Industrial park energy structure low-carbonization intelligent optimization method and system

Abstract

The application provides an industrial park energy structure low-carbon intelligent optimization method and system, belonging to the technical field of low-carbon intelligent optimization. The above scheme calculates the dynamic carbon emission intensity factor of the electrical connection node of the park, generates the interactive power feasible interval and the operation cost and carbon emission mapping relationship of each functional partition within the equipment physical constraint and production operation constraint, the park total control layer forms the global feasible domain together with the power balance, line capacity, transformer capacity and node voltage constraint, and solves the interactive power set point with the lowest comprehensive operation cost under the upper limit constraint of the total carbon emission. When there is no feasible solution, the infeasible state is output and the original scheduling instruction is maintained. The scheme of the application can generate the interactive power set point which meets the partition equipment constraint and the park power network constraint under the condition of single measurement of carbon emission.

Description

Technical Field

[0001] This application relates to the field of low-carbon intelligent optimization technology, and more specifically, to a method and system for low-carbon intelligent optimization of the energy structure of industrial parks. Background Technology

[0002] Low-carbon intelligent optimization refers to the integration of intelligent technologies such as artificial intelligence, machine learning, and big data analysis to dynamically model, predict, and make decisions on systems in high-carbon emission fields such as energy production, industrial manufacturing, transportation and logistics, and building operation, so as to minimize carbon emission intensity and resource consumption while ensuring economic and functional needs.

[0003] However, energy dispatching in industrial parks typically needs to simultaneously meet constraints related to equipment capacity, production continuity, and the safety of the power distribution network. In existing hierarchical dispatching, if the power ranges reported by each functional zone are only feasible within that zone, the allocation scheme obtained by directly combining these ranges at the upper level may still not meet the park's power balance, line capacity, transformer capacity, or node voltage limits. Furthermore, if node electricity consumption carbon emissions are repeatedly accumulated on both the zone side and the power supply side, it will distort the carbon emission constraints. Therefore, existing methods struggle to generate interactive power setpoints that simultaneously satisfy both zone equipment constraints and the park's power network constraints under the condition of a single carbon emission measurement. Summary of the Invention

[0004] This application provides a method and system for intelligent optimization of the low-carbon energy structure of industrial parks, which is used to generate an interactive power setpoint that simultaneously satisfies the constraints of zoned equipment and the park's power network under the condition of single carbon emission measurement.

[0005] Firstly, this application provides a method for intelligent optimization of the energy structure of industrial parks to achieve low carbon emissions, comprising the following steps: Obtain real-time operating data of each energy-consuming and energy-supplying unit within the industrial park, as well as historical power generation output data; Based on the real-time operating data and the historical output data, the dynamic carbon emission intensity factor of each electrical connection node in the industrial park is determined, and each dynamic carbon emission intensity factor and time-of-use electricity price information are sent as a coordination signal to the coordination layer of each functional zone in the industrial park. Based on the received coordination signals, each functional zone coordination layer uses the physical and operational constraints of its various adjustable resources as boundaries to generate feasible adjustment ranges of the interaction power between each functional zone coordination layer and the overall control layer of the park, as well as the corresponding mapping relationship between operational costs and carbon emissions. The feasible adjustment ranges and the mapping relationship between operational costs and carbon emissions are then fed back to the overall control layer of the park. After receiving the feasible adjustment ranges and the mapping relationship between operating costs and carbon emissions from all functional zone coordination layers, the overall control layer of the park optimizes the operating costs under the carbon emission ceiling constraint within the global feasible domain jointly defined by each feasible adjustment range and the park's power network constraint, with the industrial park's preset total carbon emission ceiling as the constraint and the goal of minimizing the comprehensive operating cost. When a feasible solution exists, the interactive power setpoints of each functional zone coordination layer are generated and issued for execution; when no feasible solution exists, an infeasible state is output and no new setpoints are issued.

[0006] In some embodiments, acquiring real-time operating data of each energy-consuming unit and energy-supplying unit within the industrial park, as well as historical power generation output data, specifically includes: Smart meters and sensors are deployed in various energy-consuming and energy-supplying units within the industrial park to collect active power, reactive power, and voltage amplitude data, thereby forming real-time operating data. Extract time-series power data of photovoltaic, wind and biomass power generation that includes at least one complete annual cycle from the park's historical database, and perform scenario reduction on the time-series power data to obtain historical power output data of energy generation.

[0007] In some embodiments, determining the dynamic carbon emission intensity factor of each electrical connection node within the industrial park based on the real-time operating data and the historical output data specifically includes: The distribution ratio of power supplied by each power supply unit to each electrical connection node is determined based on the real-time operating data. Based on the historical output data and the preset carbon emission intensity of each energy supply unit, the unit carbon emission intensity of each energy supply unit within the preset time window is predicted. The carbon emission intensity of each unit's power supply is weighted and summed according to the corresponding distribution ratio to obtain the dynamic carbon emission intensity factor of each electrical connection node in the industrial park updated with the forecast period.

[0008] In some embodiments, the connection point between the power supply unit and the busbar, the connection point between the busbars connected by lines, and the branch point where the busbar feeds power to the power consumption unit are all defined as different electrical connection nodes.

[0009] In some embodiments, sending dynamic carbon emission intensity factors and time-of-use electricity price information together as coordination signals to the coordination layers of each functional zone in the industrial park specifically includes: Obtain time-of-use electricity price information from the power trading center; The dynamic carbon emission intensity factors are time-series aligned with the time-of-use electricity price information and encoded into a unified format data message. The data packets are broadcast to the edge gateways of all functional zone coordination layers at a preset coordination period via the industrial Ethernet or 5G private network within the industrial park.

[0010] In some embodiments, each functional zone coordination layer, based on the received coordination signals and using the physical and operational constraints of its various adjustable resources as boundaries, generates a feasible adjustment range for the interaction power between each functional zone coordination layer and the overall control layer of the park, as well as the corresponding mapping relationship between operational costs and carbon emissions. Specifically, this includes: Each functional zone coordination layer parses the received coordination signals to obtain the dynamic carbon emission intensity factor and time-of-use electricity price information of each functional zone coordination layer; Determine the physical and operational constraints of various adjustable resources within the coordination layer of each functional zone; Using the physical constraints and the operational constraints as boundaries, the joint feasible domain within the partition is projected onto the interaction power dimension between each functional partition coordination layer and the overall control layer of the park, thereby generating the feasible adjustment range of each functional partition coordination layer. For each power value point within each feasible adjustment range, the minimum operating cost to satisfy the internal constraints of the partition is calculated according to the time-of-use electricity price information, and the carbon emission of the corresponding electricity is calculated according to the dynamic carbon emission intensity factor of the connected nodes of the partition. The mapping relationship between interactive power and operating cost and carbon emission is constructed respectively.

