New energy power generation technology supervision and management method, device, equipment and medium

Abstract

The invention relates to a new energy power generation technology supervision and management method and device, equipment and a medium. The method comprises the following steps: acquiring a synchronous data set containing key data such as direct-current bus voltage and alternating-current power grid frequency, and generating a pulse width modulation signal through deviation calculation so as to control a power converter; forming to-be-verified data by combining actual generating capacity and the like, and generating uplink data through a consensus mechanism; calculating a contract completion index, a consensus participation rate index and a dynamic weight coefficient based on the uplink data, and generating and screening users according to a dynamic reputation value to obtain a matched transaction pair set; and determining an initial excitation price according to the dynamic reputation value, adjusting the initial excitation price in combination with a photovoltaic power fluctuation prediction value, and generating a final excitation price list and a demand response instruction in combination with system state flag compensation. According to the method, the new energy power generation supervision efficiency, the power grid operation stability and the power fluctuation consumption capability are improved by means of synchronous data set construction, dynamic reputation matching transaction, fluctuation adaptive excitation optimization and the like.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, and in particular relates to methods, devices, equipment and media for supervising and managing new energy power generation technology. Background Technology

[0002] With the large-scale application of new energy power generation technologies such as photovoltaics, the safe and stable operation and efficient supervision of hybrid microgrids have become core industry requirements. Specifically, the current supervision of new energy power generation relies on multi-source data, including actual DC bus voltage, actual AC grid frequency, actual photovoltaic output current, and user-declared electricity plans, to support control decisions and transaction management.

[0003] However, traditional renewable energy power generation monitoring technologies rely heavily on centralized databases for storing key data such as actual power generation and user-declared power plans. This lack of effective consensus mechanisms ensures data credibility, making the generated data susceptible to tampering and failing to provide a reliable data foundation for subsequent transaction matching. Furthermore, in user credit assessment, traditional monitoring methods often employ static indicators without considering the dynamic changes in system status indicators and historical data. This leads to unreasonable dynamic weighting coefficients, and the integrated calculation of contract completion and consensus participation indicators lacks specificity. Consequently, it is difficult to generate dynamic credit values ​​that truly reflect users' ability to fulfill their obligations and their willingness to participate, resulting in insufficient precision in selecting matching transaction pairs. In addition, existing demand response incentive mechanisms often use fixed prices or simple tiered pricing models, failing to accurately optimize and adjust the initial incentive price based on photovoltaic power fluctuation forecasts or apply targeted compensation based on system status indicators. This makes it difficult for the final incentive price list and demand response instructions to effectively guide users to adjust their power output or consumption, hindering the efficient absorption of renewable energy power fluctuations. The overall monitoring methods lack closed-loop optimization capabilities and adaptability, restricting the operational efficiency and stability of hybrid microgrids. Summary of the Invention

[0004] Therefore, it is necessary to provide methods, devices, equipment and media for supervising and managing new energy power generation technologies to address the above-mentioned technical issues. The aim is to improve the safety, stability and regulatory efficiency of hybrid microgrid operation, enhance data credibility, the accuracy of user credit assessment and the effectiveness of demand response incentive mechanisms.

[0005] Firstly, this application provides methods for supervising and managing new energy power generation technologies, including:

[0006] Acquire a synchronous dataset including actual DC bus voltage, actual AC grid frequency, actual photovoltaic output current, and user-reported electricity consumption plans;

[0007] The actual DC bus voltage and the actual AC grid frequency are compared with their corresponding rated values ​​to calculate the voltage deviation and frequency deviation. When the voltage deviation or frequency deviation meets the preset activation condition, the system status flag is set to the active state, and proportional-integral calculation is performed based on the voltage deviation to generate a pulse width modulation signal. The pulse width modulation signal is used to control the power converter.

[0008] Based on the actual value of photovoltaic output current, the actual power generation is generated. Based on the actual power generation, the user's declared power plan value and the system status flag, the data to be verified is obtained. The data to be verified is verified through the consensus mechanism to generate on-chain data.

[0009] Based on the historical electricity data and user consensus participation records in the on-chain data, the contract completion rate index and consensus participation rate index are calculated. Combined with the system status flag and historical system status flag, the dynamic weight coefficient is determined. Based on the contract completion rate index, consensus participation rate index and dynamic weight coefficient, a dynamic reputation value is generated. Users are screened according to the dynamic reputation value to obtain a set of matching trading pairs.

[0010] The system obtains the predicted value of photovoltaic power fluctuation, determines the initial incentive price based on the dynamic credit value in the matching trading pair set, sets the target response amount based on the predicted value of photovoltaic power fluctuation, optimizes and adjusts the initial incentive price based on the target response amount, applies compensation based on the system status flag, and generates the final incentive price table and demand response instructions.

[0011] In one embodiment, when the voltage deviation or frequency deviation meets a preset activation condition, the system status flag is set to the active state, and a proportional-integral operation is performed based on the voltage deviation to generate a pulse width modulation signal, including:

[0012] The voltage deviation is obtained by calculating the absolute value of the difference between the actual DC bus voltage and the rated DC voltage.

[0013] The frequency deviation is obtained by calculating the absolute value of the difference between the actual AC power grid frequency and the rated frequency.

[0014] When the voltage deviation is greater than the preset voltage threshold or the frequency deviation is greater than the preset frequency threshold, the system status flag is set to 1 as the active state; otherwise, it is set to 0.

[0015] When the system status flag is 1, the voltage deviation is calculated using proportional-integral operations to generate a pulse width modulation signal.

[0016] In one embodiment, the actual power generation is generated based on the actual value of the photovoltaic output current. Based on the actual power generation, the user's declared power plan value, and the system status flag, data to be verified is obtained. This data is then verified through a consensus mechanism to generate on-chain data, including:

[0017] The actual photovoltaic output current is multiplied by the rated DC voltage, and the result is integrated within a set time window to generate the actual power generation.

[0018] The actual power generation, the user's declared power plan value, and the system status flag are bound to a timestamp to form data to be verified.

[0019] The data to be verified is verified by nodes through a practical Byzantine fault-tolerant consensus mechanism. When more than two-thirds of the nodes pass the verification, the data is generated and uploaded to the chain.

