Zero-carbon digital park energy management and control method and device based on power gap technology

Abstract

The invention relates to a zero-carbon digital park energy management and control method and device based on the power gap technology, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting equipment data of multi-energy equipment in the zero-carbon digital park in real time through a multi-protocol communication module; based on an edge computing engine of a power operating system, performing protocol unified conversion on the equipment data to generate a real-time data stream; analyzing the energy supply and demand state of the zero-carbon digital park through an edge side optimization algorithm in combination with the real-time data stream, and generating a dynamic energy optimization control instruction; the energy optimization control instruction comprises at least one of an energy storage equipment charging and discharging instruction, a flexible load adjusting instruction and a grid-connected and off-grid switching instruction; and issuing the energy optimization control instruction to a corresponding execution mechanism so as to realize real-time optimization scheduling of energy of the zero-carbon digital park. By adopting the method, the energy management and control intelligence of the park can be improved.

Description

Technical Field

[0001] This application relates to the field of energy management and control technology, and in particular to a zero-carbon digital park energy management method and device based on the Hongmeng Electric Power technology. Background Art

[0002] With the continuous growth of energy demand and the increasing complexity of the energy structure, traditional energy management methods are no longer able to meet the needs of modern energy systems. Traditional energy management methods often rely on human experience and simple control strategies, lacking systematicity and intelligence, resulting in low energy utilization efficiency and severe energy waste. In addition, the operating status monitoring and maintenance of energy equipment rely on manual inspections, making it difficult to detect and address equipment failures in a timely manner, resulting in unstable energy supply and potential safety hazards.

[0003] Modern energy systems involve a wide range of energy equipment and facilities, encompassing multiple links including power generation, transmission, distribution, and consumption. This generates vast and complex data volumes. Existing energy management systems lack effective data collection and processing capabilities, preventing comprehensive, real-time understanding of the operating status of energy equipment and energy usage. Data silos are prevalent, making data interoperability and sharing difficult across systems. This results in low levels of information and intelligence in energy management, impacting the overall efficiency and reliability of the energy system. Summary of the Invention

[0004] Based on this, it is necessary to provide a zero-carbon digital park energy management method, device, computer equipment, computer-readable storage medium and computer program product based on the power Hongmeng technology to improve the intelligence of energy management and control in response to the above technical problems.

[0005] In the first aspect, this application provides a zero-carbon digital park energy management and control method based on the power Hongmeng technology, including:

[0006] The multi-protocol communication module is used to collect real-time equipment data of multiple energy devices in the zero-carbon digital park; the multi-energy equipment includes at least one of photovoltaic equipment, energy storage equipment, charging piles and flexible load equipment; the equipment data includes at least one of energy production data, energy consumption data and equipment operation status data;

[0007] An edge computing engine based on the power operating system performs unified protocol conversion on the device data to generate a real-time data stream;

[0008] By using an edge-side optimization algorithm and combining it with the real-time data stream, the energy supply and demand status of the zero-carbon digital park is analyzed to generate dynamic energy optimization control instructions; the energy optimization control instructions include at least one of energy storage device charging and discharging instructions, flexible load adjustment instructions, and on-grid and off-grid switching instructions;

[0009] The energy optimization control instruction is issued to the corresponding execution agency to achieve real-time optimization scheduling of the energy of the zero-carbon digital park.

[0010] In one embodiment, the edge-side optimization algorithm includes an automatic power generation control algorithm or an automatic voltage control algorithm. The edge-side optimization algorithm is combined with the real-time data stream to analyze the energy supply and demand status of the zero-carbon digital park and generate dynamic energy optimization control instructions, including:

[0011] Dynamically calculating the carbon emission intensity factor of the zero-carbon digital park by utilizing the containerized application in the edge computing engine in combination with the real-time data stream;

[0012] The carbon emission intensity factor is embedded into the peak shaving and valley filling control logic as an optimization constraint condition to obtain an optimized peak shaving and valley filling control logic;

[0013] According to the optimized peak shaving and valley filling control logic, the energy storage device charging and discharging instructions are generated.

[0014] In one embodiment, the method further comprises:

[0015] Obtaining a change in the carbon emission intensity factor according to the carbon emission intensity factor corresponding to the zero-carbon digital park after optimization;

[0016] When the change in the carbon emission intensity factor does not meet a preset change threshold, the configuration parameters of the edge-side optimization algorithm are adjusted according to a preset parameter adjustment method.

