A comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion

Abstract

The application relates to the technical field of industrial energy management, and discloses a comprehensive energy-carbon dynamic optimization management system based on multi-dimensional data fusion, wherein a source-side available power and a carbon emission fluctuation trend are respectively mapped into a real part and an imaginary part of a carbon voltage vector through a multi-dimensional data sensing module; and physical inertia and material inventory of load-side equipment are reconstructed into virtual inductance and capacitance parameters by using a virtual impedance mapping engine. Based on a dominant frequency characteristic of a source-side signal, the system adaptively reconstructs a virtual circuit topology to suppress carbon fluctuation by using inductive reactance or capacitive reactance characteristics; combined with minimum carbon loss impedance matching and virtual phase locking optimization, an optimal virtual response vector is solved, and a phase angle of the optimal virtual response vector is inversely mapped into a time delay instruction of equipment action. The application realizes isomorphic fusion and collaborative control of heterogeneous energy-carbon data, and effectively improves the consumption efficiency of low-carbon energy.

Description

Technical Field

[0001] This invention relates to the field of industrial energy management technology, specifically to a comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion. Background Technology

[0002] As the global goal of carbon neutrality progresses, industrial production systems are facing the dual pressures of energy structure transformation and carbon emission constraints. Traditional single-energy management models are no longer sufficient to meet the current needs of low-carbon manufacturing. In existing industrial scenarios, energy management systems typically focus on real-time balance of electricity supply and demand and energy efficiency analysis, while carbon management systems often focus on ex-post carbon accounting and quota management. These two systems exhibit inherent heterogeneity in data and a mismatch in time scales. Energy data typically presents as high-frequency power fluctuations, while carbon emission data is often based on static factors or long-term statistical values. This results in a disconnect between energy flow and carbon flow at the physical control level, making it difficult to achieve deep synergistic optimization on the same timeline.

[0003] Furthermore, most existing energy and carbon optimization methods rely on rule-based static scheduling strategies or offline linear programming algorithms. These methods are inadequate in handling high-frequency carbon intensity fluctuations caused by renewable energy grid integration, lacking sufficient dynamic response capabilities. When the proportion of clean energy in the external power grid experiences momentary fluctuations, traditional control logic often struggles to map these fluctuations to the operating conditions of production equipment in real time due to the lack of a unified physical model. Simultaneously, existing optimization models typically employ complex logical constraints to describe the physical characteristics of production equipment. This approach not only increases the computational burden on control algorithms but also distorts the mapping between physical objects and control models, making it difficult for the system to minimize carbon emissions while ensuring production continuity when facing complex source-load fluctuations. Therefore, a comprehensive optimization management solution that can break down energy and carbon data barriers, unify physical and information models, and possess high-frequency dynamic response capabilities is urgently needed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion. This system solves the problem that existing technologies, due to the heterogeneous fragmentation of energy flow and carbon flow data and the lack of a unified physical mapping model, make it difficult for traditional control methods that rely on static rules or complex logic to achieve real-time dynamic collaborative optimization of high-frequency carbon fluctuations while ensuring production continuity.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion, comprising: The multi-dimensional data sensing module is used to collect available power and carbon emission factors on the source side, and to normalize the collected data to construct a source-side carbon voltage vector. The virtual impedance mapping engine module is used to acquire the operating conditions and material inventory data of the load-side equipment, and reconstruct the operating characteristics of the physical equipment into virtual impedance parameters including virtual resistance, virtual inductance and virtual capacitance. An adaptive topology control module is used to analyze the frequency characteristics of the source-side carbon voltage vector and determine the virtual circuit connection relationship between each device node and the source node based on the frequency characteristics to generate a topology correlation matrix. An impedance matching optimization module is used to construct an objective function based on the source-side carbon voltage vector, the virtual impedance parameters, and the topological correlation matrix, and to calculate the optimal virtual response vector and the target impedance correction amount that optimize the objective function. The execution feedback module is used to convert the optimal virtual response vector into the power setting value and action timing of the physical device, and generate control logic interlocks based on the topological correlation matrix.

