Park energy intelligent planning method for zero-carbon transformation of agriculture and pasture

Abstract

The invention discloses an intelligent park energy planning method for agricultural and pastoral zero-carbon transformation, relates to the technical field of energy planning, and is used for solving the problem that the controllability of agricultural and pastoral park energy planning is reduced. The photovoltaic electric quantity data and the soil carbon sequestration saturation data of a to-be-measured agricultural and pastoral production area are integrated to obtain negative carbon compensation characteristics, electric power information of energy consumption metering equipment is obtained, positive carbon emission characteristics are constructed, a carbon notch coefficient is calculated by performing specific value analysis on the positive carbon emission characteristics and the negative carbon supply characteristics, and the power consumption of the to-be-measured agricultural and pastoral production area is calculated. According to the method, the carbon compensation state of the to-be-detected agricultural and pastoral production area is judged, external energy supply configuration is restrained and evaluated according to the carbon notch coefficient, an unenforceable energy transformation scheme depending on external energy supply is prevented from being formed under the condition that negative carbon resources are limited, and implementation judgment of a zero-carbon transformation scheme is achieved. And the scientificity and controllability of the energy planning of the agricultural and pastoral park are improved.

Description

Technical Field

[0001] This invention relates to the field of energy planning technology, and more specifically, to a smart energy planning method for industrial parks aimed at zero-carbon transformation of agriculture and animal husbandry. Background Technology

[0002] As a complex energy-consuming scenario integrating agricultural production, livestock breeding, primary processing and supporting energy use, agricultural and pastoral parks have increasingly complex energy and carbon emission structures. In the process of implementing zero-carbon or near-zero-carbon transformation, existing agricultural and pastoral parks usually adopt methods such as photovoltaic power generation, biomass utilization, electricity substitution and external clean energy access to optimize their energy structure.

[0003] The existing technology has the following shortcomings: Currently, existing technologies mostly rely on historical energy consumption or installed capacity to perform static energy configuration and ex-post carbon accounting for agricultural and pastoral parks. They lack dynamic assessment mechanisms that take negative carbon resources such as photovoltaic power generation capacity and soil carbon sequestration saturation as pre-constraints. This makes it difficult to quantify the matching relationship between positive carbon emissions and negative carbon compensation during the energy planning stage, which reduces the controllability of energy planning in agricultural and pastoral parks. This can easily lead to unsustainable transformation schemes that rely on external energy supply to compensate for carbon gaps under the condition of limited negative carbon resources. Therefore, a smart energy planning method for parks oriented towards zero-carbon transformation of agricultural and pastoral parks is proposed.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an intelligent energy planning method for agricultural and livestock parks aimed at zero-carbon transformation. This method addresses the problems mentioned in the background art by employing a coupled analysis mechanism based on positive carbon emission characteristics and negative carbon compensation characteristics within a planning evaluation cycle.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a smart energy planning method for agricultural and livestock parks aimed at zero-carbon transformation, comprising the following steps: Step S1: Retrieve the planning assessment period of the agricultural and livestock production area to be tested, detect the photovoltaic power generation data and soil carbon sequestration saturation data of the agricultural and livestock production area to be tested within the planning assessment period, and evaluate the negative carbon compensation characteristics based on the detection results. Step S2: Collect the power information of energy consumption metering equipment in the agricultural and pastoral production area to be tested and generate positive carbon emission characteristics. Calculate the carbon gap coefficient by combining the positive carbon emission characteristics and the negative carbon compensation characteristics. Generate the carbon compensation status of the agricultural and pastoral production area to be tested based on the carbon gap coefficient. Step S3: Use the carbon offset status to determine whether to access the configuration database to obtain energy configuration information within the planning evaluation period, lock the external energy supply configuration based on the energy configuration information and set the number of energy supply triggers, and use the number of energy supply triggers to analyze the energy supply concentration index. Step S4: Filter and mark external energy supply configurations based on the energy supply concentration index, perform trigger constraint processing on the marked external energy supply configurations, detect the energy supply activation duration, and determine whether to generate a transformation warning prompt based on the energy supply activation duration.

