Method and system for evaluating credible capacity of new-type business subject facing power market

Abstract

The application provides a kind of new type of operation subject of power market-oriented credible capacity evaluation method and system, comprising: according to the type identification of the operation subject to be evaluated, the corresponding calculation model is matched, and the basic capacity of the operation subject is calculated;If the type identification is non-new energy, the dynamic correction coefficient is determined by combining the operating state factor, the historical response performance factor and the preset environmental condition benchmark factor;If the type identification is new energy, the dynamic correction coefficient is determined by combining the environmental condition factor, the historical response performance factor and the preset operating state benchmark factor;The basic capacity is corrected using the dynamic correction coefficient to obtain the credible capacity and generate the credible capacity label;When the update condition is met, recalculate and update the credible capacity label.The existing technology evaluation caliber is not unified, it is difficult to reflect the dynamic influence of multi-source factors on capacity in real time, and there is no full-cycle dynamic updating mechanism, which leads to the problem that the evaluation result cannot support the accurate clearing and reliable operation of power market.

Description

Technical Field

[0001] This invention relates to the fields of power market operation and power system planning technology, specifically to a reliable capacity assessment method and system for new types of power market operators. Background Technology

[0002] During the construction of the new power system, intermittent renewable energy sources such as wind power and photovoltaics are being connected to the grid at a high proportion. New types of electricity market operators, such as Virtual Power Plants (VPPs), Energy Storage Aggregators (ESAs), Electric Vehicle Aggregators (EVAs), and Load Aggregators, are emerging on a large scale, becoming key bridges connecting distributed resources and the electricity market. In this new power system, the electricity market needs to construct a capacity valuation mechanism for capacity market clearing, electricity ancillary service settlement, and pricing of medium- and long-term electricity contracts, and its technological system needs to be adapted to the operational characteristics of diverse and heterogeneous operators.

[0003] Current mainstream capacity assessment technologies primarily target traditional conventional coal-fired power plants and other dispatchable power sources, generally using nameplate capacity and equivalent availability factor as quantitative indicators of capacity contribution. However, new operating entities are characterized by strong output randomness, diverse response modes, significant cross-energy coupling, and huge differences between aggregation levels and individual units, resulting in a serious discrepancy between their nameplate capacity and the actual reliability contribution of the system. Therefore, for new energy and aggregation-type entities, existing technologies have derived three typical assessment approaches: first, calculating the capacity value of a single type of resource based on Effective Load Carrying Capability (ELCC); second, introducing a capacity equivalence factor to reduce the theoretical output of new energy; and third, using static coefficient tables to complete the capacity equivalence conversion of energy storage and demand response resources.

[0004] The existing technical system has significant limitations: First, the assessment system is fragmented, with inconsistent capacity quantification rules for different types of market participants, making cross-category horizontal benchmarking impossible. Second, the assessment results are static and fixed, failing to consider the dynamic impact of resource operating status (such as battery state of charge and aggregated resource online rate), external environment (such as temperature, irradiance, wind speed, and extreme weather), and historical response behavior, making it difficult to characterize the real-time capacity contribution of each participant. Third, it lacks standardized data interaction specifications and engineering integration solutions, preventing deep integration between the assessment system and existing business systems such as dispatch automation, power market technical support, and aggregated operation platforms. Fourth, it lacks a full lifecycle capacity label management and rolling update mechanism, resulting in insufficient timeliness of assessment results and inability to achieve full-process traceability and market-based settlement adaptation. Fifth, it lacks real-world power system examples and comparative verification data, making it difficult to support the engineering promotion of the assessment method. With the continuous expansion of new operating entities and the increasing demands for refined operation in the power market, existing static and decentralized capacity assessment technologies can no longer meet the industry's application requirements of "unified comparability, dynamic reliability, engineering implementation, and traceable settlement." Summary of the Invention

[0005] The invention provides a reliable capacity assessment method and system for new types of operators in the electricity market, which at least solves the problems of inconsistent assessment criteria, difficulty in reflecting the dynamic impact of multiple factors on capacity in real time, and lack of a full-cycle dynamic update mechanism in existing capacity assessment technologies, resulting in assessment results that cannot support accurate clearing and reliable operation of the electricity market.

[0006] According to one aspect of the present invention, a reliable capacity assessment method for new types of operators in the electricity market is provided, comprising:

[0007] Based on the type identifier of the business entity to be evaluated, the corresponding calculation model is matched from the preset basic capacity function library to calculate the basic capacity of the business entity; wherein, the type identifier includes non-new energy and new energy;

[0008] When the type is identified as non-new energy, the operating status factor and historical response performance factor are calculated based on the operating status data and historical response performance data of the operating entity, respectively, and combined with the preset environmental condition benchmark factor, the dynamic correction coefficient of the non-new energy operating entity is determined.

[0009] When the type is identified as new energy, the environmental condition factor and historical response performance factor are calculated based on environmental meteorological data and historical response performance data, respectively, and combined with the preset operating status benchmark factor, the dynamic correction coefficient of the new energy operator is determined.

[0010] The basic capacity is adjusted using the dynamic correction coefficient to obtain the reliable capacity of the operating entity, and a corresponding reliable capacity label is generated.

[0011] If the trigger update conditions are met, the trusted capacity of the operating entity is recalculated and the corresponding trusted capacity tag is updated; wherein, the trigger update conditions include timed trigger conditions based on a preset period and event trigger conditions based on sudden changes in operating status.

[0012] As an optional solution, the non-new energy sources include virtual power plants, energy storage aggregators, and electric vehicle aggregators; the new energy sources include wind power and photovoltaic power.

[0013] The step of calculating the basic capacity of the business entity by matching the corresponding calculation model from a preset basic capacity function library based on the type identifier of the business entity to be evaluated includes:

[0014] When the type is identified as a virtual power plant, the virtual power plant basic capacity calculation model is used to perform a weighted summation of the nameplate capacity, type baseline coefficient, and aggregation reachability of each member resource within the aggregation to obtain the basic capacity of the virtual power plant.

[0015] When the type is identified as an energy storage aggregator, the basic capacity calculation model of the energy storage aggregator is adopted to obtain the basic capacity of the energy storage aggregator based on the rated power and duration segmentation coefficient of the access resources.

[0016] When the type is identified as an electric vehicle aggregator, the basic capacity calculation model of the electric vehicle aggregator is adopted. The basic capacity of the electric vehicle aggregator is calculated based on the number of connected vehicles, the average available power per vehicle, the average state of charge of connected vehicles, and the ordered charging response coefficient.

[0017] When the type is identified as new energy, the basic capacity calculation model of new energy is adopted, and the basic capacity of new energy is calculated according to the nameplate capacity, type benchmark coefficient and application correction coefficient of the new energy; wherein, the type benchmark coefficient is obtained by simulation fitting of historical power system operation data.

[0018] As an optional approach, when the type is identified as non-new energy, the operating status factor and historical response performance factor are calculated based on the operating entity's operating status data and historical response performance data, respectively, and combined with a preset environmental condition benchmark factor, to determine the dynamic correction coefficient for the non-new energy operating entity, including:

[0019] Calculate the operating status factor based on the operating status data of the operating entity;

[0020] When the type is identified as a virtual power plant, the capacity of each member resource is weighted and summed according to the online status of each member resource within the aggregation to obtain the operating status factor of the virtual power plant.

[0021] When the type is identified as an energy storage aggregator, the operating state factor of the energy storage aggregator is determined according to the piecewise linear decay rule of the battery state of charge; wherein, when the battery state of charge is lower than the first charge threshold or higher than the second charge threshold, the capacity contribution is linearly reduced relative to the full value.

[0022] When the type is identified as an electric vehicle aggregator, the operating status factor of the electric vehicle aggregator is calculated based on the proportion of vehicles connected to the grid and the average remaining power.

[0023] Calculate the corresponding historical response performance factor based on the historical response performance data of non-new energy business entities;

[0024] Obtain a preset environmental condition benchmark factor; wherein, the environmental condition benchmark factor is a preset benchmark value adapted to the non-new energy business entity;

[0025] The dynamic correction coefficient is obtained by multiplying the operating status factor, the historical response performance factor, and the environmental condition benchmark factor.

[0026] As an optional approach, when the type is identified as new energy, the environmental condition factor and historical response performance factor are calculated based on environmental meteorological data and historical response performance data, respectively, and combined with a preset operating status benchmark factor to determine the dynamic correction coefficient for the new energy operator, including:

[0027] Calculate environmental condition factors based on environmental meteorological data;

[0028] In the case of wind power identified as a new energy source, the environmental condition factors of wind power are calculated using a power law function based on the relationship between the predicted wind speed and the rated wind speed of the wind turbine, the wind turbine cut-in wind speed, and the wind turbine cut-out wind speed.

[0029] In the case of photovoltaics, which are identified as new energy sources, the environmental condition factor of photovoltaics is calculated based on the ratio of the predicted irradiance value to the irradiance under standard test conditions and the temperature deviation of the photovoltaic module.

[0030] Calculate the corresponding historical response performance factor based on the historical response performance data of new energy operators;

[0031] Obtain a preset operating status benchmark factor; wherein, the operating status benchmark factor is a benchmark value preset by the new energy business entity;

[0032] The dynamic correction coefficient is obtained by multiplying the environmental condition factor, the historical response performance factor, and the operational status benchmark factor.

[0033] As an optional approach, the environmental condition benchmark factor or the value of the environmental condition factor is dynamically adjusted according to the meteorological scenario, including:

[0034] When the type is identified as non-new energy and the meteorological scenario is a conventional meteorological scenario, the environmental condition benchmark factor is taken as a preset benchmark value;

[0035] When the type is identified as non-new energy and the meteorological scenario is an extreme meteorological scenario, the environmental condition benchmark factor is the value obtained by correcting the environmental condition benchmark factor under the conventional meteorological scenario using the extreme meteorological correction coefficient.

[0036] When the type is identified as new energy and the meteorological scenario is a conventional meteorological scenario, the environmental condition factor is taken as the value calculated based on the environmental meteorological data;

[0037] When the type is identified as new energy and the meteorological scenario is an extreme meteorological scenario, the environmental condition factor is the value obtained by correcting the environmental condition factor under the conventional meteorological scenario using the extreme meteorological correction coefficient.

