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

Abstract

The present disclosure provides an agricultural multi-microgrid collaborative optimization method and system based on carbon accounting and spatiotemporal interconnection, which relates to the field of microgrid dispatching technology, including constructing an interaction model of a multi-micro energy grid system in an 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 a spatiotemporal interconnection dispatching strategy for the multi-micro energy grid system; based on the interaction model of the multi-micro energy grid system in the agricultural park, the carbon emission accounting model and the spatiotemporal interconnection dispatching 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, and introducing the transmission between micro energy grid systems as a constraint condition, establishing and solving a rural multi-micro energy grid system collaborative optimization model, and obtaining an optimized dispatching scheme for the rural multi-micro energy grid system collaborative optimization model.

Description

Technical Field

[0001] The present disclosure relates to the field of microgrid dispatching technology, and in particular to a method and system for collaborative optimization of agricultural multi-microgrids based on carbon accounting and spatiotemporal interconnection. Background Art

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

[0003] Currently, the large-scale use of fossil fuels in agricultural production generates significant amounts of greenhouse gases, making agricultural carbon emissions a significant factor in global change. With the continuous advancement of energy technology, some agricultural parks are advocating for the integration of diverse energy development methods, such as biomass cogeneration, distributed photovoltaic and wind power, to form intelligent and efficient regional energy networks and integrated energy cascade utilization systems. This would meet the energy needs of agricultural modernization while reducing agricultural carbon emissions. However, since research on carbon accounting for agricultural multi-micro energy grid systems is still in its early stages, and agricultural park carbon dioxide emissions primarily come from the use of fertilizers, feed, and fuels, and coal-fired heating and waste incineration in rural areas also contribute significant carbon dioxide emissions, there are issues with the clarity of carbon dioxide emission accounting.

[0004] To address these issues, existing research methods have optimized the carbon emissions of energy systems from an environmental perspective by introducing carbon tax calculation strategies. Other existing studies have constructed a two-layer source-load carbon emission model for integrated energy systems, promoting energy conservation and emission reduction. However, these methods still have problems:

[0005] 1) It mainly focuses on the low-carbon operation of independent systems, ignoring the energy transactions and transmission behaviors between different entities, which is not conducive to further exploring the coordinated emission reduction effects of agricultural multi-micro energy network system clusters.

[0006] 2) For multi-energy systems that implement energy trading, energy purchases come from different entities, and the above carbon emission calculation method is no longer applicable.

[0007] 3) Currently, the optimization scheduling strategy for multi-micro energy grid systems only considers conventional renewable energy units such as wind power generation and photovoltaic power generation, but has not yet considered agricultural renewable energy units such as biogas power generation units and biomass cogeneration units, which has limitations in decision-making. Summary of the Invention

[0008] In order to solve the above problems, the present disclosure proposes an agricultural multi-microgrid collaborative optimization method and system based on carbon accounting and spatiotemporal interconnection. Aiming at agricultural parks, it fully considers the output characteristics of renewable energy sources such as wind, photovoltaic, and biogas, constructs equipment carbon accounting models, micro-energy network internal and external transaction carbon accounting models, and agricultural production carbon accounting models, takes into account the seasonal energy supply characteristics of agricultural parks, formulates spatiotemporal interconnection scheduling strategies, and establishes an agricultural multi-micro energy network system collaborative optimization scheduling model. Based on the carbon accounting mechanism and spatiotemporal interconnection scheduling strategy, it comprehensively optimizes the operating output level of each device in multiple parks to ensure that the system operates in an efficient and environmentally friendly state.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions:

[0010] The collaborative optimization method of agricultural multi-microgrid based on carbon accounting and spatiotemporal interconnection includes:

[0011] Based on the coordinated operation mode of agricultural multi-microgrids, a multi-micro energy grid system interaction model for agricultural parks is constructed;

[0012] Considering the carbon emission transfer in the energy trading process of multi-micro energy grid, a carbon emission accounting model is constructed;

[0013] Taking into account the seasonal energy supply characteristics of agricultural parks, corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems are formulated for summer, winter, and transition seasons. The real-time changes and transmission of renewable energy output, energy storage equipment, and agricultural loads among multiple parks in the interaction model of the agricultural park multi-micro energy grid system are analyzed.

[0014] Based on the interaction model of multi-micro energy grid systems in agricultural parks, the carbon emission accounting model, and the spatiotemporal interconnection scheduling process, an objective function is constructed 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. The transmission between micro energy grid systems is introduced as a constraint condition, and a collaborative optimization model of rural multi-micro energy grid systems is established and solved to obtain the optimal scheduling scheme of the collaborative optimization model of rural multi-micro energy grid systems.

[0015] According to some embodiments, the present disclosure adopts the following technical solutions:

[0016] The agricultural multi-microgrid collaborative optimization system based on carbon accounting and spatiotemporal interconnection includes:

[0017] System model building module, which is used to build a system interaction model of multi-micro energy grids in agricultural parks based on the coordinated operation mode of agricultural multi-micro grids;

[0018] A carbon accounting model construction module is used to consider the carbon emission transfer in the energy trading process of multiple micro-energy networks and build a carbon emission accounting model;

[0019] A microgrid interconnection model construction module is used to take into account the seasonal energy supply characteristics of agricultural parks, formulate corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems in summer, winter, and transition seasons, and analyze the real-time changes and transmission of renewable energy output, energy storage equipment, and agricultural loads among multiple parks in the agricultural park multi-micro energy grid system interaction model;

[0020] The optimization scheduling module is used to construct an objective function based on the interaction model of the multi-micro energy grid system in the agricultural park, the carbon emission accounting model, and the spatiotemporal 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. It also introduces the transmission between micro energy grid systems as a constraint condition, establishes and solves the rural multi-micro energy grid system collaborative optimization model, and obtains the optimal scheduling plan for the rural multi-micro energy grid system collaborative optimization model.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection is implemented.

