and the cost of processing crop straw and livestock manure can also be reduced, thereby reducing the autonomy cost of the integrated energy microgrid while reducing carbon dioxide emissions. A more reasonable production and consumption complementarity between agricultural production and energy suppliers is achieved to reduce the autonomy cost and carbon dioxide emissions of the system including energy suppliers and agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of an integrated energy microgrid according to an embodiment of the present invention is shown;

[0063] Figure 2 shows a structural block diagram of a computing device 200 according to one embodiment of the present invention;

[0064] Figure 3 A flowchart of a method 300 for constructing a planning model for an integrated energy microgrid according to an embodiment of the present invention is shown;

[0065] Figure 4 A structural diagram of a planning model building device 400 for an integrated energy microgrid according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0066] The integrated energy microgrid in this invention, also known as an integrated energy microgrid, includes energy suppliers and energy consumers. Energy suppliers can provide energy to energy consumers. This invention does not limit the specific form of energy suppliers; for example, energy suppliers can be understood as renewable energy systems. Energy consumers can be understood as users who purchase energy from energy suppliers. This invention also does not limit the type of energy consumers; for example, energy consumers include residents, agricultural enterprises, and animal husbandry.

[0067] In some embodiments, the integrated energy microgrid is Figure 1As shown, the energy production and consumption system includes energy producers and energy consumers. Energy producers can be understood as integrated energy microgrids in remote areas. Furthermore, integrated energy microgrids are renewable energy systems in remote areas. Renewable energy systems in remote areas include energy production equipment (distributed photovoltaic generators, distributed wind turbines, and biogas generators), energy storage equipment (thermal energy storage equipment, biogas energy storage equipment, and electrical energy storage equipment), electric boilers, and combined heat and power units. Energy consumers include the agricultural sector, animal husbandry, and residents.

[0068] In the present invention, the energy supplier provides energy for the planting industry, animal husbandry and residents, including electricity, heat and gas energy, while the planting industry and animal husbandry generate biomass energy based on the energy provided by the energy supplier, and use biomass energy as the power for the energy production of the biogas unit. By providing power for the energy production of the biogas unit with biomass energy, it is possible to fully utilize biomass energy and achieve full utilization of waste. It can also reduce the carbon dioxide produced by biomass energy in a natural state, and reduce the amount of coal required for the biogas unit in energy production. At the same time, the autonomy cost of the integrated energy microgrid is minimized. That is, by rationally planning energy suppliers and biomass energy, the carbon emission reduction of the integrated energy microgrid is maximized (i.e., the carbon dioxide emissions of the integrated energy microgrid are minimized) and the autonomy cost of the integrated energy microgrid is minimized. Biomass energy includes crop straw and livestock and poultry manure, and biomass energy produces carbon dioxide in a natural state.

[0069] The method for constructing a planning model for an integrated energy microgrid provided by the present invention is suitable for execution in a computing device. Figure 2 FIG2 shows a block diagram of a computing device 200 according to an embodiment of the present invention. Figure 2 The computing device 200 shown is only an example. In practice, the computing device used to implement the screenshot processing method of the present invention can be any type of device, and its hardware configuration can be different from the above. Figure 2 The computing device 200 shown is the same as Figure 2 The computing device 200 shown is different. In practice, the computing device used to implement the screenshot processing method of the present invention can be Figure 2 The hardware components of the computing device 200 shown are added or deleted, and the present invention does not limit the specific hardware configuration of the computing device.

[0070] like Figure 2 As shown, in a basic configuration 202, computing device 200 typically includes system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between processor 204 and system memory 206.

[0071] Depending on the desired configuration, the processor 204 can be any type of processor, including, but not limited to, a microprocessor (μP), a microcontroller (μC), a digital signal processing unit (DSP), or any combination thereof. The processor 204 can include one or more levels of cache, such as a level 1 cache 210 and a level 2 cache 212, a processor core 214, and registers 216. An example processor core 214 can include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller 218 can be used with the processor 204, or in some implementations, the memory controller 218 can be an internal part of the processor 204.

[0072] Depending on the desired configuration, system memory 206 can be any type of memory, including, but not limited to, volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 206 can include an operating system 220, one or more applications 222, and program data 224. In some embodiments, application 222 can be arranged to operate on the operating system using program data 224. Application 222 is used to execute the instructions of method 300.

[0073] Computing device 200 also includes a storage device 232, which includes a removable storage device 236 and a non-removable storage device 238. Both removable storage device 236 and non-removable storage device 238 are connected to storage interface bus 234. In the present invention, data related to various events occurring during program execution and information indicating the time when each event occurred can be stored in storage device 232. Operating system 220 is suitable for managing storage device 232. Storage device 232 can be a magnetic disk.

[0074] The computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to the basic configuration 202 via the bus / interface controller 230. Example output devices 242 include an image processing unit 248 and an audio processing unit 250. These can be configured to facilitate communication with various external devices such as a display or speakers via one or more A / V ports 252. Example peripheral interfaces 244 may include a serial interface controller 254 and a parallel interface controller 256, which can be configured to facilitate communication with external devices such as input devices (e.g., a keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., a printer, scanner, etc.) via one or more I / O ports 258. Example communication devices 246 may include a network controller 260, which can be arranged to facilitate communication with one or more other computing devices 262 via a network communication link via one or more communication ports 264.

[0075] A network communication link can be an example of a communication medium. Communication media can generally be embodied as computer-readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A "modulated data signal" can be a signal in which one or more of its data sets or changes thereto can be performed in a manner that encodes information in the signal. As non-limiting examples, communication media can include wired media such as a wired network or a dedicated line network, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR) or other wireless media. The term computer-readable medium as used herein can include both storage media and communication media.

[0076] The computing device 200 can be implemented as a server, such as a file server, a database server, an application server, and a web server, or as part of a small portable (or mobile) electronic device, such as a cellular phone, a personal digital assistant (PDA), a personal media player, a wireless network browsing device, a personal head-mounted device, an application-specific device, or a hybrid device that can include any of the above functions. The computing device 200 can also be implemented as a personal computer including desktop and notebook computer configurations.

[0077] The present invention is Figure 1 Taking the integrated energy microgrid shown in as an example, the planning model construction method of the integrated energy microgrid is studied.

[0078] Figure 3A flowchart of a method 300 for constructing a planning model of an integrated energy microgrid according to an embodiment of the present invention is shown. Figure 2 The computing device 200 is shown. Figure 3 As shown, method 300 includes steps 310 to 330 .

