Dynamic assessment method and system for multi-dimensional response potential of resources

Abstract

The invention relates to a resource multi-dimensional response potential dynamic assessment method and system, and the method comprises the steps: obtaining multi-source data of a target assessment resource, the multi-source data at least comprising resource inherent attribute data, external dynamic factor data and real-time operation state data; fractal-dimension adjustable capability boundary calculation is carried out, specifically, based on the multi-source data, adjustable capability boundaries of the resources under at least two different dimensions are calculated respectively, and the adjustable capability boundaries are power boundaries; on the basis of external dynamic factor data in a future time period, the fractal-dimension adjustable capability boundary calculation is executed in a rolling mode, and a dynamic potential boundary sequence under all dimensions in the future time period is generated; and according to the dynamic potential boundary sequence, generating a constraint condition for the resource scheduling power, and outputting the priority of each dimension according to a predefined scene rule. According to the method, the accuracy and the real-time performance of the evaluation result can be improved, and the maximization and the optimal utilization of the resource potential are realized.

Description

Technical Field

[0001] This application relates to the field of energy internet technology, and in particular to a dynamic assessment method and system for the multi-dimensional response potential of resources. Background Technology

[0002] With the development of the energy internet and demand-side response technologies, effectively assessing and utilizing the regulation potential of distributed resources (such as industrial and commercial users, residential users, and energy storage systems) has become crucial. These resources can respond to grid or market signals in different dimensions, such as adjusting electricity consumption strategies to obtain economic benefits (economic benefit dimension), avoiding frequent equipment start-ups and shutdowns to extend equipment lifespan (equipment lifespan dimension), ensuring basic user comfort (user comfort dimension), or providing frequency and voltage support to the grid (grid support dimension).

[0003] Existing resource response potential assessment methods mostly focus on a single dimension, such as considering only economic optimization or setting fixed upper and lower limits for power regulation as technical constraints. The idea behind these methods is to define a static, single adjustable capacity boundary for the resource based on historical data or simple models. For example, the potential of an energy storage system is fixed at its rated power and capacity, ignoring its actual operating conditions and the influence of the external environment. Using this static, fixed boundary value to define the resource's adjustability ignores the impact of dynamically changing external factors such as weather, real-time electricity prices, and user behavior, resulting in inaccurate and unreal-time assessment results. This coarse-grained assessment makes it difficult to maximize the utilization of resource response potential. Summary of the Invention

[0004] Based on this, the purpose of this invention is to solve the above-mentioned technical problems, and to comprehensively evaluate the response potential of resources from multiple dimensions, thereby significantly improving the accuracy and real-time nature of the evaluation results and maximizing and optimizing the utilization of resource potential.

[0005] To achieve the above-mentioned objectives, the first aspect of this application provides a dynamic evaluation method for the multi-dimensional response potential of resources, comprising: Acquire multi-source data of the target evaluation resource, wherein the multi-source data includes at least resource inherent attribute data, external dynamic factor data, and real-time operational status data; Perform dimensional adjustable capability boundary calculation, including calculating the adjustable capability boundary of the resource in at least two different dimensions based on the multi-source data, wherein the adjustable capability boundary is a power boundary; Based on external dynamic factor data for future time periods, the calculation of the adjustable capability boundary in each dimension is performed in a rolling manner to generate a dynamic potential boundary sequence for each dimension within the future time period. Based on the dynamic potential boundary sequence, constraints on the resource scheduling power are generated, and priorities for each dimension are output according to predefined scenario rules.

[0006] Preferably, in the multi-source data: The inherent attribute data of the resources include the rated power, capacity, efficiency curve, and lifespan model parameters of the equipment; The external dynamic factor data includes weather forecast data, electricity market price data, and user historical behavior data; The real-time operating status data includes the current operating power of the resource, the state of charge (SOC), and the equipment temperature.

[0007] Preferably, the at least two different dimensions include at least two of the following: economic benefit dimension, equipment lifespan dimension, user comfort dimension, and power grid support dimension; For the economic benefit dimension, based on the electricity price signal, the power boundary of the economic benefit dimension is calculated with the condition that the benefit is not less than a preset threshold. For the equipment lifespan dimension, the power boundary for the equipment lifespan dimension is calculated with the condition that the lifespan loss rate is not greater than a preset threshold. For the user comfort dimension, the power boundary of the user comfort dimension is calculated under the condition of maintaining environmental parameters within a preset range; For the power grid support dimension, the power boundary of the power grid support dimension is calculated based on the power grid dispatch command information.