[0007] In a preferred embodiment, in step S1, the planning evaluation cycle of the agricultural and livestock production area to be tested is retrieved from the planning parameter library; During the planning and evaluation period, the actual electricity consumed by the photovoltaic system in the agricultural and livestock production area under test is detected by the energy consumption metering interface of the energy consumption metering equipment to obtain the actual electricity consumption. The actual electricity consumption is multiplied by the preset photovoltaic emission reduction factor to obtain the photovoltaic power data.

[0008] In a preferred embodiment, in step S1, soil carbon sequestration saturation data of the agricultural and pastoral production area to be tested is obtained through a carbon sequestration capacity table. The soil carbon sequestration saturation data is the level of carbon sequestration per unit area of ​​soil in the agricultural and pastoral production area to be tested relative to its upper limit of carbon sequestration. After standardizing the photovoltaic power data and soil carbon sequestration saturation data respectively, the photovoltaic power coefficient and soil carbon sequestration saturation coefficient were obtained. The negative carbon compensation characteristics were calculated by combining the photovoltaic power generation coefficient and the soil carbon sequestration saturation coefficient.

[0009] In a preferred embodiment, in step S2, during the planning and evaluation period, the power information of the energy consumption metering equipment in the agricultural and livestock production area to be tested is collected through the energy consumption metering power interface, including the average operating power of the energy consumption metering equipment. The average operating power of the energy consumption metering equipment is processed by time integration to obtain the positive carbon emission of the energy consumption metering equipment within the planning and evaluation period. The product of the positive carbon emission and the preset power emission factor is used as the positive carbon emission characteristic.

[0010] In a preferred embodiment, in step S2, the ratio of negative carbon compensation characteristics to a preset negative carbon compensation threshold is used as the negative carbon supply coefficient, and the ratio of positive carbon emission characteristics to a preset positive carbon emission threshold is used as the positive carbon emission coefficient. The ratio of the negative carbon supply coefficient to the positive carbon emission coefficient is used as the carbon gap coefficient. Access the historical database to retrieve the carbon offset status benchmark of the agricultural and livestock production area to be tested; If the carbon deficit coefficient is greater than the carbon compensation state benchmark, then the carbon compensation state of the agricultural and pastoral production area to be tested is judged to be a carbon compensation matching state. Conversely, the carbon compensation status of the agricultural and pastoral production area under test is determined to be insufficient.

[0011] In a preferred embodiment, in step S3, after the carbon compensation status determination is completed, when the agricultural and livestock production area to be tested is determined to be in a state of insufficient carbon compensation, the configuration database is accessed to obtain energy configuration information within the planning assessment period. Energy configuration information takes energy supply units as the basic object and includes the type of energy source, energy supply activation status, rated energy supply capacity, and energy supply activation duration. All energy supply units were screened based on the type of energy source, and only those energy supply units whose energy source type is external energy supply to the park were retained as external energy supply configurations.

[0012] In a preferred embodiment, in step S3, the activation behavior of each external power supply configuration is statistically analyzed, with the scheduling period as the minimum time dimension: When an external power supply configuration changes from an inactive state to an active state between two adjacent scheduling cycles, it is recorded as a power supply trigger. Throughout the entire planning and evaluation period, the number of times external energy supply configurations are triggered is accumulated to obtain the corresponding number of energy supply triggers; The energy concentration index is obtained by summing the squares of the number of energy supply triggers for each external energy supply configuration and normalizing it with the square of the total number of energy supply triggers.

