A green electricity direct connection high load electric carbon synergistic risk regulation method

Abstract

The application discloses a kind of green electricity direct connection high load energy load electric carbon collaborative risk regulation methods, comprising: considering the extremely low temperature of alpine high altitude area, complex topographic factor extracts multidimensional spatiotemporal heterogeneity characteristics, to build the spatiotemporal complementary comprehensive matching degree index of green electricity direct connection combination;Establish green electricity direct connection combination constraint set, construct green electricity direct connection combination planning model based on spatiotemporal complementary comprehensive matching degree index, under the condition of satisfying green electricity direct connection combination constraint set, it is solved to obtain optimal green electricity direct connection combination scheme set;Build heterogeneous high load energy load electric carbon collaborative marginal risk loss model;For wind light storage direct connection data center and carborundum load in optimal green electricity direct connection combination scheme set, based on heterogeneous high load energy load electric carbon collaborative marginal risk loss model to electric carbon collaborative comprehensive loss minimum as target function is built, comprehensively consider green electricity direct connection heterogeneous high load energy load safety regulation constraint, regulation strategy is obtained.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and specifically to a method for coordinated risk control of electricity and carbon emissions by directly connecting green electricity to high energy-consuming loads. Background Technology

[0002] my country's western high-altitude and cold regions are rich in wind and solar energy resources, and the installed capacity of new energy sources continues to expand. However, due to factors such as dispersed load centers and limited local absorption capacity, there is a phenomenon of wind and solar curtailment. High-energy-consuming enterprises such as ferroalloys, electrolytic aluminum, data centers, silicon carbide, and salt lake chemicals have large energy consumption scales and high carbon emission intensity. The regional advantages of green electricity have not been translated into improved carbon efficiency, resulting in a disconnect between clean energy consumption and carbon emission reduction targets. Affected by factors such as low temperatures, strong radiation, variable weather, and complex terrain, the output of new energy in high-altitude and cold regions exhibits significant temporal fluctuations and spatial differences. There is a clear spatiotemporal mismatch between new energy supply and the continuous energy demand of high-energy-consuming loads, leading to insufficient green electricity supply during certain periods. Against this backdrop, how to scientifically conduct spatiotemporal matching of various types of new energy sources with high-energy-consuming loads under the green electricity direct connection model, and how to propose a method for coordinated carbon risk control of heterogeneous high-energy-consuming loads in scenarios with insufficient green electricity supply are key issues that urgently need to be addressed.

[0003] Current methods for regulating high-energy-consuming loads primarily determine power reduction or transfer amounts based on power balance constraints, total carbon emission constraints, and hard constraints on safe operation. They fail to adequately consider the impact of the spatiotemporal matching degree between new energy sources and high-energy-consuming loads on the green electricity guarantee rate under high-altitude and cold conditions. Furthermore, existing methods neglect the dynamic differences in process safety risks and carbon emission risks of high-energy-consuming loads under different process states, making it difficult to coordinate and allocate regulating power according to the marginal safety risks and carbon emission risks of different high-energy-consuming loads. This may lead to an imbalance between the low-carbon operation goals of green power direct-connection systems and the safe and stable operation of high-energy-consuming loads. Therefore, there is an urgent need to research a method for coordinated electricity-carbon risk regulation of green power direct-connection heterogeneous high-energy-consuming loads to achieve synergistic optimization of green electricity guarantee, safe and stable operation of high-energy-consuming loads, and carbon emission reduction benefits in green power direct-connection systems. Summary of the Invention

[0004] In view of this, the present invention provides a method for co-regulating the carbon risk of green electricity directly connected to high energy-consuming loads, in order to at least solve the problem of the lack of existing technology for co-regulating the carbon risk of green electricity directly connected to heterogeneous high energy-consuming loads.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for coordinating carbon risk control through direct connection of green electricity to high energy-consuming loads includes the following steps: S1. Considering the impact of extreme low temperatures and complex terrain in high-altitude and cold regions on the direct connection matching of existing and unmatched various types of new energy sources with high-energy-consuming loads, multi-dimensional spatiotemporal heterogeneous features are extracted, and green electricity direct connection combinations are constructed based on these features. Spatiotemporal complementary comprehensive matching degree index ; S2. Establish a set of constraints for direct green energy connections and construct a comprehensive matching index based on spatiotemporal complementarity. The green energy direct connection combination planning model is solved under the condition of satisfying the green energy direct connection combination constraint set, and the optimal green energy direct connection combination scheme set is obtained. S3. Construct a marginal risk loss model for heterogeneous high-energy-consuming load electricity-carbon synergy, specifically including: a latency risk loss function based on the latency flexibility of data center computing power tasks, considering the difference in computing power task deadlines and computing power capacity limitations; and a model for characterizing the output loss of silicon carbide resistance furnace load power adjustment based on the thermal inertia of silicon carbide load resistance furnace. S4. For the wind-solar-storage direct connection data center and silicon carbide load in the optimal green power direct connection combination scheme set, an objective function is constructed based on the marginal risk loss model of electric-carbon synergy for heterogeneous high energy loads, with the goal of minimizing the comprehensive loss of electric-carbon synergy. Taking into account the safety regulation constraints of green power direct connection to heterogeneous high energy loads, the risk regulation strategy of electric-carbon synergy for green power direct connection to heterogeneous high energy loads is obtained.

[0006] Preferably, the specific content of S1 includes: Multidimensional spatiotemporal heterogeneous features include: green electricity direct connection combination Multi-scale temporal distribution overlap of source and load power Green electricity direct connection combination Nonlinear complexity of random fluctuations in power of various types of new energy sources Green electricity direct connection combination China's energy storage system's coverage of extreme green electricity shortages Green electricity direct connection combination Corresponding standard transmission line volume ; Green electricity direct connection combination constructed based on multidimensional spatiotemporal heterogeneous features Spatiotemporal complementary comprehensive matching degree index for: ; In the formula, The extremely small positive number introduced to prevent the denominator from being zero. This is a collection of green electricity direct connection combinations.