[0079] In step 310, basic parameters are obtained. Basic data is the input data of the model, and the basic parameters include: the rated output power of the distributed wind turbine Distributed wind turbine wind speed cut-in, cut-out and rated speed v ci 、v co 、v r , real-time wind speed v on a typical day in a certain area t , Rated output power of distributed photovoltaic generator set Rated irradiance, rated temperature and temperature power coefficient G of distributed photovoltaic generator sets r 、T r , τ, the typical daytime real-time illumination radiation and ambient temperature G in a certain area t 、 Biochemical methane potential E of methane o , the total volatile solid concentration S of the material o , hydraulic retention time H of the material RT , power parameter K of biogas unit, volume V of biogas unit r , the mass of material entering the biogas unit m j , material biogas conversion rate α j , the density and specific heat capacity ρ of the material entering the biogas unit b 、C b , the real-time temperature inside the biogas unit on a typical day in a certain area Total heat transfer coefficient and total heat dissipation area U, A of the biogas unit, and real-time indoor temperature of a greenhouse on a typical day in a certain area Greenhouse volume, internal gas density, internal gas specific heat capacity, internal gas mass and greenhouse heat loss coefficient V c , ρ c 、C c 、m c ,ξ c , density of irrigation water, gravitational acceleration, geometric head, standard humidity of air and fitting coefficient ρ g ,g,Z,h s 、 Real-time air humidity on a typical day in a certain area The total amount of crop straw that can be collected in a certain area, the total amount of livestock and poultry manure produced in a certain area Y 畜禽 , the heating efficiency of the electric boiler η EB , the conversion efficiency of electric and thermal energy of the cogeneration unit Electricity distribution coefficient φ1 of electric boiler, electricity and heat distribution coefficient φ2 of cogeneration unit, straw methane emission factor EF 秸秆 , methane emission factor EF of livestock and poultry manure 畜禽 , crop planting area AD j , the number of livestock and poultry AP j , daily volatile solid excretion of livestock and poultry VS j , the maximum methane production capacity of livestock and poultry manure The proportion of load reduction, transfer and substitution in a region that participates in demand response to the total electricity load is δ1, δ2, δ3, and the compensation cost coefficient δ for load reduction, transfer and substitution by users participating in demand response cut , δ mov , δ re , unit capacity investment costs of distributed wind turbines, distributed photovoltaic generators, biogas units, cogeneration units, electric boilers, and various energy storage devices Annual fixed maintenance cost per unit capacity Annual discount rate μ, equipment life y, and subsidy amount for collecting straw or livestock and poultry Carbon emission coefficient of biogas combustion Typical solar load, heat load, and gas load in a certain area A 0-1 variable u indicating whether the CHP unit is on or off t , the upper limit of the electrical output power of the cogeneration unit Lower limit and thermal output power upper limit Lower limit Electric boiler output power upper and lower limits Q EB,min , Q EB,max , the upper and lower limits of the capacity configuration of the electric energy storage equipment M ES,min 、M ES,max , the upper and lower limits of thermal energy storage equipment capacity configuration M HS,min 、M HS ,max , gas energy storage equipment capacity configuration upper and lower limits M QS,min 、M QS,max , 0-1 variables for the charge / heat / gas status of electrical energy storage devices, thermal energy storage devices, and gas energy storage devices 0-1 variables for the discharge / heat / gas state of electrical, thermal, and gas energy storage devices Power-to-capacity ratio η of electrical energy storage equipment, thermal energy storage equipment, and gas energy storage equipment ES ,η HS ,η QS One or more of .

[0080] The output data of the model include: initial investment cost of the system, operation and maintenance cost, demand response cost and waste disposal subsidy.

[0081] Then, in step 320, a planning model of an integrated energy microgrid taking energy suppliers and agricultural production into consideration is established through multi-objective optimal planning theory, where the model includes an objective function and constraints.

[0082] In some embodiments, a planning model for an integrated energy microgrid that takes into account energy suppliers and agricultural production includes a first model and a second model. The first model corresponds to the autonomy cost of the integrated energy microgrid, and aims to minimize the autonomy cost of the integrated energy microgrid to achieve economic optimization of the integrated energy microgrid. The first model corresponds to a first objective function and a first constraint. The second model corresponds to the carbon dioxide emission reduction of the integrated energy microgrid, and aims to maximize the carbon dioxide emission reduction of the integrated energy microgrid. In other words, the second model aims to minimize the carbon dioxide emissions of the integrated energy microgrid, and the second model corresponds to a second objective function and a second constraint.

[0083] The first objective function includes:

[0084] minF1=C inv +C opt +C DR -C BT

[0085] Where, F1 represents the autonomous cost of the integrated energy microgrid, C inv Indicates the initial investment cost of the system, C opt represents the system operation and maintenance cost, C DR represents the energy supplier's demand response cost to energy consumers, C BT Represents the cost of biomass processing.

[0086] The first objective function specifically includes:

[0087] minF1=C inv +C opt +C DR -C BT

[0088]

[0089] Where M i Indicates the capacity of the device. represents the annual fixed maintenance cost per unit capacity of equipment i, μ represents the annual discount rate of the equipment, which can be understood as the annual cost converted from the total assets of the equipment, and y represents the project life in years. represents the annual fixed maintenance cost per unit capacity of equipment i, Y represents the subsidy amount for the jth type of crop straw or livestock and poultry, j Represents the total amount of the jth type of crop straw or livestock and poultry.

[0090] The first constraint conditions corresponding to the first objective function include: energy power balance constraint, energy production equipment output constraint, energy storage constraint, energy storage equipment charging and discharging power constraint, energy consumer load compensation cost constraint, biogas output rate constraint, material entering the biogas unit constraint, biogas unit thermal balance constraint, planting energy consumption constraint, animal husbandry energy consumption constraint and one or more of the load constraints of each energy source.

