Agricultural multi-microgrid collaborative optimization method and system based on carbon accounting and space-time interconnection

Abstract

The invention provides an agricultural multi-micro-grid collaborative optimization method and system based on carbon accounting and space-time interconnection, and relates to the technical field of micro-grid dispatching, and the method comprises the steps: constructing an interaction model of an agricultural park multi-micro energy grid system; carbon emission transfer in the energy transaction process of the multi-micro energy network is considered, and a carbon emission accounting model is constructed; considering seasonal energy supply characteristics of the agricultural park, and making a space-time interconnection scheduling strategy of the multi-micro energy network system; based on an agricultural park multi-micro energy network system interaction model, a carbon emission accounting model and a space-time interconnection scheduling process, a target function is constructed by taking the lowest comprehensive cost of system operation, the lowest carbon emission cost of the system and the lowest transmission loss cost in the system as targets, and transmission between micro energy network systems is introduced as a constraint condition. And establishing and solving a rural multi-micro energy network system collaborative optimization model to obtain an optimal scheduling scheme of the rural multi-micro energy network system collaborative optimization model.

Description

Technical Field

[0001] The present disclosure relates to the technical field of microgrid scheduling, and particularly to a collaborative optimization method and system for agricultural multi-microgrids based on carbon accounting and spatio-temporal interconnection. Background Art

[0002] The statements in this part merely provide background technical information related to the present disclosure and do not necessarily constitute prior art.

[0003] Currently, due to the large-scale use of fossil fuels in agricultural production, a large amount of greenhouse gases are generated, making agricultural carbon emissions one of the important factors in global change. With the continuous progress of energy technology, some agricultural parks advocate organically combining various energy development methods such as biomass cogeneration, distributed photovoltaic and wind power to form an intelligent and efficient regional energy network and an energy integrated cascade utilization system, which can meet the energy demand in agricultural modernization construction while reducing agricultural carbon emissions. However, since the relevant research on carbon accounting of agricultural multi-micro energy network systems is still in its infancy, and the main sources of carbon dioxide emissions in agricultural parks are the use of chemical fertilizers, feeds and fuels, and rural coal heating and waste incineration also cause a large amount of carbon dioxide emissions, there are problems such as unclear carbon dioxide emissions accounting.

[0004] To solve these problems, existing research methods optimize the carbon emissions of energy systems from the environmental aspect by introducing carbon tax calculation strategies. There are also existing studies that have constructed a source-load double-layer carbon emission model for integrated energy systems, which promotes energy conservation and emission reduction of the system. However, the above methods still have problems: 1) It mainly focuses on the low-carbon operation of independent systems, ignoring the energy trading and transmission behaviors between different entities, which is not conducive to further exploring the collaborative emission reduction effect of agricultural multi-micro energy network system clusters.

[0005] 2) For multi-energy systems implementing energy trading, where energy purchases come from different entities, the above carbon emission calculation methods are no longer applicable.

[0006] 3) Currently, the optimization scheduling strategies for multi-micro energy network systems only consider conventional renewable energy units such as wind power generation and photovoltaic power generation, and do not consider agricultural renewable energy generating units such as biogas generating units and biomass cogeneration units, resulting in limitations in decision-making. Summary of the Invention

[0007] To solve the above problems, the present disclosure proposes an agricultural multi - microgrid collaborative optimization method and system based on carbon accounting and spatio - temporal interconnection. For agricultural parks, fully considering the output characteristics of renewable energy sources such as wind power, photovoltaic power, and biogas, constructing an equipment carbon accounting model, an internal and external transaction carbon accounting model of the micro - energy grid, and an agricultural production carbon accounting model, taking into account the seasonal energy supply characteristics of the agricultural park, formulating a spatio - temporal interconnection scheduling strategy, and establishing a collaborative optimization scheduling model for the agricultural multi - micro - energy grid system. Based on the carbon accounting mechanism and the spatio - temporal interconnection scheduling strategy, comprehensively optimize the operation output level of each device in multiple parks to ensure that the system operates in an efficient and environmentally friendly state.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: An agricultural multi - microgrid collaborative optimization method based on carbon accounting and spatio - temporal interconnection, comprising: Based on the collaborative operation mode of the agricultural multi - microgrid, constructing an interaction model of the multi - micro - energy grid system in the agricultural park; Considering the carbon emission transfer in the energy trading process of the multi - micro - energy grid, constructing a carbon emission accounting model; Taking into account the seasonal energy supply characteristics of the agricultural park, formulating corresponding spatio - temporal interconnection scheduling strategies for the multi - micro - energy grid system in the agricultural park around summer, winter, and the transition season, and analyzing the real - time changes and transmission conditions of renewable energy output, energy storage devices, and agricultural loads among multiple parks in the interaction model of the multi - micro - energy grid system in the agricultural park; Based on the interaction model of the multi - micro - energy grid system in the agricultural park, the carbon emission accounting model, and the spatio - temporal interconnection scheduling process, constructing an objective function with the goal of minimizing the comprehensive cost of system operation, the carbon emission cost of the system, and the transmission loss cost within the system, introducing the transmission between micro - energy grid systems as a constraint condition, establishing and solving a collaborative optimization model for the rural multi - micro - energy grid system, and obtaining an optimized scheduling plan for the collaborative optimization model of the rural multi - micro - energy grid system.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: An agricultural multi - microgrid collaborative optimization system based on carbon accounting and spatio - temporal interconnection, comprising: A system model construction module, configured to construct an interaction model of the multi - micro - energy grid system in the agricultural park based on the collaborative operation mode of the agricultural multi - microgrid; A carbon accounting model construction module, configured to construct a carbon emission accounting model considering the carbon emission transfer in the energy trading process of the multi - micro - energy grid; A microgrid interconnection model construction module, configured to take into account the seasonal energy supply characteristics of the agricultural park, formulate corresponding spatio - temporal interconnection scheduling strategies for the multi - micro - energy grid system in the agricultural park around summer, winter, and the transition season, and analyze the real - time changes and transmission conditions of renewable energy output, energy storage devices, and agricultural loads among multiple parks in the interaction model of the multi - micro - energy grid system in the agricultural park; An optimization scheduling module, which is used to construct an objective function based on the interaction model of the multi-micro energy network system in the agricultural park, the carbon emission accounting model, and the spatio-temporal interconnection scheduling process, with the goal of minimizing the comprehensive cost of system operation, the carbon emission cost of the system, and the transmission loss cost within the system, introducing the transmission between micro energy network systems as a constraint condition, establishing and solving a collaborative optimization model for the rural multi-micro energy network system, and obtaining an optimized scheduling plan for the collaborative optimization model of the rural multi-micro energy network system.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium for storing computer instructions, which when executed by a processor, implement the collaborative optimization method for agricultural multi-micro networks based on carbon accounting and spatio-temporal interconnection.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory to enable the electronic device to execute the collaborative optimization method for agricultural multi-micro networks based on carbon accounting and spatio-temporal interconnection.