[0017] In one embodiment, the use of containerized applications in the edge computing engine in combination with the real-time data stream to dynamically calculate the carbon emission intensity factor of the zero-carbon digital park includes:

[0018] Determine photovoltaic power generation, energy storage charging and discharging efficiency, and load energy consumption using the containerized application in combination with the real-time data stream;

[0019] Determining the net carbon emissions of the zero-carbon digital park based on the photovoltaic power generation and the energy storage charging and discharging efficiency;

[0020] The carbon emission intensity factor is determined according to the ratio information between the net carbon emissions of the zero-carbon digital park and the load energy consumption.

[0021] In one embodiment, determining the net carbon emissions of the zero-carbon digital park based on the photovoltaic power generation and the energy storage charging and discharging efficiency includes:

[0022] Calculate the carbon emission reduction contributed by clean energy based on the photovoltaic power generation;

[0023] Based on the energy storage charging and discharging efficiency, correcting the actual charging and discharging loss of the energy storage device to obtain the carbon emissions of electricity;

[0024] The net carbon emissions are determined based on the electricity carbon emissions and the carbon emission reduction.

[0025] In one embodiment, the on-grid and off-grid switching instruction is used to instruct the actuator to operate in parallel with the grid when the grid is normal, and to switch to the autonomous power supply mode when the grid fails.

[0026] Secondly, this application also provides a zero-carbon digital campus energy management and control device based on the power Hongmeng technology, including:

[0027] An acquisition module is configured to collect, in real time, device data of multiple energy devices within the zero-carbon digital park through a multi-protocol communication module; the multiple energy devices include at least one of photovoltaic devices, energy storage devices, charging piles, and flexible load devices; and the device data includes at least one of energy production data, energy consumption data, and device operating status data;

[0028] A conversion module, configured to perform protocol unified conversion on the device data based on the edge computing engine of the power operating system to generate a real-time data stream;

[0029] A generation module is configured to analyze the energy supply and demand status of the zero-carbon digital park through an edge-side optimization algorithm in combination with the real-time data stream, and generate dynamic energy optimization control instructions; the energy optimization control instructions include at least one of an energy storage device charging and discharging instruction, a flexible load adjustment instruction, and an on-grid and off-grid switching instruction;

[0030] The sending module is used to send the energy optimization control instruction to the corresponding execution agency to achieve real-time optimization scheduling of the energy of the zero-carbon digital park.

[0031] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0032] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0033] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0034] The above-mentioned zero-carbon digital park energy management and control method, device, computer equipment, computer-readable storage medium and computer program product based on the power Hongmeng technology collects the equipment data of multiple energy equipment in the zero-carbon digital park in real time through a multi-protocol communication module; the multiple energy equipment includes at least one of photovoltaic equipment, energy storage equipment, charging piles and flexible load equipment; the equipment data includes at least one of energy production data, energy consumption data and equipment operation status data; the edge computing engine based on the power operating system performs unified protocol conversion on the equipment data to generate a real-time data stream; through the edge-side optimization algorithm, combined with the real-time data stream, the energy supply and demand status of the zero-carbon digital park is analyzed to generate dynamic energy optimization control instructions; the energy optimization control instructions include at least one of the energy storage equipment charging and discharging instructions, flexible load adjustment instructions and on-grid switching instructions; the energy optimization control instructions are sent to the corresponding execution agency to realize the real-time optimization scheduling of the energy of the zero-carbon digital park.

[0035] In this way, unified access to heterogeneous equipment such as photovoltaics, energy storage, and charging piles within the park is achieved through multi-protocol communication modules, reducing protocol development and debugging costs and significantly improving system compatibility and deployment efficiency. The edge computing engine based on the power operating system completes data protocol conversion and generates high-precision real-time data streams to meet the rapid control requirements of scenarios such as island operation and frequency modulation. Through edge-side optimization algorithms combined with real-time data streams, the energy supply and demand status of the zero-carbon digital park is analyzed to generate dynamic energy optimization control instructions. The energy optimization control instructions include at least one of the energy storage equipment charging and discharging instructions, flexible load adjustment instructions, and on-grid and off-grid switching instructions. The energy optimization control instructions are sent to the corresponding actuators, which can realize dynamic adjustment of the energy storage equipment charging and discharging strategy and flexible load power distribution, optimize the energy supply and demand balance, reduce the peak load of the park, and improve energy utilization efficiency, thereby effectively improving the energy management intelligence of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 A flowchart of a zero-carbon digital park energy management and control method based on the power Hongmeng technology in one embodiment;