[0011] In some embodiments, determining the physical and operational constraints of various adjustable resources within each functional partition coordination layer specifically includes: The aforementioned adjustable resources are classified into energy storage resources, flexible load resources, and distributed generation resources according to their response characteristics. For the energy storage resources, identify the upper and lower limits of the state of charge, the maximum charge and discharge power, and the charge and discharge efficiency curves of the energy storage resources, thereby constructing physical constraints that include power limiting and energy windows. For the flexible load type resources, by collecting the historical adjustable power curves of the flexible load type resources, and based on the preset production process priority, the power adjustment range, the power adjustment range and the maximum continuous adjustment time of the flexible load type resources are determined, thereby constructing an operation constraint that includes adjustment capacity and duration. For the distributed generation resources, the active power output prediction range and reactive power regulation range of the distributed generation resources are determined based on real-time meteorological forecast data and inverter status, thereby constructing a joint constraint boundary that includes volatility and supportability. The device-level constraints of each adjustable resource are retained, and the device variables are combined into a joint feasible constraint set of partitions through the partition power balance relationship.

[0012] In some embodiments, after receiving the feasible adjustment ranges and the mapping relationship between operating costs and carbon emissions from all functional zone coordination layers, the park's overall control layer optimizes the operating costs under the carbon emission ceiling constraint within the global feasible domain jointly defined by each feasible adjustment range and the park's power network constraint, using the industrial park's preset total carbon emission ceiling as a constraint and minimizing the overall operating cost as the objective. When a feasible solution exists, interactive power setpoints for each functional zone coordination layer are generated and issued for execution; when no feasible solution exists, an infeasible state is output and no new setpoints are issued. Specifically, this includes: The overall control layer of the park will take the intersection of the feasible adjustment range of each functional zone with the park's power balance, line capacity, transformer capacity and node voltage constraints to form the global feasible domain of interactive power; Based on the mapping relationship between operating costs and carbon emissions fed back by the coordination layer of each functional zone, the operating costs and carbon emissions of each zone corresponding to the same power allocation scheme are accumulated to obtain the total operating costs and total carbon emissions of the park. The carbon emissions of each zone are verified by calculating the dynamic carbon emission intensity factor and the interactive electricity of the corresponding node, and the same electricity is not counted repeatedly on the energy supply side. Obtain the pre-set carbon emission cap for the industrial park; Within the global feasible domain, power allocation schemes with total carbon emissions not exceeding the upper limit of total carbon emissions are selected, and the allocation scheme with the lowest total operating cost of the park is solved from them. When there is no scheme that meets the conditions, an infeasible state is output and no new setpoint is issued.

[0013] The interaction power value corresponding to each functional zone coordination layer is extracted from the allocation scheme and used as the optimal setting point. This value is then sent to the corresponding functional zone coordination layer for execution through the communication interface of the park's overall control layer.

[0014] In some embodiments, determining an allocation scheme within the globally feasible domain that satisfies the total carbon emission cap and minimizes the total operating cost of the park specifically includes: Obtain an initial interactive power allocation scheme that satisfies the constraints of the park's power network; Starting with the initial interactive power allocation scheme, and constrained by the upper limit of total carbon emissions, power balance of the park, line capacity, transformer capacity and node voltage, the interactive power allocation scheme is solved with the goal of minimizing the total operating cost of the park, and the infeasible state is returned when there is no feasible solution.

[0015] Secondly, this application provides an intelligent optimization system for the low-carbon energy structure of industrial parks, comprising: The acquisition module is used to acquire real-time operating data of each energy-consuming unit and energy-supplying unit in the industrial park, as well as historical power generation data. The processing module is used to determine the dynamic carbon emission intensity factor of each electrical connection node in the industrial park based on the real-time operation data and the historical output data, and send each dynamic carbon emission intensity factor and time-of-use electricity price information as a coordination signal to the coordination layer of each functional zone in the industrial park. The processing module is also used to generate, based on the received coordination signals, the feasible adjustment range of the interaction power between each functional zone coordination layer and the park's overall control layer, as well as the corresponding mapping relationship between operating costs and carbon emissions, using the physical constraints and operational constraints of various adjustable resources within its internal boundaries, and to feed back each feasible adjustment range and the mapping relationship between operating costs and carbon emissions to the park's overall control layer. The execution module is used by the overall control layer of the park to receive the feasible adjustment ranges and the mapping relationship between operating costs and carbon emissions from all functional zone coordination layers. It optimizes the operating costs under the carbon emission upper limit constraint within the global feasible domain jointly defined by each feasible adjustment range and the park's power network constraint, with the preset carbon emission upper limit of the industrial park as the constraint and the goal of minimizing the comprehensive operating cost. When a feasible solution exists, it generates the interactive power setpoints of each functional zone coordination layer and issues them for execution. When no feasible solution exists, it outputs an infeasible state and does not issue a new setpoint.

[0016] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described intelligent optimization method for low-carbon energy structure of industrial parks.

[0017] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned intelligent optimization method for low-carbon energy structure in industrial parks.

[0018] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: In the intelligent optimization method and system for low-carbon energy structure of industrial parks provided in this application, the dynamic carbon emission intensity factor of each electrical connection node is obtained by weighted summing of the corresponding unit power supply carbon emission intensity according to the power supply ratio of each power supply unit of the node. This factor represents the average emission of unit power supply within the corresponding prediction period, thereby ensuring that the carbon emission of electricity consumption at the node is subject to a single measurement caliber. Each functional zone coordination layer constructs a joint feasible domain based on the physical constraints and production operation constraints of various adjustable resources within the zone, and projects it onto the interactive power dimension between the zone and the overall control layer of the park. The resulting feasible adjustment interval represents the adjustable capability boundary of the zone under the condition of not relaxing equipment safety constraints and hard production constraints. When the projection result is empty, the zone returns to an infeasible state. Therefore, the upper layer obtains not the equipment variables within the zone, but the interactive power boundary that can directly participate in the upper layer's solution. Each power value point within the interval corresponds to the calculation of time-of-use electricity price. The minimum operating cost and the carbon emissions calculated from the node's dynamic carbon emission intensity factor and interactive power are used to make each candidate power a comparable evaluation quantity. The park's overall control layer takes the intersection of the combination of feasible adjustment intervals of each zone with the park's power balance, line capacity, transformer capacity, and node voltage constraints to form a global feasible region. Power combinations that are feasible within a zone but cannot be executed by the distribution network are eliminated here, thus making the zone's adjustability and the park's power network constraints work together in the same solution process. The total carbon emissions of the park are directly accumulated from the carbon emissions of each zone, and the energy supply side emissions already included in the node factor are no longer added, thus ensuring that the upper limit of total carbon emissions is established within the metering boundary consistent with the zone mapping. Within this global feasible region, schemes with total carbon emissions not exceeding the upper limit are selected and the scheme with the lowest total operating cost is solved. If a feasible solution exists, an interactive power setpoint is issued; if no feasible solution exists, an infeasible state is output and the original scheduling command is maintained. Therefore, this application can generate an interactive power setpoint that simultaneously satisfies the zone equipment constraints and the park's power network constraints under the condition of a single carbon emission metering. Attached Figure Description

[0019] Figure 1 This is an exemplary flowchart of a method for intelligent optimization of the low-carbon energy structure of industrial parks, as shown in some embodiments of this application. Figure 2 This is an exemplary flowchart illustrating the determination of dynamic carbon emission intensity factors according to some embodiments of this application; Figure 3 This is a schematic diagram illustrating the hierarchical interaction principle between the overall control layer and the functional zoning coordination layer of the park, based on some embodiments of this application. Figure 4 This is a schematic diagram of the structure of an intelligent optimization system for low-carbon energy structure in industrial parks, as shown in some embodiments of this application. Figure 5This is a schematic diagram of the structure of a computer device for implementing a method for intelligent optimization of the low-carbon energy structure of an industrial park, according to some embodiments of this application. Detailed Implementation

[0020] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] refer to Figure 1 The figure is an exemplary flowchart of an intelligent optimization method for decarbonizing the energy structure of an industrial park, according to some embodiments of this application. The intelligent optimization method for decarbonizing the energy structure of an industrial park mainly includes the following steps: In step 101, real-time operating data of each energy-consuming unit and energy-supplying unit in the industrial park, as well as historical power generation data, are obtained.