[0012] Compared with the prior art, the beneficial effects of the present disclosure are: The collaborative optimization method for agricultural multi-micro networks based on carbon accounting and spatio-temporal interconnection of the present disclosure is oriented to agricultural parks, fully considers the output characteristics of renewable energy sources such as wind power, photovoltaic power, and biogas, establishes a collaborative optimization model for the rural multi-micro energy network system based on an energy router, comprehensively optimizes the operation output level of each device in multiple parks, ensures that the system operates in an efficient and environmentally friendly state, and improves the operation efficiency and stability of the agricultural multi-micro energy network system.

[0013] The collaborative optimization method for agricultural multi-micro networks based on carbon accounting and spatio-temporal interconnection of the present disclosure aims at problems such as unclear carbon emission calculation in the transaction scenario of the agricultural multi-micro energy network system, considers the carbon emission transfer in the energy transaction process of the multi-micro energy network, constructs a carbon emission accounting model, which includes an equipment carbon accounting model, an internal and external transaction carbon accounting model of the micro energy network, and an agricultural production carbon accounting model, accurately calculates the system carbon emissions, and provides data support for formulating differential emission reduction strategies in the future.

[0014] The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection of the present disclosure takes into account the seasonal energy supply characteristics of agricultural parks, formulates corresponding spatio-temporal interconnection dispatching strategies for the multi-micro energy network system of agricultural parks around summer, winter and transitional seasons, analyzes the real-time changes and transmission conditions of renewable energy output, energy storage devices and agricultural loads among multiple parks in the interaction model of the multi-micro energy network system of agricultural parks, improves the dispatchability of renewable energy, and realizes the dynamic supply-demand balance of the multi-microgrid system.

[0015] The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection of the present disclosure establishes a collaborative optimization dispatch model for the agricultural multi-micro energy network system with the minimum of the system comprehensive operation cost, carbon dioxide emission cost and transmission loss cost as the objective function. Based on the carbon accounting mechanism and spatio-temporal interconnection dispatching strategy, this model takes into account agricultural characteristic renewable energies such as biogas and straw, realizes the resource utilization of agricultural waste, and effectively helps the agricultural multi-microgrid system to achieve the green and low-carbon goal. This model comprehensively optimizes the operation output level of each device in multiple parks, and fairly and accurately identifies the carbon emissions of each micro energy network system, ensuring that the system operates in an efficient and environmentally friendly state while effectively improving the energy utilization rate and promoting the sustainable development of the agricultural field. Brief Description of the Drawings

[0016] The specification drawings forming a part of the present disclosure are used to provide a further understanding of the present disclosure. The schematic embodiments and descriptions thereof of the present disclosure are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0017] Figure 1 It is the collaborative operation mode architecture diagram of the agricultural multi-microgrid of the embodiment of the present disclosure; Figure 2 It is the structure diagram of the multi-micro energy network system of the agricultural park of the embodiment of the present disclosure; Figure 3 It is the architecture diagram of the carbon emission accounting model of the embodiment of the present disclosure; Figure 4 It is the application method process diagram of the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection of the embodiment of the present disclosure. Detailed Description of the Embodiments

[0018] The present disclosure will be further described below in conjunction with the drawings and embodiments.

[0019] It should be noted that the following detailed descriptions are all illustrative and are intended to provide a further description of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present disclosure belongs.

[0020] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0021] Embodiment 1 In an embodiment of the present disclosure, a collaborative optimization method for agricultural multi-microgrids based on carbon accounting and spatio-temporal interconnection is provided. The steps include: Step 1: Based on the collaborative operation mode of the agricultural multi-microgrid, construct an interaction model of the multi-micro energy network system in the agricultural park; Step 2: Considering the carbon emission transfer in the energy trading process of the multi-micro energy network, construct a carbon emission accounting model; Step 3: Considering the seasonal energy supply characteristics of the agricultural park, formulate corresponding spatio-temporal interconnection scheduling strategies for the multi-micro energy network system in the agricultural park around summer, winter, and the transition season, and analyze the real-time changes and transmission conditions of renewable energy output, energy storage devices, and agricultural loads among multiple parks in the interaction model of the multi-micro energy network system in the agricultural park; Step 4: Based on the interaction model of the multi-micro energy network system in the agricultural park, the carbon emission accounting model, and the spatio-temporal interconnection scheduling process, construct an objective function with the lowest comprehensive cost of system operation, carbon emission cost of the system, and transmission loss cost within the system as the goal, and introduce the transmission between micro energy network systems as a constraint condition to establish and solve a collaborative optimization model for the rural multi-micro energy network system, and obtain an optimized scheduling plan for the collaborative optimization model of the rural multi-micro energy network system.