[0023] According to some embodiments, the present disclosure adopts the following technical solutions:

[0024] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The disclosed agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection is aimed at agricultural parks, fully considers the output characteristics of renewable energy sources such as wind, photovoltaic, and biogas, and establishes a rural multi-micro energy network system collaborative optimization model based on energy routers. It comprehensively optimizes the operating output level of each device in the multi-park, ensures that the system operates in an efficient and environmentally friendly state, and improves the operating efficiency and stability of the agricultural multi-micro energy network system.

[0027] The disclosed agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection addresses the problem of unclear carbon emission calculation in agricultural multi-micro energy grid system transaction scenarios, considers the carbon emission transfer in the multi-micro energy grid energy transaction process, and constructs a carbon emission accounting model. The carbon emission accounting model includes an equipment carbon accounting model, a carbon accounting model for transactions within and outside the micro energy grid, and an agricultural production carbon accounting model, which realizes accurate accounting of system carbon emissions and provides data support for the formulation of differentiated emission reduction strategies in the future.

[0028] The disclosed agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection takes into account the seasonal energy supply characteristics of agricultural parks, formulates corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems around summer, winter and transition seasons, analyzes the real-time changes and transmission of renewable energy output, energy storage equipment and agricultural loads among multiple parks in the agricultural park multi-micro energy grid system interaction model, improves the dispatchability of renewable energy, and realizes the dynamic supply and demand balance of the multi-microgrid system.

[0029] The disclosed agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection establishes a collaborative optimization scheduling model for agricultural multi-micro energy grid systems with the objective function of minimizing the system's comprehensive operating cost, carbon dioxide emission cost, and transmission loss cost. This model is based on the carbon accounting mechanism and spatiotemporal interconnection scheduling strategy, taking into account agricultural-specific renewable energy such as biogas and straw, realizing the resource utilization of agricultural waste, and effectively helping agricultural multi-microgrid systems achieve green and low-carbon goals. This model comprehensively optimizes the operating output level of each device in multiple parks, and fairly and accurately identifies the carbon emissions of each micro energy grid system, ensuring that the system operates in an efficient and environmentally friendly state while effectively improving energy utilization and promoting sustainable development in the agricultural field. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0031] Figure 1 This is an architecture diagram of the collaborative operation mode of agricultural multi-microgrids according to an embodiment of the present disclosure;

[0032] Figure 2 This is a structural diagram of the multi-micro energy network system in an agricultural park according to an embodiment of the present disclosure;

[0033] Figure 3 This is a diagram of the carbon emission accounting model architecture of an embodiment of the present disclosure;

[0034] Figure 4 This is a process diagram of the application method of the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0035] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0037] 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 intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0038] Example 1

[0039] In one embodiment of the present disclosure, a method for collaborative optimization of agricultural multi-microgrids based on carbon accounting and spatiotemporal interconnection is provided, comprising the following steps:

[0040] Step 1: Based on the coordinated operation mode of agricultural multi-microgrids, a system interaction model of multi-micro energy grids in agricultural parks is constructed;

[0041] Step 2: Consider the carbon emission transfer in the energy trading process of multiple micro-energy networks and build a carbon emission accounting model;

[0042] Step 3: Taking into account the seasonal energy supply characteristics of agricultural parks, develop corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems in summer, winter, and transition seasons. Analyze the real-time changes and transmission of renewable energy output, energy storage equipment, and agricultural loads among multiple parks in the agricultural park multi-micro energy grid system interaction model.

[0043] Step 4: Based on the interaction model of the multi-micro energy grid system in the agricultural park, the carbon emission accounting model, and the spatiotemporal interconnection scheduling process, an objective function is constructed 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. The transmission between micro energy grid systems is introduced as a constraint condition, and a rural multi-micro energy grid system collaborative optimization model is established and solved to obtain the optimal scheduling plan of the rural multi-micro energy grid system collaborative optimization model.

[0044] As an example, the disclosed method for collaborative optimization of agricultural multi-microgrids based on carbon accounting and spatiotemporal interconnection is targeted at agricultural parks. It fully considers the output characteristics of renewable energy sources such as wind, photovoltaics, and biogas, and establishes an interaction model for agricultural park multi-microgrid systems based on energy routers. Furthermore, to address the unclear carbon emission calculation issues in agricultural multi-microgrid system transaction scenarios, an energy flow-carbon coupled carbon emission accounting mechanism is proposed. This analysis analyzes the carbon flow of agricultural multi-microgrid systems and constructs models for equipment carbon accounting, carbon accounting for transactions within and outside the microgrid, and carbon accounting for agricultural production, achieving accurate carbon emission accounting for the system. Furthermore, taking into account the seasonal energy supply characteristics of agricultural parks, a spatiotemporal interconnection scheduling strategy for agricultural park multi-microgrid systems is developed for summer, winter, and transition seasons. Based on the above, the present disclosure establishes a collaborative optimization model for rural multi-microgrid systems, aiming to minimize the comprehensive cost of system operation, the system's carbon emission costs, and the transmission loss costs within the system. Based on the carbon accounting mechanism and spatiotemporal interconnected scheduling strategy, the model comprehensively optimizes the operating output level of each device in multiple parks to ensure that the system operates in an efficient and environmentally friendly state. The specific implementation process is as follows:

[0045] Step 1: Based on the coordinated operation mode of agricultural multi-microgrids, a multi-micro energy grid system interaction model for agricultural parks is constructed, including:

[0046] Step 1.1 Build a multi-micro energy grid system model for an agricultural park

[0047] first, Figure 1 This diagram shows the architecture of a coordinated multi-microgrid operation model for agriculture. The agricultural park multi-microgrid energy grid system consists of three microgrids, each consisting of a wind turbine, a photovoltaic system, a combined heat and power (CHP) unit, a biogas generator, a biomass CHP unit, an energy storage system, and local loads. The energy router, as an intelligent interface, interconnects the microgrids and the energy routers. The energy router's purpose is to maximize the multi-microgrid system's absorption of biomass and other energy sources, reducing reliance on the upstream distribution network and achieving dynamic supply and demand balance across the entire system.