[0022] In some embodiments, obtaining real-time operating data of each energy-consuming unit and energy-supplying unit within the industrial park, as well as historical power generation output data, can be achieved through the following steps: Smart meters and sensors are deployed in various energy-consuming and energy-supplying units within the industrial park to collect active power, reactive power, and voltage amplitude data, thereby forming real-time operating data. Extract time-series power data of photovoltaic, wind and biomass power generation that includes at least one complete annual cycle from the park's historical database, and perform scenario reduction on the time-series power data to obtain historical power output data of energy generation.

[0023] It should be noted that the real-time operational data in this application is a dataset used to reflect the electrical operating status of each energy-consuming unit and each energy-supplying unit within the industrial park at the current moment, serving as the basis for subsequent power distribution calculations and optimized scheduling. The historical power output data is a time-series power data set used to characterize the output patterns of photovoltaic, wind, and biomass power generation units within the industrial park under different meteorological conditions, serving as the basis for predicting carbon emission intensity per unit of electricity supply in future periods. These different meteorological conditions specifically include sunny, cloudy, overcast, rainy, strong wind, and windless conditions. Scene reduction is a data processing method used to classify, aggregate, and extract the large amount of redundant raw time-series power data of photovoltaic, wind, and biomass power generation collected over a complete annual cycle, thereby obtaining a dataset that can represent the output patterns under different meteorological conditions.

[0024] Energy-consuming units within an industrial park refer to various terminal equipment or systems that consume electrical energy to achieve functions such as production, office work, warehousing, and living. Specifically, this includes electric motors, electric heating equipment, and automated production lines in production workshops; lighting systems, air conditioning systems, computers, and servers in office areas; electric forklifts and cold storage compressors in warehousing areas; and elevators, water pumps, and charging piles in public facilities. Energy-supplying units within an industrial park refer to various equipment or systems that can generate, convert, or store electrical energy and inject power into the park's internal power grid. Specifically, this includes photovoltaic power generation arrays, wind turbine generators, biomass power generators, gas turbines, fuel cells, as well as electrochemical energy storage systems and flywheel energy storage systems. Energy-consuming units are the collection of end-loads that consume electrical energy and convert it into other forms of energy such as mechanical energy, thermal energy, and solar energy, while energy-supplying units are the collection of source equipment that convert primary energy or stored energy into electrical energy and inject it into the park's power grid.

[0025] In practice, smart meters and multi-parameter sensors are first installed at the electrical connection point of each energy-consuming unit and the electrical output point of each energy-supplying unit within the industrial park. All smart meters and sensors are then activated synchronously to collect the active power, reactive power, and voltage amplitude values ​​of each energy-consuming and energy-supplying unit. After data collection, all collected active power, reactive power, and voltage amplitude values ​​are organized according to the time stamp and unit number at the time of collection, thus forming real-time operational data.

[0026] In practice, the implementation involves: first, extracting time-series power data for photovoltaic (PV), wind, and biomass power generation units from the industrial park's historical database, ensuring the extracted data covers at least a full annual cycle. Then, scene reduction processing is performed on the extracted time-series power data for each type of energy generation. This process uses a mean clustering algorithm combined with meteorological feature association. Specifically, the daily power output curves for each day of the year are categorized according to the corresponding meteorological features. Multiple daily power output curves within the same meteorological feature category are then clustered using mean clustering, and the center curve of each cluster is selected as the output pattern curve for that meteorological feature category. After scene reduction processing, multiple daily power output pattern curves for each of the PV, wind, and biomass power generation units are obtained. These output pattern curves are then summarized and labeled according to meteorological feature types, thus forming the historical power output data for energy generation.

[0027] In step 102, the dynamic carbon emission intensity factor of each electrical connection node in the industrial park is determined based on the real-time operation data and the historical output data, and each dynamic carbon emission intensity factor and time-of-use electricity price information are sent as a coordination signal to the coordination layer of each functional zone in the industrial park.

[0028] In some embodiments, reference Figure 2 The figure is an exemplary flowchart for determining the dynamic carbon emission intensity factor in some embodiments of this application. In this embodiment, the determination of the dynamic carbon emission intensity factor of each electrical connection node in the industrial park based on the real-time operating data and the historical output data can be achieved by the following steps: In step 1021, the distribution ratio of the power supply from each power supply unit to each electrical connection node is determined based on the real-time operating data; In step 1022, the carbon emission intensity per unit of power supply of each energy supply unit is predicted within a future preset time window based on the historical power output data and the preset carbon emission intensity of each energy supply unit. In step 1023, the carbon emission intensity of each unit's power supply is weighted and summed according to the corresponding distribution ratio to obtain the dynamic carbon emission intensity factor of each electrical connection node in the industrial park updated with the forecast period.

[0029] It should be noted that the distribution ratio represents the share of power supplied by each energy supply unit to the electrical connection node through power flow during the forecast period. The unit power supply carbon emission intensity represents the average emission corresponding to a unit of power supplied within the forecast period and the unified metering boundary, and does not represent the marginal emission caused by adding an additional unit of load. The unit power supply carbon emission intensity is weighted and summed according to the corresponding distribution ratio to obtain the dynamic carbon emission intensity factor of the electrical connection node.

[0030] Based on the actual power network layout within the industrial park, the output terminals of all energy supply units, the access terminals of all energy consumption units, and the various levels of distribution busbars are abstracted as independent electrical connection nodes. Each electrical connection node has a unique number and clear spatial location information, and also records the voltage level, the type of connected equipment, and the rated capacity of the node. When constructing nodes, the connection points between energy supply units and busbars, the connection points between busbars via lines, and the branch points where busbars feed power to energy consumption units are all defined as different electrical connection nodes.

[0031] In practice, the active power injection of each energy supply unit, the active power demand of each energy consumption unit, and the power flow direction and active power of each branch are extracted from real-time operating data, and the campus power grid topology is read. Power flow tracing is performed according to the branch power conservation relationship to calculate the power contribution of each energy supply unit to each electrical connection node through each branch. The power contribution of a certain energy supply unit to a node is divided by the sum of the power contributions of all energy supply units at that node to obtain the distribution ratio of the power supplied by that energy supply unit to that node. The sum of the distribution ratios is one within the allowable numerical error range, and the power supply ratio is not replaced by the carbon emission flow ratio of each source.