[0007] The preferred method for obtaining multidimensional spatiotemporal heterogeneous features includes the following specific details: Green electricity direct connection combination Multi-scale temporal distribution overlap of source and load power : ; In the formula, For the collection of months of the year, Indicates the month sequence number; For the first A discrete time-domain sample set within one month; for Discrete time period index; The duration of a single discrete sampling period; For multiple types of new energy entities Direct connection with green electricity The mapping variable between them represents the relationship between the main body of new energy sources. Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. and Respectively, the main body of new energy High energy load In the Month Power during a given time period; For heterogeneous high energy loads Direct connection with green electricity The mapping variables between them, when high energy load Access to Green Power Direct Connection Combination If the value is 1, then the value is 0; otherwise, the value is 0. and The time indicates the main body of new energy With high energy load Direct connection matching; As the main body of new energy Directly Connected to Green Electricity No. Month Equivalent transmission efficiency over a given time period; and These are respectively a collection of various types of new energy entities that have been built but are yet to be matched and a collection of high energy-consuming loads; Green electricity direct connection combination Nonlinear complexity of random fluctuations in power of various types of new energy sources : ; In the formula, The length of the permutation pattern represents the number of sampling points included when constructing the new energy power fluctuation subsequence; Indicates length is The total number of permutations that can be formed by the power subsequence; The sampling delay step size represents the number of discrete sampling periods between two adjacent sampling points when constructing the main power fluctuation sequence of new energy sources. The index for the arrangement pattern; As the main body of new energy No. Monthly arrangement pattern Frequency of occurrence; As the main body of new energy Installed capacity; Green electricity direct connection combination China's energy storage system's coverage of extreme green electricity shortages : ; ; ; In the formula, For green electricity direct connection combination The set of periods of extreme green electricity shortage; The threshold for determining extreme green electricity shortages; This serves as an index for periods of continuous extreme green energy shortages. This represents the total number of consecutive periods of extreme green energy shortages. For green electricity direct connection combination No. A set of consecutive periods of extreme green energy shortage; For energy storage system indexing, A collection of energy storage systems; For energy storage systems Direct connection with green electricity The mapping variables, when the energy storage system Access to Green Power Direct Connection Combination hour Select 1 if the value is 1, otherwise select 0. and energy storage system Maximum and minimum capacity; Energy storage system for high-altitude and cold regions The discharge efficiency; For energy storage systems Maximum discharge power; in, The specific calculation method is as follows: ; ; In the formula, New energy main body to high energy load No. Month Direct transmission efficiency during a given time period; Respectively, the main body of new energy to high energy load Resistance per unit length of a directly connected line; and Respectively, the main body of new energy With high energy load Spatial distance and terrain correction factor between them; As the main body of new energy With high energy load Corona loss correction factor for direct-connected lines; and Respectively, the main body of new energy With high energy load The rated voltage of the direct connection between them and the corona initiation voltage under standard conditions; The phase difference angle between voltage and current in a directly connected line; Power factor for directly connected lines; and These are the altitude correction factor and the weather correction factor, respectively. ; In the formula, This represents a function for selecting the standard conductor cross-section. and Respectively, the main body of new energy Maximum design power transmission capacity and main body of high energy-consuming load Maximum design power received; For economic current density; , and Respectively, the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node and load-side power receiving nodes Highest energy load main body Rated voltage of a direct-connected line; , and Representing the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node Load-side power receiving node Highest energy load main body Spatial distance; , and Representing the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node Load-side power receiving node Highest energy load main body Spatial correction coefficient.

[0008] Preferably, the green electricity direct connection combination constraint set in S2 specifically includes: Green electricity consumption replacement threshold constraint: The proportion of self-generated and self-consumed electricity from new energy sources in the green electricity direct connection system shall not be less than 60% of the total available power generation and not less than 30% of the total electricity consumption. ; In the formula, As the main body of new energy In the Month The power that was not absorbed or fed into the grid during the specified period; For high energy loads In the Month Power purchased from the main power grid during the specified period; Direct connection space constraints: ; In the formula, Indicates when the main body of new energy Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. Indicates when high energy load Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. , and Respectively, the main body of new energy and New energy main body With high energy load and high energy load main body and Spatial distance; , and These are the upper limits of direct connection distances between new energy entities, between new energy entities and high energy-consuming load entities, and between high energy-consuming load entities, respectively. , and Representing the main body of new energy to New energy main body Maximum energy load High energy load main body to Spatial correction factor; Unique membership constraint for resources: ; In the formula, For the decision variables of the direct-connection combination, when the green electricity direct-connection combination The value is 1 when it is determined to be the final green electricity direct connection combination scheme, and 0 otherwise.

[0009] The preferred green energy direct connection combined planning model in S2 is as follows: ; The green power direct connection combination planning model belongs to the discrete optimization decision problem with multiple candidate combinations. The mixed integer optimization method is used to solve the green power direct connection combination planning model, and finally the optimal green power direct connection combination scheme set is obtained.

[0010] Preferably, S3 includes a latency risk loss function that considers the differences in deadlines for computing power tasks and limitations in computing power capacity. for: ; In the formula, This is an index of the control period within the rolling control cycle. Index for data center computing power tasks For the first The total number of computing power tasks that the data center can delay executing during a given time period; For the first QoS sensitivity coefficient of each computing power task; For the first The workload of each computing task; For the first The proportion of latency for each computing task; , , and The first The deadline, execution time, resumption time after delay, and original start time of each computing task; This is a non-linear amplification exponent for latency loss in computing tasks, indicating that the closer the computing task is to the deadline, the greater the latency risk loss. This is the penalty coefficient for latency risk in computing power tasks. For data center servers in the first The latency and computing power load of the task during the time period; For from the first The set of future time periods that can be used to resume the execution of delayed tasks up to the deadline of the delayed computing power tasks; Index for future execution periods; and The execution of the next phase will be resumed in the future. The data center servers can handle the maximum computing load and the raw computing load during the specified period. It is the set of all discrete control periods within the rolling control cycle; The model for characterizing the impact of load power regulation on production loss in silicon carbide resistance furnaces includes: ; ; ; ; In the formula, and The first The first resistance furnace Furnace temperature at the end of the period and furnace temperature at the beginning; For the first The power of the electric resistance furnace participating in demand response reduction; and The first Taiwanese resistance furnace during the period Internal heat dissipation feedback coefficient and reaction endothermic feedback coefficient; For the first The equivalent heat capacity of the electric resistance furnace; The duration of a single control period within the rolling control cycle; A collection of silicon carbide resistance furnaces participating in demand response regulation. Index for electric resistance furnaces; , and These are the convective heat transfer coefficient, effective heat dissipation area, and surface thermal emissivity of the electric resistance furnace surface, respectively. It is the Stefan-Boltzmann constant; The molar enthalpy of reaction during the silicon carbide reaction process; For the first Taiwanese resistance furnace during the period The reaction rate-temperature correlation coefficient; For Arrhenius pre-exponential factors; This is the activation energy for the silicon carbide reaction process; It is the universal gas constant; For the first The first resistance furnace Production loss during the period; For the first The reaction rate-yield correlation coefficient of a single resistance furnace; For the first The first resistance furnace The equivalent thermal inertia time constant for a given period of time.