[0038] The extreme weather scenarios mentioned include cold waves, continuous rain, typhoons, or thunderstorms.

[0039] As an optional approach, the historical response performance factor is calculated on a rolling basis based on the ratio of the actual response volume to the promised response volume of the operating entity within the historical assessment period. If the ratio fails to reach the preset ratio threshold twice consecutively, a penalty reduction mechanism is activated to obtain the reduced historical response performance factor.

[0040] As an optional approach, the step of using the dynamic correction coefficient to recalculate and correct the basic capacity to obtain the reliable capacity of the operating entity and generating a corresponding reliable capacity label includes:

[0041] The reliable capacity of the operating entity is obtained by multiplying the dynamic correction coefficient by the basic capacity.

[0042] By combining the fields of trusted capacity, tag number, entity number, version number, effective start and end time, evaluation criteria, and certification status, a trusted capacity tag for the business entity is generated.

[0043] The trusted capacity tag is fed back to the power dispatch automation system or the power market technical support system.

[0044] As an optional approach, before generating the trusted capacity label for the operating entity, the method further includes performing a consistency check on the aggregated level and the individual level response for multiple operating entities when the type identifier is non-new energy; the consistency check includes:

[0045] Obtain the aggregate-level measured response power and the individual-weighted measured response power of the business entity during the assessment period, and calculate the consistency deviation;

[0046] If the consistency deviation exceeds the preset deviation threshold, the consistency verification is deemed unsuccessful, a formal trusted capacity label is not issued, and parameter review and re-identification are triggered.

[0047] If the consistency deviation does not exceed the preset deviation threshold, the consistency verification is deemed successful, the authentication status of the trusted capacity tag is set to valid, and the generation and feedback of the trusted capacity tag are executed.

[0048] As an optional approach, the step of recalculating the trusted capacity of the operating entity and updating the corresponding trusted capacity tag when the trigger update condition is met includes:

[0049] When the type is identified as non-new energy, timed and event-triggered updates are performed.

[0050] The operating status factor is updated according to the first preset cycle, the historical response performance factor is updated according to the second preset cycle, and the basic capacity is updated according to the third preset cycle. The trusted capacity of the operating entity is recalculated and the corresponding trusted capacity label is updated.

[0051] If any of the following situations are detected: battery charge state exceeding the limit, virtual power plant member resource online status switching, operator failing to meet the standard twice in a row, or operator equipment malfunctioning or undergoing maintenance, then the operator's trusted capacity will be recalculated and the corresponding trusted capacity label will be updated.

[0052] When the type is identified as new energy, updates are performed using both timed and event-triggered methods.

[0053] The environmental condition factor is updated according to the update frequency of meteorological forecast data, the historical response performance factor is updated according to the second preset cycle, the basic capacity is updated according to the third preset cycle, the reliable capacity of the operating entity is recalculated, and the corresponding reliable capacity label is updated.

[0054] If any of the following situations are detected: an extreme weather warning is issued, the operator fails to meet the standard twice in a row, or the operator's equipment malfunctions or is under maintenance, the operator's trusted capacity will be recalculated and the corresponding trusted capacity label will be updated.

[0055] According to another aspect of the present invention, a reliable capacity assessment system for new types of operators in the electricity market is also provided, which is applied to a reliable capacity assessment method for new types of operators in the electricity market, comprising:

[0056] The basic capacity calculation module is used to calculate the basic capacity of the business entity by matching the corresponding calculation model from the preset basic capacity function library according to the type identifier of the business entity to be evaluated; wherein, the type identifier includes non-new energy and new energy.

[0057] The dynamic correction coefficient calculation module is used to calculate the operating status factor and historical response performance factor based on the operating status data and historical response performance data of the operating entity when the type is identified as non-new energy, and to determine the dynamic correction coefficient of the non-new energy operating entity in combination with the preset environmental condition benchmark factor; and to calculate the environmental condition factor and historical response performance factor based on the environmental meteorological data and historical response performance data when the type is identified as new energy, and to determine the dynamic correction coefficient of the new energy operating entity in combination with the preset operating status benchmark factor.

[0058] The tag generation module is used to calculate and correct the basic capacity using the dynamic correction coefficient to obtain the reliable capacity of the business entity and generate a corresponding reliable capacity tag.

[0059] The update management module is used to recalculate the trusted capacity of the operating entity and update the corresponding trusted capacity tag when the trigger update conditions are met; wherein, the trigger update conditions include timed trigger conditions based on a preset period and event trigger conditions based on sudden changes in operating status.

[0060] The beneficial effects of this invention are as follows:

[0061] 1. This invention utilizes a pre-defined basic capacity function library to employ a unified hierarchical quantification framework of "basic capacity × dynamic correction coefficient" for different types of operating entities. This allows different operating entities to calculate reliable capacity under the same metric, solving the problems of inconsistent assessment standards and the inability to make horizontal comparisons between different types of operating entities in existing technologies. For non-energy operating entities, the dynamic impact of operating status factors and historical response performance factors is mainly considered; for new energy operating entities, the dynamic impact of environmental condition factors and historical response performance factors is mainly considered. This reflects the nonlinear coupling effect of the main influencing factors on capacity contribution in different types of operating entities, solving the problem that static reduction coefficients cannot track dynamic changes.

[0062] 2. The invention employs a dual-trigger mechanism of timed triggering and event triggering, which can ensure periodic refresh in normal scenarios, ensure the timeliness of calculation, and also enable immediate response to key boundary conditions, ensuring the sensitivity of calculation. It balances calculation efficiency and evaluation accuracy, and realizes full lifecycle management of trusted capacity from beginning to end.

[0063] 3. The present invention generates reliable capacity tags that are engineerable, settlementable, roll-upable, and traceable. These tags can be directly used for capacity market clearing, ancillary service market settlement, and medium- and long-term contract pricing. This enables system scheduling and transaction decisions to be based on more realistic reliability contributions, significantly reducing the risk of load loss, settlement deviations, and performance defaults caused by capacity misjudgment. Attached Figure Description

[0064] To more clearly illustrate the embodiments of the present invention or the technical solutions in 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, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart of the reliable capacity assessment method for new types of operators in the electricity market, created by this invention.

[0066] Figure 2 This is a schematic diagram of the system structure of an embodiment of the present invention.

[0067] Figure 3 This is a schematic diagram of the structure of the electronic device created by this invention. Detailed Implementation

[0068] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0069] Example 1

[0070] like Figure 1 As shown, this invention provides a reliable capacity assessment method for new types of operators in the electricity market, comprising:

[0071] Step S11: Based on the type identifier of the business entity to be evaluated, match the corresponding calculation model from the preset basic capacity function library to calculate the basic capacity of the business entity; wherein, the type identifier includes non-new energy and new energy.

[0072] In this embodiment of the invention, non-new energy operators include virtual power plants, energy storage aggregators, and electric vehicle aggregators; new energy operators include wind power and photovoltaic power. Different types of operators possess differentiated resource regulation characteristics, and corresponding calculation models are configured for different types of operators to achieve differentiated basic capacity solutions.

[0073] The aforementioned basic capacity function library is a pre-built set of calculation models, which encapsulates the basic capacity calculation logic for different types of business entities. It automatically retrieves the matching calculation model based on the input type identifier to perform standardized calculation of basic capacity.

[0074] Step S12: When the type is identified as non-new energy, calculate the operating status factor and historical response performance factor based on the operating status data and historical response performance data of the operating entity, and determine the dynamic correction coefficient of the non-new energy operating entity in combination with the preset environmental condition benchmark factor.

[0075] The aforementioned operational status factors are quantitative indicators calculated based on real-time operational status data reflecting the current equipment health status and real-time availability of non-new energy operators. These factors characterize the actual physical availability of non-new energy operators at any given moment. The operational status data includes, but is not limited to, equipment start-up / shutdown status, availability, forced outage records, and planned maintenance status.

[0076] Specifically, for different non-new energy operators, the operating status factor adopts differentiated calculation logic to ensure that different non-new energy operators can still reflect the essential differences in their physical characteristics and operating constraints under the same assessment framework, thereby improving the precision and engineering applicability of credible capacity assessment.

[0077] The aforementioned historical response performance factor is a quantitative indicator calculated based on the historical response performance data of the operating entity's actual response volume and committed response volume within the historical assessment period. It is used to quantify the reliability of the operating entity's long-term participation in power regulation.

[0078] The aforementioned environmental condition benchmark factors are preset physical reference anchors designed to adapt to non-new energy operators. Specifically, under normal weather conditions, given the relatively stable output characteristics of non-new energy operators, which are mainly constrained by the physical state of equipment and dispatch commands, the environmental condition benchmark factors are typically set to preset benchmark values ​​to reflect the dominant role of the non-new energy operator's operating status in reliable capacity. Meanwhile, under extreme weather conditions, considering that environmental stress may significantly constrain non-new energy equipment (such as energy storage battery performance and gas turbine intake efficiency), the environmental condition benchmark factors will be dynamically adjusted or corrected based on weather warning levels, etc. This ensures the simplicity of calculations under normal weather conditions while also guaranteeing the safety and accuracy of assessment results under extreme weather conditions.

[0079] Step S13: When the type is identified as new energy, calculate the environmental condition factor and historical response performance factor based on environmental meteorological data and historical response performance data respectively, and determine the dynamic correction coefficient of the new energy operator by combining the preset operating status benchmark factor.

[0080] The aforementioned environmental condition factors are quantitative indicators calculated based on environmental meteorological data affecting the actual output of new energy operators. They are used to characterize the theoretical maximum output capacity or resource availability ratio of new energy power generation equipment under normal meteorological scenarios. Environmental meteorological data includes solar irradiance, wind speed, and extreme weather warnings. New energy output exhibits strong randomness and volatility, directly determining the upper limit of reliable capacity.

[0081] Specifically, different calculation logics are used for environmental condition factors for different new energy operators to ensure that the assessment results accurately match the physical conversion characteristics of different new energy operators. In addition, under extreme weather scenarios, an extreme weather correction coefficient is introduced to reduce the theoretical output under normal weather scenarios, reflecting the nonlinear impact of severe weather on equipment output and safe operation.