[0032] In addition, in specific implementation, the daily power generation mode curves of photovoltaic power generation units, wind power generation units, and biomass power generation units are first extracted from the historical power output data of energy generation. At the same time, the preset carbon emission intensity of each power supply unit is obtained, where the preset carbon emission intensity represents the amount of carbon emissions generated by the power supply unit per unit of electricity output under rated operating conditions. Then, a preset future time window is set, the length of which is four hours in the future. This time window is divided into several equally spaced prediction periods, each prediction period being 15 minutes. The four-hour time window length is determined after comprehensively considering the typical charge and discharge cycle of the energy storage system in the industrial park, the adjustable duration of flexible loads, and the common time span of the time-of-use pricing policy of the upper-level power grid. This duration can cover a complete response process of most adjustable resources in the park, while avoiding a significant decrease in the accuracy of weather and load forecasts due to excessively long prediction times. The 15-minute forecast period is aligned with the coordination cycle of the overall control layer of the park, the data sampling and reporting cycle of smart meters, and the update frequency of carbon emission factors and time-of-use pricing in the electricity market. Furthermore, the length of this period is exactly one-quarter of an hour, facilitating integration with the grid's commonly used 15-minute power balance assessment cycle. For each forecast period, different forecasting methods are used for each energy supply unit, depending on its type: if the energy supply unit is a zero-carbon emission unit, its unit power supply carbon emission intensity is directly set to zero; if the energy supply unit is a fossil fuel unit, based on the relationship between its power generation efficiency curve and load rate, combined with historical power output patterns extracted from historical power output data under similar weather conditions, a Kalman filter correction algorithm is used to predict the unit's unit power supply carbon emission intensity within the future forecast period. The Kalman filter correction algorithm uses real-time operating data to dynamically calibrate historical power output patterns, reducing short-term forecast bias. After performing the above forecasts for all energy supply units and all forecast periods, the unit power supply carbon emission intensity for each energy supply unit within each forecast period in the future preset time window is obtained.

[0033] In addition, in specific implementation, for each prediction period, the carbon emission intensity per unit of power supply of each energy supply unit is weighted and summed according to its distribution ratio to the power supply of the corresponding electrical connection node, to obtain the dynamic carbon emission intensity factor of that node in that prediction period. After calculation for each prediction period, a factor sequence arranged by period is formed. The factors are updated at the time period boundaries, without pre-assuming continuous changes between adjacent time periods.

[0034] In some embodiments, the following steps can be used to send the dynamic carbon emission intensity factors and time-of-use electricity price information together as a coordination signal to the coordination layers of each functional zone in the industrial park: Obtain time-of-use electricity price information from the power trading center; The dynamic carbon emission intensity factors are time-series aligned with the time-of-use electricity price information and encoded into a unified format data message. The data packets are broadcast to the edge gateways of all functional zone coordination layers at a preset coordination period via the industrial Ethernet or 5G private network within the industrial park.

[0035] It should be noted that the time-of-use electricity price information in this application refers to a time-price data set used to reflect the differences in electricity purchase prices of the power grid where the industrial park is located at different times, and serving as the basis for economic adjustment in subsequent coordination signals. The unified format data message is used to organize the dynamic carbon emission intensity factor and time-of-use electricity price information into a structured data string along the same time axis, and serves as a standardized data carrier for transmission within the park's internal network. The edge gateway is a communication device used to receive coordination signals issued by the park's overall control layer and to serve as the data entry point between the internal networks of each functional zone's coordination layer and the park's overall control layer.

[0036] In some embodiments, the park's central control layer first establishes a communication connection with the power trading center through a dedicated data interface. This data interface employs a security authentication mechanism to ensure the legitimacy of the data source. Then, a time-of-use (TOU) electricity price data request is sent to the power trading center. This request includes the industrial park's user identifier and the requested time range, covering the period from the current moment to the next 24 hours. Upon responding to the request, the power trading center returns TOU electricity price data within that time range, with hourly intervals. The data includes the start and end times for each hour, as well as the electricity purchase price for that period. After receiving the returned data, the park's central control layer performs a data integrity check, removing outliers and filling in missing values, thereby obtaining complete TOU electricity price information covering the next 24 hours.

[0037] In some embodiments, the dynamic carbon emission intensity factor of each electrical connection node, updated over the predicted period, can be obtained first. This dynamic carbon emission intensity factor covers a four-hour time window with a 15-minute interval. Simultaneously, the time-of-use electricity price information obtained in the first step is acquired, covering a twenty-four-hour time window with an hourly interval. To distribute both information together, time-series alignment is required: using 15 minutes as the basic time unit, the hourly electricity price is maintained as a segmented constant within the corresponding 15-minute sub-period according to the start and end times of each electricity price period, ensuring that each 15-minute time moment simultaneously contains both the dynamic carbon emission intensity factor and the corresponding electricity price value. Then, a unified format data message is constructed, consisting of a header and a data body. The header records the current message's generation timestamp, valid start time, valid end time, and message version number. The data body records, in chronological order, the timestamp of each 15-minute time moment, the dynamic carbon emission intensity factor corresponding to each electrical connection node at that time, and the corresponding time-of-use electricity price value at that time. All data is converted into a compact binary or text format according to preset encoding rules, ensuring that the data message structure is fixed and the parsing is unique.

[0038] In some embodiments, it can be first confirmed that an industrial Ethernet network or a 5G mobile communication private network covering all functional zone coordination layers has been deployed within the industrial park. Each functional zone coordination layer is configured with an edge gateway, which has a unique network address and maintains a real-time connection with the communication server of the park's central control layer. Then, a preset coordination cycle of five minutes is set, meaning a distribution action is performed every five minutes. At the end of each coordination cycle, the park's central control layer uses the unified format data packet generated in the second step as the coordination signal for the current cycle, and simultaneously broadcasts the data packet to the edge gateways of all functional zone coordination layers via the industrial Ethernet or 5G mobile communication private network. After receiving the data packet, the edge gateway first performs integrity verification and parsing to confirm that the packet source is legitimate and the data is complete. Then, it forwards the obtained dynamic carbon emission intensity factor and time-of-use electricity price information to the internal controller of its functional zone coordination layer. Finally, the unified format data packet successfully distributed to each edge gateway serves as the coordination signal basis for each functional zone coordination layer to perform internal optimized scheduling.

[0039] In step 103, each functional zone coordination layer generates a feasible adjustment range of the interaction power between each functional zone coordination layer and the overall control layer of the park, as well as the corresponding mapping relationship between the operation cost and carbon emission, based on the received coordination signal and the physical and operational constraints of its various adjustable resources. The feasible adjustment range and the mapping relationship between the operation cost and carbon emission are then fed back to the overall control layer of the park.

[0040] In some embodiments, each functional zone coordination layer, based on the received coordination signals and using the physical and operational constraints of its various adjustable resources as boundaries, generates a feasible adjustment range for the interaction power between each functional zone coordination layer and the overall control layer of the park, as well as the corresponding mapping relationship between operating costs and carbon emissions. This can be achieved through the following steps: Each functional zone coordination layer parses the received coordination signals to obtain the dynamic carbon emission intensity factor and time-of-use electricity price information of each functional zone coordination layer; Determine the physical and operational constraints of various adjustable resources within the coordination layer of each functional zone; Using the physical constraints and the operational constraints as boundaries, the joint feasible domain within the partition is projected onto the interaction power dimension between each functional partition coordination layer and the overall control layer of the park, thereby generating the feasible adjustment range of each functional partition coordination layer. For each power value point within each feasible adjustment range, the minimum operating cost to satisfy the internal constraints of the partition is calculated according to the time-of-use electricity price information, and the carbon emission of the corresponding electricity is calculated according to the dynamic carbon emission intensity factor of the connected nodes of the partition. The mapping relationship between interactive power and operating cost and carbon emission is constructed respectively.