[0008] Preferably, the calculation of the dimensional adjustable capability boundary specifically includes: The calculation of the power boundary in the economic benefit dimension includes: Establish the payoff function:

[0009] in, For resources in time The power is positive, indicating that electricity is drawn from the grid, and negative, indicating that electricity is discharged back to the grid or that electricity consumption is reduced. The electricity price at time t; For time intervals; For resource reasons The resulting internal costs; Predict or acquire the natural power of resources when they are not participating in the response. ; Set a minimum profit threshold By solving This inequality, combined with the physical limits of resources, yields a power range. ; The power boundary for the economic benefit dimension is calculated using the following formula:

[0010]

[0011] in, and These refer to the maximum power adjustment capabilities for upward and downward movements, respectively. and The power boundary represents the economic benefit dimension; The calculation of the power boundary in the device lifetime dimension includes: Choosing a stress model as the lifetime model, lifetime attrition rate The model is as follows:

[0012] Where k and z are model parameters, fitted using experimental data; is the activation energy; R is the gas constant; This refers to the internal temperature of the battery. The charging and discharging current, and the power Related; C_n is the rated capacity, in Ah; The state of charge of the device at time t; It is a function that takes into account the influence of SOC; Set a maximum permissible instantaneous lifetime decay rate. , will the current moment and Substitute the lifetime attrition rate and solve the inequality:

[0013] because yes and For an increasing function, the inequality becomes a constraint on the absolute value of power:

[0014] in, The power limit for the lifetime dimension is obtained by solving this inequality; The power boundary in the equipment lifetime dimension is the intersection of the economic benefit dimension boundary and the power limit in the lifetime dimension, ensuring that both are satisfied simultaneously:

[0015]

[0016] in, and The power boundary represents the device's lifetime dimension; The calculation of the power boundary for the user comfort dimension includes: Establish thermodynamic and comfort models, including building thermodynamic models and comfort models: The building thermodynamic model is as follows:

[0017] in, Indicates indoor temperature; Indicates the building's heat capacity; Indicates the air conditioner's cooling or heating capacity; Indicates indoor heat gain; Indicates indoor heat loss; The comfort model uses the Predicted Average Voting Value (PMV), which is a function of temperature, humidity, wind speed, clothing, and activity intensity, simplified to a temperature range. ; In the given To maintain the indoor temperature at Inside, air conditioning power It is necessary to deduce, within a specific range, the result by solving the thermodynamic differential equation or its discrete form. Permissible range ; Convert air conditioner power into electrical power:

[0018] in, This represents the energy efficiency ratio at time t; The power boundary for the comfort dimension is calculated using the following formula:

[0019]

[0020] in, Indicates maintaining temperature Power at that time; Indicates maintaining temperature Power at that time; This indicates the power required to maintain the set temperature. Based on the power grid dispatch instructions, the power boundary of the power grid support dimension is calculated using the following expression:

[0021]

[0022] in, , Represents the power boundary in the power grid support dimension; , These represent the upward or downward regulation capacity required by the power grid at time t, respectively. , Calculated based on power grid conditions:

[0023]

[0024] in, Indicates frequency deviation; Indicates the adjustment dead zone; , This is the adjustment coefficient.

[0025] Preferably, the rolling execution of the dimensional adjustable capability boundary calculation specifically includes: The calculation process of the dimensional adjustable capability boundary is encapsulated into a boundary calculation function:

[0026] in, It refers to the internal state of a resource, including its inherent attribute data and real-time operational status data. It is data on external dynamic factors in the future time period; From the initial state Initially, combined with predictions Calculate in sequence The boundaries of each dimension of time; When calculating the boundary at each future time step, it is necessary to consider the impact of the current action on the future state, and use a state transition model for prediction:

[0027] in, It is about , , Relational model; The final output is a sequence of dynamic potential boundaries in each dimension for the future time period.