[0091] 1) Energy power balance constraints include electric power balance constraints, thermal power balance constraints, and gas power balance constraints. The electric power balance constraint can be understood as the sum of the output of distributed wind turbines, distributed photovoltaic units, cogeneration units, and electric energy storage output, equal to the power demand of various users in the integrated energy microgrid, the power consumption of electric boilers, and the power storage of electric energy storage. Specifically, the electric power balance constraints include:

[0092]

[0093] In an integrated energy microgrid, heat is primarily provided by cogeneration units and electric boilers. This must meet the heat requirements of biogas units for temperature increase and the heat load requirements of various users. Thermal power balance constraints include:

[0094]

[0095] The gas power balance constraint, also known as biogas power balance, can be understood as the sum of the biogas unit's gas production and gas storage release volume should be equal to the sum of the combined heat and power unit's gas consumption, user gas consumption, and gas storage tank's gas capacity. The gas power balance constraint includes:

[0096]

[0097] Where, represents the output power of distributed wind turbines, Indicates the output power of distributed photovoltaic generators, Indicates the output power of the cogeneration unit. Indicates the discharge power of the energy storage, It is represented by the electricity load of various users at time t, Indicates the electric power required by the electric boiler. Indicates the charging power of the energy storage device, Indicates the thermal power output of the cogeneration unit, Indicates the output power of the electric boiler. Indicates the heat release power of the thermal energy storage device, Indicates the thermal power supplied to the biogas unit, It is represented by the heat load of various users in the integrated energy microgrid at time t, Indicates the thermal energy storage charging power, Indicates the biogas power generated by the biogas unit. Indicates the deflation power of gas energy storage equipment, Indicates the thermal power required by the cogeneration unit, It is represented by the gas load of various users in the integrated energy microgrid at time t, Indicates the charging power of the gas energy storage device.

[0098] 2) Energy production equipment output constraints include combined heat and power system output constraints and electric boiler output constraints. Combined heat and power system output constraints include:

[0099]

[0100] Electric boiler output constraints, that is, the output power of electric boilers must meet their respective capacity constraints. Electric boiler output constraints include:

[0101] Q EB,min ≤Q t EB ≤Q EB,max

[0102] Where u t A variable indicating whether the cogeneration unit is on or off at a given moment. The value of this variable ranges from 0 to 1. They respectively represent the upper and lower limits of the electrical output power of the cogeneration unit, in KW. represents the electrical power of the cogeneration unit, Respectively represent the upper and lower limits of the thermal output power of the cogeneration unit, in KW, Indicates the thermal power of the cogeneration unit, Q EB,min Indicates the minimum input of the electric boiler, Indicates the heat release power of the electric boiler, Q EB,max Indicates the maximum input of the electric boiler.

[0103] 3) To prevent overcharging and overdischarging of energy storage devices, the variable range of their stored energy needs to be constrained. Energy storage constraints include:

[0104]

[0105]

[0106] Where M ES,min 、M ES,max They represent the maximum and minimum configuration capacity of the electric energy storage, Represents the capacity of the electric energy storage device, M HS,min 、M HS,maxIndicates the maximum and minimum configuration capacity of gas energy storage, Represents the capacity of thermal energy storage equipment, M QS,min 、M QS,max They represent the maximum and minimum configuration capacity of gas energy storage respectively, in KW. Indicates the capacity of gas energy storage equipment.

[0107] 4) Charging, discharging, and heating cannot be performed simultaneously. Constraints on the charging and discharging power of the energy storage device are also required. The constraints on the charging and discharging power of the energy storage device include:

[0108]

[0109] Where, express, Variables representing the charging / heating / gas status of electric energy storage, thermal energy storage, and gas energy storage, respectively. The value range of this variable is 0 to 1. Represent the discharge / heat / gas state variables of electric energy storage, thermal energy storage and gas energy storage respectively, and the value range of this variable is 0 to 1. η ES ,η HS ,η QS Represent the ratio of power to capacity of electric energy storage, thermal energy storage and gas energy storage respectively, M ES,max Indicates the maximum configuration capacity of electric energy storage, M HS,max Indicates the maximum configuration capacity of gas energy storage, M QS,max Indicates the maximum configuration capacity of gas energy storage, P t ES,ch Indicates the charging power of the energy storage device, Indicates the charging power of thermal energy storage equipment, Indicates the charging power of the gas energy storage device, Indicates the discharge power of the electric energy storage device, Indicates the heat release power of the thermal energy storage device, Indicates the deflation power of gas energy storage equipment.

[0110] 5) Due to the low power quality in remote areas and the high energy requirements of off-grid integrated energy microgrids (i.e., power grids), in order to improve the stability of the integrated energy microgrid, reduce the peak-to-valley difference in load, and reduce the planning and construction costs of the integrated energy microgrid, consideration is given to implementing demand-side response (DR) for the agricultural, animal husbandry, and residential loads within the permitted range. When multiple industrial loads participate in DR, appropriate economic compensation will be given to the industrial loads. The energy consumer load compensation cost constraints include:

[0111]

[0112] Where C DRrepresents the cost of industrial load demand response compensation, δ cut , δ mov , δ re denote the compensation cost coefficients for DR reduction, transfer and replacement load, They represent DR reduction, transfer and replacement loads respectively, and δ1, δ2 and δ3 represent the proportion of industrial loads that can be reduced, transferred and replaced respectively.

[0113] 6) Collect straw, the main residue produced by the planting industry, and livestock and poultry manure, the main residue produced by animal husbandry, as the energy source for the biogas unit to produce biogas. The biogas produced by anaerobic fermentation of the biogas unit can not only meet the energy needs of agricultural production, but also meet the energy needs of residents. At the same time, the remaining biogas liquid and biogas residue in the biogas unit can be used as fertilizer to obtain income. In the present invention, the biogas unit adopts a medium-temperature fermentation method, and the internal temperature of the fermentation tank is about 35°C. The biogas production process of the biogas unit is relatively stable. If the fermentation conditions remain unchanged, the hourly biogas output rate constraints include:

[0114]

[0115] V CH4 =γ V ×V r

[0116]

[0117] Where, Indicates the biogas production rate per hour, V CH4 represents the daily biogas production, γ V Indicates the volumetric methane production rate, V r Indicates the volume of the biogas unit, E o represents the biochemical methane potential of methane, S o Indicates the total volatile solid concentration of the material, H RT represents the hydraulic retention time, μ m represents the intermediate parameter, K represents the power parameter of the biogas unit, Indicates the internal temperature of the biogas unit, m i Indicates the material entering the biogas production equipment.

[0118] 7) The materials entering the biogas unit can be of various types, and the quality of each material is different. The material constraints entering the biogas unit include: Where, α j Indicates the material biogas conversion rate.

