A power consumption information monitoring system in an industrial park

Abstract

This invention discloses an electricity consumption information monitoring system for industrial parks, belonging to the field of industrial energy management technology. It includes three main modules: carbon emission baseline generation, regional electricity consumption control, and photovoltaic-storage coordinated scheduling. The carbon emission baseline generation module divides time-series units to construct a monitoring database, calculates and iteratively optimizes the dynamic carbon emission baseline, and uses it as the upper limit for carbon emission control during specific time periods. The regional electricity consumption control module calculates the purchased electricity volume and total electricity consumption threshold based on the dynamic baseline, divides electricity consumption areas and calculates load priorities, and implements differentiated control and carbon emission over-limit alarms according to load type. The photovoltaic-storage coordinated scheduling module executes scheduling based on real-time carbon emission scenarios, collects operational data, and transmits it back to the model to correct parameters, forming a closed-loop optimization. This invention can solve the problems of rigid carbon emission control, extensive electricity consumption regulation, and isolated photovoltaic-storage energy scheduling in industrial parks, balancing production continuity and low-carbon control needs, improving the efficiency of clean energy consumption, and optimizing the coordinated control effect of electricity consumption and carbon emission in industrial parks.

Description

Technical Field

[0001] This invention belongs to the field of industrial energy management technology, specifically relating to an electricity consumption information monitoring system in an industrial park. Background Technology

[0002] Industrial parks are the core carriers of industrial energy consumption and carbon emissions, and low-carbon electricity management is a key focus for industry development. Currently, industrial parks suffer from rigid carbon emission control thresholds, inefficient electricity regulation methods, and isolated photovoltaic and energy storage systems with electricity load dispatch, making it difficult to simultaneously address production stability and low-carbon management needs. Therefore, this invention urgently aims to solve the following technical problems: The carbon emission control in industrial parks uses fixed standards, which cannot be adapted to the actual situation of production, electricity consumption and clean energy supply in the parks, resulting in poor carbon emission control. The park's electricity management fails to differentiate between load importance and regional differences, and unified control is prone to interfering with core production. Electricity allocation lacks scientific rationality. The lack of coordinated scheduling between photovoltaic, energy storage and electricity load in the industrial park, and insufficient absorption of clean energy make it difficult to achieve low-carbon electricity management through energy dispatch. To address this, we propose an electricity information monitoring system for industrial parks. Summary of the Invention

[0003] The purpose of this invention is to provide an electricity consumption information monitoring system for industrial parks to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an electricity consumption information monitoring system for industrial parks, comprising: Carbon emission baseline generation module: Divide time series units and collect multi-dimensional operation data to build a time series database for electricity consumption supervision; calculate the benchmark electricity load, photovoltaic predicted output and production load fluctuation coefficient of each time series unit to obtain the dynamic carbon emission baseline and perform rolling iterative optimization, and use the corrected baseline as the upper limit of carbon emission control for the time period; Regional power consumption control module: Based on the dynamic carbon emission baseline of time sequence units, calculate the maximum allowable grid purchase volume and total power consumption threshold of the park; divide the power consumption area and calculate the load priority coefficient of the area to determine the upper limit of power consumption control in each area; divide the load type according to priority, execute regional power consumption control, calculate carbon emissions in real time and trigger regular control, global emergency flexible control and over-limit alarm; Photovoltaic-storage coordinated scheduling module: collects real-time data on photovoltaics, energy storage, electricity consumption and electricity purchase, performs photovoltaic-storage-electricity consumption coordinated scheduling based on real-time cumulative carbon emissions scenarios, generates scheduling schemes and issues control commands; collects post-scheduling operation data and sends back model correction parameters to support the management and control of the next time series unit.

[0005] Preferably, the specific process for constructing a time-series database for electricity consumption monitoring is as follows: The complete electricity consumption monitoring cycle of the industrial park is divided into several continuous and non-overlapping time units according to a fixed and equal time granularity. Connect to the park's smart electricity consumption data collection terminal, production execution management system, distributed photovoltaic monitoring terminal and weather forecasting platform to collect all the park's multi-dimensional raw operational data. All raw data are matched and aligned according to time series units, so that each data item corresponds to a unique time series unit, and a time series database for monitoring electricity consumption in the park is constructed. Data preprocessing is then carried out on the raw data in the database.

[0006] The preferred method for analyzing dynamic carbon emission baselines is as follows: Based on the time-series database of electricity consumption monitoring in the park, for each time-series unit, the actual electricity load of the park in multiple historical periods in the same period as the current time-series unit is extracted, and the benchmark electricity load of the park in that time-series unit is calculated by arithmetic average. The measured photovoltaic output data of multiple preceding time series units are extracted and weighted by the preset weighting coefficients of each preceding time series unit to obtain the predicted photovoltaic output of that time series unit. Read the actual production task volume of the day, calculate the average production task volume of the same period in history, and obtain the production load fluctuation coefficient by comparing the two. Obtain the carbon emission coefficient per unit of electricity generated by the power grid officially released by the energy management department of the area where the park is located. After normalizing and dedimensionalizing the benchmark electricity load, photovoltaic predicted output and production load fluctuation coefficient, substitute them into the dynamic carbon emission baseline calculation model to obtain the dynamic carbon emission baseline for this time series unit.

