Intelligent collaborative scheduling method for multi-energy complementary power system of low-carbon park

Abstract

This application discloses an intelligent collaborative scheduling method for multi-energy complementary power systems in low-carbon industrial parks, comprising the following steps: S1, acquiring first data of energy nodes within the power system, the first data being used to characterize the carbon intensity of the energy source of the energy node at the current moment; S2, acquiring the network topology of the power system, and determining second data based on the network topology to characterize the physical constraints of energy transport within the power system; S3, generating third data based on the first and second data to uniformly characterize the carbon intensity of the energy source and the physical constraints of energy transport; S4, determining target scheduling instructions for controllable resources within the power system based on the third data; This invention deeply integrates the carbon attributes and physical constraints of energy through carbon weighted transport impedance, thereby endogenizing conflicting economic and low-carbon goals within a single optimization process; collaboratively achieving optimization of energy selection, transport paths, and network topology, obtaining dual gains in cost and carbon emission reduction.

Description

Technical Field

[0001] This application relates to the field of power system dispatching technology, and in particular to a method for intelligent collaborative dispatching of multi-energy complementary power systems for low-carbon industrial parks. Background Technology

[0002] Guided by the "dual carbon" goals, data-driven intelligent management systems are playing an increasingly important role in energy management in modern industrial and commercial parks. Treating the complex energy systems of these parks as dynamic economic entities requiring meticulous operation, and utilizing advanced data processing and optimization algorithms to minimize operating costs, effectively control carbon emissions, and maintain the long-term value of energy assets while meeting energy demands, has become the core direction of technological development in this field.

[0003] However, existing park-level multi-energy collaborative dispatch and management systems still face the following core challenges in practice: Traditional scheduling methods typically use minimizing electricity purchase and operation and maintenance costs as the sole optimization objective. When low-carbon objectives need to be introduced, external constraints such as setting a total carbon emission cap or introducing a fixed carbon tax cost are often employed. This separate approach makes "economic optimization" and "environmental optimization" two conflicting and independent objectives that require repeated trade-offs. The system cannot find a truly globally optimal solution within a unified mathematical framework, often resulting in scheduling outcomes that compromise between economic efficiency and low-carbon performance.

[0004] The "quality" of energy (such as carbon intensity) is disconnected from its physical "transportation processes" (such as network topology and line losses) in existing models. When making decisions, the system may choose clean energy sources but ignore the possibility of high physical losses or network congestion along the transmission path, resulting in suboptimal actual costs and carbon footprints. This information silo effect limits the system's ability to perform integrated optimization from the perspective of the entire "source-grid-load" chain. Summary of the Invention

[0005] The main objective of this application is to provide an intelligent collaborative scheduling method for multi-energy complementary power systems in low-carbon industrial parks, and to construct an intelligent assessment and control framework with a digital twin wear prediction engine enhanced with physical information as its core. The aim is to break down the barriers of information silos in existing technologies, achieving a fundamental shift from decentralized perception to integrated prediction and control; thereby addressing the background technical challenges.

[0006] To achieve the above objectives, this application provides the following technical solution: A method for intelligent coordinated dispatch of multi-energy complementary power systems for low-carbon industrial parks includes the following steps: S1. Obtain the first data of the energy nodes in the power system. The first data is used to characterize the carbon intensity of the energy source of the energy nodes at the current moment. S2. Obtain the network topology of the power system and determine the second data based on the network topology to characterize the physical constraints of energy transport within the power system. S3. Based on the first and second data, generate third data to uniformly characterize the carbon intensity of energy sources and the physical constraints of energy transport; S4. Based on the third data, determine the target scheduling instructions for controllable resources within the power system.

[0007] Furthermore, in S1, the first data source periodically obtains a benchmark carbon intensity index characterizing the external power grid to which the power system is connected from an external energy data platform via an application programming interface.

[0008] Furthermore, the second data in S2 is the basic transmission impedance matrix, which is determined based on the preset line parameters of the power system and the real-time collected operating status parameters.

[0009] Furthermore, the steps for obtaining the first data are as follows: Input the carbon intensity prediction model order determined in the offline phase, and the exogenous meteorological prediction data obtained in real time by the application programming interface; The latest benchmark carbon intensity index is obtained through the application programming interface. If the acquisition is successful, the predicted value of the exogenous regression variable time series prediction model for the current moment in the previous period is obtained. The latest benchmark carbon intensity index is subtracted from the predicted value to obtain the model prediction error. The model prediction error is input into the initialized exogenous regression variable time series prediction model, and the internal algorithm of the exogenous regression variable time series prediction model updates the historical state vector to correct the model bias. If the acquisition fails, this update step is skipped, and the model state of the previous period is used directly. Using the updated model state and the acquired exogenous meteorological forecast data as external input to the time series forecast model of exogenous regression variables, a multi-step forward forecast calculation is performed; the calculation will generate a time series containing carbon intensity forecasts for the next N time steps from the current moment. The final output is a time series of predicted carbon intensity values; this is named the carbon intensity prospective curve. The value of the first time point in the carbon intensity predicted value time series is used as the first data point at the current moment.

[0010] Furthermore, the steps for obtaining the second data are as follows: Input the nominal resistance, temperature coefficient of resistance, coefficient of linear expansion of conductor, and nominal geometric parameters read from the local equipment ledger database; the convective heat dissipation coefficient and radiative heat dissipation coefficient obtained from the lookup table; and the ambient temperature and real-time line current obtained from the external system. The process begins by setting an initial estimate of the conductor temperature. Based on this estimate, the real-time resistance of the conductor is calculated using a linear relationship derived from the temperature effect of conductor resistance. Then, based on the real-time current of the line and the calculated real-time resistance, the heat dissipation power of the conductor is calculated using a physical relationship derived from Joule's law. Finally, based on the current estimated conductor temperature and the ambient temperature, and combining the convective and radiative heat dissipation coefficients, the total heat dissipation power of the conductor is calculated using a combined physical relationship derived from Newton's law of cooling and Stefan-Boltzmann's law. The heat dissipation power and the heat generation power are compared. If the difference is less than a preset convergence threshold used to control calculation accuracy, the current estimated conductor temperature is the final solution, the process ends, and the next step is initiated. Otherwise, the estimated conductor temperature is adjusted according to the magnitude and direction of the difference, and the calculation is repeated until convergence. Obtain the conductor's steady-state temperature; based on the conductor's steady-state temperature, reference temperature, and conductor's coefficient of linear expansion, calculate the conductor's elongation using a physical formula derived from the linear thermal expansion of solids; based on the elongation and the nominal geometric parameters of the line span, calculate the line's real-time sag under the current temperature and load using a mathematical formula derived from the catenary theory. Obtain the real-time resistance value, which is the resistance component of the dynamic impedance; based on the real-time sag, which determines the average equivalent distance between the conductor and the ground and between phase conductors, the Carlson formula or similar formula derived from electromagnetic field theory for calculating transmission line parameters is used to calculate the real-time inductance and capacitance of the line, and finally converted into the reactance component of the dynamic impedance. The final output is a complex number, with the real part being the resistance component and the imaginary part being the reactance component. The calculation is repeated for each line in the park's power grid, and all the results are combined to form the basic transmission impedance matrix of the final second data.