[0119] 8) The heat load of a biogas unit can be divided into two parts. The first part of the heat is used to preheat the materials entering the biogas unit, and the second part of the heat is used to offset the heat dissipation to the environment through the walls, ceiling, and bottom of the biogas unit. Therefore, the heat balance constraints of the biogas unit include:

[0120]

[0121] Where, ρ b 、C b Respectively represent the density and specific heat capacity of the material. Since the solid content in crop straw and livestock manure is small (generally less than 2%), the density and specific heat capacity of the material can be approximately determined according to the specific heat capacity and density of water. r Indicates integral operation, which has no practical meaning. Indicates the heat input to the biogas unit, Indicates the amount of heat dissipated by the biogas unit to the external environment. Indicates the internal temperature of the biogas unit. Indicates the ambient temperature. The temperature of the inlet material is approximately equal to the ambient temperature. U indicates the total heat transfer coefficient. When additional insulation materials are used, the value is generally 10W / (m 2 K). A represents the total heat dissipation area of the biogas unit. The total heat dissipation area of the biogas unit can be set according to the actual application scenario, and the present invention is not limited to this. For example, the total heat dissipation area of the biogas unit is generally set to 60%.

[0122] 9) Energy consumption constraints in the planting industry among energy consumers include: greenhouse energy consumption constraints, irrigation energy consumption constraints and crop straw collection constraints.

[0123] Greenhouse energy constraints include:

[0124]

[0125] Irrigation energy constraints include:

[0126]

[0127] The amount of crop straw that can be collected is estimated using the grass-to-grain ratio and collectibility coefficient method. The grass-to-grain ratio and collectibility coefficient for different crops are based on the values recommended by the Ministry of Agriculture and Rural Affairs. The constraints on the amount of crop straw collected include:

[0128]

[0129] Where, ρ c Indicates the air density, V c Indicates the volume of the greenhouse, C c represents the specific heat capacity of indoor air, Indicates the heat load power required to supply the greenhouse, mc Indicates indoor air quality, represents the indoor temperature of the building at time t, Indicates the ambient temperature, represents heat loss, ξ c represents the building heat loss coefficient, The electrical load power of the water pump representing the irrigation energy, represents the irrigation water flow rate, ρ g represents the density of irrigation water, g represents the acceleration of gravity, Z represents the geometric head, represents the irrigation water flow rate, h s 、 They respectively represent the standard humidity of the air and the humidity of the air in time period t. The standard humidity of the air can be set according to the actual application scenario, and the present invention is not limited to this. For example, the standard humidity of the air is 50%. Represent the fitting coefficients, Y 秸秆 represents the total amount of crop straw that can be collected in the area, Y j represents the yield of the jth crop, λ j represents the grass-to-grain ratio of the jth crop, η j represents the collectability coefficient of the j-th crop.

[0130] 10) Energy constraints for animal husbandry include energy constraints for animal husbandry and constraints on the amount of livestock and poultry manure collected. Energy constraints for animal husbandry include:

[0131]

[0132] The amount of livestock and poultry manure is calculated using the production and discharge coefficient method, which is based on the livestock and poultry breeding volume, breeding cycle, and excretion coefficient. The constraints on the amount of livestock and poultry manure collected include:

[0133]

[0134] Where, Indicates the electric load power of the farm, η a Indicates the electricity consumption of livestock and poultry per unit area, S j Indicates the breeding area, represents the number of livestock and poultry j at time t, Y 畜禽 Represents the total production of livestock and poultry manure, N j represents the output of livestock and poultry of category j, O j represents the j-th livestock and poultry feeding cycle, ω j It represents the excretion coefficient of feces and urine of the jth type of livestock and poultry.

[0135] 11) The off-grid integrated energy microgrid established by the present invention not only includes renewable energy sources such as wind energy, solar energy, and biogas, but also includes energy conversion equipment and storage equipment, etc., so as to improve the flexibility and reliability of the off-grid integrated energy microgrid energy supply. In addition, the energy conversion / transfer coupling matrix of wind, solar, and biomass energy flows to heat, electricity, and multi-energy flows formed by the energy hub scheduling principle is used to describe the optimal allocation and conversion relationship of the complementary and mutually beneficial wind-solar-biomass energy input to heat-electricity-gas multi-energy output. The load constraints of each energy source include:

[0136]

[0137] Where, L e 、L h 、L g They represent the electric load, heat load and gas load in the integrated energy microgrid respectively, β represents the distribution coefficient of electric energy to electric boilers, φ1 and φ2 represent the electric and heat distribution coefficients of biogas to cogeneration units respectively, They represent the conversion efficiency of electric energy and thermal energy of the cogeneration unit, η EB Indicates the heat production efficiency of the electric boiler, P W Indicates that P VT Indicates that P ES , Q HS , G QS Represent the output power of electric energy storage equipment, thermal energy storage equipment and gas energy storage equipment respectively, G BIO Indicates the output of the biogas unit.

[0138] In some embodiments, the second objective function corresponding to the second model is:

[0139]

[0140] Where F2 represents the carbon emission reduction of the integrated energy microgrid, Δt represents the time interval, which can be one hour, and T represents the period, which can be one year. C02 Indicates the amount of carbon emissions reduced after processing crop straw and livestock manure. represents the biogas carbon emission coefficient, Indicates the biogas produced by the biogas unit at each moment.

[0141] The second constraint corresponding to the second objective function includes a biomass carbon emission constraint. Crop straw and livestock manure naturally produce significant amounts of greenhouse gases. Integrated energy microgrids recycle these materials and utilize biogas units for anaerobic fermentation to generate biogas, providing significant energy while also reducing carbon dioxide emissions. This paper uses the natural carbon dioxide emissions from crop straw and livestock manure as primary indicators for evaluating the carbon dioxide emission capacity of integrated energy microgrids.

[0142] The carbon dioxide emissions from crop straw are calculated by multiplying the area of different types of crops by the corresponding crop methane emission factors. The carbon dioxide emissions from livestock and poultry manure are calculated by multiplying the year-end inventory of different types of livestock and poultry by the corresponding emission factors. Therefore, the carbon emission constraints for biomass energy include:

[0143] E C02 =E 秸秆 +E 畜禽

[0144]

[0145] E 畜禽 =EF 畜禽 ×AP j ×10 -7

[0146]

[0147] Where, E C02 Indicates that E 秸秆 represents the total methane emission from rice fields, E 畜禽 Indicates methane emissions from livestock and poultry manure, EF j,秸秆 represents the methane emission factor of the jth type of straw, AD j represents the sown area of the jth crop, EF represents the coefficient of converting methane and nitrous oxide into carbon dioxide equivalent. 畜禽 represents the methane emission factor of the j-th livestock and poultry manure management, AP j represents the number of the jth type of livestock and poultry, VS j represents the daily volatile solid excretion of animal species j, B oi represents the maximum methane production capacity of manure of livestock species j.