[0006] Preferably, the multidimensional data sensing module constructs the source-side carbon voltage vector by: mapping the per-unit value of the available power amplitude on the source side to the real part of a complex vector; mapping the product of the differential value of the carbon emission factor with respect to time and the energy-carbon weighting coefficient to the imaginary part of a complex vector; and, based on the complex number construction rule, linearly superimposing the real part with the imaginary part multiplied by the imaginary unit to generate the source-side carbon voltage vector; the multidimensional data sensing module determines the source-side energy supply status by calculating the phase angle of the source-side carbon voltage vector.

[0007] Preferably, the virtual impedance mapping engine module calculates the virtual impedance parameters in the following ways: The virtual resistance is defined as the ratio of the square of the reference virtual voltage to the minimum power required to sustain the equipment process. The ratio of the physical inertia coefficient to the maximum power ramp rate allowed by the device is defined as the virtual inductance; The difference between the maximum inventory capacity and the current inventory is defined as the virtual capacitance.

[0008] Preferably, the virtual impedance mapping engine module is also used to establish a material energy mutual inductance coupling model between devices. When the inventory of downstream devices is detected to exceed the warning threshold, a virtual mutual inductance coefficient is generated based on the impact rate of the power change of upstream devices on the inventory of downstream devices, and mutual inductance impedance is introduced in the virtual circuit calculation to reduce the current of the upstream device branch.

[0009] Preferably, the adaptive topology control module performs time-frequency transformation analysis on the imaginary part of the source-side carbon voltage vector to obtain the dominant frequency. When the dominant frequency is higher than a preset high-frequency threshold, the device node with virtual inductance is configured as a virtual series connection; when the dominant frequency is lower than a preset low-frequency threshold, the device node with virtual capacitance is configured as a virtual parallel connection.

[0010] Preferably, the objective function constructed by the impedance matching optimization module is specifically a carbon flow transport efficiency objective function, which includes two parts: the first part is used to maximize the directional consistency between the source-side carbon voltage vector and the system's total virtual response current vector, and the second part is used to minimize the magnitude of the sum of the imaginary part of the system's equivalent complex impedance and the imaginary part of the source-side internal impedance.

[0011] Preferably, the impedance matching optimization module introduces a virtual phase-locking constraint when solving for the optimal virtual response vector. The virtual phase-locking constraint requires adjusting the device parameters so that the sum of the inductive reactance generated by all virtual inductors, the sum of the capacitive reactance generated by all virtual capacitors, and the algebraic sum of the imaginary part of the source-side impedance at the dominant frequency point approaches zero.

[0012] Preferably, the method by which the execution feedback module performs physical inverse mapping includes: calculating the physical active power setpoint based on the real part of the product of the conjugate vector of the optimal virtual response vector and the corresponding node virtual voltage; calculating the relative delay in the physical time domain based on the phase angle of the optimal virtual response vector divided by the dominant angular frequency; when the relative delay is positive, the control device starts with a delay; when the relative delay is negative, the control device preloads in advance.

[0013] Preferably, the execution feedback module maps the topology correlation matrix to control logic interlocking in the following ways: mapping the virtual serial relationships defined in the topology correlation matrix to sequential control logic, and connecting the start signal of the downstream device in series with the operating status signal of the upstream device; mapping the virtual parallel relationships defined in the topology correlation matrix to synchronous control logic, and allocating load tasks according to the proportion of the virtual admittance values ​​of each device.

[0014] Preferably, the execution feedback module is also used to monitor the actual operating impedance of the device in real time after the instruction is issued, and only when the percentage deviation between the actual operating impedance and the target impedance value exceeds a preset threshold, the impedance matching optimization module is triggered to recalculate the target impedance correction amount.

[0015] This invention provides a comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion. It has the following beneficial effects: 1. This invention constructs a source-side carbon voltage vector, creatively mapping steady-state energy supply capacity to a complex real part and non-steady-state carbon emission fluctuation trend to a complex imaginary part. This effectively solves the problem of inconsistent dimensions and difficulty in coordinated control of energy and carbon data in traditional systems, and realizes vectorized isomorphism of multi-source heterogeneous data. This enables the system to accurately calculate and optimize energy supply stability and carbon emission fluctuation simultaneously in a unified complex domain mathematical model.