[0022] As an embodiment, the collaborative optimization method of agricultural multi - microgrids based on carbon accounting and spatio - temporal interconnection of the present disclosure is targeted at agricultural parks. It fully considers the output characteristics of renewable energy sources such as wind power, photovoltaic power, and biogas, and establishes an interaction model of the multi - micro - energy network system in the agricultural park based on an energy router. Secondly, aiming at problems such as unclear carbon emission calculation in the trading scenario of the agricultural multi - micro - energy network system, an energy - flow - carbon - coupled carbon emission accounting mechanism is proposed. The carbon flow of the agricultural multi - micro - energy network system is analyzed, and a device carbon accounting model, an internal and external trading carbon accounting model of the micro - energy network, and an agricultural production carbon accounting model are respectively constructed to accurately account for the system carbon emissions. Thirdly, considering the seasonal energy supply characteristics of the agricultural park, spatio - temporal interconnection scheduling strategies for the multi - micro - energy network system in the agricultural park are formulated for summer, winter, and transition seasons. Based on the above, the present disclosure takes the minimum of the comprehensive cost of system operation, the carbon emission cost of the system, and the transmission loss cost within the system as the goal, and establishes a collaborative optimization model for the rural multi - micro - energy network system. This model is based on the carbon accounting mechanism and spatio - temporal interconnection scheduling strategy, and comprehensively optimizes the operation output level of each device in the multi - park to ensure that the system operates in an efficient and environmentally friendly state. The specific implementation process is as follows: Step 1: Based on the collaborative operation mode of the agricultural multi - microgrid, construct an interaction model of the multi - micro - energy network system in the agricultural park, including: Step 1.1 Construct a model of the multi - micro - energy network system in the agricultural park Firstly, Figure 1 The architecture diagram of the collaborative operation mode of the agricultural multi - microgrid is shown. The multi - micro - energy network system in the agricultural park consists of three microgrids, and each microgrid is composed of a wind turbine, a photovoltaic unit, a combined heat and power unit, a biogas generator set, a biomass combined heat and power unit, an energy storage system, and a local load. The energy router, as an intelligent interface, realizes the interconnection between microgrids and the interconnection between the microgrid and the energy router. The purpose of the energy router is to maximize the absorption of energy such as biomass energy by the multi - microgrid system, reduce the dependence on the superior distribution network, and thus achieve the dynamic supply - demand balance of the entire system.

[0023] Each microgrid is assigned an IP address by the energy router and plays a role in the real - time optimal scheduling process of the multi - micro - energy network system in the agricultural park. The energy router can monitor the load demand in each microgrid, the output power of renewable energy, and the charge / discharge state of the energy storage system. In addition, the power router can also collect relevant state information from each microgrid and, based on the spatio - temporal complementarity of renewable energy and load demand, dispatch the remaining renewable energy of each system to achieve the optimal scheduling of the multi - microgrid system.

[0024] Secondly, as Figure 2As shown, the energy devices in each microgrid include wind turbines, photovoltaic units, combined heat and power units, biogas generating units, biomass combined heat and power units, and energy storage systems (batteries). System models are constructed for each energy device respectively.

[0025] (1)Photovoltaic power generation model The output of the photovoltaic generating unit mainly depends on the solar radiation intensity, and the output power can be modeled as: (1) Among them, is t the light intensity at time is the i th t power output by the photovoltaic unit at the th time in the th microgrid, is the temperature parameter for the energy conversion of the photovoltaic generating unit, is the ambient temperature, is the normal temperature at which the generating unit operates, is the reference temperature, is the working efficiency of the energy conversion of the photovoltaic generating unit,

[0026] (2)Wind power generation model The output power of the wind turbine is related to the wind speed and can be modeled as: (2) Among them, is the i th t power output by the wind turbine at the th time in the , and are the cut-in, rated, and cut-out wind speeds respectively.

[0027] (3)Biogas power generation model The biogas digester uses straw, manure, etc. for anaerobic fermentation to produce biogas at a controllable rate. The biogas produced is used for power generation after being washed by the device. The output power of this unit can be modeled as: (3) (4) Among them, is the i tht The power output by the biogas unit at a certain moment, is t the biogas flow rate flowing into the biogas unit in the i th microgrid at the moment, is t the biogas flow rate at the moment, is the efficiency of the water washing device. is the output efficiency of the biogas unit, is the conversion coefficient of heat units, is the calorific value of biogas.

[0028] (4) Combined heat and power unit The CHP unit burns natural gas and supplies heat and electricity simultaneously, and can be modeled as: (5) (6) (7) Among them, and are respectively the electric power and heat power output by the combined heat and power unit in the i th microgrid at the t th moment, and are respectively the natural gas consumption and the lower calorific value, , and are respectively the electric conversion efficiency, heat conversion efficiency and loss efficiency of the combined heat and power unit is the efficiency of the combined heat and power unit for generating electricity after burning natural gas. The economic operation of the combined heat and power unit is achieved by restricting the maximum and minimum outputs of the system. Among them is the maximum output of the combined heat and power unit, is a binary function. When the operating power of the combined heat and power unit is less than 35% of its rated capacity, is 0, otherwise is 1.