[0007] Preferably, the process of dynamically iteratively optimizing the carbon emission baseline and using the revised baseline as the upper limit for carbon emission control during a given period is as follows: The cumulative value of carbon emission baselines for all time units within the regulatory period shall not exceed the park’s preset total carbon emission control target. If the cumulative value exceeds the target, it shall be uniformly and proportionally reduced based on the dynamic carbon emission baseline ratio of each time unit, and the cumulative value shall be recalculated until it meets the requirements. After a single time unit is completed, the actual power load and measured photovoltaic output data are collected and substituted into the model to obtain the actual fitted baseline value. The relative deviation rate between the actual fitted baseline value and the original calculated baseline is calculated. If the deviation exceeds the preset allowable range, the baseline of the next time series unit is corrected proportionally. The repeated calibration process realizes the rolling iterative optimization of the baseline, and the corrected baseline is used as the carbon emission control upper limit of the corresponding time series unit.

[0008] Preferably, the specific process for calculating the maximum allowable electricity purchase from the power grid and the total electricity consumption threshold for the park is as follows: For each time series unit, the dynamic carbon emission baseline after rolling calibration and the carbon emission coefficient of the power grid unit electricity officially released by the energy management department of the park are obtained. After completing the parameter normalization process, the maximum allowable power grid purchase electricity of the park for the time series unit is calculated. The predicted photovoltaic output corresponding to the time series unit is added to the maximum allowable grid power purchase in the park to obtain the total power consumption threshold of the park for the time series unit.

[0009] Preferably, the specific process of dividing electricity consumption areas, calculating the load priority coefficient of each area, and determining the upper limit of electricity consumption control for each area is as follows: The areas within the park equipped with independent intelligent electricity consumption collection terminals are marked as electricity consumption areas and given unique numbers. The actual electricity consumption of each electricity consumption area is collected in real time and synchronized to the electricity consumption supervision time series database. The historical average electricity load and rated total operating power of production equipment for each electricity consumption area and the entire park are obtained. The regional electricity importance coefficient and regional equipment power proportion coefficient are obtained by ratio calculation. The two types of coefficients are calculated in combination with the preset weight coefficient to obtain the load priority coefficient of the electricity consumption area. Based on the load priority coefficient of each power consumption area, the power grid purchase and allocation coefficient of each power consumption area is calculated by the proportion method. After normalizing and dimensionlessly processing the regional historical average electricity load, photovoltaic power output, maximum allowable grid purchase volume in the park, and grid purchase allocation coefficient, a comprehensive analysis was conducted to obtain the upper limit of electricity control for each electricity consumption area. The sum of the electricity consumption thresholds for all sub-areas is equal to the total electricity consumption threshold of the park.

[0010] Preferably, the specific process of classifying load types by priority, implementing regional power consumption control, calculating carbon emissions in real time, and triggering routine control, global emergency flexible control, and over-limit alarms is as follows: The load priority coefficient is preset to control the threshold. The load priority coefficient of each power consumption area is obtained under the time sequence unit and compared with the preset threshold to divide the absolutely guaranteed load area and the flexibly adjustable load area. When the actual electricity consumption in the flexibly adjustable load area is greater than or equal to the upper limit of electricity control, the power will be reduced in a step-by-step manner according to a preset ratio until the actual electricity consumption is lower than the upper limit of control. When the actual electricity consumption in the guaranteed load area is greater than or equal to the upper limit of electricity control, an over-limit warning will be triggered and pushed to the park's electricity monitoring terminal, and no electricity control will be implemented. The system summarizes the total electricity consumption of the park in real time, extracts the measured output of photovoltaic power, and calculates the real-time cumulative carbon emissions of the time sequence unit. When the real-time cumulative carbon emissions are greater than or equal to the dynamic carbon emission baseline, the system performs global emergency flexible control, reduces the operating power of equipment in the flexible load area by the preset reduction ratio after the increase, and stops reducing when the equipment reaches the preset cumulative reduction ratio threshold. When all equipment in the flexibly adjustable load area reaches the preset cumulative reduction ratio threshold, a carbon emission control over-limit alarm is triggered and pushed to the park's electricity consumption monitoring terminal for manual intervention.

[0011] Preferably, the specific process of performing photovoltaic-storage-power coordinated scheduling in different scenarios is as follows: Real-time data collection of the park's measured photovoltaic power output, energy storage equipment status charge, park's real-time total electricity consumption, and real-time electricity purchased from the grid within the time-series unit; Set safe operating constraints for the energy storage equipment at preset minimum and maximum safe states of charge, and establish a real-time power balance relationship in the park; When the measured output of photovoltaic power is greater than or equal to the total real-time electricity consumption of the park, the photovoltaic power is supplied to the electricity consumption area in full according to the load priority from high to low. If the energy storage device does not reach the preset maximum safe charge state, the surplus photovoltaic power is used for charging, the grid purchase power is set to 0 and no load regulation is performed. When the real-time cumulative carbon emissions are within the system's preset warning coefficient range, the system will prioritize photovoltaic power, supplement energy storage, and dispatch power grid as a backup. The power shortage will be addressed by purchasing electricity according to the maximum allowable power grid purchase limit for the park. In areas with flexibly adjustable loads, only conventional tiered power reduction will be implemented. When the real-time cumulative carbon emissions exceed the warning range, the energy storage device will replace the grid power purchase with a preset full-power discharge, and simultaneously trigger global emergency flexible control. In areas where flexible control is possible, the power will be reduced by a preset downward ratio after the increase, and the load area will be absolutely guaranteed to only trigger the over-limit warning. When all equipment in the flexibly adjustable area reaches the preset cumulative reduction ratio threshold but still fails to meet the standard, a carbon emission control over-limit alarm is triggered and pushed to the park's electricity consumption monitoring terminal.

