Park energy carbon supervision method and system

Abstract

The embodiment of the invention provides a park energy carbon supervision method and system, and belongs to the technical field of energy carbon supervision. The method is executed by an energy-carbon management platform, the energy-carbon management platform is in communication connection with area control modules arranged on all energy sides based on an energy internet-of-things base, and the method comprises the steps that energy consumption data and equipment operation data which are collected in real time and correspond to all enterprises are recycled; updating a sub-energy grid of each enterprise based on the equipment operation data, and updating a main energy grid of each enterprise based on the energy consumption data of each enterprise; wherein the lowest carbon emission is taken as an optimization target, and real-time regulation and control of the energy scheduling scheme of each enterprise in the park are executed based on the updated main energy grid and the updated sub-energy grid; and monitoring the regulation and control process of each enterprise energy scheduling scheme, executing park data operation and maintenance based on a monitoring result, and generating a regulation and control report in real time. The scheme of the invention not only improves the accuracy of energy management, but also effectively promotes the realization of the carbon emission reduction target of the park.

Description

Technical Field

[0001] The present invention relates to the technical field of energy carbon regulation, and in particular to a park energy carbon regulation method and a park energy carbon regulation system. Background Art

[0002] As global climate change intensifies, reducing greenhouse gas emissions, especially carbon dioxide emissions, has become one of the top priorities for governments and international organizations. As the main source of energy consumption and carbon emissions, industrial parks play a vital role in achieving global climate goals. However, the current energy consumption and carbon emission management methods of industrial parks have many shortcomings and are in urgent need of improvement and optimization.

[0003] Most of the existing energy management solutions for industrial parks are based on macro-control of the overall energy consumption of the park, and lack independent and refined energy and carbon emission data management for each enterprise. Enterprises in the park usually report their energy consumption and carbon emission data on their own, and the management method is relatively extensive. This method has problems such as data opacity, poor real-time performance, and difficulty in accurate supervision, which makes it impossible to effectively evaluate the low-carbonization degree of each enterprise. In addition, the energy use between enterprises cannot be accurately calculated, and there is a lack of in-depth analysis and comparison of carbon emission levels between enterprises within the park, which makes the entire park lack scientific and refined standards in energy and carbon emission management.

[0004] In the specific carbon emission management, enterprises often fail to closely integrate energy consumption with the production process, and lack key indicators for unit energy consumption and unit carbon emission intensity. This is not only not conducive to the energy efficiency optimization of each enterprise according to its own production activities, but also difficult to achieve accurate carbon emission reduction effects in the park. At the same time, the existing scheme lacks a clear division and monitoring mechanism for the use of green electricity (such as renewable energy such as photovoltaics) and municipal electricity (traditional fossil energy), resulting in the park's inability to accurately calculate the energy consumption data and carbon emissions of each enterprise. This deficiency not only limits the realization of the park's energy efficiency optimization and carbon emission reduction goals, but also brings potential disputes to energy transactions and carbon emission rights transactions between enterprises.

[0005] Therefore, there is an urgent need for a system and method that can finely manage the energy consumption and carbon emissions of multiple enterprises in an industrial park. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide a park energy and carbon supervision method and system, so as to at least solve the problem that the park is currently unable to achieve refined supervision and regulation in terms of energy and carbon emission management.

[0007] In order to achieve the above-mentioned purpose, the first aspect of the present invention provides a park energy carbon supervision method, which is applied to carbon emission monitoring and carbon emission optimization in industrial parks, and is characterized in that the method is executed by an energy carbon management platform, and the energy carbon management platform is communicated with the regional control modules arranged on each energy side based on the energy Internet of Things base. The method includes: based on the energy Internet of Things base, recovering the energy consumption data and equipment operation data corresponding to each enterprise collected in real time by each regional control module; updating the sub-energy grid of each enterprise based on the equipment operation data, and updating the main energy grid of each enterprise based on the energy consumption data of each enterprise; wherein the main energy grid is used to characterize the total consumption of all energy types of each enterprise; the sub-energy grid is used to characterize the energy usage of each equipment; with the lowest carbon emission as the optimization goal, the energy scheduling scheme of each enterprise in the park is executed in real time based on the updated main energy grid and the updated sub-energy grid; monitoring the regulation process of the energy scheduling scheme of each enterprise, and executing park data operation and maintenance and generating regulation reports in real time based on the monitoring results.

[0008] Optionally, each regional control module includes a smart meter and a gateway; the smart meter is used to collect output / consumption status information of each energy type; the gateway is used to upload the output / consumption status information of each energy type after preprocessing to the energy IoT base.

[0009] Optionally, the updating of the sub-energy grid of each enterprise based on the equipment operation data includes: determining the power consumption of each equipment and the consumption of other energy types other than power consumption based on the equipment operation data; distinguishing between municipal power consumption and photovoltaic power consumption, and mapping the distinction results and the consumption of other energy types other than power consumption to the corresponding sub-energy grid.

[0010] Optionally, the updating of the main energy grid of each enterprise based on the energy consumption data of each enterprise includes: calculating the total energy consumption data of each sub-energy grid based on the energy consumption data; calculating the consumption proportion of each energy type of each enterprise based on the total energy consumption data of each sub-energy network summarized by each enterprise, and the photovoltaic power generation of each enterprise, and mapping the calculation results to the main energy grid; identifying the consumption of each energy type of each enterprise's public equipment in the total energy consumption data of each sub-energy network, and updating the main energy grid based on the identification results.

[0011] Optionally, the optimization objective is to minimize carbon emissions, and real-time regulation of energy dispatch plans for enterprises in the park is performed based on the updated main energy grid and the updated sub-energy grid, including: structuring data of the updated main energy grid and the updated sub-energy grid to obtain structured energy consumption data; performing data processing of corresponding carbon emission management index items based on the structured energy consumption data to obtain index data of each carbon emission management index item; determining direct carbon emission data and indirect carbon emission data based on the structured energy consumption data; simulating the carbon emission curve corresponding to each enterprise based on the direct carbon emission data and the indirect carbon emission data to characterize the carbon emissions of each enterprise; based on the carbon emission curve corresponding to each enterprise, with the lowest carbon emissions as the optimization objective, optimizing the index data of each carbon emission management index item, and performing real-time regulation of energy dispatch plans for each enterprise based on the optimization results.

[0012] Optionally, the structured processing of the updated main energy grid and the updated sub-energy grid data to obtain structured energy consumption data includes: identifying the energy consumption data of the enterprise or park level by level based on the hierarchical structure of the metering topology, and constructing energy usage in different time dimensions; determining the corresponding energy type and usage period based on the energy usage in different time dimensions, and comparing and analyzing the consumption of various types of energy through the energy grid view based on the energy type and usage period; and forming a structured energy usage data table for various types of energy consumption data according to energy type as structured energy consumption data.

[0013] Optionally, the energy usage based on different time dimensions determines the corresponding energy type and usage period, and compares and analyzes the consumption of various types of energy through the energy grid view based on the energy type and usage period, including: based on the metering topology, one or more multi-level metering topology nodes are selected according to the enterprise, and the load and energy type consumption data within the selected time period are collected and analyzed; based on the load and energy type consumption data within the selected time period, based on the energy grid view, a detailed analysis of the consumption data of each energy type is performed according to the energy grid configured by the enterprise to obtain the power consumption situation; wherein, the power consumption situation includes any one or more of power peak, power peak, level and power valley; within each time period, the power consumption situation is compared across preset time periods, and the consumption situation of the energy class is obtained based on the comparison results.

[0014] Optionally, the carbon emission management index items include: any one or more of energy loss index items, photovoltaic absorption index items, energy use economic index items and energy use cost index items; among which, the energy loss index item is used to characterize the energy efficiency loss of each enterprise's power distribution network, each transformer and each predetermined key equipment; the photovoltaic absorption index item is used to characterize the absorption rate, online power and income of photovoltaic power generation; the energy use economic index item is used to characterize the economic benefits of energy use of each enterprise; the energy use cost index item is used to characterize the total energy cost of each enterprise in different time periods.

[0015] Optionally, the data processing rules for executing the energy loss index items on the structured energy consumption data are as follows: based on the preconfigured relationship between the upper and lower loss models, the power loss and loss rate of the power distribution network of each enterprise are obtained based on the structured energy consumption data, as the index data of the energy loss index items of the power distribution network of each enterprise; based on the structured energy consumption data, the maximum load rate, loss rate and duration distribution of different load states of each transformer in a selected time period are identified, as the index data of the energy loss index items of each transformer; based on the structured energy consumption data, the standby status of each predetermined key equipment is identified, and based on the standby status, the standby energy loss of each predetermined key equipment is obtained, as the index data of the energy loss index items of each predetermined key equipment.

[0016] Optionally, the data processing rules for executing the photovoltaic consumption index item on the structured energy consumption data are as follows: configure the correspondence between photovoltaic access points and grid incoming lines, and obtain photovoltaic consumption overview information according to the selected time period; wherein the photovoltaic consumption overview information of the park includes any one or more of photovoltaic power generation installed capacity, power generation, photovoltaic grid-connected power, photovoltaic absorbed power and absorption rate; based on the photovoltaic consumption overview information, respectively simulate the photovoltaic grid-connected income and photovoltaic absorption income to obtain photovoltaic absorption income information; based on the structured energy consumption data of each time period, separate the municipal electricity and photovoltaic green electricity to obtain the usage ratio information of municipal electricity and photovoltaic green electricity in the energy grid; determine the photovoltaic absorption adjustable amount based on the photovoltaic absorption overview information, the photovoltaic absorption income information and the usage ratio information of municipal electricity and photovoltaic green electricity; and use the photovoltaic absorption adjustable amount as the indicator data of the photovoltaic consumption index item.

[0017] Optionally, the photovoltaic grid-connected income and photovoltaic absorption income simulation are respectively performed based on the photovoltaic absorption overview information to obtain the photovoltaic absorption income information, including: determining the total usage of municipal electricity and photovoltaic green electricity in the park based on the photovoltaic absorption overview information; calculating the photovoltaic absorption electricity of each photovoltaic access point based on the minimum value of photovoltaic power generation and load power consumption, and calculating the corresponding photovoltaic green electricity ratio; configuring a main energy grid and a sub-energy grid for each enterprise, associating meters on the high-voltage side and the low-voltage side, and subdividing the usage of municipal electricity and green electricity according to the photovoltaic green electricity ratio; multiplying the total electricity in each sub-energy grid by the green electricity ratio, calculating the municipal electricity and photovoltaic green electricity of each sub-energy grid, and summarizing them into the main energy grid to obtain the usage ratio of municipal electricity and photovoltaic green electricity; based on the usage ratio of municipal electricity and photovoltaic green electricity, respectively simulate the photovoltaic grid-connected income and photovoltaic absorption income, and display them at the energy grid level.