[0148] Then, in step 330, the basic parameters are substituted into the model, and the model is solved with the goal of minimizing the autonomy cost of the integrated energy microgrid and maximizing the carbon dioxide emission reduction of the integrated energy microgrid. The first model outputs the capacity configuration plan for each device in the integrated energy microgrid when the autonomy cost of each device is minimized. The second model outputs the capacity configuration plan for each device in the integrated energy microgrid when the carbon emission reduction is maximized.

[0149] It should be understood that there are many methods for solving the model, and the present invention is not limited to a specific implementation. All methods capable of solving the above-mentioned model are within the scope of protection of the present invention. For example, the model can be solved using a multi-objective genetic algorithm. Of course, the present invention is not limited to multi-objective genetic algorithms. For example, the model can be solved using the NSGA-II algorithm within the multi-objective genetic algorithm.

[0150] Taking the NSGA-Ⅱ algorithm as an example, the process of solving the model is as follows:

[0151] In order to improve the stability of the model, the inventor first conducts a robust analysis of the load source uncertainty of the model, and the obtained uncertainty set model of renewable energy and load is as follows:

[0152]

[0153] Where W w 、W d Represents renewable energy output and load column vector respectively, P r represents the base vector, P w Represents the renewable energy output column vector, S represents the vector set, Represent the expected column vectors of renewable energy output and load respectively, λ1 and σ1 both represent uncertainty set parameters, T represents transpose (mathematical symbol, no practical meaning), P d represents the load output column vector, λ2 and σ2 represent the covariance uncertainty set parameters, ε ψ , ε ζ All represent the variance of renewable energy power, ψ represents the actual sample, ζ represents the control sample, ξ ψζ Represents the correlation coefficient between renewable energy electric fields, ε' ψ represents the variance of the ψth load point.

[0154] The model is then solved using the NSGA-II algorithm, which is used to solve multi-objective optimization problems. NSGA-II is a fast, non-dominated, multi-objective optimization algorithm based on Pareto optimality and an elite-preserving strategy. The process of solving the model using the NSGA-II algorithm includes:

[0155] 1) Initialize the population and set the relevant parameters of the NSGA-II algorithm. These parameters include setting the maximum number of iterations of NSGA-II to 1000, the population size to 100, the crossover probability to 0.9, and the mutation probability to 0.1. During the optimization process, the system simulation cycle is 24 hours.

[0156] 2) Afterwards, for each individual in the population, the interval boundaries of the objective function and constraints are calculated based on the interval optimization method.

[0157] 3) Through fast non-dominated sorting and crowding calculation, the population is graded and sorted to determine the individual fitness of each population.

[0158] 4) The first generation offspring population is obtained through the three basic operations of genetic algorithm: selection, crossover and mutation.

[0159] 5) Determine whether the convergence condition is met, and use the maximum evolutionary generation as the convergence condition. If the evolutionary generation is greater than the set condition, the maximum solution set is output. Otherwise, the parent population is selected, mutated, and crossover operations are performed to generate the next generation population.

[0160] 6) Repeat steps (2) and (5), and so on, until the conditions for the program to end are met and the Pareto optimal solution set is obtained.

[0161] After obtaining the Pareto optimal set, in order to select the optimal solution from the Pareto final solution set, the fuzzy membership function is introduced to determine the final solution through the fuzzy membership function, that is, the solution of the fuzzy membership function is the final solution. Calculate the fuzzy membership μ of the i-th objective function corresponding to the m-th solution in the final solution set i (m) and aggregation function μ(m). The fuzzy membership expression is:

[0162]

[0163] γ i =F imax -θ i (F imax -F imin ),0≤θ i ≤1

[0164] Where, F i (m) represents the i-th objective function value corresponding to the m-th solution, where the objective function F1(m) is the autonomous cost of the integrated energy microgrid and F2(m) is the system carbon dioxide emissions. imin 、F imax Represented as the highest and lowest values of the i-th objective function, γ i Indicates that θ i Represents the proportion of the i-th objective function, where the decision maker can determine θ according to the load demand for system operation in different regions and at different times. i The specific value of .

[0165] Figure 4 FIG. 4 shows a structural block diagram of a device 400 for constructing a planning model of an integrated energy microgrid according to an embodiment of the present invention. The device 400 may reside in a computing device 200, such as Figure 4 As shown, the apparatus 400 includes: a parameter acquisition unit 410 , a model construction unit 420 and a model solving unit 430 .

[0166] The parameter acquisition module 410 is adapted to acquire basic parameters.

[0167] The model building module 420 is suitable for establishing a planning model for an integrated energy microgrid taking into account energy suppliers and agricultural production. The model includes a first model and a second model. The first model includes a first objective function and a first constraint condition, and the second model includes a second objective function and a second constraint condition.

[0168] The model solving unit 430 is suitable for substituting the basic parameters into the first model, solving the first model with the goal of minimizing the autonomous cost of the integrated energy microgrid, and outputting the capacity configuration plan of each device in the integrated energy microgrid when the autonomous cost of each device is minimized, and is suitable for substituting the basic parameters into the second model, solving the second model with the goal of maximizing the carbon dioxide emission reduction of the integrated energy microgrid, and outputting the capacity configuration plan of each device in the integrated energy microgrid when the carbon emission reduction is maximized.

[0169] Among them, the first objective function includes: minF1=C inv +C opt +C DR -C BT , where F1 represents the autonomous cost of the integrated energy microgrid, C inv Indicates the initial investment cost of the system, C opt represents the system operation and maintenance cost, C DR represents the energy supplier's demand response cost to energy consumers, C BT represents the biomass energy processing cost. The second objective function includes: Where F2 represents the carbon emission reduction of the integrated energy microgrid, Δt represents the time interval, and E C02 Represents the cycle, represents the biogas carbon emission coefficient, Indicates the biogas produced by the biogas unit at each moment.