[0016] 2. This invention utilizes virtual impedance mapping technology to equate the operating inertia of physical equipment and material inventory to inductance and capacitance parameters in a virtual circuit, respectively, and constructs a virtual impedance network based on Kirchhoff's laws. The mechanism abandons the traditional complex conditional logic judgment code and utilizes the inherent DC-passing, AC-blocking, or charging and discharging physical characteristics of circuit elements to automatically realize flexible adjustment of the timing of production equipment actions, reducing the complexity of the control algorithm and improving the system's adaptive ability to cope with sudden changes in operating conditions.

[0017] 3. This invention adopts an adaptive topology reconstruction and virtual phase-locking strategy based on carbon frequency characteristics, dynamically switching the virtual series-parallel connection relationship between devices according to the dominant frequency of carbon emission fluctuations; by using inductive reactance to suppress carbon jitter in high-frequency series connection and capacitive reactance to absorb low-carbon energy in low-frequency parallel connection, and combined with phase-time conversion to control device start and stop, it achieves precise timing matching between production load and clean energy fluctuations without changing the hardware facilities, effectively reducing the overall carbon loss in the production process. Attached Figure Description

[0018] Figure 1 This is a system functional module architecture diagram of the present invention; Figure 2 This is a flowchart of the overall process of the energy-carbon synergistic optimization control method based on virtual carbon impedance of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example: Please see the appendix Figure 1This invention provides a comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion. The system includes: a multi-dimensional data sensing module 100, a virtual impedance mapping engine module 200, an adaptive topology control module 300, an impedance matching optimization module 400, and an execution feedback module 500. The above modules interact with each other through an internal bus or industrial Ethernet.

[0021] The multi-dimensional data sensing module 100 is connected to the physical sensor network and external data interface of the industrial site. The multi-dimensional data sensing module 100 is configured to collect real-time operating data from the source side, grid side, and load side. The source side data includes the real-time power of distributed energy sources and the real-time carbon emission factor published by the external power grid. The load side data includes the electrical parameters of energy-consuming equipment and the process status parameters of non-electrical quantities. The electrical parameters include voltage, current, and power factor. The process status parameters include equipment temperature, speed, vibration frequency, pressure value, and material inventory level. The multi-dimensional data sensing module 100 performs time synchronization and filtering processing on the collected raw signals to form a multi-dimensional state vector with a unified time scale, and transmits it to the virtual impedance mapping engine module 200.

[0022] The virtual impedance mapping engine module 200 receives data from the multidimensional data sensing module 100. The virtual impedance mapping engine module 200 is configured to construct a complex domain carbon potential vector model on the source side and a virtual impedance model of the load-side equipment. Based on the rate of change of the real-time carbon emission factor, the virtual impedance mapping engine module 200 constructs a source-side carbon voltage vector, converting the carbon emission attribute into the vector's imaginary part. Simultaneously, the module utilizes state-space equations to analyze the process characteristics of each energy-consuming device, calculating resistance parameters characterizing rigid power consumption, inductance parameters characterizing adjustment inertia, and capacitance parameters characterizing buffering capacity. The virtual impedance mapping engine module 200 transforms physical devices into virtual circuit elements with dynamic parameters, outputting a parameter set containing the impedance values ​​of each node to the adaptive topology control module 300 and the impedance matching optimization module 400.

[0023] The adaptive topology control module 300 is connected to the virtual impedance mapping engine module 200. The adaptive topology control module 300 is configured to analyze the spectral characteristics of the source-side carbon voltage vector and dynamically reconstruct the logical connection relationship between each virtual circuit element based on the spectral characteristics. The adaptive topology control module 300 has multiple preset sets of topology switching logic, which are used to generate the corresponding topology correlation matrix when high-frequency carbon fluctuations or low-frequency energy flows are detected. The topology correlation matrix defines the series, parallel, or bypass connection state of each device node relative to the source node. The adaptive topology control module 300 calculates the system equivalent impedance under the current topology structure and transmits the topology state information to the impedance matching optimization module 400.