[0041] It should be noted that the dynamic carbon emission intensity factor and time-of-use electricity price information are evaluation information for each power value point, and do not change the feasible adjustment range limited by equipment physical constraints and production operation constraints. The time-of-use electricity price is used to calculate the operating cost, and the dynamic carbon emission intensity factor is used to calculate the carbon emissions corresponding to the interactive electricity consumption. The two form the operating cost mapping and carbon emission mapping, respectively.

[0042] The operating cost represents the energy loss, energy storage loss, load regulation production loss, power curtailment loss, and reserve capacity cost generated by the functional area under the corresponding interactive power, excluding the carbon emissions already measured in the carbon emission mapping.

[0043] In practice, each functional zone coordination layer's edge gateway first receives a uniformly formatted data packet broadcast from the park's central control layer. This data packet includes a header and a data body. The header records the packet's generation timestamp, effective start time, effective end time, and version number. The data body records, in chronological order, the timestamp for each 15-minute interval, the dynamic carbon emission intensity factor corresponding to each electrical connection node at that time, and the corresponding time-of-use electricity price. After verifying the data packet's integrity and source legitimacy, the edge gateway forwards the data packet to the internal controller of its functional zone coordination layer. The internal controller, based on the electrical connection node number corresponding to its functional zone, extracts the dynamic carbon emission intensity factor sequence matching that node number from the data packet, and simultaneously extracts the time-of-use electricity price sequence aligned with the time axis. After extraction, the dynamic carbon emission intensity factor sequence and the time-of-use electricity price sequence are stored in the local cache of the functional zone coordination layer in chronological order.

[0044] In some embodiments, a joint feasible region within a partition can be constructed based on the physical constraints and operational constraints of each resource, and the internal scheduling scheme that satisfies the partition's net exchange power equation can be projected onto the interaction power dimension to obtain a feasible adjustment interval. Equipment safety constraints and hard production constraints are not relaxed even if the interval is empty. If the projection result is empty, the partition infeasibility state is returned to the overall control layer of the campus.

[0045] In some embodiments, the feasible adjustment interval can be divided into multiple power value points. For each value point, the internal scheduling within the partition is solved without relaxing equipment safety constraints and hard production constraints to obtain the minimum operating cost. The carbon emission is then calculated by multiplying the interactive power corresponding to that value point by the dynamic carbon emission intensity factor of the connected node. Two sets of mappings are formed by each power value point, its operating cost, and its carbon emission. If no internal scheduling scheme satisfying the constraints exists for a certain value point, that value point is deleted from the feasible interval.

[0046] In addition, in some embodiments, the physical and operational constraints of various adjustable resources within each functional partition coordination layer can be determined using the following steps: The aforementioned adjustable resources are classified into energy storage resources, flexible load resources, and distributed generation resources according to their response characteristics. For the energy storage resources, identify the upper and lower limits of the state of charge, the maximum charge and discharge power, and the charge and discharge efficiency curves of the energy storage resources, thereby constructing physical constraints that include power limiting and energy windows. For the flexible load type resources, by collecting the historical adjustable power curves of the flexible load type resources, and based on the preset production process priority, the power adjustment range, the power adjustment range and the maximum continuous adjustment time of the flexible load type resources are determined, thereby constructing an operation constraint that includes adjustment capacity and duration. For the distributed generation resources, the active power output prediction range and reactive power regulation range of the distributed generation resources are determined based on real-time meteorological forecast data and inverter status, thereby constructing a joint constraint boundary that includes volatility and supportability. The device-level constraints of each adjustable resource are retained, and the device variables are combined into a joint feasible constraint set of partitions through the partition power balance relationship.

[0047] It should be noted that the resource categories classified by response characteristics in this application are used to distinguish the essential attributes of energy storage resources, flexible load resources, and distributed generation resources in terms of power regulation speed, regulation direction, and sustainability, and to provide a classification basis for subsequently constructing different constraints. Physical constraints are hard boundary conditions based on electrochemical characteristics and hardware safety limits that energy storage resources must not exceed during power regulation. These physical constraints include two components: power limit and energy window. Operational constraints are flexible boundary conditions based on production continuity and process safety requirements that flexible load resources must meet when participating in power regulation. These operational constraints include two core parameters: regulation capacity and duration. Joint constraint boundaries are combined constraints that simultaneously limit the active power output range and reactive power regulation capability of distributed generation resources. These joint constraint boundaries include two dimensions: volatility characteristics and sustainability characteristics. Physical constraints are hard boundary conditions based on electrochemical characteristics and hardware safety limits that energy storage resources must not exceed during power regulation. Operational constraints are used to define flexible boundary conditions for flexible load resources to participate in power regulation based on production continuity and process safety requirements.

[0048] In practice, each functional zone coordination layer first conducts a comprehensive inventory of all adjustable resources within its zone, establishing an adjustable resource list. This list records the equipment name, equipment type, rated power, and connection location of each adjustable resource. Then, each adjustable resource is categorized based on its response characteristics during power adjustment: if the resource can bidirectionally absorb or release electrical energy and has a clear energy storage / discharge window, it is classified as an energy storage resource, including electrochemical energy storage devices and flywheel energy storage devices; if the resource can only unidirectionally consume electrical energy but its power can be continuously adjusted within a certain range without affecting the safety of core production processes, it is classified as a flexible load resource, including adjustable air conditioning systems, adjustable lighting systems, and some interruptible production equipment; if the resource can convert primary energy from nature into electrical energy for output, and its output fluctuates due to weather conditions, it is classified as a distributed generation resource, including rooftop photovoltaic power generation units, small wind power generation units, and biomass power generation units. After classifying all adjustable resources as described above, we obtain resource lists corresponding to the three resource categories.

[0049] In some embodiments, each energy storage unit can be selected first from the list of energy storage resources. For each energy storage unit, the current state of charge (SOC) value of the unit is obtained through a real-time connected battery management system. Simultaneously, the SOC lower limit threshold, SOC upper limit threshold, maximum charging power limit, and maximum discharging power limit, pre-configured in the battery management system parameter table, are read. The SOC lower limit threshold is typically set to 20% to prevent over-discharge damage to the battery; the SOC upper limit threshold is typically set to 90% to prevent overcharging from causing safety risks. The maximum charging power limit and maximum discharging power limit are determined by the inverter capacity of the energy storage unit, in kilowatts. Then, based on the above parameters, a power limiting constraint is constructed: the charging power of the energy storage unit at any time must not exceed the maximum charging power limit, and the discharging power must not exceed the maximum discharging power limit. Simultaneously, an energy window constraint is constructed: the SOC of the energy storage unit must not be lower than the SOC lower limit threshold or higher than the SOC upper limit threshold at any time. The above power limiting constraint and energy window constraint together constitute the physical constraints of the energy storage resource. After performing the above operations on all energy storage units in sequence, summarize the physical constraints of all energy storage units.

[0050] Additionally, in some embodiments, the adjustable power range, allowable adjustment period, maximum duration of a single adjustment, and recovery time of the flexible load can be read individually. The maximum duration of a single adjustment is only used to limit the continuous adjustment time and is not considered as the response speed. When the ramp rate or command response time can be obtained, it is included as an independent constraint in the device-level feasible domain of the flexible load.