[0170] It should be noted that the working principle of the planning model construction device 400 for the integrated energy microgrid is similar to the above-mentioned planning model construction method 300 for the integrated energy microgrid. For relevant details, please refer to the description of the above-mentioned method 300, which will not be repeated here.

[0171] As can be seen from the above, in the present invention, by using a large amount of crop straw and livestock manure produced by the planting industry and animal husbandry as the production power (i.e., thermal power) of the biogas unit in the energy supplier, it can not only provide thermal power for the energy producer, but also effectively reduce the carbon dioxide produced by crop straw and livestock manure in the natural state. And because it provides production power for the biogas unit in the energy supplier, it greatly reduces the amount of coal purchased by the energy supplier, and can also reduce the cost of processing crop straw and livestock manure, thereby reducing the autonomy cost of the integrated energy microgrid while reducing carbon dioxide emissions. A more reasonable production and consumption complementarity between agricultural production and energy suppliers is achieved to reduce the autonomy cost and carbon dioxide emissions of the system including energy suppliers and agricultural production.

[0172] The various techniques described herein may be implemented in conjunction with hardware or software, or a combination thereof. Thus, the methods and apparatus of the present invention, or certain aspects or portions of the methods and apparatus of the present invention, may be implemented in the form of program codes (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes an apparatus for practicing the present invention.

[0173] When the program code is executed on a programmable computer, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store the program code, and the processor is configured to execute the integrated energy microgrid planning model construction method of the present invention according to the instructions in the program code stored in the memory.

[0174] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media store information such as computer-readable instructions, data structures, program modules, or other data. Communication media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and include any information delivery medium. Combinations of any of the above are also included within the scope of readable media.

[0175] In the description provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used in conjunction with the examples of the present invention. Based on the above description, it is apparent that the structure required for constructing such systems is well understood. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages may be utilized to implement the present invention described herein, and the description of specific languages above is provided for the purpose of disclosing the preferred embodiment of the present invention.

[0176] 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.

[0177] Similarly, it should be understood that in order to streamline the disclosure and aid understanding of one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0178] Those skilled in the art will appreciate that the modules, units, or components of the devices in the examples disclosed herein may be arranged in the device described in the embodiment, 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 further divided into multiple submodules.

[0179] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0180] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

[0181] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions described. Thus, a processor having the necessary instructions for implementing the method or method element forms a device for implementing the method or method element. Furthermore, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0182] As used herein, 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 are not intended to imply that the objects so described must have a given order in time, space, ranking, or in any other manner.

[0183] Although the present invention has been described with respect to a limited number of embodiments, it will be apparent to those skilled in the art, having benefit of the foregoing description, that other embodiments are contemplated within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been selected primarily for readability and didactic purposes, rather than for the purpose of explaining or limiting the subject matter of the present invention. Consequently, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A method for constructing a planning model for an integrated energy microgrid, suitable for execution in a computing device, wherein the integrated energy microgrid includes energy suppliers and energy consumers, wherein the energy consumers include crop farming, animal husbandry, and residents, and wherein the crop farming and animal husbandry generate biomass energy. The method comprises: Get basic parameters; A planning model for an integrated energy microgrid taking into account energy suppliers and agricultural production is established through multi-objective optimal planning theory, the model comprising a first model and a second model, the first model comprising a first objective function and a first constraint, the first constraint comprising one or more of an energy power balance constraint, an energy production equipment output constraint, an energy storage constraint, an energy storage equipment charging and discharging energy power constraint, an energy consumer load compensation cost constraint, a biogas output rate constraint, a material input to a biogas unit constraint, a biogas unit heat balance constraint, a crop production energy consumption constraint, an animal husbandry energy consumption constraint, and load constraints of various energy sources, the second model comprising a second objective function and a second constraint; Substituting the basic parameters into the first model, solving the first model with the goal of minimizing the autonomy cost of the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when the autonomy cost of each device is minimized; Substituting the basic parameters into the second model, solving the second model with the goal of maximizing carbon dioxide emission reduction in the integrated energy microgrid, and outputting a capacity configuration plan for each device in the integrated energy microgrid when carbon emission reduction is maximized; Wherein, the first objective function includes: minF1=C inv +C opt +C DR -C BT , where F1 represents the autonomous cost of the integrated energy microgrid, C inv Indicates the initial investment cost of the system, C opt represents the system operation and maintenance cost, C DR represents the energy supplier's demand response cost to energy consumers, C BT represents the biomass processing cost, Where M i Indicates the capacity of the device. represents the annual fixed maintenance cost per unit capacity of equipment i, μ represents the annual discount rate of the equipment, y represents the project life in years, represents the annual fixed maintenance cost per unit capacity of equipment i, Y represents the subsidy amount for the jth type of crop straw or livestock and poultry, j represents the total amount of crop straw or livestock and poultry of the jth type; Wherein, the second objective function includes: Where F2 represents the carbon emission reduction of the integrated energy microgrid, Δt represents the time interval, T represents the cycle, and E C02 Indicates the amount of carbon emissions reduced after processing crop straw and livestock manure. represents the biogas carbon emission coefficient, Indicates the biogas produced by the biogas unit at each moment.

2. The method according to claim 1, wherein The energy power balance constraints include electric power balance constraints, thermal power balance constraints and gas power balance constraints; Electric power balance constraints include: P t WT +P t PV +P t CHP +P t ES,dch =P t load +P t EB +P t ES,ch Thermal power balance constraints include: Gas power balance constraints include: Where, P t WT Represents the output power of distributed wind turbines, P t PV Represents the output power of distributed photovoltaic generators, P t CHP Indicates the output power of the cogeneration unit, P t ES,dch Indicates the energy storage discharge power, P t load It is represented by the electricity load of various users at time t, P t EB Indicates the electric power required by the electric boiler, P t ES,ch Indicates the charging power of the energy storage device, Indicates the thermal power output of the cogeneration unit, Indicates the output power of the electric boiler. Indicates the heat release power of the thermal energy storage device, Indicates the thermal power supplied to the biogas unit, It is represented by the heat load of various users in the integrated energy microgrid at time t, Indicates the thermal energy storage charging power, Indicates the biogas power generated by the biogas unit. Indicates the deflation power of gas energy storage equipment, Indicates the thermal power required by the cogeneration unit, It is represented by the gas load of various users in the integrated energy microgrid at time t, Indicates the charging power of the gas energy storage device.