[0048] Each microgrid is assigned an IP address by the Energy Router, which plays a role in the real-time optimization and scheduling of the multi-microgrid energy system in the agricultural park. The Energy Router monitors the load demand, renewable energy output, and the charge / discharge status of the energy storage system within each microgrid. Furthermore, the Energy Router collects relevant status information from each microgrid and, based on the temporal and spatial complementarity between renewable energy and load demand, dispatches the remaining renewable energy in each system, thereby achieving optimal scheduling of the multi-microgrid system.

[0049] Secondly, if Figure 2As shown in Figure 1, the energy equipment in each microgrid includes wind turbines, photovoltaic units, cogeneration units, biogas generators, biomass cogeneration units, and energy storage systems (batteries). System models are constructed for each energy device.

[0050] (1) Photovoltaic power generation model

[0051] The output of photovoltaic generator sets mainly depends on the intensity of solar radiation and the output power Can be modeled as:

[0052] (1)

[0053] in, yes t The light intensity at the moment, It is i Microgrid No. t The power output of the photovoltaic unit at a certain moment, is the temperature parameter of energy conversion of photovoltaic generator set, is the ambient temperature, Is the normal operating temperature of the generator set, is the reference temperature, It is the efficiency of energy conversion of photovoltaic generator sets. is the area of ​​a single photovoltaic panel in a rural park, is the total number of photovoltaic panels in the rural park.

[0054] (2) Wind power generation model

[0055] Output power of wind turbines and wind speed Related, can be modeled as:

[0056] (2)

[0057] in, It is i Microgrid No. t The power output of the wind turbine at a given moment, is the rated output power of the wind turbine, , and They are cut-in, rated and cut-out wind speeds.

[0058] (3) Biogas power generation model

[0059] The biogas tank uses straw, feces, etc. for anaerobic fermentation to produce biogas at a controllable rate. The generated biogas is washed with water and then used for power generation. Can be modeled as:

[0060] (3)

[0061] (4)

[0062] in, It is i Microgrid No. t The power output of the biogas unit at each moment, yes t Moment i The biogas flow to the biogas unit in each microgrid is yes t Biogas flow at the time, is the efficiency of the water washing device. is the output efficiency of the biogas unit, is the conversion factor for thermal units, is the calorific value of biogas.

[0063] (4) Combined heat and power units

[0064] The CHP unit burns natural gas and provides heat and electricity at the same time, which can be modeled as:

[0065] (5)

[0066] (6)

[0067] (7)

[0068] in, and They are i Microgrid No. t The electrical power and thermal power output of the cogeneration unit at each moment, and are natural gas consumption and lower calorific value, 、 and They are the electrical conversion efficiency, thermal conversion efficiency and loss efficiency of the cogeneration unit The efficiency of the combined heat and power unit in generating electricity after burning natural gas. The economic operation of the combined heat and power unit is achieved by limiting the maximum and minimum output of the system. is the maximum output of the cogeneration unit, is a binary function. When the operating power of the cogeneration unit is less than 35% of its rated capacity, is 0, otherwise is 1.

[0069] (5) Biomass cogeneration unit

[0070] Dry matter such as straw and kitchen waste decomposes into combustible gases at high temperatures. These gases burn at high temperatures to drive internal combustion engines to generate electricity, and the high-temperature waste heat of the flue gas can be used to provide heat through waste heat boilers. This can be modeled as:

[0071] (8)

[0072] (9)

[0073] (10)

[0074] in, and It is i Microgrid No. t The electrical power and thermal power output of the biomass cogeneration unit at each moment, It is t Moment i The combustible gas flowing to the biomass cogeneration unit in each microgrid, is the calorific value of the combustible gas, is the generating efficiency of the unit, is the heat loss rate, is the heating efficiency of the unit, It is the maximum power supply of the unit.

[0075] (6) Battery

[0076] Battery energy storage status It changes with the change of charge and discharge:

[0077] (11)

[0078] (12)

[0079] in, and They are the charge and discharge efficiency of the battery respectively. and are the initial and final storage states of the battery, respectively.

[0080] During battery operation, the battery's storage state and charge and discharge power have upper and lower limits, and the following conditions must be met:

[0081] (13)

[0082] (14)

[0083] (15)

[0084] in, It is i Microgrid No. t The charging power of the battery at a moment, It is i Microgrid No. t The discharge power of the battery at a certain moment. and are the minimum and maximum storage states of the battery, and They are the maximum charging power and the maximum discharging power respectively.

[0085] Step 1.2: Construct a multi-micro energy grid system interaction model for the agricultural park;

[0086] According to the characteristics of the agricultural park, the multi-micro energy grid system of the agricultural park is set up as a livestock farm park, a planting farm park and a residential user park respectively. When the renewable energy supply of the multi-micro energy grid system of the agricultural park is insufficient or excessive, the multi-micro energy grid will interact with the superior energy grid and choose to purchase from the superior energy grid according to the energy management strategy. The electricity and heat transaction process between the multi-micro energy grids is defined, and an interaction model of the multi-micro energy grid system of the agricultural park is constructed.

[0087] First, the energy characteristics of different parks, including livestock farm parks, planting farm parks, and residential user parks, are shown in Table 1.

[0088] Table 1 Energy characteristics of different parks

[0089]

[0090] In the table, √ indicates that the park contains the unit, and × indicates that the park does not have the unit.