[0018] Optionally, the data processing rules for executing the energy use economic index item on the structured energy consumption data are as follows: based on the structured energy consumption data, the monthly city electricity bill and photovoltaic grid-connected income bill of the park are obtained; the park electricity bill is decomposed according to the actual usage of each enterprise, and the city electricity and photovoltaic power consumption of each enterprise is analyzed based on the decomposition results; the city electricity fee and photovoltaic consumption rate corresponding to the city electricity and photovoltaic power consumption of each enterprise are calculated, and economic data are generated based on the city electricity fee and photovoltaic consumption rate; based on the economic data, an economic bill of each enterprise is generated as index data for the energy use economic index item.

[0019] Optionally, the data processing rules for executing the energy usage cost index item on the structured energy consumption data are as follows: according to the electricity price model adopted by the park and the enterprise, maintain the unit price and effective time of the time-of-use tiered electricity price, photovoltaic consumption electricity price and on-grid electricity price; set the time period for each electricity consumption situation published by the power grid, and associate it with the time-of-use electricity price model; wherein the electricity consumption situation includes any one or more of electricity peak, electricity peak, level period and electricity valley period; based on the electricity price billing method triggered by the user, set corresponding unit prices for different time periods, and update the time-of-use electricity price model; based on the updated time-of-use electricity price model, process the structured energy consumption data, calculate the energy usage cost of each enterprise, and use it as the index data of the energy usage cost index item.

[0020] Optionally, the carbon emission curve simulation corresponding to each enterprise is performed based on the direct carbon emission data and the indirect carbon emission data to characterize the carbon emissions of each enterprise, including: performing data normalization on the direct carbon emission data and the indirect carbon emission data respectively; performing direct carbon emissions calculation based on the normalized direct carbon emission data; performing indirect carbon emissions calculation based on the normalized indirect carbon emission data; obtaining the total carbon emissions of each enterprise based on the direct carbon emissions and the indirect carbon emissions, and constructing the carbon emission curve corresponding to each enterprise based on the time series data of the total carbon emissions of each enterprise.

[0021] Optionally, based on the carbon emission curve corresponding to each enterprise and taking the lowest carbon emission as the optimization target, the indicator data of each carbon emission management indicator item is optimized, and the energy scheduling plan of each enterprise is adjusted in real time based on the optimization result, including: calculating whether the total carbon emissions of the current park are greater than a preset total carbon emission threshold based on the carbon emission curve corresponding to each enterprise; if the total carbon emissions of the current park are greater than the preset total carbon emission threshold, triggering the carbon emission optimization plan; if the total carbon emissions of the current park are not greater than the preset total carbon emission threshold, performing carbon emission prediction within a predetermined time in the future based on the carbon emission curve corresponding to each enterprise to obtain a carbon emission prediction value; if the carbon emission prediction value is greater than the preset carbon emission prediction value threshold, the carbon emission optimization plan is triggered; if the carbon emission prediction value is not greater than the preset carbon emission prediction value threshold, the carbon emission optimization plan is not triggered; based on the triggered carbon emission optimization plan, taking the lowest carbon emission as the optimization target, real-time adjustment of the energy scheduling plan of each enterprise is performed.

[0022] Optionally, with the lowest carbon emissions as the optimization target, real-time regulation of the energy scheduling plans of each enterprise is performed, including: with the lowest carbon emissions as the optimization target, respectively optimizing the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item, to obtain the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item under the optimal carbon emissions state, and constructing a corresponding initial regulation plan; with the highest profit as the optimization target, respectively optimizing the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item, to obtain the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item under the optimal profit state, and constructing a corresponding revised regulation plan; revising the initial regulation plan based on the revised regulation plan, and obtaining and executing the real-time regulation plan of each enterprise.

[0023] Optionally, the monitoring of the regulation and control process of each enterprise's energy scheduling plan, executing park data operation and maintenance and generating regulation and control reports in real time based on the monitoring results, includes: responding to a request for intervention in the regulation process, performing identity authentication on the requesting user; reading based on the verification result whether the current user has access rights corresponding to the intervention request; if so, opening the intervention function and reclaiming the current user's intervention instructions; if not, rejecting the current user's intervention request.

[0024] Optionally, the monitoring of the energy dispatching plan control process of each enterprise, executing park data operation and maintenance and generating control reports in real time based on the monitoring results, includes: entering the power grid bill every predetermined period based on the monitoring results within the corresponding period; wherein, the power grid bill includes: electricity consumption, total electricity charge, municipal electricity charge, market-based electricity purchase charge, basic electricity charge, power factor adjustment charge, photovoltaic grid-connected electricity, photovoltaic grid-connected unit price, photovoltaic grid-connected electricity charge, and any one or more of various subsidy funds.

[0025] The second aspect of the present invention provides an energy carbon management platform, which is applied to carbon emission monitoring and carbon emission optimization in industrial parks. The energy carbon management platform is connected to the regional control modules arranged on each energy side based on the energy Internet of Things base. The energy carbon management platform includes: a collection unit, which is used to recover the energy consumption data and equipment operation data corresponding to each enterprise collected in real time by each regional control module based on the energy Internet of Things base; a processing unit, which is used to update the sub-energy grid of each enterprise based on the equipment operation data, and to update the main energy grid of each enterprise based on the energy consumption data of each enterprise; wherein the main energy grid is used to characterize the total consumption of all energy types of each enterprise; the sub-energy grid is used to characterize the energy usage of each device; a control unit, which is used to perform real-time control of the energy scheduling scheme of each enterprise in the park based on the updated main energy grid and the updated sub-energy grid with the lowest carbon emission as the optimization goal; a monitoring unit, which is used to monitor the control process of the energy scheduling scheme of each enterprise, and perform park data operation and maintenance and generate control reports in real time based on the monitoring results.

[0026] Optionally, the processing unit includes: an energy online monitoring module, which is used to monitor the energy consumption of various energy types of various enterprises in the park in real time, and provide metering topology and energy grid view analysis; a distribution network energy efficiency module, which is used to analyze the loss of the park's power distribution network, the standby status of key equipment, and the load and loss of the transformer; a photovoltaic consumption module, which is used to manage the access, consumption and access to the Internet of photovoltaic power generation in the park; a lean electricity fee module, which is used to manage the electricity bills of park enterprises and the decomposition of multilateral bills; a carbon emission management module, which is used to monitor the carbon emissions of each enterprise and calculate direct carbon emission data and indirect carbon emission data.

[0027] Optionally, the monitoring unit includes: a price model module, which is used to maintain the time-of-use electricity price model of various energy media in the park; an enterprise entity maintenance module, which is used to maintain the metering and settlement information, energy usage information and green electricity profit sharing ratio of the park and the enterprise; a user authority module, which is used to manage user roles and authorities to ensure that different enterprises and personnel can only access and operate corresponding functional modules.

[0028] Optionally, each regional control module includes a smart meter and a gateway; the smart meter is used to collect output / consumption status information of each energy type; the gateway is used to upload the output / consumption status information of each energy type after preprocessing to the energy IoT base.

[0029] The third aspect of the present invention provides a park energy carbon supervision system, which is applied to carbon emission monitoring and carbon emission optimization in industrial parks, and is characterized in that the park energy carbon supervision system includes the above-mentioned energy carbon management platform; an energy Internet of Things base arranged on one side of the energy carbon supervision system and communicatively connected to the energy carbon supervision system; a regional control module arranged on the other side of the energy Internet of Things base and communicatively connected to the energy Internet of Things base; the regional control module transmits the energy consumption data and equipment operation data corresponding to each enterprise collected in real time to the energy carbon management platform based on the energy Internet of Things base.

[0030] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed on a computer, enables the computer to execute the above-mentioned park energy carbon supervision method.

[0031] Through the above technical scheme, the scheme of the present invention recovers the energy consumption and equipment operation data of each enterprise in real time based on the energy Internet of Things base, and realizes the accurate collection and processing of energy data. By updating the sub-energy grid and the main energy grid, the energy usage of each device and each enterprise is characterized respectively, and detailed energy consumption distribution and carbon emission accounting are provided. Secondly, the method takes the lowest carbon emissions as the optimization goal, and adjusts the energy scheduling plan of each enterprise in real time based on the updated grid data, so as to minimize carbon emissions while ensuring energy supply. In addition, the system generates a control report by real-time monitoring of the scheduling execution effect, which provides a scientific basis and feedback mechanism for the energy and carbon management and data operation and maintenance of the park. Overall, this method not only improves the accuracy of energy management, but also effectively promotes the realization of the park's carbon reduction goals.

[0032] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0034] Figure 1 It is a flowchart of the steps of a park energy carbon supervision method provided by an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the structure of an energy carbon management platform provided by one embodiment of the present invention;

[0036] Figure 3 It is a schematic diagram of the structure of each module of the energy carbon management platform provided by one embodiment of the present invention;

[0037] Figure 4 This is a system structure diagram of a park energy carbon supervision system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0039] Figure 1 This is a method flow chart of a park energy carbon supervision method provided by an embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a method for regulating carbon emissions in a park, the method comprising:

[0040] Step S10: Based on the energy IoT base, the energy consumption data and equipment operation data corresponding to each enterprise collected in real time by the control modules in each region are recovered.

[0041] Specifically, each regional control module includes a smart meter and a gateway; the smart meter is used to collect output / consumption status information of each energy type; the gateway is used to upload the output / consumption status information of each energy type after preprocessing to the energy IoT base.

[0042] In the embodiment of the present invention, each regional control module is composed of a smart meter and a gateway, which are used to sense and collect data of different energy types. The smart meter is responsible for real-time monitoring of the output and consumption status of various energy sources such as electricity, water, cold, heat, compressed air, and natural gas to ensure that the energy usage information of each enterprise can be accurately obtained. The gateway is the core device for data collection and preprocessing, with a built-in intelligent collection algorithm, which can perform preliminary processing on the original energy data, including data cleaning, integration, and noise removal, and remove redundant information or erroneous data to improve the accuracy and effectiveness of the data.

[0043] Furthermore, after the gateway completes preprocessing, the optimized energy data is uploaded to the energy IoT base through a wired or wireless connection. The gateway converts the data into MQTT format through the protocol, and uses SSL / TLS encryption technology during data transmission to ensure data security and privacy protection. The energy IoT base is responsible for communicating with the regional control module, providing one-stop access management for devices such as smart meters and gateways, and storing the data reported by the gateway in the local database. Afterwards, the energy IoT base uploads this data to the energy carbon management platform through the API interface so that the upper-level applications can perform further data analysis and decision support.

