A method for quantifying demand priority and dynamic threshold control in an energy system

Abstract

The application discloses a demand priority quantification and dynamic threshold control method in an energy system, and relates to the field of energy control, and comprises the following steps: step 1: parameterizing description is carried out on the energy-using demand entering the energy system, and each energy-using demand is mapped into a multi-dimensional demand characteristic set containing urgency, continuity, influence range, energy intensity and system contribution degree; step 2: based on the multi-dimensional demand characteristic set, a demand priority quantification value with comparability is generated through weighted calculation; step 3: a state parameter set including energy surplus, safety margin and energy structure state is acquired in real time; the application ensures that various demands are fully met when the system capacity is sufficient, and the most critical load is preferentially guaranteed when the capacity is tight, thereby enhancing the flexibility of the system when facing internal and external disturbances, and enabling the system to realize dynamic balance among multiple targets through parallel autonomous decision-making of a large number of modules without centralized optimization calculation.

Description

Technical Field

[0001] This invention relates to the field of energy control technology, specifically to a method for demand priority quantification and dynamic threshold control in an energy system. Background Technology

[0002] As energy systems evolve towards distributed, modular, and multi-energy complementary models, energy demand is becoming increasingly diversified and concurrent, rendering traditional energy allocation and control methods inadequate for system operation. Current technologies primarily employ centralized scheduling or fixed-priority strategies for energy system demand response: centralized scheduling, through a central dispatch center, calculates and issues execution instructions, requiring the processing of massive amounts of demand and system status data, resulting in high computational complexity, significant response delays, and the risk of single points of failure; fixed-priority strategies pre-determine demand ranking rules, failing to dynamically adjust according to system energy supply capacity, and are prone to supply-demand imbalances during energy fluctuations, leading to unmet high-urgency demands or system overload.

[0003] Furthermore, in existing methods, the energy function modules rely entirely on external scheduling instructions to respond, lacking autonomous decision-making capabilities. When the scheduling center fails or communication is interrupted, the modules cannot operate independently, making it difficult to provide demand adjustment suggestions in the event of abnormal energy supply scheduling. At the same time, when adding new demand types or modules, the scheduling algorithm needs to be redesigned, resulting in poor system scalability.

[0004] The aforementioned problems result in low stability and operational efficiency of existing energy systems under scenarios of concurrent demand and fluctuating energy supply, limiting the large-scale development of new energy forms such as distributed integrated energy systems and virtual power plants.

[0005] Therefore, there is an urgent need for an energy allocation control method that can achieve quantitative expression of demand priority, dynamic threshold adaptation, and autonomous module response, in order to solve the technical defects of existing technologies such as rigid static rules, complex scheduling, delayed response, and insufficient scalability. Summary of the Invention

[0006] (a) Technical problems to be solved In view of the above-mentioned shortcomings of the existing technology, the present invention provides a method for demand priority quantification and dynamic threshold control in energy systems, which can effectively solve the problems of the existing technology.