[0038] Figure 2 A flowchart of a zero-carbon digital park energy management and control method based on the power Hongmeng technology in another embodiment;

[0039] Figure 3 This is a structural block diagram of a zero-carbon digital campus energy management and control system based on the power Hongmeng technology in one embodiment;

[0040] Figure 4 This is a structural block diagram of a zero-carbon digital park energy management and control device based on the power Hongmeng technology in one embodiment;

[0041] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0043] It should be noted that the terms "first", "second", etc. used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "including" and "having" used in this application and any variations thereof are intended to cover non-exclusive inclusions. The term "plurality" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions or any combination of multiple solutions.

[0044] In one embodiment, Figure 1 As shown, a zero-carbon digital park energy management and control method based on the power Hongmeng technology is provided. This embodiment uses the method applied to a computer device as an example. It can be understood that the computer device can be a terminal, a server, or a system including a terminal and a server. In this embodiment, the method includes the following steps:

[0045] Step S110 , collecting device data of multiple energy devices in the zero-carbon digital park in real time through a multi-protocol communication module.

[0046] Among them, the multi-energy equipment includes at least one of photovoltaic equipment, energy storage equipment, charging piles and flexible load equipment.

[0047] The equipment data includes at least one of energy production data, energy consumption data and equipment operation status data.

[0048] Among them, energy production data includes at least one of the real-time power generation power (kW) of photovoltaic equipment, the charging and discharging power (kW) and charging and discharging efficiency (%) of energy storage equipment, and the actual output of renewable energy (kWh).

[0049] The energy consumption data includes at least one of the real-time power consumption (kW) of the charging pile, the energy consumption (kWh) of the air-conditioning system, and the operating power (kW) of the production equipment.

[0050] Among them, the equipment operating status data includes the temperature of the equipment ( ), voltage (V), current (A), communication status (online / offline) and at least one of the switch signal.

[0051] Step S120: Based on the edge computing engine of the power operating system, the device data is converted into a unified protocol to generate a real-time data stream.

[0052] Among them, the power operating system can be the power Hongmeng operating system.

[0053] In specific implementation, computer equipment can perform unified protocol conversion on device data based on the edge computing engine of the power operating system to generate real-time data streams.

[0054] Furthermore, based on the edge computing engine of the power Hongmeng operating system, device data can be standardized and converted into unified protocols to generate real-time data streams.

[0055] In some embodiments, the edge computing engine adopts a containerized deployment method to support the rapid loading of user-defined AI energy-saving models.

[0056] In step S130 , the energy supply and demand status of the zero-carbon digital park is analyzed through the edge-side optimization algorithm in combination with the real-time data stream, and dynamic energy optimization control instructions are generated.

[0057] The energy optimization control instructions include at least one of energy storage device charging and discharging instructions, flexible load adjustment instructions, and on-grid and off-grid switching instructions.

[0058] Grid-connected and off-grid switching refers to operating in parallel with the grid (on-grid) when the grid is normal, and switching to autonomous power supply (off-grid) in the event of a fault. For example, when the grid's carbon intensity is too high, on-grid and off-grid switching instructions can be triggered to optimize energy sources. Grid-connected and off-grid switching instructions can include both on-grid switching instructions and off-grid switching instructions.

[0059] Among them, edge-side optimization algorithms include AGC (Automatic Generation Control) or AVC (Automatic Voltage Control) algorithms, which are used to achieve peak shaving and valley filling and plan curve tracking, and reduce the peak load of the park by dynamically adjusting the power distribution of energy storage equipment and flexible loads.

[0060] Energy storage device charge and discharge instructions are specifically used to adjust the energy storage device's charge and discharge strategy based on carbon emission reduction targets, prioritizing the use of photovoltaic power generation to reduce grid dependence. Energy storage device charge and discharge instructions can be generated based on peak shaving and valley filling control logic.

[0061] Among them, the flexible load adjustment instruction is used to optimize the power distribution of flexible loads based on net carbon emissions (including: dynamically adjusting the charging pile power, air conditioning set temperature and production equipment operation mode based on real-time carbon footprint thresholds).