[0044] Based on the solution of the present invention, the solution of the present invention can monitor the energy usage of each enterprise in the park in real time and ensure the security and integrity of the data transmission process. This mechanism not only improves the efficiency of obtaining energy data, but also enables accurate analysis of consumption data and equipment operation data of each energy type through the layered collaboration of smart meters and gateways. At the same time, the energy IoT base realizes data storage and management through the rule engine, providing an efficient energy monitoring solution to assist the park in real-time monitoring and management of energy efficiency optimization and carbon emission control. This solution provides strong support for the park's carbon emission management and ensures the realization of low-carbon goals.

[0045] Step S20: updating the sub-energy grid of each enterprise based on the equipment operation data, and updating the main energy grid of each enterprise based on the energy consumption data of each enterprise.

[0046] Specifically, based on the equipment operation data, the power consumption of each device and the consumption of other energy types except power consumption are determined; the power consumption is distinguished between municipal power consumption and photovoltaic power consumption, and based on the distinction results and the consumption of other energy types except power consumption, they are mapped to the corresponding sub-energy grid.

[0047] Furthermore, the updating of the main energy grid of each enterprise based on the energy consumption data of each enterprise includes: calculating the total energy consumption data of each sub-energy grid based on the energy consumption data; calculating the consumption proportion of each energy type of each enterprise based on the total energy consumption data of each sub-energy network summarized by each enterprise, and the photovoltaic power generation of each enterprise, and mapping the calculation results to the main energy grid; identifying the consumption of each energy type of each enterprise's public equipment in the total energy consumption data of each sub-energy network, and updating the main energy grid based on the identification results.

[0048] In an embodiment of the present invention, the power consumption and other types of energy (such as natural gas, water, compressed air, etc.) of each device are first determined by real-time collection of device operation data. The power consumption needs to be further refined and divided into mains power consumption and photovoltaic power consumption. Such a distinction helps to clarify the actual use of mains power and renewable energy for each enterprise. Next, the power consumption and other energy consumption of each device are accurately mapped to the corresponding sub-energy grid to achieve accurate tracking and recording of the energy consumption of each device.

[0049] Furthermore, based on the energy consumption data of each enterprise, the energy consumption data of each sub-energy grid is calculated and updated. On this basis, by summarizing the total energy consumption data in the sub-energy grid of each enterprise and combining the use of photovoltaic power generation of each enterprise, the consumption proportion of different energy types of each enterprise is calculated. These calculation results will be mapped to the main energy grid of the enterprise to ensure that the main energy grid can fully reflect the total energy consumption of the enterprise. In addition, the energy consumption of shared equipment between enterprises will be identified and apportioned, and included in the total energy consumption data of the main energy grid for update. This identification ensures the accurate allocation of public energy and further improves the transparency of energy management.

[0050] Based on the scheme of the present invention, the scheme of the present invention can accurately track the energy usage of each device and enterprise, and realize the detailed management of energy consumption. The distinction between municipal electricity and photovoltaic electricity helps enterprises to clearly understand their own renewable energy usage and optimize the energy consumption structure on this basis. By updating and calculating the sub-energy grid, the system can accurately calculate the total energy consumption data of each enterprise to ensure the fairness and accuracy of energy distribution. In addition, the energy consumption identification and allocation of enterprise public equipment in the main energy grid ensures the transparency of the overall energy consumption data of the park, reduces disputes and uncertainties in energy use, and provides reliable data support for enterprises to formulate more reasonable energy optimization measures.

[0051] Step S30: Taking the lowest carbon emission as the optimization goal, the energy scheduling plans of various enterprises in the park are implemented in real time based on the updated main energy grid and the updated sub-energy grid.

[0052] Specifically, the updated main energy grid and the updated sub-energy grid data are structured to obtain structured energy consumption data; based on the structured energy consumption data, data processing of corresponding carbon emission management index items is performed to obtain index data of each carbon emission management index item; based on the structured energy consumption data, direct carbon emission data and indirect carbon emission data are determined; based on the direct carbon emission data and the indirect carbon emission data, the carbon emission curve simulation corresponding to each enterprise is performed to characterize the carbon emission situation of each enterprise; based on the carbon emission curve corresponding to each enterprise, the index data of each carbon emission management index item is optimized with the lowest carbon emission as the optimization goal, and the energy dispatch plan of each enterprise is controlled in real time based on the optimization result.

[0053] In an embodiment of the present invention, the data of the updated main energy grid and sub-energy grid are structured, and complex energy consumption data can be converted into clear and easy-to-analyze structured data. These data not only cover the energy consumption of electricity, water, natural gas, photovoltaics and other energy sources of each enterprise, but also provide a solid foundation for further carbon emission management. Based on these structured energy consumption data, the system can process the management index items related to carbon emissions and generate specific values ​​for various carbon emission indicators. Through these data, the system can further distinguish between direct carbon emissions (such as the use of fossil fuels such as coal and natural gas) and indirect carbon emissions (such as the use of municipal electricity), so as to more accurately calculate the overall carbon emissions of the enterprise.

[0054] Furthermore, based on these direct and indirect carbon emission data, the system simulates the carbon emission of each enterprise and displays its carbon emission trend in a visual way. These curves represent the carbon emissions of enterprises in different periods and under different energy structures, providing an intuitive basis for subsequent carbon emission reduction optimization. On this basis, the system will optimize each carbon emission management indicator with the lowest carbon emission as the optimization goal. Through the optimization results, the system can generate the optimal enterprise energy scheduling plan and perform regulation in real time to ensure that the park and enterprises minimize carbon emissions while meeting production needs.

[0055] Based on the scheme of the present invention, through structured data processing and curve simulation, the system can not only fully grasp the energy use and carbon emissions of enterprises, but also dynamically adjust and control based on real-time optimization results, so as to achieve the best configuration of energy use and minimize carbon emissions. This optimization scheme greatly improves the low-carbon management capabilities of enterprises and helps the park move towards the goal of near-zero carbon emissions.

[0056] Specifically, the structured processing of the updated main energy grid and the updated sub-energy grid data to obtain structured energy consumption data includes: identifying the energy consumption data of the enterprise or park level by level based on the hierarchical structure of the metering topology, and constructing energy usage in different time dimensions; determining the corresponding energy type and usage period based on the energy usage in different time dimensions, and comparing and analyzing the consumption of various types of energy through the energy grid view based on the energy type and usage period; and forming a structured energy usage data table according to the energy type of various types of energy consumption data as structured energy consumption data.

[0057] Furthermore, the energy usage based on different time dimensions determines the corresponding energy type and usage period, and compares and analyzes the consumption of various types of energy through the energy grid view based on the energy type and usage period, including: based on the metering topology, one or more multi-level metering topology nodes are selected according to the enterprise, and the load and consumption data of each energy type in the selected time period are collected and analyzed; based on the load and consumption data of each energy type in the selected time period, based on the energy grid view, the consumption data of each energy type is segmented and analyzed according to the energy grid configured by the enterprise to obtain the power consumption situation; wherein, the power consumption situation includes any one or more of power peak, power peak, level and power valley; in each time period, the power consumption situation is compared across the preset time period, and the consumption situation of the energy class is obtained based on the comparison result.

[0058] In an embodiment of the present invention, the system first determines the usage of various types of energy according to the time dimension (such as day, month, year), and performs a detailed comparative analysis of the consumption of electricity, water, gas and other energy sources through the energy grid view according to different energy types and usage periods. The energy grid view supports the hierarchical display of energy consumption by enterprise or region, and can effectively present the usage characteristics of each energy type in different time periods. Through comparative analysis, the system refines various types of energy data according to energy type to form a structured energy usage data table. These structured data not only provide enterprises with comprehensive energy consumption records, but also provide basic data for subsequent carbon emission management and optimization. For example, electricity consumption can be subdivided into electricity peaks, peaks, flat sections and valley sections, and the energy consumption proportion of each time period is displayed. This multi-dimensional energy analysis method allows enterprises to better understand their own energy usage patterns and take corresponding energy-saving measures.

[0059] Furthermore, based on the energy usage in different time dimensions, the system can perform detailed analysis of various types of energy consumption data according to the energy grid configured by the enterprise. In this process, the system collects and analyzes the load and energy consumption data within a specific time period by selecting one or more multi-level metering topology nodes. By displaying the energy grid view of these data, electricity consumption, water consumption, natural gas consumption, etc. can be further subdivided, and comparative analysis of multiple enterprises and multiple nodes can be performed within the selected time period. For example, in the electricity consumption analysis, the system supports the display of peak, high, flat and valley electricity consumption, and can compare these electricity consumption patterns for multiple days or even years to generate a detailed electricity consumption ranking analysis. This cross-time period comparative analysis can help enterprises identify energy consumption peaks and optimize energy use strategies.

[0060] Furthermore, the metering topology view of electric energy not only supports energy consumption comparison of multiple metering nodes within a single enterprise, but also allows parallel analysis of energy consumption data of different enterprises. For example, the power load, peak and valley power consumption of multiple enterprises in the same time period can be compared horizontally through the view display, thereby providing an optimization basis for the overall energy management of the park. Similarly, the consumption data of other energy types such as water energy, cold energy, compressed air and natural gas can also be hierarchically analyzed and processed according to the same logic.

[0061] Based on the scheme of the present invention, through this structured processing, the system realizes refined management and multi-dimensional analysis of energy consumption, which not only improves the visualization of energy data, but also provides strong data support for energy conservation and emission reduction of parks and enterprises. Enterprises can identify high energy consumption periods and equipment based on comparative analysis of various energy consumption data, so as to formulate more effective energy management and optimization plans. This system can not only improve the energy utilization efficiency of enterprises, but also help the park achieve the overall low-carbon emission goals and promote green and sustainable development.

[0062] Specifically, the carbon emission management index items include: any one or more of energy loss index items, photovoltaic absorption index items, energy use economic index items and energy use cost index items; among which, the energy loss index item is used to characterize the energy efficiency loss of each enterprise's power distribution network, each transformer and each predetermined key equipment; the photovoltaic absorption index item is used to characterize the absorption rate, online power and income of photovoltaic power generation; the energy use economic index item is used to characterize the economic benefits of energy use of each enterprise; the energy use cost index item is used to characterize the total energy cost of each enterprise in different time periods.

[0063] Specifically, the data processing rules for executing energy loss index items on the structured energy consumption data are as follows: based on the preconfigured relationship between the upper and lower loss models, the power loss and loss rate of the power distribution network of each enterprise are obtained based on the structured energy consumption data, as the index data of the energy loss index items of the power distribution network of each enterprise; based on the structured energy consumption data, the maximum load rate, loss rate and duration distribution of different load states of each transformer in a selected time period are identified, as the index data of the energy loss index items of each transformer; based on the structured energy consumption data, the standby status of each predetermined key equipment is identified, and based on the standby status, the standby energy loss of each predetermined key equipment is obtained, as the index data of the energy loss index items of each predetermined key equipment.