[0011] Furthermore, the third data is a carbon weighted transport impedance matrix; The carbon weighted transport impedance matrix is ​​calculated based on the carbon-induced virtual impedance calculated from the first data, and then superimposed with the second data in a complex manner; The calculation of carbon-induced virtual impedance involves: inputting the first data into a nonlinear sigmoid response function to generate a normalized response factor; Carbon-induced virtual impedance is a complex number. The real part is used to characterize the direct economic cost of carbon emissions, and the imaginary part is used to characterize the systemic risks associated with high-carbon energy.

[0012] Furthermore, the steps for generating the carbon-weighted transport impedance matrix are as follows: The input is a line in the park's power grid with an energy node at the beginning and a node at the end, to obtain the basic transmission impedance; the carbon intensity index of the energy node; and the carbon pricing signal obtained from the outside. The dynamic parameter calculation process is performed, and the current virtual impedance saturation amplitude and carbon intensity response steepness are calculated in real time based on the input carbon pricing signal. The input carbon intensity index is converted into a standardized nonlinear response factor through an S-shaped response function; Generate the final virtual impedance complex number; multiply the obtained nonlinear response factor with the virtual impedance saturation amplitude, and the result is defined as the carbon-induced virtual resistance; multiply the carbon-induced virtual resistance with the preset virtual reactance to resistance ratio, and the result is defined as the carbon-induced virtual reactance; combine the virtual resistance as the real part and the virtual reactance as the imaginary part to form a complex number, which is the final carbon-induced virtual impedance. Perform a complex addition operation to add the input fundamental transport impedance to the carbon-induced virtual impedance; The final output is a complex sum, defined as the element in the carbon weighted transport impedance matrix that is a node from the energy node to the terminal. This process is repeated for all lines in the power grid to generate the complete carbon weighted transport impedance matrix.

[0013] Furthermore, the target scheduling instruction determined in S4 includes: the cooperative scheduling engine performing an optimization solution process with the objective of minimizing the total cost function, wherein the calculation of the total cost function is based on third-party data.

[0014] Furthermore, the steps for generating the target scheduling instruction are as follows: Input the carbon weight transport impedance matrix; real-time collected power grid operation data; the collaborative scheduling engine initializes all state variables based on the input current power grid state. The collaborative scheduling engine executes a programmed process to construct the total cost minimization function; generates a mathematical expression describing the minimization of total operating cost; calculates the product of the total electricity purchased from the external grid and the real-time electricity purchase price signal; calculates the total charging and discharging energy of the energy storage system during the scheduling cycle, multiplying the total charging and discharging energy by the energy storage degradation cost; for each line in the grid, it obtains the voltage, phase angle, and power flowing through the line at both ends, which are intermediate decision variables of the model; based on the intermediate decision variables and the carbon weight transport impedance matrix corresponding to the line, it uses a formula derived from circuit theory to calculate line power loss to calculate the generalized power loss of the line, which includes both physical losses and carbon weight costs; sums the generalized power losses of all lines; calculates the difference between the predicted renewable energy output and the actual dispatched output, i.e., the amount of curtailment, multiplying the amount of curtailment by the renewable energy curtailment penalty coefficient; The final total cost objective function is obtained by multiplying the total electricity purchased from the external grid by the real-time electricity purchase price signal, multiplying the total charging and discharging energy by the energy storage degradation cost, summing the generalized power losses of all lines, multiplying the amount of abandoned electricity by the renewable energy abandonment penalty coefficient, and so on. The collaborative scheduling engine synchronously constructs mathematical constraints to describe physical laws and operational boundaries; these mainly include: node power balance constraints, power flow constraints, equipment operation constraints, and topology logic constraints. The collaborative scheduling engine combines the generated minimum total cost function and all generated constraints into a complete, large-scale mixed-integer second-order cone programming problem, and passes the mixed-integer second-order cone programming problem to a commercial or open-source mathematical optimization solver with the key properties for solving mixed-integer second-order cone programming problems; The collaborative scheduling engine waits for the solver to return the optimal solution; the optimal solution is a vector containing the optimal values ​​of all decision variables; the collaborative scheduling engine parses this vector and extracts the active power output of all generators; the reactive power output of generators, the values ​​of continuous variables such as the charging and discharging power of energy storage systems, and the values ​​of binary variables such as the states of all network switches and interruptible loads. The final output is a set of optimal continuous and discrete control instructions; the control instructions are defined as target scheduling instructions and sent to each execution unit in the park for physical execution.

[0015] Furthermore, it also includes triggering a preset degradation strategy in S1 when it is detected that the first data cannot be obtained. The degradation strategy includes setting all values ​​used to characterize the carbon intensity of the energy source to a preset 0 in S3, and continuing to execute S3 and subsequent steps.

[0016] The beneficial effects of this invention are: This invention constructs a decision-making foundation that can unify, quantify, and integrate multi-dimensional information, thereby endogenously and integrally integrating the fragmented optimization objectives into a single scheduling model, and completing a paradigm shift by introducing carbon weight transport impedance. This invention no longer regards the carbon intensity of energy merely as an external environmental indicator that needs to be constrained. Instead, it transforms the carbon intensity of energy into a virtual impedance with the same dimensions as the physical parameters of the power grid through a nonlinear response function. The magnitude of the virtual impedance directly reflects the carbon weight cost required to obtain energy, thus completing the mapping from the abstract concept of carbon intensity to the concrete impedance parameter of electrical calculation, laying the foundation for unified optimization. This invention superimposes the virtual impedance bound to energy quality with the real impedance on the physical transmission path to construct a carbon weight transport impedance matrix, which is the core of the decision-making process. Each element in the carbon weight transport impedance matrix indivisibly contains three pieces of information: the economic cost of obtaining energy, the inherent carbon emission attributes, and the physical network constraints that need to be overcome to transport the energy to the target location. When the collaborative scheduling engine of this invention optimizes the solution with the goal of minimizing the total cost, the single decision-making behavior is endowed with a deep linkage effect. When the optimization algorithm finds the path with the minimum impedance, it no longer simply avoids physical losses, but automatically avoids energy sources with high carbon sources and transmission paths with high physical losses under the same mathematical logic, and actively constructs an end-to-end new energy artery with the lowest impedance by changing the network topology. It has the ability to seamlessly unify the selection of energy quality, the optimization of physical transmission and the management of network topology under a single mathematical model. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall technical framework of the intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks, as described in this application. Figure 2 This is a schematic diagram illustrating the overall steps of the intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks, as described in this application. Figure 3 This is a schematic diagram illustrating the first data acquisition steps of the intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks, as described in this application. Figure 4 This is a schematic diagram illustrating the steps for acquiring the second data in the intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks, as described in this application. Figure 5 This is a schematic diagram illustrating the steps of the carbon weighting transport impedance matrix in the intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks, as described in this application. Figure 6 This diagram illustrates the steps involved in generating target dispatch instructions for the intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks, as described in this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] like Figures 1 to 6As shown, the intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks includes the following steps: S1. Obtain the first data of the energy nodes in the power system. The first data is used to characterize the carbon intensity of the energy source of the energy nodes at the current moment. In S1, the first data source periodically obtains a benchmark carbon intensity index representing the external power grid connected to the power system from an external energy data platform through an application programming interface (API). It also initiates data requests to a third-party energy data platform, such as Electricity Maps, every preset time interval (in this embodiment, the preset time is set to 5 minutes). The data request aims to obtain the marginal carbon intensity at the current moment corresponding to the geographical area or power grid area where the park is located. The API receives the returned data packet containing carbon intensity values ​​(in this embodiment, JSON format is used), parses out the benchmark carbon intensity index, and uses the externally obtained benchmark carbon intensity index as a correction benchmark based on a preset exogenous regression variable time series prediction model. It also integrates high-frequency weather forecast data to generate a carbon intensity forward curve in the park that has the ability to predict the next hour to several hours and can still operate reliably even when the external data source is briefly interrupted.