[0028] Preferably, the constraint condition for generating the resource scheduling power is specifically as follows: For each time point t, the resource scheduling power Constraints must be satisfied in all dimensions: .

[0029] Preferably, in the constraint modeling and priority output step, outputting the priority of each dimension according to predefined scene rules specifically involves: Multiple scene identifiers and their corresponding priority rules are predefined. The corresponding priority rules are activated based on the real-time scene recognition results. The priority rules are used to determine the weight of each dimension in the optimization objective function.

[0030] To achieve the aforementioned objectives, a second aspect of this application provides a dynamic evaluation system for multi-dimensional resource response potential, applying the dynamic evaluation method for multi-dimensional resource response potential described above. The system includes: The data acquisition module is used to acquire multi-source data of the target evaluation resource, wherein the multi-source data includes at least resource inherent attribute data, external dynamic factor data, and real-time operating status data; The dimensional boundary calculation module is used to calculate the adjustable capability boundary of the resource in at least two different dimensions based on the multi-source data, wherein the adjustable capability boundary is a power boundary. The dynamic evaluation module is used to generate a dynamic potential boundary sequence for each dimension in the future time period by rolling calls to the multi-dimensional boundary calculation module based on predicted external dynamic factor data and combined with the state transition model. The constraint and priority output module is used to generate constraints on the resource scheduling power based on the dynamic potential boundary sequence, and output the priority of each dimension according to the predefined scenario rules.

[0031] Preferably, the multidimensional boundary calculation module is specifically used for: For at least two of the following dimensions—economic benefits, equipment lifespan, user comfort, and grid support—the power boundaries for each dimension are calculated by satisfying the preset thresholds for each dimension.

[0032] Preferably, the constraint and priority output module includes: The constraint modeling unit is used to determine the intersection of the power boundaries of each dimension in the dynamic potential boundary sequence at the same time as the power scheduling constraint range of the resource at that time. The priority judgment unit has pre-stored priority rules corresponding to different scene identifiers. It is used to activate the corresponding priority rules according to the real-time scene recognition results. The priority rules are used to determine the weight of each dimension in the optimization objective function so as to output the priority of each dimension.

[0033] Compared with the prior art, the beneficial effects of this invention are: This invention overcomes the shortcomings of existing technologies that rely on a single evaluation dimension by calculating the adjustable capacity boundary of resources across multiple dimensions, thus comprehensively reflecting the response potential of resources under multiple objectives. Furthermore, by calculating the dynamic potential boundary sequence under each dimension over a future time period, it achieves dynamic and refined assessment of resource potential, significantly improving the accuracy and real-time nature of the assessment results and avoiding the rigidity of static assessments. The dynamic potential boundary sequence and priorities provide a scientific and quantitative constraint model and decision-making basis for subsequent multi-objective optimization decisions, thereby maximizing and optimizing the utilization of resource potential and effectively improving the operational efficiency and economy of the integrated energy system. Attached Figure Description

[0034] Figure 1 A flowchart illustrating the steps of a dynamic assessment method for the multi-dimensional response potential of resources; Figure 2 A schematic diagram illustrating the calculation of the adjustable capability boundary across dimensions; Figure 3 This is a schematic diagram of a dynamic assessment system for the multi-dimensional response potential of resources. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention. The following embodiments are used to illustrate the invention but are not intended to limit its scope.

[0036] Example 1 Embodiment 1 of this application provides a dynamic evaluation method for the multi-dimensional response potential of resources, such as... Figure 1 As shown, it includes the following steps: S1: Obtain multi-source data of the target evaluation resource, wherein the multi-source data includes at least resource inherent attribute data, external dynamic factor data, and real-time operating status data; S2: Perform dimensional adjustable capability boundary calculation, including calculating the adjustable capability boundary of the resource in at least two different dimensions based on the multi-source data, wherein the adjustable capability boundary is a power boundary; S3: Based on external dynamic factor data for future time periods, the calculation of the adjustable capability boundary in each dimension is performed in a rolling manner to generate a dynamic potential boundary sequence for each dimension within the future time period. S4: Generate constraints on the resource scheduling power based on the dynamic potential boundary sequence, and output the priority of each dimension according to the predefined scenario rules.