[0024] The impedance matching optimization module 400 is connected to the virtual impedance mapping engine module 200 and the adaptive topology control module 300, respectively. The impedance matching optimization module 400 is configured to solve for the optimal control parameters that maximize carbon flow transmission efficiency based on the maximum transmission theorem and the phase conjugate principle. The impedance matching optimization module 400 uses the matching of the system's total equivalent impedance with the source-side internal impedance as a constraint, and calculates the target virtual current vector and target impedance correction for each controllable device node. This calculation process includes the simultaneous solution of current amplitude and phase angle, used to determine the power setpoints and action timing of the physical devices.

[0025] The execution feedback module 500 is connected to the impedance matching optimization module 400. The execution feedback module 500 is configured to inversely map the virtual electrical parameters output by the impedance matching optimization module 400 into control commands for the physical device. The execution feedback module 500 sends power adjustment commands, start / stop timing commands, and interlocking control signals to the underlying PLC controller or DCS control system through the communication interface, driving the physical device to adjust its operating state, thereby changing its actual virtual impedance characteristics and realizing closed-loop control.

[0026] See attached document Figure 2 , Figure 2 This is a flowchart illustrating a comprehensive energy and carbon dynamic optimization management method according to an embodiment of the present invention; the method is executed based on the aforementioned comprehensive energy and carbon dynamic optimization management system and mainly includes the following steps: The multi-dimensional data sensing module 100 collects source-side energy data, grid carbon emission factor data, load-side equipment operating condition data, and material inventory data in real time, and performs preprocessing and vectorization alignment on the multi-source heterogeneous data.

[0027] The virtual impedance mapping engine module 200 is used to perform the mapping from physical objects to virtual circuit models. This step includes: constructing a source-side carbon voltage vector in the complex domain based on the fluctuation characteristics of carbon emission factors; and calculating the virtual carbon resistance, virtual carbon inductance, and virtual carbon capacitance parameters of each device based on the state-space equations of the devices, and establishing a virtual impedance network model that includes mutual inductance coupling relationships.

[0028] The adaptive topology control module 300 performs frequency domain analysis on the source-side carbon voltage vector to extract the dominant frequency characteristics. Based on the dominant frequency characteristics, it adaptively selects the logical connection method of each device node in the virtual impedance network, generates a dynamic topology correlation matrix, and calculates the equivalent total impedance of the system under the current topology based on Kirchhoff's laws.

[0029] An optimization function aimed at maximizing carbon flow transmission efficiency is constructed using the impedance matching optimization module 400. Under the constraint of phase conjugate matching, the optimal virtual current response of the system and the corresponding target value of device impedance correction are solved to determine the operating reference of each device at the next moment.

[0030] The calculated optimal virtual current and impedance correction target values ​​are converted into physical control signals by the execution feedback module 500. The control signals include the equipment power setpoint and action delay time, which are sent to the actuator to adjust the operating state of the production equipment so that the dynamic characteristics of the physical system approximate the calculated optimal matching state.

[0031] The energy-carbon synergistic optimization control method implemented by this system and the detailed processing flow of each module are as follows: Vectorized reconstruction of source-side carbon data: This step aims to establish a unified mathematical model that describes the characteristics of energy supply capacity and carbon emission fluctuations, and to solve the problem that heterogeneous data cannot be integrated at the same control level.

[0032] Data Acquisition and Standardized Preprocessing: The system acquires the maximum available clean energy power from the source side via an industrial communication interface. and real-time carbon emission factors released by external power grids .

[0033] Because the sampling frequencies of different data sources are inconsistent, the system first performs polynomial interpolation on the low-frequency carbon emission data to unify the time resolution.

[0034] Subsequently, to eliminate differences in physical dimensions, the system introduced a normalization process. A system reference power was then set. and benchmark carbon emission change rate (The value is taken as the maximum rated value allowed by the system design).

[0035] The collected data is converted into per-unit values ​​by dividing it by the reference value, which facilitates dimensionless operations in the same complex plane.

