A smart operation and maintenance management platform for distributed power plants

Abstract

The application relates to the technical field of operation and maintenance management systems, and discloses a smart operation and maintenance management platform for distributed power stations, which realizes credible decoupling of natural attenuation and active regulation of power in an all-round way by dynamically generating a first physical constraint surface through high-precision micro-meteorological sensing and coupled modeling, and accurately quantifying the natural power generation capacity boundary caused by weather, and ensures the fairness of value measurement. Through an instruction intelligent resolution algorithm, a safe and feasible second instruction response surface is generated by taking the physical surface as a constraint, the dispatching instruction is converted into an absolute safe and dynamically optimized execution scheme, equipment overrun risks are eliminated, and the regulation quality is improved through smooth redistribution. The system finally realizes a complete technical closed loop from accurate sensing, safe control to credible measurement, and provides reliable support for high-proportion participation of distributed power sources in the power market.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance management system technology, and in particular to a smart operation and maintenance management platform for distributed power plants. Background Technology

[0002] A smart operation and maintenance management platform for large-scale distributed photovoltaic power plants is the core hub for achieving safe and efficient operation of the power plants and their participation in grid market services. This platform integrates functions such as meteorological monitoring, power forecasting, equipment monitoring, and dispatch command reception to provide a comprehensive understanding and intelligent control of the power plant's power generation status. Its core mission is to maximize power generation revenue while ensuring the safety of power generation equipment, and to reliably execute grid dispatch commands, providing technical support for the power plant's participation in ancillary services markets such as peak shaving.

[0003] Currently, the existing technical solutions for such platforms mostly rely on historical power generation data from the same period or predictive models based on macro-meteorological parameters such as total irradiance and temperature to construct their operating baselines. After receiving dispatch instructions, the platform usually uses this static or roughly dynamic baseline as a reference to control the power plant output to track the target curve, but it lacks the ability to model and respond in real time to fine-grained meteorological disturbances such as sandstorms that cause drastic changes in spectral characteristics.

[0004] The aforementioned and existing related technologies often suffer from the following drawbacks: When sandstorms cause a sharp decrease in atmospheric transmittance, the baseline generated by macroscopic models cannot accurately reflect the instantaneous theoretical power generation capacity of the power plant caused by the weather. If a load reduction command is executed at this time, the platform cannot distinguish between unavoidable natural attenuation and valuable proactive adjustments within the actual power reduction. This results in the power plant's true adjustment contribution being drowned out or obscured by weather noise, leading to a severe underestimation of its revenue in the ancillary services market. Furthermore, if the platform forcibly follows the command to compensate for weather losses, it may force equipment to operate beyond its safe operating capacity, triggering risks. This dual dilemma of value conflation and safety inaccuracies stems from the lack of a precise computational framework in existing technologies that effectively decouples transient physical environments from scheduling requirements. Summary of the Invention

[0005] The technical problem to be solved by this invention is that the existing technology lacks a precise computing framework that can decouple transient physical environment and scheduling requirements. To address this, we propose a smart operation and maintenance management platform for distributed power plants.

[0006] To achieve the above objectives, this application adopts the following technical solution: a smart operation and maintenance management platform for distributed power stations, comprising: a fine particulate matter damage precision sensing module, used to acquire micro-meteorological data of the power station and generate a first physical constraint surface reflecting the real-time physical limitations of the current weather process on power generation capacity; a dispatching instruction coupling analysis module, used to parse power dispatching instructions and, in conjunction with the first physical constraint surface, dynamically construct a second instruction response surface characterizing the regulation capacity boundary of the power station under physical constraints; a value decoupling verification module, used to compare the actual output curve of the power station with the first physical constraint surface during the dispatching period, generate atomic regulation evidence units, and perform compliance verification and value integration based on the second instruction response surface; a dynamic operation strategy control module, used to adjust the resource allocation strategy within the power station in real time according to the state of the atomic regulation evidence units and the second instruction response surface, so as to ensure that the regulation process meets the physical constraints and dispatching requirements; and a trusted evidence storage and traceability module, used to perform hash processing and chain storage on the first physical constraint surface, the second instruction response surface, the atomic regulation evidence units and their value integration results, forming an immutable and verifiable regulation service value evidence chain.

[0007] Furthermore, the fine particulate matter-induced damage precision sensing module includes: a microscale meteorological field monitoring unit, deployed at key locations in the power plant array, used to synchronously collect power plant micro-meteorological data including fine particulate matter mass concentration, particle size distribution, relative humidity, and wind speed and direction; and a physical constraint surface modeling unit, which calculates and outputs a first physical constraint surface that varies over time as the theoretical maximum instantaneous power limit of the power plant, based on the photovoltaic module physical model and the power plant micro-meteorological data. The calculation includes: calculating the equivalent deposition rate and optical path attenuation coefficient of the current aerosol particle swarm on the glass cover surface of the photovoltaic module based on the particle size distribution and relative humidity data; calculating the effective received irradiance attenuation function of the module surface caused by the combined action of deposited particles and suspended particles, and coupling the attenuation function with the basic power characteristic curve of the power plant to generate the first physical constraint surface.