[0082] The aforementioned operational status benchmark factor is a preset benchmark value adapted to new energy operators and is not affected by operational parameters such as equipment operating status. Specifically, the operational status benchmark factor is set based on the physical characteristics of new energy operators, which operate on an "on-demand" basis and typically do not have large-scale fuel reserves. Unlike non-new energy operators, the theoretical availability rate of equipment for new energy operators is usually set as a constant close to 100%, and fluctuations in actual output are mainly due to natural resources (environmental conditions) rather than the operational status of the operator itself. Therefore, by eliminating the interference of controllable factors of the new energy operator itself on the assessment results and using it as a reference anchor point in the calculation of dynamic correction coefficients, the constraints of the natural environment on reliable capacity are reflected more purely.

[0083] Step S14: The basic capacity is adjusted using a dynamic correction coefficient to obtain the credible capacity of the operating entity, and a corresponding credible capacity label is generated.

[0084] The aforementioned reliable capacity refers to the effective capacity that operating entities can stably participate in electricity market regulation at the present moment and in the short term, after being corrected by a dynamic correction coefficient.

[0085] The aforementioned trusted capacity label not only includes trusted capacity, but also identifiers such as operating status and validity period. It serves as a direct basis for electricity market transaction clearing, ancillary service call-up, and grid security verification, ensuring the fairness of market transactions and the safety of grid operation.

[0086] Step S15: If the trigger update conditions are met, recalculate the trusted capacity of the operating entity and update the corresponding trusted capacity label; wherein, the trigger update conditions include timed trigger conditions based on a preset period and event trigger conditions based on sudden changes in operating status.

[0087] The aforementioned triggering update conditions refer to both timed triggering conditions and event triggering conditions. These two triggering mechanisms work together to continuously ensure the real-time effectiveness of the trusted capacity label. Specifically, the timed triggering condition updates each correction factor and the base capacity periodically according to a fixed preset cycle; the event triggering condition immediately triggers a recalculation of the trusted capacity when a sudden event is detected.

[0088] The execution entity for the above steps can be a local edge computing terminal, a data aggregation gateway, or a server deployed on the power market management and control platform. Specifically, the steps of matching the basic capacity model, calculating the basic capacity, and collecting and solving the basic data can be completed by a data acquisition chip in conjunction with a local microcontroller; the steps of solving for operating status factors, environmental condition factors, and historical response performance factors, dynamically adjusting coefficients, and calculating trusted capacity can be implemented by a digital signal processor (DSP), a field-programmable gate array (FPGA), an industrial-grade edge computing unit, or a general-purpose processor executing computer program instructions; and the final trusted capacity tag generation, timed triggering and event-triggered update logic, and tag push output can be implemented by a site monitoring host or a cloud-based market assessment server. This invention is not limited to a single physical entity; any computing device capable of data acquisition, multi-factor calculation, capacity calculation, and tag update logic can serve as the execution entity.

[0089] Specifically, in step S11, based on the type identifier of the business entity to be evaluated, the corresponding calculation model is matched from the preset basic capacity function library to calculate the basic capacity of the business entity, which includes the following steps:

[0090] Step S111: When the type is identified as a virtual power plant, the virtual power plant basic capacity calculation model is used to perform a weighted summation of the nameplate capacity, type baseline coefficient, and aggregation reachability of each member resource within the aggregation to obtain the basic capacity of the virtual power plant.

[0091] In this embodiment, the virtual power plant aggregates various heterogeneous resources, including industrial adjustable loads, commercial building air conditioning, user-side energy storage, rooftop photovoltaics, and charging piles. A weighted summation of the resources within each member is used to construct the virtual power plant's basic capacity calculation model. The expression for calculating the basic capacity of the virtual power plant using this model is as follows: ; ; In the formula, This represents the basic capacity of the virtual power plant; Indicates the first The nameplate capacity of each member's resource; Indicates the first The type benchmark coefficient of each member's resource; Indicates the first Aggregate reachability of resources for each member; Indicates the communication success rate; Indicates the measurement accuracy coefficient; Indicates the call response rate; This indicates the resource sequence number of a member within the virtual power plant.

[0092] Among them, the communication success rate, metering accuracy coefficient and call response rate are obtained by fitting the assessment data of the virtual power plant trial operation for 30 days, and the value range is [0, 1.0].

[0093] It should be noted that the type benchmark coefficients can be obtained by ELCC simulation fitting based on local historical data, or by direct reference to publicly available data from mature international markets (PJM, CAISO), or by static assignment based on industry standards.

[0094] Step S112: When the type is identified as an energy storage aggregator, the basic capacity calculation model of the energy storage aggregator is adopted to obtain the basic capacity of the energy storage aggregator based on the rated power and duration segmentation coefficient of the access resources.

[0095] In this embodiment, the capacity contribution of an energy storage aggregator's energy storage resources is strongly correlated with its continuous discharge duration. A duration segmentation coefficient is introduced to correct the impact of rated power on the base capacity, thereby constructing a base capacity calculation model for the energy storage aggregator. The expression for calculating the base capacity of the energy storage aggregator using this model is as follows: ; In the formula, This indicates the basic capacity of the energy storage aggregator; This represents the sum of the rated power of the resources connected to the energy storage aggregator; Indicates the number of hours of continuous discharge at rated power; This represents a piecewise function indicating the duration.

[0096] The duration piecewise function is a quantitative index obtained by fitting simulation results of the energy storage effective load carrying capacity (ELCC) under a typical load curve. To accurately reflect energy storage characteristics, the duration piecewise function is defined as a piecewise linear function, specifically expressed as: .

[0097] Piecewise function of duration at the breakpoint and The continuity of the duration prevents abrupt changes in capacity assessment results due to minor variations in duration, ensuring a smooth transition of the assessment model. Simultaneously, the piecewise function of duration exhibits a monotonically non-decreasing trend and eventually approaches saturation, accurately reflecting the objective law that as the continuous discharge capacity of energy storage increases, its contribution to reducing peak system load gradually increases and eventually reaches its physical limit.

[0098] Step S113: When the type is identified as an electric vehicle aggregator, the basic capacity calculation model of the electric vehicle aggregator is adopted. Based on the number of connected vehicles, the average available power per vehicle, the average state of charge of connected vehicles, and the ordered charging response coefficient, the basic capacity of the electric vehicle aggregator is calculated.

[0099] In this embodiment, a basic capacity calculation model for electric vehicle aggregators is constructed based on the number of connected vehicles, the average available power per vehicle, the average state of charge of connected vehicles, and the ordered charging response coefficient. The expression for calculating the basic capacity of electric vehicle aggregators using this basic capacity calculation model is as follows: ; In the formula, This represents the basic capacity of the electric vehicle aggregation; This indicates the number of electric vehicles connected to the aggregator's management system; This indicates the average available power per vehicle; This indicates the average state of charge of the connected vehicles; This indicates the orderly charging response rate.

[0100] Specifically, the average available power per vehicle is obtained from historical charging pile power statistics; the orderly charging response rate is obtained by fitting the assessment data of electric vehicle aggregators, which is used to reflect the proportion of vehicles that can actually participate in the adjustment under the dispatch signal.

[0101] Step S114: When the type is identified as new energy, the basic capacity calculation model of new energy is adopted. Based on the nameplate capacity, type benchmark coefficient and application correction coefficient of new energy, the basic capacity of new energy is calculated. Among them, the type benchmark coefficient is obtained by simulation fitting of historical power system operation data.

[0102] For wind power operators, the basic capacity of wind power is calculated using a basic capacity calculation model, and its expression is as follows: ; In the formula, Indicates the base capacity of wind power; Indicates the nameplate capacity of the wind turbine; Indicates the benchmark coefficient for wind power type; This indicates the correction factor for wind power application.

[0103] Specifically, the benchmark coefficients for wind power types include onshore wind power benchmark coefficients and offshore wind power benchmark coefficients. Among them, the offshore wind power benchmark coefficients are obtained by fitting the hourly output data of local coastal wind farms over the past 5 years.

[0104] Specifically, the wind power application correction factor is a custom setting made by the user based on actual needs.

[0105] In a preferred embodiment, the value range of the onshore wind power reference coefficient is [0.30, 0.40]; and the value range of the offshore wind power reference coefficient is [0.45, 0.55].

[0106] For photovoltaic (PV) operators, a basic capacity calculation model is used to calculate the basic capacity of PV systems, and its expression is as follows: ; In the formula, Indicates the basic capacity of photovoltaic power; Indicates the nameplate capacity of the photovoltaic system; Indicates the type benchmark coefficient of photovoltaics; This indicates the correction factor for photovoltaic (PV) applications.

[0107] Specifically, the benchmark coefficients for photovoltaic (PV) types include the benchmark coefficient for fixed PV and the benchmark coefficient for tracking PV. The benchmark coefficient for tracking PV is obtained by fitting local distributed PV output data.

[0108] In a preferred embodiment, the value range of the fixed photovoltaic reference coefficient is [0.12, 0.18]; the value range of the tracking photovoltaic reference coefficient is [0.22, 0.28].

[0109] Specifically, in step S12, when the type is identified as non-new energy, the operating status factor and historical response performance factor are calculated based on the operating entity's operating status data and historical response performance data, respectively. Combined with the preset environmental condition benchmark factor, the dynamic correction coefficient for the non-new energy operating entity is determined. This includes the following steps:

[0110] Step S121: Calculate the operating status factor based on the operating status data of the business entity.

[0111] Step S1211: When the type is identified as a virtual power plant, the capacity of each member resource is weighted and summed according to the online status of each member resource within the aggregation to obtain the operating status factor of the virtual power plant.

[0112] In this embodiment, the operating state factor of the virtual power plant is obtained by capacity-weighted summation of the online rates of its internal member resources. The expression for the operating state factor of the virtual power plant is as follows: ; In the formula, The operating status factor of the virtual power plant; Indicates the first The capacity weight of each member resource in the virtual power plant is used to reflect the contribution of each member resource to the overall aggregated capacity of the virtual power plant. Indicates the first The online indicator function for each member resource is used to characterize the real-time connection status of the member resource. The value is 1 when the member resource is online (available) and 0 when the member resource is offline (unavailable).