[0011] Preferably, the objective function in S4 for: ; In the formula, For the data center The power reduction of computing power tasks with time-lapse latency; The target control combination is selected from the optimal green electricity direct connection combination scheme set, specifically the green electricity direct connection combination that includes data centers and silicon carbide loads; For green electricity direct connection combination No. Power purchased from the main power grid during specific time periods; , and These are the conversion factors for data center service quality loss, silicon carbide production loss, and carbon emission reduction, respectively. For the first The first resistance furnace The time period considers the equivalent output loss after risk boundary correction; For the first large power grid Real-time electricity price for a given time period; For the first large power grid Dynamic carbon emission factors over a given period; and They are respectively and The molar mass.

[0012] Preferably, a risk-corrected equivalent production loss model is constructed by introducing a boundary risk penalty term to characterize the nonlinear amplification of risk loss caused by unit power adjustment as the operating state of the resistance furnace gradually approaches the process safety boundary. The first resistance furnace Equivalent output loss after risk boundary correction within the time period for: ; In the formula, For the first The boundary risk penalty coefficient for the electric resistance furnace; and They represent the first The warning temperature and minimum process temperature of the resistance furnace.

[0013] Preferably, the data center Power reduction of time-lapse computing power tasks for: ; In the formula, and These are the ratios of server dynamic power to operating frequency and computing power to operating frequency, respectively. For data center servers in the first The computing load during a given time period; Data center power efficiency is the ratio of total energy consumption of the data center to the energy consumption of IT equipment. For the first The number of servers that are open in the data center during a given time period; This represents the expected utilization rate of data center servers.

[0014] Preferably, the safety regulation constraints for direct green power connection to heterogeneous high-energy-consuming loads in S4 specifically include: grid-connected capacity constraints for direct green power connection systems, maximum latency tolerance constraints for offline tasks in data centers, and safety constraints for silicon carbide crystal growth processes. Green electricity direct connection system grid connection capacity constraints: ; In the formula, and For the first Total power of data center and total power of silicon carbide load during the time period; For green electricity direct connection combination No. The discharge power of the energy storage system during a given time period; For green electricity direct connection combination No. New energy power during a given period; For green electricity direct connection combination The upper limit of grid-connected capacity to be declared; For green electricity direct connection combination The upper limit of the variation in grid-connected switching power between adjacent time periods; Maximum latency constraints for offline tasks in data centers: ; Safety constraints for silicon carbide crystal growth process: ; In the formula, This is the silicon carbide crystal growth process stage; This is a collection of stages in the silicon carbide crystal growth process. For the first The first resistance furnace The indicator variable for the process stage of the time period, when the first... The first resistance furnace The period is in a phase The value is 1 if the condition is met, otherwise it is 0. For the stage The sequential coding is used to characterize the order of different process stages in the silicon carbide crystal growth process; and The first Taiwan resistance furnace stage Lower and upper limits for the permissible rate of temperature change; and The first Taiwan resistance furnace stage The lower and upper limits of the temperature required to meet quality requirements.

[0015] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads, which has the following beneficial effects: 1. To address the lack of existing research on the impact of extreme low temperatures and complex terrain on uncertain direct connection combinations of various types of new energy sources and high-energy-consuming loads, this invention extracts the overlap degree of multi-scale temporal distribution of source-load power in high-altitude and cold regions, the nonlinear complexity of random fluctuations, and the coverage of extreme green electricity shortages by energy storage capacity. It also introduces altitude gain and terrain slope operators to characterize the influence mechanism of high-altitude geographical features on the volume and transmission efficiency of direct connection lines. A comprehensive matching degree index for the spatiotemporal complementarity of various types of new energy sources and high-energy-consuming loads, integrating multi-dimensional spatiotemporal heterogeneous characteristics, is constructed. A green electricity direct connection combination constraint set, including green electricity consumption and substitution thresholds, direct connection line spatial constraints, and unique resource affiliation, is established. A green electricity direct connection combination planning model based on global comprehensive matching degree equilibrium is constructed, and the optimal direct connection matching schemes for wind-solar-storage direct connection to data centers and silicon carbide, wind-storage direct connection to electrolytic aluminum, and photovoltaic direct connection to salt lake chemical industry are obtained.