[0062] For example, if the net carbon emissions exceed the target value (e.g. 100kg of carbon dioxide), the following actions are triggered: Energy storage discharge: instruct the energy storage device to release 100 kWh of electricity, reducing the power purchase from the grid; Flexible load adjustment: reduce the charging pile power from 50 kW to 30 kW, and increase the air conditioning temperature by 2 , reducing load energy consumption by 50 kWh; grid-connected switching: If the carbon intensity of the grid is too high, switch to off-grid mode and give priority to photovoltaic and energy storage power supply.

[0063] Step S140: Send the energy optimization control instruction to the corresponding execution agency to achieve real-time optimization scheduling of energy in the zero-carbon digital park.

[0064] In specific implementation, computer equipment can send energy optimization control instructions to corresponding execution agencies to achieve real-time optimization scheduling of energy in the zero-carbon digital park.

[0065] For example, the grid-connected and off-grid switching instructions are sent to the relay protection device or energy storage converter; the flexible load adjustment instructions are sent to the charging pile controller and air conditioning group control system; the energy storage equipment charging and discharging instructions are sent to the energy storage battery management system.

[0066] In some embodiments, the method further includes: uploading the carbon emission data and optimization results to the zero-carbon management platform through the northbound communication interface to generate carbon quota trading recommendations and energy efficiency reports.

[0067] In the above-mentioned zero-carbon digital park energy management and control method based on the power Hongmeng technology, the equipment data of multiple energy equipment in the zero-carbon digital park is collected in real time through a multi-protocol communication module; the multiple energy equipment includes at least one of photovoltaic equipment, energy storage equipment, charging piles and flexible load equipment; the equipment data includes at least one of energy production data, energy consumption data and equipment operation status data; the edge computing engine based on the power operating system performs unified protocol conversion on the equipment data to generate a real-time data stream; through the edge-side optimization algorithm, combined with the real-time data stream, the energy supply and demand status of the zero-carbon digital park is analyzed to generate dynamic energy optimization control instructions; the energy optimization control instructions include at least one of the energy storage equipment charging and discharging instructions, flexible load adjustment instructions and on-grid switching instructions; the energy optimization control instructions are sent to the corresponding execution agency to realize the real-time optimization scheduling of the energy of the zero-carbon digital park.

[0068] In this way, unified access to heterogeneous equipment such as photovoltaics, energy storage, and charging piles within the park is achieved through multi-protocol communication modules, reducing protocol development and debugging costs and significantly improving system compatibility and deployment efficiency. The edge computing engine based on the power operating system completes data protocol conversion and generates high-precision real-time data streams to meet the rapid control requirements of scenarios such as island operation and frequency modulation. Through edge-side optimization algorithms combined with real-time data streams, the energy supply and demand status of the zero-carbon digital park is analyzed to generate dynamic energy optimization control instructions. The energy optimization control instructions include at least one of the energy storage equipment charging and discharging instructions, flexible load adjustment instructions, and on-grid and off-grid switching instructions. The energy optimization control instructions are sent to the corresponding actuators, which can realize dynamic adjustment of the energy storage equipment charging and discharging strategy and flexible load power distribution, optimize the energy supply and demand balance, reduce the peak load of the park, and improve energy utilization efficiency, thereby effectively improving the energy management intelligence of the park.

[0069] In one embodiment, the edge-side optimization algorithm includes an automatic power generation control algorithm or an automatic voltage control algorithm. Through the edge-side optimization algorithm, combined with real-time data streams, the energy supply and demand status of the zero-carbon digital park is analyzed to generate dynamic energy optimization control instructions, including: using containerized applications in the edge computing engine, combined with real-time data streams, to dynamically calculate the carbon emission intensity factor of the zero-carbon digital park; embedding the carbon emission intensity factor as an optimization constraint condition into the peak shaving and valley filling control logic to obtain the optimized peak shaving and valley filling control logic; generating energy storage equipment charging and discharging instructions based on the optimized peak shaving and valley filling control logic.

[0070] In the specific implementation, when the computer equipment analyzes the energy supply and demand status of the zero-carbon digital park through edge-side optimization algorithms and combines real-time data streams to generate dynamic energy optimization control instructions, for the charging and discharging instructions of the energy storage equipment, the containerized application in the edge computing engine can be used in combination with the real-time data stream to dynamically calculate the carbon emission intensity factor of the zero-carbon digital park; the carbon emission intensity factor is embedded in the peak shaving and valley filling control logic as an optimization constraint condition to obtain the optimized peak shaving and valley filling control logic; according to the optimized peak shaving and valley filling control logic, the charging and discharging instructions of the energy storage equipment are generated.