3. The method according to claim 1, wherein The energy production equipment output constraints include cogeneration system output constraints and electric boiler output constraints; The output constraints of the cogeneration system include: Electric boiler output constraints include: Where u t A 0-1 variable indicating whether the cogeneration unit is on or off at the moment. Respectively represent the upper and lower limits of the electrical output power of the cogeneration unit, P t CHP represents the electrical power of the cogeneration unit, Respectively represent the upper and lower limits of the thermal output power of the cogeneration unit, Indicates the thermal power of the cogeneration unit, Q EB,min Indicates the minimum input of the electric boiler, Indicates the heat release power of the electric boiler, Q EB,max Indicates the maximum input of the electric boiler.

4. The method according to claim 1, wherein The energy storage constraints include: Where M ES,min 、M ES,max They represent the maximum and minimum configuration capacity of the electric energy storage, Represents the capacity of the electric energy storage device, M HS,min 、M HS,max Indicates the maximum and minimum configuration capacity of gas energy storage, Represents the capacity of thermal energy storage equipment, M QS,min 、M QS,max They represent the maximum and minimum configuration capacities of gas energy storage, Indicates the capacity of gas energy storage equipment.

5. The method according to claim 1, wherein The energy storage device charging and discharging energy power constraints include: Where, Variables representing the charge / heat / gas status of electric energy storage, thermal energy storage, and gas energy storage, respectively, are the discharge / heat / gas state variables of electric energy storage, thermal energy storage and gas energy storage, η ES ,η HS ,η QS Represent the ratio of power to capacity of electrical energy storage, thermal energy storage and gas energy storage respectively, M ES,max Indicates the maximum configuration capacity of electric energy storage, M HS,max Indicates the maximum configuration capacity of gas energy storage, M QS,max Indicates the maximum configuration capacity of gas energy storage, P t ES,ch Indicates the charging power of the energy storage device, Indicates the charging power of thermal energy storage equipment, Indicates the charging power of the gas energy storage device, P t ES,dch Indicates the discharge power of the electric energy storage device, Indicates the heat release power of the thermal energy storage device, Indicates the deflation power of gas energy storage equipment.

6. The method of claim 1, wherein: The energy consumer load compensation cost constraints include: Where C DR represents the cost of industrial load demand response compensation, δ cut , δ mov , δ re are the compensation cost coefficients for DR reduction, transfer and replacement load, P t cut,i 、P t mov,i 、P t re,i They represent DR reduction, transfer and replacement loads respectively, and δ1, δ2 and δ3 represent the proportion of industrial loads that can be reduced, transferred and replaced respectively.

7. The method of claim 1, wherein: The biogas production rate constraints include: V CH4 =c V ×V r Constraints on materials entering a biogas production facility include: Where, Indicates the biogas production rate per hour, V CH4 represents the daily biogas production, γ V Indicates the volumetric methane production rate, V r Indicates the volume of the biogas unit, E o represents the biochemical methane potential of methane, S o Indicates the total volatile solid concentration of the material, H RT represents the hydraulic retention time, μ m represents the intermediate parameter, K represents the power parameter of the biogas unit, T t b Indicates the internal temperature of the biogas unit, m j Indicates the material entering the biogas unit, α j Indicates the material biogas conversion rate.

8. The method of claim 1, wherein: The thermal balance constraints of the biogas unit include: Where, ρ b 、C b Represent the density and specific heat capacity of the material, T r represents the integral operation, Indicates the heat input to the biogas unit, Indicates the amount of heat dissipated by the biogas unit to the external environment, T t b Indicates the internal temperature of the biogas unit, T t a represents the ambient temperature, U represents the total heat transfer coefficient, and A represents the total heat dissipation area of the biogas unit.

9. The method of claim 1, wherein: The energy consumption constraints of the planting industry include greenhouse energy consumption constraints, irrigation energy consumption constraints and crop straw collection constraints; Greenhouse energy constraints include: Irrigation energy constraints include: The crop straw collection constraints include: Where, ρ c Indicates the air density, V c Indicates the volume of the greenhouse, C c represents the specific heat capacity of indoor air, Indicates the heat load power required to supply the greenhouse, m c Indicates indoor air quality, T t c.r represents the indoor temperature of the building at time t, T t a Indicates the ambient temperature, represents heat loss, ξ c represents the building heat loss coefficient, The electrical load power of the water pump representing the irrigation energy, represents the irrigation water flow rate, ρ g represents the density of irrigation water, g represents the acceleration of gravity, Z represents the geometric head, represents the irrigation water flow rate, h s 、 They represent the standard humidity of the air and the humidity of the air during period t, respectively. Represent the fitting coefficients, Y 秸秆 represents the total amount of crop straw that can be collected in the area, Y j represents the yield of the jth crop, λ j represents the grass-to-grain ratio of the jth crop, η j represents the collectability coefficient of the j-th crop.

10. The method of claim 1, wherein: The animal husbandry energy consumption constraints include animal husbandry energy consumption constraints and livestock and poultry manure collection constraints; Energy constraints in the livestock industry include: Constraints on the amount of livestock and poultry manure collected include: Where, Indicates the electric load power of the farm, η a Indicates the electricity consumption of livestock and poultry per unit area, S j Indicates the breeding area, N t j represents the number of livestock and poultry j at time t, Y 畜禽 Represents the total production of livestock and poultry manure, N j represents the output of livestock and poultry of category j, O j represents the j-th livestock and poultry feeding cycle, ω j It represents the excretion coefficient of feces and urine of the jth type of livestock and poultry.

11. The method of claim 1, wherein: The load constraints of each energy source include: Where, L e 、L h 、L g They represent the electric load, heat load and gas load in the integrated energy microgrid respectively, β represents the distribution coefficient of electric energy to electric boilers, φ1 and φ2 represent the electric and heat distribution coefficients of biogas to cogeneration units respectively, Respectively represent the conversion efficiency of electric energy and thermal energy of the cogeneration unit, η EB Indicates the heat production efficiency of the electric boiler, P W Represents the output of distributed wind turbines, P VT Represents the output of distributed photovoltaic generators, P ES , Q HS , G QS Represent the output power of electric energy storage equipment, thermal energy storage equipment and gas energy storage equipment respectively, G BIO Indicates the output of the biogas unit.