[0016] 2. This invention constructs a risk marginal loss model for the coordinated electricity-carbon emissions of heterogeneous high-energy-consuming loads based on the latency flexibility of data center computing power tasks and the thermal inertia of silicon carbide load resistance furnaces. A risk penalty factor is introduced to characterize the surge in risk loss when high-energy-consuming loads approach the safety boundary. The model quantifies the degradation of data center computing power service quality, silicon carbide output loss, and carbon emission increment caused by adjusting unit power. Taking into account the constraints of green electricity direct grid connection capacity, the maximum latency tolerance of data center offline tasks, and the safety constraints of silicon carbide crystal growth processes, a multi-type renewable energy direct-connected data center and silicon carbide load coordinated electricity-carbon emissions risk control model is constructed to address the risk of green electricity shortage. This model achieves optimized decision-making based on the degree and duration of green electricity shortage and the marginal adjustment loss of the load. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a method for coordinating electricity and carbon risk control in direct connection of green electricity to high energy-consuming loads provided by the present invention; Figure 2 This is a schematic diagram comparing the risks of direct green electricity connection to data centers and silicon carbide load-coordinated carbon emissions control before and after implementation, provided in an embodiment of the present invention. Figure 2 (a) before regulation, Figure 2 (b) is after regulation. 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] This invention provides a method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads, comprising the following steps: S1. Considering the impact of extreme low temperatures and complex terrain in high-altitude and cold regions on the direct connection matching of existing and unmatched various types of new energy sources with high-energy-consuming loads, multi-dimensional spatiotemporal heterogeneous features are extracted, and green electricity direct connection combinations are constructed based on these features. Spatiotemporal complementary comprehensive matching degree index ; S2. Establish a set of constraints for direct green energy connections and construct a comprehensive matching index based on spatiotemporal complementarity. The green energy direct connection combination planning model is solved under the condition of satisfying the green energy direct connection combination constraint set, and the optimal green energy direct connection combination scheme set is obtained. S3. Construct a marginal risk loss model for heterogeneous high-energy-consuming load electricity-carbon synergy, specifically including: a latency risk loss function based on the latency flexibility of data center computing power tasks, considering the difference in computing power task deadlines and computing power capacity limitations; and a model for characterizing the output loss of silicon carbide resistance furnace load power adjustment based on the thermal inertia of silicon carbide load resistance furnace. S4. For the wind-solar-storage direct connection data center and silicon carbide load in the optimal green power direct connection combination scheme set, an objective function is constructed based on the marginal risk loss model of electric-carbon synergy for heterogeneous high energy loads, with the goal of minimizing the comprehensive loss of electric-carbon synergy. Taking into account the safety regulation constraints of green power direct connection to heterogeneous high energy loads, the risk regulation strategy of electric-carbon synergy for green power direct connection to heterogeneous high energy loads is obtained.

[0021] It should be noted that: This invention considers the impact of extreme low temperatures and complex terrain in high-altitude and cold regions on the direct matching of existing and unmatched new energy sources with high-energy-consuming loads. It characterizes the matching degree of new energy sources, energy storage and high-energy-consuming loads in the time domain by extracting the overlap degree of multi-scale time-domain distribution of source and load in high-altitude and cold regions, the nonlinear complexity of random fluctuations, and the coverage of energy storage systems to extreme green electricity shortages.

[0022] To further implement the above technical solution, the specific content of S1 includes: Multidimensional spatiotemporal heterogeneous features include: green electricity direct connection combination Multi-scale temporal distribution overlap of source and load power Green electricity direct connection combination Nonlinear complexity of random fluctuations in power of various types of new energy sources Green electricity direct connection combination China's energy storage system's coverage of extreme green electricity shortages Green electricity direct connection combination Corresponding standard transmission line volume ; Green electricity direct connection combination constructed based on multidimensional spatiotemporal heterogeneous features Spatiotemporal complementary comprehensive matching degree index for: ; In the formula, The extremely small positive number introduced to prevent the denominator from being zero. This is a collection of green electricity direct connection combinations.

[0023] To further implement the above technical solution, the specific content of the method for obtaining multidimensional spatiotemporal heterogeneous features includes: Green electricity direct connection combination Multi-scale temporal distribution overlap of source and load power : ; In the formula, For the collection of months of the year, Indicates the month sequence number; For the first A discrete time-domain sample set within one month; for Discrete time period index; The duration of a single discrete sampling period; For multiple types of new energy entities Direct connection with green electricity The mapping variable between them represents the relationship between the main body of new energy sources. Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. and Respectively, the main body of new energy High energy load In the Month Power during a given time period; For heterogeneous high energy loads Direct connection with green electricity The mapping variables between them, when high energy load Access to Green Power Direct Connection Combination If the value is 1, then the value is 0; otherwise, the value is 0. and The time indicates the main body of new energy With high energy load Direct connection matching; As the main body of new energy Directly Connected to Green Electricity No. Month Equivalent transmission efficiency over a given time period; and These are respectively a collection of various types of new energy entities that have been built but are yet to be matched and a collection of high energy-consuming loads; Green electricity direct connection combination Nonlinear complexity of random fluctuations in power of various types of new energy sources : ; In the formula, The length of the permutation pattern represents the number of sampling points included when constructing the new energy power fluctuation subsequence; Indicates length is The total number of permutations that can be formed by the power subsequence; The sampling delay step size represents the number of discrete sampling periods between two adjacent sampling points when constructing the main power fluctuation sequence of new energy sources. The index for the arrangement pattern; As the main body of new energy No. Monthly arrangement pattern Frequency of occurrence; As the main body of new energy Installed capacity; Green electricity direct connection combination China's energy storage system's coverage of extreme green electricity shortages : ; ; ; In the formula, For green electricity direct connection combination The set of periods of extreme green electricity shortage; The threshold for determining extreme green electricity shortages; This serves as an index for periods of continuous extreme green energy shortages. This represents the total number of consecutive periods of extreme green energy shortages. For green electricity direct connection combination No. A set of consecutive periods of extreme green energy shortage; For energy storage system indexing, A collection of energy storage systems; For energy storage systems Direct connection with green electricity The mapping variables, when the energy storage system Access to Green Power Direct Connection Combination hour Select 1 if the value is 1, otherwise select 0. and energy storage system Maximum and minimum capacity; Energy storage system for high-altitude and cold regions The discharge efficiency; For energy storage systems Maximum discharge power; in, The specific calculation method is as follows: ; ; In the formula, New energy main body to high energy load No. Month Direct transmission efficiency during a given time period; Respectively, the main body of new energy to high energy load Resistance per unit length of a directly connected line; and Respectively, the main body of new energy With high energy load Spatial distance and terrain correction factor between them; As the main body of new energy With high energy load Corona loss correction factor for direct-connected lines; and Respectively, the main body of new energy With high energy load The rated voltage of the direct connection between them and the corona initiation voltage under standard conditions; The phase difference angle between voltage and current in a directly connected line; Power factor for directly connected lines; and These are the altitude correction factor and the weather correction factor, respectively. ; In the formula, This represents a function for selecting the standard conductor cross-section. and Respectively, the main body of new energy Maximum design power transmission capacity and main body of high energy-consuming load Maximum design power received; For economic current density; , and Respectively, the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node and load-side power receiving nodes Highest energy load main body Rated voltage of a direct-connected line; , and Representing the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node Load-side power receiving node Highest energy load main body Spatial distance; , and Representing the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node Load-side power receiving node Highest energy load main body Spatial correction coefficient.