[0037] Step S1 specifically includes: (1) Data acquisition steps: Acquire multi-source data of the target evaluation resources. The multi-source data includes: Resource inherent attribute data: such as equipment rated power, capacity, efficiency curve, life model parameters, etc.

[0038] External dynamic factors data include weather forecast data (such as temperature, humidity, and light intensity), real-time electricity market prices and future forecast electricity prices, and historical user behavior data (such as energy consumption habits and comfort preference settings).

[0039] Real-time operating status data: current operating power of resources, state of charge (SOC, referring to the charge state of energy storage devices), device temperature, etc.

[0040] like Figure 2 As shown, step S2 specifically includes: (2) Steps for calculating adjustable capability boundaries by dimension: This step is the core, aiming to quantify the abstract "potential" into concrete power boundaries (i.e., maximum / minimum allowable power values) that can be used by optimization algorithms. The following is a detailed explanation of each dimension: (2.1) Calculation of the boundary of the economic benefit dimension Objective: To determine the maximum economic benefit that a resource can obtain by adjusting its own power under a given electricity price signal, and to calculate the power adjustment range to achieve this benefit.

[0041] Specific methods (taking adjustable load as an example): 1. Establish a revenue model: The revenue function of a resource can be expressed as:

[0042] in, For resources in time The power is positive, indicating that electricity is drawn from the grid, and negative, indicating that electricity is discharged back to the grid or that electricity consumption is reduced. The electricity price at time t; For time intervals; For resource reasons The resulting internal costs (such as equipment wear and tear, decreased comfort, etc.) will be calculated in detail in other dimensions, but here we can simplify them into a single cost. Related functions or constants.

[0043] 2. Determine the baseline operating point: First, predict or obtain the natural power of the resource when it is not participating in the response. This is the baseline for the calculation.

[0044] 3. Calculate the power boundary: Set an acceptable minimum return threshold (For example, the return must be greater than zero, or greater than a certain minimum rate of return).

[0045] By solving This inequality, combined with the physical limits of resources (such as rated power) A power range can be obtained. .

[0046] The power boundary in the economic benefit dimension was ultimately determined as follows:

[0047]

[0048] in, and These refer to the maximum power adjustment capabilities for upward and downward movements, respectively. and This represents the power boundary in the economic benefit dimension.

[0049] (2.2) Boundary calculation of equipment lifespan dimension (taking battery energy storage system BESS as an example) Objective: To limit the aging rate of the battery to an acceptable range, thereby determining the power boundary that satisfies the lifetime constraint.

[0050] Specific methods: 4. Select a life model: Use a relatively accurate and computationally feasible model, such as a stress model.

[0051]

[0052] Where k and z are model parameters, fitted using experimental data; is the activation energy; R is the gas constant; The internal temperature of the battery (which can be estimated or measured). The charging and discharging current (A) and power Related ( (V is voltage); C_n is rated capacity, in Ah; The state of charge of the device at time t; It is a function that takes into account the impact of SOC, for example, at extremely high or low SOC, device aging will be accelerated.

[0053] 5. Set a lifespan degradation threshold: Set a maximum allowable instantaneous lifespan degradation rate. For example, it can be set to guarantee the average degradation rate of the battery over its 10-year lifespan.

[0054] 6. Calculate the power boundary: At the current moment and Substitute the lifespan loss rate.

[0055] Solve the inequalities ≤ .

[0056] because yes (Too The inequality can be directly transformed into a constraint on the absolute value of power, where the power is an increasing function.

[0057] in It is a power limit obtained by solving the above inequality.

[0058] The power boundary in the lifetime dimension is the intersection of the economic benefit boundary and the power limit in the lifetime dimension, ensuring that both are satisfied simultaneously:

[0059] (2.3) Calculation of the boundary of user comfort dimension (taking commercial building air conditioning system as an example) Objective: To ensure that the indoor temperature is within the user's acceptable comfort range and to calculate the corresponding air conditioning power adjustment boundary.

[0060] Specific methods: 1. Establish a thermodynamic and comfort model: Building thermodynamic model:

[0061] Indoor temperature.

[0062] C: Building heat capacity.

[0063] Air conditioning cooling / heating power (kW, negative indicates cooling).

[0064] Indoor heat gain (personnel, equipment, solar radiation).