[0020] In one embodiment, based on historical electricity data and user consensus participation records in the on-chain data, a contract completion rate indicator and a consensus participation rate indicator are calculated. A dynamic weighting coefficient is determined by combining system status flags and historical system status flags. A dynamic reputation value is then generated based on the contract completion rate indicator, the consensus participation rate indicator, and the dynamic weighting coefficient, including:

[0021] Extract historical electricity data within a preset number of transaction periods from the on-chain data. The historical electricity data includes historical actual power generation and historical user-declared electricity plan values.

[0022] Based on historical actual power generation and historical user-declared power generation plans, calculate the ratio of the minimum cumulative delivered power to the cumulative declared power to generate a contract completion index;

[0023] Based on user consensus participation records, the ratio of the number of times a user successfully participated in block verification to the total number of calls in the on-chain data is calculated to generate a consensus participation rate metric.

[0024] Extract historical system status flags within a preset number of transaction periods from the on-chain data, calculate the ratio of the number of historical system status flags in an active state to the total number, and generate dynamic weighting coefficients based on the ratio.

[0025] Based on dynamic weighting coefficients, a weighted summation of consensus participation rate and contract completion rate is performed to generate a dynamic reputation value.

[0026] In one embodiment, an initial incentive price is determined based on the dynamic reputation value in the matching trading pair set, a target response amount is set in conjunction with the photovoltaic power fluctuation forecast, the initial incentive price is optimized and adjusted based on the target response amount, and compensation is applied in conjunction with system status flags to generate a final incentive price table and demand response instructions, including:

[0027] The initial incentive price is generated by dividing the dynamic credit value of each user in the matching transaction pair set by the maximum dynamic credit value and then multiplying it by the preset base electricity price.

[0028] The target response is generated by multiplying the predicted photovoltaic power fluctuation by the absorption coefficient.

[0029] Based on the historical user response records in the on-chain data, the price sensitivity is fitted, and the initial incentive price is iteratively optimized by gradient descent with the target response volume as the benchmark to generate the optimized incentive price.

[0030] When the system status flag is active, the optimized incentive price will be multiplied by the voltage stabilization compensation coefficient to generate the final incentive price table.

[0031] Based on the final incentive price list and the target response amount, a demand response instruction is generated, which is used to instruct the adjustment of power output or consumption according to the target response amount.

[0032] In one embodiment, the method further includes:

[0033] Calculate the voltage stability index based on the actual value of the DC bus voltage fed back by the power converter;

[0034] When the voltage stability index is greater than the preset stability threshold, the proportional coefficient of the proportional-integral operation in the system status flag is adjusted according to the deviation ratio between the voltage stability index and the preset stability threshold, so as to obtain the adjusted proportional coefficient.

[0035] The transaction fulfillment rate is calculated based on the actual electricity volume responded to by the user's response to the demand command.

[0036] When the transaction fulfillment rate is less than the preset fulfillment rate threshold, the dynamic weight coefficient is adjusted according to the deviation between the transaction fulfillment rate and the preset fulfillment rate threshold to obtain the updated dynamic weight coefficient.

[0037] Integrate the adjusted proportional coefficients and the updated dynamic weight coefficients to generate parameter update instructions.

[0038] In one embodiment, the dynamic weighting coefficient is calculated using the following formula:

[0039]

[0040] in, For dynamic weighting coefficients, It is the balance coefficient, and , This represents the ratio of the number of active states in the historical system status flags to the total number of active states. This represents the number of active states in the historical system status flags. For the first Voltage deviation during the second activation state For the first Frequency deviation during the next activation state The nonlinear coefficient is the deviation coefficient. , For the first The time interval between the previous activation state and the current state. The time decay coefficient, , and These are the historical maximum voltage deviation and the historical maximum frequency deviation, respectively.

[0041] Secondly, this application also provides a monitoring and management device for new energy power generation technology, including:

[0042] The data synchronization acquisition module is used to acquire a synchronized dataset including the actual value of DC bus voltage, the actual value of AC grid frequency, the actual value of photovoltaic output current, and the value of user-submitted electricity plans.

[0043] The status monitoring and signal generation module is used to calculate the difference between the actual value of the DC bus voltage and the actual value of the AC grid frequency and the corresponding rated value, respectively, to obtain the voltage deviation and frequency deviation. When the voltage deviation or frequency deviation meets the preset activation condition, the system status flag is set to the active state, and proportional integral calculation is performed based on the voltage deviation to generate a pulse width modulation signal. The pulse width modulation signal is used to control the power converter.

[0044] The data verification and on-chain module is used to generate actual power generation based on the actual value of photovoltaic output current, obtain data to be verified based on the actual power generation, user-declared power plan value and system status flag, and verify the data to be verified through consensus mechanism to generate on-chain data.

[0045] The reputation assessment and transaction matching module is used to calculate the contract completion rate index and consensus participation rate index based on the historical electricity data and user consensus participation records in the on-chain data. It determines the dynamic weight coefficient by combining the system status flag and historical system status flag, and generates a dynamic reputation value based on the contract completion rate index, consensus participation rate index and dynamic weight coefficient. Users are screened according to the dynamic reputation value to obtain a set of matching transaction pairs.

[0046] The price optimization and instruction generation module is used to obtain the photovoltaic power fluctuation forecast value, determine the initial incentive price based on the dynamic credit value in the matching trading pair set, set the target response amount in combination with the photovoltaic power fluctuation forecast value, optimize and adjust the initial incentive price based on the target response amount, apply compensation in combination with system status flags, and generate the final incentive price table and demand response instructions.

[0047] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the first aspect.

[0048] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the first aspect.