[0036] Constructing the source-side carbon voltage vector: This system employs a complex domain modeling method to construct the source-side carbon voltage vector. This vector maps steady-state energy supply capacity to the real part and non-steady-state carbon emission fluctuation trends to the imaginary part.

[0037] Source-side carbon voltage vector The calculation expression is as follows: ; Note: For ease of writing, the formulas here will use the same characters directly in subsequent formulas. and Refers to the normalized value.

[0038] The simplified expression is: ; in: :time The per-unit value of the available power amplitude on the source side; :time The derivative of the carbon emission factor with respect to time, i.e. This characterizes the instantaneous fluctuation trend of carbon intensity; Imaginary unit, satisfying ; Carbon weighting coefficient. This coefficient is used to adjust the system's sensitivity to carbon fluctuations; in practical implementation, The value can be dynamically set based on the current carbon tax price or environmental indicators: when the carbon price is high, increase. This value increases the proportion of the imaginary part in the vector magnitude, thereby strengthening the system's suppression response to carbon fluctuations.

[0039] In addition, the source-side internal impedance is defined in this step. This parameter characterizes the rigidity of the power supply on the source side and is typically set to purely resistive or weakly inductive characteristics (e.g., Its value is inversely proportional to the short-circuit capacity of the power grid.

[0040] eigenparameter analysis of the source side vector: After constructing Then, the system calculates the magnitude and phase angle of the vector: carbon potential modulus : Represents the total comprehensive energy and carbon pressure faced by the system at the current moment.

[0041] carbon potential phase angle : Through calculation get.

[0042] when When this occurs, it indicates that the source side is in a steady-state power supply state; when When the intensity increases, it indicates that there are high-frequency carbon intensity fluctuations on the source side, and the system needs to enter the fluctuation suppression mode.

[0043] Equipment-level virtual carbon impedance and mutual inductance coupling modeling: The virtual impedance mapping engine module reconstructs the operating characteristics of physical devices into virtual circuit network parameters through state space mapping, realizing the equivalent transformation from physical objects to circuit objects.

[0044] Equipment physical status extraction: The system reads the real-time operating conditions and material inventory data of each energy-consuming device on the load side, and constructs an equipment status vector. .

[0045] Virtual impedance parameter calculation for individual devices: The system calculates the virtual impedance parameters of each device based on the correspondence between physical characteristics and circuit component characteristics; during this process, the system presets a reference virtual voltage. It is used to establish a reference for the conversion between power and impedance.

[0046] Virtual resistance : Characterizes the rigid power requirement of the equipment; the calculation formula is: ; in For the first The equipment maintains the minimum power required for the current process.

[0047] Virtual Inductance : Characterizes the inertia of equipment or its ability to impede changes in power; the calculation formula is: ; in: The physical inertia coefficient; The maximum power ramp rate allowed by the equipment; : Inductance dimension conversion factor. This factor ensures the accuracy of the calculated value. Time constant generated in virtual circuits It is consistent with the actual response time constant of the physical device.

[0048] Virtual Capacitor : Characterizes the material or energy storage elasticity of equipment; the calculation formula is: ; in: Maximum inventory capacity; This represents the current inventory level. : Capacitance-dimension conversion factor. This factor maps the remaining space of the material to a capacitance value, and its magnitude is proportional to the energy density contained in a unit of material.

[0049] Modeling of material and energy mutual inductance coupling between equipment: In order to characterize the constraints between upstream and downstream production lines, the system introduces a virtual mutual inductance coefficient. : ; in: Sensitivity coefficient, representing the upstream equipment Power changes affect downstream equipment The impact rate of inventory; : Indicator function, when downstream inventory Exceeding the warning threshold The value is 1 if the condition is met, and 0 otherwise. : Coupling strength gain coefficient; This model enables mutual inductance impedance to take effect when downstream blockage occurs, automatically generating reverse induced electromotive force in virtual circuit calculation, reducing the current in the upstream branch, and automatically achieving logical decoupling between devices.

[0050] Adaptive topology reconstruction based on carbon frequency characteristics: This step dynamically adjusts the connection logic of the virtual circuit based on the frequency characteristics of the source-side signal.