[0007] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: This invention discloses a method for demand priority quantification and dynamic threshold control in an energy system. It is applied to a pull-type modular energy system composed of multiple energy functional modules with autonomous decision-making capabilities. Each energy functional module independently assesses and responds to energy demands based on system instructions and its own state. This method supports concurrent decision-making for multiple demands, and the response priority of concurrent demands is determined solely by comparing quantified values ​​with dynamic thresholds, without relying on external sorting instructions. The method includes the following steps: Step 1: Parametrically describe the energy demand entering the energy system, mapping each energy demand to a multi-dimensional set of demand characteristics that includes at least urgency, continuity, scope of impact, energy intensity, and system contribution; the dimensions of the multi-dimensional set of demand characteristics are expandable, and the expanded dimensions include at least one of environmental attributes, economic value attributes, or substitutability attributes of the demand, in order to adapt to refined demand assessment under different scenarios. Step 2: Based on a multi-dimensional set of demand features, generate comparable quantitative values ​​for demand priority through weighted calculation; Step 3: Real-time acquisition of a set of state parameters, including at least energy margin, safety margin, and energy structure status, wherein the set of state parameters reflects the overall energy supply capacity of the system; Step 4: Based on the set of state parameters, adaptively generate a dynamic threshold range for demand response adjudication. This threshold range expands or contracts adaptively as the system's power supply capacity increases or decreases. The update period of the dynamic threshold range is adaptively matched with the frequency of change of the system's state parameters. Step 5: Without external execution instructions, the energy function module autonomously compares the demand priority quantification value with the locally cached dynamic threshold range. Only when the preset decision conditions are met will it automatically enter the operating state corresponding to the energy demand. The preset decision conditions include the demand priority quantification value being within the dynamic threshold range and simultaneously meeting the operating capacity constraints of the energy function module itself. Step 6: For energy demand that does not meet the preset adjudication conditions, analyze the composition of its demand priority quantification value, mark the key demand characteristics and associated system state parameters that lead to adjudication failure, and generate strategy information that includes demand adjustment suggestions to be fed back to the demand side. Step 7: When any multi-dimensional demand feature set of energy demand changes, or when the state parameter set changes above a preset threshold, the priority recalculation and decision update process starting from Step 2 is automatically triggered; the change of the state parameter set is defined by a preset dead zone threshold or change rate threshold to prevent frequent global update calculations due to small parameter fluctuations, thereby reducing the computational load while ensuring the real-time response of the system.

[0008] Furthermore, during the weighted calculation process in step 2, the weight allocation rules for different demand characteristics can be dynamically adjusted according to the system operation objectives. The system operation objectives on which the weight allocation rules are dynamically adjusted include at least one of reliability priority, energy efficiency priority, cost priority, or environmental protection priority. The system switches between different objectives according to preset strategies or real-time scheduling instructions to change the contribution ratio of each demand characteristic to the final priority quantification value.

[0009] Furthermore, the set of state parameters in step 3 also includes: energy quality parameters including the overall system and key node response rate, voltage or thermal energy quality, and at least one of the instantaneous output ratio of renewable energy; parameters used to reflect the quality and dynamic characteristics of energy supply.

[0010] Furthermore, the adaptive generation process of the dynamic threshold interval in step 4 includes: when the set of state parameters indicates that the system's energy supply capacity is sufficient, the dynamic threshold interval expands towards a lower priority direction, allowing more types of energy demand to be responded to; when the set of state parameters indicates that the system's energy supply capacity is strained, the dynamic threshold interval shrinks towards a higher priority direction, responding only to critical demands.

[0011] Furthermore, the operational capability constraints of the energy function module itself in step 5 include at least one of the following: the current load rate of the module, the response capability boundary defined by the maximum responsive power or flow, and the safe operation threshold defined by temperature, pressure or current; the energy function module must self-assess and satisfy the above constraints while confirming that the demand priority quantification value meets the standard before it is determined to meet the preset decision conditions.

[0012] Furthermore, in step 5, if the demand priority quantification value does not meet the preset adjudication conditions, the energy function module generates a demand delay identifier that includes the expected fulfillment time or suggested modification parameters, and maintains the current operating state without performing any energy supply response actions related to the energy demand that does not meet the adjudication conditions.

[0013] Furthermore, the marking process in step 6 is as follows: Identify the types of failures that do not meet the preset decision conditions: if the failure is due to the quantified value of the demand priority not falling within the dynamic threshold range, it is determined that the system power supply capacity is insufficient; if the failure is due to the failure to meet the module's own operating capacity constraints, it is determined that the local module's capacity is limited. When the system's power supply capacity is insufficient, reverse the calculation process of the demand priority quantification value, identify one or more demand characteristics that have a high weight under the current system operation target, but whose specific value is lower than the preset threshold, and mark them as key improvement characteristics. Parameters in the set of state parameters whose values ​​are lower than the standard threshold and which directly limit the dynamic threshold range are marked as associated bottleneck parameters; For cases where local module capabilities are limited, mark the violated runtime capability constraints and their current values.