[0049] The aforementioned methods, devices, equipment, and media for supervising and managing new energy power generation technologies first acquire a synchronous dataset containing key data such as the actual value of DC bus voltage and the actual value of AC grid frequency, providing a data foundation for subsequent deviation calculation and control decisions. Secondly, voltage and frequency deviations are calculated through difference calculations. Based on preset activation conditions, system status flags are set and pulse width modulation signals are generated, achieving precise real-time control of the power converter and solving the problem of lag in traditional control responses. Furthermore, data to be verified is formed by combining actual power generation, user-reported power plans, and system status flags. This data is then generated on-chain through a consensus mechanism, ensuring data credibility and avoiding the problem of easy tampering in centralized storage. Finally, contract completion and consensus participation indicators are calculated based on the on-chain data. Dynamic weight coefficients are determined using system status flags to generate dynamic reputation values ​​for screening and matching trading pairs. The initial incentive price is then optimized and compensation is applied based on the photovoltaic power fluctuation prediction value, generating the final incentive price table and demand response instructions. This effectively solves the problems of static reputation assessment and insufficient adaptability of incentive mechanisms in traditional methods, significantly improving the efficiency of new energy power generation supervision, the accuracy of trading matching, and the ability to absorb power fluctuations. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of a new energy power generation technology supervision and management method provided as an exemplary embodiment of the present invention;

[0052] Figure 2 A flowchart of a method for generating dynamic reputation values ​​is provided as an exemplary embodiment of the present invention;

[0053] Figure 3 A schematic diagram of a new energy power generation technology supervision and management device provided as an exemplary embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] In one embodiment, such as Figure 1 As shown, a method for supervising and managing new energy power generation technology is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0056] S101: Obtain a synchronous dataset including the actual value of DC bus voltage, the actual value of AC grid frequency, the actual value of photovoltaic output current, and the user's declared power consumption plan value.

[0057] Specifically, in the operation of hybrid microgrids, real-time acquisition of synchronous datasets, including actual DC bus voltage, actual AC grid frequency, actual photovoltaic output current, and user-declared electricity consumption plans, is fundamental to effective regulation. Among these, DC bus voltage and AC grid frequency are key indicators for measuring the grid's operational status; deviations between their actual and rated values ​​directly reflect the grid's stability and security. Actual photovoltaic output current reflects the actual output of renewable energy generation, enabling further assessment of power generation efficiency and power fluctuations. User-declared electricity consumption plans provide anticipated user-side data for transaction management and demand response. Synchronously collecting this data provides comprehensive and real-time data support for subsequent control decisions and transaction management, ensuring the data integrity and timeliness of the regulatory system.

[0058] S102: The actual value of the DC bus voltage and the actual value of the AC grid frequency are calculated with the corresponding rated values ​​to obtain the voltage deviation and frequency deviation. When the voltage deviation or frequency deviation meets the preset activation condition, the system status flag is set to the active state, and proportional-integral operation is performed based on the voltage deviation to generate a pulse width modulation signal. The pulse width modulation signal is used to control the power converter.

[0059] Specifically, the deviations between the actual and rated values ​​of the DC bus voltage and the AC grid frequency are core indicators reflecting whether the power grid is operating stably. When these deviations exceed allowable limits, they can lead to risks such as grid disconnection of renewable energy generation equipment and damage to electrical equipment. The core function of the preset activation condition is to define the stable and abnormal boundaries of the power grid. When the deviation reaches this condition, it indicates that grid intervention is needed to restore stability. Therefore, the system status flag can be set to an active state, providing a status basis for subsequent data storage, incentive optimization, and other processes. Furthermore, proportional-integral calculations can be performed based on the voltage deviation, allowing for dynamic adjustment of the control pulse width modulation (PWM) signal according to the magnitude and trend of the deviation, achieving precise control of the power converter. This PWM signal can effectively regulate the DC bus voltage and AC grid frequency by adjusting the switching frequency and duty cycle of the power converter, thereby ensuring the safe and stable operation of the renewable energy generation system and the power grid.

[0060] S103: Based on the actual value of photovoltaic output current, generate actual power generation. Based on the actual power generation, the user's declared power plan value and system status flag, obtain the data to be verified. Verify the data to be verified through the consensus mechanism and generate on-chain data.

[0061] Specifically, actual power generation is a crucial output indicator for new energy power generation systems, reflecting the power generation efficiency and output of photovoltaic systems. Actual power generation can be calculated based on the correlation between the actual photovoltaic output current and the rated DC voltage. The difference between actual power generation and the user's declared power plan directly reflects the user's fulfillment of their obligations, while the system status flag indicates the grid's operating status at the time of data generation. The integrated data to be verified comprehensively covers the core information related to the transaction and forms the basis for subsequent transaction management and data storage. Because traditional centralized data storage models are susceptible to data tampering and forgery, a consensus mechanism is used to verify the data to be verified and generate on-chain data. This allows for collaborative verification by multiple nodes, ensuring the authenticity and consistency of the data to be verified. Only data jointly recognized by the nodes is recorded and stored. On-chain data verified through the consensus mechanism possesses the characteristics of immutability and traceability, avoiding transaction disputes caused by falsified power generation data and providing authentic and complete historical data support for subsequent dynamic reputation value calculations, thus ensuring the fairness and reliability of the transaction process.

[0062] S104: Based on the historical electricity data and user consensus participation records in the on-chain data, calculate the contract completion rate index and consensus participation rate index, combine the system status flag and historical system status flag to determine the dynamic weight coefficient, and generate a dynamic reputation value based on the contract completion rate index, consensus participation rate index and dynamic weight coefficient, and filter users to obtain a set of matching trading pairs based on the dynamic reputation value.

[0063] Specifically, historical electricity data stored on the blockchain serves as the core basis for measuring a user's ability to fulfill obligations. The contract completion rate metric calculated using this data directly reflects the quality of a user's historical transactions. User consensus participation records demonstrate a user's active participation and cooperation in the data trust verification process. The consensus participation rate metric calculated from these records further reflects a user's willingness to cooperate. System status flags and historical system status flags reflect the dynamic changes in power grid operation. Dynamic weighting coefficients determined based on these flags allow reputation assessments to adapt to different power grid operating states. For example, during power grid anomalies, the weight of the consensus participation rate metric can be appropriately increased to encourage user participation in data verification and ensure data trustworthiness. By fusing the contract completion rate metric and the consensus participation rate metric using dynamic weighting coefficients, the generated dynamic reputation value comprehensively and accurately reflects a user's current transaction reliability. Furthermore, by filtering users based on this dynamic reputation value, priority can be given to matching high-reputation users to form a set of trading pairs, thereby reducing the risk of transaction default and improving the overall efficiency of new energy trading.