[0051] Frequency domain feature extraction of the source-side carbon voltage vector: The system extracts the source-side carbon voltage vector... The imaginary part is used to perform time-frequency transform analysis to obtain the spectral distribution within the current time window and identify the dominant angular frequency with the largest energy proportion. .

[0052] Generation of dynamic topological correlation matrix: system comparison of dominant angular frequencies With the preset high-frequency threshold and low frequency threshold Generate the topological correlation matrix This matrix defines the connection method between each device node and the source node: High-frequency suppression mode The system configures device nodes with large virtual inductance (high inertia) and sensitive device nodes in virtual series. This utilizes the inductive reactance of the inductor. The characteristic of filtering out high-frequency carbon fluctuations on the source side as the frequency increases.

[0053] Low-frequency absorption mode The system configures device nodes with large virtual capacitors (large energy storage) as virtual parallel connections; utilizing the capacitive reactance of the capacitors... It has the characteristic of high impedance at low frequencies but rechargeability under DC components, thus absorbing low-carbon energy.

[0054] The system's equivalent impedance is calculated as follows: the system is based on the current topological correlation matrix. The equivalent impedance of the system is calculated using Kirchhoff's laws and the series-parallel formula for complex impedances. : For a virtual series branch, the impedances are added together: ; For virtual parallel branches, the admittances are summed: Then the equivalent impedance ; If mutual inductance coupling exists, a mutual inductance voltage term is introduced into the loop equation. Make corrections.

[0055] Minimum carbon loss impedance matching and phase-locking optimization: This step achieves optimal matching between the source and load by adjusting the operating parameters of the controllable equipment.

[0056] Constructing the carbon flow transport efficiency objective function: System construction objective function The objective function aims to maximize effective power transfer and minimize reactive power fluctuation losses. The specific optimization objective function is as follows: ; in: : The vector of control parameters of the device to be optimized; The system's total virtual response current vector. ; : The conjugate of complex numbers; : Construct the source-side equivalent internal impedance defined in the source-side carbon voltage vector; The system equivalent impedance calculated in the synthesis calculation of system equivalent impedance is denoted as follows: The function; , : These are the weighting coefficients for the power factor term and the impedance mismatch penalty term, respectively, used to balance the optimization preference between multi-energy utilization and stable fluctuations.

[0057] Phase-locked constraint based on virtual C-PLL: To achieve timing synchronization, the system introduces a virtual phase-locked constraint; this constraint requires that the imaginary part of the system's equivalent complex impedance and the imaginary part of the source-side internal impedance are numerically canceled out. ; in: Dominant angular frequency, usually measured in radians per second, refers to the main frequency or resonant frequency of a circuit during operation.

[0058] The summation symbol indicates that the terms within a specified range are summed.

[0059] : Component index, used to identify a specific component in the set.

[0060] The set of inductive elements refers to the collection of all elements in a circuit loop that exhibit inductive behavior.

[0061] : No. The virtual inductance of each component corresponds to The index in the set is The element is represented by the henry (H).

[0062] A set of capacitive components refers to the collection of all capacitive components in a circuit loop.

[0063] : No. The virtual capacitance of each component. Corresponding to The index in the set is The component is measured in farads.

[0064] Source-side internal impedance refers to the complex impedance inside the signal source or power supply, usually expressed as: In the form of.

[0065] The imaginary part operator extracts the imaginary part of the complex number within parentheses.

[0066] This constraint ensures that the system can maximize the transmission efficiency of low-carbon energy by adjusting the device parameters so that the total inductive reactance and total capacitive reactance resonate at the dominant frequency.

[0067] Solving for the optimal virtual response vector: Under the premise of satisfying the above constraints, the system uses a numerical optimization algorithm to solve for the objective function. Output: Optimal Virtual Current Vector For each device node, the current vector contains optimal amplitude and optimal phase information; Target impedance correction The impedance change values ​​that need to be adjusted for each device.

[0068] Physical inverse mapping of virtual parameters and closed-loop execution: This step converts the calculated electrical parameters into specific industrial control commands.