[0064] In an embodiment of the present invention, the distribution network energy efficiency module can accurately analyze and manage the power distribution network loss of each enterprise in the park by performing data processing of energy loss indicators on structured energy consumption data. First, based on the pre-configured upper and lower loss model relationships, the module uses structured energy consumption data to calculate the power loss and loss rate of each enterprise's power distribution network, and then forms the energy loss indicators of each enterprise's power distribution network. This process can accurately identify the difference between input power and output power by displaying the loss node relationship, helping enterprises understand the energy loss caused by transmission and equipment efficiency problems in the distribution network.

[0065] Furthermore, in terms of transformer loss analysis, the module identifies the maximum load rate and loss rate of each transformer in the selected period, as well as the time distribution of the transformer in different load states (such as no load, light load, normal, heavy load and overload) based on structured energy consumption data. By analyzing the trend of transformer loss rate and load rate, enterprises can clearly understand the operating efficiency of their transformer equipment under different working conditions, so as to take appropriate maintenance or adjustment measures to avoid unnecessary energy loss and improve the overall distribution efficiency.

[0066] Furthermore, the distribution network energy efficiency module also supports in-depth analysis of the standby status of key equipment. Based on the standby status of the equipment, the module can identify the standby energy loss of major energy-consuming equipment within a specific time range, and display the number of standby devices, total standby power and standby time. Through trend analysis, enterprises can understand the standby mode of each device and evaluate the rationality of its standby power. For those devices that are in standby for a long time but are not fully utilized, this analysis can be used to find out the energy-saving potential and further optimize the energy management strategy in the standby state.

[0067] Specifically, the distribution network energy efficiency module also provides loss model configuration and maintenance functions, allowing parks or enterprises to flexibly manage loss models according to different energy types, equipment levels, plant levels, and even enterprise levels. By setting loss thresholds for each device and transformer, the system can monitor the energy loss of these devices. When the loss rate exceeds the set threshold, the system will trigger an alarm to prompt the enterprise to take necessary energy-saving measures. For example, the loss rate of the transformer under no-load, light-load, and heavy-load conditions will be monitored in real time. When the loss of a certain device exceeds the preset range, enterprise managers can take timely measures to reduce unnecessary energy waste.

[0068] Based on the solution of the present invention, the distribution network energy efficiency module significantly improves the energy management capabilities of the park and enterprises. By accurately calculating the losses of each enterprise's power distribution network, the enterprise can clearly understand the flow of electric energy and at which links the losses occur. Detailed load rate and loss rate analysis of transformers and equipment helps enterprises optimize the efficiency of equipment use and avoid energy waste under overload or no-load conditions. At the same time, the standby analysis function of key equipment allows enterprises to find energy-saving potential and adjust equipment operation strategies to reduce energy waste during standby. Through this set of loss analysis and management mechanisms, enterprises can fully grasp the weak links in energy use, thereby implementing precise energy-saving and emission reduction measures, ultimately improving energy utilization efficiency, reducing operating costs, and achieving sustainable development goals.

[0069] Specifically, the data processing rules for executing the photovoltaic consumption index item on the structured energy consumption data are as follows: configure the corresponding relationship between the photovoltaic access point and the grid incoming line, and obtain the photovoltaic consumption overview information according to the selected time period; wherein the photovoltaic consumption overview information of the park includes any one or more of the installed capacity of photovoltaic power generation, power generation, photovoltaic grid-connected power, photovoltaic consumption power and consumption rate; based on the photovoltaic consumption overview information, respectively simulate the photovoltaic grid-connected income and photovoltaic consumption income to obtain the photovoltaic consumption income information; based on the structured energy consumption data of each time period, separate the municipal electricity and photovoltaic green electricity to obtain the usage ratio information of municipal electricity and photovoltaic green electricity in the energy grid; determine the photovoltaic consumption adjustable amount based on the photovoltaic consumption overview information, the photovoltaic consumption income information and the usage ratio information of municipal electricity and photovoltaic green electricity; and use the photovoltaic consumption adjustable amount as the indicator data of the photovoltaic consumption index item.

[0070] Furthermore, the photovoltaic grid-connected income and photovoltaic absorption income simulation are respectively performed based on the photovoltaic absorption overview information to obtain the photovoltaic absorption income information, including: determining the total usage of municipal electricity and photovoltaic green electricity in the park based on the photovoltaic absorption overview information; calculating the photovoltaic absorption electricity of each photovoltaic access point based on the minimum value of photovoltaic power generation and load power consumption, and calculating the corresponding photovoltaic green electricity ratio; configuring a main energy grid and a sub-energy grid for each enterprise, associating meters on the high-voltage side and the low-voltage side, and subdividing the usage of municipal electricity and green electricity according to the photovoltaic green electricity ratio; multiplying the total electricity in each sub-energy grid by the green electricity ratio, calculating the municipal electricity and photovoltaic green electricity of each sub-energy grid, and summarizing them in the main energy grid to obtain the usage ratio of municipal electricity and photovoltaic green electricity; based on the usage ratio of municipal electricity and photovoltaic green electricity, respectively simulate the photovoltaic grid-connected income and photovoltaic absorption income, and display them at the energy grid level.

[0071] In an embodiment of the present invention, the photovoltaic absorption module analyzes the structured energy consumption data and executes the data processing rules of the photovoltaic absorption index items to realize the effective use of photovoltaic power generation in the industrial park. First of all, the photovoltaic absorption module needs to configure the correspondence between the photovoltaic access point and the grid incoming line to ensure the coordination between photovoltaic power generation and municipal electricity use in the park. On this basis, the module obtains the overview information of photovoltaic absorption according to the selected time period. This information includes any one or more of the installed capacity of photovoltaic power generation, actual power generation, photovoltaic grid-connected power, photovoltaic absorption power and photovoltaic absorption rate, which helps the park to clearly grasp the overall situation of photovoltaic power generation and use.

[0072] Furthermore, based on the overview information of these photovoltaic consumption, the photovoltaic consumption module further simulates and calculates the photovoltaic grid-connected income and photovoltaic consumption income to generate photovoltaic consumption income information. This process is mainly based on the comparison of photovoltaic power generation, grid-connected electricity and consumed electricity. By analyzing the use of photovoltaic power generation in different time periods, it helps the park predict and calculate the economic benefits under different power generation strategies. The income information includes two aspects: the grid-connected income and consumption income of photovoltaic power. Through the calculation of these two dimensions, enterprises can optimize their energy use strategies, thereby maximizing the economic return of photovoltaic power.

[0073] Furthermore, when executing the photovoltaic absorption data processing, the photovoltaic absorption module will also distinguish between municipal electricity and photovoltaic green electricity based on the structured energy consumption data of each time period. In this way, the system can obtain the actual usage ratio information of municipal electricity and photovoltaic green electricity in the energy grid. These ratios can help enterprises understand their usage of photovoltaic electricity and municipal electricity in a certain period of time, so as to make more optimized decisions in subsequent energy scheduling. Next, based on the photovoltaic absorption overview information, photovoltaic absorption income information and the usage ratio information of municipal electricity and photovoltaic green electricity, the photovoltaic absorption module can determine the adjustable amount of photovoltaic absorption. The adjustable amount of photovoltaic absorption indicates the proportion of photovoltaic electricity usage that can be adjusted within a certain range in the park, which provides energy management flexibility and ensures the maximum use of photovoltaic electricity under different load conditions.

[0074] Furthermore, the photovoltaic consumption module not only performs real-time analysis during the consumption process, but also simulates the photovoltaic grid-connected income and photovoltaic consumption income respectively through the photovoltaic consumption overview information to obtain detailed information on the photovoltaic consumption income. This process first determines the total usage of municipal electricity and photovoltaic green electricity in the park based on the photovoltaic consumption overview information, and then calculates the photovoltaic consumption of each photovoltaic access point based on the minimum value of photovoltaic power generation and load power consumption, and further calculates the proportion of photovoltaic green electricity. These calculation results can help enterprises clarify the actual consumption of their photovoltaic electricity, and optimize the combined use of photovoltaic electricity and municipal electricity through comparative analysis.

[0075] Furthermore, configuring the main energy grid and sub-energy grid for each enterprise is an important step in photovoltaic consumption management. The main energy grid and sub-energy grid represent the energy consumption of enterprises at different levels. By configuring the meters on the high-voltage side and the low-voltage side, the photovoltaic consumption module can clearly define the energy usage of each enterprise in different time periods. According to the proportion of photovoltaic green electricity, the system further subdivides the usage of municipal electricity and green electricity to ensure that the actual consumption of these two energy sources is clearly distinguished within the enterprise. After that, the system will multiply the total electricity in each sub-energy grid by the proportion of green electricity, calculate the municipal electricity and photovoltaic green electricity of each sub-energy grid, and summarize these results in the main energy grid to obtain the overall usage ratio of municipal electricity and photovoltaic green electricity.

[0076] Furthermore, on this basis, the photovoltaic consumption module will simulate the photovoltaic grid-connected income and photovoltaic consumption income based on the usage ratio of municipal electricity and photovoltaic green electricity, and display these results at the energy grid level. This detailed analysis helps enterprises to understand their energy usage from the overall to the detailed level, and formulate corresponding energy management and optimization plans based on these data. The functions provided by the photovoltaic consumption module are not limited to clearly displaying the use of photovoltaic electricity, but also help enterprises find the best power generation and consumption strategies through detailed profit analysis to achieve the goal of energy saving and cost reduction.

[0077] Furthermore, the photovoltaic absorption module consists of multiple key functions, including photovoltaic absorption configuration, photovoltaic power generation overview, photovoltaic clearing analysis and photovoltaic absorption improvement function. Photovoltaic absorption configuration is used to set the name of the photovoltaic access point in the park and determine the correspondence between the photovoltaic access point and the grid incoming line. This step ensures that photovoltaic electricity can be smoothly connected to the park power grid and provides a basis for subsequent photovoltaic absorption data processing. The photovoltaic power generation overview shows the photovoltaic absorption overview information of the park as a whole and each grid-connected point. This information covers photovoltaic installed capacity, power generation, photovoltaic grid-connected power, photovoltaic absorption power, etc., to help enterprises understand power generation and usage in different time periods.

[0078] Furthermore, for photovoltaic consumption in different time periods, the system also supports further refining the data of the daily dimension into the minute level, the data of the monthly dimension into the day level, and the data of the annual dimension into the month level. This refined data display method helps enterprises analyze the consumption of photovoltaic power in a more detailed time range, so as to perform real-time scheduling when needed. In addition, the changing trend of the photovoltaic consumption rate can reveal the degree of dependence of enterprises on photovoltaic power in different time periods, providing data support for enterprises to optimize energy use. Photovoltaic revenue analysis helps enterprises evaluate the changes in their photovoltaic power grid-connected revenue and consumption revenue. By analyzing the average grid-connected price and the average consumption price, enterprises can find the best power generation and use strategy.