[0064] S105: Obtain the photovoltaic power fluctuation forecast value, determine the initial incentive price based on the dynamic credit value in the matching trading pair set, set the target response amount in combination with the photovoltaic power fluctuation forecast value, optimize and adjust the initial incentive price based on the target response amount, apply compensation in combination with the system status flag, and generate the final incentive price table and demand response instructions.

[0065] Specifically, photovoltaic (PV) power generation is highly volatile and intermittent, and its power fluctuations can impact grid stability. However, through a reasonable incentive mechanism, users can be guided to adjust their power output or consumption according to grid demand. The PV power fluctuation forecast is derived from the power output characteristics of the PV system, allowing for advance understanding of PV power fluctuation trends. The target response amount set based on this forecast can accurately match the grid's current fluctuation absorption needs. Since users with high dynamic credit scores are more credible and willing to participate in demand response, the initial incentive price can be determined based on the dynamic credit scores of users in the matching trading pair set. Subsequently, the initial incentive price can be optimized and adjusted based on the target response amount to ensure a precise match between the incentive price and absorption demand. For example, the incentive intensity can be appropriately increased when fluctuations are large to ensure the target response amount is fully achieved. Furthermore, compensation can be applied in conjunction with system status flags to strengthen incentives for abnormal grid conditions, further enhancing users' willingness to respond during critical grid periods. Ultimately, a final incentive price list and demand response instructions can be obtained, providing users with clear behavioral guidance and expected returns. This step, through precise incentive price adjustments and compensation mechanisms, significantly enhances users' willingness and accuracy in responding to demand, effectively absorbs photovoltaic power fluctuations, reduces the impact of fluctuations on the power grid, and improves the overall operating efficiency and stability of the hybrid microgrid.

[0066] The aforementioned method first provides a comprehensive data foundation for subsequent control and supervision by acquiring a synchronized dataset. Secondly, it calculates and controls voltage and frequency deviations, addressing the issues of untimely state monitoring and lagging control response. Furthermore, it verifies and uploads power generation and user-reported data to the blockchain through a consensus mechanism, resolving the problem of insufficient data credibility and improving data authenticity and security. Further, it filters users and matches transaction pairs based on dynamic reputation values, addressing the issues of unreasonable user reputation assessments and low accuracy in transaction pair selection, thus enhancing the precision of transaction management. Finally, it optimizes incentive prices and demand response instructions by combining photovoltaic power fluctuation predictions, addressing the issues of untargeted incentive mechanisms and poor demand response effects, improving the absorption capacity of new energy power generation fluctuations and the closed-loop optimization capability of supervision, and significantly improving the operational efficiency and stability of hybrid microgrids.

[0067] In one embodiment, when the voltage deviation or frequency deviation meets a preset activation condition, the system status flag is set to the active state, and a proportional-integral operation is performed based on the voltage deviation to generate a pulse width modulation signal, including:

[0068] The voltage deviation is obtained by calculating the absolute value of the difference between the actual DC bus voltage and the rated DC voltage.

[0069] The frequency deviation is obtained by calculating the absolute value of the difference between the actual AC power grid frequency and the rated frequency.

[0070] When the voltage deviation is greater than the preset voltage threshold or the frequency deviation is greater than the preset frequency threshold, the system status flag is set to 1 as the active state; otherwise, it is set to 0.

[0071] When the system status flag is 1, the voltage deviation is calculated using proportional-integral operations to generate a pulse width modulation signal.

[0072] Specifically, the rated DC voltage value is the preset benchmark for the matching operation of the new energy power generation system with the power grid. Fluctuations in the actual DC bus voltage value directly affect power conversion efficiency and equipment safety. Illustratively, after the analog voltage signal is converted into a digital signal by an analog-to-digital converter, the arithmetic unit can calculate the absolute value of the difference between the actual DC bus voltage value and the rated DC voltage value to obtain the voltage deviation. This ensures that dynamic voltage fluctuations can be captured in real time, avoiding omissions due to calculation lag. The AC grid frequency is the basis for the synchronous operation of the power system. The rated frequency is usually set at 50Hz or 60Hz. Frequency deviation can lead to problems such as loss of synchronization between power generation equipment and the grid, and shortened lifespan of electrical equipment. Therefore, the absolute value of the difference between the actual AC grid frequency value and the rated frequency value can be calculated to obtain the frequency deviation. For example, the actual AC grid frequency value can be obtained by sampling through a synchronization phasor measurement unit. The calculation process can share the same arithmetic unit as the voltage deviation calculation to ensure data processing synchronization.

[0073] Furthermore, voltage and frequency are two core dimensions for stable power grid operation; anomalies in either dimension can threaten system security.

[0074] Therefore, when the voltage deviation exceeds a preset voltage threshold or the frequency deviation exceeds a preset frequency threshold, the system status flag can be set to 1 as the active state; otherwise, it can be set to 0. This clarifies the boundary between grid stability and anomalies, providing a clear basis for triggering control strategies. The preset voltage and frequency thresholds can be set based on grid safety operation standards and equipment tolerance limits. For example, the preset voltage threshold can be set to ±5% of the rated DC voltage, and the preset frequency threshold to ±0.1Hz. These thresholds can be adjusted through the system parameter configuration interface according to different application scenarios (such as distributed photovoltaic power stations and microgrid clusters), and after adjustment, a calibration process is required to ensure that the thresholds match the system's dynamic characteristics. The system status flag uses a simplified binary 0 / 1 identifier, facilitating rapid identification and retrieval by subsequent functional modules without a complex status parsing process. 0 corresponds to a stable grid state requiring no control, while 1 corresponds to an abnormal grid state requiring control.