[0013] In a preferred embodiment, in step S4, when the energy supply concentration index is lower than the concentration determination threshold, it is determined that the current energy planning scheme has a decentralized dependence on external energy supply, and the corresponding external energy supply configuration is marked as a high dependence configuration. When the energy supply concentration index is higher than or equal to the concentration judgment threshold, the external energy supply activation behavior is judged to be relatively concentrated, and the corresponding external energy supply configuration is not marked as risk. Trigger constraint processing is performed on external power supply configurations marked as high-dependency configurations. The trigger constraint processing includes limiting the maximum number of power supply triggers allowed for an external power supply configuration within a single planning evaluation period, and reducing the activation priority of external power supply configurations in scheduling decisions.

[0014] In a preferred embodiment, in step S4, after completing the trigger constraint processing, the power supply activation duration of each external power supply configuration within the planning evaluation period is recalculated. The power supply activation duration is the product of the number of scheduling periods in which the external power supply configuration is in the activated state under the constraint conditions and the scheduling period duration. When the constrained energy supply activation time exceeds the preset activation time threshold, it is determined that the zero-carbon transformation target is difficult to achieve, and a transformation early warning is generated to indicate that the current planning scheme is not feasible under the actual negative carbon conditions and requires major adjustments. When the constrained energy supply activation time is lower than or equal to the preset activation time threshold, the current energy smart planning scheme is determined to be feasible within the planning evaluation cycle, and the corresponding zero-carbon transformation planning result is output.

[0015] The technical effects and advantages of this invention are as follows: This invention uses the planning and evaluation cycle as a time benchmark, integrates photovoltaic power generation data and soil carbon sequestration saturation data of the agricultural and pastoral production area to obtain negative carbon compensation characteristics, acquires power information from energy consumption metering equipment, constructs positive carbon emission characteristics, and calculates the carbon gap coefficient by performing ratio analysis on the positive carbon emission characteristics and negative carbon supply characteristics. Based on this, the carbon compensation status of the agricultural and pastoral production area to be tested is determined, and the external energy supply configuration is constrained and evaluated according to the carbon gap coefficient. This avoids the formation of unfeasible energy transformation schemes that rely on external energy supply under the condition of limited negative carbon resources. By introducing negative carbon resources as a pre-constraint into the energy planning process, the feasibility of zero-carbon transformation schemes is determined, and the scientificity and controllability of energy planning in agricultural and pastoral parks are improved. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of an intelligent energy planning method for zero-carbon transformation of agricultural and livestock parks according to the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the steps of an intelligent energy planning method for zero-carbon transformation of agricultural and livestock parks according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention uses the planning and evaluation cycle as a time benchmark, integrates photovoltaic power generation data and soil carbon sequestration saturation data of the agricultural and pastoral production area to be tested to obtain negative carbon compensation characteristics, acquires power information of energy consumption metering equipment, constructs positive carbon emission characteristics, and calculates the carbon gap coefficient by performing ratio analysis on the positive carbon emission characteristics and negative carbon supply characteristics. Based on this, the carbon compensation status of the agricultural and pastoral production area to be tested is determined, and the external energy supply configuration is constrained and evaluated according to the carbon gap coefficient. This avoids the formation of unfeasible energy transformation schemes that rely on external energy supply under the condition of limited negative carbon resources. By introducing negative carbon resources as a pre-constraint into the energy planning process, the feasibility of zero-carbon transformation schemes is determined.

[0020] Example 1, such as Figures 1 to 2 As shown, a smart energy planning method for agricultural and livestock parks aimed at zero-carbon transformation includes the following steps: Step S1: Retrieve the planning assessment period of the agricultural and livestock production area to be tested, detect the photovoltaic power generation data and soil carbon sequestration saturation data of the agricultural and livestock production area to be tested within the planning assessment period, and evaluate the negative carbon compensation characteristics based on the detection results. Step S2: Collect the power information of energy consumption metering equipment in the agricultural and pastoral production area to be tested and generate positive carbon emission characteristics. Calculate the carbon gap coefficient by combining the positive carbon emission characteristics and the negative carbon compensation characteristics. Generate the carbon compensation status of the agricultural and pastoral production area to be tested based on the carbon gap coefficient. Step S3: Use the carbon offset status to determine whether to access the configuration database to obtain energy configuration information within the planning evaluation period, lock the external energy supply configuration based on the energy configuration information and set the number of energy supply triggers, and use the number of energy supply triggers to analyze the energy supply concentration index. Step S4: Filter and mark external energy supply configurations based on the energy supply concentration index, perform trigger constraint processing on the marked external energy supply configurations, detect the energy supply activation duration, and determine whether to generate a transformation warning prompt based on the energy supply activation duration.