[0051] In some embodiments, each distributed generation unit can be selected first from a list of distributed generation resources. For each distributed generation unit, a weather forecasting service system is accessed to obtain real-time weather forecast data for a preset future time window. This real-time weather forecast data includes predicted light intensity, predicted wind speed, and ambient temperature. This forecast data is input into the power prediction model calibrated at the factory of the distributed generation unit. Based on the correlation between meteorological parameters and historical power output data, the model outputs the active power output prediction range for the unit at various future times. This prediction range consists of a lower limit prediction value and an upper limit prediction value, reflecting the fluctuation characteristics of power output caused by weather changes. Simultaneously, the real-time status parameters of the inverter configured for the distributed generation unit are read, and its reactive power regulation capability range is obtained from the inverter's parameter table. This range is usually given in the form of a power factor, for example, the power factor can be continuously adjusted between 0.9 leading and 0.9 lagging. This reactive power regulation capability range reflects the unit's ability to support the grid voltage, i.e., the supportability characteristic. Combining the active power output prediction range and the reactive power regulation capability range forms the joint constraint boundary of the distributed generation resource. After performing the above operations on all distributed generation units in sequence, the joint constraint boundaries of all distributed generation units are summarized.

[0052] Furthermore, resource constraints in this application can be retained for each device variable, without directly taking the intersection of the state-of-charge windows or power boundaries of different devices. During partition aggregation, the power of each device is summed according to the power balance relationship, while retaining the state-of-charge and charge / discharge limits of each energy storage device, the regulation capacity and duration of each flexible load, and the active and non-active power capacity constraints of each distributed power source. These constraints together define the joint feasible region of the partition.

[0053] In specific implementation, after generating feasible adjustment ranges for various adjustable resources within each functional zone and the corresponding mapping relationship between operating costs and carbon emissions, the coordination layer first encodes the feasible range of interaction power between each zone and the overall control layer of the park in the form of segmented intervals. At the same time, it expresses the mapping relationship between operating costs and carbon emissions as a parameter matrix of a piecewise linear function or a quadratic function. Subsequently, through industrial Ethernet, 5G or real-time database communication interfaces, the above interval boundaries, cost function coefficients and timestamps are packaged and uploaded to the collaborative optimization server of the overall control layer of the park in standardized data frames.

[0054] In step 104, after receiving the feasible adjustment ranges and the mapping relationship between operating costs and carbon emissions from all functional zone coordination layers, the park's overall control layer optimizes the operating costs under the carbon emission ceiling constraint within the global feasible domain jointly defined by each feasible adjustment range and the park's power network constraint, with the industrial park's preset carbon emission ceiling as a constraint and the goal of minimizing the overall operating cost. When a feasible solution exists, the interactive power setpoints of each functional zone coordination layer are generated and issued for execution; when no feasible solution exists, an infeasible state is output and no new setpoints are issued.

[0055] In some embodiments, after receiving the feasible adjustment ranges and the mapping relationship between operating costs and carbon emissions from all functional zone coordination layers, the park's overall control layer optimizes the operating costs under the carbon emission ceiling constraint within the global feasible domain jointly defined by each feasible adjustment range and the park's power network constraint, using the industrial park's preset total carbon emission ceiling as a constraint and minimizing the overall operating cost as the objective. When a feasible solution exists, interactive power setpoints for each functional zone coordination layer are generated and issued for execution; when no feasible solution exists, an infeasible state is output and no new setpoints are issued. This can be achieved through the following steps: The overall control layer of the park will take the intersection of the feasible adjustment range of each functional zone with the park's power balance, line capacity, transformer capacity and node voltage constraints to form the global feasible domain of interactive power; Based on the mapping relationship between operating costs and carbon emissions fed back by the coordination layer of each functional zone, the operating costs and carbon emissions of each zone corresponding to the same power allocation scheme are accumulated to obtain the total operating costs and total carbon emissions of the park. The carbon emissions of each zone are verified by calculating the dynamic carbon emission intensity factor and the interactive electricity of the corresponding node, and the same electricity is not counted repeatedly on the energy supply side. Obtain the pre-set carbon emission cap for the industrial park; Within the global feasible domain, power allocation schemes with total carbon emissions not exceeding the upper limit of total carbon emissions are selected, and the allocation scheme with the lowest total operating cost of the park is solved from them. When there is no scheme that meets the conditions, an infeasible state is output and no new setpoint is issued.

[0056] In practice, the interaction power value corresponding to each functional partition coordination layer can be extracted from the allocation scheme as the optimal setting point, and then sent to the corresponding functional partition coordination layer for execution through the communication interface of the park's overall control layer.

[0057] It should be noted that the global feasible region is not a simple Cartesian product of the feasible adjustment intervals of each partition, but rather the intersection of the Cartesian product with constraints on park power balance, line capacity, transformer capacity, and node voltage. The park's overall control layer evaluates candidate power allocation schemes only within this intersection and solves the problem with the upper limit of total carbon emissions as a constraint and the goal of minimizing overall operating costs.

[0058] In some embodiments, the overall control layer of the park first forms the Cartesian product of the feasible adjustment intervals of each zone, and then superimposes the active and reactive power balance, line transmission capacity, transformer capacity and node voltage upper and lower limits of each time period in the park. Power combinations that do not meet any network constraints are eliminated, and the remaining set constitutes the global feasible region.

[0059] In addition, in some embodiments, for each power allocation scheme within the global feasible domain, the interactive power of each partition is substituted into the corresponding operating cost mapping and carbon emission mapping, and the total operating cost and total carbon emission of the park are accumulated.

[0060] In some embodiments, the carbon emissions of a zone can be calculated from the interactive electricity volume of that zone and the dynamic carbon emission intensity factor of the corresponding electrical connection node. The dynamic carbon emission intensity factor already includes the unit power supply emissions allocated according to the power flow of each energy supply unit connected to the park's power grid. Therefore, the total carbon emissions of the park are directly added to the carbon emissions of each zone, without adding the power generation emissions of the same energy supply unit. Direct process emissions that are unrelated to power flow and are not included in the node factor can be recorded separately, but are not repeatedly included in the carbon emissions from electricity consumption.

[0061] Furthermore, in this application, the overall control layer of the park can read the pre-set carbon emission limit and verify that its applicable time period and measurement boundary are consistent with the carbon emission mapping of each zone. The comprehensive operating cost is directly calculated by the cost mapping of each zone, serving as the minimization target under constraints, without setting a separate expected cost value that needs to be approximated.

[0062] In practice, the overall control layer of the park can parse the interaction power value corresponding to each functional zone coordination layer from the allocation scheme. This value represents the net exchange power target that the zone should maintain with the overall control layer. The interaction power value of each zone is extracted separately and organized into a setpoint list according to the zone number. Each item in this list contains a zone identifier and its corresponding power value. Then, the overall control layer establishes a connection with the edge gateways of each functional zone coordination layer through its communication interface, which uses an industrial Ethernet or 5G mobile communication private network. The overall control layer encapsulates the optimal interaction power setpoint for each zone in the setpoint list into a control command. This command includes a command type field, a timestamp field, a target zone field, and a power value field. After encapsulation, the control command is sent to the edge gateway of the corresponding functional zone coordination layer. Upon receiving the command, each edge gateway forwards the optimal interaction power setpoint to its internal controller. The internal controller then adjusts the power allocation of various adjustable resources based on this setpoint to achieve the required power exchange with the overall control layer. Finally, the optimal setpoint for interactive power issued to the coordination layer of each functional area will be used as the final execution instruction for this round of low-carbon optimization scheduling.