[0008] Furthermore, the physical constraint surface modeling unit generates the first physical constraint surface in the following ways: determining the base power ratio based on the ratio of the effective received irradiance on the component surface to the irradiance under standard test conditions; determining the comprehensive meteorological influence factor based on the fine particulate matter deposition damage coefficient, the equivalent deposition mass concentration of fine particulate matter on the component surface, and the exponential function of relative humidity, and correcting the base power ratio by subtracting this factor from 1; then performing angle correction based on the cosine value of the solar incidence angle; and finally performing efficiency correction based on the inverter's average conversion efficiency and the internal transmission line loss efficiency to obtain the instantaneous maximum theoretical power limit, i.e., the first physical constraint surface.

[0009] Furthermore, the scheduling instruction coupling analysis module includes: an instruction depth parsing unit, used to parse the target power curve of the scheduling instruction and perform superposition analysis with the first physical constraint surface on the time axis to identify conflict periods and conflict amounts where the instruction requirement exceeds the current physical constraint capability; the superposition analysis specifically involves comparing the target power curve and the first physical constraint surface point by point in the same time coordinate system, and when the power value on the target power curve is continuously greater than the power value at the corresponding point on the first physical constraint surface, and the excess exceeds a preset error tolerance threshold, the period is determined to be a conflict period, and the integral value of the excess is the conflict amount in that period; and a response surface dynamic construction unit, used to dynamically construct a second instruction response surface based on the conflict identification result, and in non-conflict periods, the response surface dynamic construction unit directly sets the second instruction response surface to be consistent with the target power curve. During the identified conflict period, the dynamic construction unit of the response surface executes a conflict resolution strategy: it determines the upper limit of the power response for that period based on the second command response surface generation formula, and initiates a conflict redistribution algorithm. The algorithm takes the physical constraint margin and adjustment rate limit of adjacent non-conflict periods as input, and takes the conservation of total adjustment power and smooth power change as optimization objectives. It optimizes the allocation of power deficits that cannot be responded to during the conflict period to the adjacent periods that have physical response capabilities. The allocation is weighted according to the proportion of physical margins and time proximity of each adjacent period to generate a set of dynamic power compensation values. The dynamic construction unit of the response surface uses the power trajectory after power upper limit constraint and conflict redistribution processing as the output of the second command response surface for that conflict period, thereby forming a dynamic executable response trajectory that globally satisfies physical constraints, locally smooths adjustment, and the total adjustment is equivalent to the original command requirements.

[0010] Furthermore, the method by which the dynamic construction unit of the response surface generates the second command response surface includes: using the instantaneous power upper limit of the first physical constraint surface as the physical constraint upper limit; combining the expected adjustment power of the scheduling command and the conflict-distributed power to obtain the initial target adjustment power; taking the minimum of the two; multiplying it by a smooth transition function that varies with time, determined based on the start time of the scheduling time window, the total time window length, and the adjustment smoothing coefficient; and finally multiplying it by the ratio of the actual adjustment rate to the maximum adjustment rate required by the command to obtain the instantaneously safe response adjustment power boundary, i.e., the second command response surface.

[0011] Furthermore, the value decoupling verification module includes: a real-time arbitration unit for output deviation, which samples the real-time total output of the power station using the first physical constraint surface as a dynamic baseline. When the sampling point remains below the baseline for more than a preset number of consecutive sampling points or a duration threshold, arbitration logic is initiated. Combining the inverter's operating status and control command logs, equipment fault factors are ruled out, confirming that the output reduction is an active adjustment behavior, and generating an atomized adjustment evidence unit. This unit at least includes a timestamp, an adjusted power value, the corresponding first physical constraint surface value, and an arbitration logic identifier. The chain compliance verification unit is used to fit the continuously generated atomic regulation evidence units into the actual regulation curve and compare it with the second instruction response surface to verify whether it exceeds the boundary of the surface. Evidence units that exceed the boundary are marked and discounted or directly eliminated according to the preset penalty coefficient during value integration. The value integration and labeling unit is used to integrate the sequence of atomic regulation evidence units that have passed the verification over time to obtain the total regulation power, calculate the economic value according to the preset value mapping function, and finally output the regulation service value measurement data with complete verification path labels.

[0012] Furthermore, the dynamic control module of the operation strategy includes: a feasible domain monitoring and early warning unit, used to calculate in real time the distance between the actual output point of the power station and the boundary of the second command response surface, i.e., the load margin. When the margin is lower than the preset first-level threshold, an early warning signal is issued; when the margin is lower than the second-level threshold, an emergency intervention signal is issued. A multi-dimensional resource coordination and optimization unit, which, upon receiving an early warning or emergency intervention signal, or in order to actively improve the overall regulation quality, initiates an online optimization algorithm. The online optimization algorithm, in the spatial dimension, reallocates the output tasks of different inverter clusters to balance device losses or avoid local meteorological degradation points identified by the microscale meteorological field monitoring unit. In the resource dimension, it decides whether and how to call the on-site energy storage system for power compensation to ensure that the actual regulation curve smoothly and accurately follows the second command response surface.