[0021] The specific implementation is as follows: In step S1, the planning assessment cycle of the agricultural and pastoral production area to be tested is retrieved from the planning parameter library. The planning assessment cycle refers to the time period for assessing the energy supply structure and carbon compensation capacity of the agricultural and pastoral production area to be tested, so as to avoid forming an unfeasible energy allocation plan under the condition of limited negative carbon resources. During the planning and evaluation period, the actual electricity consumed by the photovoltaic system in the agricultural and livestock production area under test is detected by the energy consumption metering interface of the energy consumption metering equipment to obtain the actual electricity consumption. The actual electricity consumption is multiplied by the preset photovoltaic emission reduction factor to obtain the photovoltaic power data, which reflects the positive carbon emission reduction capacity of photovoltaic power generation during the planning and evaluation cycle. Soil carbon sequestration saturation data of the agricultural and pastoral production area to be tested were obtained through the carbon sequestration capacity table. Soil carbon sequestration saturation data is the level of carbon sequestration per unit area of ​​soil in the current agricultural and pastoral production area to be tested relative to its upper limit of carbon sequestration. After standardizing the photovoltaic power data and soil carbon sequestration saturation data respectively, the photovoltaic power coefficient and soil carbon sequestration saturation coefficient were obtained. The higher the photovoltaic power coefficient, the higher the effective emission reduction contribution of photovoltaic power generation to offset positive carbon emissions; the higher the soil carbon sequestration saturation coefficient, the smaller the remaining space for soil carbon sequestration. The combined photovoltaic power generation coefficient and soil carbon sequestration saturation coefficient generate negative carbon compensation characteristics: ,in, and As a preset weighting factor, This is the soil carbon sequestration saturation coefficient. The photovoltaic power coefficient, It exhibits negative carbon compensation characteristics; The larger the negative carbon compensation characteristic, the stronger the negative carbon supply capacity of the tested agricultural and pastoral production area; the smaller the negative carbon compensation characteristic, the higher the degree of limitation of negative carbon resources in the tested agricultural and pastoral production area.

[0022] It should be noted that the planning parameter library is a configuration database used to store the planning evaluation cycle related to the agricultural and livestock production area to be tested; the energy consumption metering interface is the power acquisition interface in the energy consumption metering equipment used to collect the actual consumption of photovoltaic power generation by the energy consumption metering equipment; the preset photovoltaic emission reduction factor can be set according to the type of power grid connected to the agricultural and livestock production area to be tested and the historical carbon emission results; the carbon sequestration capacity table is a pre-established benchmark data table used to record the upper limit parameters of soil carbon sequestration under different soil types, management methods and environmental conditions in different agricultural and livestock production areas to be tested; the standardization processing methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization method or normalization method based on nonlinear mapping function, and the application methods of standardization processing will not be elaborated here; the preset weight factor can be set according to the relative contribution ratio of photovoltaic power generation and soil carbon sequestration in the agricultural and livestock production area to be tested.

[0023] By integrating the effective absorption capacity of photovoltaic power generation with the remaining space for soil carbon sequestration during the planning and evaluation cycle, negative carbon compensation characteristics are generated, so that negative carbon resources can participate in decision-making as a constraint in the energy planning stage, thus avoiding the formation of unfeasible zero-carbon transformation schemes that rely on external energy supply under the condition of limited negative carbon.