[0071] In this way, carbon-energy coordinated optimization is achieved by embedding the dynamically calculated carbon emission intensity factor as the core constraint into the edge-side AGC / AVC peak shaving and valley filling control logic.

[0072] In one embodiment, the method also includes: obtaining a change in the carbon emission intensity factor based on the carbon emission intensity factor corresponding to the zero-carbon digital park after optimization; when the change in the carbon emission intensity factor does not meet a preset change threshold, adjusting the configuration parameters of the edge-side optimization algorithm according to a preset parameter adjustment method.

[0073] In a specific implementation, the computer equipment can obtain the change in the carbon emission intensity factor based on the carbon emission intensity factor corresponding to the zero-carbon digital park after optimization; when the change in the carbon emission intensity factor does not meet the preset change threshold, the configuration parameters of the edge-side optimization algorithm are adjusted according to the preset parameter adjustment method.

[0074] For example, if the change in the carbon emission intensity factor is less than the preset change threshold, if the root cause is analyzed to be energy storage delay, the power deviation correction coefficient will be increased; if the root cause is analyzed to be photovoltaic forecast deviation, the weight of clean energy will be increased and photovoltaic dependence will be reduced; if the root cause is analyzed to be load mutation, the economic weight (electricity price cost factor) will be reduced.

[0075] In this way, by tracking the changes in the carbon emission intensity factor in real time, the edge optimization algorithm parameters are automatically adjusted when the preset threshold is not reached, thereby achieving dynamic and adaptive carbon-energy coordinated control.

[0076] In some embodiments, when computer equipment utilizes containerized applications in an edge computing engine and combines them with real-time data streams to dynamically calculate the carbon emission intensity factor of a zero-carbon digital park, it can utilize containerized applications and combine them with real-time data streams to determine the net carbon emissions and load energy consumption of the zero-carbon digital park; and determine the carbon emission intensity factor based on the ratio information between the net carbon emissions and load energy consumption of the zero-carbon digital park.

[0077] Furthermore, containerized applications can be used in combination with real-time data streams to determine photovoltaic power generation, energy storage charging and discharging efficiency, and load energy consumption; the net carbon emissions of the zero-carbon digital park can be determined based on photovoltaic power generation and energy storage charging and discharging efficiency; and the carbon emission intensity factor can be determined based on the ratio information between the net carbon emissions of the zero-carbon digital park and the load energy consumption.

[0078] The load energy consumption may refer to the total energy consumption of the park.

[0079] Among them, in the process of determining the net carbon emissions of the zero-carbon digital park based on photovoltaic power generation and energy storage charging and discharging efficiency, the carbon emission reduction contributed by clean energy can be calculated based on photovoltaic power generation; based on the energy storage charging and discharging efficiency, the actual charging and discharging losses of the energy storage equipment are corrected to obtain the carbon emissions of electricity.

[0080] Specifically, the carbon reduction contribution from clean energy can be determined by multiplying photovoltaic power generation by the clean energy correction coefficient. Electricity carbon emissions can be determined by multiplying grid power purchases by the grid carbon intensity coefficient. By correcting the actual charge and discharge losses of energy storage equipment based on energy storage charge and discharge efficiency, grid power purchases can be recalculated to obtain a corrected grid power purchase. Electricity carbon emissions can then be determined by multiplying this corrected grid power purchases by the grid carbon intensity coefficient. This dynamic correction of energy storage losses based on charge and discharge efficiency avoids false carbon reduction claims and reduces carbon accounting errors.

[0081] In this way, the net carbon emissions can be determined based on the carbon emissions from electricity and the carbon reductions.

[0082] In practical applications, the calculation formula for net carbon emissions is:

[0083] .

[0084] in," " represents the carbon emissions of electricity;" ” represents carbon emission reduction.

[0085] The calculation formula for the carbon emission intensity factor is:

[0086] .

[0087] In this way, through edge-side containerized applications, real-time correlation analysis of photovoltaic power generation, energy storage efficiency and load energy consumption can be carried out, the carbon emission model can be dynamically corrected, and the carbon footprint of the park can be accurately calculated, providing a reliable basis for carbon reduction strategies and helping to effectively reduce carbon emission intensity.

[0088] In some embodiments, energy optimization control instructions can be issued in real time to corresponding actuators, and device data can be updated through a feedback mechanism, forming a closed-loop optimization scheduling mechanism to minimize carbon emissions. Specifically, actuator response data can be collected and updated in real-time data streams. When the actual adjustment of the flexible load does not meet a preset threshold, an equipment health warning is triggered, and compensation control instructions are regenerated and issued again.