[0091] Secondly, in the coordinated operation of multiple micro-energy grid systems in agricultural parks, each micro-energy grid system acts as a producer and seller of renewable energy. It collects status information and energy flow data of the energy supply / demand park through energy routers, and then distributes the information to the transmission path to realize intelligent management and scheduling of energy.

[0092] As an example, the energy router serves as an intelligent interface between the agricultural park and the higher-level energy grid. It coordinates the management of renewable energy and load demand to achieve optimal energy utilization. When the agricultural micro-energy grid system experiences insufficient or excessive renewable energy, the micro-energy grid interacts with the higher-level energy grid and chooses to purchase or sell energy from the higher-level grid based on energy management strategies. (The agricultural micro-energy grid system purchases natural gas from the higher-level grid, but does not sell it.) Electricity and heat transactions between micro-energy grids are defined as follows:

[0093] (16)

[0094] (17)

[0095] in, and Respectively represent the amount of electricity and heat traded between the micro energy grid system and the upper energy grid. and When it is greater than or equal to 0, it is defined as the amount of electricity purchased and heat Otherwise it is defined as the amount of electricity sold and heat .

[0096] (18)

[0097] (19)

[0098] in, and Respectively represent The total amount of electricity and heat purchased by each micro energy grid system from other micro energy grid systems, and Represents micro energy grid system From micro energy grid system Electricity and heat purchased. and Respectively indicate time No. The total amount of electricity and heat sold by a micro energy grid system to other micro energy grid systems, and Indicates time Micro energy grid system Micro Energy Grid System Electricity and heat sold.

[0099] Step 2: Consider the carbon emission transfer in the energy trading process of multiple micro-energy networks and build a carbon emission accounting model;

[0100] Specifically, traditional methods for calculating carbon emissions within a multi-microgrid energy system only consider the carbon emissions from purchasing electricity, heat, and gas from the parent energy grid, while ignoring carbon emissions from energy transfers between these systems and from agricultural production activities. Energy trading involves multi-directional and multi-category energy flows between multiple trading entities. Due to the coupling between energy and carbon flows, carbon emissions transfers within these multi-microgrid energy trading processes should be considered when calculating the carbon emissions of each trading entity. Furthermore, agriculture is a significant source of greenhouse gases such as carbon dioxide, and due to its complex characteristics, carbon emissions from agricultural production activities such as crop cultivation should also be considered.

[0101] Carbon flow analysis Figure 3 As shown, the carbon emission sources of each micro energy grid in the present disclosure are considered from the following three parts: first, carbon emission transfer generated by energy transactions between the micro energy grid and the upper energy grid; second, carbon emission generated by energy transactions within the micro energy grid; and third, carbon emission generated by agricultural production activities within the park. t The carbon emissions of multiple micro-energy networks at a given moment are the sum of the carbon emissions generated by the three activities. That is, according to the source of carbon emissions of each micro-energy network, a carbon emission accounting model is constructed, which specifically includes an equipment carbon accounting model, a micro-energy network internal and external transaction carbon accounting model, and an agricultural production carbon accounting model. The agricultural production carbon accounting model measures the carbon emissions of planting farms and livestock farms. The carbon source of planting farms is the actual consumption of agricultural materials, namely fertilizers, pesticides, agricultural plastic films, agricultural machinery, namely diesel, and agricultural irrigation electricity. The carbon emissions of livestock farms are calculated by converting the greenhouse gas emission coefficient of animal husbandry according to the life cycle, and then calculating the carbon emissions. The specific analysis of each part is as follows:

[0102] Step 2.1 Equipment Carbon Accounting Model

[0103] This paper constructs a carbon emission accounting model for energy flow-carbon coupling in agricultural parks. In order to calculate carbon emissions, it is first necessary to clarify the carbon emission intensity of various energy sources in all micro-energy systems. The carbon emission intensity of green energy electricity, heat, and gas is fixed, while the carbon emission intensity of electricity and heat in the system is closely related to the output power of internal devices. i The carbon accounting formula for different equipment within the system is as follows:

[0104] (20)

[0105] (twenty one)

[0106] (twenty two)

[0107] (twenty three)

[0108] (twenty four)

[0109] (25)

[0110] (26)

[0111] (27)

[0112] (28)

[0113] in, is the carbon emission intensity of biogas generator sets, is the carbon emission intensity of electricity. and are the carbon emission intensities of electricity generation and heating from cogeneration units, and are the carbon emission intensities of power generation and heating from biomass cogeneration units, is the electrothermal conversion coefficient. yes t The electricity purchased from the superior energy grid at all times, yes t The carbon emission intensity of batteries at all times, yes t The carbon emission intensity of battery power generation at all times. and They are t Moment Micro Energy Network System i Carbon intensity of internal electricity and heat.

[0114] Step 2.2 Carbon accounting model for internal and external transactions in micro-energy grids

[0115] Based on the above content, this paper constructs a carbon accounting model for internal and external transactions in the micro-energy network system as follows:

[0116] (29)

[0117] (30)

[0118] (31)

[0119] in, yes t Moment Micro Energy Network System i Formulas (30) and (31) use piecewise functions to determine the carbon intensity of electricity and heat traded between micro-energy grid systems. 、 and They represent the carbon emission intensity corresponding to electricity, heat and gas traded outside the micro energy grid system, and Represents micro energy grid system i and j The carbon emission intensity of electricity and heat traded between them.