[0024] In step S2, during the planning and evaluation period, the power information of the energy consumption metering equipment in the agricultural and livestock production area to be tested is collected through the power metering interface. The power information is the power data of the energy consumption intensity of the energy consumption metering equipment in the agricultural and livestock production area to be tested during operation, including the average operating power of the energy consumption metering equipment. Among them, the energy consumption metering equipment refers to the electrical energy consumption equipment in the agricultural and livestock production area to be measured; the average operating power is the overall power consumption intensity level of the energy consumption metering equipment during the evaluation period. The average operating power of the energy consumption metering equipment is processed by time integration to obtain the positive carbon emission of the energy consumption metering equipment within the planning and evaluation period. The product of the positive carbon emission and the preset power emission factor is used as the positive carbon emission characteristic. A negative carbon compensation threshold and a positive carbon emission threshold are preset respectively. The ratio of the negative carbon compensation characteristic to the preset negative carbon compensation threshold is used as the negative carbon supply coefficient, and the ratio of the positive carbon emission characteristic to the preset positive carbon emission threshold is used as the positive carbon emission coefficient. The ratio of the negative carbon supply coefficient to the positive carbon emission coefficient is used as the carbon gap coefficient. The larger the carbon deficit coefficient, the more sufficient the negative carbon supply capacity is relative to the positive carbon emission demand, and the smaller the carbon offset pressure; the smaller the carbon deficit coefficient, the less sufficient the negative carbon supply capacity is relative to the positive carbon emission demand, and the greater the carbon offset pressure. Access the historical database to retrieve the carbon compensation status benchmark of the agricultural and livestock production area to be tested. The carbon compensation status benchmark refers to the carbon gap coefficient benchmark of the agricultural and livestock production area to be tested under the carbon compensation matching state. It is set according to the statistical results of the carbon gap coefficient under the historical carbon compensation matching state and then stored in the historical database. The carbon deficit coefficient is compared with the carbon compensation status benchmark to determine the carbon compensation status of the agricultural and livestock production area under test. If the carbon deficit coefficient is greater than the carbon compensation state benchmark, then the carbon compensation state of the agricultural and pastoral production area to be tested is judged to be a carbon compensation matching state. Conversely, the carbon compensation status of the agricultural and pastoral production area under test is determined to be insufficient.

[0025] It should be noted that the energy consumption metering power interface is the power data acquisition interface set in the energy consumption metering equipment; the preset power emission factor can be set according to the average positive carbon emission intensity corresponding to different time periods and different power structure conditions; the preset negative carbon compensation threshold can be set according to the historical negative carbon supply capacity assessment results and ecological carrying capacity of the agricultural and livestock production area to be tested; the preset positive carbon emission threshold can be set according to the production scale and historical energy consumption intensity of the agricultural and livestock production area to be tested; the historical database is used to store the historical data of the agricultural and livestock production area to be tested within the historical planning assessment period.

[0026] By quantifying the power information of energy consumption metering equipment in the agricultural and pastoral production areas to be tested, positive carbon emission characteristics are generated. The carbon gap coefficient is obtained by comparing the positive carbon emission demand with the negative carbon supply characteristics. This allows the matching relationship between positive carbon emission demand and negative carbon supply capacity to be quantitatively determined, providing a reliable basis for subsequent screening of external energy supply configurations and feasibility assessment of zero-carbon transformation.

[0027] In step S3, after the carbon compensation status determination is completed, when the agricultural and livestock production area to be tested is determined to be in a state of insufficient carbon compensation, the configuration database is accessed to obtain energy configuration information within the planning assessment period. Energy configuration information is based on the energy supply unit and includes the following fields: Energy supply sources include two types: internal energy supply within the park and external energy supply from outside the park. The power supply activation status indicates whether the power supply unit is activated in each scheduling cycle, including two states: not activated and activated. Rated power supply capacity is used to characterize the maximum energy output that the power supply unit can provide when it is activated; The duration of energy supply activation is used to reflect the actual usage of the energy supply unit during the planning and evaluation period.

[0028] Based on the energy source type field, all energy supply units are filtered, and only those energy supply units whose energy source type is external energy supply to the park are retained and configured as external energy supply units.