[0089] Among them, in the event of a power grid failure, off-grid switching instructions are executed first, and energy storage capacity is dynamically allocated based on carbon footprint calculation results to ensure zero-carbon power supply for critical loads.

[0090] For example, in a normal scenario, flexible load adjustment instructions are sent to the charging pile via the 4G network, reducing the charging power from 100kW to 70kW to achieve peak shaving.

[0091] Failure scenario: Upon detecting a sudden grid voltage drop, the system issues an off-grid switch command to the charging station, which immediately disconnects from the grid and switches to off-grid mode. The supercapacitor activates to maintain power to the main control unit for six seconds, ensuring the energy storage device can continue to supply critical loads (such as data centers). A relay controls the start-up of a backup diesel generator to fill the power gap.

[0092] In another embodiment, Figure 2 As shown, a flowchart of a zero-carbon digital park energy management and control method based on the power Hongmeng technology is provided, including the following steps:

[0093] Step S202: collecting device data of multiple energy devices in the zero-carbon digital park in real time through a multi-protocol communication module.

[0094] Step S204: Based on the edge computing engine of the power operating system, the device data is converted into a unified protocol to generate a real-time data stream.

[0095] Step S206: Utilize containerized applications in combination with real-time data streams to determine photovoltaic power generation, energy storage charging and discharging efficiency, and load energy consumption.

[0096] Step S208: Calculate the carbon emission reduction contributed by clean energy based on the photovoltaic power generation.

[0097] In step S210 , based on the energy storage charging and discharging efficiency, the actual charging and discharging loss of the energy storage device is corrected to obtain the carbon emissions of the electricity.

[0098] Step S212: determining the net carbon emissions based on the electricity carbon emissions and the carbon emission reductions.

[0099] Step S214: determining a carbon emission intensity factor based on the ratio between the net carbon emissions of the zero-carbon digital park and the load energy consumption.

[0100] Step S216 , embedding the carbon emission intensity factor as an optimization constraint into the peak shaving and valley filling control logic to obtain an optimized peak shaving and valley filling control logic.

[0101] Step S218: Generate charging and discharging instructions for the energy storage device according to the optimized peak shaving and valley filling control logic.

[0102] Step S220: Send the energy storage device charge and discharge instructions to the corresponding execution mechanism to achieve real-time optimization and scheduling of energy in the zero-carbon digital park.

[0103] It should be noted that the specific limitations of the above steps can be found in the specific limitations of a zero-carbon digital park energy management method based on Hongmeng Electric Power technology.

[0104] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of the steps or stages in other steps or other steps. It is understandable that the various steps in different embodiments can be freely combined as needed, and the various non-contradictory schemes formed by the combination all fall within the scope of protection of this application.

[0105] In some other embodiments, Figure 3 As shown, a block diagram of a zero-carbon digital park energy management and control system based on electric power Hongmeng technology is provided for implementing the above-mentioned zero-carbon digital park energy management and control method based on electric power Hongmeng technology. Specifically, the device structure includes:

[0106] Hardware layer:

[0107] Main control unit: uses domestically produced dual-core processors (Arm Cortex-A55 and Cortex-R5F), supporting parallel processing of real-time and non-real-time tasks.

[0108] Communication module: Integrates 8-channel RS-485, 3-channel Ethernet, and 4G / 5G wireless communication, and is compatible with protocols such as Modbus, IEC 104, and MQTT.

[0109] Data acquisition module: supports high-precision acquisition (0.5 level accuracy) of three-phase voltage / current, ambient temperature and humidity, equipment switching quantity and other signals.

[0110] Control output module: provides multiple relay outputs, supports remote control of equipment start and stop, and grid-connection and off-grid switching.

[0111] Supercapacitor: Built-in energy storage unit, can maintain power supply for 6 seconds after power failure, ensuring key data reporting and emergency control.

[0112] Hardware implementation:

[0113] Device size: 19-inch standard rack design, terminal configuration includes:

[0114] Power input: AC 220V or DC 18-75V dual redundant design.

[0115] Communication interface: 8-way RS-485 (Modbus RTU), 3-way Ethernet (Modbus TCP / IP), 4G / 5G antenna interface.

[0116] Control output: 2 relays (8A / 250VAC), supporting the issuance of on-grid and off-grid switching commands.

