CN-122022006-A - Dynamic evaluation method and system for multi-dimensional response potential of resources

CN122022006ACN 122022006 ACN122022006 ACN 122022006ACN-122022006-A

Abstract

The application relates to a dynamic evaluation method and a system for multi-dimensional response potential of resources, wherein the method comprises the steps of obtaining multi-source data of target evaluation resources, wherein the multi-source data at least comprise inherent attribute data of the resources, external dynamic factor data and real-time running state data, carrying out dimension-dividing adjustable capability boundary calculation, respectively calculating adjustable capability boundaries of the resources in at least two different dimensions based on the multi-source data, wherein the adjustable capability boundaries are power boundaries, carrying out rolling execution on the dimension-dividing adjustable capability boundary calculation based on the external dynamic factor data of a future time period to generate a dynamic potential boundary sequence in each dimension in the future time period, generating constraint conditions for scheduling power of the resources according to the dynamic potential boundary sequence, and outputting priorities of the dimensions according to predefined scene rules. The application can improve the accuracy and the real-time performance of the evaluation result and realize the maximization and the optimal utilization of the resource potential.

Inventors

LI ZHICAI
HUANG ZHONGBIN
Deng Hanbiao
CAO LEI
YANG MINGJING
ZHANG TINGKAI
HE JIASHENG
ZHENG QINGYUE
Wei Haimei
XIE HAOLIN

Assignees

广州南方电力集团科技发展有限公司

Dates

Publication Date: 20260512
Application Date: 20251226

Claims (10)