[0029] It should be noted that the configuration database is the basic data table in the park's intelligent energy planning system. It is used to uniformly and structurally store and manage various energy supply units that can be scheduled within the planning evaluation period. The configuration database uses the energy supply unit as the smallest data object. Each energy supply unit is assigned a unique identifier when it is entered into the database, and its energy source attributes, operation and scheduling characteristics, and capacity boundary information are recorded in the form of discrete fields.

[0030] After locking in the external power supply configuration, the activation behavior of each external power supply configuration is statistically analyzed, using the scheduling cycle as the minimum time dimension. When an external power supply configuration changes from an inactive state to an active state between two adjacent scheduling cycles, it is recorded as a power supply trigger. Throughout the entire planning and evaluation period, the number of power supply triggers for external power supply configurations is accumulated to obtain the corresponding power supply trigger count.

[0031] After obtaining the number of power supply triggers for all external power supply configurations, a power supply concentration index is further constructed to quantify whether the external power supply dependency exhibits concentrated or dispersed characteristics. Specifically, the power supply concentration index is obtained by summing the squares of the number of power supply triggers for each external power supply configuration and normalizing the sum with the square of the total number of power supply triggers. The specific expression is as follows: ; in, The energy supply concentration index, The total number of units configured for external power supply. Configure the number of power supply triggers for the j-th external power supply within the planning evaluation period. Index value configured for external power supply.

[0032] The energy concentration index is a dimensionless quantity with a value range of (0,1]. The closer the energy concentration index is to 1, the more concentrated the external energy supply triggers are on a few configurations. This indicates that under the condition of limited negative carbon, the energy solution tends to be corrected by local and controllable external energy supplementation. When the energy concentration index approaches 0, it indicates that external energy supply is used more frequently and in a more decentralized manner, reflecting a high degree of dependence on external energy supply in energy planning schemes, making it difficult to converge to the zero-carbon boundary under the condition of limited negative carbon resources.

[0033] In step S4, after the calculation of the energy concentration index is completed, the locked external energy supply configurations are further screened and marked.

[0034] Specifically, the energy supply concentration index is compared with the preset concentration judgment threshold. When the energy supply concentration index is lower than the concentration judgment threshold, it is determined that the current energy planning scheme has a decentralized dependence on external energy supply, and there is a risk that external energy supply will be frequently used. The corresponding external energy supply configuration is marked as a high dependence configuration. When the energy supply concentration index is higher than or equal to the concentration judgment threshold, the activation behavior of external energy supply is determined to be relatively concentrated, and the corresponding external energy supply configuration is not marked as risk.

[0035] It should be noted that the concentration threshold is used to define the dividing point between whether external power supply triggering behavior is concentrated or dispersed, and to quantify the distribution of the number of external power supply triggers among different external power supply configurations. Specifically, based on the number of external power supply configurations within the planning and evaluation period, the power supply concentration index is calculated under both completely uniformly dispersed and completely concentrated activation scenarios. The concentration threshold is defined as the midpoint between the power supply concentration indices obtained under the two extreme conditions.

[0036] For external energy supply configurations marked as high-dependency configurations, trigger constraint processing is applied. Trigger constraint processing includes limiting the maximum number of energy supply triggers allowed for an external energy supply configuration within a single planning evaluation cycle, and reducing the activation priority of external energy supply configurations in scheduling decisions, so that they are only allowed to be activated when the energy supply capacity within the park is insufficient and there is still room for negative carbon offsetting.

[0037] By triggering constraints, we can avoid the frequent introduction of new positive carbon emission sources when negative carbon resources are already limited.

[0038] After completing the trigger constraint processing, the power supply activation duration of each external power supply configuration within the planning evaluation period is recalculated. The power supply activation duration is the product of the number of scheduling cycles in which the external power supply configuration is in the activated state under the constraint conditions and the duration of the scheduling cycle, reflecting the degree of intervention of external power supply at the actual operation level.