[0012] Preferably, the specific process of generating a scheduling scheme and issuing control commands, collecting post-scheduling running data and transmitting back model correction parameters to support the management and control of the next time-series unit is as follows: Based on the scenario-based scheduling results, a photovoltaic-storage-electricity co-schedule scheme under carbon emission constraints is generated. The scheme is then transformed into standardized control commands and sent to the photovoltaic grid-connected monitoring terminal, the energy storage charge and discharge controller, and the load control terminal of each electricity consumption area to complete the coordinated control of photovoltaic and energy storage resources and electricity load. The system collects and schedules the actual carbon emissions, total electricity consumption in the park, measured photovoltaic output, electricity consumption data of each area, and load control execution results in real time. It then transmits the actual carbon emissions, total electricity consumption in the park, and measured photovoltaic output data back to the dynamic carbon emission baseline calculation model for baseline rolling calibration in the next time series unit. Simultaneously, the electricity consumption data and load control execution results of each region are fed back to the load priority coefficient and electricity control upper limit calculation model to correct the load priority coefficient, regional electricity allocation coefficient and electricity control upper limit calculation coefficient, providing support for the allocation of regional electricity control thresholds in the next time series unit, forming a closed-loop iterative optimization.

[0013] Compared with the prior art, the beneficial effects of the present invention are: (1) This electricity consumption information monitoring system in an industrial park divides the park's electricity consumption cycle into time-series units, collects multi-dimensional data such as electricity consumption, production, and photovoltaics to construct a time-series database, calculates a dynamic carbon emission baseline by combining historical load and production status, and iterates and corrects the baseline based on the overall control target and actual operating data. This breaks away from the limitations of fixed carbon emission control thresholds, making the carbon emission control benchmark more consistent with the actual production, electricity consumption, and clean energy supply status of the park, and improving the adaptability and scientific nature of carbon emission control. (2) The electricity information monitoring system in the industrial park calculates the maximum allowable electricity purchase and total electricity consumption threshold based on the dynamic carbon emission baseline, divides the electricity consumption units according to regional attributes and calculates the load priority, determines the upper limit of electricity control in each region, and implements differentiated regulation based on the importance of the load. It only issues warnings and does not regulate the core guaranteed load, and adjusts the flexible load in stages. This improves the situation where the traditional extensive electricity control is prone to affecting production, and takes into account the overall needs of the park's production continuity and electricity and carbon emission control.