[0075] Specifically, when the system status flag is 1, a proportional-integral operation can be performed on the voltage deviation to generate a pulse-width modulation signal for precise control based on abnormal grid conditions. The calculation formula can be expressed as:

[0076]

[0077] in, It is a pulse width modulation signal. This is the proportionality coefficient. The integral coefficient is... This represents the voltage deviation, where t is the current time. This proportionality coefficient... The integral coefficient determines the sensitivity to the deviation. This determines the ability to eliminate static deviations; the initial values ​​of both can be preset based on the system identification results, such as... =0.5, =0.1. Illustratively, based on this formula, when the system status flag is 1, the arithmetic unit can automatically call a preset... and The parameters are continuously calculated based on the real-time updated voltage deviation. The calculation result is mapped to the duty cycle of the pulse width modulation signal. The larger the voltage deviation, the greater the duty cycle adjustment range. In turn, the switching time of the power converter is controlled by the drive circuit to achieve dynamic regulation of the DC bus voltage.

[0078] In one embodiment, the actual power generation is generated based on the actual value of the photovoltaic output current. Based on the actual power generation, the user's declared power generation plan, and the system status flag, data to be verified is obtained. This data is then verified through a consensus mechanism to generate on-chain data, including:

[0079] The actual photovoltaic output current is multiplied by the rated DC voltage, and the result is integrated within a set time window to generate the actual power generation.

[0080] The actual power generation, the user's declared power plan value, and the system status flag are bound to a timestamp to form data to be verified.

[0081] The data to be verified is verified by nodes through a practical Byzantine fault-tolerant consensus mechanism. When more than two-thirds of the nodes pass the verification, the data is generated and uploaded to the chain.

[0082] Specifically, power generation is essentially the accumulation of electrical energy over time, and the instantaneous power of electrical energy equals the product of voltage and current. Therefore, power generation can be calculated by integrating power over time. Furthermore, by selecting the rated DC voltage value instead of the actual DC bus voltage value for calculation, the interference of actual voltage fluctuations on the metering results can be avoided, establishing a unified and fair metering benchmark. If fluctuating actual voltages are used, metering standards will differ under different grid conditions, easily leading to metering disputes between the trading parties. Illustratively, the actual value of photovoltaic output current can be obtained through a smart meter, converted into a digital signal after analog-to-digital conversion, and the calculation unit first performs a real-time power calculation of "actual photovoltaic output current value × rated DC voltage value," and then uses a trapezoidal integral algorithm to integrate the real-time power within a set time window. This set time window can be configured according to the trading cycle requirements.

[0083] Specifically, since actual power generation is the core measurement basis for transactions, user-declared power generation plans serve as the benchmark for judging user performance capabilities, and system status flags indicate the grid's operating status (stable or abnormal) at the time of data generation, these three can be combined to obtain the data to be verified, reflecting the complete data dimensions of a single power generation transaction scenario. Furthermore, during the integration process, timestamps can be generated using high-precision synchronous clocks (such as GPS timing clocks) to ensure time synchronization with multi-source data collection. The binding process can be performed using hash calculations; that is, the original data of actual power generation, user-declared power generation plans, system status flags, and timestamps can be input into a hash function such as SHA-256 to generate a unique hash value. This hash value, together with the original data, constitutes the data to be verified, ensuring that any tampering during data transmission will result in a change in the hash value, facilitating rapid identification of data integrity during node verification. Finally, a structurally complete, spatiotemporally unique, and quickly verifiable original data packet—the data to be verified—can be obtained. This not only fully records the core information related to the transaction but also provides an easily verifiable data packet structure for subsequent consensus verification, providing the first layer of assurance for data credibility.

[0084] Specifically, new energy power generation transactions involve multiple stakeholders such as power generation users, electricity users, and regulatory agencies. The Practical Byzantine Fault-Tolerant (PBFT) consensus mechanism, as an efficient consensus algorithm suitable for consortium blockchain scenarios, can tolerate no more than one-third of malicious nodes (i.e., Byzantine nodes) in the network. It can also achieve collaborative verification among nodes through a three-phase protocol of "pre-preparation-preparation-commit," ensuring the consistency and reliability of verification results. Therefore, by using the PBFT consensus mechanism to verify the data to be verified by nodes, and generating on-chain data when more than two-thirds of the nodes pass verification, decentralized collaborative verification can resist malicious node attacks, ensuring the authenticity and immutability of the on-chain data, and achieving trusted data storage. For example, nodes participating in consensus verification can include power generation nodes, electricity user nodes, regulatory nodes, and third-party notary nodes, deployed in different physical locations to avoid single points of failure. During the verification process, each node first verifies whether the hash value of the data to be verified is consistent with the original data to ensure that the data has not been tampered with. Next, the validity of the timestamps is verified to avoid data with timeouts or forged timestamps. Finally, the logical consistency between the actual power generation, the user's declared power plan value, and the system status flag can be verified. For example, if the system status flag is abnormal, can the actual power generation still fluctuate within a reasonable range? When more than two-thirds of the participating nodes (i.e., the fault tolerance threshold of the PBFT mechanism) report that the verification has passed, it indicates that the data to be verified has been recognized by all nodes in the network. The data, along with the verification results and node signatures, can be automatically packaged into a block and linked to the existing blockchain ledger to generate on-chain data.

[0085] In one embodiment, such as Figure 2 As shown, based on historical electricity data and user consensus participation records in the on-chain data, contract completion rate and consensus participation rate are calculated. Dynamic weighting coefficients are determined by combining system status flags and historical system status flags. A dynamic reputation value is then generated based on the contract completion rate, consensus participation rate, and dynamic weighting coefficients, including:

[0086] S201: Extract historical electricity data within a preset number of transaction periods from the on-chain data. The historical electricity data includes historical actual power generation and historical user-declared electricity plan values.

[0087] S202: Based on historical actual power generation and historical user-declared power plan values, calculate the ratio of the minimum delivered cumulative power value to the declared cumulative power value, and generate a contract completion index;

[0088] S203: Based on user consensus participation records, calculate the ratio of the number of times a user successfully participated in block verification to the total number of calls in the on-chain data, and generate a consensus participation rate indicator;

[0089] S204: Extract historical system status flags within a preset number of transaction periods from the on-chain data, calculate the ratio of the number of active historical system status flags to the total number, and generate dynamic weight coefficients based on the ratio.

[0090] S205: Based on dynamic weighting coefficients, a dynamic reputation value is generated by weighted summation of consensus participation rate and contract completion rate indicators.