[0117] Specifically, Figure 3 As shown in the figure, the upper layer is embedded software adaptation, enabling the deployment of software-defined functions on the Dianhong system with the CoreChip D9 high-performance core A55, and running the power prediction AI embedded model on the Dianhong D9 development board. The lower layer is operating system adaptation, enabling the deployment of the Dianhong system with the CoreChip D9 high-performance core A55 and the RTOS system with the real-time core R5. Based on the real-time core R5, the decoding, time synchronization, and timekeeping functions of the Beidou B code were developed and transplanted.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a zero-carbon digital campus energy management and control device based on electric power Hongmeng technology for implementing the above-mentioned zero-carbon digital campus energy management and control method based on electric power Hongmeng technology. The implementation solution to the problem provided by the device is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more zero-carbon digital campus energy management and control devices based on electric power Hongmeng technology provided below can be found in the above-mentioned limitations on the zero-carbon digital campus energy management and control method based on electric power Hongmeng technology, and will not be repeated here.

[0119] In an exemplary embodiment, Figure 4 As shown, a zero-carbon digital park energy management and control device based on the power Hongmeng technology is provided, including: a collection module 410, a conversion module 420, a generation module 430 and a sending module 440, wherein:

[0120] The acquisition module 410 is used to collect the equipment data of multiple energy equipment in the zero-carbon digital park in real time through the multi-protocol communication module; the multiple energy equipment includes at least one of photovoltaic equipment, energy storage equipment, charging piles and flexible load equipment; the equipment data includes at least one of energy production data, energy consumption data and equipment operation status data.

[0121] The conversion module 420 is used to perform unified protocol conversion on the device data based on the edge computing engine of the power operating system to generate a real-time data stream.

[0122] Generation module 430 is used to analyze the energy supply and demand status of the zero-carbon digital park through an edge-side optimization algorithm in combination with the real-time data stream, and generate dynamic energy optimization control instructions; the energy optimization control instructions include at least one of energy storage equipment charging and discharging instructions, flexible load adjustment instructions and on-grid switching instructions.

[0123] The sending module 440 is used to send the energy optimization control instruction to the corresponding execution mechanism to achieve real-time optimization scheduling of the energy of the zero-carbon digital park.

[0124] In one embodiment, the edge-side optimization algorithm includes an automatic power generation control algorithm or an automatic voltage control algorithm, and the generation module 430 is specifically used to utilize the containerized application in the edge computing engine, combined with the real-time data stream, to dynamically calculate the carbon emission intensity factor of the zero-carbon digital park; embed the carbon emission intensity factor as an optimization constraint condition into the peak shaving and valley filling control logic to obtain the optimized peak shaving and valley filling control logic; and generate the energy storage device charging and discharging instructions according to the optimized peak shaving and valley filling control logic.

[0125] In one embodiment, the device also includes: an adjustment module for obtaining a change in the carbon emission intensity factor based on the carbon emission intensity factor corresponding to the zero-carbon digital park after optimization; when the change in the carbon emission intensity factor does not meet a preset change threshold, adjusting the configuration parameters of the edge-side optimization algorithm according to a preset parameter adjustment method.

[0126] In one embodiment, the generation module 430 is specifically used to use the containerized application in combination with the real-time data stream to determine the photovoltaic power generation, energy storage charging and discharging efficiency and load energy consumption; determine the net carbon emissions of the zero-carbon digital park based on the photovoltaic power generation and the energy storage charging and discharging efficiency; determine the carbon emission intensity factor based on the ratio between the net carbon emissions of the zero-carbon digital park and the load energy consumption.

[0127] In one embodiment, the generation module 430 is specifically used to calculate the carbon emission reduction contributed by clean energy based on the photovoltaic power generation; based on the energy storage charging and discharging efficiency, correct the actual charging and discharging loss of the energy storage device to obtain the carbon emissions of electricity; and determine the net carbon emissions based on the carbon emissions of electricity and the carbon emission reduction.

[0128] In one embodiment, the on-grid and off-grid switching instruction is used to instruct the actuator to operate in parallel with the grid when the grid is normal, and to switch to the autonomous power supply mode when the grid fails.

[0129] Each module in the aforementioned zero-carbon digital campus energy management and control device based on Hongmeng Electric Power technology can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device's memory in software form, so that the processor can call and execute the corresponding operations of each of these modules.

[0130] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. Among them, the processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store device data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a zero-carbon digital park energy management and control method based on the power Hongmeng technology is realized.