[0014] (3) The electricity information monitoring system in the industrial park performs photovoltaic-storage-electricity coordinated scheduling according to the real-time cumulative carbon emission level and follows the principle of photovoltaic priority, energy storage supplementation and grid backup in allocating energy. The surplus photovoltaic power is used for energy storage charging. When the carbon emission is too high, the full power discharge of energy storage replaces the purchase of electricity from the grid, thereby improving the degree of clean energy consumption in the park, reducing the dependence on the purchase of electricity from the grid, reducing the carbon emission of the park, and making the coordinated management of photovoltaic and energy storage resources and electricity load more in line with the actual operation scenario. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1; Please see Figure 1 This invention provides an electricity consumption information monitoring system for industrial parks, comprising: Carbon emission baseline generation module: Divides the park's electricity consumption monitoring cycle into continuous time series units, collects multi-dimensional raw operational data on park electricity consumption, and constructs a time series database for park electricity consumption monitoring; calculates the benchmark electricity load, photovoltaic predicted output, and production load fluctuation coefficient for each time series unit to obtain a dynamic carbon emission baseline, and iteratively optimizes it. The corrected baseline is used as the upper limit for carbon emission control during the time period. The specific process is as follows: The complete electricity consumption monitoring cycle of the industrial park will be divided into several continuous and non-overlapping time units t, t=1,2,...,M, where M is the total number of time units within the monitoring cycle; Connect to the park's smart electricity consumption data collection terminal, production execution management system, distributed photovoltaic monitoring terminal and meteorological forecasting platform, etc., to collect all the original operational data of the park's electricity consumption information from multiple dimensions; All collected raw data are matched and aligned according to time series index t, so that each data item corresponds to a unique time series unit, and a time series database for monitoring electricity consumption in the park is constructed. The raw data in the database is then standardized and preprocessed, including outlier removal and missing value completion. The multi-dimensional raw operational data includes: Historical electricity consumption data: Actual electricity load data for each time period in the park during the same historical period (stored in the park's historical electricity consumption database); Production data: The actual production task of each time unit on the current day, and the average production task of each time unit in the same historical period (stored in the production management system). Photovoltaic data: Measured photovoltaic output data for each time series unit (stored in the photovoltaic grid-connected monitoring terminal database); Based on the time-series database of electricity consumption monitoring in the park, for each time-series unit t: Extract the actual electricity load of the park for each of the K historical periods corresponding to the current time period t. k = 1, 2, ..., K, where K is the preset total number of historical periods; Substitute the obtained K historical electricity load data into the following formula: The baseline power load of the park for time series unit t is obtained. ; Extract the measured photovoltaic power output data of the first J time-series units. j = 1, 2, ..., J, where J is the preset number of preceding timing units, and the formula is used: The photovoltaic power output of time series unit t is obtained. ,in, The preset weighting coefficient for the j-th preceding time unit; Read the actual production task of the day for timing unit t ; Extract the production task volume of each time series unit t in the same historical period, and calculate the average production task volume of time series unit t in the same historical period using the arithmetic mean method. ; Using the formula: The production load fluctuation coefficient of time series unit t is obtained. ; Obtain the carbon emission coefficient per unit of electricity in the power grid (Directly obtain the fixed constants officially released by the energy management department of the area where the park is located), and calculate the park's benchmark electricity load corresponding to time series unit t. Photovoltaic power output forecast and production load fluctuation coefficient After normalization and dimensionless processing, the values ​​are substituted into the dynamic carbon emission baseline calculation model: The dynamic carbon emission baseline of time series unit t is obtained. ,in, To take the maximum value, if the photovoltaic power generation exceeds the total electricity demand of the park, the calculation result in parentheses will be negative. In this case, the baseline is 0, which means that there is no need to purchase electricity from the grid and no carbon emission quota is required during this period. Setting overall constraints: Cumulative baseline values ​​of carbon emissions for all time series units within the regulatory period. The cumulative carbon emission control target shall not exceed the park's preset total carbon emission control target. If the cumulative value exceeds the total control target, the carbon emission baseline of each time series unit will be used to adjust the carbon emission control target for all time series units. The cumulative value will be uniformly and proportionally reduced according to a preset ratio, and then recalculated until the cumulative value meets the overall carbon emission control target requirements. After a single time series unit t is completed, real operational data of the actual power load and measured photovoltaic output of the park within that time series unit are collected. Substitute real operational data into the dynamic carbon emission baseline calculation model to obtain the actual fitted baseline value for that period. Calculate the relative deviation rate between the actual fitted baseline value and the original calculated dynamic carbon emission baseline for that period, and determine whether the relative deviation rate exceeds the preset allowable deviation range. If it exceeds the range, then based on the value and direction of the deviation rate, the original calculated dynamic carbon emission baseline for the next adjacent time unit t+1 is corrected proportionally. After each timing unit is completed, the above calibration process is repeated to achieve rolling iterative optimization of the baseline. Complete the revised dynamic carbon emission baseline for the next time-series unit. As the core rigid carbon emission constraint for electricity consumption supervision of this time series unit, that is: when performing electricity consumption supervision, load regulation, and energy dispatch for this time series unit, the modified carbon emission constraint shall be used. The upper limit for carbon emission control is set to ensure that the actual carbon emissions of the park in this time period do not exceed the revised baseline value.

[0018] It should be noted that by dividing the electricity consumption monitoring cycle of the park into time-series units, and connecting with platforms such as smart electricity consumption collection, production management, and photovoltaic monitoring to complete data collection, alignment, and basic preprocessing, the format and timing of multi-source data are standardized, providing stable and reliable data support for dynamic carbon emission baseline calculation. The method employs a multi-parameter coupled calculation approach, which uses arithmetic average to calculate the baseline electricity load, weighted calculation to predict photovoltaic output, and ratio to derive the production load fluctuation coefficient. This approach fully integrates the park's historical electricity consumption patterns, photovoltaic output characteristics, and actual production intensity, abandoning the rigid setting of static carbon emission baselines. It ensures that the dynamic carbon emission baseline is fully aligned with the park's real-time operating conditions, significantly improving the matching degree between the baseline and the actual situation on site, and avoiding the disconnect between carbon emission control and production electricity consumption. Using the park's pre-set carbon emission control target as the top-level constraint, the baseline for the entire cycle is calibrated as a whole. At the same time, deviations are corrected based on the actual operating data of a single time-series unit. Through the two-dimensional rolling iteration of overall constraints and local corrections, the upper limit of carbon emission control is dynamically and adaptively adjusted. This not only strictly adheres to the overall carbon emission control red line of the park, but also accurately corrects deviations based on real-time operating status, making carbon emission control more scientific, flexible, and executable.