[0014] Furthermore, the strategy information fed back in step 6 includes: For requests marked with key boosting features, generate negotiation suggestions to boost the corresponding feature values; For cases where associated bottleneck parameters are marked, additional explanations should be provided regarding the current system's constrained state and estimated recovery time. For requirements marked as having limited local module capabilities, suggestions are provided to route them to other parallel or backup functional modules, along with the estimated time to wait for the module's capabilities to be released; Based on the above data, generate one or more quantitative adjustment schemes that can raise the quantitative value of demand priority to an acceptable level, for the demand side to choose from.

[0015] Furthermore, the external execution instructions in step 5 include start / stop instructions, power adjustment instructions, or operating mode switching instructions issued by the scheduling center, host computer, or other related modules.

[0016] Furthermore, after generating strategy information in step 6, the system establishes a feature and status association file for unmet needs; when similar energy needs are initiated again, the system prioritizes predicting the success rate of the decision based on the association file, and when the predicted failure probability is higher than a preset threshold, it proactively pushes pre-adjustment suggestions to the demand side to optimize the demand feature parameters in advance and improve the system's first response efficiency.

[0017] (III) Beneficial Effects Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: 1. By parameterizing energy demand into multi-dimensional features and calculating quantified priority values, an objective comparison benchmark is provided for massive heterogeneous demands. Combined with threshold ranges that adapt to system state, each energy function module can make autonomous response decisions based on local information without waiting for central instructions. This not only significantly shortens response time and improves the speed of responding to renewable energy fluctuations and load changes, but also ensures that various demands are fully met when system capacity is sufficient and that the most critical loads are prioritized when capacity is tight through dynamic matching of priorities and thresholds. This enhances the system's resilience in the face of internal and external disturbances.

[0018] 2. By using dynamic weight allocation rules, the system's operational objectives are transformed into priority calculation parameters, guiding the quantitative evaluation of each requirement. Dynamic thresholds serve as a bridge connecting the global state and local decision-making, avoiding the computational and communication bottlenecks caused by strong centralization and overcoming the blindness of fully distributed autonomy. This enables the system to achieve dynamic balance among multiple objectives through the parallel autonomous decision-making of a large number of modules without centralized optimization computation.

[0019] 3. By performing feature and state correlation analysis on unmet demands and feeding back structured strategy information to the demand side, the traditional one-way, extensive, and mandatory load control is transformed into two-way, refined, and negotiated interactive guidance. This greatly respects and utilizes the diversity and flexibility of user demands, enhances user participation and satisfaction, and guides the demand side to proactively optimize its own energy consumption characteristics to match the system state, thereby more deeply exploring the flexibility resources of the demand side. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0021] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] The present invention will be further described below with reference to embodiments.

[0024] Example 1 This embodiment presents a method for demand priority quantification and dynamic threshold control in an energy system. The method is applicable to integrated energy systems that complement multiple energy sources, including cooling, heating, electricity, and gas, such as park-level, regional-level energy systems, or virtual power plants. Figure 1As shown, this includes a pull-type modular energy system composed of multiple energy function modules with autonomous decision-making capabilities. The system is characterized in that each energy function module independently assesses and responds to energy demands based on system instructions and its own state. This method supports concurrent decision-making for multiple demands, and the response priority of concurrent demands is determined solely by comparing quantified values ​​with dynamic thresholds, without relying on external sorting instructions. The method includes the following steps: Step 1: Parametrically describe the energy demand entering the energy system, mapping each energy demand to a multi-dimensional set of demand characteristics that includes at least urgency, continuity, scope of impact, energy intensity, and system contribution. The dimensions of the multi-dimensional demand characteristic set are expandable, and the expanded dimensions include at least one of the environmental attributes, economic value attributes, or substitutability attributes of the demand, in order to adapt to refined demand assessments under different scenarios.

[0025] Step 2: Based on the multidimensional set of demand features, generate comparable demand priority quantification values ​​through weighted calculation; during the weighted calculation process, the weight allocation rules for different demand features can be dynamically adjusted according to the system operation goals. The system operation goals on which the weight allocation rules are dynamically adjusted include at least one of reliability priority, energy efficiency priority, cost priority, or environmental protection priority. The system switches between different goals according to preset strategies or real-time scheduling instructions to change the contribution ratio of each demand feature to the final priority quantification value.