[0091] Specifically, the on-chain data is a historical dataset continuously stored after verification using a practical Byzantine fault-tolerant consensus mechanism. It includes records of user consensus participation for each transaction cycle, such as historical actual power generation, historical user-declared power generation plans, the number of successful user participations in block verification, and the total number of calls. Simultaneously, the on-chain data also stores system state flags for each transaction cycle, forming a historical system state flag sequence. In the new energy trading scenario, the user-declared power generation plan represents the trading expectation, historical actual power generation represents the actual delivery capacity, and the minimum delivered power cumulative value (i.e., the sum of the smaller value between the actual power generation and the declared plan value for each cycle) reflects the effective contribution of the user's actual performance. The declared power cumulative value is the total amount of the user's transaction commitment, and the ratio of the two directly reflects the degree of user fulfillment. Therefore, for each transaction cycle, the minimum value between historical actual power generation and historical user-declared power generation plans can be selected, and this minimum value from all cycles can be accumulated to obtain the minimum delivered power cumulative value. Similarly, the historical user-declared power generation plan values ​​from all cycles can be accumulated to obtain the declared power cumulative value. The contract completion index can be obtained by comparing the minimum delivered power cumulative value with the declared power cumulative value. This metric presents the reliability of a user's historical performance in a quantitative form; the higher the value, the more stable the user's performance.

[0092] Specifically, in a blockchain-based new energy regulatory system, the enthusiasm and success rate of users participating in block verification directly affect the credibility and efficiency of data on-chain. Users with more successful participation contribute more to ensuring data consensus and resisting malicious tampering. For example, the ratio of the total number of times a user is called to participate in block verification and the number of times a user successfully completes verification and generates a valid digital signature can be extracted from the blockchain's node participation logs. This ratio is the consensus participation rate indicator. This indicator can form a two-dimensional evaluation basis with the contract completion indicator, avoiding the one-sided characterization of credibility caused by a single performance dimension. Furthermore, in a new energy power grid, the activation frequency and deviation of system status flags directly reflect the stability of the grid. More activated states and greater deviations indicate that the grid needs more user support in terms of credible data participation. Therefore, historical system status flags within a preset number of transaction periods can be extracted from the on-chain data. The ratio of the number of activated historical system status flags to the total number can be calculated. Based on this ratio and combined with deviation characteristics, a dynamic weighting coefficient can be generated to adapt the credibility assessment weight to the power grid scenario. For example, this dynamic weighting coefficient can be calculated using the following formula:

[0093]

[0094] in, For dynamic weighting coefficients, It is the balance coefficient, and , This represents the ratio of the number of active states in the historical system status flags to the total number of active states. This represents the number of active states in the historical system status flags. For the first Voltage deviation during the second activation state For the first Frequency deviation during the next activation state The nonlinear coefficient is the deviation coefficient. , For the first The time interval between the previous activation state and the current state. The time decay coefficient, , and These are the historical maximum voltage deviation and the historical maximum frequency deviation, respectively.

[0095] The above formula allows the dynamic weighting coefficient to dynamically adapt to the historical operating state of the power grid. Specifically, when the power grid is frequently activated and exhibits large deviations, the coefficient shifts towards consensus participation rate, emphasizing the user's credible data contribution. When the power grid is stable, the coefficient shifts towards contract completion rate, emphasizing the user's ability to fulfill obligations, thus achieving precise, scenario-based adjustment of the weights. Subsequently, the dynamic weighting coefficient can be multiplied by the consensus participation rate indicator, added to the remaining weight multiplied by the contract completion rate indicator, and superimposed with the reputation value from the previous period to generate a dynamic reputation value, comprehensively reflecting user value.

[0096] In one embodiment, an initial incentive price is determined based on the dynamic credit value in the matching trading pair set, a target response amount is set in conjunction with the photovoltaic power fluctuation forecast, the initial incentive price is optimized and adjusted based on the target response amount, and compensation is applied in conjunction with system status flags to generate a final incentive price table and demand response instructions, including:

[0097] The initial incentive price is generated by dividing the dynamic credit value of each user in the matching transaction pair set by the maximum dynamic credit value and then multiplying it by the preset base electricity price.

[0098] The target response is generated by multiplying the predicted photovoltaic power fluctuation by the absorption coefficient.

[0099] Based on the historical user response records in the on-chain data, the price sensitivity is fitted, and the initial incentive price is iteratively optimized by gradient descent with the target response volume as the benchmark to generate the optimized incentive price.

[0100] When the system status flag is active, the optimized incentive price will be multiplied by the voltage stabilization compensation coefficient to generate the final incentive price table.

[0101] Based on the final incentive price list and the target response amount, a demand response instruction is generated, which is used to instruct the adjustment of power output or consumption according to the target response amount.

[0102] Specifically, in the context of new energy trading, dynamic credit score is a comprehensive quantitative reflection of a user's ability to fulfill obligations and the reliability of their participation. Users with higher credit scores have lower transaction risks and higher cooperation value; therefore, a pricing mechanism is needed to provide positive incentives. First, the dynamic credit scores of all users can be filtered from the set of matching trading pairs, and the maximum value (denoted as MaxCR) can be determined. Then, for each user... Calculate its dynamic reputation score The ratio of MaxCR (i.e.) MaxCR), then multiply the ratio by the preset base electricity price (such as the market benchmark electricity price). Then you can get the user's information. initial incentive price This preset base electricity price can be set based on market supply and demand balance and operating costs, ensuring that the pricing is competitive in the market while covering costs.

[0103] Furthermore, the predicted photovoltaic power fluctuation is then multiplied by the absorption coefficient to generate the target response, which quantifies the absorption demand for photovoltaic power fluctuations. The predicted photovoltaic power fluctuation is based on multi-source information such as weather forecasts and historical power generation data, and is generated by a time series prediction model such as LSTM to predict the future power fluctuation range. The absorption coefficient is a proportional coefficient, such as 0.8-1.2, determined based on factors such as the grid's current available demand response resources and equipment adjustment margin, used to convert the predicted fluctuation into the target amount that users actually need to respond to. In addition, price sensitivity can be fitted based on the user's historical response records in the on-chain data. Using the target response amount as a benchmark, the initial incentive price is iteratively optimized using gradient descent to generate an optimized incentive price, achieving a precise match between the incentive price and user response behavior. The user's historical response records are stored in the on-chain data and contain data on the actual electricity adjustments made by users under different prices. By fitting this data, the user's price sensitivity curve (i.e., the degree of impact of price changes on the response electricity) can be obtained. During the gradient descent iterative optimization process, a price sensitivity model can be constructed first based on the historical incentive price and corresponding response electricity in the on-chain data, using linear regression or nonlinear fitting (such as logarithmic functions). Then you can use Let P be the loss function. The price P is iteratively updated using the gradient descent algorithm until the loss function is less than a preset threshold, such as 0.01. The price at this point is the optimized incentive price.