[0019] The regional electricity consumption management module calculates the maximum allowable grid purchase volume and total electricity consumption threshold of the park based on the dynamic carbon emission baseline of the time-series unit; it divides the electricity consumption areas and calculates the load priority coefficient of each area to determine the upper limit of electricity consumption management for each area; it classifies the load type according to priority, executes regional electricity consumption control, calculates carbon emissions in real time, and triggers regular control, global emergency flexible control, and over-limit alarms. The specific process is as follows: For each time series unit t, obtain the corresponding dynamic carbon emission baseline after rolling calibration. The carbon emission coefficient per unit of electricity generated by the power grid, as well as the official carbon emission coefficient per unit of electricity generated by the energy management department of the area where the park is located. After parameter normalization, the formula is used: The maximum allowable electricity purchase from the power grid for the time-series unit t is obtained. ; The predicted photovoltaic output corresponding to the time series unit is added to the maximum allowable grid power purchase of the park to obtain the total electricity consumption threshold of the park for time series unit t. The calculation formula is: ; All areas within the park that have independent intelligent power consumption data collection terminals are marked as power consumption areas and uniquely numbered i, i=1,2,...,N; N is the total number of power consumption areas; Real-time collection of actual electricity consumption in each electricity consumption area and synchronization to the electricity consumption monitoring time sequence database; For each power consumption area i, obtain the average power load of the power consumption area in the same historical period of the current time series unit t and the rated total operating power of the production equipment; Obtain the average electricity load of the park during the historical same period in the current time series unit t and the rated total operating power of all production equipment in the park; The regional electricity importance coefficient is obtained by dividing the historical average electricity load of the electricity consumption area by the historical average electricity load of the industrial park during the same period. ; The regional equipment power ratio coefficient is obtained by dividing the rated total operating power of production equipment in the power consumption area by the rated total operating power of all production equipment in the park. ; By substituting the electricity importance coefficient of the electricity consumption area and the power proportion coefficient of the equipment in the area into the formula: The load priority coefficient of power consumption area i under the current time sequence unit t is obtained. ;in, and Preset weighting coefficients; Based on the load priority coefficient of each power consumption area i Calculate the power grid purchase and allocation coefficient for each power consumption area in time series unit t using the proportion method. The calculation formula is: ;in, ; For each electricity consumption area i, after normalizing and dimensionlessly processing the historical average electricity load of the area during the same period, the photovoltaic predicted output corresponding to the time series unit, the maximum allowable grid purchase volume of the time series unit t, and the grid purchase allocation coefficient of the electricity consumption area, the formula is used: The upper limit of power consumption control for power consumption area i in time sequence unit t is obtained. ; in, The photovoltaic power output is predicted for the corresponding time-series unit; The historical average electricity load for the same period in electricity consumption area i; The maximum allowable electricity purchase from the power grid for the time-series unit t in the park; The power grid purchase and allocation coefficient for the power consumption area; The proportion of photovoltaic power allocated to electricity consumption area i; The sum of the electricity consumption thresholds for all sub-areas equals the total electricity consumption threshold for the park. ; Preset load priority coefficient control threshold Under time sequence unit t, the corresponding load priority coefficient is obtained for each power consumption area i. and the corresponding preset threshold Compare: like Then the electricity consumption area is marked as an absolutely guaranteed load area; like Then the electrical region is marked as a flexibly adjustable load region; For areas with flexibly adjustable loads, if the actual electricity consumption in the area is greater than or equal to the upper limit of the power control for the application, a step-by-step power reduction regulation will be implemented according to a preset ratio. Specifically, the operating power of the equipment in the area will be reduced uniformly according to a single preset reduction ratio. After regulation, the actual electricity consumption will be compared with the upper limit of the power control for the application until the actual electricity consumption is lower than the upper limit of the power control for the application. For areas with absolutely guaranteed load, if the actual electricity consumption in the area is greater than or equal to the upper limit of the electricity control for the application, an absolute guaranteed load over-limit warning will be triggered. No electricity control actions will be performed, and the warning information will only be pushed to the park's electricity monitoring terminal to remind people to pay attention but not to interfere with the load operation. Real-time summary of total electricity consumption in the park Simultaneously extract the measured photovoltaic power output And using the formula: The real-time cumulative carbon emissions within the time series unit are obtained. ; If carbon emissions are accumulated in real time within the time unit Greater than or equal to the dynamic carbon emission baseline Then, global emergency flexible control will be implemented; The specific implementation of global emergency flexible control is as follows: For all equipment marked as having adjustable load areas, the operating power is uniformly reduced according to the pre-set single-time reduction ratio, and the real-time cumulative carbon emissions are recalculated after the control is completed. ,like Still greater than or equal to If the cumulative reduction ratio of the equipment has not reached the corresponding preset cumulative reduction ratio threshold, then the reduction operation will be repeated until... ; For devices that have reached the corresponding preset cumulative reduction ratio threshold, no further reduction operations will be performed; If all equipment marked as flexibly adjustable load areas reaches the preset cumulative reduction ratio threshold, a carbon emission control over-limit alarm will be triggered and pushed to the park's electricity consumption monitoring terminal for manual intervention.

[0020] It should be noted that the maximum allowable grid purchase volume and total electricity consumption threshold of the park are determined based on the dynamic carbon emission baseline. The total electricity consumption of the park is controlled by combining the photovoltaic power forecast. Then, the load priority coefficient and grid purchase volume allocation coefficient are determined according to the regional electricity consumption and equipment power ratio. The upper limit of electricity consumption control in each region is reasonably set so that the total electricity consumption threshold of the park is compatible with the regional electricity consumption control, thereby reducing the carbon emission exceedance caused by exceeding the limit for electricity purchase. Based on the load priority coefficient, the system distinguishes between absolutely guaranteed load areas and flexibly adjustable load areas. For flexibly adjustable load areas, a step-by-step power reduction method is adopted to control electricity consumption. For absolutely guaranteed load areas, only over-limit warnings are issued and no control operations are carried out. This can maintain the stable operation of the core production load of the park and also adjust non-core loads, taking into account both production continuity and electricity consumption control requirements. The system calculates the cumulative carbon emissions within a time-series unit in real time and compares them with the dynamic carbon emission baseline. It establishes a hierarchical control and over-limit alarm mechanism. When carbon emissions are within a reasonable range, it conducts regular flexible control in different areas. When carbon emissions exceed the baseline, it initiates global emergency flexible control. After the equipment is adjusted to its limit, it pushes a manual handling alarm, forming a complete management and control process of carbon emission monitoring, hierarchical control, and over-limit early warning, making the coordinated management of electricity consumption and carbon emissions in the park more detailed.