[0026] Step 3: Real-time acquisition of a set of state parameters including at least energy margin, safety margin, and energy structure status. The set of state parameters reflects the overall energy supply capacity of the system. The set of state parameters also includes: energy quality parameters including the overall system and key node response rate, voltage or thermal quality, and at least one of the instantaneous output ratio of renewable energy; parameters used to reflect the quality and dynamic characteristics of energy supply.

[0027] Step 4: Based on the set of state parameters, adaptively generate a dynamic threshold range for demand response adjudication. This threshold range expands or contracts adaptively as the system's energy supply capacity rises or falls. The update cycle of the dynamic threshold range is adaptively matched with the frequency of change of the system's state parameters. The adaptive generation process of the dynamic threshold range includes: when the set of state parameters indicates that the system's energy supply capacity is sufficient, the dynamic threshold range expands towards lower priority, allowing more types of energy demand to be responded to; when the set of state parameters indicates that the system's energy supply capacity is strained, the dynamic threshold range contracts towards higher priority, responding only to critical demands.

[0028] Step 5: Without external execution commands, the energy function module autonomously compares the quantified demand priority value with the locally cached dynamic threshold range. Only when the preset decision conditions are met does it automatically enter the operating state corresponding to the energy demand. External execution commands include start / stop commands, power adjustment commands, or operating mode switching commands issued by the dispatch center, host computer, or other related modules. The preset decision conditions include the quantified demand priority value being within the dynamic threshold range and simultaneously meeting the operating capacity constraints of the energy function module itself. The operating capacity constraints of the energy function module itself include at least one of the following: the module's current load rate, the response capacity boundary defined by the maximum responsive power or flow, and the safe operating threshold defined by temperature, pressure, or current. The energy function module must confirm that the quantified demand priority value meets the standard and self-assess that it meets the above constraints before determining that the preset decision conditions are met. If the demand priority quantification value does not meet the preset adjudication conditions, the energy function module generates a demand delay flag containing the expected fulfillment time or suggested modification parameters, and maintains the current operating state without executing any energy supply response actions related to the energy demand that does not meet the adjudication conditions.

[0029] Step 6: For energy demands that do not meet the preset adjudication conditions, analyze the composition of their demand priority quantification values, mark the key demand characteristics that lead to adjudication failure and the associated system state parameters, and generate strategy information containing demand adjustment suggestions to be fed back to the demand side accordingly; the marking process is as follows: Identify the types of failures that do not meet the preset decision conditions: If the failure is due to the demand priority quantification value not falling within the dynamic threshold range, it is determined that the system power supply capacity is insufficient; if the failure is due to the failure to meet the module's own operating capacity constraints, it is determined that the local module's capacity is limited. When the system's power supply capacity is insufficient, reverse the calculation process of the demand priority quantification value, identify one or more demand characteristics that have a high weight under the current system operation target, but whose specific value is lower than the preset threshold, and mark them as key improvement characteristics. Parameters in the state parameter set whose values ​​are below the standard threshold and which directly limit the dynamic threshold range are marked as associated bottleneck parameters; In cases where local module capabilities are limited, mark the violated runtime capability constraints and their current values; The feedback strategy information includes: For requests marked with key boosting features, generate negotiation suggestions to boost the corresponding feature values; For cases where associated bottleneck parameters are marked, additional explanations should be provided regarding the current system's constrained state and estimated recovery time. For requirements marked as having limited local module capabilities, suggestions are provided to route them to other parallel or backup functional modules, along with the estimated time to wait for the module's capabilities to be released; Based on the above data, generate one or more quantitative adjustment schemes that can raise the quantitative value of demand priority to an acceptable level, for the demand side to choose from; After generating strategy information, the system establishes a profile of the characteristics and status of unmet needs. When similar energy needs are initiated again, the system prioritizes predicting the success rate of the decision based on the profile. When the predicted failure probability is higher than a preset threshold, the system proactively pushes pre-adjustment suggestions to the demand side to optimize the demand characteristic parameters in advance and improve the system's first response efficiency.