[0020] S2. Obtain the network topology of the power system and determine the second data based on the network topology to characterize the physical constraints of energy transport within the power system. The second data in S2 is the basic transmission impedance matrix, which is determined based on the preset line parameters of the power system and the real-time collected operating status parameters. In a specific implementation, preset power grid topology information is read from a local configuration file (in this embodiment, the local configuration file is a spreadsheet file). The power grid topology information includes the nominal resistance and nominal reactance of each line. The operating status parameters related to the lines are acquired in real time. In this embodiment, the operating status parameters are line temperature, Joule heating effect, sag change caused by thermal expansion and contraction, and the effect of sag change on line inductance and capacitance. Based on the electrothermal multiphysics coupling model, the geometric influence of line temperature, Joule heating effect, sag change caused by thermal expansion and contraction, and sag change on line inductance and capacitance is uniformly modeled to calculate the dynamic nonlinear impedance that is strongly correlated with the real-time power flow magnitude and construct a basic transmission impedance matrix.

[0021] In this embodiment, the key parameters involved in obtaining the first data and the second data are as follows: The parameter symbol for the benchmark carbon intensity index is: This refers to the regional average carbon emission intensity per unit of electricity generated, published by an external authoritative organization, which characterizes the external power grid to which the park is connected. The parameter sign of the order of the carbon intensity prediction model is: and , where are integer parameters used to uniquely determine the structure of the time series prediction model for exogenous regression variables; where , The autoregression order, differencing order, and moving average order of the time series prediction model for exogenous regression variables are defined respectively. The seasonal autoregression order, seasonal difference order, seasonal moving average order, and seasonal cycle length of the time series prediction model for exogenous regression variables are defined respectively. A historical benchmark carbon intensity index series of at least one year is obtained, and the predefined order combinations are traversed. The time series prediction model for exogenous regression variables is trained for each combination, and the Akaike information criterion or Bayesian information criterion score is calculated. The order combination that minimizes the information criterion score is selected as the fixed model structure parameters in this embodiment. In one specific embodiment, the order of the carbon intensity prediction model and The offline model optimization process systematically traverses a set of predefined order combinations, including the following steps: Setting the search range for non-seasonal parameters, specifically: the autoregressive order p ranges from 0 to 5; the difference order d ranges from 0 to 2; and the moving average order q ranges from 0 to 5. Simultaneously, setting the search range for seasonal parameters, specifically: the seasonal autoregressive order P ranges from 0 to 2; the seasonal difference order D ranges from 0 to 2; and the seasonal moving average order Q ranges from 0 to 2. The seasonal cycle length m is determined based on the characteristics of historical data. The value is fixed at 24 to correspond to the daily periodicity of the power system; all parameters within the range are automatically and exhaustively combined to form a parameter grid containing thousands of possible combinations; for each parameter combination in the grid, the acquired historical benchmark carbon intensity index sequence is used as training data to fit the corresponding exogenous regression variable time series prediction model, and the Akaike Information Criterion value of the exogenous regression variable time series prediction model is calculated after fitting; after all combinations have been traversed, the Akaike Information Criterion values ​​of all records are compared, and the parameter combination with the smallest Akaike Information Criterion value is selected as the final model structure parameter fixed in the system.

[0022] The parameter symbols for exogenous meteorological forecast data are: This refers to the predicted value sequence of non-periodic external variables that influence future carbon intensity; in this embodiment, exogenous meteorological prediction data... This includes the hourly average cloud cover percentage and average wind speed forecast for the park's location over the next 24 hours; obtained through an application programming interface (API) by communicating with a commercial weather service platform. The sign of the parameter for model prediction error is This is the latest benchmark carbon intensity index. The difference between the actual value of and the predicted value of in the time series prediction model of exogenous regression variables; The symbol for the nominal resistance parameter is , is the DC resistance of a conductor per unit length at a reference temperature (in this embodiment, the reference temperature is set to 20 degrees Celsius); read from the local equipment ledger database, which pre-stores the model specifications and corresponding national standard parameters of all power lines in the park; The symbol for the temperature coefficient of resistance is: , where is the linear rate of change of resistivity of the conductor material with temperature; is read from the local equipment ledger database; The symbol for the ambient temperature parameter is: The air temperature of the environment where the power lines are located is obtained through the park's environmental monitoring system, which has the key attribute of providing minute-level average temperature from multiple monitoring points within the park. The parameter symbol for the real-time current of the line is: This represents the effective value of the current flowing through the target line; it is obtained through a monitoring and data acquisition system, which has the key attribute of providing the second-level effective value of the current of critical lines. The parameter symbol for the convective heat dissipation coefficient is: , is a coefficient characterizing the ability of a conductor to exchange heat with the surrounding air through convection; it is determined by consulting a preset heat balance equation parameter table based on the line installation environment (in this embodiment, the line installation environment includes overhead and cable trench) and average wind speed. The parameter symbol for the radiative heat dissipation coefficient is: , is a coefficient characterizing the ability of a conductor to dissipate heat outward through thermal radiation; like the convection heat dissipation coefficient, it is determined by consulting the parameter table.