[0013] Furthermore, the optical path attenuation coefficient is calculated in the physical constraint surface modeling unit through the following steps: using the monitored particle size distribution data, the extinction efficiency factor of aerosol particles of different sizes in the photovoltaic module response band range is calculated based on Mie scattering theory; combined with the real-time measured relative humidity data, the equivalent complex refractive index and particle size of the aerosol particles are corrected by the deliquescence growth model to obtain a more accurate particle swarm extinction cross-section distribution under the current humidity conditions; combining the corrected extinction cross-section distribution with the real-time monitored fine particulate matter mass concentration data, the vertical aerosol optical thickness characterizing the total attenuation capability of aerosols within a unit vertical air column is calculated; based on the vertical aerosol optical thickness and the real-time solar zenith angle, the total aerosol optical thickness on the inclined path is calculated by approximating the planar parallel layer of atmospheric radiation transmission, and this total optical thickness is the core parameter of dynamic optical path attenuation used to construct the effective received irradiance attenuation function of the module surface.

[0014] Furthermore, when the multi-dimensional resource coordination and optimization unit performs spatial dimension optimization, it specifically acquires the real-time operating status, historical failure rate, and cluster-level local meteorological data provided by the microscale meteorological field monitoring unit for each inverter cluster within the power station; establishes a mathematical optimization model that includes equipment operation constraints and power balance constraints, with the optimization objective of maximizing the overall regulation load margin or minimizing the root mean square error between the output of each cluster and the required value of the second command response surface; and dynamically outputs a new set of inverter cluster power allocation commands by solving the mathematical optimization model. This set of commands ensures that, under the premise of meeting the overall regulation objective, local high-loss or low-efficiency operating points are avoided, thereby achieving adaptive optimization scheduling of resources within the station.

[0015] Furthermore, the trusted evidence storage and traceability module is specifically used to: generate a unique hash value for each atomic adjustment evidence unit; construct a Merkle tree in chronological order by combining the hash values ​​of all evidence units generated within the same scheduling event period with the final hash values ​​of the corresponding adjustment service value measurement data; submit the root hash value of the Merkle tree, the parameter snapshots of the key process data, and the timestamp to a permissioned blockchain network or a trusted timestamp service center for evidence storage; and generate a verifiable digital certificate containing the root hash value, the evidence storage time, and the blockchain transaction ID, which serves as an appendix to the adjustment service value measurement data for independent and efficient authenticity verification by third parties.

[0016] The technical effects and advantages of this invention are as follows: By deploying a high spatiotemporal resolution microscale meteorological field monitoring unit and constructing a physical constraint surface modeling algorithm that integrates multiple parameters such as fine particulate matter concentration, particle size spectrum, and humidity, the platform achieves minute-level dynamic and precise quantification of the effects of photovoltaic module surface deposition and atmospheric light path attenuation caused by weather such as sandstorms. This solves the core metrological problem that traditional methods, which rely on macroscopic meteorological data or historical models, cannot construct an accurate theoretical power generation capacity benchmark at the moment of sudden weather changes. This technology, for the first time, transforms complex meteorological disturbances into a continuously changing theoretical maximum output boundary of the power station (the first physical constraint surface), providing an objective and unique dynamic benchmark for value decoupling. It fundamentally achieves precise separation of natural attenuation components and active regulation components. When grid dispatch instructions and severe weather occur simultaneously, the platform can distinguish the physical causes of power reduction with second-level precision, irreversibly attributing the output below the dynamic benchmark to effective regulation services, ensuring the fairness and accuracy of power station regulation value assessment, and breaking through the core obstacle of value aliasing in market transactions.

[0017] In this invention, by constructing a mechanism for deep command analysis and dynamic response surface generation, the platform innovatively achieves online coupling and intelligent resolution of scheduling commands and real-time physical capabilities. When a command requirement exceeds the theoretical power generation capacity under current weather constraints (i.e., a conflict occurs), the system uses a first physical constraint surface as a rigid upper limit and initiates a conflict redistribution algorithm based on the dual objectives of "total quantity conservation" and process smoothing to generate a safe and feasible second command response surface. This solves the operational safety problems that may be caused by rigid command execution in traditional methods, such as equipment overload or power quality degradation during the regulation process, and intelligently transforms external scheduling commands into an internally absolutely safe and dynamically optimized execution framework. On the one hand, it ensures that the power plant's response behavior is always within the safe capability range of the physical equipment, eliminating risks; on the other hand, it optimizes the power change curve through smooth redistribution, improving its frequency friendliness to the power grid. Ultimately, while meeting the macro-regulation needs of the power grid, it also ensures the safe and stable operation of the power plant and the lifespan of its equipment. Attached Figure Description

[0018] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0019] Figure 1 This is a diagram of the overall platform framework of the present invention; Figure 2 This is a flowchart of the physical constraint surface generation process of the present invention; Figure 3 This is the value decoupling verification logic diagram of the present invention; Figure 4 This is the safety closed-loop control logic diagram of the present invention. Detailed Implementation

[0020] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0021] The present invention will be described in detail using a large-scale distributed photovoltaic power station cluster in a dusty region in northwestern my country as an example. The power station has a total installed capacity of 100 MW and consists of multiple photovoltaic sub-arrays. It is connected to the power grid through a key 110 kV line and frequently participates in the provincial power grid's peak-shaving ancillary service market.