[0065] Indoor heat loss.

[0066] Comfort model: Utilizing the Predicted Average Voting Value (PMV). PMV is a function of temperature, humidity, wind speed, clothing, and activity level. It can be simplified to an acceptable temperature range. .

[0067] 2. Calculate the power boundary: Given outdoor temperature, solar radiation, etc. To maintain the indoor temperature at Inside, air conditioning power It must be within a certain range.

[0068] By solving the thermodynamic differential equations (or their discrete forms), one can deduce... Permissible range .

[0069] Convert air conditioner power into electrical power (COP stands for Energy Efficiency Ratio).

[0070] The power boundary for the comfort dimension is:

[0071] Indicates maintaining temperature Power at that time; Indicates maintaining temperature Power at that time; This indicates the power required to maintain the set temperature.

[0072] (2.4) Calculation of the boundary of the power grid support dimension (taking the provision of frequency regulation reserve as an example) Objective: To quantify the ability of resources to provide the power grid with rapid and frequent power adjustments (such as AGC regulation).

[0073] Specific methods: 1. Define support capability metrics: for example, adjustment rate (RR), adjustment range (P_agc_up, P_agc_down), and sustain time (ST).

[0074] 2. Calculate the power boundary required by the power grid: Receive power grid dispatch instructions or automatically calculate requirements:

[0075]

[0076] in, , Represents the power boundary in the power grid support dimension; , These represent the upward or downward regulation capacity required by the power grid at time t, respectively. in, , Calculated based on grid conditions (such as frequency deviation):

[0077]

[0078] in, Indicates frequency deviation; Indicates the adjustment dead zone; , This is the adjustment coefficient.

[0079] 3. Calculate the available adjustment capabilities: The resource needs to commit to a reference power point. Choose within the economic benefit boundary.

[0080] Power grids require resources to be around There is an up and down adjustment range .

[0081] The calculation of this range needs to take into account other dimensional boundaries and dynamic characteristics of the resources:

[0082]

[0083] At the same time, the duration depends on energy limitations (such as battery SOC). For example, the upward adjustment of the duration.

[0084] The duration of downward adjustment:

[0085] in, Indicates capacity.

[0086] 4. Calculate the ramp rate. The ramp rate (RR) of a resource characterizes its power change capability, including both upward and downward ramp rates:

[0087]

[0088] in: The maximum technical regulation rate (kW / min) of a resource is determined by its physical characteristics. Actual power at the current moment , Total power boundary integrating all dimensions : The time interval for adjustment (e.g., 1 minute, used to standardize units). The adjustment rate is an instantaneous capability, unlike the power boundary, and is usually used as a derivative constraint in optimization problems.

[0089] Step S3 specifically includes: (3) Dynamic assessment and potential prediction steps: This step aims to transform static boundaries into dynamic predictions of future potential.

[0090] Objective: To generate power boundary curves for resources across various dimensions over a future time period (e.g., the next 24 hours, in 15-minute intervals). , (i represents dimension).

[0091] Specific methods: 1. Input prediction data: Obtain or predict data on external dynamic factors for future time periods.

[0092] Meteorological data: temperature, humidity, solar radiation intensity (used for comfort and photovoltaic power output forecasting).

[0093] Market data: Electricity prices predict.

[0094] User behavior data: Building occupancy prediction, used for calculation .

[0095] Power grid status data: frequency deviation prediction, voltage over-limit risk prediction.

[0096] 2. Perform boundary calculations in a rolling manner: The calculation process designed for the "current moment" in step (2) is encapsulated into a boundary calculation function:

[0097] in It is the internal state of a resource (such as SOC). ), It is external environmental data (such as temperature, electricity price).

[0098] With an initial state Initially, combined with predictions Calculate in sequence The boundaries of each dimension of time.

[0099] When calculating the boundaries at each future time step, the impact of the current action on the future state needs to be considered. For example, the charging / discharging power at time t will affect the battery's state of charge (SOC) at time t+1. This requires a state transition model to predict:

[0100] It is about , , The relational model, namely the relational model concerning the internal state of a resource, its boundary function, and the external state of the resource, describes the relationship in the current state. Control power action and external disturbances Under the combined influence of these factors, how does the system state evolve into its state at the next moment? . It can be obtained by constructing precise mathematical equations based on first principles (physical / chemical laws), treating the system as a "black box" (e.g., differential equations, difference equations, transfer functions), learning the mapping relationship from input to output entirely through data-driven learning (e.g., neural networks, decision trees, support vector machines), or by combining physical laws with data-driven approaches and calibrating unknown parameters with data within a physical framework (e.g., physical information neural networks, mechanism models after parameter identification).