[0113] Among them, the capacity weights of each member resource in the virtual power plant are... Defined as the first The proportion of the basic capacity of each member's resources to the sum of the basic capacities of all members' resources in the virtual power plant is specifically expressed as follows: ; In the formula, Indicates the first The basic capacity of each member's resources; Indicates the first The basic capacity of each member's resources; This indicates the resource sequence number of a member within the virtual power plant.

[0114] By calculating the operating status factor of the virtual power plant, the capacity ratio of online member resources at the current moment is characterized, and it is positively correlated with the overall online rate of the virtual power plant. This objectively and quantitatively reflects the actual physical availability of the virtual power plant at the current moment, with clear logic and in line with engineering practice.

[0115] Step S1212: When the type is identified as an energy storage aggregator, the operating state factor of the energy storage aggregator is determined according to the piecewise linear decay rule of the battery state of charge; wherein, when the battery state of charge is lower than the first charge threshold or higher than the second charge threshold, the capacity contribution is linearly reduced relative to the full value.

[0116] In this embodiment, the operating state factor of the energy storage aggregator is calculated based on a piecewise linear decay rule of the battery's state of charge (SOC). The expression for the operating state factor of the energy storage aggregator is as follows: ; In the formula, This represents the operating status factor of the energy storage aggregator; This indicates the battery's state of charge, with a value ranging from [0, 1.0]. Indicates the first charge threshold; This represents the second charge threshold.

[0117] Specifically, the first charge threshold Second charge threshold It is designed based on the battery chemistry system.

[0118] In a preferred embodiment, the first charge threshold Set to 0.2, second charge threshold Set to 0.8. This is the first charge threshold when the battery is lithium iron phosphate. Set to 0.1, second charge threshold Set to 0.9; First charge threshold when the battery is lithium titanate. Set to 0.3, second charge threshold Set it to 0.7.

[0119] The piecewise function described above ensures that the operating state factor of the energy storage aggregator is at the boundary point. and The continuity of the energy storage resources of the energy storage aggregator reflects that when the battery state of charge (SOC) crosses the safety boundary, that is, below 0.2 or above 0.8, the discharge capacity at low charge and the charging absorption capacity at high charge are limited due to the battery protection mechanism, resulting in a linear decay in the effective capacity contribution.

[0120] Step S1213: When the type is identified as an electric vehicle aggregator, the operating status factor of the electric vehicle aggregator is calculated based on the proportion of vehicles connected to the grid and the average remaining power.

[0121] In this embodiment, the operating status factor of the electric vehicle aggregator is a comprehensive assessment based on the proportion of connected vehicles and the average remaining battery power, used to reflect the actual physical capacity of the electric vehicle aggregator cluster to participate in regulation at the current moment. The expression for the operating status factor of the electric vehicle aggregator is: ; In the formula, This represents the operating status factor of electric vehicle aggregators; This indicates the number of vehicles connected to the power grid at the time of assessment. This indicates the total number of vehicles in the electric vehicle aggregator; This indicates the average state of charge of the connected vehicles; Indicates the maximum charge value of the connected vehicle; This indicates the minimum charge value of the connected vehicle.

[0122] In a preferred embodiment, the minimum charge value of the connected vehicle The default value is 0.3, meaning vehicles with values ​​below this are typically prioritized to ensure user travel needs and are not included in the adjustment; the maximum charge value of the connected vehicle. The default value is 0.95, meaning that vehicles with a value higher than this will have their battery power limited or stop charging.

[0123] The operating status factor of electric vehicle aggregators increases with the increase of the proportion of connected vehicles and the average remaining battery power, showing a positive correlation. Specifically, when all vehicles are connected and the average remaining battery power is within the optimal adjustment range, the operating status factor of electric vehicle aggregators approaches 1; when too many vehicles are offline, or the average remaining battery power is too high or too low, the operating status factor of electric vehicle aggregators decreases significantly, so as to accurately quantify the real-time available capacity of electric vehicle aggregators.

[0124] Step S122: Calculate the corresponding historical response performance factor based on the historical response performance data of non-new energy business entities.

[0125] In this embodiment, a rolling evaluation mechanism is adopted. The historical response performance factor is calculated based on the ratio of the actual response electricity to the promised response electricity of non-new energy operators within the historical assessment period. The specific expression is as follows: ; In the formula, This represents the historical response performance factor of non-new energy business entities. , Represents a virtual power plant. Indicates an energy storage aggregator. Indicates an electric vehicle aggregator; This indicates that non-new energy operators were in the historical assessment period. The actual response power; This indicates that non-new energy operators were in the historical assessment period. The promised response power; Indicates the number of assessment cycles.

[0126] Specifically, the number of assessment cycles The default value is 90 days, which can be adjusted to 30 days, 180 days or 365 days according to actual needs.

[0127] In practice, a 90-day observation period is set for newly connected business entities. During the observation period, due to the lack of sufficient historical data, their historical response performance factor is initialized to 1.0 by default to ensure that newly connected business entities can participate in market transactions normally.

[0128] Within the assessment window, if an operator's response rate (the ratio of its actual response volume to its promised response volume) fails to reach the preset threshold for two consecutive periods during the historical assessment cycle, it is deemed to have a substandard response rate. This triggers a penalty reduction mechanism, adjusting the historical response performance factor to obtain the reduced historical response performance factor for non-new energy operators. The expression for the reduced historical response performance factor for non-new energy operators is as follows: ; In the formula, This represents the historical response performance factor of non-new energy business entities after deduction; This indicates the number of consecutive recovery cycles that meet the standards.

[0129] Specifically, after the response rate resumes meeting the standard, the consecutive compliance recovery period is incremented by 1 for each consecutive period of compliance. The maximum cumulative limit for the consecutive compliance recovery period is 1, which means that the historical response performance factor of non-new energy business entities after reduction will be reduced to a minimum of 0.90 times the original value and a maximum of 0.95 times the original value.

[0130] In a preferred embodiment, the preset ratio threshold is set to 80%.

[0131] In addition, once the penalty reduction mechanism is triggered, the system will automatically trigger a warning sign in the authentication status field of the trusted capacity label, indicating that the current operating entity has a risk of default.

[0132] Step S123: Obtain the preset environmental condition benchmark factor.

[0133] In this embodiment, the environmental condition benchmark factor is a preset benchmark value adapted to non-new energy business entities, which can be adaptively adjusted according to the actual situation.

[0134] In a preferred embodiment, under normal meteorological scenarios, the environmental condition baseline factor is set to 1.

[0135] When encountering extreme weather scenarios such as cold waves, continuous rain, typhoons, and thunderstorms, environmental stress exerts a certain physical inhibition on non-new energy equipment. To more accurately assess these environmental constraints, an extreme weather correction coefficient is introduced to correct environmental condition factors under conventional weather scenarios, resulting in a baseline environmental condition factor for non-new energy operators under extreme weather scenarios. The expression for this baseline environmental condition factor for non-new energy operators under extreme weather scenarios is as follows: ; In the formula, This represents the environmental condition benchmark factor for non-new energy business entities under extreme weather scenarios; This represents the environmental condition benchmark factor for non-new energy business entities under conventional meteorological scenarios; This represents the correction factor for extreme weather conditions; Represents the minimum environmental factor; This represents the maximum value function.

[0136] It should be noted that if the system receives an extreme weather warning signal, it will automatically call the extreme weather correction coefficient to correct the environmental condition factors.

[0137] In this embodiment, extreme weather scenarios include cold waves, continuous rain, typhoons, or thunderstorms. The extreme weather correction coefficient is determined based on historical statistical data, such as measured power output data during extreme weather events over the past five years, to characterize the degree of attenuation of power generation capacity caused by specific extreme weather events.

[0138] In a preferred embodiment, the extreme weather correction factor is 0.40 for cold wave extreme weather scenarios; 0.30 for continuous rain extreme weather scenarios; 0.15 for typhoon extreme weather scenarios; and 0.50 for thunderstorm extreme weather scenarios.

[0139] In this embodiment, the minimum environmental factor is set to 0.1 to prevent the calculation results from approaching zero under extreme weather conditions, thereby ensuring that the system still retains a minimum adjustment capacity and maintains the stability and observability of the system.

[0140] Step S124: Multiply the operating status factor, historical response performance factor and environmental condition benchmark factor to obtain the dynamic correction coefficient for non-new energy business entities.

[0141] The expression for the dynamic correction coefficient for non-new energy business entities is as follows: ; In the formula, This represents the dynamic correction coefficient for non-new energy business entities; Factors representing the operational status of non-new energy business entities; Indicates the environmental condition benchmark factor; This represents the historical response performance factor of non-new energy business entities.

[0142] Specifically, in step S13, when the type is identified as new energy, the environmental condition factor and historical response performance factor are calculated based on environmental meteorological data and historical response performance data, respectively. Combined with the preset operating status benchmark factor, the dynamic correction coefficient of the new energy operator is determined, which includes the following steps:

[0143] Step S131: Calculate environmental condition factors based on environmental meteorological data.

[0144] Step S1311: In the case of wind power identified as a new energy source, the environmental condition factors of wind power are calculated using a power law function based on the relationship between the predicted wind speed and the rated wind speed, the wind speed cut-in wind speed, and the wind speed cut-out wind speed.

[0145] In this embodiment, the environmental condition factor for wind power is composed of a wind speed prediction correction term and is approximated using a power-law function of the wind turbine power curve. The expression for the environmental condition factor for wind power is: ; In the formula, This represents environmental condition factors of a wind farm; This indicates the predicted cabin wind speed for the day or very short term; Indicates the rated wind speed of the fan; Indicates the fan cut-in wind speed; This indicates the cut-off wind speed of the fan.

[0146] In a preferred embodiment, the rated wind speed of the fan The value is taken as 12 m / s; the fan cut-in velocity. The value is 3 m / s; the fan cut-off velocity. The value is 25 m / s.