[0024] It should be noted that: This invention introduces altitude gain and terrain slope operators to characterize the influence mechanism of climate and terrain features in high-altitude and cold regions on the volume and transmission efficiency of direct-connection lines for various types of new energy sources and high-energy-consuming loads, based on an altitude correction coefficient. Weather correction factor Get .

[0025] To further implement the above technical solution, the green electricity direct connection combination constraint set in S2 specifically includes: Green electricity consumption replacement threshold constraint: The proportion of self-generated and self-consumed electricity from new energy sources in the green electricity direct connection system shall not be less than 60% of the total available power generation and not less than 30% of the total electricity consumption. ; In the formula, As the main body of new energy In the Month The power that was not absorbed or fed into the grid during the specified period; For high energy loads In the Month Power purchased from the main power grid during the specified period; Direct connection space constraints: ; In the formula, Indicates when the main body of new energy Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. Indicates when high energy load Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. , and Respectively, the main body of new energy and New energy main body With high energy load and high energy load main body and Spatial distance; , and These are the upper limits of direct connection distances between new energy entities, between new energy entities and high energy-consuming load entities, and between high energy-consuming load entities, respectively. , and Representing the main body of new energy to New energy main body Maximum energy load High energy load main body to Spatial correction factor; Unique membership constraint for resources: ; In the formula, For the decision variables of the direct-connection combination, when the green electricity direct-connection combination The value is 1 when it is determined to be the final green electricity direct connection combination scheme, and 0 otherwise.

[0026] To further implement the above technical solutions, the green electricity direct connection combination planning model in S2 is as follows: ; The green power direct connection combination planning model belongs to the discrete optimization decision problem with multiple candidate combinations. The mixed integer optimization method is used to solve the green power direct connection combination planning model, and finally the optimal green power direct connection combination scheme set is obtained.

[0027] It should be noted that: This invention constructs an objective function that takes into account both the improvement of the overall matching degree and the penalty for the balance. The first term adopts the geometric mean form to characterize the overall green electricity direct connection matching level between multiple types of new energy sources and high energy-consuming loads. The second term constructs an exponential decay function for the relative mean deviation of the matching degree of different direct connection combination schemes, and corrects the matching degree differences of different high energy-consuming load entities.

[0028] The multi-type new energy and high energy load green electricity direct connection combination planning model constructed in this invention belongs to the discrete optimization decision problem with multiple candidate combinations. It is solved by the mixed integer optimization method. Finally, it obtains a set of green electricity direct connection combination schemes such as wind power, photovoltaic, energy storage direct connection to data center and silicon carbide consortium, wind power and energy storage direct connection to electrolytic aluminum enterprise, photovoltaic direct connection to salt lake chemical industry, and photovoltaic direct connection to cement load.

[0029] To further implement the above technical solution, S3 incorporates a latency risk loss function that considers differences in computing power task deadlines and computing power capacity limitations. for: ; In the formula, This is an index of the control period within the rolling control cycle. Index for data center computing power tasks For the first The total number of computing power tasks that the data center can delay executing during a given time period; For the first QoS sensitivity coefficient of each computing power task; For the first The workload of each computing task; For the first The proportion of latency for each computing task; , , and The first The deadline, execution time, resumption time after delay, and original start time of each computing task; This is a non-linear amplification exponent for latency loss in computing tasks, indicating that the closer the computing task is to the deadline, the greater the latency risk loss. This is the penalty coefficient for latency risk in computing power tasks. For data center servers in the first The latency and computing power load of the task during the time period; For from the first The set of future time periods that can be used to resume the execution of delayed tasks up to the deadline of the delayed computing power tasks; Index for future execution periods; and The execution of the next phase will be resumed in the future. The data center servers can handle the maximum computing load and the raw computing load during the specified period. It is the set of all discrete control periods within the rolling control cycle; The model for characterizing the impact of load power regulation on production loss in silicon carbide resistance furnaces includes: ; ; ; ; In the formula, and The first The first resistance furnace Furnace temperature at the end of the period and furnace temperature at the beginning; For the first The power of the electric resistance furnace participating in demand response reduction; and The first Taiwanese resistance furnace during the period Internal heat dissipation feedback coefficient and reaction endothermic feedback coefficient; For the first The equivalent heat capacity of the electric resistance furnace; The duration of a single control period within the rolling control cycle; A collection of silicon carbide resistance furnaces participating in demand response regulation. Index for electric resistance furnaces; , and These are the convective heat transfer coefficient, effective heat dissipation area, and surface thermal emissivity of the electric resistance furnace surface, respectively. It is the Stefan-Boltzmann constant; The molar enthalpy of reaction during the silicon carbide reaction process; For the first Taiwanese resistance furnace during the period The reaction rate-temperature correlation coefficient; For Arrhenius pre-exponential factors; This is the activation energy for the silicon carbide reaction process; It is the universal gas constant; For the first The first resistance furnace Production loss during the period; For the first The reaction rate-yield correlation coefficient of a single resistance furnace; For the first The first resistance furnace The equivalent thermal inertia time constant for a given period of time.

[0030] It should be noted that: This invention constructs a model to characterize the production loss of silicon carbide resistance furnace load power regulation based on the transmission mechanism of "unit power regulation - thermal equilibrium state evolution - reaction rate decay - cumulative production loss".

[0031] To further implement the above technical solution, the objective function in S4... for: ; In the formula, For the data center The power reduction of computing power tasks with time-lapse latency; The target control combination is selected from the optimal green electricity direct connection combination scheme set, specifically the green electricity direct connection combination that includes data centers and silicon carbide loads; For green electricity direct connection combination No. Power purchased from the main power grid during specific time periods; , and These are the conversion factors for data center service quality loss, silicon carbide production loss, and carbon emission reduction, respectively. For the first The first resistance furnace The time period considers the equivalent output loss after risk boundary correction; For the first large power grid Real-time electricity price for a given time period; For the first large power grid Dynamic carbon emission factors over a given period; and They are respectively and The molar mass.