[0039] Based on the updated energy supply activation duration, the overall usage intensity of external energy supply within the planning assessment period is further determined. Specifically, the energy supply activation duration is compared with a preset activation duration threshold. If the constrained energy supply activation duration still exceeds the preset activation duration threshold, it indicates that within the existing negative carbon resource constraint boundary, even with constraints on external energy supply, the energy planning scheme still needs to rely on external energy supply support for a relatively long period of time. This indicates that the zero-carbon transformation target is difficult to achieve, and a transformation early warning is generated accordingly to indicate that the current planning scheme is not feasible under the current negative carbon conditions and requires significant adjustments. When the constrained energy supply activation time is lower than or equal to the preset activation time threshold, it indicates that the external energy supply dependence has been effectively compressed to the range allowed by negative carbon resources. It is determined that the current energy smart planning scheme is feasible to implement within the planning evaluation cycle, and the corresponding zero-carbon transformation planning results are output.

[0040] It should be noted that the activation duration threshold is used to limit the maximum allowed time proportion of external energy supply within the planning assessment period. Its definition is derived from the time carrying capacity boundary of negative carbon offsetting capacity against external positive carbon emissions. Within the planning assessment period, the equivalent total negative carbon emissions that can be used to offset external energy supply emissions are calculated based on the negative carbon offsetting characteristics. Combined with the carbon emission intensity per unit time of the external energy supply configuration, the maximum allowed continuous activation time of external energy supply without exceeding the negative carbon offsetting boundary is calculated and used as the activation duration threshold.

[0041] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0042] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0043] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0044] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0045] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart energy planning method for agricultural and livestock parks aimed at zero-carbon transformation, characterized by: Includes the following steps: Step S1: Retrieve the planning assessment period of the agricultural and livestock production area to be tested, detect the photovoltaic power generation data and soil carbon sequestration saturation data of the agricultural and livestock production area to be tested within the planning assessment period, and evaluate the negative carbon compensation characteristics based on the detection results. Step S2: Collect the power information of energy consumption metering equipment in the agricultural and pastoral production area to be tested and generate positive carbon emission characteristics. Calculate the carbon gap coefficient by combining the positive carbon emission characteristics and the negative carbon compensation characteristics. Generate the carbon compensation status of the agricultural and pastoral production area to be tested based on the carbon gap coefficient. Step S3: Use the carbon offset status to determine whether to access the configuration database to obtain energy configuration information within the planning evaluation period, lock the external energy supply configuration based on the energy configuration information and set the number of energy supply triggers, and use the number of energy supply triggers to analyze the energy supply concentration index. Step S4: Filter and mark external energy supply configurations based on the energy supply concentration index, perform trigger constraint processing on the marked external energy supply configurations, detect the energy supply activation duration, and determine whether to generate a transformation warning prompt based on the energy supply activation duration.

2. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 1, is characterized in that: In step S1, the planning evaluation cycle of the agricultural and livestock production area to be tested is retrieved from the planning parameter library; During the planning and evaluation period, the actual electricity consumed by the photovoltaic system in the agricultural and livestock production area under test is detected by the energy consumption metering interface of the energy consumption metering equipment to obtain the actual electricity consumption. The actual electricity consumption is multiplied by the preset photovoltaic emission reduction factor to obtain the photovoltaic power data.

3. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 1, is characterized in that: In step S1, the soil carbon sequestration saturation data of the agricultural and pastoral production area to be tested is obtained through the carbon sequestration capacity table. The soil carbon sequestration saturation data is the level of carbon sequestration per unit area of ​​soil in the agricultural and pastoral production area to be tested relative to its upper limit of carbon sequestration. After standardizing the photovoltaic power data and soil carbon sequestration saturation data respectively, the photovoltaic power coefficient and soil carbon sequestration saturation coefficient were obtained. The negative carbon compensation characteristics were calculated by combining the photovoltaic power generation coefficient and the soil carbon sequestration saturation coefficient.

4. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 1, is characterized in that: In step S2, during the planning and evaluation period, the power information of the energy consumption metering equipment in the agricultural and livestock production area to be tested is collected through the energy consumption metering power interface, including the average operating power of the energy consumption metering equipment. The average operating power of the energy consumption metering equipment is processed by time integration to obtain the positive carbon emission of the energy consumption metering equipment within the planning and evaluation period. The product of the positive carbon emission and the preset power emission factor is used as the positive carbon emission characteristic.

5. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 4, is characterized in that: In step S2, the ratio of negative carbon compensation characteristics to a preset negative carbon compensation threshold is used as the negative carbon supply coefficient, and the ratio of positive carbon emission characteristics to a preset positive carbon emission threshold is used as the positive carbon emission coefficient. The ratio of the negative carbon supply coefficient to the positive carbon emission coefficient is used as the carbon gap coefficient. Access the historical database to retrieve the carbon offset status benchmark of the agricultural and livestock production area to be tested; If the carbon deficit coefficient is greater than the carbon compensation state benchmark, then the carbon compensation state of the agricultural and pastoral production area to be tested is judged to be a carbon compensation matching state. Conversely, the carbon compensation status of the agricultural and pastoral production area under test is determined to be insufficient.

6. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 5, is characterized in that: In step S3, after the carbon compensation status determination is completed, when the agricultural and livestock production area to be tested is determined to be in a state of insufficient carbon compensation, the configuration database is accessed to obtain energy configuration information within the planning assessment period. Energy configuration information takes energy supply units as the basic object and includes the type of energy source, the activation status of energy supply, the rated capacity of energy supply, and the activation duration of energy supply. All energy supply units were screened based on their energy source type, and only those energy supply units whose energy source type was external energy supply from the park were retained as external energy supply configurations.

7. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 6, is characterized in that: In step S3, the activation behavior of each external power supply configuration is statistically analyzed, with the scheduling cycle as the minimum time dimension: When an external power supply configuration changes from an inactive state to an active state between two adjacent scheduling cycles, it is recorded as a power supply trigger. Throughout the entire planning and evaluation period, the number of times external energy supply configurations are triggered is accumulated to obtain the corresponding number of energy supply triggers; The energy concentration index is obtained by summing the squares of the number of energy supply triggers for each external energy supply configuration and normalizing the sum of the total number of energy supply triggers.

8. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 7, is characterized in that: In step S4, when the energy supply concentration index is lower than the concentration judgment threshold, it is determined that the current energy planning scheme has a decentralized dependence on external energy supply, and the corresponding external energy supply configuration is marked as a high dependence configuration. When the energy supply concentration index is higher than or equal to the concentration judgment threshold, the external energy supply activation behavior is judged to be relatively concentrated, and the corresponding external energy supply configuration is not marked as risk. Trigger constraint processing is performed on external power supply configurations marked as high-dependency configurations. The trigger constraint processing includes limiting the maximum number of power supply triggers allowed for an external power supply configuration within a single planning evaluation period, and reducing the activation priority of external power supply configurations in scheduling decisions.

9. The intelligent energy planning method for agricultural and livestock parks oriented towards zero-carbon transformation, as described in claim 1, is characterized in that: In step S4, after completing the trigger constraint processing, the power supply activation duration of each external power supply configuration within the planning evaluation period is recalculated. The power supply activation duration is the product of the number of scheduling periods in which the external power supply configuration is in the activated state under the constraint conditions and the scheduling period duration. When the constrained energy supply activation time exceeds the preset activation time threshold, it is determined that the zero-carbon transformation target is difficult to achieve, and a transformation early warning is generated to indicate that the current planning scheme is not feasible under the actual negative carbon conditions and requires major adjustments. When the constrained energy supply activation time is lower than or equal to the preset activation time threshold, the current energy smart planning scheme is determined to be feasible within the planning evaluation cycle, and the corresponding zero-carbon transformation planning result is output.

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