[0023] The parameter symbol for the coefficient of linear expansion of a conductor is . , which is the linear rate of change of the conductor material length with temperature; read from the local equipment ledger database; The parameter symbols for nominal geometric parameters are: , which are parameters describing the spatial geometry of the line under reference temperature and no-load conditions, mainly including the line span and the initial sag of the conductor between the two suspension points; read from the local equipment ledger database; The steps to obtain the first data are as follows: Input the order of the carbon intensity prediction model, which has been determined in the offline phase. and And exogenous meteorological forecast data obtained in real time through application programming interfaces. ; Obtain the latest benchmark carbon intensity index through application programming interface. If successful, retrieve the predicted values ​​of the exogenous regression variables from the previous period's time series forecast model for the current moment; then retrieve the latest benchmark carbon intensity index. Subtracting the predicted value from the actual predicted value yields the model prediction error. ; model prediction error The historical state vector is updated by the internal algorithm of the exogenous regression variable time series prediction model to correct model bias when the input is entered into the model. If the acquisition fails, this update step is skipped and the model state of the previous period is used directly. Use the updated model state and the obtained exogenous weather forecast data. As an external input to the time series prediction model of exogenous regression variables, a multi-step forward prediction calculation is performed; the calculation will generate a time series containing carbon intensity predictions for the next N time steps (in this embodiment, one point per hour for the next 24 hours) starting from the current moment. The final output is a time series of predicted carbon intensity values, named the carbon intensity prospective curve. The value of the first time point in the carbon intensity predicted value time series is used as the first data point at the current time. The steps for obtaining the second data are as follows: Input the nominal resistance read from the local equipment ledger database. Temperature coefficient of resistance Coefficient of linear expansion of conductor Nominal geometric parameters Convection heat dissipation coefficient obtained from table lookup and radiation heat dissipation coefficient ; and the ambient temperature obtained in real time from the external system. and line real-time current ; An initial guess value for the conductor temperature is set; in this embodiment, the initial guess value is set to the ambient temperature. Based on the current guess value, the real-time resistance of the conductor is calculated using a linear relationship derived from the temperature effect of conductor resistance. The real-time resistance of the conductor is then calculated based on the line current. Using the calculated real-time resistance and the physical relationship derived from Joule's law, the conductor's heat output is calculated; based on the current estimated conductor temperature and ambient temperature, and combined with the convective heat dissipation coefficient... and radiation heat dissipation coefficient The total heat dissipation power of the conductor is calculated using a combination of physical relationships derived from Newton's law of cooling and Stefan-Boltzmann's law. The heat dissipation power is compared with the heat generation power. If the difference between the two is less than a preset convergence threshold used to control the calculation accuracy (in this embodiment, the convergence threshold is set to 0.1 watts / meter), then the current conductor temperature guess is the final solution, the process ends and proceeds to the next step; otherwise, the conductor temperature guess is adjusted according to the magnitude and direction of the difference (in this embodiment, if the heat generation is greater than the heat dissipation, the guess is increased), and the calculation is repeated until convergence. Obtain the conductor's steady-state temperature; based on the conductor's steady-state temperature, reference temperature, and conductor's coefficient of linear expansion. Using physical relationships derived from the linear thermal expansion of solids, the elongation of the conductor length is calculated; based on the elongation and the nominal geometric parameters of the line span, mathematical relationships derived from the catenary theory are used to calculate the real-time sag of the line under the current temperature and load. Obtain the real-time resistance value, which is the resistance component of the dynamic impedance; based on the real-time sag, which determines the average equivalent distance between the conductor and the ground and between phase conductors, the Carlson formula or similar formula derived from electromagnetic field theory for calculating transmission line parameters is used to calculate the real-time inductance and capacitance of the line, and finally converted into the reactance component of the dynamic impedance. The final output is a complex number, with the real part being the resistance component and the imaginary part being the reactance component. The calculation is repeated for each line in the park's power grid, and all the results are combined to form the basic transmission impedance matrix of the final second data.

[0024] The convergence threshold is determined using an iterative solution strategy based on the bisection method to find the steady-state temperature. The specific process is as follows: Before the iteration begins, an initial lower bound (equal to the current ambient temperature in this embodiment) and an upper bound (equal to the maximum allowable operating temperature of the conductor according to national standards, 90 degrees Celsius in this embodiment) are set. In the loop, the following operations are performed: the midpoint value between the current upper and lower bounds is calculated, and the midpoint value is used as the conductor temperature guess value for this iteration; the aforementioned steps are repeated to calculate the corresponding heat generation power and heat dissipation power based on the conductor temperature guess value; the difference between the heat generation power and the heat dissipation power is calculated; and the search interval is narrowed according to the sign of the difference. If the difference is positive, it means that the current guessed temperature is too low, and the true solution is located between the current midpoint and the original upper bound. Therefore, the search lower bound is updated to the current midpoint value. Conversely, if the difference is negative, the search upper bound is updated to the current midpoint value. The absolute value of the difference between the updated search upper bound and lower bound is checked. If the absolute value is less than the preset temperature convergence threshold used to control the calculation accuracy (in this embodiment, the temperature convergence threshold is set to 0.01 degrees Celsius), the iteration terminates, and the midpoint temperature of the current interval is determined as the final conductor steady-state temperature solution. The process proceeds to the next step. Otherwise, the iteration steps are returned to continue the next round of calculation until the convergence condition is met.

[0025] S3. Based on the first and second data, generate third data to uniformly characterize the carbon intensity of energy sources and the physical constraints of energy transport; The third data is the carbon weighted transport impedance matrix.

[0026] The carbon weighted transport impedance matrix is ​​calculated based on the carbon-induced virtual impedance calculated from the first data, and then superimposed with the second data in a complex manner; The calculation of carbon-induced virtual impedance involves: inputting the first data into a nonlinear sigmoid response function to generate a normalized response factor; Carbon-induced virtual impedance is a complex number. The real part is used to characterize the direct economic cost of carbon emissions, and the imaginary part is used to characterize the systemic risks associated with high-carbon energy.