[0069] Power setpoint analysis: The system is based on the optimal virtual current vector. and node virtual voltage Calculate the physical active power setpoint for each device. : ; in: : No. The active power setting value of each node, indicating that the index is... The actual power target set by the node or device, usually in watts.

[0070] The real part operator extracts the real part of the complex number within the parentheses, since the real part of complex power is the active power.

[0071] : No. The node virtual voltage of a node, usually in volts.

[0072] : No. The optimal virtual current vector of each node, with subscripts This usually represents the optimized value, referring to the complex value of the current obtained by the node in the optimization calculation, and the unit is usually ampere.

[0073] : Complex conjugate symbol, acting on the preceding current phasor , indicates taking the conjugate value of the complex number.

[0074] : Index number, used to identify the first in the system Each node or device unit.

[0075] This setting is then linearly mapped to the control signal of the underlying actuator.

[0076] Action timing phase control: The system utilizes the phase angle of the optimal virtual current vector. The timing of the control equipment's actions. The system converts the phase angle into a relative delay in the physical time domain. : ; in: Relative delay refers to the amount of time a signal needs to move along the time axis to achieve a specific phase condition; the unit is usually seconds.

[0077] : Optimize phase angle, sign Phase refers to the ideal phase difference calculated to achieve a certain performance index of the system, and the unit is usually radians.

[0078] Dominant frequency, symbol Represents angular frequency, subscript The dominant frequency component refers to the main frequency component of the system's operating signal, and its unit is usually radians per second.

[0079] The formula It describes the physical relationship between phase and time, representing the phase at a specific frequency. If we want to achieve this The phase difference needs to be applied in the controller. The time delay is a common step in control systems to convert the calculated phase angle into a time-triggered signal that can be executed by actual hardware.

[0080] like (Inductive response): Delay the start-up of control equipment to avoid carbon emission peaks; like (Capacitive response): Control equipment is preloaded in advance to take advantage of carbon emission troughs.

[0081] Interlocking execution of topological logic: The system will execute the topological correlation matrix. Mapped to logic interlocks in a programmable logic controller: Virtual Serial Sequential control logic: The start signal of the downstream device is connected in series with the operating status signal of the upstream device; Virtual parallel Synchronous control logic: Multiple devices are triggered in parallel, and the load distribution ratio is based on their virtual admittance values. The proportion is determined.

[0082] Closed-loop feedback and error correction: The system sends instructions to the underlying controller and monitors the actual operating status of the equipment in real time.

[0083] To prevent frequent control oscillations caused by minor fluctuations on the source side, the system introduces a dead-zone control mechanism in the feedback loop: a new round of impedance matching optimization calculation is triggered only when the percentage deviation between the actual operating impedance and the target impedance exceeds a preset threshold; otherwise, the current control command remains unchanged.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion, characterized in that, include: A multi-dimensional data sensing module (100) is used to collect available power and carbon emission factors on the source side, and to normalize the collected data to construct a source-side carbon voltage vector. The virtual impedance mapping engine module (200) is used to obtain the operating conditions and material inventory data of the load-side equipment, and reconstruct the operating characteristics of the physical equipment into virtual impedance parameters including virtual resistance, virtual inductance and virtual capacitance. An adaptive topology control module (300) is used to analyze the frequency characteristics of the source-side carbon voltage vector and determine the virtual circuit connection relationship between each device node and the source node based on the frequency characteristics to generate a topology correlation matrix; Impedance matching optimization module (400) is used to construct an objective function based on the source-side carbon voltage vector, the virtual impedance parameters and the topological correlation matrix, and to calculate the optimal virtual response vector and the target impedance correction amount that make the objective function optimal. The execution feedback module (500) is used to convert the optimal virtual response vector into the power setting value and action timing of the physical device, and generate control logic interlocks based on the topology correlation matrix.

2. The integrated energy and carbon dynamic optimization management system based on multi-dimensional data fusion according to claim 1, characterized in that, The multidimensional data sensing module (100) constructs the source-side carbon voltage vector by: mapping the per-unit value of the available power amplitude on the source side to the real part of a complex vector, mapping the product of the differential value of the carbon emission factor with respect to time and the energy-carbon weighting coefficient to the imaginary part of a complex vector, and, based on the complex number composition rule, linearly superimposing the real part with the imaginary part multiplied by the imaginary unit to generate the source-side carbon voltage vector; the multidimensional data sensing module (100) determines the source-side energy supply status by calculating the phase angle of the source-side carbon voltage vector.