[0022] In spring, the region often experiences a sharp decline in atmospheric transmittance due to sandstorms and dust storms. If the power grid happens to issue a load reduction dispatch order at this time, the actual power reduction at the power plant will result in a severe overlap between the natural meteorological attenuation component and the active regulation response component. Traditional operation and maintenance methods, whether based solely on historical meteorological data for prediction or simply monitoring the execution of dispatch orders, cannot construct an accurate theoretical benchmark for natural power generation capacity during real-time dynamic weather degradation. Consequently, they are fundamentally unable to distinguish between these two physically distinct power components. This leads to a severe underestimation of the true value of the regulation services provided by the power plant, or to regulation actions taken to compensate for weather losses exceeding the equipment's safe operating capacity, thus posing operational risks.

[0023] To address the core technical challenge of decoupling aliased power, this embodiment deploys a smart operation and maintenance management platform for distributed power stations. This platform, through an analysis and verification system that combines a first physical constraint surface and a second command response surface, achieves precise value extraction, safety control, and reliable metering in complex scenarios. The specific implementation of this platform is described in detail below with reference to the accompanying drawings and embodiments.

[0024] Reference Figure 1 As shown, this invention provides a technical solution: a smart operation and maintenance management platform for distributed power plants, comprising: a fine particulate matter damage precision sensing module, which includes a microscale meteorological field monitoring unit and a physically constrained surface modeling unit. The microscale meteorological field monitoring unit is deployed at representative key locations within the power plant array to synchronously collect high spatiotemporal resolution micrometeorological data of the power plant. This data includes at least the mass concentration of fine particulate matter, complete particle size distribution, ambient relative humidity, and wind speed and direction. All monitoring data is transmitted in real time to the physically constrained surface modeling unit.

[0025] Upon receiving a continuous stream of micrometeorological data, the physical constraint surface modeling unit performs core modeling calculations. This calculation process first uses monitored particle size distribution data and relative humidity data to calculate the equivalent deposition rate and optical path attenuation coefficient of the current aerosol particle swarm on the photovoltaic module's glass cover surface.

[0026] Specifically, this unit utilizes Mie scattering theory to calculate the extinction efficiency factor of particles of different sizes within the response band of the photovoltaic module based on the particle size distribution. Simultaneously, by incorporating real-time relative humidity, a deliquescence growth model is used to correct the equivalent complex refractive index and optical particle size of the particles, thereby obtaining a more accurate particle swarm extinction cross-section distribution under the current humidity conditions. Subsequently, by combining this corrected extinction cross-section distribution with real-time monitored fine particulate matter mass concentration data, this unit calculates the vertical aerosol optical thickness, characterizing the total aerosol attenuation capacity per unit vertical air column.

[0027] Furthermore, based on this vertical aerosol optical thickness and the real-time solar zenith angle, the total aerosol optical thickness reaching the module surface along the inclined path is calculated using the planar parallel layer approximation model of atmospheric radiation transmission. This result is the core parameter for the dynamic optical path attenuation of the attenuation function of the effective received irradiance on the module surface. Combining this attenuation function with the solar incident angle, the actual effective irradiance received on the module surface can be solved. Finally, the physical constraint surface modeling unit begins the calculation process to generate the first physical constraint surface. This unit first obtains the current effective received irradiance on the module surface from the micrometeorological data. And combined with irradiance under standard test conditions The baseline power ratio was calculated. Next, the fine particulate matter deposition damage coefficient was introduced. Equivalent deposition mass concentration of fine particulate matter on the component surface as monitored in real time Multiply, then pass through an exponential function Considering relative humidity The enhanced effect of sediment-induced damage is used to obtain a comprehensive meteorological impact factor. This factor is subtracted from 1 (i.e., 100% ideal state) to correct the baseline power ratio, i.e. Then, multiply by the angle of solar incidence. The cosine value is used to correct for the effects of changes in the sun's position. It's important to note that when the sun's angle of incidence... Make When the calculated cosine value is negative, it indicates that sunlight cannot reach the component surface, and the power of the physically constrained surface is reduced. The value should be set to zero, not negative, to ensure correct physical meaning. If other parameters are abnormal, such as the efficiency value exceeding the [0,1] range, the system will automatically perform data cleaning or use historical average values ​​to avoid calculation failures. Finally, the average conversion efficiency of the inverter will be comprehensively considered. and internal transmission line loss efficiency The power value is then finalized. Through the above series of steps, the unit dynamically calculates the upper limit of the instantaneous maximum theoretical power at each moment. This generates a physically meaningful power plant maximum output boundary that changes continuously over time, namely the first physical constraint surface.

[0028] This surface, for the first time, uses a refined and structured mathematical representation of the coupling effect of fine particulate matter mass concentration, particle size distribution, and humidity on component surface deposition and atmospheric attenuation through deposition damage coefficient and humidity correction coefficient. It also integrates real-time variables such as solar position and equipment efficiency, achieving real-time and accurate quantification of the key benchmark of the theoretical power generation capacity of the power station under the influence of only the current actual severe weather when there are no dispatch instructions. This fundamentally provides an objective, unique, and dynamic measurement benchmark for solving the "value aliasing" problem, and produces the unexpected technical effect of transforming complex meteorological influences into calculable power hard constraints.