[0029] (5) Biomass combined heat and power unit Dry substances such as straw and food waste are decomposed into combustible gases at high temperatures. These gases burn at high temperatures to drive an internal combustion engine to generate electricity, and the high-temperature waste heat of the flue gas can supply heat through a waste heat boiler. It can be modeled as: (8) (9) (10) Among them, and are the iThe electric power and thermal power output by the biomass cogeneration unit at the t th moment of the th microgrid, t is the combustible gas flowing into the biomass cogeneration unit in the i th microgrid at the th moment, is the calorific value of the combustible gas, is the power generation efficiency of the unit, is the heat loss rate, is the heat supply efficiency of the unit,

[0030] (6) Battery The state of charge of the battery changes continuously with charging and discharging: (11) (12) Among them, and are the charging and discharging efficiencies of the battery respectively. and are the initial and final storage states of the battery respectively.

[0031] During the operation of the battery, both the storage state and the charging and discharging power of the battery have upper and lower limits, and the following conditions need to be met: (13) (14) (15) Among them, is the charging power of the battery at the i th moment of the t th microgrid, is the discharging power of the battery at the i th moment of the t th microgrid. and are the minimum and maximum storage states of the battery respectively, and are the maximum charging power and maximum discharging power respectively.

[0032] Step 1.2 Build an interaction model of the multi-micro energy network system in the agricultural park; According to the characteristics of the agricultural park, the multi-micro energy network system of the agricultural park is respectively set as a livestock farm park, a planting farm park, and a residential user park. When the renewable energy supply of the multi-micro energy network system in the agricultural park is insufficient or excessive, the multi-micro energy network will interact with the superior energy network and choose to purchase from the superior energy network according to the energy management strategy. Define the electro-thermal trading process between multi-micro energy networks and construct an interaction model for the multi-micro energy network system in the agricultural park.

[0033] First, the energy characteristics of different parks such as the livestock farm park, the planting farm park, and the residential user park are shown in Table 1.

[0034] Table 1 Energy characteristics of different parks

[0035] Among them, √ in the table indicates that the park contains the unit, and × indicates that the park does not have the unit.

[0036] Secondly, in the coordinated operation of the multi-micro energy network system in the agricultural park, each micro energy network system, as a producer and seller of renewable energy, collects the status information and energy flow data of the energy supply / demand park through the energy router, and then distributes the information to the transmission path to achieve intelligent management and scheduling of energy.

[0037] As an embodiment, the energy router is an intelligent interface between the agricultural park and the superior energy network. It coordinates the management of renewable energy and load demand to achieve the best energy utilization efficiency. When the renewable energy supply of the agricultural micro energy network system is insufficient or excessive, the micro energy network will interact with the superior energy network and choose to purchase or sell energy from the superior energy network according to the energy management strategy (the agricultural micro energy network system purchases natural gas from the superior energy network and does not sell). The electro-thermal trading between micro energy networks is defined as follows: (16) (17) Among them, and respectively represent the electricity quantity and heat quantity traded between the micro energy network system and the superior energy network. When and are greater than or equal to 0, they are defined as the electricity quantity purchase volume and heat ; otherwise, they are defined as the electricity quantity sale volume and heat .

[0038] (18) (19) Among them, and respectively represent the total electricity and heat purchased by the th micro - energy network system from other micro - energy network systems, and respectively represent the electricity and heat purchased by the micro - energy network system from the micro - energy network system. and respectively represent the total electricity and heat sold by the th micro - energy network system to other micro - energy network systems at time , and and represent the electricity and heat sold by the micro - energy network system to the micro - energy network system at time .

[0039] Step 2: Consider the carbon emission transfer in the multi - micro - energy network energy trading process and construct a carbon emission accounting model; Specifically, when calculating the carbon emissions of multi - micro - energy network systems using traditional methods, only the carbon emissions generated from purchasing electricity, heat, and gas from the superior energy network are considered, while the carbon emissions generated due to energy transfer between multi - micro - energy network systems and the carbon emissions in agricultural production activities are ignored. In the energy trading process, there are multi - directional and multi - category energy flows among multiple trading entities. Due to the coupling relationship between the energy flow and the carbon flow, the carbon emission transfer in the multi - micro - energy network energy trading process should be considered when calculating the carbon emissions of each trading entity. On the other hand, agriculture is also an important emission source of greenhouse gases such as carbon dioxide, and the characteristics of agriculture are complex, so the carbon emissions from agricultural production activities such as planting should also be considered.

[0040] As shown in the carbon flow analysis Figure 3 , the carbon emission sources of each micro - energy network in the present disclosure are considered from the following three parts. One is the carbon emission transfer generated by the energy trading between the micro - energy network and the superior energy network, the second is the carbon emissions generated by the energy trading within the micro - energy network, and the third is the carbon emissions generated by the agricultural production activities within the park. Then, a certain tThe carbon emissions of the multi - micro energy network at a certain moment are the sum of the carbon emissions generated by three parts of activities. That is, according to the carbon emission sources of each micro - energy network, a carbon emission accounting model is constructed, specifically including an equipment carbon accounting model, an internal and external transaction carbon accounting model of the micro - energy network, and an agricultural production carbon accounting model. The agricultural production carbon accounting model measures the carbon emissions of the planting farm park and the livestock farm park. The carbon source of the planting farm park is the consumption of agricultural materials, that is, the actual usage data of chemical fertilizers, pesticides, agricultural plastic films, agricultural machinery (diesel), and agricultural irrigation electricity. The carbon emission measurement of the livestock farm park is to convert according to the emission coefficient of greenhouse gases in the livestock industry in the life cycle and then measure the carbon emissions. The specific analysis of each part is as follows: Step 2.1 Equipment carbon accounting model This disclosure constructs an energy flow - carbon coupling carbon emission accounting model for agricultural parks. To calculate carbon emissions, it is first necessary to clarify the carbon emission intensities of various types of energy in all micro - energy systems. The carbon emission intensities of electricity, heat, and gas from green energy are fixed, while the carbon emission intensities of electricity and heat in the system are closely related to the output power of internal devices. Taking the agricultural micro - energy network system i as an object, the carbon accounting formulas for different devices within the system are as follows: (20) (21) (22) (23) (24) (25) (26) (27) (28) Among them, is the carbon emission intensity of biogas generator sets for power generation, is the carbon emission intensity of electric energy. and are the carbon emission intensities of combined heat and power units for power generation and heat supply respectively, and are the carbon emission intensities of biomass combined heat and power units for power generation and heat supply respectively, is the electro - thermal conversion coefficient. is t the electric energy purchased from the superior energy network at time is t the carbon emission intensity of the battery at time is tCarbon emission intensity of the battery power generation at a certain moment. and are respectively t the carbon emission intensities of electricity and heat within the micro energy grid system i at a certain moment.