[0104] Indicatively, when the system status flag is active, it indicates that the power grid is in a critical state of voltage or frequency anomalies, requiring stronger incentives to guide users to respond quickly and assist the grid in restoring stability. The voltage stabilization compensation coefficient is an amplification factor set according to the degree of grid anomaly, such as 1.2-1.5. When the system status flag is 1 (active), this coefficient can be triggered to amplify the optimized incentive price, resulting in the final incentive price. If the system status flag is 0, the final incentive price equals the optimized incentive price. By compensating for price anomalies during grid anomalies, the user's response priority can be significantly improved, ensuring that the grid can quickly obtain sufficient demand response resources to restore stability. Simultaneously, excessive incentives are avoided when the grid is stable, ensuring reasonable operating costs. Finally, a demand response instruction can be generated based on the final incentive price table and the target response quantity. This demand response instruction can be in JSON format, containing key information such as user identifier, incentive price, target response quantity, and response time limit. This instruction can be pushed to the user's smart terminal via a secure communication channel, such as an encrypted dedicated power line, to guide users to adjust their power consumption according to the target, achieving efficient absorption of photovoltaic power fluctuations and stable grid operation.

[0105] In one embodiment, the method further includes:

[0106] Calculate the voltage stability index based on the actual value of the DC bus voltage fed back by the power converter;

[0107] When the voltage stability index is greater than the preset stability threshold, the proportional coefficient of the proportional-integral operation in the system status flag is adjusted according to the deviation ratio between the voltage stability index and the preset stability threshold, so as to obtain the adjusted proportional coefficient.

[0108] The transaction fulfillment rate is calculated based on the actual electricity volume responded to by the user's response to the demand command.

[0109] When the transaction fulfillment rate is less than the preset fulfillment rate threshold, the dynamic weight coefficient is adjusted according to the deviation between the transaction fulfillment rate and the preset fulfillment rate threshold to obtain the updated dynamic weight coefficient.

[0110] Integrate the adjusted proportional coefficients and the updated dynamic weight coefficients to generate parameter update instructions.

[0111] Specifically, as the core equipment connecting the new energy power generation system and the power grid, the actual value of the DC bus voltage fed back by the power converter reflects the control effect. That is, the smaller the voltage fluctuation amplitude and the faster the convergence speed, the more stable the system control. Illustratively, within a set time window, the voltage stability index can be obtained by calculating the average of the sum of squares of the deviations between the actual DC bus voltage value and the rated DC voltage value. This index amplifies the impact of larger deviations through squaring, better reflecting the degree of voltage instability risk. The preset stability threshold is a critical value set based on the safe operation requirements of power grid equipment. When the voltage stability index exceeds this threshold, it indicates that the current proportional coefficient of the proportional-integral (PI) calculation cannot meet the stable control requirements. Furthermore, a proportional coefficient that is too small will lead to a slow response, while a coefficient that is too large may cause overshoot oscillations. Therefore, the deviation ratio between the voltage stability index and the preset stability threshold can be calculated first, and then the adjusted proportional coefficient can be calculated using the following formula. :

[0112]

[0113] in, To preset the stability threshold, Here, k represents the voltage stability index, and k is the adjustment gain, used to control the magnitude of parameter adjustments to prevent system instability caused by drastic fluctuations. Dynamic adjustment of the proportional gain allows proportional-integral (PI) calculations to adapt to changes in voltage stability, improving system response speed while maintaining control accuracy, effectively suppressing voltage fluctuations, and enhancing grid stability. Furthermore, the target response quantity explicitly stated in the demand response command is the user's committed adjustment benchmark. The deviation between the actual response quantity and the target response quantity directly reflects the user's fulfillment reliability; the smaller the deviation, the higher the user's cooperation with grid dispatch. Therefore, the ratio of the actual response quantity to the target response quantity can be calculated to obtain the transaction fulfillment rate index. And when the actual response power exceeds the target response power, the calculation is based on the target response power (i.e., (≤1) This avoids excessive response that could incur additional costs for users. This metric can supplement the dynamic basis of user credit assessment at the transaction execution level, forming a dual assessment dimension of short-term performance behavior and long-term performance capability together with historical contract completion indicators, making credit characterization more timely.

[0114] Furthermore, a preset fulfillment rate threshold, such as 0.8, serves as the minimum standard to ensure effective demand response. When a user's actual fulfillment rate falls below this threshold, it indicates poor performance. In such cases, the weight of the contract completion rate indicator in the dynamic weighting coefficient should be reduced to lessen its positive impact on credit evaluation, thereby incentivizing users to improve fulfillment quality. This deviation value = preset fulfillment rate threshold - Updated dynamic weight coefficients It can be calculated using the following formula:

[0115]

[0116] in, These are the original dynamic weighting coefficients. This is the deviation value. This is a weight adjustment coefficient used to control the magnitude of weight reduction, ensuring that the adjusted weight still reflects the user's overall performance. Through negative feedback adjustment of the dynamic weight coefficient, effective constraints can be placed on users with low performance, while simultaneously guiding users with high performance to maintain good results, thus improving the overall reliability of transaction matching and the effectiveness of demand response. By integrating the adjusted proportional coefficient and the updated dynamic weight coefficient to generate a parameter update instruction, the coordinated updating of core system parameters can be achieved, ensuring that the optimization parameters of the control layer and the transaction layer take effect synchronously. This parameter update instruction can include the adjusted proportional coefficient (…). ), updated dynamic weight coefficients ( The system includes the effective timestamp and the data, which is encapsulated in a standardized data format and a confirmation message is returned to the system's central hub.