[0101] Therefore, this is a rolling prediction process with state constraints. The final output is a multi-dimensional potential boundary sequence for future time steps, taking into account dynamic coupling relationships.

[0102] Step S4 specifically includes: (4) Constraint modeling and priority output steps: This step transforms the dynamic potential into the input of the optimization problem.

[0103] Objective: To provide mathematical constraints for the upper-level optimizer and to offer suggestions on the priority of optimization objectives in different scenarios.

[0104] Specific methods: 1. Constraint Modeling: The multidimensional power boundary predicted in step (3) is transformed into inequality constraints in the optimization problem.

[0105] For each time point t, the actual scheduling power of the resources All constraints in all dimensions must be satisfied simultaneously:

[0106] This constraint explicitly expresses the intersection relationship between the potentials of different dimensions, that is, the final feasible power range is the common part of the allowable ranges of each dimension. This directly solves the problem of "lack of quantitative analysis of mutual constraints" in the background technology.

[0107] 2. Priority determination: Rule base method: Establish a predefined rule base and activate different priorities based on scene identifiers.

[0108] Scene identifier example: Scenario_Power Grid Emergency: Triggered when the power grid frequency deviation exceeds the threshold or the risk of voltage exceeding the limit is high.

[0109] Scenario_Economic Operation: The default scenario, triggered when electricity prices fluctuate significantly.

[0110] Scenario - Extreme Weather: Triggered when the temperature is far above the comfortable range.

[0111] Priority rule example: IF Scenario_Power Grid Emergency THEN Priority = [Power Grid Support >> Equipment Lifespan > User Comfort > Economic Benefits] IF Scenario_Economic Operation THEN Priority = [Economic Benefits > User Comfort > Equipment Lifespan > Power Grid Support] IF Scenario - Extreme Weather THEN Priority = [User Comfort > Equipment Lifespan > Economic Benefits > Power Grid Support] Multi-objective optimization weighting method: quantifies the priority into weights of the optimization objective function.

[0112] The objective function of the upper-level optimization problem can be designed as follows:

[0113] in, It is a function of the returns in each dimension. It's the weight.

[0114] The output of the priority determination module is this set of dynamic weights. In scenario _power grid emergency, When a value is set to a very large value, other weights are reduced accordingly.

[0115] Example 2 Embodiment 2 of this application, based on Embodiment 1, provides a dynamic evaluation system for multi-dimensional resource response potential, applying the dynamic evaluation method for multi-dimensional resource response potential described in Embodiment 1 above, such as... Figure 3 As shown, the system includes: Data acquisition module 10 is used to acquire multi-source data of the target evaluation resource, wherein the multi-source data includes at least resource inherent attribute data, external dynamic factor data and real-time operating status data; The dimensional boundary calculation module 20 is used to calculate the adjustable capability boundary of the resource in at least two different dimensions based on the multi-source data, wherein the adjustable capability boundary is a power boundary. The dynamic evaluation module 30 is used to generate a dynamic potential boundary sequence for each dimension in the future time period by rolling calls to the multi-dimensional boundary calculation module based on predicted external dynamic factor data and combined with the state transition model. The constraint and priority output module 40 is used to generate constraints on the resource scheduling power based on the dynamic potential boundary sequence, and output the priority of each dimension according to the predefined scenario rules.

[0116] Example 3 This embodiment 3 is based on embodiment 2, and further explains the structure and function of the constraint and priority output module.

[0117] The constraint and priority output module 40 includes: The constraint modeling unit is used to determine the intersection of the power boundaries of each dimension in the dynamic potential boundary sequence at the same time as the power scheduling constraint range of the resource at that time. The priority judgment unit has pre-stored priority rules corresponding to different scene identifiers. It is used to activate the corresponding priority rules according to the real-time scene recognition results. The priority rules are used to determine the weight of each dimension in the optimization objective function so as to output the priority of each dimension.