[0041] Step 2.2 Carbon accounting model for internal and external transactions of the micro energy grid Based on the above content, the present disclosure constructs a carbon accounting model for internal and external transactions of the micro energy grid system as follows: (29) (30) (31) wherein, is t the internal and external transaction carbon emissions of the micro energy grid system i at a certain moment. Formulas (30) and (31) use piecewise functions to determine the carbon intensity of electricity and heat transactions between micro energy grid systems. 、 and respectively represent the carbon emission intensities corresponding to the external transactions of electricity, heat, and gas from the micro energy grid system, and respectively represent i and j the carbon emission intensities of electricity and heat transactions between the micro energy grid systems.

[0042] Formula (29) shows that when both the energy transaction volume and the carbon emission intensity are variables, the carbon accounting model is non - linear, which is not conducive to solving the energy scheduling problem. Therefore, to ensure the convenience of solving in the carbon accounting process, the present disclosure assumes that the carbon emission intensity of each micro energy grid system engaged in internal electricity - heat transactions is known in each iteration process. And by solving the energy flow scheduling problem, the electricity carbon emission intensity and heat carbon emission intensity of each micro energy grid system are updated according to the results. For the piecewise functions in Formulas (30) and (31), the corresponding binary variables are introduced for transformation, and the formulas are as follows: (32) (33) (34) (35) (36) wherein, Formulas (32 - 35) use inequality constraints to convert the electricity and heat transaction variables into purchases and sales. and respectively refer to the iThe micro energy grid system gives electricity and heat to the t th micro energy grid at the j th moment. And respectively refer to the electricity and heat obtained by the i th micro energy grid system from the t th micro energy grid at the j th moment. , , and refer to binary variables, is a very large numerical constant. Equation (36) ensures that the i th micro energy grid system cannot obtain or give the same type of energy simultaneously.

[0043] Combining equations (32 - 36), the carbon accounting model for internal and external transactions of the agricultural micro energy grid system can be expressed as: (37) where Carbon emissions represents the total carbon emissions of the i th micro energy grid system.

[0044] Step 2.3 Agricultural production carbon accounting model In the research on agricultural production activities, the characteristics of agricultural production activities are complex. There are not only many carbon sources, but also overlaps and mutual influences among carbon emission sources, which brings certain difficulties to the accounting of agricultural carbon sources. The present disclosure conducts in - depth analysis on the carbon emissions of planting and animal husbandry and makes accurate calculations of carbon emissions.

[0045] (1) Planting The main carbon source in agricultural production is the consumption of agricultural materials, namely the actual usage data of chemical fertilizers, pesticides, agricultural plastic films, agricultural machinery and diesel, as well as agricultural irrigation electricity. Its carbon emission calculation formula is in the following form: (38) where is the physical quantity of the i th fossil energy, in ten thousand or hundred million ; is the unit calorific value of the i th energy, or ; is the carbon content per unit calorific value of the i th energy, ; is the carbon oxidation rate during the combustion process.

[0046] Furthermore, the carbon emission coefficients of chemical fertilizers, pesticides, and agricultural films are shown in Table 2 below.

[0047] Table 2 Carbon Emission Coefficients of Chemical Fertilizers, Pesticides, and Agricultural Films

[0048] (2) Animal Husbandry The main sources of greenhouse gas emissions during livestock breeding are animal intestinal fermentation and animal manure management. There are microorganisms parasitizing in the animal intestines, which are the main sources of gas emissions. In addition, a large amount of manure will be emitted during the animal growth cycle, and it needs to be stored before being applied to the soil, and greenhouse gases will also be generated during this process.

[0049] Due to the differences in the growth cycles of livestock and poultry such as pigs and rabbits, the present disclosure converts the greenhouse gas emission coefficients of animal husbandry according to the life cycle and then conducts carbon emission measurement. The formula is as follows: (39) (40) Among them, is the life cycle of livestock and poultry i , represents the annual slaughter volume of the i th type of livestock and poultry, represents the year-end inventory volume of the i th type of livestock and poultry in the t th year, is the carbon content of animal husbandry.

[0050] Step 2.4 Carbon Emission Accounting Model In summary, the carbon accounting model of the i th micro energy network system is as follows: (41) Step 3: Considering the seasonal energy supply characteristics of the agricultural park, formulate corresponding spatio-temporal interconnection scheduling strategies for the multi-micro energy network system of the agricultural park around summer, winter, and the transitional season, and analyze the real-time changes and transmission conditions of renewable energy output, energy storage devices, and agricultural loads among multiple parks in the multi-park interaction model of the multi-micro energy network system of the agricultural park; Specifically, the spatio-temporal interaction scheduling strategy proposed by the present disclosure is the real-time changes of renewable energy output, energy storage devices, and agricultural loads among three agricultural parks. Through the flexible scheduling and collaborative management of the energy router, the optimal allocation of resources is achieved. The system detects the operating status and parameters of the energy system within the proposed optimal scheduling strategy and conducts scheduling and control based on real-time data. Therefore, the energy differences and in the agricultural multi-micro energy network system are: (42) Among them, and are respectively t the electrical load and the heat load at a certain moment.