[0032] To further implement the above technical solution, a boundary risk penalty term is introduced to construct a risk-corrected equivalent production loss model to characterize the nonlinear amplification of risk loss caused by unit adjustment power when the resistance furnace operating state gradually approaches the process safety boundary. The first resistance furnace Equivalent output loss after risk boundary correction within the time period for: ; In the formula, For the first The boundary risk penalty coefficient for the electric resistance furnace; and They represent the first The warning temperature and minimum process temperature of the resistance furnace.

[0033] To further implement the above technical solutions, the data center... Power reduction of time-lapse computing power tasks for: ; In the formula, and These are the ratios of server dynamic power to operating frequency and computing power to operating frequency, respectively. For data center servers in the first The computing load during a given time period; Data center power efficiency is the ratio of total energy consumption of the data center to the energy consumption of IT equipment. For the first The number of servers that are open in the data center during a given time period; This represents the expected utilization rate of data center servers.

[0034] To further implement the above technical solutions, the specific safety control constraints for green power direct connection to heterogeneous high-energy-consuming loads in S4 include: grid-connected capacity constraints for green power direct connection systems, maximum latency tolerance constraints for offline tasks in data centers, and safety constraints for silicon carbide crystal growth processes. Green electricity direct connection system grid connection capacity constraints: ; In the formula, and For the first Total power of data center and total power of silicon carbide load during the time period; For green electricity direct connection combination No. The discharge power of the energy storage system during a given time period; For green electricity direct connection combination No. New energy power during a given period; For green electricity direct connection combination The upper limit of grid-connected capacity to be declared; For green electricity direct connection combination The upper limit of the variation in grid-connected switching power between adjacent time periods; Maximum latency constraints for offline tasks in data centers: ; Safety constraints for silicon carbide crystal growth process: ; In the formula, This is the silicon carbide crystal growth process stage; This is a collection of stages in the silicon carbide crystal growth process. For the first The first resistance furnace The indicator variable for the process stage of the time period, when the first... The first resistance furnace The period is in a phase The value is 1 if the condition is met, otherwise it is 0. For the stage The sequential coding is used to characterize the order of different process stages in the silicon carbide crystal growth process; and The first Taiwan resistance furnace stage Lower and upper limits for the permissible rate of temperature change; and The first Taiwan resistance furnace stage The lower and upper limits of the temperature required to meet quality requirements.

[0035] To verify the feasibility of the proposed green electricity direct connection technology for high-energy-consuming loads and its associated carbon-electricity synergistic risk control, this case study focuses on wind power, photovoltaic power plants, data centers, and silicon carbide loads already built in a high-altitude, cold region in Northwest my country. First, based on the comprehensive matching degree index of spatiotemporal complementarity between various types of new energy sources and high-energy-consuming loads, the study optimizes the decision to select green electricity direct connection combinations with high source-load temporal matching degree and good spatial access feasibility. On this basis, a typical daily operation scenario is further constructed to analyze the impact of wind and solar power output fluctuations and green electricity deficits on the direct connection system. The results of elastic control of data center and silicon carbide loads are shown below. Figure 2 As shown.

[0036] Figure 2After adopting the electricity-carbon synergy marginal risk control method proposed in this invention, during periods of insufficient green electricity supply, data centers and silicon carbide loads prioritize the use of elastic loads with lower electricity-carbon synergy marginal risk losses to reduce electricity demand, and compensate for losses during periods of abundant green electricity. After the control, the power purchase of the main grid decreased from 489.97 MWh to 434.98 MWh, a reduction of 11.2%.

[0037] To verify the effectiveness and advancement of the proposed method, three control schemes were compared and simulated. Based on the integrated spatiotemporal matching degree of source and load, the proposed method further considers the marginal risk of electricity-carbon synergy, utilizing the latency flexibility of offline tasks in data centers and the thermal inertia characteristics of silicon carbide loads for elastic control. 1) Scheme 1 is a high-energy-loading load control method that does not consider the integrated spatiotemporal matching degree of source and load; 2) Scheme 2 is a high-energy-loading load control method that does not consider the marginal risk of electricity-carbon synergy; 3) The method proposed in this invention. Comparisons of the results for different schemes are shown in Table 1.

[0038] Scheme 1 fails to consider the spatiotemporal matching of source and load, resulting in a significant mismatch between the power consumption timing of data centers and silicon carbide loads and the output of new energy sources. This leads to increased power purchases from the main grid by the green electricity direct connection system, resulting in the lowest green electricity guarantee rate and the highest daily carbon emissions. Scheme 2 fails to characterize the complex relationship between the power regulation depth of high-energy-consuming loads and marginal safety losses and carbon emission reductions, easily leading to unreasonable power regulation allocation, resulting in silicon carbide production losses and excessive overdue computing power workloads in data centers. Compared to Schemes 1 and 2, the method proposed in this invention further introduces the risk of electricity-carbon synergy based on the spatiotemporal matching of source and load, enabling the deficit power to be optimally allocated among different high-energy-consuming loads according to marginal losses. The green electricity guarantee rate of the method proposed in this invention is improved by 8.98% and 2.70% compared to Schemes 1 and 2, respectively, and daily carbon emissions are reduced by 7.56% and 3.46% compared to Schemes 1 and 2, respectively. Simultaneously, daily silicon carbide production losses and daily overdue computing power workloads in data centers are also reduced to some extent. It is evident that the method proposed in this invention considers the spatiotemporal matching degree between new energy sources and high energy-consuming loads. When green electricity security is insufficient, it coordinates the adjustment power of different high energy-consuming loads based on the marginal risk of electricity-carbon synergy. This achieves a comprehensive optimization effect of higher green electricity security rate, less loss of high energy-consuming loads, and less carbon emissions under the green electricity direct connection mode, verifying the advantages of the method proposed in this invention in the safe and low-carbon operation of green electricity direct connection to high energy-consuming loads.