[0029] Meanwhile, the dispatch command coupling analysis module begins operation. This module consists of a command depth parsing unit and a response surface dynamic construction unit. The command depth parsing unit receives dispatch commands issued by the power grid automation system through the power dispatch data network and parses them into a clear target power curve. Subsequently, this unit performs a time-axis overlay analysis on the target power curve and the previously generated first physical constraint surface. The overlay analysis is achieved by comparing the two curves point by point in the same time coordinate system. When it is found that the power value on the target power curve is continuously greater than the power value on the first physical constraint surface at the corresponding time, and the excess exceeds a preset error tolerance threshold, the time period is determined to be a conflict period, and the integral value of all the excess is calculated as the conflict amount within that time period. This identification process reveals the potential contradiction between traditional dispatch commands and real-time physical capabilities.

[0030] In response to the identified conflict, the dynamic construction unit of the response surface initiates a dynamic construction process, the core task of which is to generate a second command response surface. During non-conflict periods, this unit directly sets the second command response surface to be consistent with the target power curve.

[0031] During conflict periods, the unit executes a complete conflict resolution strategy. First, the unit uses the first physical constraint surface as an insurmountable hard constraint to determine the upper limit of the power response for that period. Second, the unit initiates a conflict redistribution algorithm. This algorithm uses the remaining power space (i.e., physical constraint margin) below the first physical constraint surface curve in the non-conflict periods adjacent to the conflict period and the system's allowed adjustment rate limit as its main inputs.

[0032] The algorithm's optimization objectives are dual: first, to ensure that the total regulated power output of the power plant's actual response is strictly conserved within the entire scheduling time window, matching the total regulated power output required by the scheduling command's target power curve, thus meeting the macro-regulation needs of the power grid; and second, to pursue smoothness in the power change process, minimizing steep changes in the power curve to protect power plant equipment and maintain grid frequency stability. To achieve these objectives, the algorithm performs weighted calculations based on the physical constraint margin of each adjacent available time period and its temporal proximity to conflicting time periods, generating a set of dynamic power compensation values. This intelligently and smoothly redistributes the power deficit that cannot be responded to immediately during conflicting time periods to the physically permissible time periods before and after it.

[0033] This redistribution algorithm, through the dual optimization objectives of total quantity conservation and process smoothing, not only addresses the security risks of rigidly executing unreachable commands in traditional scheduling but also proactively optimizes power quality during the regulation process, resulting in a synergistic effect of improving grid regulation service friendliness and equipment lifespan. Finally, the response surface dynamic construction unit, based on the conflict resolution strategy, begins generating the second command response surface. This unit first obtains the instantaneous power upper limit of the first physical constraint surface. As an insurmountable physical constraint. Simultaneously, combined with the desired power adjustment required by the scheduling instructions. Power amortization of conflict amount generated by the conflict amount redistribution algorithm The preliminary target regulation power is calculated, i.e. Then, the minimum value operation is used. This ensures that the response plan at any given time does not exceed physical limits. Based on this, a smooth transition function that varies with time is introduced. ,in The start time of the scheduling instruction time window. This represents the total time window length. To adjust the smoothing coefficient, this function makes the response trajectory naturally smooth at the beginning and end of the time window, avoiding power steps. It should be noted that if... A value of zero indicates that the scheduling instruction requires instantaneous completion; in this case, the smooth transition function degenerates to 1, meaning smooth transition is not considered. If... If the value is negative, the absolute value is taken. Finally, multiply by the actual regulating rate of the power station. Maximum adjustment rate required by the scheduling command ratio This dynamically couples the actual regulation capacity of the power plant with the command requirements, ensuring the dynamic feasibility of the response. It should be noted that the maximum regulation rate given in the dispatch command... It is usually a positive value; in very rare cases, if... If the value is zero or undefined, the system will automatically replace it with the maximum safe regulation rate designed for the power plant to avoid division by zero errors. Simultaneously, the anomaly will be recorded for verification by operation and maintenance personnel. Through the above steps, the unit calculates the instantaneous safe-response regulation power boundary at each moment. That is, the second instruction response surface.

[0034] The generation of this surface is the mathematical core of the hyperboloid collaborative logic in this scheme. It ensures that the response plan is absolutely respected and constrained by physical limits at all times, fundamentally eliminating safety hazards. At the same time, through smooth transition and rate coupling, it realizes the intelligent transformation from external dispatch instructions to internal executable, safe and optimized action programs, resulting in the technical effect of maximizing the satisfaction of power grid regulation needs while ensuring the safe and stable operation of the power plant.

[0035] Subsequently, the value decoupling and verification module is activated to accurately measure the scheduling and execution process. This module includes a real-time arbitration unit for output deviation, an evidence chain compliance verification unit, and a value scoring and labeling unit. (Refer to...) Figure 3 As shown, within the time window of the dispatch command execution, the real-time arbitration unit for output deviation uses the first physical constraint surface as the unique and dynamic baseline to perform high-frequency sampling of the total power output of the power station.

[0036] When the actual output at multiple consecutive sampling points or over a sustained period of time remains stably below the baseline of the first physical constraint surface, the unit initiates arbitration logic. This logic simultaneously queries the inverter's operating status and the platform's control command logs. After ruling out non-dispatch factors such as equipment failure, it confirms that the output drop is an active adjustment behavior in response to dispatch commands. Once confirmed, the unit immediately generates an atomic adjustment evidence unit. This process utilizes the first physical constraint surface as a precise weather stripper, achieving for the first time the irreversible and verifiable attribution of output deviations below the theoretical natural power generation capacity baseline to active adjustment behavior. This completely solves the fundamental metrological problem of the inability to decouple aliased electricity, creating a technical prerequisite for fair value accounting.