[0079] Furthermore, the photovoltaic clearing analysis function conducts a detailed analysis of the total usage of grid power and photovoltaic green power in the park and each enterprise, showing the power usage of grid power and green power in different time periods. This function can be refined to the energy grid level to ensure that enterprises have a clear understanding of the consumption of grid power and photovoltaic power in peak, high, flat and valley time periods. With these data, enterprises can optimize their power consumption strategies, rely more on green power during periods of high photovoltaic power generation, reduce the use of grid power, and thus reduce energy costs.

[0080] Based on the scheme of the present invention, the photovoltaic consumption module realizes the refined management and maximum utilization of photovoltaic power generation through in-depth processing of structured energy consumption data. Through the consumption and benefit analysis functions provided by the module, enterprises can not only grasp the economic benefits of photovoltaic power generation, but also optimize their energy use strategies, while ensuring production, to achieve the goal of energy saving and cost reduction. In addition, the module supports detailed analysis of photovoltaic power generation in different time periods, ensuring that enterprises can grasp energy usage in real time and make dynamic adjustments when necessary. The realization of this series of functions has improved the energy management efficiency within the park, promoted the widespread application of renewable energy in industrial production, and provided strong support for enterprises to achieve green and low-carbon development.

[0081] Specifically, the data processing rules for executing the energy use economic index item on the structured energy consumption data are as follows: based on the structured energy consumption data, the monthly city electricity bill and photovoltaic grid-connected income bill of the park are obtained; the park electricity bill is decomposed according to the actual usage of each enterprise, and the city electricity and photovoltaic power consumption of each enterprise is analyzed based on the decomposition results; the city electricity fee and photovoltaic consumption rate corresponding to the city electricity and photovoltaic power consumption of each enterprise are calculated, and economic data are generated based on the city electricity fee and photovoltaic consumption rate; based on the economic data, an economic bill of each enterprise is generated as the indicator data of the energy use economic index item.

[0082] In an embodiment of the present invention, the lean electricity fee module aims to improve the accuracy and transparency of the park's electricity fee and photovoltaic revenue management by executing data processing rules for energy use economy indicators on structured energy consumption data. First, the module generates the park's monthly city electricity bill and photovoltaic grid-connected revenue bill based on the structured energy consumption data. The city electricity bill records the total electricity consumption and corresponding fees of the city electricity in the park, including detailed information such as market-based electricity purchase charges, transmission and distribution charges, basic electricity charges, and power factor adjustment charges. At the same time, the photovoltaic grid-connected revenue bill includes power generation, grid-connected electricity, grid-connected unit price, and various types of subsidy information for photovoltaic power generation. These data provide a reliable basis for the park to evaluate the economic benefits of photovoltaic power generation.

[0083] After the bill is generated, the lean electricity fee module further decomposes the total electricity bill of the park according to the actual electricity consumption of each enterprise. Through this decomposition, the system can accurately calculate the consumption of municipal electricity and photovoltaic electricity by each enterprise in the selected time period, and generate the municipal electricity fee and photovoltaic consumption fee exclusive to the enterprise. This process not only provides transparent electricity cost accounting for each enterprise in the park, but also dynamically adjusts the energy cost sharing of each enterprise according to the ratio of photovoltaic consumption and municipal electricity use.

[0084] Furthermore, the module calculates the economic data of energy use based on the city electricity charges and photovoltaic power consumption rates of each enterprise. These economic data include the specific amounts of city electricity charges and photovoltaic power consumption charges, and can further analyze the cost-effectiveness of each enterprise in energy use. These data can help enterprises optimize their energy use structure and reduce production costs based on their actual electricity consumption and photovoltaic power consumption.

[0085] Furthermore, the lean electricity fee module will generate an economic bill for each enterprise based on these economic data as the indicator data for the economic index of energy use. These economic bills reflect the total electricity cost, photovoltaic power consumption income, and possible savings potential of each enterprise in a certain period of time. Enterprises can adjust their energy use strategies based on the data in the bills, increase the proportion of photovoltaic power use, and reduce dependence on municipal electricity, thereby achieving the effect of energy saving and cost reduction.

[0086] Based on the solution of the present invention, the lean electricity fee module improves the transparency of energy cost management by accurately allocating the total electricity cost of the park to each enterprise. Enterprises can clearly understand the actual consumption of their municipal electricity and photovoltaic electricity, and gain insight into the cost-effectiveness of energy use through economic bills, thereby optimizing their electricity use strategies. This module not only helps enterprises reduce energy costs, but also promotes the widespread use of photovoltaic power generation in the park, achieving efficient use of energy and sustainable development. At the same time, this module provides strong support for the overall energy efficiency optimization and cost control of the park, helping the park achieve the goal of low-carbon energy saving.

[0087] Specifically, the data processing rules for executing the energy usage cost index item on the structured energy consumption data are as follows: according to the electricity price model adopted by the park and the enterprise, maintain the unit price and effective time of the time-of-use tiered electricity price, photovoltaic consumption electricity price and on-grid electricity price; set the time period for each electricity consumption situation published by the power grid, and associate the time-of-use electricity price model; wherein the electricity consumption situation includes any one or more of peak electricity consumption, peak electricity consumption, level electricity consumption period and valley electricity consumption period; based on the electricity fee pricing method triggered by the user, set the corresponding unit price for different time periods, and update the time-of-use electricity price model; based on the updated time-of-use electricity price model, process the structured energy consumption data, calculate the energy usage cost of each enterprise, and use it as the index data of the energy usage cost index item.

[0088] In an embodiment of the present invention, the data processing rules of the energy use cost indicator items are executed on the structured energy consumption data. First, based on the electricity price model adopted by the park and the enterprise, the system maintains the unit prices including time-sharing tiered electricity prices, photovoltaic consumption electricity prices and on-grid electricity prices, as well as their effective time. The setting of time-sharing tiered electricity prices is segmented according to the different electricity consumption conditions published by the power grid, such as peak, peak, flat and valley periods. For each electricity consumption condition, the system will be associated with the corresponding time-sharing electricity price model and configure different unit prices for each time period. This method allows enterprises to consume electricity at different electricity prices in different time periods, thereby finely managing electricity costs.

[0089] Furthermore, according to the electricity pricing method triggered by the user, the system will update the corresponding electricity price for each time period, and after updating the time-of-use electricity price model, it will process the structured energy consumption data based on this model. This process calculates the energy usage cost of the enterprise in different time periods by combining the actual electricity usage of the enterprise. Finally, the system uses these calculation results as indicator data for energy usage cost indicators for enterprises to analyze and optimize energy usage strategies.

[0090] Based on the solution of the present invention, through this cost calculation rule, the system can help enterprises accurately understand the energy use cost in different periods, especially through the application of time-of-use tiered electricity prices, so that enterprises can flexibly adjust energy consumption between peak and off-peak periods, and reduce peak electricity costs. In addition, by combining photovoltaic consumption and grid-connected electricity prices, enterprises can better utilize renewable energy and reduce overall energy costs. This data processing rule provides enterprises with a clear cost structure and optimization path, helping them to manage energy use more efficiently, reduce operating costs and achieve sustainable development goals.

[0091] Specifically, the carbon emission curve simulation corresponding to each enterprise is performed based on the direct carbon emission data and the indirect carbon emission data to characterize the carbon emission situation of each enterprise, including: performing data normalization processing on the direct carbon emission data and the indirect carbon emission data respectively; performing direct carbon emission calculation based on the normalized direct carbon emission data; performing indirect carbon emission calculation based on the normalized indirect carbon emission data; obtaining the total carbon emissions of each enterprise based on the direct carbon emissions and the indirect carbon emissions, and constructing the carbon emission curve corresponding to each enterprise based on the time series data of the total carbon emissions of each enterprise.

[0092] Specifically, based on the carbon emission curve corresponding to each enterprise and taking the lowest carbon emission as the optimization goal, the indicator data of each carbon emission management indicator item is optimized, and the energy scheduling plan of each enterprise is adjusted in real time based on the optimization result, including: calculating whether the total carbon emissions of the current park are greater than the preset total carbon emission threshold based on the carbon emission curve corresponding to each enterprise; if the total carbon emissions of the current park are greater than the preset total carbon emission threshold, the carbon emission optimization plan is triggered; if the total carbon emissions of the current park are not greater than the preset total carbon emission threshold, the carbon emissions within a predetermined time in the future are predicted based on the carbon emission curve corresponding to each enterprise to obtain a predicted carbon emissions value; if the predicted carbon emissions value is greater than the preset predicted carbon emissions value threshold, the carbon emission optimization plan is triggered; if the predicted carbon emissions value is not greater than the preset predicted carbon emissions value threshold, the carbon emission optimization plan is not triggered; based on the triggered carbon emission optimization plan, taking the lowest carbon emissions as the optimization goal, the energy scheduling plan of each enterprise is adjusted in real time.

[0093] Furthermore, with the lowest carbon emissions as the optimization goal, real-time regulation of the energy dispatch plans of various enterprises is performed, including: with the lowest carbon emissions as the optimization goal, respectively optimizing the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item, obtain the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item under the optimal carbon emissions state, and construct a corresponding initial regulation plan; with the highest profit as the optimization goal, respectively optimizing the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item, obtain the energy loss index item, photovoltaic absorption index item, energy use economic index item and energy use cost index item under the optimal profit state, and construct a corresponding revised regulation plan; based on the revised regulation plan, the initial regulation plan is revised to obtain and execute the real-time regulation plan of each enterprise.

[0094] In an embodiment of the present invention, in order to ensure that carbon emission data from different sources can be compared and analyzed under the same standard, it is necessary to perform data normalization on direct carbon emission data and indirect carbon emission data. Direct carbon emission data generally includes carbon emissions generated by the use of fossil fuels (such as coal, natural gas, gasoline, etc.) by enterprises, and these emissions can be calculated by the amount of fuel used and the corresponding emission factors. Indirect carbon emission data mainly involves carbon emissions generated by the power production process when enterprises use energy such as municipal electricity. The calculation of such indirect carbon emissions needs to be combined with the carbon emission coefficient of the power grid and the electricity consumption obtained by the enterprise from the power grid. Through normalization, the system can convert carbon emission data from different units and sources into a unified dimension, thereby ensuring the accuracy of calculation and analysis. Next, direct carbon emission calculation is performed based on the normalized direct carbon emission data, and indirect carbon emission calculation is performed based on the normalized indirect carbon emission data. Through these two processes, the system can obtain the direct and indirect carbon emissions of each enterprise respectively, which lays the foundation for constructing the overall carbon emission curve of the enterprise.