[0120] Formula (29) shows that when energy trading volume and carbon emission intensity are variables at the same time, the carbon accounting model is nonlinear, which is not conducive to solving the energy scheduling problem. Therefore, in order to ensure the convenience of solving the carbon accounting process, this disclosure assumes that the carbon emission intensity of each micro-energy grid system engaged in internal electricity and heat trading 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 to transform them, and the formula is as follows:

[0121] (32)

[0122] (33)

[0123] (34)

[0124] (35)

[0125] (36)

[0126] Among them, formulas (32-35) use inequality constraints to convert electricity and heat transaction variables into purchases and sales. and Refers to the i A micro energy grid system t Give the first moment j The electricity and heat of a micro energy grid. and Refers to the i A micro energy grid system t Get the first j The electricity and heat of a micro energy grid. 、 、 and refers to a binary variable, is a large numerical constant. Formula (36) ensures that i Two micro-energy grid systems cannot obtain or provide the same type of energy at the same time.

[0127] Combining formulas (32-36), the carbon accounting model for internal and external transactions in the agricultural micro-energy network system can be expressed as:

[0128] (37)

[0129] Among them, Carbon emissions means i The total carbon emissions of a micro energy grid system.

[0130] Step 2.3 Agricultural production carbon accounting model

[0131] Agricultural production activities are complex, with numerous carbon sources and overlapping influences between them, making agricultural carbon source accounting difficult. This paper conducts an in-depth analysis of carbon emissions from crop and animal husbandry, providing precise carbon emission measurements.

[0132] (1) Planting industry

[0133] The main carbon source in agricultural production is the consumption of agricultural materials, namely the actual usage of fertilizers, pesticides, agricultural plastic films, agricultural machinery and diesel, as well as electricity used for agricultural irrigation. The carbon emission calculation formula is as follows:

[0134] (38)

[0135] in, For the i Physical amount of fossil energy, 10,000 or 100 million ; For the i Energy unit calorific value, or ; For the i Carbon content per unit calorific value of energy, ; is the carbon oxidation rate during combustion.

[0136] Furthermore, the carbon emission coefficients of fertilizers, pesticides and agricultural films are shown in Table 2 below.

[0137] Table 2 Carbon emission coefficients of fertilizers, pesticides and agricultural films

[0138]

[0139] (2) Animal Husbandry

[0140] The primary sources of greenhouse gas emissions from livestock farming are intestinal fermentation and the management of animal manure. Microorganisms living in the animal intestines are a major source of emissions. Furthermore, animals excrete large amounts of manure during their growth cycles, which needs to be stored before being applied to the soil, a process that also generates greenhouse gases.

[0141] Due to the differences in the growth cycles of livestock and poultry such as pigs and rabbits, this paper analyzes the greenhouse gas emission coefficients of animal husbandry according to their life cycle. Convert and then calculate carbon emissions. The formula is as follows:

[0142] (39)

[0143] (40)

[0144] in, It's livestock i life cycle, Indicates the i The annual number of breeding livestock and poultry slaughtered, Indicates the i Breeding livestock and poultry t Number of livestock on hand at the end of the year, The carbon content of animal husbandry.

[0145] Step 2.4 Carbon Emissions Accounting Model

[0146] In summary, i The carbon accounting model of a micro energy grid system is as follows:

[0147] (41)

[0148] Step 3: Taking into account the seasonal energy supply characteristics of agricultural parks, formulate corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems in summer, winter, and transition seasons. Analyze the real-time changes and transmission of renewable energy output, energy storage equipment, and agricultural loads among multiple parks in the agricultural park multi-micro energy grid system interaction model.

[0149] Specifically, the spatiotemporal interactive scheduling strategy proposed in this paper is based on the real-time changes in renewable energy output, energy storage equipment, and agricultural loads among the three agricultural parks. Through the flexible scheduling and collaborative management of energy routers, optimal resource allocation is achieved. The system detects the operating status and parameters of the energy system within the proposed optimal scheduling strategy and performs scheduling and control based on real-time data. Therefore, the energy difference in the agricultural multi-micro energy grid system is and for:

[0150] (42)

[0151] in, and They are t The electrical and thermal loads at each moment.

[0152] Furthermore, based on seasonal differences, this disclosure develops corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems for summer, winter, and transitional seasons, as follows:

[0153] (1) Summer

[0154] In summer, sunlight intensity is high but wind speeds are low, so photovoltaic generator output reaches its maximum while wind turbine output reaches its minimum. High summer temperatures are conducive to the growth and reproduction of microorganisms, so biogas generators and biomass cogeneration units reach their maximum output. However, due to lower heat demand and the high heat supply from biomass cogeneration units, cogeneration unit output reaches its minimum.

[0155] Based on the current situation, the renewable energy supply in the planting farm park and the residential user park exceeds the demand, and the renewable energy demand in the livestock farm park exceeds the supply. The following interactions exist:

[0156] (a) The livestock farm park will give priority to the micro-energy grid system with a shorter path and sufficient renewable energy for energy supply. If it cannot meet the load demand, another micro-energy grid system with excess renewable energy will be selected for energy supply.

[0157] (b) If the energy needs of the livestock farm park are met, the excess energy will be distributed to the local battery and the upper energy grid in turn.

[0158] (c) If the livestock farm park’s energy needs cannot be met, the park will choose to use local batteries and purchase energy from the higher-level energy network.

[0159] (2) Winter

[0160] In winter, wind speeds are high and sunlight intensity is low, so wind turbine output reaches its maximum and photovoltaic output reaches its minimum. Winter temperatures are low, and the decomposition rate of raw materials is low, so the output of biogas generators and biomass cogeneration units reaches its minimum. However, due to the high demand for heat in winter, the output of cogeneration units also reaches its maximum.

[0161] Based on the current situation, the supply of renewable energy in the livestock farm park exceeds the demand, and the demand for renewable energy in the residential user park and the planting farm park exceeds the supply. The following interactions exist:

[0162] (a) The livestock farm park gives priority to transmitting energy to a micro-energy grid system with a shorter distance, and then sends it to another micro-energy grid system with insufficient power.

[0163] (b) If the livestock farm park meets the energy needs of the residential user park and the crop farm park, the excess energy will be distributed to the local battery and the upper energy grid in turn.