[0051] Furthermore, according to the seasonal difference characteristics, the present disclosure formulates corresponding spatio-temporal interconnection scheduling strategies for the multi-micro energy network system in the agricultural park for summer, winter and transitional seasons, as follows: (1) Summer In summer, the light intensity is high, but the wind speed is low. When the output of the photovoltaic generator set reaches the maximum value, the output of the wind turbine generator set reaches the minimum value. The temperature in summer is relatively high, which is conducive to the growth and reproduction of microorganisms. Therefore, the output of the biogas generator set and the biomass cogeneration unit reaches the maximum value. However, the heat demand in summer is low, and the biomass cogeneration unit supplies more heat. Therefore, the output of the cogeneration unit reaches the lowest value.

[0052] Based on the current situation, the renewable energy supply in the planting farm park and the residential user park is greater than the demand, and the renewable energy demand in the livestock farm park is greater than the supply. Then there are the following interactions: (a) The livestock farm park preferentially selects the micro energy network system with a shorter path and sufficient renewable energy for power supply. If the load demand cannot be met, it will select another micro energy network system with surplus renewable energy for power supply.

[0053] (b) If the energy demand of the livestock farm park is satisfied, the surplus energy will be sequentially allocated to the local storage battery and the superior energy network.

[0054] (c) If the energy demand of the livestock farm park cannot be met, the park will select the local storage battery and purchase energy from the superior energy network.

[0055] (2) Winter In winter, the wind speed is high and the light intensity is low. Therefore, the output of the wind turbine generator set reaches the maximum value, and the output of the photovoltaic generator set reaches the minimum value. The temperature in winter is relatively low, and the raw material decomposition rate is relatively low. Therefore, the output of the biogas generator set and the biomass cogeneration unit reaches the minimum value. However, the heat demand in winter is large, and the output of the cogeneration unit also reaches the maximum value.

[0056] Based on the current situation, the renewable energy supply in the livestock farm park is greater than the demand, and the renewable energy demand in the residential user park and the planting farm park is greater than the supply. Then there are the following interactions: (a) The livestock farm park preferentially selects to deliver energy to the micro energy network system with a shorter distance, and then sends it to another micro energy network system with insufficient power.

[0057] (b)If the livestock farm park meets the energy demands of the residential user park and the planting farm park, the excess energy will be sequentially allocated to the local storage battery and the superior energy grid.

[0058] (c)If the livestock farm park fails to meet the energy demands of the residential user park and the planting farm park, the micro energy grid system with insufficient power will choose to purchase energy from the local storage battery and the superior energy grid.

[0059] (3)Transition season During the transition season, the temperature is suitable, and the output of the renewable energy supply unit is at the average value. Therefore, the supply and demand of renewable energy in the three parks are roughly the same. The specific strategy changes according to the load demand. If the load demand is greater than the energy supply, the storage battery will be selected for power supply and energy will be purchased from the superior energy grid. If the load demand is less than the energy supply, the micro energy grid system will give priority to charging the storage battery and sell the other excess energy to the superior energy grid.

[0060] Step 4: Based on the interaction model of multi - micro energy grid systems in the agricultural park, the carbon emission accounting model, and the spatio - temporal interconnection dispatching process, with the goal of minimizing the comprehensive cost of system operation, the carbon emission cost of the system, and the transmission loss cost within the system, construct an objective function, introduce the transmission between micro energy grid systems as a constraint condition, establish and solve the collaborative optimization model of rural multi - micro energy grid systems, and obtain the optimal dispatching plan of the collaborative optimization model of rural multi - micro energy grid systems, specifically including: Step 4.1 Construct the objective function The construction of the objective function is considered from three aspects: the comprehensive cost of system operation, the carbon emission cost of the system, and the transmission loss cost within the system. The linear weighted method is used to define the objective function and a set of weights is determined Construct the objective function as follows: (43) Among them, is the total cost of system operation Among them, should satisfy: (44) Furthermore, (1) the comprehensive cost of system operation : The comprehensive cost of system operation includes the operation and maintenance cost and the transaction cost , and can be expressed as follows: (45) First of all, the system operation and maintenance cost The equipment operation and maintenance costs of a system including a wind power generation unit, a photovoltaic power generation unit, a biogas power generation unit, a biomass cogeneration unit, a cogeneration unit, and a battery can be expressed as: (46) Among them, 、 、 、 、 and are the equipment operation and maintenance coefficients of the wind power generation unit, the photovoltaic power generation unit, the biogas power generation unit, the biomass cogeneration unit, the cogeneration unit, and the battery, respectively.

[0061] Secondly, the system trading cost consists of the trading cost between the micro energy network system and the superior energy network and the trading cost between the micro energy network systems. (47) Among them, 、 and respectively represent the real-time prices of electricity, heat, and gas purchased by the agricultural multi-micro energy network system from the superior energy network, 、 and respectively represent the numerical values of electricity, heat, and gas energy purchased from the superior energy network system. and respectively represent the real-time prices of electricity, heat, and gas sold by the agricultural multi-micro energy network system to the superior energy network, and respectively represent the numerical values of electricity, heat, and gas energy sold to the superior energy network. and respectively represent the trading volumes of the electric and heat units i and j of the micro energy network system, and represent the unit trading price i and j between the micro energy network systems.