[0037] The evidence chain compliance verification unit is responsible for verifying the security of the aforementioned evidence chain. This unit fits the atomized adjustment evidence units generated over a period of time into an actual adjustment curve and compares it with the boundary of the second command response surface. Any adjustment behavior exceeding the boundary of the second command response surface is considered an over-limit operation, and its corresponding evidence unit is marked as over-limit. This means that value accounting is not only based on whether it is proactive, but also on whether it is safe and compliant. The safety boundary second command response surface is directly internalized into a rigid rule for value verification, ensuring that every unit of electricity measured is within a safe and feasible range, resulting in an unexpected management effect that deeply integrates operational safety and economic value.

[0038] The value integration and labeling unit performs the final quantification and output. This unit first integrates the sequence of atomic regulation evidence units that have passed compliance verification (or been adjusted for penalties) in chronological order to obtain the total effective regulation volume contributed purely by proactive regulation behavior in this scheduling event. Next, based on a preset value mapping function, the economic value corresponding to this portion of the volume is calculated. Finally, this unit outputs a structured regulation service value measurement data. This data is a direct result of the "hyperbolic surface collaboration" system, and it comes with complete verification path labels, making each value report traceable to the original physical constraint benchmark and safety response boundary, possessing unprecedented technical transparency and auditability.

[0039] Meanwhile, the dynamic adjustment module for operational strategies, as a crucial part of the security closed loop, began real-time monitoring and optimization. (Refer to...) Figure 4 As shown, this module consists of a feasible domain monitoring and early warning unit and a multi-dimensional resource coordination and optimization unit. The feasible domain monitoring and early warning unit continuously calculates the distance between the actual output point of the power plant and the boundary of the second command response surface, which is defined as the load margin. This margin reflects in real time the proximity of the power plant's operating status to the safety boundary. The platform has preset multiple early warning thresholds. When the margin falls below the first threshold, the unit issues an early warning signal; when the margin continues to decrease to the even lower second threshold, an emergency signal requiring immediate intervention is issued. This real-time margin monitoring based on the second command response surface transforms traditional passive protection into proactive prevention based on predictive models, providing the power grid with a safety buffer at the minute or even second level, significantly enhancing the dynamic safety level of the power grid under high-proportion renewable energy access.

[0040] The multi-dimensional resource coordination and optimization unit is triggered upon receiving an early warning or emergency intervention signal, or when the platform actively improves regulation quality. This unit executes an online optimization algorithm with the specific optimization objective of minimizing the root mean square error between the actual output of each inverter cluster and the required value of the second command response surface, while ensuring that the total power output of the power plant closely follows the second command response surface.

[0041] To achieve this goal, the unit first acquires real-time operating status and historical reliability data of each inverter cluster within the power plant, as well as more refined cluster-level local meteorological data provided by the microscale meteorological field monitoring unit. Subsequently, the unit establishes a mathematical optimization model incorporating equipment operation constraints and power balance constraints, and solves it rapidly using a solver. The solution results in a new, dynamic set of inverter cluster power allocation instructions. This instruction set can automatically avoid operating points with high local losses or inefficiencies due to weather degradation while meeting global regulation objectives. It achieves intelligent redistribution of regulation tasks in the spatial dimension, thus ensuring safety while actively optimizing the power plant's internal operating efficiency and equipment lifespan during the process of following the second instruction response surface, resulting in a secondary optimization effect that improves overall economic benefits.

[0042] Finally, the trusted evidence storage and traceability module completes the technical loop of the entire value creation process. This module generates a unique cryptographic hash value for each atomic adjustment evidence unit. After a complete scheduling event cycle, this module constructs a Merkle tree from the hash values ​​of all evidence units, along with the hash values ​​of the finally generated adjustment service value measurement data, in chronological order. Then, the root hash value of the Merkle tree, the hash digest of the key process data of this scheduling event, and the authoritative timestamp are submitted together through the application programming interface to a permissioned blockchain network or a nationally authorized trusted timestamp service center for solidification and evidence storage.

[0043] After successful notarization, the module generates a verifiable digital certificate. This certificate is not simply the original data uploaded to the blockchain, but rather a cryptographically solidified chain of value conclusions with a tight internal logical connection, formed after the aforementioned "hyperbolic surface collaboration" analysis, decoupling, and verification. This chain is an immutable and independently verifiable technical fingerprint. This achieves a leap from "trustworthy technical logic" to "trustworthy commercial certificate," enabling the output of complex computational processes that originally existed in private systems to be accepted undisputed by all market participants. This removes trust barriers for distributed power sources to participate in high-level electricity market transactions, resulting in the commercial effect of transforming complex technical solutions into standardized and trustworthy commodities.