[0131] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0132] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0134] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0135] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0136] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0137] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0138] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A zero-carbon digital park energy management and control method based on electric Hongmeng technology, characterized in that: The method comprises: The multi-protocol communication module is used to collect real-time equipment data of multiple energy devices in the zero-carbon digital park; the multi-energy equipment includes at least one of photovoltaic equipment, energy storage equipment, charging piles and flexible load equipment; the equipment data includes at least one of energy production data, energy consumption data and equipment operation status data; An edge computing engine based on the power operating system performs unified protocol conversion on the device data to generate a real-time data stream; By using an edge-side optimization algorithm and combining it with the real-time data stream, the energy supply and demand status of the zero-carbon digital park is analyzed to generate dynamic energy optimization control instructions; the energy optimization control instructions include at least one of energy storage device charging and discharging instructions, flexible load adjustment instructions, and on-grid and off-grid switching instructions; The energy optimization control instruction is issued to the corresponding execution agency to achieve real-time optimization scheduling of the energy of the zero-carbon digital park.

2. The method according to claim 1, characterized in that The edge-side optimization algorithm includes an automatic power generation control algorithm or an automatic voltage control algorithm. The edge-side optimization algorithm is combined with the real-time data stream to analyze the energy supply and demand status of the zero-carbon digital park and generate dynamic energy optimization control instructions, including: Dynamically calculating the carbon emission intensity factor of the zero-carbon digital park by utilizing the containerized application in the edge computing engine in combination with the real-time data stream; The carbon emission intensity factor is embedded into the peak shaving and valley filling control logic as an optimization constraint condition to obtain an optimized peak shaving and valley filling control logic; According to the optimized peak shaving and valley filling control logic, the energy storage device charging and discharging instructions are generated.

3. The method according to claim 2, characterized in that The method further comprises: Obtaining a change in the carbon emission intensity factor according to the carbon emission intensity factor corresponding to the zero-carbon digital park after optimization; When the change in the carbon emission intensity factor does not meet a preset change threshold, the configuration parameters of the edge-side optimization algorithm are adjusted according to a preset parameter adjustment method.

4. The method according to claim 2, characterized in that The method of dynamically calculating the carbon emission intensity factor of the zero-carbon digital park by utilizing the containerized application in the edge computing engine in combination with the real-time data stream includes: Determine photovoltaic power generation, energy storage charging and discharging efficiency, and load energy consumption using the containerized application in combination with the real-time data stream; Determining the net carbon emissions of the zero-carbon digital park based on the photovoltaic power generation and the energy storage charging and discharging efficiency; The carbon emission intensity factor is determined according to the ratio information between the net carbon emissions of the zero-carbon digital park and the load energy consumption.

5. The method according to claim 4, characterized in that The determining of the net carbon emissions of the zero-carbon digital park based on the photovoltaic power generation and the energy storage charging and discharging efficiency includes: Calculate the carbon emission reduction contributed by clean energy based on the photovoltaic power generation; Based on the energy storage charging and discharging efficiency, correcting the actual charging and discharging loss of the energy storage device to obtain the carbon emissions of electricity; The net carbon emissions are determined based on the electricity carbon emissions and the carbon emission reduction.

6. The method according to claim 1, characterized in that The on-grid and off-grid switching instruction is used to instruct the actuator to operate in parallel with the grid when the grid is normal, and to switch to the autonomous power supply mode when the grid fails.

7. A zero-carbon digital park energy management and control device based on electric Hongmeng technology, characterized in that: The device comprises: An acquisition module is configured to collect, in real time, device data of multiple energy devices within the zero-carbon digital park through a multi-protocol communication module; the multiple energy devices include at least one of photovoltaic devices, energy storage devices, charging piles, and flexible load devices; and the device data includes at least one of energy production data, energy consumption data, and device operating status data; A conversion module, configured to perform protocol unified conversion on the device data based on the edge computing engine of the power operating system to generate a real-time data stream; A generation module is configured to analyze the energy supply and demand status of the zero-carbon digital park through an edge-side optimization algorithm in combination with the real-time data stream, and generate dynamic energy optimization control instructions; the energy optimization control instructions include at least one of an energy storage device charging and discharging instruction, a flexible load adjustment instruction, and an on-grid and off-grid switching instruction; The sending module is used to send the energy optimization control instruction to the corresponding execution agency to achieve real-time optimization scheduling of the energy of the zero-carbon digital park.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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