[0027] In this embodiment, the key parameters used to generate the carbon weighted transport impedance matrix are as follows: The parameter symbol for the carbon pricing signal is: This serves as a quantitative signal characterizing the economic cost or policy penalty intensity per unit of carbon emission; it communicates with external financial or policy information platforms that possess key attributes such as providing real-time transaction prices or official guidance prices for regional or national carbon trading markets via application programming interfaces, periodically acquiring carbon pricing signals at intervals not exceeding five minutes. The latest value; The parameter symbol for the virtual impedance saturation amplitude is: This represents the upper limit of the virtual resistance penalty applied when the carbon strength approaches infinity; ensuring that the virtual impedance remains within a reasonable and bounded range, and that the virtual impedance saturation amplitude... With carbon pricing signals Dynamic correlation; in this embodiment, the calculation logic is as follows: obtain real-time carbon pricing signals. ; carbon pricing signal Multiply by a preset economic-impedance conversion factor for uniform dimension; the economic-impedance conversion factor is calibrated through offline experiments so that the calculated virtual impedance saturation amplitude at the highest historical carbon price is equal to a preset multiple of the physical impedance of typical lines in the park. In this embodiment, the preset multiple is set to five times. The parameter sign at the midpoint of the carbon intensity response is: ; is the carbon intensity value corresponding to the center of symmetry of the S-shaped response function, at which point the virtual impedance reaches half of the saturation amplitude; the midpoint of the carbon intensity response. Its function is to define the range most sensitive to carbon intensity; the midpoint of the carbon intensity response. The carbon intensity response midpoint is preset by the park management based on long-term carbon management strategies; in this embodiment, it will be set as the national or industry-recommended carbon emission intensity baseline. The value was set at 400 grams of carbon dioxide per kilowatt-hour; this represents the carbon intensity threshold at which park managers believe it is necessary to begin strong intervention. The symbol for the parameter of carbon intensity response steepness is: , is a decisive parameter for the slope of the S-shaped response function near the midpoint of the response. The larger the value, the steeper the curve and the more drastic the response to changes in carbon intensity. The calculation logic is as follows: carbon intensity response steepness. Similarly, with carbon pricing signals Dynamic correlation; in this embodiment, real-time carbon pricing signals are obtained. The second step is to signal carbon pricing. Divide by the historical average carbon price to obtain a standardized price volatility factor; multiply the price volatility factor by the base steepness value; the base steepness value is determined through offline simulation experiments, so that under the average carbon price, a 10% fluctuation in carbon intensity near the midpoint of the response can cause a change of about 50% in the virtual impedance. The symbol for the virtual reactance to resistance ratio is: , which is the ratio of the reactance component to the resistance component of the carbon-induced virtual impedance; its function is to simulate the reactive costs that high-carbon energy may bring in addition to the direct economic costs, such as grid stability risks, policy uncertainty risks, etc.; in this embodiment, the virtual reactance to resistance ratio is... The value is set to 0.2; this reflects the management strategy of this park, which considers the reactive power risk cost caused by carbon emissions to be approximately 20% of the direct economic cost; virtual reactance to resistance ratio It can also be dynamically output from an offline risk assessment model; The specific implementation of the offline risk assessment model is based on the fuzzy comprehensive evaluation method. The construction and calculation process is as follows: Define a risk factor set, which includes three dimensions: energy predictability risk. In this embodiment, the fluctuations in photovoltaic power output due to weather conditions and supply chain stability risks are addressed. In this embodiment, the risks include the possibility of widespread power outages caused by extreme weather in the external power grid, and policy uncertainty risks. In this embodiment, to account for the possibility of sudden adjustments to the carbon tax policy, a risk level rating set is defined, which in this embodiment is {extremely low, relatively low, moderate, relatively high, extremely high}, and assigned values ​​{0.1, 0.3, 0.5, 0.7, 0.9} respectively; for each external energy node connected to the industrial park, such as the main grid and adjacent industrial parks, a group of technical and economic experts score each risk factor according to the specific circumstances; in this embodiment, for the main grid node, energy predictability risk The score is extremely low, resulting in a membership vector of (1, 0, 0, 0, 0); supply chain stability risk. The risk factor is rated as low, resulting in a membership vector of (0, 1, 0, 0, 0). The membership vectors of all factors are combined to form a fuzzy evaluation matrix. The importance weights of different risk factors for the park's management objectives are determined using the analytic hierarchy process (AHP) or expert survey. In this embodiment, the weight vector W = {Energy Predictability Risk} is obtained. Weight: 0.5, Supply Chain Stability Risk Weight: 0.3, Policy uncertainty risk The weights are set to 0.2. A matrix multiplication operation is performed, multiplying the obtained weight vector W with the obtained fuzzy evaluation matrix to obtain a comprehensive evaluation result vector. The comprehensive evaluation result vector describes the membership degree of the overall risk of the energy node at each level. A weighted average calculation is performed on the obtained comprehensive evaluation result vector to obtain a single quantitative comprehensive risk score, with a value range between 0 and 1. The comprehensive risk score is multiplied by a preset maximum risk conversion coefficient. In this embodiment, the maximum risk conversion coefficient is set to 0.5. The result is the dynamic virtual reactance to resistance ratio of the energy node in the current evaluation period. The upper limit shall not exceed 50% of the virtual resistance.

[0028] The steps for generating the carbon-weighted transport impedance matrix are as follows: The input is for a specific energy node in the park's power grid. Terminal as node The line, obtain the basic transmission impedance Acquire energy nodes carbon intensity index ; and carbon pricing signals obtained from external sources. ; Perform dynamic parameter calculations based on the input carbon pricing signal. The current virtual impedance saturation amplitude is calculated in real time. And carbon intensity response steepness ; Input carbon intensity index The sigmoid response function is used to convert the response into a standardized nonlinear response factor. The calculation process originates from the logistic function in statistics, and the calculation logic is as follows: Obtain the input carbon intensity index. And subtract the preset carbon intensity response midpoint from it. The difference was obtained; the difference was then compared with the kurtosis of the carbon intensity response. Perform a multiplication operation to obtain an intermediate product; take the negative value of the intermediate product; calculate the power of the negative value of the natural constant e to obtain the exponential result; add 1 to the exponential result; divide 1 by the sum of the exponential result and 1 to finally obtain the nonlinear response factor with a value range between zero and one.

[0029] Generate the final virtual impedance complex number; the calculation logic is as follows: combine the obtained nonlinear response factor with the virtual impedance saturation amplitude. The result of the multiplication operation is defined as the carbon-induced virtual resistance; the carbon-induced virtual resistance is then compared with a preset virtual reactance-resistance ratio. The result of the multiplication operation is defined as the carbon-induced virtual reactance; the virtual resistance is taken as the real part and the virtual reactance as the imaginary part, and the combination is a complex number, which is the final carbon-induced virtual impedance. Performing a complex addition operation will change the input fundamental transport impedance. Add to the carbon-induced virtual impedance; The final output is a complex sum, defined in the carbon weighted transport impedance matrix, from the energy node. To the terminal as a node elements This process is repeated for all lines in the power grid to generate a complete carbon weighted transport impedance matrix.

[0030] S4. Based on third-party data, determine the target dispatch instructions for controllable resources within the power system; The target scheduling instructions in S4 include: the cooperative scheduling engine performing an optimization process with the objective of minimizing the total cost function, wherein the calculation of the total cost function is based on third-party data; The collaborative scheduling engine receives the third data generated by S3. The core task of the collaborative scheduling engine is to solve the objective function, which aims to minimize the total cost function consisting of the electricity purchase cost and the generalized transmission cost. The generalized transmission cost is directly related to the product of the power flowing through each line and the corresponding value of the line in the carbon weighted transmission impedance matrix. By running mathematical optimization algorithms such as quadratic programming, the collaborative scheduling engine solves for a set of control variables that minimizes the total cost function. These control variables constitute the specific target scheduling instructions for the energy storage system's charging and discharging power and the controllable resources under interruptible load conditions. It also includes triggering a preset degradation strategy in S1 when it is detected that the first data cannot be obtained. The degradation strategy includes setting all values ​​used to characterize the carbon intensity of the energy source to a preset 0 in S3, and continuing to execute S3 and subsequent steps. If the application programming interface in S1 fails to obtain a valid carbon intensity index from an external energy data platform within several consecutive preset periods, the problem persists. If the data source fails, a data source failure flag is sent to S3; upon receiving the data source failure flag, S3 will trigger a degraded operation mode: during weighted processing, the carbon intensity index of all energy nodes will be temporarily reduced. Forced to 0; in this mode, the carbon weighted transport impedance matrix will be completely equivalent to the fundamental transport impedance. A message is generated and sent to the operation and maintenance monitoring platform, stating "Carbon data source lost, automatically switched to pure physical efficiency optimal scheduling mode".