3. The integrated energy and carbon dynamic optimization management system based on multi-dimensional data fusion according to claim 1, characterized in that, The virtual impedance mapping engine module (200) calculates virtual impedance parameters in the following ways: The virtual resistance is defined as the ratio of the square of the reference virtual voltage to the minimum power required to sustain the equipment process. The ratio of the physical inertia coefficient to the maximum power ramp rate allowed by the device is defined as the virtual inductance; The difference between the maximum inventory capacity and the current inventory is defined as the virtual capacitance.

4. The integrated energy and carbon dynamic optimization management system based on multi-dimensional data fusion according to claim 3, characterized in that, The virtual impedance mapping engine module (200) is also used to establish a material energy mutual inductance coupling model between devices. When the inventory of downstream devices is detected to exceed the warning threshold, a virtual mutual inductance coefficient is generated based on the impact rate of the power change of upstream devices on the inventory of downstream devices, and mutual inductance impedance is introduced in the virtual circuit calculation to reduce the current of the upstream device branch.

5. The integrated energy and carbon dynamic optimization management system based on multi-dimensional data fusion according to claim 1, characterized in that, The adaptive topology control module (300) performs time-frequency transformation analysis on the imaginary part of the source-side carbon voltage vector to obtain the dominant frequency. When the dominant frequency is higher than a preset high-frequency threshold, the device node with virtual inductance is configured as a virtual series connection. When the dominant frequency is lower than a preset low-frequency threshold, the device node with virtual capacitance is configured as a virtual parallel connection.

6. The integrated energy and carbon dynamic optimization management system based on multi-dimensional data fusion according to claim 1, characterized in that, The objective function constructed by the impedance matching optimization module (400) is specifically a carbon flow transport efficiency objective function. The objective function includes two parts: the first part is used to maximize the directional consistency between the source-side carbon voltage vector and the system's total virtual response current vector, and the second part is used to minimize the magnitude of the sum of the imaginary part of the system's equivalent complex impedance and the imaginary part of the source-side internal impedance.

7. A comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion as described in claim 6, characterized in that, The impedance matching optimization module (400) introduces a virtual phase-locking constraint when solving for the optimal virtual response vector. The virtual phase-locking constraint requires adjusting the device parameters so that the sum of the inductive reactance generated by all virtual inductors, the sum of the capacitive reactance generated by all virtual capacitors, and the algebraic sum of the imaginary part of the source-side impedance at the dominant frequency point approaches zero.

8. The integrated energy and carbon dynamic optimization management system based on multi-dimensional data fusion according to claim 1, characterized in that, The execution feedback module (500) performs physical inverse mapping in the following ways: calculating the physical active power setpoint based on the real part of the product of the conjugate vector of the optimal virtual response vector and the corresponding node virtual voltage; calculating the relative delay in the physical time domain based on the phase angle of the optimal virtual response vector divided by the dominant angular frequency; when the relative delay is positive, the control device starts with a delay; when the relative delay is negative, the control device preloads in advance.

9. A comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion according to claim 5, characterized in that, The execution feedback module (500) maps the topological correlation matrix to control logic interlocking in the following ways: mapping the virtual serial relationships defined in the topological correlation matrix to sequential control logic, and connecting the start signal of the downstream device in series with the operating status signal of the upstream device; mapping the virtual parallel relationships defined in the topological correlation matrix to synchronous control logic, and allocating load tasks according to the proportion of virtual admittance values ​​of each device.

10. A comprehensive energy and carbon dynamic optimization management system based on multi-dimensional data fusion as described in claim 1, characterized in that, The execution feedback module (500) is also used to monitor the actual operating impedance of the device in real time after the instruction is issued. Only when the percentage deviation between the actual operating impedance and the target impedance value exceeds a preset threshold, the impedance matching optimization module (400) is triggered to recalculate the target impedance correction amount.

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