12. The method of claim 1, wherein: The biomass energy includes crop straw and livestock and poultry manure. The second constraint includes a biomass energy carbon emission constraint, which includes: AND C02 =And 秸秆 +E 畜禽 E 畜禽 =EF 畜禽 ×AP j ×10 -7 Where, E C02 Indicates that E 秸秆 represents the total methane emission from rice fields, E 畜禽 Indicates methane emissions from livestock and poultry manure, EF 秸秆 represents the straw methane emission factor, AD j represents the sown area of the jth crop, EF represents the coefficient of converting methane and nitrous oxide into carbon dioxide equivalent. 畜禽 represents the methane emission factor for livestock and poultry manure management, AP j represents the number of the jth type of livestock and poultry, VS j represents the daily volatile solid excretion of animal species j, B oi represents the maximum methane production capacity of manure of livestock species j.

13. The method of claim 1, wherein: The model is solved using a multi-objective genetic algorithm.

14. The method of claim 1, wherein: The basic parameters include the rated output power of distributed wind turbines, wind speed cut-in, cut-out and rated speed of distributed wind turbines, real-time wind speed on a typical day in a certain area, rated output power of distributed photovoltaic generators, rated light radiation, rated temperature and temperature power coefficient of distributed photovoltaic generators, real-time light radiation and ambient temperature on a typical day in a certain area, biochemical methane potential of methane, total volatile solids concentration of materials, hydraulic retention time of materials, power parameters of biogas units, volume of biogas units, mass of materials entering the biogas units, biogas conversion rate of materials, density and specific heat capacity of materials entering the biogas units, and biogas on a typical day in a certain area. The real-time temperature inside the unit, the total heat transfer coefficient and total heat dissipation area of the biogas unit, the real-time temperature of the greenhouse indoors on a typical day in a certain area, the volume of the greenhouse, the internal gas density, the internal gas specific heat capacity, the internal gas mass and the heat loss coefficient of the greenhouse, the density of irrigation water, the acceleration of gravity, the geometric head, the standard humidity and fitting coefficient of the air, the real-time air humidity on a typical day in a certain area, the total amount of crop straw that can be collected in a certain area, the total amount of livestock and poultry manure produced in a certain area, the heat production efficiency of the electric boiler, the conversion efficiency of the electric heat energy of the cogeneration unit, the electricity distribution coefficient of the electric boiler, the electricity and heat distribution coefficient of the cogeneration unit, the straw methane emission factor, livestock and poultry Methane emission factor from manure, crop planting area, number of livestock and poultry, daily volatile solid excretion of livestock and poultry, maximum methane production capacity of livestock and poultry manure, proportion of load reduction, transfer and substitution participating in demand response in a certain region to total electricity load, compensation cost coefficient of load reduction, transfer and substitution by users participating in demand response, unit capacity investment cost of distributed wind turbines, distributed photovoltaic generators, biogas units, cogeneration units, electric boilers, and various energy storage equipment, annual fixed maintenance cost per unit capacity, annual discount rate, equipment life and subsidy amount for collecting straw or livestock and poultry, carbon emission coefficient of biogas combustion, typical daily load and heat load in a certain region load, gas load, a 0-1 variable indicating whether the cogeneration unit is on or off, the upper and lower limits of the electrical output power and the upper and lower limits of the thermal output power of the cogeneration unit, the upper and lower limits of the output power of the electric boiler, the upper and lower limits of the capacity configuration of the electric energy storage device, the upper and lower limits of the capacity configuration of the thermal energy storage device, the upper and lower limits of the capacity configuration of the gas energy storage device, a 0-1 variable indicating the charging / heat / gas status of the electric energy storage device, the thermal energy storage device, and the gas energy storage device, a 0-1 variable indicating the discharging / heat / gas status of the electric energy storage device, the thermal energy storage device, and the gas energy storage device, and one or more of the power to capacity ratio of the electric energy storage device, the thermal energy storage device, and the gas energy storage device.

15. A device for constructing a planning model for an integrated energy microgrid, adapted for execution in a computing device, wherein the integrated energy microgrid includes energy suppliers and energy consumers, wherein the energy consumers include agriculture, animal husbandry, and residents, wherein the agriculture and animal husbandry generate biomass energy, the device comprising: Parameter acquisition module, suitable for obtaining basic parameters; a model building module adapted to establish a planning model for an integrated energy microgrid taking into account energy suppliers and agricultural production through multi-objective optimal planning theory, the model comprising a first model and a second model, the first model comprising a first objective function and a first constraint, the first constraint comprising one or more of an energy power balance constraint, an energy production equipment output constraint, an energy storage constraint, an energy storage equipment charging and discharging energy power constraint, an energy consumer load compensation cost constraint, a biogas output rate constraint, a material input to a biogas unit constraint, a biogas unit heat balance constraint, a crop production energy consumption constraint, an animal husbandry energy consumption constraint, and load constraints for each energy source, the second model comprising a second objective function and a second constraint; a model solving module, adapted to substitute the basic parameters into the first model, solve the first model with the goal of minimizing the autonomy cost of the integrated energy microgrid, and output a capacity configuration plan for each device in the integrated energy microgrid when the autonomy cost of each device is minimized; and adapted to substitute the basic parameters into the second model, solve the second model with the goal of maximizing carbon dioxide emission reduction of the integrated energy microgrid, and output a capacity configuration plan for each device in the integrated energy microgrid when the carbon emission reduction is maximized; Wherein, the first objective function includes: minF1=C inv +C opt +C DR -C BT , where F1 represents the autonomous cost of the integrated energy microgrid, C inv Indicates the initial investment cost of the system, C opt represents the system operation and maintenance cost, C DR represents the energy supplier's demand response cost to energy consumers, C BT represents the biomass processing cost, Where M i Indicates the capacity of the device. represents the annual fixed maintenance cost per unit capacity of equipment i, μ represents the annual discount rate of the equipment, y represents the project life in years, represents the annual fixed maintenance cost per unit capacity of equipment i, Y represents the subsidy amount for the jth type of crop straw or livestock and poultry, j represents the total amount of crop straw or livestock and poultry of the jth type; Wherein, the second objective function includes: Where F2 represents the carbon emission reduction of the integrated energy microgrid, Δt represents the time interval, and E C02 Represents the cycle, represents the biogas carbon emission coefficient, Indicates the biogas produced by the biogas unit at each moment.

16. A computing device comprising: at least one processor; and A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the method according to any one of claims 1 to 14.

17. A readable storage medium storing program instructions, wherein when the program instructions are read and executed by a computing device, the computing device is caused to execute the method according to any one of claims 1 to 14.

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