[0031] In this embodiment, the key parameters involved in the target scheduling instruction are as follows: The symbol for the active power output of the generator is: The parameter symbol for the reactive power output of the generator is: This refers to the active and reactive power injected into the power grid by various generators (in this embodiment, including photovoltaic, gas turbine, etc.) within the park; The parameter symbol for the charging and discharging power of an energy storage system is: , which represents the charging and discharging power of the battery energy storage system, with positive values ​​for discharging and negative values ​​for charging; The symbol for the state of charge parameter of an energy storage system is: , which is the percentage of the current remaining charge of the battery energy storage system to its total capacity; it is a state variable that changes dynamically and is affected by the charging and discharging power; The parameter symbol for the network switch status is: , is a variable that characterizes the logic state of a circuit breaker or switch in the power grid; it is a binary decision variable, with a value of 1 indicating that the switch is closed and the line is connected; and a value of 0 indicating that the switch is open and the line is disconnected; The parameter symbol for interruptible load status is: , is a variable that characterizes whether a non-critical load is allowed to be connected to the power grid; it is also a binary decision variable, with a value of 1 indicating that the load is working normally and a value of 0 indicating that the load is disconnected; The parameter symbol for the electricity purchase price signal is: To obtain the real-time price of a unit of electricity purchased from the external power grid, the system communicates with the data platform of the power trading center, which has the key attributes of providing time-of-use pricing or real-time electricity market prices, through an application programming interface. The parameter symbol for the energy storage per-kilowatt-hour degradation cost is: This refers to the cost per unit of electricity incurred by an energy storage system after each equivalent full charge-discharge cycle, calculated as the loss of asset value. This cost is determined using an offline energy storage economic analysis model. The model's inputs are the total investment cost of the energy storage system, its design cycle life, and energy efficiency parameters; the output is the energy storage degradation cost per kilowatt-hour. In this embodiment, the energy storage per-kilowatt-hour degradation cost The depreciation of energy storage equipment is set at 0.2 yuan per kilowatt-hour, thus internalizing the long-term asset depreciation of energy storage equipment into the immediate cost of short-term operating decisions. This avoids the uneconomical behavior of overusing energy storage in pursuit of short-term arbitrage, which leads to premature degradation of its lifespan. The parameter sign of the renewable energy abandonment penalty coefficient is: This is a virtual cost penalty imposed on each unit of electricity reduced when photovoltaic renewable energy output is forced to be reduced due to network congestion or other reasons; it reflects the policy orientation of maximizing green electricity consumption in the optimization objectives; it is set by the park manager based on environmental commitments or relevant subsidy policies; in this embodiment, the renewable energy abandonment penalty coefficient The price is set to a value much higher than the local maximum electricity purchase price; in this embodiment, it is set to 10 yuan per kilowatt-hour.

[0032] The steps for generating target scheduling instructions are as follows: Input the carbon weight transport impedance matrix; real-time collected power grid operation data, including node load and photovoltaic predicted output in this embodiment; the collaborative scheduling engine initializes all state variables based on the input current power grid state; The collaborative scheduling engine executes a programmatic process to construct the function that minimizes the total operating cost; it generates a mathematical expression to describe minimizing the total operating cost; the calculation logic is as follows: Calculate the total electricity purchased from the external power grid and the real-time electricity purchase price signal. The product of and ; calculate the total charge and discharge energy of the energy storage system during the scheduling cycle. In this embodiment, is the sum of absolute values. The total charge and discharge energy is multiplied by the energy storage unit degradation cost. Multiply the values; for each line in the power grid, obtain the voltage, phase angle, and power flowing through the line at both ends, which are intermediate decision variables for the model; based on the intermediate decision variables and the carbon weight transport impedance matrix corresponding to the line, use the formula derived from circuit theory for calculating line power loss to calculate the generalized power loss of the line. The calculation of generalized power loss includes both physical losses (i.e., from the real part of the impedance) and carbon weight costs (i.e., from the real part of the impedance); sum the generalized power losses of all lines; calculate the difference between the predicted renewable energy output and the actual dispatched output, i.e., the amount of curtailment, and the amount of curtailment and the renewable energy curtailment penalty coefficient. Multiply; The total electricity purchased from the external power grid is compared with the real-time electricity purchase price signal. The product of total charge / discharge energy and energy storage degradation cost Multiply the generalized power losses of all lines, sum the curtailment amount, and calculate the renewable energy curtailment penalty factor. Multiply and sum to obtain the final total cost objective function; The cooperative scheduling engine synchronously constructs mathematical constraints to describe physical laws and operational boundaries; these mainly include: Node power balance constraint: Ensure that for each node in the power grid, the sum of all incoming power (i.e., generators, energy storage, and line inputs) is always equal to the sum of all outgoing power (i.e., loads, energy storage, and line outputs); Power flow constraints: For each line, the voltage at both ends, the phase angle, and the power flowing through it must satisfy the nonlinear AC power flow equations. In this embodiment, a second-order cone relaxation technique is used to transform the original non-convex power flow equations into a set of convex, linear second-order cone inequality constraints. Equipment operation constraints include the upper and lower limits of generator output, the upper and lower limits of energy storage state of charge, charging and discharging rate limits, and line transmission capacity limits. Topological logic constraints: Associating network switch states (in this embodiment, binary variables) with power flow constraints; in this embodiment, when a switch state is 0, the power flow of the corresponding line must be forcibly constrained to zero; The collaborative scheduling engine combines the generated minimum total cost function and all generated constraints into a complete, large-scale mixed-integer second-order cone programming problem, and passes the mixed-integer second-order cone programming problem to a commercial or open-source mathematical optimization solver with the key properties for solving mixed-integer second-order cone programming problems; The collaborative scheduling engine waits for the solver to return the optimal solution; the optimal solution is a vector containing the optimal values ​​of all decision variables; the collaborative scheduling engine analyzes this vector and extracts the active power output of all generators. Reactive power output of generator Energy storage system charging and discharging power The values ​​of continuous variables, as well as the values ​​of binary variables for all network switch states and interruptible load states; The final output is a set of optimal continuous and discrete control instructions; the control instructions are defined as target scheduling instructions and sent to various execution units in the park (in this embodiment, including generator controllers, energy storage PCS, smart switches, etc.) for physical execution.

[0033] To verify the effectiveness of the intelligent coordinated dispatch method for multi-energy complementary power systems in low-carbon industrial parks, this embodiment establishes a simulation environment based on the IEEE 33-node standard distribution network system. This environment includes a photovoltaic power station, a gas turbine, a battery energy storage system, and a connection line to the external main grid. The simulation platform is MATLAB, and the Gurobi solver is used to perform MISOCP optimization. The experiment aims to compare the performance differences between the method of this invention and existing technologies. Existing technologies employ traditional optimization dispatch methods that only aim to minimize economic costs, without considering carbon intensity, and with a fixed grid topology. Calculate the total electrical energy consumed by all terminal loads within the park during a single scheduling cycle; calculate the total carbon emissions generated by all energy inputs to supply this electrical energy during the same period; divide the obtained total electrical energy by the obtained total carbon emissions; compare the simulation data as shown in Table 1 below: Table 1: Performance Comparison Simulation Data of the Invention Method and Existing Technologies in Different Scenarios