[0164] (c) If the livestock farm park cannot meet the energy needs of the residential user park and the planting farm park, the micro-energy grid system with insufficient power will choose local batteries and purchase energy from the superior energy grid.

[0165] (3) Transition Season

[0166] During the transition season, temperatures are moderate, and the output of renewable energy generators is average. Therefore, the supply and demand of renewable energy across the three parks are roughly equal. Specific strategies vary based on load demand. If load demand exceeds energy supply, battery power and energy purchases from the upstream energy grid will be used. If load demand is less than energy supply, the micro-energy grid system prioritizes battery charging and sells any excess energy to the upstream energy grid.

[0167] Step 4: Based on the agricultural park multi-micro energy grid system interaction model, carbon emission accounting model, and spatiotemporal interconnected scheduling process, an objective function is constructed with the goal of minimizing the comprehensive cost of system operation, the system's carbon emission cost, and the transmission loss cost within the system. The transmission between micro energy grid systems is introduced as a constraint condition. A rural multi-micro energy grid system collaborative optimization model is established and solved to obtain the optimal scheduling scheme for the rural multi-micro energy grid system collaborative optimization model, which specifically includes:

[0168] Step 4.1: Construct the objective function

[0169] The objective function is constructed 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 determine a set of weights. Construct the objective function as follows:

[0170] (43)

[0171] in, Total cost of running the system

[0172] in, Should meet:

[0173] (44)

[0174] Furthermore, (1) the comprehensive cost of system operation :

[0175] Comprehensive cost of system operation Including operation and maintenance costs and transaction costs , which can be expressed as follows:

[0176] (45)

[0177] First, the system operation and maintenance costs The equipment operation and maintenance costs, including wind turbines, photovoltaic generators, biogas generators, biomass cogeneration units, combined heat and power units, and batteries, can be expressed as:

[0178] (46)

[0179] in, 、 、 、 、 and They are the equipment operation and maintenance coefficients of wind turbines, photovoltaic generators, biogas generators, biomass cogeneration units, combined heat and power units and batteries.

[0180] Secondly, system transaction costs The transaction costs between the micro energy grid system and the upper energy grid Transaction costs between the micro energy grid system composition. The calculation is as follows:

[0181] (47)

[0182] in, 、 and They represent the real-time prices of electricity, heat and gas purchased by the agricultural multi-micro energy grid system from the upper energy grid. 、 and They respectively represent the values ​​of electricity, heat and gas energy purchased from the superior energy grid system. and They represent the real-time prices of electricity, heat and gas sold by the agricultural multi-micro energy grid system to the upper energy grid. and They respectively represent the values ​​of electricity, heat and gas energy sold to the superior energy grid. and Represents micro energy grid system i and j The trading volume of electric heating units, and Represents micro energy grid system i and j The unit transaction price between .

[0183] (2) Transmission loss cost within the system

[0184] The specific energy transmitted interactively in the agricultural multi-micro energy grid system is determined based on the energy supply and demand between the three parks. The energy router issues instructions to achieve spatiotemporal interaction between the parks. The interaction between electrical energy and thermal energy is achieved through transmission loss. Therefore, this disclosure considers the transmission loss cost between the micro energy grid system as the system spatiotemporal interconnection cost. The spatiotemporal interconnection cost mainly includes the electrical and thermal transmission loss between the micro energy grid and the energy router. and , the specific formula is as follows:

[0185] (48)

[0186] (49)

[0187] in, and Indicates the unit loss price of electric heat during the transmission and distribution process, Indicates the allowable voltage during the transmission and distribution process.

[0188] (3) System carbon emission costs

[0189] Currently, in order to promote the low-carbon operation of the micro-energy grid system, a carbon emission factor is introduced based on step 2 to calculate the carbon emission cost of the system. t Moment, i The carbon emission cost of a micro energy grid system is:

[0190] (50)

[0191] in, is the carbon emission factor. is the carbon emission factor.

[0192] Furthermore, the constraints include power balance constraints, electric and heat output constraints, restrictions on energy trading with the upper energy grid, and transmission between micro-energy grid systems. The rural multi-micro-energy grid system collaborative optimization model is a mixed integer nonlinear model, which is converted into a mixed integer linear programming problem using piecewise linearization. The details are as follows:

[0193] (1) Power balance constraints:

[0194] (51)

[0195] (52)

[0196] (2) Electric heating output constraints:

[0197] (53)

[0198] (54)

[0199] (3) Restrictions on energy transactions with higher-level energy grids:

[0200] (55)

[0201] (4) Transmission constraints between micro-energy grid systems:

[0202] (56)

[0203] in, and Micro Energy Grid System i and j The electric heating power transmitted between and is the maximum value of electrical heat transfer, and Indicates the maximum and minimum voltages allowed on the transmission path.

[0204] Finally, the constructed collaborative optimization model of the rural multi-micro energy grid system is a mixed integer nonlinear model. Therefore, piecewise linearization processing is required to convert 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.

[0205] Example 2

[0206] In one embodiment of the present disclosure, a multi-microgrid collaborative optimization system for agriculture based on carbon accounting and spatiotemporal interconnection is provided, comprising:

[0207] System model building module, which is used to build a system interaction model of multi-micro energy grids in agricultural parks based on the coordinated operation mode of agricultural multi-micro grids;

[0208] A carbon accounting model construction module is used to consider the carbon emission transfer in the energy trading process of multiple micro-energy networks and build a carbon emission accounting model;

[0209] A microgrid interconnection model construction module is used to take into account the seasonal energy supply characteristics of agricultural parks, formulate corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems in summer, winter, and transition seasons, and analyze the real-time changes and transmission of renewable energy output, energy storage equipment, and agricultural loads among multiple parks in the agricultural park multi-micro energy grid system interaction model;

[0210] The optimization scheduling module is used to construct an objective function based on the interaction model of the multi-micro energy grid system in the agricultural park, the carbon emission accounting model, and the spatiotemporal 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. It also introduces the transmission between micro energy grid systems as a constraint condition, establishes and solves the rural multi-micro energy grid system collaborative optimization model, and obtains the optimal scheduling plan for the rural multi-micro energy grid system collaborative optimization model.