[0147] Specifically, in the wind turbine start-up and ramp-up zone ( Within the specified range, the wind turbine output power increases cubically with increasing wind speed. Therefore, the environmental factors for wind power are nonlinearly corrected using the cube of the ratio of the predicted wind speed to the rated wind speed of the turbine to match the actual power curve. In the full-power zone (…),… Within the designated area, when the wind speed exceeds the turbine's rated wind speed but does not reach the turbine's cut-out wind speed, the turbine maintains its rated power output through pitch control. At this time, the wind power's environmental condition factor remains constant at 1.0, indicating that the turbine is in optimal operating condition. In the shutdown area (… or Within a given timeframe, if the wind speed is too low to drive the generator, or if the wind speed is too high to trigger the safety protection mechanism, the wind turbine will stop outputting power. In this case, the environmental condition factor for wind power will be set to 0.

[0148] It should be noted that the power law function is used to calculate the environmental condition factors of wind power in this embodiment. In practical applications, the interpolation function of the measured power curve provided by the manufacturer can also be used for calculation.

[0149] Step S1312: In the case of photovoltaics with the type identified as new energy, the environmental condition factor of photovoltaics is calculated based on the ratio of the irradiance prediction value to the irradiance under standard test conditions and the temperature deviation of the photovoltaic module.

[0150] In this embodiment, the environmental condition factor for photovoltaics is the product of an irradiance prediction correction term and a temperature correction term. The expression for the photovoltaic environmental condition factor is: ; In the formula, Indicates environmental condition factors for photovoltaics; This indicates the predicted daytime or ultra-short-term radiation value; Indicates irradiance under standard test conditions; Indicates the temperature coefficient of a photovoltaic module; This indicates the predicted temperature of the component's backsheet. This indicates the standard test condition temperature.

[0151] In a preferred embodiment, standard test condition irradiance Value: 1000W / m 2 Temperature coefficient of photovoltaic modules Value taken as 0.004℃; standard test condition temperature. The value is set to 25℃.

[0152] When encountering extreme weather scenarios such as cold waves, continuous rain, typhoons, and thunderstorms, the actual output of new energy power generation equipment is often significantly suppressed. Similar to step S123, new energy operators also introduce an extreme weather correction coefficient to correct the environmental condition factors of new energy operators under normal weather scenarios, resulting in the environmental condition factors of new energy operators under extreme weather scenarios, specifically expressed as follows: ; In the formula, This represents environmental condition factors for new energy operators under extreme weather scenarios; This represents environmental condition factors for new energy operators under conventional meteorological scenarios.

[0153] It should be noted that if the system receives an extreme weather warning signal, it will automatically call the extreme weather correction coefficient to correct the environmental condition factors.

[0154] Step S132: Calculate the corresponding historical response performance factor based on the historical response performance data of the new energy business entity.

[0155] In this embodiment, the calculation method for the historical response performance factor of new energy operators is logically consistent with the calculation method used for the historical response performance factor of non-new energy operators in step S122, both employing a rolling evaluation mechanism. The expression for the historical response performance factor of new energy operators is as follows: ; In the formula, This represents the historical response performance factor of new energy business entities. , Indicates wind power, Indicates photovoltaic; This indicates that new energy operators are in the historical assessment period The actual response power; This indicates that new energy operators are in the historical assessment period The promised response power; This indicates the number of assessment periods, with a default value of 90 days.

[0156] Similar to step S122, a penalty reduction mechanism is also configured for new energy operators. If the penalty reduction mechanism is triggered, the historical response performance factor of the new energy operator after reduction is obtained, and its expression is: ; In the formula, This represents the historical response performance factor of new energy business entities after deduction.

[0157] Specifically, after the response rate resumes meeting the standard, the consecutive compliance recovery period is incremented by 1 for each consecutive period of compliance. The maximum cumulative limit for the consecutive compliance recovery period is 1, which means that the historical response performance factor of the new energy business entity after reduction will be reduced to a minimum of 0.90 times the original value and a maximum of 0.95 times the original value.

[0158] In addition, once the penalty reduction mechanism is triggered, the system will automatically trigger a warning sign in the authentication status field of the trusted capacity label, indicating that the current new energy operator has a risk of default.

[0159] Step S133: Obtain the preset operating state baseline factor.

[0160] In this embodiment, the operating status benchmark factor is a preset benchmark value adapted to the new energy operator, used to characterize the benchmark contribution level of the availability of new energy equipment to capacity under standard operating conditions. Since wind power, photovoltaic and other new energy power generation equipment usually do not involve complex energy storage charging and discharging state switching or fluctuations in the online rate of aggregated resources during normal operation, the physical availability of the equipment itself is relatively stable. Therefore, in the conventional assessment scenario, the operating status benchmark factor is set to 1.0 to eliminate the interference of the equipment's own status on capacity assessment, so that the dynamic correction coefficient more purely reflects the impact of environmental conditions and historical performance.

[0161] It should be noted that in practical engineering applications, if the actual availability rate of a new energy operator deviates significantly from the rated operating condition due to equipment failure, planned maintenance, or other reasons, an operating status correction coefficient can be introduced based on the real-time operating status of the equipment to correct the operating status benchmark factor and achieve adaptive adjustment of the operating status benchmark factor.

[0162] The operation status correction coefficient is determined based on data such as the real-time availability of the new energy operator's own equipment. Its introduction can adapt to the additional needs of assessing the status of the new energy operator in specific market or scheduling scenarios, but does not affect the simplicity and universality of the default value in conventional scenarios.

[0163] Step S134: Multiply the environmental condition factor, historical response performance factor, and operational status benchmark factor to obtain the dynamic correction coefficient of the new energy business entity.

[0164] The expression for the dynamic correction coefficient of new energy operators is as follows: ; In the formula, This represents the dynamic correction coefficient for new energy business entities; Indicates the baseline factor for operating status; Indicates environmental condition factors; This represents the historical response performance factor of new energy business entities.

[0165] It should be noted that in this embodiment, the dynamic correction coefficient is calculated by multiplying the environmental condition factor, the historical response performance factor, and the operating status factor. However, in practical applications, it can also be obtained by weighted summation of the environmental condition factor, the historical response performance factor, and the operating status factor, or by nonlinear mapping based on neural networks.

[0166] Specifically, in step S14, the basic capacity is adjusted using a dynamic correction coefficient to obtain the reliable capacity of the operating entity, and a corresponding reliable capacity label is generated. This includes the following steps:

[0167] Step S141: Multiply the dynamic correction coefficient by the basic capacity to calculate the reliable capacity of the operating entity.

[0168] The expression for the reliable capacity of the operating entity is as follows: ; In the formula, Indicates the business entity Trusted capacity; Indicates the business entity Basic capacity; Indicates the business entity The dynamic correction coefficient; Indicates the type of business entity. .

[0169] Step S142: Combine the fields of trusted capacity, tag number, subject number, version number, effective start and end time, assessment criteria and certification status to generate a trusted capacity tag for the business entity.

[0170] In this embodiment, before generating the trusted capacity label for an operating entity, a consistency check is performed on the aggregated level and the individual level response for multiple operating entities when the type identifier is non-new energy. The consistency check specifically includes the following steps:

[0171] Obtain the aggregate-level measured response power and the individual-weighted measured response power of the operating entity within the assessment period, and calculate the consistency deviation.

[0172] If the consistency deviation exceeds the preset deviation threshold, the consistency verification is deemed unsuccessful, a formal trusted capacity label is not issued, and parameter review and re-identification are triggered.

[0173] If the consistency deviation does not exceed the preset deviation threshold, the consistency verification is deemed successful, the authentication status of the trusted capacity label is set to valid, and the generation and feedback of the trusted capacity label are executed.

[0174] The expression for consistency deviation is: ; In the formula, Indicates consistency deviation; This represents the measured response power at the aggregation level; Indicates the first Measured response power of each member.

[0175] For both non-new energy business entities and new energy business entities that have passed the consistency verification, the fields of trusted capacity, label number, entity number, version number, effective start and end time, assessment caliber, and certification status are combined to generate corresponding trusted capacity labels.

[0176] It should be noted that in practical applications, the technology can be expanded according to the specific business needs of the electricity market, such as adding fields for carbon emission conversion, cross-market settlement, and ancillary service product segmentation.

[0177] Step S143: Feed back the trusted capacity tag to the power dispatch automation system or the power market technical support system.

[0178] As an optimized engineering implementation, in order to achieve persistent storage of evaluation data, efficient querying, and secure interaction with external systems, this embodiment also constructs a standardized database table structure and data interaction interface.

[0179] Specifically, a relational database is used to store various types of data during the evaluation process, including:

[0180] (1) The main registry T_ENTITY is used as the basic index table of the system to store the static attributes of all market entities participating in the evaluation.

[0181] The key fields of the main registry T_ENTITY include the main entity number ENTITY_ID, the main entity type TYPE, the aggregation level LEVEL, the operator OPERATOR, and the registration time REG_TIME.

[0182] (2) Basic capacity parameter table T_BASE_CAP, used to record the reference physical quantities required for calculation.

[0183] The key fields of the basic capacity parameter table T_BASE_CAP include ENTITY_ID, nameplate capacity P_NAMEPLATE, type base coefficient ALPHA_TYPE, declaration correction coefficient BETA_DECLARE, basic capacity C_BASE, and calculation time CALC_TIME.

[0184] (3) Dynamic correction coefficient table T_DYN_FACTOR, which is used as the core data table of the system to store state factors that fluctuate frequently over time.

[0185] The key fields of the dynamic correction coefficient table T_DYN_FACTOR include the subject number ENTITY_ID, timestamp TIMESTAMP, operating status factor K_STATE, environmental condition factor K_ENV, historical response performance factor K_PERF, and dynamic correction coefficient K_DYN.

[0186] (4) Trusted capacity tag table T_TRUST_TAG, used to output the final evaluation results and its lifecycle management.

[0187] The key fields of the Trusted Capacity Tag Table T_TRUST_TAG include Tag ID, Entity ID, Trusted Capacity C_TRUST, Version ID, Effective Start and End Time VALID_FROM, VALID_TO, Evaluation Criteria EVAL_REF, and Authentication Status STATUS.

[0188] (5) Aggregate the verification record table T_AGG_CHECK for post-audit and deviation analysis.