1. The dynamic evaluation method for the multi-dimensional response potential of the resource is characterized by comprising the following steps of: Acquiring multi-source data of a target evaluation resource, wherein the multi-source data at least comprises inherent attribute data of the resource, external dynamic factor data and real-time running state data; Performing dimension-division adjustable capacity boundary calculation, including respectively calculating adjustable capacity boundaries of the resources under at least two different dimensions based on the multi-source data, wherein the adjustable capacity boundaries are power boundaries; based on external dynamic factor data of a future time period, performing the dimension-based adjustable capacity boundary calculation in a rolling way to generate a dynamic potential boundary sequence in each dimension in the future time period; and generating constraint conditions for scheduling power for the resources according to the dynamic potential boundary sequence, and outputting priorities of all dimensions according to predefined scene rules.
2. The method of claim 1, wherein, in the multi-source data: the inherent attribute data of the resource comprises rated power, capacity, efficiency curve and life model parameters of the equipment; The external dynamic factor data comprise weather forecast data, electric power market price data and user historical behavior data; the real-time operational state data includes a current operational power, state of charge, SOC, and device temperature of the resource.
3. The method of claim 1, wherein the at least two different dimensions include at least any two of an economic benefit dimension, a device lifetime dimension, a user comfort dimension, and a grid support dimension; Aiming at the economic benefit dimension, calculating a power boundary of the economic benefit dimension on the condition that the benefit is not smaller than a preset threshold based on the electricity price signal; calculating a power boundary of the equipment life dimension on the condition that the life loss rate is not greater than a preset threshold value aiming at the equipment life dimension; Aiming at the user comfort dimension, calculating a power boundary of the user comfort dimension on the condition that the environmental parameter is maintained within a preset range; and calculating the power boundary of the power grid supporting dimension based on the power grid dispatching instruction information aiming at the power grid supporting dimension.
4. A method according to claim 3, wherein the dimension-wise adjustability boundary calculation specifically comprises: the calculation of the power boundary for the economic benefit dimension includes: establishing a profit function: Wherein, the At time for resource Positive values indicate taking power from the grid, negative values indicate discharging or reducing power to the grid; electricity price for time t; Is a time interval; For the reason of resource And the resulting internal costs; Predicting or deriving natural power of a resource when not participating in a response ; Setting a minimum profit threshold By solving for This inequality, combined with the physical limits of the resources, yields a power range ; Calculating the power boundary of the economic benefit dimension according to the following: Wherein, the And Maximum power adjustment capability up and down, respectively; And A power boundary representing an economic benefit dimension; the calculation of the power boundary for the device lifetime dimension includes: selecting stress model as life model and life loss rate Modeling is as follows: Wherein k and z are model parameters, and fitting is performed through experimental data; r is the gas constant; is the internal temperature of the battery; For charging and discharging current and power C_n is rated capacity, and the unit is Ah; the state of charge of the device at time t; is a function that takes into account the influence of SOC; Setting a maximum allowable instantaneous life loss rate Will be at the current time And Substituting the life loss rate and solving the inequality: Due to Is that And The inequality translates into a limit on the absolute value of the power: Wherein, the A power limit in the lifetime dimension obtained by solving the inequality; the power boundary of the device lifetime dimension is the intersection of the economic benefit dimension boundary and the power limit of the lifetime dimension, ensuring that both are satisfied at the same time: Wherein, the And A power boundary representing a device lifetime dimension; the calculation of the power boundary for the user comfort dimension includes: and establishing thermodynamic and comfort models, including a building thermodynamic model and a comfort model: the building thermodynamic model is as follows: Wherein, the Indicating the indoor temperature; representing the heat capacity of the building; Representing the refrigerating or heating power of the air conditioner; indicating indoor heating; indicating indoor heat loss; the comfort model adopts a predictive average voting index PMV which is a function of temperature, humidity, wind speed, clothing and activity intensity and is simplified into a temperature range ; At a given point Under the condition, the indoor temperature is maintained In, air conditioner power Must be back-deduced within a specific range by solving thermodynamic differential equations or discrete forms thereof Is not limited by the allowable range of (2) ; Converting air conditioning power into electrical power: Wherein, the The energy efficiency ratio at the time t is represented; the power boundary for the comfort dimension is calculated by: Wherein, the Indicating the maintenance temperature Power at that time; Indicating the maintenance temperature Power at that time; indicating the power at which the set temperature is maintained; According to the power grid dispatching instruction information, calculating the power boundary of the power grid supporting dimension by the following expression: Wherein, the 、 A power boundary representing a grid support dimension; 、 Respectively representing the upward or downward adjustment capacity required by the power grid at the moment t; 、 Calculating according to the state of the power grid: Wherein, the Representing the frequency deviation; representing an adjustment dead zone; 、 For adjusting the coefficients.
5. The method of claim 4, wherein the scrolling performing the dimension-wise adjustability boundary calculation specifically comprises: The calculation process of the dimension-dividing adjustable capacity boundary is packaged into a boundary calculation function: Wherein, the Is the internal state of the resource, including the inherent attribute data of the resource and the real-time running state data; External dynamic factor data of a future time period; From the initial state Initially, predicted is combined Sequentially calculate Boundaries of each dimension at the moment; in calculating the boundary of each future time, the influence of the current action on the future state needs to be considered, and a state transition model is used for prediction: Wherein, the Is about 、、 Is a relational model of (a); And finally outputting the dynamic potential boundary sequences in each dimension in the future time period.
6. The method according to claim 5, wherein the constraint for generating the resource scheduling power is specifically: for each point in time t, the scheduling power of the resource All dimensional constraints need to be satisfied: 。
7. The method according to claim 6, wherein in the constraint modeling and priority outputting step, the priorities of the dimensions according to the predefined scene rules are specifically: And predefining a plurality of scene identifications and corresponding priority rules thereof, and activating the corresponding priority rules according to the real-time scene identification result, wherein the priority rules are used for determining the weight of each dimension in the optimization objective function.
8. A system for dynamic assessment of a multi-dimensional response potential of a resource, applying a method for dynamic assessment of a multi-dimensional response potential of a resource according to any one of claims 1-7, the system comprising: The data acquisition module is used for acquiring multi-source data of the target evaluation resource, wherein the multi-source data at least comprises inherent attribute data of the resource, external dynamic factor data and real-time running state data; The dimension-based boundary calculation module is used for respectively calculating adjustable capacity boundaries of the resources under at least two different dimensions based on the multi-source data, wherein the adjustable capacity boundaries are power boundaries; The dynamic evaluation module is used for generating a dynamic potential boundary sequence in each dimension in a future time period by rolling and calling the dimension boundary calculation module based on predicted external dynamic factor data and combining a state transition model; And the constraint and priority output module is used for generating constraint conditions for scheduling power for the resources based on the dynamic potential boundary sequence and outputting priorities of all dimensions according to a predefined scene rule.
9. The system of claim 8, wherein the dimension-wise boundary calculation module is specifically configured to: And calculating to obtain the power boundary of each dimension according to at least two dimensions of economic benefit dimension, equipment service life dimension, user comfort dimension and power grid support dimension and respectively taking the condition that the preset threshold of each dimension is met.
10. The system of claim 8, wherein the constraint and priority output module comprises: The constraint modeling unit is used for determining the intersection of the power boundaries of each dimension in the dynamic potential boundary sequence at the same moment as the power scheduling constraint range of the resource at the moment; the priority judging unit is used for pre-storing priority rules corresponding to different scene identifications and activating the corresponding priority rules according to the real-time scene identification result, wherein the priority rules are used for determining the weight of each dimension in the optimization objective function so as to output the priority of each dimension.