[0095] Furthermore, based on the calculation results of direct carbon emissions and indirect carbon emissions, the system can further combine the total carbon emissions of each enterprise. By recording the time series data of these total carbon emissions, the system can build a corresponding carbon emissions curve for each enterprise. These curves can intuitively reflect the carbon emission trends of enterprises in a specific period of time and help enterprises identify periods and activities with high carbon emissions.

[0096] Furthermore, in order to further optimize carbon emission management, the system optimizes the data of each carbon emission management indicator item with the lowest carbon emission as the optimization goal. These indicators may include the efficiency of energy use, the operating status of equipment, the consumption of photovoltaic power, etc. By analyzing the carbon emission curve of the enterprise, the system can calculate whether the total carbon emissions of the current park exceed the preset carbon emission threshold. If the current total carbon emissions exceed the threshold, the system will trigger the carbon emission optimization plan to reduce carbon emissions. If the current carbon emissions are lower than the threshold, the system will predict future carbon emissions based on the carbon emission curve and evaluate whether it will exceed the threshold in the future.

[0097] In this process, the carbon emission optimization plan mainly includes real-time regulation of the energy scheduling plan. If the carbon emission forecast value exceeds the preset threshold, the system will automatically perform carbon emission optimization to ensure that the company can reduce carbon emissions by optimizing energy scheduling. In the regulation plan, the system not only considers the reduction of carbon emissions, but also analyzes the efficiency of different energy sources, photovoltaic absorption rate and energy cost-effectiveness to find the optimal energy scheduling strategy.

[0098] Furthermore, during the execution of the energy dispatch plan, the system will continue to optimize multiple indicators with the goal of minimizing carbon emissions, including energy loss indicators, photovoltaic consumption indicators, energy use economic indicators, and energy use cost indicators. Through optimal calculation, the system can construct an initial control plan under the optimal carbon emission state. This plan is based on the goal of minimizing carbon emissions, considering how enterprises can make the best choices in terms of energy loss, photovoltaic consumption, etc., so as to achieve the lowest carbon emissions.

[0099] Furthermore, however, simply targeting carbon emissions may not be enough to fully meet the needs of enterprises. In order to further optimize economic benefits, the system will also use the highest benefit as the optimization target and conduct secondary optimization for the same energy loss index items, photovoltaic consumption index items, etc. Through this step, the system can construct a control plan with the best benefits, ensuring that enterprises can not only reduce carbon emissions, but also achieve the best economic benefits.

[0100] Specifically, after obtaining two plans (i.e., the initial control plan and the revised control plan), the system will revise the initial control plan based on the revised control plan with the best benefits, and finally form and execute a real-time control plan. This plan can achieve the lowest carbon emissions while maximizing the economic benefits of the enterprise.

[0101] Step S40: Monitor the energy scheduling and control process of each enterprise, perform park data operation and maintenance based on the monitoring results, and generate control reports in real time.

[0102] Specifically, in response to a request for intervention in the regulation process, identity authentication is performed on the requesting user; based on the verification result, it is read whether the current user has access rights corresponding to the intervention request; if so, the intervention function is opened and the intervention instruction of the current user is recovered; if not, the intervention request of the current user is rejected.

[0103] In the embodiment of the present invention, in the process of energy and carbon management of the park and each enterprise, it is crucial to ensure the operational security and authority control of different roles and users. In response to the intervention request in the regulation process, the system will first perform strict identity authentication on the requesting user to ensure the legitimacy of the user. The identity authentication process can be carried out in a variety of ways, including username and password verification, two-factor identity authentication (such as SMS verification code, fingerprint recognition, etc.). When the user passes the verification, the system will read the corresponding role and authority configuration based on its identity, and check whether the current user has the access right to execute the intervention request.

[0104] Furthermore, if the user's role permissions allow intervention in the current control process, the system will open the corresponding intervention function, allowing the user to submit and retrieve intervention instructions. The instructions may involve adjustments to modules such as online monitoring of energy consumption, distribution network energy efficiency, and photovoltaic consumption, ensuring that users can intervene and optimize energy management and carbon emission management in a timely manner. However, if the user does not have the corresponding permissions, the system will reject the intervention request and issue a prompt to the user that the permissions are insufficient to prevent unauthorized operations from affecting the security of the system and the normal operation of energy management.

[0105] Based on the solution of the present invention, this strict authority management mechanism can not only ensure the security of the system and prevent unauthorized users from interfering with the control process, but also clarify the scope of responsibilities of each role and ensure the standardization of system operations. Through the refined control of role permissions, the system can allocate management resources more effectively, ensuring that users of different levels can perform corresponding operations according to their permissions, while also improving the overall security and operational efficiency of the system.

[0106] Furthermore, at every predetermined period, a power grid bill is entered based on the monitoring results within the corresponding period; wherein the power grid bill includes: electricity consumption, total electricity charge, city electricity charge, market-based electricity purchase charge, basic electricity charge, power factor adjustment charge, photovoltaic grid-connected electricity, photovoltaic grid-connected unit price, photovoltaic grid-connected electricity charge, and any one or more of various subsidy funds.

[0107] In an embodiment of the present invention, every predetermined period (for example, one month), the park and the enterprise need to enter the power grid bill based on the actual energy usage and monitoring results within the period to ensure the accuracy of energy management and financial accounting. Specifically, the entry of the power grid bill covers the detailed electricity usage of the enterprise during the period and the corresponding expenses. First, the bill will include the total electricity consumption of the enterprise, which is the total amount of electricity consumed by the enterprise during the entire period. Based on this electricity consumption, the system will calculate the total electricity bill and further subdivide it into cost categories such as municipal electricity charges, market-based electricity purchase charges, basic electricity charges, and power factor adjustment charges.

[0108] Among them, the utility electricity fee is the fee incurred by enterprises for using power from the power grid, which is usually calculated based on the time-of-use tiered electricity price model. The time-of-use electricity price model sets different electricity prices for each time period based on the different time periods released by the power grid, such as peak, high, flat and valley electricity consumption. The electricity fee for enterprises is relatively high during peak electricity consumption periods, while the electricity fee is lower during valley electricity consumption periods. This pricing mechanism encourages enterprises to use more electricity during valley electricity consumption periods, thereby balancing the load of the power grid.

[0109] The market-based electricity purchase fee is the fee that enterprises need to pay when purchasing electricity from the power market through a market-based mechanism. This is usually determined by the supply and demand relationship in the market. With the gradual opening up of the energy market, enterprises can obtain more flexible electricity price options through market-based electricity purchase, which may be more economical than traditional grid electricity prices.

[0110] The basic electricity fee is related to the company's electricity capacity or demand. There are two ways to calculate the basic electricity fee: capacity-based and demand-based. If the company chooses the capacity-based method, the basic electricity fee is related to its electricity capacity (i.e. the highest possible electricity consumption); if the demand-based method is chosen, the basic electricity fee is linked to the actual electricity demand. The system will calculate the basic electricity fee for the period based on the company's choice and the corresponding electricity consumption.

[0111] In addition, the bill will also include a power factor adjustment fee, which is a fee adjusted based on the company's power factor (i.e. the effective use rate of electricity). If the company's power factor is low, it means that its electricity use efficiency is not high and it needs to pay a higher electricity fee. Through this adjustment mechanism, companies can reduce electricity expenses by optimizing the power factor.

[0112] In the case of photovoltaic power generation, the bill will also include photovoltaic grid-connected electricity, photovoltaic grid-connected unit price and photovoltaic grid-connected electricity fee. The photovoltaic power generated by the enterprise can be sold to the grid through the grid to generate income. Photovoltaic grid-connected electricity refers to the total amount of photovoltaic power that the enterprise feeds back to the grid during the cycle, and the photovoltaic grid-connected unit price is the grid-connected electricity price paid by the grid to the enterprise according to the market or agreement. The photovoltaic grid-connected electricity fee is obtained by multiplying the grid-connected electricity by the unit price, which can bring considerable benefits to the enterprise.

[0113] In addition, enterprises may also enjoy subsidies from the government or other institutions, and their bills may include subsidy funds, such as photovoltaic power generation subsidies. These subsidies help enterprises further reduce energy costs and encourage more enterprises to use renewable energy.

[0114] Based on the solution of the present invention, by periodically entering the grid bill every month, enterprises can more transparently and accurately grasp their energy usage and related expenses. This periodic bill management not only helps enterprises to conduct financial accounting, but also provides important data support for their energy usage optimization. Through detailed bill sub-items, enterprises can clearly understand their electricity costs in different time periods, especially the usage ratio between municipal electricity and photovoltaic electricity. This provides a reference for enterprises to formulate more economical and environmentally friendly energy usage strategies.

[0115] Furthermore, the system adjusts the electricity fee setting through time-of-use electricity prices and power factors, encouraging enterprises to optimize the electricity usage period and efficiency, thereby reducing peak electricity consumption and improving power factors. This can not only help enterprises reduce electricity costs, but also effectively balance the grid load and reduce energy waste.

[0116] Furthermore, the record of photovoltaic grid-connected electricity charges and related subsidy funds provides economic incentives for enterprises to introduce renewable energy, and promotes enterprises to gradually reduce their dependence on traditional fossil energy. This not only helps enterprises reduce long-term operating costs, but also contributes to the sustainable development of the park. Ultimately, through detailed bill management, enterprises can better track and manage energy use, improve the overall efficiency of energy management, and promote green development.

[0117] Example:

[0118] There are three companies within a certain science and technology industrial park, which has built 18.5MW of solar photovoltaics. The above methods and systems can be adopted in achieving the management of a near-zero-carbon park.

[0119] 1) Smart collection meters and aggregation gateways for various energies such as electricity, water, cold, heat, compressed air, etc. are arranged in the workshops, factories, shared stations and other business areas of each enterprise in the park. The collection points of the above-mentioned smart collection meters are summarized and uploaded to the energy IoT base through the configuration of the gateway.

[0120] 2) Maintain the topological hierarchical relationship between meters of various energy types on the Energy IoT base, and upload the metering topological relationship and information such as the points collected by each meter to the Energy Carbon Management Platform. The Energy Carbon Management Platform interacts with the Energy IoT base data through API calls.

[0121] 3) Maintain the basic information of the park and each enterprise in the enterprise maintenance module. The basic information of the park needs to maintain the transformers used in the park and the cost price models of related electricity, water, natural gas, cold, heat, compressed air and other energy sources, and set the photovoltaic income sharing ratio of each enterprise in the park based on the investment relationship of the photovoltaic in the park. The transformer information maintained in a certain park is shown in Table 1:

[0122] Table 1 Park transformer information

[0123]

[0124]

[0125] 4) Maintain the basic information of each enterprise in the park, establish a main energy grid for each enterprise, where electricity, water, natural gas, cold, heat, compressed air and other energy are 100% owned by a certain enterprise. For electric energy, configure a sub-energy grid with the same number of high-voltage side meters in the main energy grid according to whether it contains multiple high-voltage side meters. The main energy grids of each energy type shared by each enterprise need to be maintained in the virtual entity public energy consumption, and set the energy sharing coefficient of each enterprise.