[0039] Table 1 Comparison of results from different schemes ; The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads, characterized in that, Includes the following steps: S1. Considering the impact of extreme low temperatures and complex terrain in high-altitude and cold regions on the direct connection matching of existing and unmatched various types of new energy sources with high-energy-consuming loads, multi-dimensional spatiotemporal heterogeneous features are extracted, and green electricity direct connection combinations are constructed based on these features. Spatiotemporal complementary comprehensive matching degree index ; S2. Establish a set of constraints for direct green energy connections and construct a comprehensive matching index based on spatiotemporal complementarity. The green energy direct connection combination planning model is solved under the condition of satisfying the green energy direct connection combination constraint set, and the optimal green energy direct connection combination scheme set is obtained. S3. Construct a marginal risk loss model for heterogeneous high-energy-consuming load electricity-carbon synergy, specifically including: a latency risk loss function based on the latency flexibility of data center computing power tasks, considering the difference in computing power task deadlines and computing power capacity limitations; and a model for characterizing the output loss of silicon carbide resistance furnace load power adjustment based on the thermal inertia of silicon carbide load resistance furnace. S4. For the wind-solar-storage direct connection data center and silicon carbide load in the optimal green power direct connection combination scheme set, an objective function is constructed based on the marginal risk loss model of electric-carbon synergy for heterogeneous high energy loads, with the goal of minimizing the comprehensive loss of electric-carbon synergy. Taking into account the safety regulation constraints of green power direct connection to heterogeneous high energy loads, the risk regulation strategy of electric-carbon synergy for green power direct connection to heterogeneous high energy loads is obtained.

2. The method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 1, characterized in that, The specific content of S1 includes: Multidimensional spatiotemporal heterogeneous features include: green electricity direct connection combination Multi-scale temporal distribution overlap of source and load power Green electricity direct connection combination Nonlinear complexity of random fluctuations in power of various types of new energy sources Green electricity direct connection combination China's energy storage system's coverage of extreme green electricity shortages Green electricity direct connection combination Corresponding standard transmission line volume ; Green electricity direct connection combination constructed based on multidimensional spatiotemporal heterogeneous features Spatiotemporal complementary comprehensive matching degree index for: ； In the formula, The extremely small positive number introduced to prevent the denominator from being zero. This is a collection of green electricity direct connection combinations.

3. The method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 2, characterized in that, The specific details of the method for obtaining multidimensional spatiotemporal heterogeneous features include: Green electricity direct connection combination Multi-scale temporal distribution overlap of source and load power : ； In the formula, For the collection of months of the year, Indicates the month sequence number; For the first A discrete time-domain sample set within one month; for Discrete time period index; The duration of a single discrete sampling period; For multiple types of new energy entities Direct connection with green electricity The mapping variable between them represents the relationship between the main body of new energy sources. Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. and Respectively, the main body of new energy High energy load In the Month Power during a given time period; For heterogeneous high energy loads Direct connection with green electricity The mapping variables between them, when high energy load Access to Green Power Direct Connection Combination If the value is 1, then the value is 0; otherwise, the value is 0. and The time indicates the main body of new energy With high energy load Direct connection matching; As the main body of new energy Directly Connected to Green Electricity No. Month Equivalent transmission efficiency over a given time period; and These are respectively a collection of various types of new energy entities that have been built but are yet to be matched and a collection of high energy-consuming loads; Green electricity direct connection combination Nonlinear complexity of random fluctuations in power of various types of new energy sources : ； In the formula, The length of the permutation pattern represents the number of sampling points included when constructing the new energy power fluctuation subsequence; Indicates length is The total number of permutations that can be formed by the power subsequence; The sampling delay step size represents the number of discrete sampling periods between two adjacent sampling points when constructing the main power fluctuation sequence of new energy sources. The index for the arrangement pattern; As the main body of new energy No. Monthly arrangement pattern Frequency of occurrence; As the main body of new energy Installed capacity; Green electricity direct connection combination China's energy storage system's coverage of extreme green electricity shortages : ； ； ； In the formula, For green electricity direct connection combination The set of periods of extreme green electricity shortage; The threshold for determining extreme green electricity shortages; This serves as an index for periods of continuous extreme green energy shortages. This represents the total number of consecutive periods of extreme green energy shortages. For green electricity direct connection combination No. A set of consecutive periods of extreme green energy shortage; For energy storage system indexing, A collection of energy storage systems; For energy storage systems Direct connection with green electricity The mapping variables, when the energy storage system Access to Green Power Direct Connection Combination hour Select 1 if the value is 1, otherwise select 0. and energy storage system Maximum and minimum capacity; Energy storage system for high-altitude and cold regions The discharge efficiency; For energy storage systems Maximum discharge power; in, The specific calculation method is as follows: ； ； In the formula, New energy main body to high energy load No. Month Direct transmission efficiency during a given time period; Respectively, the main body of new energy to high energy load Resistance per unit length of a directly connected line; and Respectively, the main body of new energy With high energy load Spatial distance and terrain correction factor between them; As the main body of new energy With high energy load Corona loss correction factor for direct-connection lines; and Respectively, the main body of new energy With high energy load The rated voltage of the direct connection between them and the corona initiation voltage under standard conditions; The phase difference angle between voltage and current in a directly connected line; For the power factor of a directly connected line; and These are the altitude correction factor and the weather correction factor, respectively. ； In the formula, This represents a function for selecting the standard conductor cross-section. and Respectively, the main body of new energy Maximum design power transmission capacity and main body of high energy-consuming load Maximum design power received; For economic current density; , and Respectively, the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node and load-side power receiving nodes Highest energy load main body Rated voltage of a direct-connected line; , and Representing the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node Load-side power receiving node Highest energy load main body Spatial distance; , and Representing the main body of new energy to power supply side aggregation node Power supply side aggregation node to the load-side power receiving node Load-side power receiving node Highest energy load main body Spatial correction coefficient.

4. The method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 3, characterized in that, The specific set of green electricity direct connection combination constraints in S2 includes: Green energy consumption replacement threshold constraint: The proportion of self-generated and self-consumed electricity from new energy sources in the green electricity direct connection system shall not be less than 60% of the total available power generation and not less than 30% of the total electricity consumption. ； In the formula, As the main body of new energy In the Month The power that was not absorbed or fed into the grid during the specified period; For high energy load In the Month Power purchased from the main power grid during the specified period; Direct connection space constraints: ； In the formula, Indicates when the main body of new energy Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. Indicates when high energy load Access to Green Power Direct Connection Combination The value is 1 if the condition is met, otherwise it is 0. , and Respectively, the main body of new energy and New energy main body With high energy load and high energy load main body and Spatial distance; , and These are the upper limits of direct connection distances between new energy entities, between new energy entities and high energy-consuming load entities, and between high energy-consuming load entities, respectively. , and Representing the main body of new energy to New energy main body Maximum energy load High energy load main body to Spatial correction factor; Unique membership constraint for resources: ； In the formula, For the decision variables of the direct-connection combination, when the green electricity direct-connection combination The value is 1 when it is determined to be the final green electricity direct connection combination scheme, and 0 otherwise.