[0034] Data table analysis Scenario 1 (Ample solar power during the day): Data Comparison and In-Depth Analysis: In this scenario, the decision-making logic of the method of this invention demonstrates its ability to comprehensively weigh multiple cost factors. The table data shows that the carbon intensity of the main grid (0.8) is actually higher than that of the gas turbine (0.6); the collaborative scheduling engine of this invention does not completely exclude purchasing electricity from the main grid; the internal optimization process calculates that although the carbon cost of the gas turbine is lower, its overall operating cost (including but not limited to fuel cost and maintenance cost, which are uniformly converted and reflected in the total cost) is relatively higher. Simultaneously, this invention, through the carbon weighted transport impedance matrix generated by S3, accurately quantifies the generalized transport costs of purchasing electricity from the main grid and using local gas turbines. Demonstration of effectiveness: By reducing the gas turbine output from 500kW to 200kW while purchasing 300kW of electricity from the main grid and utilizing an energy storage system for power balancing (the dynamic behavior of energy storage is not fully shown in the static table, but its effect is already included in the optimization result of the total cost); this seemingly counterintuitive decision is actually the optimal balance point achieved between electricity purchase price, generation cost, line physical losses, and carbon credit costs; this decision reduces the total operating cost of this invention by 12.4% compared to existing technologies (calculation basis: obtaining a baseline value of 1850 and a comparison value of 1620; calculation difference). The value is 230; dividing the difference by the benchmark value of 1850 yields 12.4%); despite purchasing some grid electricity with higher carbon intensity, the end-to-end carbon efficiency still improved by 17.4% due to the optimization of the overall energy mix (calculation basis: obtaining the benchmark value of 1.55 and the comparison value of 1.82; calculating the difference of 0.27; dividing the difference by the benchmark value of 1.55 yields 17.4%). This data quantitatively proves that this invention transcends simple, single-indicator-based decision-making logic and can find a globally optimal solution that balances economy and environment in complex scenarios through joint optimization of multi-dimensional costs. Scenario 2 (Peak Nighttime Load): Data comparison: At night when there is no photovoltaic output, it is necessary to rely on gas turbines and the main grid; due to the high carbon pricing signal at this time (80 yuan / ton), the carbon aversion effect of this invention is enhanced, further reducing the output of high-carbon gas turbines and relying more on the main grid; Demonstration of effectiveness: This decision reduced total operating costs by 8.6% ((4320-3950) / 4320) and improved end-to-end carbon efficiency by 13.7% ((1.08-0.95) / 0.95); it proves that the present invention can dynamically adjust the carbon aversion of the scheduling strategy according to the real-time carbon price, and realize the effective transmission of economic signals to physical execution; Scenario 3 (Daytime solar power disruption, critical scenario): Data Comparison: The simulation assumes a blockage on a critical line connecting to the photovoltaic station (simulated by setting the physical impedance to a maximum value); due to the fixed topology, existing technologies cannot transmit all photovoltaic power, forcing the curtailment of 700kW of solar power and the activation of a large number of high-cost, high-carbon-emission gas turbines to meet the load; while the mixed-integer second-order cone programming of this invention identifies the extremely high cost of curtailment and gas turbine operation during the optimization process, and discovers that a new, although slightly longer, usable transmission path can be constructed by closing a bypass switch.

[0035] Demonstration of effects: The control decision brought about a disruptive effect; the invention achieved 100% full consumption of photovoltaic power and significantly reduced the output of gas turbines; the total operating cost was reduced by 28.9% ((3150-2240) / 3150) compared with the existing technology, and the end-to-end carbon efficiency soared by 53.5% ((1.75-1.14) / 1.14).

[0036] Value statement: These data not only prove the technical feasibility of the present invention, but more importantly, quantitatively reveal that the present invention can solve the problem of energy wastage caused by physical blockage, which cannot be solved by existing technologies, by jointly solving continuous power flow optimization and discrete topology reconstruction. This results in nonlinear and huge synergistic gains, demonstrating the outstanding substantive features and significant progress of the present invention.

[0037] Figure 1 This is a schematic diagram of the overall technical framework: by combining the physical view of the application scenario with the logical flowchart of the core method, the isometric view on the left intuitively shows the physical environment of the application—a modern low-carbon park integrating photovoltaic, wind power, power grid, energy storage and load, which constitutes the target of this scheduling method; the technical roadmap on the right precisely corresponds to the core technical steps, in which carbon intensity data acquisition corresponds to S1, which performs the operation of obtaining the first data of carbon intensity of each energy node in the park; physical network topology analysis corresponds to S2, which performs the operation of obtaining the second data representing physical constraints; carbon weighted impedance fusion modeling corresponds to S3, which performs the operation of fusing the first data and the second data to generate a unified third data (i.e., carbon weighted transport impedance matrix); and collaborative scheduling instruction generation corresponds to S4, which performs the operation of determining the final target scheduling instruction. The entire diagram clearly reveals how this invention starts from obtaining multi-dimensional data, constructs a unified fusion model, and ultimately realizes intelligent collaborative scheduling of complex multi-energy systems.

[0038] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0039] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0040] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A method for intelligent collaborative dispatching of multi-energy complementary power systems in low-carbon industrial parks, characterized in that: Includes the following steps: S1. Obtain the first data of the energy nodes in the power system. The first data is used to characterize the carbon intensity of the energy source of the energy nodes at the current moment. S2. Obtain the network topology of the power system and determine the second data based on the network topology to characterize the physical constraints of energy transport within the power system. S3. Based on the first and second data, generate third data to uniformly characterize the carbon intensity of energy sources and the physical constraints of energy transport; S4. Based on the third data, determine the target scheduling instructions for controllable resources within the power system.

2. The intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks according to claim 1, characterized in that, In S1, the first data source periodically obtains a benchmark carbon intensity index characterizing the external power grid to which the power system is connected from an external energy data platform through an application programming interface.

3. The intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks according to claim 2, characterized in that, The second data in S2 is the basic transmission impedance matrix, which is determined based on the preset line parameters of the power system and the real-time collected operating status parameters.

4. The intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks according to claim 3, characterized in that, The steps to obtain the first data are as follows: Input the carbon intensity prediction model order determined in the offline phase, and the exogenous meteorological prediction data obtained in real time by the application programming interface; The latest benchmark carbon intensity index is obtained through the application programming interface. If the acquisition is successful, the predicted value of the exogenous regression variable time series prediction model for the current moment in the previous period is obtained. The latest benchmark carbon intensity index is subtracted from the predicted value to obtain the model prediction error. The model prediction error is input into the initialized exogenous regression variable time series prediction model, and the internal algorithm of the exogenous regression variable time series prediction model updates the historical state vector to correct the model bias. If the acquisition fails, this update step is skipped, and the model state of the previous period is used directly. Using the updated model state and the acquired exogenous meteorological forecast data as external input to the time series forecast model of exogenous regression variables, a multi-step forward forecast calculation is performed; the calculation will generate a time series containing carbon intensity forecasts for the next N time steps from the current moment. The final output is a time series of predicted carbon intensity values; this is named the carbon intensity prospective curve. The value of the first time point in the carbon intensity predicted value time series is used as the first data point at the current moment.