[0126] In all the above sub-energy grids and the main energy grid without sub-energy grids, configure the energy meter corresponding to the energy grid. The main energy grid and sub-energy grid configuration of each enterprise in a certain park are shown in Table 2 and Table 3:

[0127] Table 2 Division of the main energy grid of enterprise electricity energy

[0128]

[0129]

[0130] The main energy grid corresponding to plant 1# can be divided into sub-energy grids as shown in Table 3.

[0131] Table 3 Division of sub-energy grids

[0132]

[0133] The main energy grid of the electric energy type shared by all enterprises in a certain park is maintained in the virtual entity public energy consumption, and the allocation coefficient of energy consumption of each enterprise is set as shown in Table 4:

[0134] Table 4 Division of sub-energy grids

[0135] South District Restaurant, Office Building Enterprise 1 50% Enterprise 2 50% Joint Station Building Enterprise 1 50% Enterprise 2 50% Boiler Room Enterprise 1 49.86% Enterprise 2 50.14% Refrigeration station Enterprise 1 49.86% Enterprise 2 50.14% ... ... ...

[0136] 5) Configure the photovoltaic meter associated with the photovoltaic access point name, the corresponding relationship between it and the grid incoming line, and the topological relationship between the photovoltaic access point and the high-voltage side load meter under the grid incoming line.

[0137] 6) Maintain the key energy-consuming equipment owned by each enterprise, and synchronize the monthly output and output value of each enterprise from the production system.

[0138] 7) Maintain the price model based on the unit price of each energy type in the park. For a single-price energy model, set the unit price and effective time. For the time-of-use electricity price model, first maintain the currently effective time-of-use period, and then maintain the unit price for each period.

[0139] Maintain the monthly power grid bill and photovoltaic grid-connected income bill of the park, and initialize various loss models of the park and enterprises.

[0140] 9) Maintain the carbon emission factors and standard coal equivalent coefficients of the energy types used by the park and enterprises, and automatically calculate carbon emissions and carbon emissions through the activity data of the corresponding energy.

[0141] 10) Set corresponding roles and permissions for the park and each enterprise, and analyze, view and maintain the energy consumption online monitoring module, distribution network energy efficiency module, photovoltaic absorption module, lean electricity fee module, carbon emission management module, price model module, enterprise entity maintenance module and user authority module provided by the energy and carbon management platform based on the corresponding role permissions.

[0142] Figure 2 FIG. 1 is a structural diagram of an energy carbon management platform provided by an embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides an energy carbon management platform, which includes: a collection unit, which is used to recycle the energy consumption data and equipment operation data corresponding to each enterprise collected in real time by the control module of each region based on the energy Internet of Things base; a processing unit, which is used to update the sub-energy grid of each enterprise based on the equipment operation data, and to update the main energy grid of each enterprise based on the energy consumption data of each enterprise; wherein the main energy grid is used to characterize the total consumption of all energy types of each enterprise; the sub-energy grid is used to characterize the energy usage of each device; a control unit, which is used to perform real-time control of the energy scheduling scheme of each enterprise in the park based on the updated main energy grid and the updated sub-energy grid with the lowest carbon emission as the optimization goal; a monitoring unit, which is used to monitor the control process of the energy scheduling scheme of each enterprise, and execute park data operation and maintenance and generate control reports in real time based on the monitoring results.

[0143] like Figure 3The processing unit includes: an energy online monitoring module, which is used to monitor the energy consumption of various energy types of various enterprises in the park in real time, and provide metering topology and energy grid view analysis; a distribution network energy efficiency module, which is used to analyze the loss of the park's power distribution network, the standby status of key equipment, and the load and loss of the transformer; a photovoltaic consumption module, which is used to manage the access, consumption and access to the Internet of photovoltaic power generation in the park; a lean electricity fee module, which is used to manage the electricity bills of park enterprises and the decomposition of multilateral bills; a carbon emission management module, which is used to monitor the carbon emissions of each enterprise and calculate direct carbon emission data and indirect carbon emission data.

[0144] like Figure 3 The monitoring unit includes: a price model module, which is used to maintain the time-of-use electricity price model of various energy media in the park; an enterprise entity maintenance module, which is used to maintain the metering and payment information, energy usage information and green electricity income sharing ratio of the park and the enterprise; a user authority module, which is used to manage user roles and authorities to ensure that different enterprises and personnel can only access and operate corresponding functional modules.

[0145] Preferably, each regional control module includes a smart meter and a gateway; the smart meter is used to collect output / consumption status information of each energy type; the gateway is used to upload the output / consumption status information of each energy type after preprocessing to the energy IoT base.

[0146] like Figure 4 The third aspect of the present invention provides a park energy carbon supervision system, which is applied to carbon emission monitoring and carbon emission optimization in industrial parks, and is characterized in that the park energy carbon supervision system includes the above-mentioned energy carbon management platform; an energy Internet of Things base arranged on one side of the energy carbon supervision system and communicatively connected to the energy carbon supervision system; a regional control module arranged on the other side of the energy Internet of Things base and communicatively connected to the energy Internet of Things base; the regional control module transmits the energy consumption data and equipment operation data corresponding to each enterprise collected in real time to the energy carbon management platform based on the energy Internet of Things base.

[0147] The embodiment of the present invention also provides a computer-readable storage medium, on which instructions are stored, which, when executed on a computer, enable the computer to execute the above-mentioned park energy carbon supervision method.

[0148] Those skilled in the art will understand that all or part of the steps in the method for implementing the above-mentioned embodiments can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for making a single-chip microcomputer, a chip or a processor (processor) perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0149] The optional embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the technical concept of the embodiments of the present invention, the technical scheme of the embodiments of the present invention can be subjected to a variety of simple modifications, and these simple modifications all belong to the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0150] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the embodiments of the present invention, they should also be regarded as the contents disclosed in the embodiments of the present invention.

Claims

1. A park energy carbon supervision method, applied to carbon emission monitoring and carbon emission optimization in industrial parks, characterized in that: The method is executed by an energy carbon management platform, which is connected to a regional control module arranged on each energy side based on an energy IoT base, and the method includes: Based on the energy IoT base, the control modules in each area collect the energy consumption data and equipment operation data of each enterprise in real time; The sub-energy grid of each enterprise is updated based on the equipment operation data, and the main energy grid of each enterprise is updated based on the energy consumption data of each enterprise; wherein, The main energy grid is used to represent the total energy consumption of all energy types of each enterprise; The sub-energy grid is used to characterize the energy usage of each device; With the lowest carbon emissions as the optimization goal, the energy dispatch plans of various enterprises in the park are implemented in real time based on the updated main energy grid and updated sub-energy grids; Monitor the energy dispatching and control process of each enterprise, perform park data operation and maintenance and generate control reports in real time based on the monitoring results.

2. The method according to claim 1, characterized in that Each regional control module includes a smart meter and a gateway; The smart meter is used to collect output / consumption status information of each energy type; The gateway is used to upload the output / consumption status information of each energy type after preprocessing to the energy IoT base.

3. The method according to claim 1, characterized in that The updating of the sub-energy grid of each enterprise based on the equipment operation data includes: Based on the equipment operation data, determine the power consumption of each equipment and the consumption of other energy types besides power consumption; The electricity consumption is distinguished between mains electricity consumption and photovoltaic electricity consumption, and based on the distinction result and the consumption of other energy types except the electricity consumption, they are mapped to the corresponding sub-energy grid.

4. The method according to claim 1, characterized in that: The updating of the main energy grid of each enterprise based on the energy consumption data of each enterprise includes: Based on the energy consumption data, calculate the total energy consumption data of each sub-energy grid; Based on the total energy consumption data of each enterprise corresponding to each sub-energy network and the photovoltaic power generation of each enterprise, the consumption ratio of each energy type of each enterprise is calculated, and the calculation results are mapped to the main energy grid; In the total energy consumption data of each sub-energy network, the consumption of each energy type of each enterprise's public equipment is identified, and the main energy grid is updated based on the identification result.

5. The method according to claim 1, characterized in that The optimization goal is to minimize carbon emissions, and the energy scheduling plans of various enterprises in the park are implemented in real time based on the updated main energy grid and the updated sub-energy grid, including: Structuring the updated main energy grid and the updated sub-energy grid data to obtain structured energy consumption data; Perform data processing of corresponding carbon emission management index items based on structured energy consumption data to obtain index data of each carbon emission management index item; Based on structured energy consumption data, determine direct and indirect carbon emission data; Based on direct carbon emission data and indirect carbon emission data, the carbon emission curve simulation of each enterprise is performed to characterize the carbon emission situation of each enterprise; Based on the carbon emission curves corresponding to each enterprise, with the lowest carbon emission as the optimization goal, the indicator data of each carbon emission management indicator item is optimized, and the energy scheduling plan of each enterprise is adjusted in real time based on the optimization results.

6. The method according to claim 5, characterized in that The structured processing of the updated main energy grid and the updated sub-energy grid data to obtain structured energy consumption data includes: Based on the hierarchical structure of metering topology, the energy consumption data of enterprises or parks is identified step by step, and the energy usage in different time dimensions is constructed; Determine the corresponding energy type and usage period based on energy usage in different time dimensions, and compare and analyze the consumption of various energy types through the energy grid view based on energy type and usage period; Various types of energy consumption data are organized into a structured energy usage data table according to energy type as structured energy consumption data.

7. The method according to claim 6, characterized in that The energy usage based on different time dimensions determines the corresponding energy type and usage period, and compares and analyzes the consumption of various energy sources through the energy grid view based on the energy type and usage period, including: Based on the metering topology, one or more multi-level metering topology nodes are selected by enterprise to collect and analyze the load and energy consumption data of each type in the selected time period; Based on the load and energy consumption data of each type in the selected time period, the energy grid view is used to analyze the consumption data of each type of energy according to the energy grid configured by the enterprise to obtain the power consumption situation; The power consumption situation includes any one or more of power consumption peak, power consumption peak, power consumption level section and power consumption valley section; In each time period, the power consumption is compared across preset time periods, and the energy consumption is obtained based on the comparison results.

8. The method according to claim 5, characterized in that The carbon emission management indicators include: Any one or more of energy loss index, photovoltaic consumption index, energy use economic index and energy use cost index; among which, The energy loss index item is used to characterize the energy efficiency loss of each enterprise's power distribution network, each transformer and each predetermined key equipment; Photovoltaic consumption index items are used to characterize the consumption rate, grid-connected electricity and revenue of photovoltaic power generation; The energy use economic index item is used to characterize the economic benefits of energy use of each enterprise; The energy cost indicator is used to characterize the total energy cost of each enterprise in different time periods.