5. The method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 3, characterized in that, The specific planning model for direct green energy connection in S2 is as follows: ； The green power direct connection combination planning model belongs to the discrete optimization decision problem with multiple candidate combinations. The mixed integer optimization method is used to solve the green power direct connection combination planning model, and finally the optimal green power direct connection combination scheme set is obtained.

6. The method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 1, characterized in that, S3 considers the latency risk loss function due to differences in deadlines for computing tasks and limitations in computing capacity. for: ； In the formula, This is an index of the control period within the rolling control cycle. Index for data center computing power tasks For the first The total number of computing power tasks that the data center can delay executing during a given time period; For the first QoS sensitivity coefficient of each computing power task; For the first The workload of each computing task; For the first The proportion of latency for each computing task; , , and The first The deadline, execution time, resumption time after delay, and original start time of each computing task; This is a non-linear amplification exponent for latency loss in computing tasks, indicating that the closer the computing task is to the deadline, the greater the latency risk loss. This is the penalty coefficient for latency risk in computing power tasks. For data center servers in the first The latency and computing power load of the task during the time period; For from the first The set of future time periods that can be used to resume the execution of delayed tasks up to the deadline of the delayed computing power tasks; Index for future execution periods; and The execution of the next phase will be resumed in the future. The data center servers can handle the maximum computing load and the raw computing load during the specified period. It is the set of all discrete control periods within the rolling control cycle; The model for characterizing the impact of load power regulation on production loss in silicon carbide resistance furnaces includes: ； ； ； ； In the formula, and The first The first resistance furnace Furnace temperature at the end of the period and furnace temperature at the beginning; For the first The power of the electric resistance furnace participating in demand response reduction; and The first Taiwanese resistance furnace during the period Internal heat dissipation feedback coefficient and reaction endothermic feedback coefficient; For the first The equivalent heat capacity of the electric resistance furnace; The duration of a single control period within the rolling control cycle; A collection of silicon carbide resistance furnaces participating in demand response regulation. Index for electric resistance furnaces; , and These are the convective heat transfer coefficient, effective heat dissipation area, and surface thermal emissivity of the electric resistance furnace surface, respectively. It is the Stefan-Boltzmann constant; The molar enthalpy of reaction during the silicon carbide reaction process; For the first Taiwanese resistance furnace during the period The reaction rate-temperature correlation coefficient; For Arrhenius pre-exponential factors; This is the activation energy for the silicon carbide reaction process; It is the universal gas constant; For the first The first resistance furnace Production loss during the period; For the first The reaction rate-yield correlation coefficient of a single resistance furnace; For the first The first resistance furnace The equivalent thermal inertia time constant for a given period of time.

7. The method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 6, characterized in that, Objective function in S4 for: ； In the formula, For the data center The power reduction of computing power tasks with time-lapse latency; The target control combination is selected from the optimal green electricity direct connection combination scheme set, specifically the green electricity direct connection combination that includes data centers and silicon carbide loads; For green electricity direct connection combination No. Power purchased from the main power grid during specific time periods; , and These are the conversion factors for data center service quality loss, silicon carbide production loss, and carbon emission reduction, respectively. For the first The first resistance furnace The time period considers the equivalent output loss after risk boundary correction; For the first large power grid Real-time electricity price for a given time period; For the first large power grid Dynamic carbon emission factors over a given period; and They are respectively and The molar mass.

8. The method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 7, characterized in that, A risk-corrected equivalent production loss model is constructed by introducing a boundary risk penalty term to characterize the nonlinear amplification of risk loss caused by unit power adjustment as the operating state of the resistance furnace gradually approaches the process safety boundary. The first resistance furnace Equivalent output loss after risk boundary correction within the time period for: ； In the formula, For the first The boundary risk penalty coefficient for the resistance furnace; and They represent the first The warning temperature and minimum process temperature of the resistance furnace.

9. A method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 7, characterized in that, Data Center Power reduction of time-lapse computing power tasks for: ； In the formula, and These are the ratios of server dynamic power to operating frequency and computing power to operating frequency, respectively. For data center servers in the first The computing load during a given time period; Data center power efficiency is the ratio of total energy consumption of the data center to the energy consumption of IT equipment. For the first The number of servers that are open in the data center during a given time period; This represents the expected utilization rate of data center servers.

10. A method for coordinated carbon risk control of green electricity directly connected to high energy-consuming loads according to claim 7, characterized in that, The specific safety regulation constraints for direct green power connection to heterogeneous high-energy-consuming loads in S4 include: grid-connected capacity constraints for direct green power connection systems, maximum latency tolerance constraints for offline tasks in data centers, and safety constraints for silicon carbide crystal growth processes. Green electricity direct connection system grid connection capacity constraints: ； In the formula, and For the first Total power of data center and total power of silicon carbide load during the time period; For green electricity direct connection combination No. The discharge power of the energy storage system during a given time period; For green electricity direct connection combination No. New energy power during a given period; For green electricity direct connection combination The upper limit of grid-connected capacity to be declared; For green electricity direct connection combination The upper limit of the variation in grid-connected switching power between adjacent time periods; Maximum latency constraints for offline tasks in data centers: ； Safety constraints for silicon carbide crystal growth process: ； In the formula, This is the silicon carbide crystal growth process stage; This is a collection of stages in the silicon carbide crystal growth process. For the first The first resistance furnace The indicator variable for the process stage of the time period, when the first... The first resistance furnace The period is in a phase The value is 1 if the condition is met, otherwise it is 0. For the stage The sequential coding is used to characterize the order of different process stages in the silicon carbide crystal growth process; and The first Taiwan resistance furnace stage Lower and upper limits for the permissible rate of temperature change; and The first Taiwan resistance furnace stage The lower and upper limits of the temperature required to meet quality requirements.

Images