[0021] The photovoltaic-storage coordinated scheduling module collects real-time data on photovoltaics, energy storage, electricity consumption, and electricity purchase; executes photovoltaic-storage-electricity consumption coordinated scheduling based on real-time cumulative carbon emissions scenarios; generates scheduling schemes and issues control commands; collects post-scheduling operation data and transmits back model correction parameters to support the management and control of the next time-series unit. The specific process is as follows: Real-time acquisition of measured photovoltaic power output within time series unit t in the park State of charge of energy storage devices Real-time total electricity consumption in the park Real-time power purchase by the power grid ; Set safety operation constraints for energy storage devices: in, Preset a minimum safe state of charge for energy storage devices. Preset the highest safe state of charge for energy storage devices; Establish a real-time power balance relationship in the park: in, This refers to the real-time discharge power of the energy storage device. Real-time charging power for energy storage devices; When the actual photovoltaic output Greater than or equal to the park's real-time total electricity consumption hour: The photovoltaic power is controlled to supply all electricity-consuming areas in the park, and supplied in order of load priority coefficient from large to small. If the energy storage device is in a charged state The preset maximum safe state of charge has not been reached. The surplus photovoltaic power is transferred to the energy storage device for charging until the operating constraints of the energy storage device are met. At this point, the power purchased by the grid is used. Set to 0, and no load control operation will be performed; When carbon emissions are accumulated in real time satisfy: At that time, among them, Preset warning coefficients for the system: The scheduling is carried out according to the logic of prioritizing photovoltaic power output, supplementing with energy storage discharge, and ensuring that the grid purchases electricity as a safety net. When there is still a power shortage between photovoltaic power generation and energy storage discharge to supplement power supply, the maximum allowable grid power purchase volume of the park shall be strictly followed according to the time sequence unit t. Implement quota-based electricity purchases; The flexibly adjustable load area will only implement a conventional step-by-step power reduction according to a preset single reduction ratio when the actual electricity consumption reaches the upper limit of the application power control, without initiating global emergency flexible control. When carbon emissions are accumulated in real time satisfy: hour: The energy storage device is controlled to discharge at a preset full power to replace the grid electricity purchase to the maximum extent, and the real-time grid electricity purchase is reduced based on the amount of electricity replaced by the energy storage discharge. Simultaneously trigger global emergency flexible control, uniformly reducing equipment operating power in all flexibly controllable load areas according to the preset reduction ratio, absolutely ensuring that the load area only triggers over-limit warnings and does not execute any control actions; if all equipment in all flexibly controllable load areas reaches the preset cumulative reduction ratio threshold, and still cannot meet the requirements... If this occurs, a carbon emission control over-limit alarm will be triggered and pushed to the park's electricity consumption monitoring terminal for manual intervention. Based on the above scenario-based scheduling results, a photovoltaic-storage-electricity co-scheduling scheme under carbon emission constraints is generated. The co-scheduling scheme is then transformed into standardized control commands, which are simultaneously sent to the photovoltaic grid-connected monitoring terminal, the energy storage charge and discharge controller, and the load control terminal of each electricity consumption area to complete the co-control of photovoltaic and energy storage resources and electricity load. Real-time collection of actual operational data of the park after scheduling execution, including: actual carbon emissions. Real-time total electricity consumption in the park Photovoltaic measured output Electricity consumption data and load control execution results for each region; The corresponding data such as actual carbon emissions, electricity load, and photovoltaic output are fed back to the dynamic carbon emission baseline calculation model as input data for the baseline rolling calibration of the next time series unit t+1. At the same time, the electricity consumption data and load control execution results of each region are fed back to the load priority coefficient and electricity control upper limit calculation model to correct the load priority coefficient, regional electricity allocation coefficient, and electricity control upper limit calculation coefficient, providing data support for the regional electricity control threshold allocation of the next time series unit t+1, forming a closed-loop iterative optimization of the entire process.