[0211] As an embodiment, the scheduling process of the collaborative optimization method of the agricultural multi-microgrid collaborative optimization system based on carbon accounting and spatiotemporal interconnection is as follows:

[0212] (1) Select a typical day, set the total scheduling time to 24 hours, and the scheduling interval to 1 hour;

[0213] (2) Input the basic parameters of the agricultural multi-micro energy grid system, including equipment parameters such as wind turbines, photovoltaic generators, and biogas generators; input data such as photovoltaic power generation, wind power generation, and load on typical days in summer, winter, and transition seasons.

[0214] (3) Based on the carbon emission accounting process, the carbon emissions in summer, winter and transition seasons are accurately calculated, and different spatiotemporal interactive scheduling strategies are proposed accordingly, taking into account the different seasonal characteristics within a year;

[0215] (4) Based on the objective function and constraints, a collaborative optimization scheduling model for the agricultural multi-micro energy grid system is established and linearized. Finally, the commercial solver CPLEX is used to solve it and achieve the optimal scheduling of the collaborative optimization model for the agricultural multi-micro energy grid system.

[0216] Example 3

[0217] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection is implemented.

[0218] Example 4

[0219] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection.

[0220] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0221] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0222] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work 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 spatiotemporal interconnection, characterized by: include: Based on the coordinated operation mode of agricultural multi-microgrids, a multi-micro energy grid system interaction model for agricultural parks is constructed; According to the characteristics of the agricultural park, the multi-micro energy grid system of the agricultural park consists of three microgrids, which are divided into the corresponding livestock farm park, planting farm park and residential user park. The livestock farm park, planting farm park and residential user park have different energy characteristics. When the renewable energy supply of the agricultural park multi-micro energy grid system is insufficient or excessive, the multi-micro energy grid will interact with the upper energy grid and choose to purchase from the upper energy grid according to the energy management strategy. The electricity and heat transaction process between the multi-micro energy grids is defined: and the interaction model of the multi-micro energy grid system of the agricultural park is constructed. in, and Respectively represent The total amount of electricity and heat purchased by each micro energy grid system from other micro energy grid systems, and Represents micro energy grid system From micro energy grid system The amount of electricity and heat purchased, and Respectively indicate time No. The total amount of electricity and heat sold by a micro energy grid system to other micro energy grid systems, and Indicates time Micro energy grid system Micro Energy Grid System Electricity and heat sold; In the coordinated operation of the multi-micro energy grid system in the agricultural park, each micro energy grid system acts as a producer and seller of renewable energy. It collects status information and energy flow data of the energy supply / demand park through the energy router, and then distributes this information to the transmission path to achieve intelligent energy management and scheduling. Each microgrid is assigned an IP address by the energy router, which plays a role in the real-time optimization and scheduling of the multi-micro energy grid system in the agricultural park. The energy router monitors the load demand in each microgrid, collects relevant status information from each microgrid, and schedules the remaining renewable energy of each system based on the temporal and spatial complementarity of renewable energy and load demand, thereby achieving optimal scheduling of the multi-microgrid system. Considering the carbon emission transfer in the energy trading process of multi-micro energy grid, a carbon emission accounting model is constructed; Taking into account the seasonal energy supply characteristics of agricultural parks, a corresponding spatiotemporal interconnection scheduling strategy for the multi-micro energy grid system of agricultural parks is formulated around the summer, winter and transition seasons. The real-time changes and transmission of renewable energy output, energy storage equipment and agricultural loads among multiple parks in the interaction model of the multi-micro energy grid system of agricultural parks are analyzed: (1) Summer In summer, the sunlight intensity is high, but the wind speed is low. The output of the photovoltaic generator set reaches its maximum value while the output of the wind turbine generator set reaches its minimum value. The high temperature in summer is conducive to the growth and reproduction of microorganisms, so the output of the biogas generator set and the biomass cogeneration unit reaches its maximum value. However, the heat demand in summer is low, and the biomass cogeneration unit provides more heat, so the output of the cogeneration unit reaches its minimum value. Based on the current situation, the renewable energy supply in the planting farm park and the residential user park exceeds the demand, and the renewable energy demand in the livestock farm park exceeds the supply. The following interactions exist: (a) The livestock farm park will give priority to using a micro-grid system with a shorter route and sufficient renewable energy for energy supply. If this cannot meet the load demand, another micro-grid system with excess renewable energy will be selected for energy supply; (b) If the energy needs of the livestock farm park are met, the excess energy will be distributed to the local battery and the upper energy grid in turn; (c) If the livestock farm park’s energy needs cannot be met, the park will choose to use local batteries and purchase energy from the higher-level energy network; (2) Winter In winter, the wind speed is high and the light intensity is low, so the output of wind turbines reaches its maximum value and the output of photovoltaic generators reaches its minimum value. In winter, the temperature is low and the decomposition rate of raw materials is low, so the output of biogas generators and biomass cogeneration units reaches its minimum value. However, the demand for heat in winter is high, so the output of cogeneration units also reaches its maximum value. Based on the current situation, the supply of renewable energy in the livestock farm park exceeds the demand, and the demand for renewable energy in the residential user park and the planting farm park exceeds the supply. The following interactions exist: (a) The livestock farm park gives priority to transmitting energy to a micro-energy grid system with a shorter distance, and then sends it to another micro-energy grid system with insufficient power; (b) If the livestock farm park meets the energy needs of the residential user park and the crop farm park, the excess energy will be distributed to the local battery and the upper energy grid in turn; (c) If the livestock farm park cannot meet the energy needs of the residential user park and the planting farm park, the insufficient power micro-energy grid system will choose to use local batteries and purchase energy from the upper energy grid; (3) Transition Season During the transition season, temperatures are moderate, and the output of renewable energy generators is at average levels. Therefore, the supply and demand of renewable energy in the three parks are roughly equal. Specific strategies vary based on load demand. If load demand exceeds energy supply, battery power supply and energy purchase from the upper-level energy grid will be used. If load demand is less than energy supply, the micro-energy grid system will prioritize battery charging and sell any excess energy to the upper-level energy grid. Based on the interaction model of the multi-micro energy grid system in the agricultural park, the carbon emission accounting model, and the spatiotemporal interconnection scheduling process, an objective function is constructed 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. The transmission between micro energy grid systems is introduced as a constraint condition. A rural multi-micro energy grid system collaborative optimization model is established and solved, and the optimal scheduling scheme of the rural multi-micro energy grid system collaborative optimization model is obtained. The objective function is: Among them, the comprehensive cost of system operation Including operation and maintenance costs and transaction costs, is the weight, The cost of space-time interconnection mainly includes the loss of electric and heat transmission between the micro energy grid and the energy router. and The specific energy interactively transmitted in the agricultural multi-micro energy grid system is determined based on the energy supply and demand between the three parks. The energy router issues instructions to realize the spatiotemporal interaction between the parks, and the interaction between electrical energy and thermal energy is realized through transmission loss. The transmission loss cost between the micro energy grid systems is considered as the spatiotemporal interconnection cost of the system.

2. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection according to claim 1 is characterized in that: The agricultural multi-microgrid consists of three microgrids, each of which consists of a wind turbine, a photovoltaic unit, a cogeneration unit, a biogas generator unit, a biomass cogeneration unit, an energy storage system and a local load. The microgrids use energy routers as intelligent interfaces to achieve interconnection between the microgrids and between the microgrids and the energy routers. Each microgrid is assigned an IP address by the energy router to achieve real-time optimization and scheduling of the agricultural multi-microgrid.

3. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection according to claim 1 is characterized in that: In the process of energy trading, there are multi-directional and multi-category energy flows between multiple trading entities. Due to the coupling relationship between energy flow and carbon flow, the carbon emission sources of each micro-energy network are considered from the following three parts: first, the carbon emission transfer generated by energy transactions between the micro-energy network and the upper energy network; second, the carbon emissions generated by energy transactions within the micro-energy network; and third, the carbon emissions generated by agricultural production activities within the park. t The carbon emissions of the multi-micro energy network at a given moment are the sum of the carbon emissions generated by the three activities.

4. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection according to claim 1 is characterized in that: According to the carbon emission sources of each micro-energy network, the constructed carbon emission accounting model includes the equipment carbon accounting model, the micro-energy network internal and external transaction carbon accounting model and the agricultural production carbon accounting model. The agricultural production carbon accounting model measures the carbon emissions of planting farms and livestock farms. The carbon source of planting farms is the actual usage data of agricultural materials, namely fertilizers, pesticides, agricultural plastic films, agricultural machinery, namely diesel, and agricultural irrigation electricity. The carbon emissions of livestock farms are calculated by converting the greenhouse gas emission coefficient of animal husbandry according to the life cycle, and then calculating the carbon emissions.

5. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection according to claim 1 is characterized in that: Based on the seasonal differences, a spatiotemporal interconnected scheduling strategy is formulated for the summer, winter and transition seasons. In the summer, the supply of renewable energy in the planting farm park and the residential user park is greater than the demand, and the demand for renewable energy in the livestock farm park is greater than the supply; in the winter, the supply of renewable energy in the livestock farm park is greater than the demand, and the demand for renewable energy in the residential user park and the planting farm park is greater than the supply; in the transition season, the output of the renewable energy power supply units is at the average value, so the supply and demand of renewable energy in the three parks are the same.

6. The agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection according to claim 1 is characterized in that: The objectives of objective function construction 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 constraints include power balance constraints, electric and thermal output constraints, restrictions on energy trading with the upper-level energy grid, and transmission between micro-energy grid systems. The rural multi-micro-energy grid system collaborative optimization model is a mixed integer nonlinear model, which is converted into a mixed integer linear programming problem using piecewise linearization.

7. An agricultural multi-microgrid collaborative optimization system based on carbon accounting and spatiotemporal interconnection, specifically implementing an agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection as described in any one of claims 1-6, characterized in that: include: System model building module, which is used to build a system interaction model of multi-micro energy grids in agricultural parks based on the coordinated operation mode of agricultural multi-micro grids; A carbon accounting model construction module is used to consider the carbon emission transfer in the energy trading process of multiple micro-energy networks and build a carbon emission accounting model; A microgrid interconnection model construction module is used to take into account the seasonal energy supply characteristics of agricultural parks, formulate corresponding spatiotemporal interconnection scheduling strategies for agricultural park multi-micro energy grid systems in summer, winter, and transition seasons, and analyze the real-time changes and transmission of renewable energy output, energy storage equipment, and agricultural loads among multiple parks in the agricultural park multi-micro energy grid system interaction model; The optimization scheduling module is used to construct an objective function based on the interaction model of the multi-micro energy grid system in the agricultural park, the carbon emission accounting model, and the spatiotemporal 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. It also introduces the transmission between micro energy grid systems as a constraint condition, establishes and solves the rural multi-micro energy grid system collaborative optimization model, and obtains the optimal scheduling plan for the rural multi-micro energy grid system collaborative optimization model.

8. 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 the processor, the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection as described in any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: 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 is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the agricultural multi-microgrid collaborative optimization method based on carbon accounting and spatiotemporal interconnection as described in any one of claims 1 to 6.

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