[0044] Through the implementation of this embodiment, the platform fully demonstrated its workflow under a typical complex operating condition of sandstorm weather and power grid frequency regulation commands superimposed. From generating a first physical constraint surface based on fine meteorological perception, to dynamically coupling scheduling commands to generate a safe and executable second command response surface, to performing value decoupling arbitration based on the first physical constraint surface and performing security and compliance verification based on the second command response surface, and finally forming reliable metering data and completing blockchain notarization, the platform has constructed a complete technical closed loop from physical perception to reliable transactions. This not only solves the metering problem of the inability to decouple mixed power and ensures the fair benefits of power plants, but also significantly improves the safety and reliability of power plants participating in grid regulation through real-time margin monitoring and resource optimization.

[0045] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A smart operation and maintenance management platform for distributed power stations, characterized in that, include: The fine particulate matter damage precision sensing module is used to acquire micro-meteorological data of the power plant and generate a first physical constraint surface that reflects the real-time physical limitations of the current weather process on power generation capacity; The fine particulate matter damage precision sensing module includes: a microscale meteorological field monitoring unit, deployed at key locations in the power plant array, used to synchronously collect micro-meteorological data of the power plant, including fine particulate matter mass concentration, particle size distribution, relative humidity, and wind speed and direction; a physical constraint surface modeling unit, which calculates and outputs a time-varying first physical constraint surface as the theoretical maximum instantaneous power limit of the power plant using the photovoltaic module physical model and the power plant micro-meteorological data; a dispatch command coupling analysis module, used to parse power dispatch commands and, combined with the first physical constraint surface, dynamically construct a second command response surface characterizing the regulation capability boundary of the power plant under physical constraints; the dispatch command coupling analysis module includes: command depth solution... The analysis unit parses the target power curve of the scheduling command and overlays it with the first physical constraint surface on the time axis to identify conflict periods and conflict amounts where the command requirements exceed the current physical constraint capabilities. The response surface dynamic construction unit dynamically constructs a second command response surface based on the conflict identification results. During non-conflict periods, the response surface dynamic construction unit directly sets the second command response surface to be consistent with the target power curve. During identified conflict periods, the response surface dynamic construction unit executes a conflict resolution strategy: determining the upper limit of the power response for that period based on the second command response surface generation formula, and initiating a conflict amount redistribution algorithm. The algorithm uses the physical constraint margin of adjacent non-conflict periods and the adjustment speed... With the rate limit as input, and aiming at maintaining the total regulation capacity and smoothing power changes, the power deficit that cannot be responded to during the conflict period is optimized and allocated to the adjacent periods with physical responsiveness. The allocation is weighted based on the proportion of physical margins and time proximity of each adjacent period, generating a set of dynamic power compensation values. The dynamic response surface construction unit outputs the power trajectory after power upper limit constraint and conflict redistribution processing as the second command response surface for that conflict period, thus forming a dynamically executable response trajectory that globally satisfies physical constraints, achieves local smooth adjustment, and whose total regulation is equivalent to the original command requirements. The dynamic response surface construction unit generates the second command response surface in the following manner. This includes: using the instantaneous power upper limit of the first physical constraint surface as the physical constraint upper limit; combining the expected regulation power of the dispatch command and the conflict-shared power to obtain the preliminary target regulation power; taking the minimum of the two; multiplying it by a smooth transition function that varies with time, determined based on the start time of the dispatch time window, the total time window length, and the regulation smoothing coefficient; and finally multiplying it by the ratio of the actual regulation rate to the maximum regulation rate required by the command to obtain the instantaneously safe response regulation power boundary, i.e., the second command response surface; and a value decoupling verification module, which is used to compare the actual power output curve of the power plant with the first physical constraint surface during the dispatch period, generate atomized regulation evidence units, and perform compliance verification and value integration based on the second command response surface.The dynamic control module for operational strategies is used to adjust the resource allocation strategy within the power plant in real time based on the state of the atomized adjustment evidence units and the second command response surface, ensuring that the adjustment process meets physical constraints and scheduling requirements. The trusted evidence storage and traceability module is used to hash and chain-store the first physical constraint surface, the second command response surface, the atomized adjustment evidence units, and their value integration results, forming an immutable and verifiable chain of evidence for the adjustment service value.

2. The intelligent operation and maintenance management platform for distributed power stations according to claim 1, characterized in that: The calculation of the physical constraint surface modeling unit includes: calculating the equivalent deposition rate and optical path attenuation coefficient of the current aerosol particle swarm on the glass cover surface of the photovoltaic module based on particle size distribution and relative humidity data; calculating the effective received irradiance attenuation function of the module surface caused by the combined effect of deposited particles and suspended particles in combination with the solar incident angle; and coupling the attenuation function with the basic power characteristic curve of the power station to generate the first physical constraint surface.

3. The intelligent operation and maintenance management platform for distributed power stations according to claim 2, characterized in that: The physical constraint surface modeling unit generates the first physical constraint surface in the following ways: determining the base power ratio based on the ratio of the effective received irradiance on the component surface to the irradiance under standard test conditions; determining the comprehensive meteorological influence factor based on the fine particulate matter deposition damage coefficient, the equivalent deposition mass concentration of fine particulate matter on the component surface, and the exponential function of relative humidity, and correcting the base power ratio by subtracting this factor from 1; then performing angle correction based on the cosine value of the solar incidence angle; and finally performing efficiency correction based on the inverter's average conversion efficiency and the internal transmission line loss efficiency to obtain the instantaneous maximum theoretical power limit, i.e., the first physical constraint surface.