[0022] It should be noted that the photovoltaic-storage-electricity coordinated scheduling is carried out according to the real-time cumulative carbon emission situation and the electricity allocation follows the principle of prioritizing photovoltaic output, supplementing energy storage discharge, and guaranteeing electricity purchase from the grid. When there is surplus photovoltaic power, the energy storage equipment is charged, which can reduce the use of grid electricity purchase in the park and rely more on clean energy for power supply, thus helping to reduce the carbon emissions of the park. By combining carbon emission levels with differentiated control measures, when carbon emissions are too high, energy storage is used to discharge at full power to replace grid power purchases. At the same time, flexible loads are adjusted, and warnings are only issued to guaranteed loads. This can not only implement carbon emission control requirements, but also ensure that the core production power consumption of the park is not affected. When control limits are reached, manual alarms are pushed to make dispatching operations more stable. The actual operation data after the scheduling is collected and sent back to the corresponding calculation model to correct parameters such as carbon emission baseline and load priority coefficient, providing data reference for the management and control of the next time series unit, forming a closed-loop iterative optimization, so that the subsequent photovoltaic and energy storage scheduling and power consumption management are more in line with the actual operation of the park. The carbon emission baseline generation module establishes the data foundation and forms a carbon emission control basis that fits the actual situation of the park. The regional electricity consumption control module implements hierarchical control of electricity consumption and carbon emissions according to region and load type. The photovoltaic-storage collaborative scheduling module optimizes electricity consumption scheduling and reduces the use of grid electricity purchases by relying on clean energy. The three modules work together to form a complete control process from carbon emission baseline calculation, regional electricity consumption control to photovoltaic-storage energy collaborative scheduling. This not only fits the actual situation of electricity consumption in the park, but also continuously reduces carbon emissions from grid electricity purchases. At the same time, through data feedback, iterative optimization of various control parameters ensures that the park's electricity consumption supervision and carbon emission control are adapted to the actual operating status in the long term, taking into account both stable production operation and low-carbon control needs.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power consumption information monitoring system in an industrial park, characterized by, include: Carbon emission baseline generation module: Divide time-series units and collect multi-dimensional operational data to build a time-series database for electricity consumption supervision; The baseline electricity load, photovoltaic power output, and production load fluctuation coefficient of each time series unit are calculated to obtain the dynamic carbon emission baseline and iteratively optimize it. The corrected baseline is used as the upper limit of carbon emission control for each time period. Regional electricity consumption management module: Based on the dynamic carbon emission baseline of time-series units, calculate the maximum allowable grid purchase volume and total electricity consumption threshold of the park; Divide the electricity consumption areas and calculate the load priority coefficients for each area to determine the upper limit of electricity consumption control in each area; Load types are classified by priority, regional power consumption control is implemented, carbon emissions are calculated in real time, and routine control, global emergency flexible control, and over-limit alarms are triggered. Photovoltaic-storage coordinated scheduling module: collects real-time data on photovoltaics, energy storage, electricity consumption and electricity purchase, performs photovoltaic-storage-electricity consumption coordinated scheduling based on real-time cumulative carbon emissions scenarios, generates scheduling schemes and issues control commands; collects post-scheduling operation data and sends back model correction parameters to support the management and control of the next time series unit.

2. The system according to claim 1, wherein: The specific process of constructing a time-series database for electricity consumption monitoring is as follows: The complete electricity consumption monitoring cycle of the industrial park is divided into several continuous and non-overlapping time units according to a fixed and equal time granularity. Connect to the park's smart electricity consumption data collection terminal, production execution management system, distributed photovoltaic monitoring terminal and weather forecasting platform to collect all the park's multi-dimensional raw operational data. All raw data are matched and aligned according to time series units, so that each data item corresponds to a unique time series unit, and a time series database for monitoring electricity consumption in the park is constructed. Data preprocessing is then carried out on the raw data in the database.

3. The electricity consumption information monitoring system in an industrial park according to claim 2, characterized in that: The analysis process for dynamic carbon emission baselines is as follows: Based on the time-series database of electricity consumption monitoring in the park, for each time-series unit, the actual electricity load of the park in multiple historical periods in the same period as the current time-series unit is extracted, and the benchmark electricity load of the park in that time-series unit is calculated by arithmetic average. The measured photovoltaic output data of multiple preceding time series units are extracted and weighted by the preset weighting coefficients of each preceding time series unit to obtain the predicted photovoltaic output of that time series unit. Read the actual production task volume of the day, calculate the average production task volume of the same period in history, and obtain the production load fluctuation coefficient by comparing the two. Obtain the carbon emission coefficient per unit of electricity generated by the power grid officially released by the energy management department of the area where the park is located. After normalizing and dedimensionalizing the benchmark electricity load, photovoltaic predicted output and production load fluctuation coefficient, substitute them into the dynamic carbon emission baseline calculation model to obtain the dynamic carbon emission baseline for this time series unit.

4. The system according to claim 3, wherein the system further comprises a power consumption information collection device. The specific process of dynamically optimizing the carbon emission baseline through rolling iterations and using the revised baseline as the upper limit for carbon emission control during a given period is as follows: The cumulative value of carbon emission baselines for all time units within the regulatory period shall not exceed the park’s preset total carbon emission control target. If the cumulative value exceeds the target, it shall be uniformly and proportionally reduced based on the dynamic carbon emission baseline ratio of each time unit, and the cumulative value shall be recalculated until it meets the requirements. After a single time unit is completed, the actual power load and measured photovoltaic output data are collected and substituted into the model to obtain the actual fitted baseline value. Calculate the relative deviation rate between the actual fitted baseline value and the original calculated baseline. If the deviation exceeds the preset allowable range, the baseline of the next time unit is corrected proportionally. The repeated calibration process enables baseline rolling iterative optimization, and the corrected baseline is used as the upper limit for carbon emission control of the corresponding time series unit.

5. The system according to claim 4, wherein: The specific process for calculating the maximum allowable electricity purchase from the power grid and the total electricity consumption threshold for the park is as follows: For each time series unit, the dynamic carbon emission baseline after rolling calibration and the carbon emission coefficient of the power grid unit electricity officially released by the energy management department of the park are obtained. After completing the parameter normalization process, the maximum allowable power grid purchase electricity of the park for the time series unit is calculated. The predicted photovoltaic output corresponding to the time series unit is added to the maximum allowable grid power purchase in the park to obtain the total power consumption threshold of the park for the time series unit.