[0118] In summary, this invention overcomes the shortcomings of existing technologies that rely on a single evaluation dimension by calculating the adjustable capacity boundary of resources across multiple dimensions, thus comprehensively reflecting the response potential of resources under multiple objectives. By integrating dynamically changing external factors such as weather, electricity prices, and user behavior, it achieves dynamic and refined assessment of resource potential, significantly improving the accuracy and real-time nature of the assessment results and avoiding the rigidity of static assessments. The output of multi-dimensional potential prediction results and priority judgments provides a scientific and quantitative constraint model and decision-making basis for subsequent multi-objective optimization decisions, supporting the maximization and optimal utilization of resource potential and effectively improving the operational efficiency and economy of integrated energy systems.

Claims

1. A dynamic evaluation method for the multi-dimensional response potential of resources, characterized in that, Includes the following steps: Acquire multi-source data of the target evaluation resource, wherein the multi-source data includes at least resource inherent attribute data, external dynamic factor data, and real-time operational status data; Perform dimensional adjustable capability boundary calculation, including calculating the adjustable capability boundary of the resource in at least two different dimensions based on the multi-source data, wherein the adjustable capability boundary is a power boundary; Based on external dynamic factor data for future time periods, the calculation of the adjustable capability boundary in each dimension is performed in a rolling manner to generate a dynamic potential boundary sequence for each dimension within the future time period. Based on the dynamic potential boundary sequence, constraints on the resource scheduling power are generated, and priorities for each dimension are output according to predefined scenario rules.

2. The method according to claim 1, characterized in that, In the multi-source data: The inherent attribute data of the resources include the rated power, capacity, efficiency curve, and lifespan model parameters of the equipment; The external dynamic factor data includes weather forecast data, electricity market price data, and user historical behavior data; The real-time operating status data includes the current operating power of the resource, the state of charge (SOC), and the equipment temperature.

3. The method according to claim 1, characterized in that, The at least two different dimensions include at least two of the following: economic benefits, equipment lifespan, user comfort, and power grid support. For the economic benefit dimension, based on the electricity price signal, the power boundary of the economic benefit dimension is calculated with the condition that the benefit is not less than a preset threshold. For the equipment lifespan dimension, the power boundary for the equipment lifespan dimension is calculated with the condition that the lifespan loss rate is not greater than a preset threshold. For the user comfort dimension, the power boundary of the user comfort dimension is calculated under the condition of maintaining environmental parameters within a preset range; For the power grid support dimension, the power boundary of the power grid support dimension is calculated based on the power grid dispatch command information.

4. The method according to claim 3, characterized in that, The calculation of the multidimensional adjustable capability boundary specifically includes: The calculation of the power boundary in the economic benefit dimension includes: Establish the payoff function: in, For resources in time The power is positive, indicating that electricity is taken from the grid, and negative, indicating that electricity is discharged to the grid or that electricity consumption is reduced. The electricity price at time t; For time intervals; For resource reasons The resulting internal costs; Predict or acquire the natural power of resources when they are not participating in the response. ; Set a minimum profit threshold By solving This inequality, combined with the physical limits of resources, yields a power range. ; The power boundary for the economic benefit dimension is calculated using the following formula: in, and These refer to the maximum power adjustment capabilities for upward and downward movements, respectively. and The power boundary represents the economic benefit dimension; The calculation of the power boundary in the device lifetime dimension includes: Choosing a stress model as the lifetime model, lifetime attrition rate The model is as follows: Where k and z are model parameters, which are fitted using experimental data; is the activation energy; R is the gas constant; This refers to the internal temperature of the battery. The charging and discharging current, and the power Related; C_n is the rated capacity, in Ah; The state of charge of the device at time t; It is a function that takes into account the influence of SOC; Set a maximum permissible instantaneous lifetime decay rate. , will the current moment and Substitute the lifetime attrition rate and solve the inequality: because yes and For an increasing function, the inequality becomes a constraint on the absolute value of power: in, The power limit for the lifetime dimension is obtained by solving this inequality; The power boundary in the equipment lifetime dimension is the intersection of the economic benefit dimension boundary and the power limit in the lifetime dimension, ensuring that both are satisfied simultaneously: in, and The power boundary represents the device's lifetime dimension; The calculation of the power boundary for the user comfort dimension includes: Establish thermodynamic and comfort models, including building thermodynamic models and comfort models: The building thermodynamic model is as follows: in, Indicates indoor temperature; Indicates the building's heat capacity; Indicates the air conditioner's cooling or heating capacity; Indicates indoor heat gain; Indicates indoor heat loss; The comfort model uses the Predicted Average Voting Value (PMV), which is a function of temperature, humidity, wind speed, clothing, and activity intensity, simplified to a temperature range. ; In the given To maintain the indoor temperature at Inside, air conditioning power It is necessary to deduce, within a specific range, the result by solving the thermodynamic differential equation or its discrete form. Permissible range ; Convert air conditioner power into electrical power: in, This represents the energy efficiency ratio at time t; The power boundary for the comfort dimension is calculated using the following formula: in, Indicates maintaining temperature Power at that time; Indicates maintaining temperature Power at that time; This indicates the power required to maintain the set temperature; Based on the power grid dispatch instructions, the power boundary of the power grid support dimension is calculated using the following expression: in, , Represents the power boundary in the power grid support dimension; , These represent the upward or downward regulation capacity required by the power grid at time t, respectively. , Calculated based on power grid conditions: in, Indicates frequency deviation; Indicates the adjustment dead zone; , This is the adjustment coefficient.