[0063] In addition, in some embodiments, determining the allocation scheme that satisfies the total carbon emission cap and minimizes the total operating cost of the park within the globally feasible domain can be achieved through the following steps: Obtain an initial interactive power allocation scheme that satisfies the constraints of the park's power network; Starting with the initial interactive power allocation scheme, and constrained by the upper limit of total carbon emissions, power balance of the park, line capacity, transformer capacity and node voltage, the interactive power allocation scheme is solved with the goal of minimizing the total operating cost of the park, and the infeasible state is returned when there is no feasible solution.

[0064] It should be noted that the preset interactive power allocation scheme in this application is a set of initial interactive power values ​​between each functional zone coordination layer and the overall park control layer, used as the initial starting point for the search process to optimize operating costs under the carbon emission ceiling constraint, and as the benchmark allocation vector for subsequent successive adjustment operations. The allocation scheme is a complete vector that specifically defines the interactive power values ​​between each functional zone coordination layer and the overall park control layer, and is a scheme that minimizes the total operating cost of the park while ensuring that the total carbon emissions do not exceed the total carbon emission ceiling.

[0065] In practice, the park's central control layer reads the feasible power allocation schemes from the previous scheduling cycle and can use the combinations formed by the midpoints of each partition interval as candidate initial values. Candidate initial values ​​are only considered feasible if they satisfy constraints related to park power balance, line capacity, transformer capacity, and node voltage. If a candidate initial value does not meet the constraints, a feasible initial value is searched within the global feasible domain; if the search result is empty, an infeasible state is output.

[0066] In some embodiments, the overall control layer of the park can enumerate or discretely search for candidate power allocation schemes within the global feasible domain. For each candidate scheme, the power of each partition is substituted into the corresponding operating cost mapping and carbon emission mapping, and the total operating cost and total carbon emission of the park are accumulated respectively. Schemes with total carbon emissions exceeding the upper limit are first eliminated, and then the scheme with the lowest total operating cost is selected from the remaining schemes. If there are no remaining schemes, an infeasible state is output and no new setpoint is issued.

[0067] In some embodiments, Figure 3 The hierarchical interaction between the overall control layer and the functional zone coordination layer is illustrated. Under this hierarchical interaction architecture, the overall control layer screens candidate schemes whose total carbon emissions do not exceed the upper limit of total carbon emissions within the global feasible domain jointly defined by the feasible adjustment range of each zone and the constraints of the park's power network. It then determines the interactive power setpoint with the lowest overall operating cost. If no candidate scheme meets the conditions, the overall control layer outputs an infeasible state and maintains the original scheduling instructions.

[0068] Furthermore, in another aspect of this application, in some embodiments, this application provides an intelligent optimization system for the low-carbon energy structure of industrial parks, with reference to... Figure 4 The figure is a schematic diagram of the structure of an intelligent optimization system for low-carbon energy structure in industrial parks, according to some embodiments of this application. The intelligent optimization system 400 for low-carbon energy structure in industrial parks includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire real-time operating data of each energy-consuming unit and energy-supplying unit in the industrial park, as well as historical power generation data. Processing module 402, in this application, is used to determine the dynamic carbon emission intensity factor of each electrical connection node in the industrial park based on the real-time operating data and the historical output data, and send each dynamic carbon emission intensity factor and time-of-use electricity price information together as a coordination signal to the coordination layer of each functional zone in the industrial park. It should be noted that, in this application, the processing module 402 is also used to generate, based on the received coordination signals and using the physical and operational constraints of its various adjustable resources as boundaries, feasible adjustment ranges of the interaction power between each functional zone coordination layer and the overall park control layer, as well as the corresponding mapping relationship between operating costs and carbon emissions, and to feed back each feasible adjustment range and the mapping relationship between operating costs and carbon emissions to the overall park control layer. The execution module 403 in this application is mainly used to optimize the operating cost under the carbon emission upper limit constraint within the global feasible domain jointly defined by each feasible adjustment range and the park power network constraint after the park's overall control layer receives the feasible adjustment range and the mapping relationship between operating cost and carbon emission from all functional partition coordination layers. It takes the preset carbon emission upper limit of the industrial park as a constraint and the minimum comprehensive operating cost as the objective. When a feasible solution exists, it generates the interactive power setpoint of each functional partition coordination layer and issues it for execution. When no feasible solution exists, it outputs an infeasible state and does not issue a new setpoint.

[0069] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described intelligent optimization method for low-carbon energy structure of industrial parks.

[0070] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a method for intelligent optimization of the low-carbon energy structure of an industrial park, according to some embodiments of this application. The intelligent optimization method for low-carbon energy structure of an industrial park in the above embodiments can... Figure 5The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0071] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0072] The communication bus 502 can be used to transmit information between the aforementioned components.

[0073] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0074] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0075] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0076] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0077] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0078] In addition, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned intelligent optimization method for low-carbon energy structure of industrial parks.

[0079] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0080] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for intelligent optimization of the energy structure of industrial parks to achieve low carbon emissions, characterized in that, Includes the following steps: Obtain real-time operating data of each energy-consuming and energy-supplying unit within the industrial park, as well as historical power generation output data; Based on the real-time operating data and the historical output data, the dynamic carbon emission intensity factor of each electrical connection node in the industrial park is determined, and each dynamic carbon emission intensity factor and time-of-use electricity price information are sent as a coordination signal to the coordination layer of each functional zone in the industrial park. Based on the received coordination signals, each functional zone coordination layer uses the physical and operational constraints of its various adjustable resources as boundaries to generate feasible adjustment ranges of the interaction power between each functional zone coordination layer and the overall control layer of the park, as well as the corresponding mapping relationship between operational costs and carbon emissions. The feasible adjustment ranges and the mapping relationship between operational costs and carbon emissions are then fed back to the overall control layer of the park. After receiving the feasible adjustment ranges and the mapping relationship between operating costs and carbon emissions from all functional zone coordination layers, the overall control layer of the park optimizes the operating costs under the carbon emission ceiling constraint within the global feasible domain jointly defined by each feasible adjustment range and the park's power network constraint, with the industrial park's preset total carbon emission ceiling as the constraint and the goal of minimizing the comprehensive operating cost. When a feasible solution exists, the interactive power setpoints of each functional zone coordination layer are generated and issued for execution; when no feasible solution exists, an infeasible state is output and no new setpoints are issued.

2. The method as described in claim 1, characterized in that, Acquiring real-time operational data of each energy-consuming and energy-supplying unit within the industrial park, as well as historical power generation output data, specifically includes: Smart meters and sensors are deployed in various energy-consuming and energy-supplying units within the industrial park to collect active power, reactive power, and voltage amplitude data, thereby forming real-time operating data. Extract time-series power data of photovoltaic, wind and biomass power generation that includes at least one complete annual cycle from the park's historical database, and perform scenario reduction on the time-series power data to obtain historical power output data of energy generation.