[0030] Step 7: When any multi-dimensional demand feature set of energy demand changes, or when the state parameter set changes above a preset threshold, the priority recalculation and decision update process starting from Step 2 is automatically triggered; the change of the state parameter set is defined by a preset dead zone threshold or change rate threshold to prevent frequent global update calculations due to small parameter fluctuations, thereby reducing the computational load while ensuring the real-time response of the system.

[0031] Example 2 At other levels, this embodiment also provides another implementation example based on embodiment 1: This embodiment is applied to a multi-energy complementary integrated energy system for cooling, heating, and power in an industrial park. The system has a pull-type modular architecture and includes 20 energy function modules, specifically several modules for power generation, cooling, and energy storage, covering various energy demands such as production load, office load, and energy storage requirements. The system employs the demand priority quantification and dynamic threshold control method of this invention. The specific implementation steps are as follows: Demand parameterization description: The energy demand entering the system is parameterized to generate a multi-dimensional set of demand features. Production load demand: urgency (9 points), continuity (8 points), scope of impact (6 points), energy intensity (4 points), system contribution (2 points), extended dimension economic value attribute (2 points); Office workload requirements: urgency (5 points), continuity (4 points), scope of impact (3 points), energy intensity (2 points), system contribution (1 point), extended dimension: economic value attribute (1 point); Energy storage demand: urgency (3 points), continuity (6 points), scope of impact (2 points), energy intensity (3 points), system contribution (2 points), extended dimension: environmental attributes (2 points); All demand characteristic parameters follow the park's unified standardized description rules to ensure that the quantitative values ​​are comparable.

[0032] Priority quantification value generation: Based on the system's "reliability first" operational objective, a weight vector of [0.3, 0.2, 0.15, 0.15, 0.1, 0.1] (corresponding to urgency, continuity, impact scope, energy intensity, system contribution, and expansion dimension) is preset, and a quantification value is generated through weighted summation. Quantitative value of production load demand: 9×0.3+8×0.2+6×0.15+4×0.15+2×0.1+2×0.1=6.8 points; Quantitative value of office workload demand: 5×0.3+4×0.2+3×0.15+2×0.15+1×0.1+1×0.1=3.15 points; Quantitative value of energy storage demand: 3×0.3+6×0.2+2×0.15+3×0.15+2×0.1+2×0.1=3.25 points.

[0033] Energy supply capacity parameter acquisition: Real-time acquisition of system status parameters: Under normal power supply conditions: Energy margin E = 15 MWh (≥ rated margin) =10MWh), safety margin S=0.9 (≥ rated margin) =0.8), and the proportion of renewable energy output K=0.3; Energy supply decline state: Energy margin E = 5MWh, safety margin S = 0.7 (< The proportion of renewable energy output is K=0.5.

[0034] Dynamic threshold interval generation: Normal power supply state: Based on the adaptive generation mechanism, the threshold range is expanded to [ =3.0 points, =10.0 points]; Energy supply decline state: the threshold interval shrinks to [ =5.0 points, =10.0 points]; The threshold update period is set to 300ms to match the frequency of changes in system state parameters.

[0035] Module autonomous response decision: Normal energy supply status: The quantitative values ​​of production load (6.8 points), office load (3.15 points), and energy storage demand (3.25 points) all fall within the range of [3.0, 10.0], and the current load rate of each corresponding energy module is ≤70%, which meets the operating capacity constraints. The modules autonomously enter the operating state to respond to the demand. Energy supply decline status: Only the production load quantification value (6.8 points) falls within the range of [5.0, 10.0], while the office load and energy storage demand quantification values ​​are below the lower limit of the threshold. The module generates a demand delay flag, does not execute any response actions, and prioritizes ensuring the production load demand.

[0036] Dynamic update execution: When renewable energy output recovers and the system energy surplus rises to 12MWh, the threshold range is automatically updated to [ =3.5 points =10.0 points], energy storage demand (3.25 points) is still not met, and office load (3.15 points) is still delayed; when the energy reserve is ≥15MWh, the threshold range returns to [3.0,10.0], and the office load and energy storage demand modules start responding autonomously.