5. The method according to claim 4, characterized in that, The rolling execution of the multi-dimensional adjustable capability boundary calculation specifically includes: The calculation process of the dimensional adjustable capability boundary is encapsulated into a boundary calculation function: in, It refers to the internal state of a resource, including its inherent attribute data and real-time operational status data. It is data on external dynamic factors in the future time period; From the initial state Initially, combined with predictions Calculate in sequence The boundaries of each dimension of time; When calculating the boundary at each future time step, it is necessary to consider the impact of the current action on the future state, and use a state transition model for prediction: in, It is about , , Relational model; The final output is a sequence of dynamic potential boundaries in each dimension for the future time period.

6. The method according to claim 5, characterized in that, The specific constraints on the resource scheduling power are as follows: For each time point t, the scheduling power of resources Constraints must be satisfied in all dimensions: 。 7. The method according to claim 6, characterized in that, In the constraint modeling and priority output step, the priority of each dimension is output according to the predefined scene rules as follows: Multiple scene identifiers and their corresponding priority rules are predefined. The corresponding priority rules are activated based on the real-time scene recognition results. The priority rules are used to determine the weight of each dimension in the optimization objective function.

8. A dynamic assessment system for multi-dimensional resource response potential, employing the dynamic assessment method for multi-dimensional resource response potential as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to acquire multi-source data of the target evaluation resource, wherein the multi-source data includes at least resource inherent attribute data, external dynamic factor data, and real-time operating status data; The dimensional boundary calculation module is used to calculate the adjustable capability boundary of the resource in at least two different dimensions based on the multi-source data, wherein the adjustable capability boundary is a power boundary. The dynamic evaluation module is used to generate a dynamic potential boundary sequence for each dimension in the future time period by rolling calls to the multi-dimensional boundary calculation module based on predicted external dynamic factor data and combined with the state transition model. The constraint and priority output module is used to generate constraints on the resource scheduling power based on the dynamic potential boundary sequence, and output the priority of each dimension according to the predefined scenario rules.

9. The system according to claim 8, characterized in that, The multi-dimensional boundary calculation module is specifically used for: For at least two of the following dimensions—economic benefits, equipment lifespan, user comfort, and grid support—the power boundaries for each dimension are calculated by satisfying the preset thresholds for each dimension.

10. The system according to claim 8, characterized in that, The constraint and priority output module includes: The constraint modeling unit is used to determine the intersection of the power boundaries of each dimension in the dynamic potential boundary sequence at the same time as the power scheduling constraint range of the resource at that time. The priority judgment unit has pre-stored priority rules corresponding to different scene identifiers. It is used to activate the corresponding priority rules according to the real-time scene recognition results. The priority rules are used to determine the weight of each dimension in the optimization objective function so as to output the priority of each dimension.

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