[0189] The key fields of the aggregated verification record table T_AGG_CHECK include verification number CHECK_ID, subject number ENTITY_ID, verification time CHECK_TIME, aggregated hierarchical response P_AGG, individual weighted response P_SUM, consistency deviation DELTA, and verification conclusion RESULT.

[0190] (6) Data quality audit table T_DATA_AUDIT, used to monitor the reliability of input data.

[0191] The key fields of the data quality audit table T_DATA_AUDIT include audit number AUDIT_ID, source source SOURCE, integrity rate INTEGRITY_RATE, accuracy rate ACCURACY_RATE, average latency AVG_LATENCY, and audit time AUDIT_TIME.

[0192] Specifically, to achieve seamless integration with external platforms such as power dispatch automation systems or power market technical support systems, a multi-protocol standardized data interface is adopted, including:

[0193] (1) Substation layer data interface, based on the IEC 61850 standard issued by the International Electrotechnical Commission (IEC) and the corresponding domestic standard DL / T 860, collects measurement data from the power dispatch automation system.

[0194] (2) The data interface of the dispatch master station is based on the Common Information Model (CIM) / Extended Markup Language (XML) standard (i.e., IEC 61970 series) and is connected with the Monitoring, Control and Data Acquisition System (SCADA), Energy Management System (EMS), and Intelligent Dispatch Automation System (D5000) to conduct data interaction.

[0195] Among them, the monitoring, control and data acquisition system (SCADA) data can replace or supplement the phase measurement unit (PMU) data or the advanced measurement system (AMI) data.

[0196] (3) Market system data interface: Based on the data specifications of the Electricity Market Technical Support System (TSS) and the Electricity Trading Clearing System (CSS), it collects winning bid information, planned output, and assessment deduction data to support capacity market clearing and settlement.

[0197] (4) Main-side equipment interface: Connect to the virtual power plant operation platform based on the Message Queuing Telemetry Transmission Protocol (MQTT) / Hypertext Transfer Protocol (HTTP) application interface; connect to the energy storage aggregator based on the energy storage power station energy management system (ESMS) data interface; connect to the electric vehicle aggregator based on the international standard ISO 15118 for charging infrastructure interoperability standards, and receive operation data reported by non-new energy operators.

[0198] (5) Meteorological data interface: based on the NetCDF (Network Common Data Format) meteorological forecast data interface provided by the meteorological department, it is used to obtain environmental meteorological data.

[0199] (6) Evaluation result feedback interface: The trusted capacity label is fed back to the market technical support system, the scheduling automation system, and the main operation platform through the REST application programming interface (REST API).

[0200] By standardizing database tables and interface design, a closed-loop engineering process is achieved, encompassing the entire chain from data acquisition, model calculation, evaluation result generation to system integration. This solves the problem that the evaluation results cannot be integrated with the existing power system and cannot support actual market operation, thus achieving standardized integration and ensuring the practicality and scalability of the evaluation method.

[0201] It should be noted that the above-described database table structure and interface protocol are merely a preferred engineering implementation, intended to provide a data foundation for the evaluation method of this invention. In practical applications, those skilled in the art can merge, split, or expand fields of the table structure (e.g., using time-series databases, NoSQL databases, etc.) or employ other equivalent communication protocols (MQTT, HTTP, Modbus, OPC UA, etc.) to achieve data feedback and interaction, based on specific data storage and interaction requirements. As long as it can achieve the data storage and retrieval functions of the trusted capacity evaluation logic described in this invention, it falls within the protection scope of this invention.

[0202] Specifically, in step S15, if the conditions for triggering the update are met, the trusted capacity of the operating entity is recalculated and the corresponding trusted capacity label is updated.

[0203] This step specifically corresponds to the rolling update phase in the trusted capacity tag lifecycle management. To ensure the continuous output of real-time and accurate trusted capacity tags, the system uses a dual-trigger update method, consisting of both scheduled and time-triggered updates, specifically including the following steps:

[0204] Step S151: If the type is identified as non-new energy, perform timed and event-triggered updates.

[0205] Step S1511: Update the operating status factor according to the first preset cycle, update the historical response performance factor according to the second preset cycle, update the basic capacity according to the third preset cycle, recalculate the trusted capacity of the operating entity, and update the corresponding trusted capacity label.

[0206] In this embodiment, the first preset period is set to 15 minutes; the second preset period is set to 90 days; and the third preset period is set to 1 year.

[0207] It should be noted that the first preset cycle can be adjusted to 5 minutes, 30 minutes, or 1 hour according to actual needs.

[0208] Step S1512: If the battery charge state exceeds the limit, the online status of virtual power plant member resources is switched, the operator fails to meet the standard twice in a row, or the operator's equipment is malfunctioning or under maintenance, the operator's trusted capacity is recalculated and the corresponding trusted capacity label is updated.

[0209] In this embodiment, the state of charge (SOC) of the battery is out of bounds when it is below the first charge threshold or above the second charge threshold.

[0210] Step S152: If the type is identified as new energy, perform timed and event-triggered updates.

[0211] Step S1521: Update the environmental condition factor according to the update frequency of meteorological forecast data, update the historical response performance factor according to the second preset cycle, update the basic capacity according to the third preset cycle, recalculate the credible capacity of the operating entity, and update the corresponding credible capacity label.

[0212] In this embodiment, the update frequency of meteorological forecast data includes ultra-short-term forecasts and day-ahead forecasts. Specifically, the update frequency of ultra-short-term forecasts is 15 minutes, and the update frequency of day-ahead forecasts is 1 day.

[0213] Step S1522: If any of the following situations are detected: an extreme weather warning is issued, the operator fails to meet the standard twice in a row, or the operator's equipment malfunctions or is under maintenance, then the operator's trusted capacity is recalculated and the corresponding trusted capacity label is updated.

[0214] It should be noted that in practical applications, for simple calculation examples, a single timed update condition or a single event-triggered update condition can be used; for highly dynamic scenarios, a dual-trigger update condition of timed and event-triggered update conditions can be used.

[0215] Based on the above steps, dynamic assessment of the trust capacity of business entities is achieved. To standardize the management and application of the assessment results, this embodiment also constructs a full lifecycle management process and triggering mechanism for trust capacity labels. This mechanism divides the trust capacity label status into six stages: application, initial review, trial operation, formal issuance, rolling update, renewal, or withdrawal. Specific triggering conditions and verification standards are set for each stage, including:

[0216] (1) Application stage.

[0217] Triggering mechanism: An event-triggered update mode is adopted, which automatically starts the application process when an operator connects to the electricity market system for the first time.

[0218] Verification criteria: Operating entities must submit a basic capacity declaration, resource allocation parameters, and declared correction coefficients. The system automatically verifies whether the completeness of the declaration file fields reaches 100%, and whether the deviation between resource participation and nameplate consistency is within ±2%.

[0219] (2) Initial verification stage.

[0220] Triggering mechanism: The update adopts an event-triggered mode, which is automatically started after the application stage is approved.

[0221] Verification criteria: Based on 90 days of historical operating data, preliminary calculations and operational accessibility verification based on Effective Load Capacity (ELCC) are conducted, requiring that the deviation between the ELCC calculation results and the declared basic capacity not exceed ±8%; operational accessibility indicators must meet market access thresholds. These operational accessibility indicators include response start-up time and adjustment rate.

[0222] (3) Trial operation phase.

[0223] Triggering mechanism: The update adopts an event-triggered mode, which is initiated after the initial core phase is passed.

[0224] Verification criteria: Conduct dynamic correction coefficient identification and assessment observation for 60 days, requiring a response rate of no less than 85%, aggregation consistency deviation of no more than 5%, and data integrity rate of no less than 98% during the observation period.

[0225] (4) Formal distribution stage.

[0226] Triggering mechanism: The update adopts an event-triggered mode and is started after the trial operation phase is passed.

[0227] Verification criteria: Trusted capacity labels are issued after aggregate verification and consistency verification, and the label version number is automatically incremented.

[0228] (5) Rolling update phase.

[0229] Triggering mechanism: Updates are performed using a timed triggering and event triggering model.

[0230] Verification criteria: Update the operating status factor according to the first preset cycle, update the historical response performance factor according to the second preset cycle, update the environmental condition factor according to the update frequency of meteorological forecast data, and perform timed updates according to the third preset cycle. If any of the following situations are detected, such as battery charge status exceeding the limit, online status switching of virtual power plant member resources, issuance of extreme weather warnings, two consecutive failures of the operator's response to meet the standards, or equipment failure and maintenance of the operator, an event-triggered update will be performed.

[0231] It should be noted that the rolling update phase is a routine maintenance process after entering formal operation, and its core is to execute the dual-trigger update logic described in step S15 above.

[0232] (6) Renewal or withdrawal stage.

[0233] Triggering mechanism: The update mode is triggered on a timed basis, and the review is initiated 30 days before the expiration of the label each year.

[0234] Verification criteria: Annual review, requiring an annual average response rate of no less than 80%, an annual average consistency deviation of no more than 5%, and an annual reliable capacity utilization rate of no less than 30%. Contracts meeting these conditions can be renewed; those failing to meet them will be downgraded or have their contracts cancelled. The reliable capacity utilization rate refers to the ratio of the actual awarded capacity to the reliable capacity.

[0235] Example 2

[0236] This embodiment provides a reliable capacity assessment method for new types of operators in the electricity market, used for reliable capacity assessment.

[0237] (1) Credible capacity assessment of virtual power plant operators.

[0238] Based on the total nameplate capacity of a virtual power plant operator within an industrial park in a certain city. Among them, the nameplate capacity of industrial adjustable load. Type benchmark coefficient Aggregation reachability Nameplate capacity of air conditioners in commercial buildings Type benchmark coefficient Aggregation reachability Nameplate capacity on the user's energy storage side Type benchmark coefficient Aggregation reachability Nameplate capacity of rooftop solar panels Type benchmark coefficient Aggregation reachability .

[0239] The calculated basic capacity of the virtual power plant is: The aggregated capacity of the virtual power plant is much smaller than the total nameplate capacity, reflecting a true depiction of the contribution to the aggregated capacity level.