6. The system according to claim 5, wherein: The specific process for dividing electricity consumption areas, calculating the load priority coefficients for each area, and determining the upper limit of electricity consumption control for each area is as follows: The areas within the park equipped with independent intelligent electricity consumption collection terminals are marked as electricity consumption areas and given unique numbers. The actual electricity consumption of each electricity consumption area is collected in real time and synchronized to the electricity consumption supervision time series database. The historical average electricity load and rated total operating power of production equipment for each electricity consumption area and the entire park are obtained. The regional electricity importance coefficient and regional equipment power proportion coefficient are obtained by ratio calculation. The two types of coefficients are calculated in combination with the preset weight coefficient to obtain the load priority coefficient of the electricity consumption area. Based on the load priority coefficient of each power consumption area, the power grid purchase and allocation coefficient of each power consumption area is calculated by the proportion method. After normalizing and dimensionlessly processing the regional historical average electricity load, photovoltaic power output, maximum allowable grid purchase volume in the park, and grid purchase allocation coefficient, a comprehensive analysis was conducted to obtain the upper limit of electricity control for each electricity consumption area. The sum of the electricity consumption thresholds for all sub-areas is equal to the total electricity consumption threshold of the park.

7. The system according to claim 6, wherein: The specific process of prioritizing load types, implementing regional power consumption control, calculating carbon emissions in real time, and triggering routine control, global emergency flexible control, and over-limit alarms is as follows: The load priority coefficient is preset to control the threshold. The load priority coefficient of each power consumption area is obtained under the time sequence unit and compared with the preset threshold to divide the absolutely guaranteed load area and the flexibly adjustable load area. When the actual electricity consumption in the flexibly adjustable load area is greater than or equal to the upper limit of electricity control, the power will be reduced in a step-by-step manner according to a preset ratio until the actual electricity consumption is lower than the upper limit of control. When the actual electricity consumption in the guaranteed load area is greater than or equal to the upper limit of electricity control, an over-limit warning will be triggered and pushed to the park's electricity monitoring terminal, and no electricity control will be implemented. Real-time summary of total electricity consumption in the park, extraction of measured photovoltaic power output, and calculation of real-time cumulative carbon emissions for time-series units; When the real-time cumulative carbon emissions are greater than or equal to the dynamic carbon emission baseline, global emergency flexible control is implemented. The operating power of equipment in the flexible load area is reduced by the preset reduction ratio after the increase. The reduction stops when the equipment reaches the preset cumulative reduction ratio threshold. When all equipment in the flexibly adjustable load area reaches the preset cumulative reduction ratio threshold, a carbon emission control over-limit alarm is triggered and pushed to the park's electricity consumption monitoring terminal for manual intervention.

8. The system according to claim 7, wherein: The specific process of implementing photovoltaic-storage-electricity coordinated scheduling according to different scenarios is as follows: Real-time data collection of the park's measured photovoltaic power output, energy storage equipment status charge, park's real-time total electricity consumption, and real-time electricity purchased from the grid within the time-series unit; Set safe operating constraints for the energy storage equipment at preset minimum and maximum safe states of charge, and establish a real-time power balance relationship in the park; When the measured output of photovoltaic power is greater than or equal to the total real-time electricity consumption of the park, the photovoltaic power is supplied to the electricity consumption area in full according to the load priority from high to low. If the energy storage device does not reach the preset maximum safe charge state, the surplus photovoltaic power is used for charging, the grid purchase power is set to 0 and no load regulation is performed. When the real-time cumulative carbon emissions are within the system's preset warning coefficient range, the system will prioritize photovoltaic power, supplement energy storage, and dispatch power grid as a backup. The power shortage will be addressed by purchasing electricity according to the maximum allowable power grid purchase limit for the park. In areas with flexibly adjustable loads, only conventional tiered power reduction will be implemented. When the real-time cumulative carbon emissions exceed the warning range, the energy storage device will replace the grid power purchase with a preset full-power discharge, and simultaneously trigger global emergency flexible control. In areas where flexible control is possible, the power will be reduced by a preset downward ratio after the increase, and the load area will be absolutely guaranteed to only trigger the over-limit warning. When all equipment in the flexibly adjustable area reaches the preset cumulative reduction ratio threshold but still fails to meet the standard, a carbon emission control over-limit alarm is triggered and pushed to the park's electricity consumption monitoring terminal.

9. The system according to claim 8, wherein: The specific process of generating a scheduling scheme and issuing control commands, collecting post-scheduling running data and transmitting back model correction parameters to support the management and control of the next time-series unit is as follows: Based on the scenario-based scheduling results, a photovoltaic-storage-electricity co-schedule scheme under carbon emission constraints is generated. The scheme is then transformed into standardized control commands and sent to the photovoltaic grid-connected monitoring terminal, the energy storage charge and discharge controller, and the load control terminal of each electricity consumption area to complete the coordinated control of photovoltaic and energy storage resources and electricity load. The system collects and schedules the actual carbon emissions, total electricity consumption in the park, measured photovoltaic output, electricity consumption data of each area, and load control execution results in real time. It then transmits the actual carbon emissions, total electricity consumption in the park, and measured photovoltaic output data back to the dynamic carbon emission baseline calculation model for baseline rolling calibration in the next time series unit. Simultaneously, the electricity consumption data and load control execution results of each region are fed back to the load priority coefficient and electricity control upper limit calculation model to correct the load priority coefficient, regional electricity allocation coefficient and electricity control upper limit calculation coefficient, providing support for the allocation of regional electricity control thresholds in the next time series unit, forming a closed-loop iterative optimization.

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