3. The method as described in claim 1, characterized in that, The determination of the dynamic carbon emission intensity factor for each electrical connection node within the industrial park based on the real-time operating data and the historical output data specifically includes: The distribution ratio of power supplied by each power supply unit to each electrical connection node is determined based on the real-time operating data. Based on the historical output data and the preset carbon emission intensity of each energy supply unit, the unit carbon emission intensity of each energy supply unit within the preset time window is predicted. The carbon emission intensity of each unit's power supply is weighted and summed according to the corresponding distribution ratio to obtain the dynamic carbon emission intensity factor of each electrical connection node in the industrial park updated with the forecast period.

4. The method as described in claim 1, characterized in that, The electrical connection nodes include connection points between the power supply unit and the busbar, connection points between the busbars connected by lines, and branch points where the busbars feed power to the power consumption unit. Each connection point participates in the determination of the dynamic carbon emission intensity factor as an independent electrical connection node.

5. The method as described in claim 1, characterized in that, The dynamic carbon emission intensity factors and time-of-use electricity price information are used together as coordination signals and sent to the coordination layers of each functional zone in the industrial park. Specifically, this includes: Obtain time-of-use electricity price information from the power trading center; The dynamic carbon emission intensity factors are time-series aligned with the time-of-use electricity price information and encoded into a unified format data message. The data packets are broadcast to the edge gateways of all functional zone coordination layers at a preset coordination period via the industrial Ethernet or 5G private network within the industrial park.

6. The method as described in claim 1, characterized in that, Based on the received coordination signals, each functional zone coordination layer, using the physical and operational constraints of its various adjustable resources as boundaries, generates a feasible adjustment range for the interaction power between each functional zone coordination layer and the overall control layer of the park, as well as the corresponding mapping relationship between operational costs and carbon emissions. Specifically, this includes: Each functional zone coordination layer parses the received coordination signals to obtain the dynamic carbon emission intensity factor and time-of-use electricity price information of each functional zone coordination layer; Determine the physical and operational constraints of various adjustable resources within the coordination layer of each functional zone; Using the physical constraints and the operational constraints as boundaries, the joint feasible domain within the partition is projected onto the interaction power dimension between each functional partition coordination layer and the overall control layer of the park, thereby generating the feasible adjustment range of each functional partition coordination layer. For each power value point within each feasible adjustment range, the minimum operating cost to satisfy the internal constraints of the partition is calculated according to the time-of-use electricity price information, and the carbon emission of the corresponding electricity is calculated according to the dynamic carbon emission intensity factor of the connected nodes of the partition. The mapping relationship between interactive power and operating cost and carbon emission is constructed respectively.

7. The method as described in claim 6, characterized in that, The physical and operational constraints of various adjustable resources within the coordination layer of each functional zone are specifically defined, including: The aforementioned adjustable resources are classified into energy storage resources, flexible load resources, and distributed generation resources according to their response characteristics. For the energy storage resources, identify the upper and lower limits of the state of charge, the maximum charge and discharge power, and the charge and discharge efficiency curves of the energy storage resources, thereby constructing physical constraints that include power limiting and energy windows. For the flexible load type resources, by collecting the historical adjustable power curves of the flexible load type resources, and based on the preset production process priority, the power adjustment range, the power adjustment range and the maximum continuous adjustment time of the flexible load type resources are determined, thereby constructing an operation constraint that includes adjustment capacity and duration. For the distributed generation resources, the active power output prediction range and reactive power regulation range of the distributed generation resources are determined based on real-time meteorological forecast data and inverter status, thereby constructing a joint constraint boundary that includes volatility and supportability. The device-level constraints of each adjustable resource are retained, and the device variables are combined into a joint feasible constraint set of partitions through the partition power balance relationship.

8. The method as described in claim 1, characterized in that, After receiving the feasible adjustment ranges and the mapping relationship between operating costs and carbon emissions from all functional zone coordination layers, the overall control layer of the industrial park optimizes the operating costs under the carbon emission ceiling constraint within the global feasible domain jointly defined by each feasible adjustment range and the park's power network constraint, using the industrial park's preset total carbon emission ceiling as a constraint and minimizing the overall operating cost as the objective. When a feasible solution exists, it generates interactive power setpoints for each functional zone coordination layer and issues them for execution; when no feasible solution exists, it outputs an infeasible state and does not issue new setpoints. Specifically, this includes: The overall control layer of the park will take the intersection of the feasible adjustment range of each functional zone with the park's power balance, line capacity, transformer capacity and node voltage constraints to form the global feasible domain of interactive power; Based on the mapping relationship between operating costs and carbon emissions fed back by the coordination layer of each functional zone, the operating costs and carbon emissions of each zone corresponding to the same power allocation scheme are accumulated to obtain the total operating costs and total carbon emissions of the park. The carbon emissions of each zone are verified by calculating the dynamic carbon emission intensity factor and the interactive electricity of the corresponding node, and the same electricity is not counted repeatedly on the energy supply side. Obtain the pre-set carbon emission cap for the industrial park; Within the global feasible domain, power allocation schemes with total carbon emissions not exceeding the upper limit of total carbon emissions are selected, and the allocation scheme with the lowest total operating cost of the park is solved from them. When there is no scheme that meets the conditions, an infeasible state is output and no new setpoint is issued. The interaction power value corresponding to each functional zone coordination layer is extracted from the allocation scheme and used as the optimal setting point. This value is then sent to the corresponding functional zone coordination layer for execution through the communication interface of the park's overall control layer.

9. The method as described in claim 8, characterized in that, Within the globally feasible domain, determining the allocation scheme that satisfies the total carbon emission cap and minimizes the total operating cost of the park specifically includes: Obtain an initial interactive power allocation scheme that satisfies the constraints of the park's power network; Starting with the initial interactive power allocation scheme, and constrained by the upper limit of total carbon emissions, power balance of the park, line capacity, transformer capacity and node voltage, the interactive power allocation scheme is solved with the goal of minimizing the total operating cost of the park, and the infeasible state is returned when there is no feasible solution.

10. A smart optimization system for low-carbon energy structure in industrial parks, comprising optimizing the energy structure of industrial parks using the smart optimization method for low-carbon energy structure as described in any one of claims 1 to 9, characterized in that, The system includes: The acquisition module is used to acquire real-time operating data of each energy-consuming unit and energy-supplying unit in the industrial park, as well as historical power generation data. The processing module is used to determine the dynamic carbon emission intensity factor of each electrical connection node in the industrial park based on the real-time operation data and the historical output data, and send each dynamic carbon emission intensity factor and time-of-use electricity price information as a coordination signal to the coordination layer of each functional area. The processing module is also used to receive and feed back the interactive power feasible adjustment range and the corresponding operation cost and carbon emission mapping relationship generated by each functional partition coordination layer with the physical constraints and operational constraints of its internal adjustable resources as the boundary. The execution module is used to define the feasible adjustment range and the park power network constraints as the global feasible domain, solve the allocation scheme with the lowest comprehensive operating cost under the constraint of the total carbon emission limit, generate an interactive power setpoint and issue it for execution when a feasible solution exists, and output an infeasible state and do not issue a new setpoint when no feasible solution exists.

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