[0037] In this embodiment, none of the energy modules receive any external execution commands. They only achieve autonomous response and decision-making through the method of the present invention. The system does not experience supply and demand imbalance or operational disorder under the scenario of energy supply fluctuation, and the stability and reliability are significantly improved.

[0038] Example 3 This embodiment also provides another implementation example based on Embodiment 1: This embodiment is applied to a regional virtual power plant system, which includes 50 distributed generation modules (photovoltaic and wind power), 30 energy storage modules, and 100 end-user energy demands. The method of this invention is used to achieve demand response control. Demand parameterization description: End-user energy demand includes industrial energy consumption, residential energy consumption, and commercial energy consumption. The multi-dimensional demand characteristic set includes urgency, continuity, scope of impact, energy intensity, system contribution, and environmental attributes. The parameterization description follows the virtual power plant demand description standard issued by the National Energy Administration.

[0039] Priority quantification value generation: Based on the "energy efficiency first" operation target, the weight vector is adjusted, and the weight of system contribution (energy efficiency related) is increased to 0.2. The quantification values ​​of various demands are obtained through weighted calculation: industrial energy consumption 6.2 points, residential energy consumption 4.5 points, and commercial energy consumption 3.8 points.

[0040] Energy supply capacity parameter acquisition: Real-time acquisition of system energy margin, safety margin, renewable energy output ratio, voltage deviation (energy quality parameter) and other status parameters.

[0041] Dynamic threshold generation: When the proportion of renewable energy output increases to 0.6%, the system's energy supply capacity is enhanced, and the threshold range expands to [ =3.0 points, =10.0 points]; When the output ratio drops to 0.2, the threshold range shrinks to [ =5.0 points, =10.0 points).

[0042] Module autonomous response: When the output ratio is 0.6, all three types of demand meet the threshold conditions, and the corresponding module responds autonomously; when the output ratio is 0.2, only industrial energy consumption (6.2 points) meets the threshold, while residential and commercial energy consumption is postponed to avoid system overload.

[0043] In this embodiment, the virtual power plant does not require instructions from a centralized dispatch center. The method of this invention enables the autonomous and collaborative operation of distributed modules, with response latency controlled within 50ms. System expansion only requires adding new module configuration parameters, demonstrating excellent scalability. In summary, this invention transforms energy demand from simple on / off commands into quantifiable characteristic values ​​and dynamically generates response thresholds based on real-time system status. This enables the system to accurately distinguish the urgency of demand, allowing energy modules to make autonomous decisions and respond only when both capacity and priority are matched. This avoids the response lag and resource mismatch caused by traditional extensive control or centralized scheduling, and improves the operational reliability and energy efficiency of complex energy systems in fluctuating scenarios. For demands that cannot be met immediately, the system intelligently analyzes the root causes of the failure and generates feedback strategies that include specific adjustment suggestions. This establishes a negotiation mechanism between energy suppliers and demanders, guides the demand side to optimize parameters, and provides alternative paths and expected timelines for demand. In this way, a one-time decision failure is transformed into an opportunity for long-term optimization of the system as a whole, improving the flexibility of supply and demand coordination and overall economic efficiency.

[0044] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for demand priority quantification and dynamic threshold control in an energy system, applied to a pull-type modular energy system composed of multiple energy functional modules with autonomous decision-making capabilities, characterized in that, The energy function module independently assesses and responds to energy demand based on system instructions and its own status, including the following steps: Step 1: Parametrically describe the energy demand entering the energy system, mapping each energy demand to a multi-dimensional set of demand characteristics including urgency, continuity, scope of impact, energy intensity, and system contribution. Step 2: Based on a multi-dimensional set of demand features, generate comparable quantitative values ​​for demand priority through weighted calculation; Step 3: Real-time acquisition of a set of state parameters, including energy margin, safety margin, and energy structure status; Step 4: Based on the set of state parameters, adaptively generate a dynamic threshold range for demand response adjudication; Step 5: Without external execution instructions, the energy function module autonomously compares the quantified value of demand priority with the dynamic threshold range of local cache. Only when the preset decision conditions are met will it automatically enter the operating state corresponding to the energy demand. Step 6: For energy demand that does not meet the preset adjudication conditions, analyze the composition of its demand priority quantification value, mark the key demand characteristics and associated system state parameters that lead to adjudication failure, and generate strategy information that includes demand adjustment suggestions to be fed back to the demand side. Step 7: When any multidimensional demand feature set of energy demand changes, or when the state parameter set changes above a preset threshold, the priority recalculation and decision update process starting from Step 2 is automatically triggered.

2. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, In the weighted calculation process of step 2, the weight allocation rules for different demand characteristics can be dynamically adjusted according to the system operation goals. The system operation goals on which the weight allocation rules are dynamically adjusted include at least one of reliability priority, energy efficiency priority, cost priority, or environmental protection priority. The system switches between different goals according to preset strategies or real-time scheduling instructions to change the contribution ratio of each demand characteristic to the final priority quantification value.

3. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, The set of state parameters in step 3 also includes at least one of the following: the overall system and key node response rate, energy quality parameters including voltage or thermal quality, and the proportion of instantaneous output of renewable energy.

4. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, The adaptive generation process of the dynamic threshold interval in step 4 includes: when the set of state parameters indicates that the system has sufficient energy supply capacity, the dynamic threshold interval expands towards a lower priority direction, allowing more types of energy demand to be responded to; when the set of state parameters indicates that the system has insufficient energy supply capacity, the dynamic threshold interval shrinks towards a higher priority direction, responding only to critical demand.

5. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, The operational capability constraints of the energy function module in step 5 include at least one of the following: the module's current load rate, the response capability boundary defined by the maximum responsive power or flow rate, and the safe operation threshold defined by temperature, pressure, or current. The energy function module must self-assess and meet the above constraints while confirming that the demand priority quantification value meets the standard before it is determined to meet the preset decision conditions.

6. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, In step 5, if the demand priority quantification value does not meet the preset adjudication conditions, the energy function module generates a demand delay identifier that includes the expected fulfillment time or suggested modification parameters, and maintains the current operating state without performing any energy supply response actions related to the energy demand that does not meet the adjudication conditions.

7. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, The marking process in step 6 is as follows: Identify the types of failures that do not meet the preset decision conditions: if the failure is due to the quantified value of the demand priority not falling within the dynamic threshold range, it is determined that the system power supply capacity is insufficient; if the failure is due to the failure to meet the module's own operating capacity constraints, it is determined that the local module's capacity is limited. When the system's power supply capacity is insufficient, reverse the calculation process of the demand priority quantification value, identify one or more demand characteristics that have a high weight under the current system operation target, but whose specific value is lower than the preset threshold, and mark them as key improvement characteristics. Parameters in the set of state parameters whose values ​​are lower than the standard threshold and which directly limit the dynamic threshold range are marked as associated bottleneck parameters; For cases where local module capabilities are limited, mark the violated runtime capability constraints and their current values.

8. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 7, characterized in that, The strategy information fed back in step 6 includes: For requests marked with key boosting features, generate negotiation suggestions to boost the corresponding feature values; For cases where associated bottleneck parameters are marked, additional explanations should be provided regarding the current system's constrained state and estimated recovery time. For requirements marked as having limited local module capabilities, suggestions are provided to route them to other parallel or backup functional modules, along with the estimated time to wait for the module's capabilities to be released; Based on the above data, generate one or more quantitative adjustment schemes that can raise the quantitative value of demand priority to an acceptable level, for the demand side to choose from.

9. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, The external execution commands in step 5 include start / stop commands, power adjustment commands, or operating mode switching commands issued by the scheduling center, host computer, or other related modules.

10. The method for demand priority quantification and dynamic threshold control in an energy system according to claim 1, characterized in that, After generating strategy information in step 6, the system establishes a feature and status association file for unmet needs. When similar energy needs are initiated again, the system prioritizes predicting the success rate of the decision based on the association file. When the predicted failure probability is higher than a preset threshold, the system proactively pushes pre-adjustment suggestions to the demand side.

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