4. The intelligent operation and maintenance management platform for distributed power stations according to claim 1, characterized in that: The superposition analysis specifically involves comparing the target power curve with the first physical constraint surface point by point in the same time coordinate system. When the power value on the target power curve is continuously greater than the power value at the corresponding point on the first physical constraint surface, and the excess exceeds a preset error tolerance threshold, the time period is determined to be a conflict period. The integral value of the excess is the conflict amount within that time period.

5. The intelligent operation and maintenance management platform for distributed power stations according to claim 1, characterized in that: The value decoupling verification module includes: a real-time output deviation arbitration unit, which samples the real-time total output of the power station using the first physical constraint surface as a dynamic baseline. When the sampling point is continuously below the baseline for more than a preset number of consecutive sampling points or a duration threshold, arbitration logic is initiated. Combining the inverter operating status and control command logs, equipment fault factors are excluded, the output reduction is confirmed as an active adjustment behavior, and an atomic adjustment evidence unit is generated. This unit includes at least a timestamp, the adjusted power value, the corresponding first physical constraint surface value, and an arbitration logic identifier; an evidence chain compliance verification unit, which fits the continuously generated atomic adjustment evidence units into an actual adjustment curve and compares it with the second command response surface to verify whether it exceeds the boundary of the surface. Evidence units that exceed the boundary are marked and discounted or directly eliminated according to a preset penalty coefficient during value integration; and a value integration and labeling unit, which integrates the verified atomic adjustment evidence unit sequence over time to obtain the total adjusted power, calculates the economic value according to a preset value mapping function, and finally outputs the adjustment service value measurement data with complete verification path labels.

6. The intelligent operation and maintenance management platform for distributed power stations according to claim 2, characterized in that: The dynamic control module of the operation strategy includes: a feasible domain monitoring and early warning unit, used to calculate in real time the distance between the actual output point of the power station and the boundary of the second command response surface, i.e., the load margin. When the margin is lower than the preset first-level threshold, an early warning signal is issued; when the margin is lower than the second-level threshold, an emergency intervention signal is issued. A multi-dimensional resource coordination and optimization unit, when receiving an early warning or emergency intervention signal, or when actively improving the overall regulation quality, starts an online optimization algorithm. The online optimization algorithm, in the spatial dimension, redistributes the output tasks of different inverter clusters to balance device losses or avoid local meteorological degradation points identified by the microscale meteorological field monitoring unit. In the resource dimension, it decides whether and how to call the on-site energy storage system for power compensation to ensure that the actual regulation curve smoothly and accurately follows the second command response surface.

7. The intelligent operation and maintenance management platform for distributed power stations according to claim 2, characterized in that: The physical constraint surface modeling unit calculates the optical path attenuation coefficient through the following steps: using the monitored particle size distribution data, the extinction efficiency factor of aerosol particles of different particle sizes in the photovoltaic module response band range is calculated based on Mie scattering theory; combined with the real-time measured relative humidity data, the equivalent complex refractive index and particle size of the aerosol particles are corrected by the deliquescence growth model to obtain a more accurate particle swarm extinction cross-sectional spectrum distribution under the current humidity conditions. By combining the corrected extinction cross-section spectral distribution with real-time monitoring of fine particulate matter mass concentration data, the vertical aerosol optical thickness, which characterizes the total attenuation capacity of aerosols within a unit vertical air column, was calculated. Based on the vertical aerosol optical thickness and the real-time solar zenith angle, the total aerosol optical thickness on the inclined path is calculated by approximating the planar parallel layer of atmospheric radiation transmission. This total aerosol optical thickness is the core parameter of dynamic optical path attenuation used to construct the attenuation function of effective irradiance received on the surface of the component.

8. The intelligent operation and maintenance management platform for distributed power stations according to claim 6, characterized in that: When the multi-dimensional resource coordination and optimization unit performs spatial dimension optimization, it specifically acquires the real-time operating status, historical failure rate, and cluster-level local meteorological data provided by the micro-scale meteorological field monitoring unit for each inverter cluster within the power station; establishes a mathematical optimization model that includes equipment operation constraints and power balance constraints, with the optimization objective of maximizing the overall regulation load margin or minimizing the root mean square error between the output of each cluster and the required value of the second command response surface; and dynamically outputs a new set of inverter cluster power allocation commands by solving the mathematical optimization model. This set of commands ensures that, under the premise of meeting the overall regulation objective, local high-loss or low-efficiency operating points are avoided, thereby achieving adaptive optimization scheduling of resources within the power station.

9. The intelligent operation and maintenance management platform for distributed power stations according to claim 1, characterized in that: The trusted evidence storage and traceability module is specifically used to: generate a unique hash value for each atomic adjustment evidence unit; and construct a Merkle tree in chronological order of all evidence unit hash values ​​generated within the same scheduling event period, together with the final hash value of the corresponding adjustment service value measurement data. The root hash value of the Merkle tree, the parameter snapshots and timestamps of the key process data are submitted to a permissioned blockchain network or a trusted timestamp service center for notarization; a verifiable digital certificate containing the root hash value, notarization time and blockchain transaction ID is generated. This certificate serves as an appendix to the data for regulating the measurement of service value, allowing third parties to conduct independent and efficient authenticity verification.

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