[0240] At 18:00 on August 1, 2025, the operating status factor was calculated based on the weighted average of member resource online rates. The environmental condition baseline factor for evening hours under normal meteorological scenarios is set as follows: Historical performance response factors are calculated based on assessment data from the past 90 days. The calculated dynamic correction coefficient is: .

[0241] Based on the virtual power plant's base capacity and dynamic correction coefficients, the reliable capacity is calculated as follows: This credible capacity serves as a capacity constraint input for the entity during electricity market clearing, accurately reflecting its contribution to the true reliability of the system during the assessment period.

[0242] (2) Credible capacity assessment of energy storage aggregator operators.

[0243] A certain independent energy storage power station, rated power Rated capacity 400 MWh, continuous discharge time , .

[0244] The calculated basic capacity of the energy storage power station is: The aggregate capacity is much smaller than the total nameplate capacity, which reflects the true contribution of the aggregate level capacity.

[0245] At 8:00 AM on January 22, 2025, during an extreme cold wave weather scenario, what is the battery's state of charge? Calculations yielded Environmental condition benchmark factors under extreme cold wave weather scenarios Historical performance response factors are calculated based on assessment data from the past 90 days. The calculated dynamic correction coefficient is: .

[0246] Based on the basic capacity and dynamic correction coefficient of the energy storage power station, the reliable capacity is calculated as follows: Under the extreme conditions of this cold wave, the traditional nameplate capacity method still calculates the system capacity as 100 MW, while this invention provides a more realistic available capacity of 29.2 MW, avoiding misjudgment of system capacity adequacy.

[0247] Example 3

[0248] This embodiment provides a reliable capacity assessment method for new types of operators in the electricity market. The effectiveness of this method is verified by comparing it with different benchmark methods under extreme weather scenarios and market conditions.

[0249] In this embodiment, the method of the present invention (M0) is compared horizontally with three existing mainstream methods. The selected benchmark methods specifically include:

[0250] Reference Method M1: Traditional nameplate capacity method, which is calculated based on the rated parameters of the equipment and does not take into account environmental factors and the effects of equipment aging.

[0251] The baseline method M2 is a single ELCC static coefficient method, which introduces probabilistic characteristics and lacks a dynamic correction mechanism for real-time states.

[0252] Benchmark Method M3: Static Equivalent Coefficient Method without Dynamic Correction.

[0253] The test scenarios were selected from three typical extreme weather days in a certain province in 2024 for comparison, in order to cover the drastic fluctuations on both sides of the source load. Specifically, these included August 1 (summer high load day), January 22 (winter cold wave day), and June 18 (plum rain continuous rain day).

[0254] The test covered 50 virtual power plants, 120 energy storage aggregators, and 80 new energy operators across the province.

[0255] Specifically, the evaluation indicators selected in this embodiment include:

[0256] (1) Assessment accuracy refers to the degree of deviation between the assessed capacity and the actual available capacity, characterized by root mean square error, with units of MW. The smaller the assessment accuracy value, the higher the assessment accuracy.

[0257] (2) Market settlement deviation refers to the economic loss caused by the difference between the day-ahead market and the real-time market settlement volume due to inaccurate capacity assessment, and the unit is RMB 10,000 / day.

[0258] (3) System load loss hours under extreme operating conditions refers to the expected time during which the system cannot meet load requirements under extreme weather scenarios, expressed in hours per year. The smaller the system load loss hours under extreme operating conditions, the higher the system reliability.

[0259] (4) Capacity assessment default rate refers to the probability of being assessed due to insufficient output during actual operation, and the unit is %.

[0260] (5) Dynamic tracking response delay refers to the time required from triggering the update to completing the tag update, in minutes.

[0261] (6) Multi-type subject coverage refers to the number of business subject types that the method can adapt to, in units of species.

[0262] (7) Engineering interface standardization refers to the compatibility and standardization of the evaluation system with the existing market interface. It is scored on a ten-point scale, with a maximum score of 10 points.

[0263] (8) Tag lifecycle integrity refers to the evaluation method covering the entire closed-loop management capability from resource registration, evaluation to deregistration. It is scored on a ten-point scale, with a maximum score of 10 points.

[0264] Through multi-scenario market clearing simulation, the credible capacity assessment of the above-mentioned business entities was carried out, and the assessment results of each method under different evaluation indicators were statistically analyzed, as shown in Table 1.

[0265] Table 1 Evaluation results of each method under different evaluation indicators

[0266] As can be seen from Table 1,

[0267] (1) The assessment accuracy is significantly improved. Specifically, compared with the best-performing M3, the error of M0 is reduced by 55.6%, and it is far better than M1. The method of this invention can correct the influence of environmental condition factors on capacity contribution in real time through the dual-trigger update mechanism of the rolling update stage, and avoid the systematic bias generated by M2 under extreme weather scenarios.

[0268] (2) Market settlement deviations were significantly reduced. Specifically, the error of M0 was reduced by 45.4% compared to the best-performing M3. This method directly reduced the assessment penalties caused by prediction deviations through high-precision capacity assessment, proving that the method can more realistically reflect the regulatory value of resources and enhance the market competitiveness of business entities.

[0269] (3) Enhanced reliability under extreme operating conditions. Specifically, compared with the best-performing M3, M0 reduces the risk of system load loss hours by 50.8% and the capacity assessment default rate by 51.0% under extreme operating conditions. This method can accurately identify the capacity degradation risk under extreme weather scenarios, providing more reliable support for the power grid safety margin.

[0270] (4) Dynamic response and engineering applicability. Specifically, M0 achieves a 15-minute response delay, which is 50% lower than the delay of M3; it also supports five types of business entities, far exceeding a single evaluation method; the interface is highly standardized and has a strong engineering implementation capability; it can achieve closed-loop management of the entire process from application, preliminary review, trial operation, formal issuance, rolling update, renewal or withdrawal.

[0271] Based on the above, the method of the present invention has significant advantages in multiple dimensions such as evaluation accuracy, dynamic tracking, security and engineering applicability, and has significant progressiveness.

[0272] Example 4

[0273] Based on the same design concept, such as Figure 2 As shown, this embodiment provides a reliable capacity assessment system for new types of operators in the electricity market, applied to a reliable capacity assessment method for new types of operators in the electricity market, including:

[0274] The basic capacity calculation module is used to calculate the basic capacity of an operating entity by matching the corresponding calculation model from a pre-set basic capacity function library based on the type identifier of the operating entity to be evaluated; the type identifier includes non-new energy and new energy. By adaptively matching different basic capacity calculation models for different types of operating entities, a one-size-fits-all evaluation approach using a uniform model for all operating entities is avoided, thereby improving the physical fit between basic capacity and actual operating conditions from the source.

[0275] The dynamic correction coefficient calculation module is used to calculate the operating status factor and historical response performance factor based on the operating entity's operating status data and historical response performance data when the type is identified as non-new energy, and to determine the dynamic correction coefficient for non-new energy operating entities by combining them with a preset environmental condition benchmark factor; and to determine the dynamic correction coefficient for new energy operating entities when the type is identified as new energy, it calculates the environmental condition factor and historical response performance factor based on environmental meteorological data and historical response performance data, and to determine the dynamic correction coefficient for new energy operating entities by combining them with a preset operating status benchmark factor. By introducing multi-dimensional influencing factors for dynamic conversion, the impact of various unreliable operating condition factors on the capacity assessment results can be eliminated, making the reliable capacity assessment results closer to the actual operating capacity, effectively eliminating the bias caused by traditional single static assessment, and realizing the upgrade from static capacity assessment to dynamic real-time assessment.

[0276] The tag generation module is used to adjust the base capacity using dynamic correction factors to obtain the reliable capacity of the operating entity and generate corresponding reliable capacity tags. By adjusting the base capacity using dynamic correction factors and generating reliable capacity tags, clear and explicit price signals can be provided to all participants in the electricity market.

[0277] The update management module is used to recalculate the trusted capacity of operating entities and update the corresponding trusted capacity tags when the trigger update conditions are met. These trigger update conditions include timed trigger conditions based on preset periods and event trigger conditions based on sudden changes in operating conditions. By using both timed and event-triggered update conditions, the system ensures both periodic synchronous updates of assessment data and immediate recalculation of trusted capacity when operating conditions change, continuously guaranteeing the real-time effectiveness of the assessed trusted capacity tags.

[0278] Example 5

[0279] Embodiments of the present invention also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0280] Example 6

[0281] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the method of an embodiment of the present invention.

[0282] Example 7

[0283] An embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method of the embodiment of the present invention.

[0284] refer to Figure 3 The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0285] like Figure 3As shown, the electronic device includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0286] Multiple components in the electronic device are connected to I / O interface 305, including: input unit 306, output unit 307, storage unit 308, and communication unit 309. Input unit 306 can be any type of device capable of inputting information into the electronic device. Input unit 306 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 307 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 308 may include, but is not limited to, disks and optical discs. Communication unit 309 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0287] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 302 and / or communication unit 309. In some embodiments, the computing unit 301 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0288] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0289] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0290] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0291] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are subject to strict compliance with relevant laws, regulations, and regulatory requirements in their collection, storage, use, processing, transmission, provision, and disclosure, and adhere to the principles of legality, legitimacy, necessity, and good faith. The acquisition of relevant information and data is premised on the user's explicit consent or other legitimate reasons, and a clear and convenient authorization management approach is provided to the user, allowing the user to independently choose to consent, withdraw consent, or refuse to provide relevant information. For functions that rely on user information, if the user does not authorize or withdraws authorization, the corresponding technical function cannot be implemented, and the technical solution of this invention is not applicable in this scenario. The steps described in the method implementation embodiments provided by the embodiments of this invention can be executed in different orders and / or in parallel. Furthermore, the method implementation embodiments may include additional steps and / or omit the steps shown. The scope of protection of this invention is not limited in this respect.