9. The method according to claim 8, characterized in that The data processing rules for executing the energy loss index item on the structured energy consumption data are as follows: Based on the pre-configured upper and lower loss model relationships, the power loss and loss rate of each enterprise's power distribution network are obtained based on the structured energy consumption data, as indicator data for energy loss index items of each enterprise's power distribution network; Based on the structured energy consumption data, the maximum load rate, loss rate and duration distribution of different load states of each transformer in the selected period are identified as the index data of the energy loss index item of each transformer; The standby status of each predetermined key device is identified based on the structured energy consumption data, and the standby energy loss of each predetermined key device is obtained based on the standby status as the index data of the energy loss index item of each predetermined key device.

10. The method according to claim 8, characterized in that The data processing rules for executing the photovoltaic consumption index item on the structured energy consumption data are as follows: Configure the correspondence between the photovoltaic access point and the grid incoming line, and obtain the photovoltaic consumption overview information according to the selected time period; The photovoltaic power consumption overview information of the park includes any one or more of the photovoltaic power generation installed capacity, power generation, photovoltaic grid-connected power, photovoltaic power consumption and consumption rate; Based on the photovoltaic consumption overview information, the photovoltaic grid-connected income and photovoltaic consumption income are simulated to obtain photovoltaic consumption income information; Based on the structured energy consumption data of each time period, the utility power and photovoltaic green power are separated to obtain the usage ratio information of utility power and photovoltaic green power in the energy grid; Determine the adjustable amount of photovoltaic consumption based on the photovoltaic consumption overview information, photovoltaic consumption income information and the use ratio of municipal electricity and photovoltaic green electricity; The adjustable amount of photovoltaic consumption is used as the indicator data of the photovoltaic consumption index item.

11. The method according to claim 10, characterized in that The photovoltaic grid-connected income and photovoltaic consumption income simulation are respectively performed based on the photovoltaic consumption overview information to obtain photovoltaic consumption income information, including: Determine the total usage of city electricity and photovoltaic green electricity in the park based on the photovoltaic consumption overview information; Based on the minimum value of photovoltaic power generation and load power consumption, calculate the photovoltaic power consumption of each photovoltaic access point and the corresponding photovoltaic green power ratio; Configure a main energy grid and a sub-energy grid for each enterprise, associate the meters on the high-voltage side and the low-voltage side, and subdivide the usage of city electricity and green electricity according to the proportion of photovoltaic green electricity; Multiply the total electricity in each sub-energy grid by the proportion of green electricity, calculate the mains electricity and photovoltaic green electricity of each sub-energy grid, and summarize them into the main energy grid to obtain the usage ratio of mains electricity and photovoltaic green electricity; Based on the usage ratio of municipal electricity and photovoltaic green electricity, the photovoltaic grid-connected income and photovoltaic consumption income are simulated respectively, and then displayed at the energy grid level.

12. The method according to claim 10, characterized in that The data processing rules for executing the energy use economy index item on the structured energy consumption data are as follows: Based on structured energy consumption data, the park's monthly utility bill and photovoltaic grid-connected income bill are obtained; Decompose the park's electricity bills according to the actual usage of each enterprise, and analyze the municipal electricity and photovoltaic power consumption of each enterprise based on the decomposition results; The municipal electricity charges and photovoltaic consumption rates corresponding to the municipal electricity and photovoltaic consumption power consumption of each enterprise are calculated, and economic data is generated based on the municipal electricity charges and photovoltaic consumption rates; An economic bill for each enterprise is generated based on the economic data as index data for the energy use economic index item.

13. The method according to claim 12, characterized in that The data processing rules for executing the energy use cost index item on the structured energy consumption data are: According to the electricity price model adopted by the park and enterprises, maintain the unit price and effective time of time-of-use tiered electricity price, photovoltaic consumption electricity price and grid-connected electricity price; Set the time period for each power consumption situation published by the power grid and associate it with the time-of-use electricity price model; The power consumption situation includes any one or more of power consumption peak, power consumption peak, power consumption level section and power consumption valley section; Based on the electricity price calculation method triggered by the user, corresponding unit prices are set for different time periods, and the time-of-use electricity price model is updated; Based on the updated time-of-use electricity price model, the structured energy consumption data is processed and the energy use cost of each enterprise is calculated as the indicator data of the energy use cost indicator item.

14. The method according to claim 5, characterized in that The carbon emission curve simulation corresponding to each enterprise is performed based on the direct carbon emission data and the indirect carbon emission data to characterize the carbon emission situation of each enterprise, including: Perform data normalization processing on direct carbon emission data and indirect carbon emission data respectively; Perform direct carbon emission calculation based on normalized direct carbon emission data; Calculate indirect carbon emissions based on the normalized indirect carbon emissions data; Based on direct carbon emissions and indirect carbon emissions, the total carbon emissions of each enterprise are obtained, and based on the time series data of the total carbon emissions of each enterprise, the corresponding carbon emissions curve of each enterprise is constructed.

15. The method according to claim 5, characterized in that Based on the carbon emission curves corresponding to each enterprise, with the lowest carbon emission as the optimization goal, the index data of each carbon emission management index item is optimized, and the energy scheduling plan of each enterprise is adjusted in real time based on the optimization results, including: Calculate whether the total carbon emissions of the current park are greater than a preset total carbon emissions threshold based on the carbon emissions curves corresponding to the enterprises; If the total carbon emissions of the current park are greater than the preset total carbon emissions threshold, the carbon emissions optimization plan will be triggered; If the total carbon emissions of the current park are not greater than the preset total carbon emissions threshold, the carbon emissions forecast within the future predetermined time is performed based on the carbon emissions curve corresponding to each enterprise to obtain the carbon emissions forecast value; If the predicted carbon emission value is greater than the preset predicted carbon emission value threshold, the carbon emission optimization plan is triggered; If the predicted carbon emission value is not greater than the preset predicted carbon emission value threshold, the carbon emission optimization plan will not be triggered; Based on the triggered carbon emission optimization plan, the energy scheduling plan of each enterprise is controlled in real time with the lowest carbon emission as the optimization goal.

16. The method according to claim 15, characterized in that With the lowest carbon emissions as the optimization goal, we implement real-time regulation of each enterprise's energy scheduling plan, including: Taking the lowest carbon emission as the optimization goal, optimize the energy loss index, photovoltaic consumption index, energy use economic index and energy use cost index respectively, obtain the energy loss index, photovoltaic consumption index, energy use economic index and energy use cost index under the optimal carbon emission state, and construct the corresponding initial control plan; Taking the highest benefit as the optimization goal, optimize the energy loss index, photovoltaic consumption index, energy use economic index and energy use cost index respectively, obtain the energy loss index, photovoltaic consumption index, energy use economic index and energy use cost index under the optimal benefit state, and construct the corresponding correction and control plan; The initial control plan is modified based on the modified control plan to obtain and execute the real-time control plan of each enterprise.

17. The method according to claim 1, characterized in that The monitoring of the energy dispatching scheme control process of each enterprise, executing park data operation and maintenance and generating control reports in real time based on the monitoring results, includes: In response to the governance process intervention request, performing identity authentication on the requesting user; Based on the verification result, read whether the current user has the access permission corresponding to the intervention request; If it exists, the intervention function is enabled and the intervention instruction of the current user is recovered; If not present, the current user's intervention request is rejected.

18. The method according to claim 1, characterized in that The monitoring of the energy dispatching scheme control process of each enterprise, executing park data operation and maintenance and generating control reports in real time based on the monitoring results, includes: At every predetermined period, the grid bill is entered based on the monitoring results within the corresponding period; wherein, The grid bill includes: Electricity consumption, total electricity charges, city electricity charges, market-based electricity purchase charges, basic electricity charges, power factor adjustment charges, photovoltaic grid-connected electricity, photovoltaic grid-connected unit price, photovoltaic grid-connected electricity charges, and any one or more of the various subsidy funds.

19. A carbon management platform, applied to carbon emission monitoring and carbon emission optimization in industrial parks, characterized in that: The energy carbon management platform is connected to the regional control modules arranged on each energy side based on the energy IoT base. The energy carbon management platform: A collection unit is used to collect energy consumption data and equipment operation data corresponding to each enterprise collected in real time by each regional control module based on the energy IoT base; A processing unit is used to update the sub-energy grid of each enterprise based on the equipment operation data, and to update the main energy grid of each enterprise based on the energy consumption data of each enterprise; wherein, The main energy grid is used to represent the total energy consumption of all energy types of each enterprise; The sub-energy grid is used to characterize the energy usage of each device; The control unit is used to optimize the lowest carbon emissions and perform real-time control of the energy dispatch plans of various enterprises in the park based on the updated main energy grid and updated sub-energy grids; The monitoring unit is used to monitor the energy scheduling and control process of each enterprise, perform park data operation and maintenance based on the monitoring results, and generate control reports in real time.

20. The energy carbon management platform according to claim 19, characterized in that: The processing unit comprises: Energy online monitoring module, which is used to monitor the energy consumption of various energy types of various enterprises in the park in real time, and provide metering topology and energy grid view analysis; The distribution network energy efficiency module is used to analyze the losses of the park's power distribution network, the standby status of key equipment, and the load and losses of transformers; Photovoltaic consumption module, used to manage the access, consumption and Internet access of photovoltaic power generation in the park; Lean electricity bill module, used to manage the electricity bills of enterprises in the park and decompose multilateral bills; The carbon emission management module is used to monitor the carbon emissions of each enterprise and calculate direct carbon emission data and indirect carbon emission data.

21. The energy carbon management platform according to claim 19, characterized in that: The monitoring unit comprises: The price model module is used to maintain the time-of-use electricity price model of various energy media in the park; The enterprise maintenance module is used to maintain the metering and payment information, energy usage information, and green electricity revenue sharing ratio of the park and enterprises; The user permission module is used to manage user roles and permissions to ensure that different companies and personnel can only access and operate corresponding functional modules.

22. The energy carbon management platform according to claim 19, characterized in that: Each regional control module includes a smart meter and a gateway; The smart meter is used to collect output / consumption status information of each energy type; The gateway is used to upload the output / consumption status information of each energy type after preprocessing to the energy IoT base.

23. A park energy carbon monitoring system is applied to carbon emission monitoring and carbon emission optimization in industrial parks, characterized in that: The park energy carbon supervision system includes the energy carbon management platform described in any one of claims 19 to 22; An energy IoT base that is arranged on one side of the energy carbon supervision system and is connected to the energy carbon supervision system for communication; A regional control module is arranged on the other side of the energy IoT base and is communicatively connected to the energy IoT base; The regional control module transmits the real-time collected energy consumption data and equipment operation data corresponding to each enterprise to the energy carbon management platform based on the energy IoT base.

24. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the park energy and carbon supervision method described in any one of claims 1-18.

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