Description

Dynamic evaluation method and system for multi-dimensional response potential of resources Technical Field The application relates to the technical field of energy Internet, in particular to a dynamic evaluation method and a dynamic evaluation system for multi-dimensional response potential of resources. Background With the development of energy internet and demand-side response technologies, it becomes critical how to effectively evaluate and utilize the regulatory potential of distributed resources (e.g., business users, residential users, energy storage systems, etc.). These resources may respond to grid or market signals in different dimensions, such as adjusting the power usage strategy to obtain economic benefits (economic benefits dimension), avoiding frequent start-up and shut-down of the device to extend lifetime (device lifetime dimension), ensuring basic comfort for the user (user comfort dimension), or providing frequency and voltage support for the grid (grid support dimension). Existing resource response potential evaluation methods mostly focus on a single dimension, for example, only considering economy optimization, or only setting fixed power adjustment upper and lower limits as technical constraints. The idea of such methods is to set a static, single adjustability boundary for the resource based on historical data or a simple model. For example, the potential of an energy storage system is fixed to its rated power and capacity, ignoring its actual operating conditions and the effects of the external environment. The static and fixed boundary value is adopted to define the adjustable capacity of the resource, and the influence of external factors of dynamic changes such as weather, real-time electricity price, user behavior habit and the like is ignored, so that the evaluation result is not accurate enough and real-time. Such coarse-grained assessment makes it difficult to achieve maximum utilization of the resource response potential. Disclosure of Invention Based on the above, the invention aims to solve the technical problems, dynamically evaluate the response potential of the resources in a comprehensive multi-dimension way, remarkably improve the accuracy and the instantaneity of the evaluation result and realize the maximization and the optimal utilization of the resource potential. To achieve the above object, a first aspect of the present application provides a method for dynamically evaluating multi-dimensional response potential of a resource, including: Acquiring multi-source data of a target evaluation resource, wherein the multi-source data at least comprises inherent attribute data of the resource, external dynamic factor data and real-time running state data; Performing dimension-division adjustable capacity boundary calculation, including respectively calculating adjustable capacity boundaries of the resources under at least two different dimensions based on the multi-source data, wherein the adjustable capacity boundaries are power boundaries; based on external dynamic factor data of a future time period, performing the dimension-based adjustable capacity boundary calculation in a rolling way to generate a dynamic potential boundary sequence in each dimension in the future time period; and generating constraint conditions for scheduling power for the resources according to the dynamic potential boundary sequence, and outputting priorities of all dimensions according to predefined scene rules. Preferably, in the multi-source data: the inherent attribute data of the resource comprises rated power, capacity, efficiency curve and life model parameters of the equipment; The external dynamic factor data comprise weather forecast data, electric power market price data and user historical behavior data; the real-time operational state data includes a current operational power, state of charge, SOC, and device temperature of the resource. Preferably, the at least two different dimensions include at least any two of an economic benefit dimension, a device lifetime dimension, a user comfort dimension, and a grid support dimension; Aiming at the economic benefit dimension, calculating a power boundary of the economic benefit dimension on the condition that the benefit is not smaller than a preset threshold based on the electricity price signal; calculating a power boundary of the equipment life dimension on the condition that the life loss rate is not greater than a preset threshold value aiming at the equipment life dimension; Aiming at the user comfort dimension, calculating a power boundary of the user comfort dimension on the condition that the environmental parameter is maintained within a preset range; and calculating the power boundary of the power grid supporting dimension based on the power grid dispatching instruction information aiming at the power grid supporting dimension. Preferably, the dimension-wise adjustable capability boundary calculation specifically includes: th