[0292] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0293] The above embodiments merely illustrate several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A reliable capacity assessment method for new types of operators in the electricity market, characterized in that, include: Based on the type identifier of the business entity to be evaluated, the corresponding calculation model is matched from the preset basic capacity function library to calculate the basic capacity of the business entity; wherein, the type identifier includes non-new energy and new energy; When the type is identified as non-new energy, the operating status factor and historical response performance factor are calculated based on the operating status data and historical response performance data of the operating entity, respectively, and combined with the preset environmental condition benchmark factor, the dynamic correction coefficient of the non-new energy operating entity is determined. When the type is identified as new energy, the environmental condition factor and historical response performance factor are calculated based on environmental meteorological data and historical response performance data, respectively, and combined with the preset operating status benchmark factor, the dynamic correction coefficient of the new energy operator is determined. The basic capacity is adjusted using the dynamic correction coefficient to obtain the trusted capacity of the operating entity, and a corresponding trusted capacity label is generated; wherein, the trusted capacity label is obtained from the fields of label number, entity number, version number, effective start and end time, evaluation caliber and certification status; If the trigger update conditions are met, the trusted capacity of the operating entity is recalculated and the corresponding trusted capacity tag is updated; wherein, the trigger update conditions include timed trigger conditions based on a preset period and event trigger conditions based on sudden changes in operating status; Before generating the trusted capacity label for the operating entity, the method further includes performing a consistency check on the aggregated level and the individual level response for multiple operating entities when the type identifier is non-new energy; the consistency check includes: Obtain the aggregate-level measured response power and the individual-weighted measured response power of the business entity during the assessment period, and calculate the consistency deviation; If the consistency deviation exceeds the preset deviation threshold, the consistency verification is deemed unsuccessful, a formal trusted capacity label is not issued, and parameter review and re-identification are triggered. If the consistency deviation does not exceed the preset deviation threshold, the consistency verification is deemed successful, the authentication status of the trusted capacity tag is set to valid, and the generation and feedback of the trusted capacity tag are executed.

2. The reliable capacity assessment method for new types of operators in the electricity market according to claim 1, characterized in that, The non-new energy sources include virtual power plants, energy storage aggregators, and electric vehicle aggregators; the new energy sources include wind power and photovoltaic power. The step of calculating the basic capacity of the business entity by matching the corresponding calculation model from a preset basic capacity function library based on the type identifier of the business entity to be evaluated includes: When the type is identified as a virtual power plant, the virtual power plant basic capacity calculation model is used to perform a weighted summation of the nameplate capacity, type baseline coefficient, and aggregation reachability of each member resource within the aggregation to obtain the basic capacity of the virtual power plant. When the type is identified as an energy storage aggregator, the basic capacity calculation model of the energy storage aggregator is adopted to obtain the basic capacity of the energy storage aggregator based on the rated power and duration segmentation coefficient of the access resources. When the type is identified as an electric vehicle aggregator, the basic capacity calculation model of the electric vehicle aggregator is adopted. The basic capacity of the electric vehicle aggregator is calculated based on the number of connected vehicles, the average available power per vehicle, the average state of charge of connected vehicles, and the ordered charging response coefficient. When the type is identified as new energy, the basic capacity calculation model of new energy is adopted, and the basic capacity of new energy is calculated according to the nameplate capacity, type benchmark coefficient and application correction coefficient of the new energy; wherein, the type benchmark coefficient is obtained by simulation fitting of historical power system operation data.

3. The reliable capacity assessment method for new types of operators in the electricity market according to claim 2, characterized in that, When the type is identified as non-new energy, the operating status factor and historical response performance factor are calculated based on the operating entity's operating status data and historical response performance data, respectively. Combined with a preset environmental condition benchmark factor, a dynamic correction coefficient for the non-new energy operating entity is determined, including: Calculate the operating status factor based on the operating status data of the operating entity; When the type is identified as a virtual power plant, the capacity of each member resource is weighted and summed according to the online status of each member resource within the aggregation to obtain the operating status factor of the virtual power plant. When the type is identified as an energy storage aggregator, the operating state factor of the energy storage aggregator is determined according to the piecewise linear decay rule of the battery state of charge; wherein, when the battery state of charge is lower than the first charge threshold or higher than the second charge threshold, the capacity contribution is linearly reduced relative to the full value. When the type is identified as an electric vehicle aggregator, the operating status factor of the electric vehicle aggregator is calculated based on the proportion of vehicles connected to the grid and the average remaining power. Calculate the corresponding historical response performance factor based on the historical response performance data of non-new energy business entities; Obtain a preset environmental condition benchmark factor; wherein, the environmental condition benchmark factor is a preset benchmark value adapted to the non-new energy business entity; The dynamic correction coefficient is obtained by multiplying the operating status factor, the historical response performance factor, and the environmental condition benchmark factor.

4. The reliable capacity assessment method for new types of operators in the electricity market according to claim 2, characterized in that, When the type is identified as new energy, environmental condition factors and historical response performance factors are calculated based on environmental meteorological data and historical response performance data, respectively. Combined with the operational status benchmark factor, a dynamic correction coefficient for the new energy operator is determined, including: Calculate environmental condition factors based on environmental meteorological data; In the case of wind power identified as a new energy source, the environmental condition factors of wind power are calculated using a power law function based on the relationship between the predicted wind speed and the rated wind speed of the wind turbine, the wind turbine cut-in wind speed, and the wind turbine cut-out wind speed. In the case of photovoltaics, which are identified as new energy sources, the environmental condition factor of photovoltaics is calculated based on the ratio of the predicted irradiance value to the irradiance under standard test conditions and the temperature deviation of the photovoltaic module. Calculate the corresponding historical response performance factor based on the historical response performance data of new energy operators; Obtain a preset operating status benchmark factor; wherein, the operating status benchmark factor is a benchmark value preset by the new energy business entity; The dynamic correction coefficient is obtained by multiplying the environmental condition factor, the historical response performance factor, and the operational status benchmark factor.

5. The reliable capacity assessment method for new types of operators in the electricity market according to claim 3 or 4, characterized in that, The environmental condition benchmark factor or the value of the environmental condition factor is dynamically adjusted according to the meteorological scenario, including: When the type is identified as non-new energy and the meteorological scenario is a conventional meteorological scenario, the environmental condition benchmark factor is taken as a preset benchmark value; When the type is identified as non-new energy and the meteorological scenario is an extreme meteorological scenario, the environmental condition benchmark factor is the value obtained by correcting the environmental condition benchmark factor under the conventional meteorological scenario using the extreme meteorological correction coefficient. When the type is identified as new energy and the meteorological scenario is a conventional meteorological scenario, the environmental condition factor is taken as the value calculated based on the environmental meteorological data; When the type is identified as new energy and the meteorological scenario is an extreme meteorological scenario, the environmental condition factor is the value obtained by correcting the environmental condition factor under the conventional meteorological scenario using the extreme meteorological correction coefficient. The extreme weather scenarios mentioned include cold waves, continuous rain, typhoons, or thunderstorms.

6. The reliable capacity assessment method for new types of operators in the electricity market according to claim 3 or 4, characterized in that, The historical response performance factor is calculated on a rolling basis based on the ratio of the actual response volume to the promised response volume of the operating entity within the historical assessment period. If the ratio fails to reach the preset ratio threshold twice consecutively, a penalty reduction mechanism is activated to obtain the reduced historical response performance factor.

7. The reliable capacity assessment method for new types of operators in the electricity market according to claim 1, characterized in that, The process of using the dynamic correction coefficient to adjust the base capacity to obtain the reliable capacity of the operating entity and generating a corresponding reliable capacity label includes: The reliable capacity of the operating entity is obtained by multiplying the dynamic correction coefficient by the basic capacity. By combining the fields of trusted capacity, tag number, entity number, version number, effective start and end time, evaluation criteria, and certification status, a trusted capacity tag for the business entity is generated. The trusted capacity tag is fed back to the power dispatch automation system or the power market technical support system.

8. The reliable capacity assessment method for new types of operators in the electricity market according to claim 1, characterized in that, The step of recalculating the trusted capacity of the operating entity and updating the corresponding trusted capacity tag when the trigger update condition is met includes: When the type is identified as non-new energy, timed and event-triggered updates are performed. The operating status factor is updated according to the first preset cycle, the historical response performance factor is updated according to the second preset cycle, and the basic capacity is updated according to the third preset cycle. The trusted capacity of the operating entity is recalculated and the corresponding trusted capacity label is updated. If any of the following situations are detected: battery charge state exceeding the limit, virtual power plant member resource online status switching, operator failing to meet the standard twice in a row, or operator equipment malfunctioning or undergoing maintenance, then the operator's trusted capacity will be recalculated and the corresponding trusted capacity label will be updated. When the type is identified as new energy, updates are performed using both timed and event-triggered methods. The environmental condition factor is updated according to the update frequency of meteorological forecast data, the historical response performance factor is updated according to the second preset cycle, the basic capacity is updated according to the third preset cycle, the reliable capacity of the operating entity is recalculated, and the corresponding reliable capacity label is updated. If any of the following situations are detected: an extreme weather warning is issued, the operator fails to meet the standard twice in a row, or the operator's equipment malfunctions or is under maintenance, the operator's trusted capacity will be recalculated and the corresponding trusted capacity label will be updated.

9. A reliable capacity assessment system for new types of operators in the electricity market, characterized in that, The reliable capacity assessment method for new types of operators in the electricity market, as described in any one of claims 1 to 8, includes: The basic capacity calculation module is used to calculate the basic capacity of the business entity by matching the corresponding calculation model from the preset basic capacity function library according to the type identifier of the business entity to be evaluated; wherein, the type identifier includes non-new energy and new energy. The dynamic correction coefficient calculation module is used to calculate the operating status factor and historical response performance factor based on the operating status data and historical response performance data of the operating entity when the type is identified as non-new energy, and to determine the dynamic correction coefficient of the non-new energy operating entity in combination with the preset environmental condition benchmark factor; and to calculate the environmental condition factor and historical response performance factor based on the environmental meteorological data and historical response performance data when the type is identified as new energy, and to determine the dynamic correction coefficient of the new energy operating entity in combination with the preset operating status benchmark factor. The tag generation module is used to calculate and correct the basic capacity using the dynamic correction coefficient to obtain the reliable capacity of the business entity and generate a corresponding reliable capacity tag. The update management module is used to recalculate the trusted capacity of the operating entity and update the corresponding trusted capacity tag when the trigger update conditions are met; wherein, the trigger update conditions include timed trigger conditions based on a preset period and event trigger conditions based on sudden changes in operating status.

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