[0062] (2)System internal transmission loss cost The specific energy for interactive transmission in the agricultural multi - micro - energy network system is determined based on the energy supply - demand amounts among three parks. The energy router issues commands to achieve spatio - temporal interaction among the parks. The interaction between electric energy and thermal energy is realized through transmission losses. Therefore, this disclosure considers the transmission loss cost between micro - energy network systems as the spatio - temporal interconnection cost of the system. The spatio - temporal interconnection cost mainly includes the electric - thermal transmission losses between the micro - energy network and the energy router and , and the specific formula is as follows: (48) (49) Among them, and represent the unit loss prices of electric - thermal energy in the power transmission and distribution process, represents the allowable voltage in the power transmission and distribution process.

[0063] (3)Carbon emission cost of the system Currently, to promote the low - carbon operation of the micro - energy network system, a carbon emission factor is introduced based on Step 2 to calculate the carbon emission cost of the system. At t time, the carbon emission cost of the i th micro - energy network system is: (50) Among them, is the carbon emission factor.

[0064] Furthermore, the constraint conditions include power balance constraints, electric - thermal output constraints, restrictions on energy trading with the superior energy network, and transmission between micro - energy network systems. The collaborative optimization model of the rural multi - micro - energy network system is a mixed - integer non - linear model, which is transformed into a mixed - integer linear programming problem by piece - wise linearization. Specifically as follows: (1)Power balance constraints: (51) (52) (2)Electric - thermal output constraints: (53) (54) (3)Restrictions on energy trading with the superior energy network: (55) (4)Transmission constraints between micro - energy network systems: (56) Among them, and is the transmission electric and thermal power between the micro - energy grid systems i and j is the maximum value of the electric - thermal transmission, and represents the maximum voltage and the minimum voltage allowed by the transmission path. and Finally, the constructed collaborative optimization model of the rural multi - micro - energy grid system is a mixed - integer non - linear model. Therefore, piece - wise linearization processing is required to transform it into a mixed - integer linear programming problem, and then the CPLEX commercial solver is used to solve it to obtain the optimal scheduling scheme of the collaborative optimization model of the rural multi - micro - energy grid system.

[0065] Example 2

[0066] In one embodiment of the present disclosure, a collaborative optimization system for agricultural multi - micro - grids based on carbon accounting and spatio - temporal interconnection is provided, including: A system model construction module, which is used to construct an interaction model of the multi - micro - energy grid system in the agricultural park based on the collaborative operation mode of the agricultural multi - micro - grid; A carbon accounting model construction module, which is used to construct a carbon emission accounting model by considering the carbon emission transfer in the energy trading process of the multi - micro - energy grid; A micro - grid interconnection model construction module, which is used to formulate spatio - temporal interconnection scheduling strategies for the multi - micro - energy grid system in the agricultural park around summer, winter, and transition seasons by taking into account the seasonal energy supply characteristics of the agricultural park, and analyze the real - time changes and transmission conditions of renewable energy output, energy storage devices, and agricultural loads among multiple parks in the interaction model of the multi - micro - energy grid system in the agricultural park; An optimal scheduling module, which is used to construct an objective function with the lowest comprehensive cost of system operation, carbon emission cost of the system, and transmission loss cost within the system as the goal based on the interaction model of the multi - micro - energy grid system in the agricultural park, the carbon emission accounting model, and the spatio - temporal interconnection scheduling process, introduce the transmission between micro - energy grid systems as a constraint condition, establish and solve a collaborative optimization model of the rural multi - micro - energy grid system, and obtain the optimal scheduling scheme of the collaborative optimization model of the rural multi - micro - energy grid system.

[0067] As an embodiment, the collaborative optimization method of the collaborative optimization system for agricultural multi - micro - grids based on carbon accounting and spatio - temporal interconnection implements the scheduling process as follows: (1) Select a typical day, set the total scheduling time to 24 h, and the scheduling interval to 1 h; (2) Input the basic parameters of the agricultural multi - micro - energy grid system, including equipment parameters such as wind turbines, photovoltaic generators, biogas generators, etc.; input data such as photovoltaic power generation, wind power generation, and load on typical days in summer, winter, and transition seasons.

[0068] (3) Based on the carbon emission accounting process, accurately account for the carbon emissions in summer, winter, and the transitional season, while taking into account the characteristics of different seasons within a year, and accordingly propose different spatio-temporal interaction scheduling strategies; (4) Establish a collaborative optimal scheduling model for the agricultural multi-micro energy network system according to the objective function and constraint conditions, linearize it, and finally use the commercial solver CPLEX to solve it to achieve the optimal scheduling of the collaborative optimization model of the agricultural multi-micro energy network system.

[0069] Example 3 In an embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the collaborative optimization method of the agricultural multi-micro grid based on carbon accounting and spatio-temporal interconnection is implemented.

[0070] Example 4 In an embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes to implement the collaborative optimization method of the agricultural multi-micro grid based on carbon accounting and spatio-temporal interconnection.

[0071] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks specified in one block or multiple blocks.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, so that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks specified in one block or multiple blocks.

[0073] Although the specific embodiments of the present disclosure have been described above in conjunction with the accompanying drawings, they are not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that various modifications or deformations that can be made without creative efforts on the basis of the technical solutions of the present disclosure are still within the scope of protection of the present disclosure.

Claims

1. An agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection, characterized in that, Including: Based on the collaborative operation mode of agricultural multi - microgrids, construct an interaction model of the multi - micro energy network system in the agricultural park; Considering the carbon emission transfer in the energy trading process of the multi - micro energy network, construct a carbon emission accounting model; Taking into account the seasonal energy supply characteristics of the agricultural park, formulate corresponding spatio - temporal interconnection dispatching strategies for the multi - micro energy network system in the agricultural park around summer, winter and transition seasons, and analyze the real - time changes and transmission conditions of renewable energy output, energy storage devices and agricultural loads among multiple parks in the interaction model of the multi - micro energy network system in the agricultural park; Based on the interaction model of the multi - micro energy network system in the agricultural park, the carbon emission accounting model and the spatio - temporal interconnection dispatching process, construct an objective function with the goal of minimizing the comprehensive cost of system operation, the carbon emission cost of the system and the transmission loss cost within the system, and introduce the transmission between micro - energy network systems as a constraint condition, establish and solve a collaborative optimization model for the rural multi - micro energy network system, and obtain an optimized dispatching plan for the collaborative optimization model of the rural multi - micro energy network system.

2. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection according to claim 1, characterized in that, The agricultural multi - microgrid consists of three microgrids. Each microgrid consists of a wind turbine, a photovoltaic unit, a combined heat and power unit, a biogas generating unit, a biomass combined heat and power unit, an energy storage system and a local load. Between each microgrid, an energy router is used as an intelligent interface to achieve the interconnection between each microgrid and the interconnection between the microgrid and the energy router. Each microgrid is assigned an IP address by the energy router to achieve real - time optimal dispatching of the agricultural multi - microgrid.

3. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection according to claim 1, wherein, According to the characteristics of the agricultural park, the multi - micro energy network system in the agricultural park is divided into a livestock farm park, a planting farm park and a residential user park respectively. When the renewable energy supply in the multi - micro energy network system in the agricultural park is insufficient or excessive, the multi - micro energy network will interact with the superior energy network and choose to purchase from the superior energy network according to the energy management strategy. Define the electro - thermal trading process between multi - micro energy networks and construct an interaction model of the multi - micro energy network system in the agricultural park.

4. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection according to claim 1, characterized in that, In the process of energy trading, there are multi-faceted and multi-category energy flows among multiple trading entities. Due to the coupling relationship between the energy flow and the carbon flow, the carbon emission sources of each micro-energy grid are considered from the following three aspects: first, the carbon emission transfer generated by the energy trading between the micro-energy grid and the superior energy grid; second, the carbon emissions generated by the energy trading within the micro-energy grid; third, the carbon emissions generated by agricultural production activities within the park. Then, the t carbon emissions of the multi-micro energy grid at a certain moment are the sum of the carbon emissions generated by the three activities.

5. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection according to claim 1, characterized in that According to the carbon emission sources of each micro - energy network, the constructed carbon emission accounting model includes an equipment carbon accounting model, an internal and external trading carbon accounting model of the micro - energy network and an agricultural production carbon accounting model. The agricultural production carbon accounting model measures the carbon emissions of the planting farm park and the livestock farm park. The carbon source of the planting farm park is the consumption of agricultural materials, that is, the actual usage data of chemical fertilizers, pesticides, agricultural plastic films, agricultural machinery and diesel, as well as agricultural irrigation electricity. The carbon emission measurement of the livestock farm park is to convert according to the emission coefficient of greenhouse gases in the livestock industry in the life cycle and then measure the carbon emissions.

6. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection according to claim 1, characterized in that According to the seasonal difference characteristics, for summer, winter and transition seasons, formulate spatio - temporal interconnection dispatching strategies. In summer, the renewable energy supply in the planting farm park and the residential user park is greater than the demand, and the renewable energy demand in the livestock farm park is greater than the supply; in winter, the renewable energy supply in the livestock farm park is greater than the demand, and the renewable energy demands in the residential user park and the planting farm park are greater than the supply; in the transition season, the output of the renewable energy supply unit is at the average value, so the renewable energy supply and demand in the three parks are the same.

7. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatio-temporal interconnection according to claim 1, characterized in that The objectives of constructing the objective function are considered from three aspects, namely the comprehensive cost of system operation, the carbon emission cost of the system, and the transmission loss cost within the system. The linear weighted method is used to define the objective function and a set of weights is determined to construct the objective function. The constraint conditions include power balance constraints, electric and heat output constraints, restrictions on energy transactions with the superior energy network, and transmissions between micro energy network systems. The collaborative optimization model of the rural multi-micro energy network system is a mixed integer non-linear model, which is transformed into a mixed integer linear programming problem by piecewise linearization.

8. An agricultural multi-microgrid collaborative optimization system based on carbon accounting and spatio-temporal interconnection, characterized in that It includes: A system model construction module, which is used to construct an interaction model of the multi-micro energy network system in the agricultural park based on the collaborative operation mode of the agricultural multi-micro grid. A carbon accounting model construction module, which is used to construct a carbon emission accounting model by considering the carbon emission transfer in the energy transaction process of the multi-micro energy network. A microgrid interconnection model construction module, which is used to consider the seasonal energy supply characteristics of the agricultural park, formulate corresponding spatio-temporal interconnection scheduling strategies for the multi-micro energy network system in the agricultural park around summer, winter and transition seasons, and analyze the real-time changes and transmissions of renewable energy output, energy storage devices and agricultural loads among multiple parks in the interaction model of the multi-micro energy network system in the agricultural park. An optimal scheduling module, which is used to construct an objective function with the lowest comprehensive cost of system operation, the carbon emission cost of the system, and the transmission loss cost within the system based on the interaction model of the multi-micro energy network system in the agricultural park, the carbon emission accounting model, and the spatio-temporal interconnection scheduling process, introduce the transmission between micro energy network systems as a constraint condition, establish and solve the collaborative optimization model of the rural multi-micro energy network system, and obtain the optimal scheduling scheme of the collaborative optimization model of the rural multi-micro energy network system.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the collaborative optimization method for agricultural multi-micro grids based on carbon accounting and spatio-temporal interconnection as described in any one of claims 1-7 is implemented.

10. An electronic device, characterized in that, It includes: A processor, a memory, and a computer program. Among them, the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes the collaborative optimization method for agricultural multi-micro grids based on carbon accounting and spatio-temporal interconnection as described in any one of claims 1-7.

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