5. The intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks according to claim 4, characterized in that, The steps for obtaining the second data are as follows: Input the nominal resistance, temperature coefficient of resistance, coefficient of linear expansion of conductor, and nominal geometric parameters read from the local equipment ledger database; and the convective heat dissipation coefficient and radiative heat dissipation coefficient obtained from the lookup table. In addition, the ambient temperature and line current are obtained in real time from external systems; The process begins by setting an initial estimate of the conductor temperature. Based on this estimate, the real-time resistance of the conductor is calculated using a linear relationship derived from the temperature effect of conductor resistance. Then, based on the real-time current of the line and the calculated real-time resistance, the heat dissipation power of the conductor is calculated using a physical relationship derived from Joule's law. Finally, based on the current estimated conductor temperature and the ambient temperature, and combining the convective and radiative heat dissipation coefficients, the total heat dissipation power of the conductor is calculated using a combined physical relationship derived from Newton's law of cooling and Stefan-Boltzmann's law. The heat dissipation power and the heat generation power are compared. If the difference is less than a preset convergence threshold used to control calculation accuracy, the current estimated conductor temperature is the final solution, the process ends, and the next step is initiated. Otherwise, the estimated conductor temperature is adjusted according to the magnitude and direction of the difference, and the calculation is repeated until convergence. Obtain the conductor's steady-state temperature; based on the conductor's steady-state temperature, reference temperature, and conductor's coefficient of linear expansion, calculate the conductor's elongation using a physical formula derived from the linear thermal expansion of solids; based on the elongation and the nominal geometric parameters of the line span, calculate the line's real-time sag under the current temperature and load using a mathematical formula derived from the catenary theory. Obtain the real-time resistance value, which is the resistance component of the dynamic impedance; based on the real-time sag, which determines the average equivalent distance between the conductor and the ground and between phase conductors, the Carlson formula or similar formula derived from electromagnetic field theory for calculating transmission line parameters is used to calculate the real-time inductance and capacitance of the line, and finally converted into the reactance component of the dynamic impedance. The final output is a complex number, with the real part being the resistance component and the imaginary part being the reactance component. The calculation is repeated for each line in the park's power grid, and all the results are combined to form the basic transmission impedance matrix of the final second data.

6. The intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks according to claim 5, characterized in that, The third data is the carbon weighted transport impedance matrix; The carbon weighted transport impedance matrix is ​​calculated based on the carbon-induced virtual impedance calculated from the first data, and then superimposed with the second data in a complex manner; The calculation of carbon-induced virtual impedance involves: inputting the first data into a nonlinear sigmoid response function to generate a normalized response factor; Carbon-induced virtual impedance is a complex number. The real part is used to characterize the direct economic cost of carbon emissions, and the imaginary part is used to characterize the systemic risks associated with high-carbon energy.

7. The intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks according to claim 6, characterized in that, The steps for generating the carbon-weighted transport impedance matrix are as follows: Input a line in the park's power grid that starts at an energy node and ends at a node, obtain the basic transmission impedance, and obtain the carbon intensity index of the energy node; And carbon pricing signals obtained from external sources; The dynamic parameter calculation process is performed, and the current virtual impedance saturation amplitude and carbon intensity response steepness are calculated in real time based on the input carbon pricing signal. The input carbon intensity index is converted into a standardized nonlinear response factor through an S-shaped response function; Generate the final virtual impedance complex number; The obtained nonlinear response factor is multiplied by the virtual impedance saturation amplitude, and the result is defined as the carbon-induced virtual resistance. The carbon-induced virtual resistance is multiplied by the preset virtual reactance to resistance ratio, and the result is defined as the carbon-induced virtual reactance. The virtual resistance is taken as the real part and the virtual reactance is taken as the imaginary part, and the combination is formed into a complex number, which is the final carbon-induced virtual impedance. Perform a complex addition operation to add the input fundamental transport impedance to the carbon-induced virtual impedance; The final output is a complex sum, defined as the element in the carbon weighted transport impedance matrix that is a node from the energy node to the terminal. This process is repeated for all lines in the power grid to generate the complete carbon weighted transport impedance matrix.

8. The intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks according to claim 7, characterized in that, The target scheduling instructions in S4 include: the cooperative scheduling engine performing an optimization process with the objective of minimizing the total cost function, wherein the calculation of the total cost function is based on third-party data.

9. The intelligent collaborative dispatch method for multi-energy complementary power systems in low-carbon industrial parks according to claim 8, characterized in that, The steps for generating target scheduling instructions are as follows: Input the carbon weight transport impedance matrix; real-time collected power grid operation data; the collaborative scheduling engine initializes all state variables based on the input current power grid state. The collaborative scheduling engine executes a programmed process to construct the total cost minimization function; generates a mathematical expression to describe the minimization of total operating cost; calculates the product of the total electricity purchased from the external grid and the real-time electricity purchase price signal; calculates the total charging and discharging energy of the energy storage system during the scheduling cycle, and multiplies the total charging and discharging energy by the energy storage unit degradation cost. For each line in the power grid, the voltage, phase angle, and power flowing through the line at both ends are obtained and used as intermediate decision variables in the model. Based on intermediate decision variables and the carbon weight transport impedance matrix corresponding to the lines, the generalized power loss of the lines is calculated using a formula derived from circuit theory for calculating line power loss. The calculation of generalized power loss includes both physical losses and carbon weight costs. The generalized power losses of all lines are summed. The difference between the predicted renewable energy output and the actual dispatched output, i.e., the amount of curtailment, is calculated and multiplied by the renewable energy curtailment penalty coefficient. The final total cost objective function is obtained by multiplying the total electricity purchased from the external grid by the real-time electricity purchase price signal, multiplying the total charging and discharging energy by the energy storage degradation cost, summing the generalized power losses of all lines, multiplying the amount of abandoned electricity by the renewable energy abandonment penalty coefficient, and so on. The collaborative scheduling engine synchronously constructs mathematical constraints to describe physical laws and operational boundaries; these mainly include: node power balance constraints, power flow constraints, equipment operation constraints, and topology logic constraints. The collaborative scheduling engine combines the generated minimum total cost function and all generated constraints into a complete, large-scale mixed-integer second-order cone programming problem, and passes the mixed-integer second-order cone programming problem to a commercial or open-source mathematical optimization solver with the key properties for solving mixed-integer second-order cone programming problems; The collaborative scheduling engine waits for the solver to return the optimal solution; the optimal solution is a vector containing the optimal values ​​of all decision variables; the collaborative scheduling engine parses this vector and extracts the active power output of all generators; the reactive power output of generators, the values ​​of continuous variables such as the charging and discharging power of energy storage systems, and the values ​​of binary variables such as the states of all network switches and interruptible loads. The final output is a set of optimal continuous and discrete control instructions; the control instructions are defined as target scheduling instructions and sent to each execution unit in the park for physical execution.

10. The intelligent collaborative dispatching method for multi-energy complementary power systems in low-carbon industrial parks according to claim 9, characterized in that, It also includes triggering a preset degradation strategy in S1 when it is detected that the first data cannot be obtained. The degradation strategy includes setting all values ​​used to characterize the carbon intensity of the energy source to a preset 0 in S3, and continuing to execute S3 and subsequent steps.

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