[0117] Based on the same inventive concept, such as Figure 3 As shown, this application also provides a new energy power generation technology supervision and management device 300 for implementing the above-mentioned new energy power generation technology supervision and management method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the new energy power generation technology supervision and management device provided below can be found in the limitations of the method embodiments above, and will not be repeated here. The device includes:

[0118] The data synchronization acquisition module 301 is used to acquire a synchronized dataset including the actual value of DC bus voltage, the actual value of AC grid frequency, the actual value of photovoltaic output current, and the user-submitted electricity plan value.

[0119] The status monitoring and signal generation module 302 is used to calculate the difference between the actual value of the DC bus voltage and the actual value of the AC grid frequency and the corresponding rated value, respectively, to obtain the voltage deviation and frequency deviation. When the voltage deviation or frequency deviation meets the preset activation condition, the system status flag is set to the active state, and proportional integral calculation is performed based on the voltage deviation to generate a pulse width modulation signal. The pulse width modulation signal is used to control the power converter.

[0120] The data verification and on-chain module 303 is used to generate actual power generation based on the actual value of photovoltaic output current, obtain data to be verified based on the actual power generation, the user's declared power plan value and system status flag, and verify the data to be verified through the consensus mechanism to generate on-chain data.

[0121] The reputation assessment and transaction matching module 304 is used to calculate the contract completion rate index and the consensus participation rate index based on the historical electricity data and user consensus participation records in the on-chain data, determine the dynamic weight coefficient by combining the system status flag and historical system status flag, and generate a dynamic reputation value based on the contract completion rate index, consensus participation rate index and dynamic weight coefficient, and filter users to obtain a set of matching transaction pairs based on the dynamic reputation value.

[0122] The price optimization and instruction generation module 305 is used to obtain the photovoltaic power fluctuation forecast value, determine the initial incentive price based on the dynamic credit value in the matching trading pair set, set the target response amount in combination with the photovoltaic power fluctuation forecast value, optimize and adjust the initial incentive price based on the target response amount, apply compensation in combination with the system status flag, and generate the final incentive price table and demand response instructions.

[0123] In one exemplary embodiment, the present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the new energy power generation technology supervision and management method of this application. A multi-core processor is preferred to improve the system's parallel processing capability. The memory provides sufficient temporary storage space to support program execution and data processing. The memory capacity should be large enough to accommodate a large amount of data and computational tasks.

[0124] In one exemplary embodiment, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the new energy power generation technology supervision and management method of this application. The computer-readable storage medium may include: a read-only memory, a random access memory (RAM), a solid-state drive (SSD), or an optical disc, etc.

[0125] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for supervising and managing new energy power generation technology, characterized in that, The method includes: Acquire a synchronous dataset including actual DC bus voltage, actual AC grid frequency, actual photovoltaic output current, and user-reported electricity consumption plans; The actual value of the DC bus voltage and the actual value of the AC grid frequency are calculated with their corresponding rated values ​​to obtain the voltage deviation and frequency deviation. When the voltage deviation or the frequency deviation meets the preset activation condition, the system status flag is set to the active state, and proportional-integral operation is performed based on the voltage deviation to generate a pulse width modulation signal. The pulse width modulation signal is used to control the power converter. Based on the actual value of the photovoltaic output current, the actual power generation is generated. Based on the actual power generation, the user's declared power plan value, and the system status flag, data to be verified is obtained. The data to be verified is then verified through a consensus mechanism to generate on-chain data. Based on the historical electricity data and user consensus participation records in the on-chain data, the contract completion rate index and consensus participation rate index are calculated. The dynamic weight coefficient is determined by combining the system status flag and historical system status flag. A dynamic reputation value is generated based on the contract completion rate index, the consensus participation rate index and the dynamic weight coefficient. Users are screened according to the dynamic reputation value to obtain a set of matching trading pairs. The photovoltaic power fluctuation forecast is obtained, the initial incentive price is determined based on the dynamic reputation value in the matching trading pair set, the target response amount is set in combination with the photovoltaic power fluctuation forecast, the initial incentive price is optimized and adjusted based on the target response amount, and compensation is applied in combination with the system status flag to generate the final incentive price table and demand response instructions.

2. The method according to claim 1, characterized in that, When the voltage deviation or the frequency deviation meets a preset activation condition, the system status flag is set to the active state, and a proportional-integral operation is performed based on the voltage deviation to generate a pulse width modulation signal, including: The voltage deviation is obtained by calculating the absolute value of the difference between the actual DC bus voltage and the rated DC voltage. The frequency deviation is obtained by calculating the absolute value of the difference between the actual value of the AC power grid frequency and the rated frequency value. When the voltage deviation is greater than a preset voltage threshold or the frequency deviation is greater than a preset frequency threshold, the system status flag is set to 1 as the active state; otherwise, it is set to 0. When the system status flag is 1, the voltage deviation is subjected to proportional-integral calculation to generate the pulse width modulation signal.

3. The method according to claim 1, characterized in that, The process involves generating actual power generation based on the actual value of the photovoltaic output current, obtaining data to be verified based on the actual power generation, the user's declared power plan value, and the system status flag, and verifying the data to be verified through a consensus mechanism to generate on-chain data, including: The actual photovoltaic output current is multiplied by the rated DC voltage, and the result is integrated within a set time window to generate the actual power generation. The actual power generation, the user-declared power plan value, and the system status flag are bound to timestamps to form the data to be verified; The data to be verified is verified by nodes using a practical Byzantine fault-tolerant consensus mechanism. When more than two-thirds of the nodes pass the verification, the on-chain data is generated.

4. The method according to claim 1, characterized in that, The process involves calculating contract completion and consensus participation rates based on historical electricity data and user consensus participation records from the on-chain data, determining dynamic weighting coefficients by combining system status flags and historical system status flags, and generating a dynamic reputation value based on the contract completion, consensus participation, and dynamic weighting coefficients. Extract historical electricity data within a preset number of transaction periods from the on-chain data. The historical electricity data includes historical actual power generation and historical user-submitted electricity plan values. Based on the historical actual power generation and the historical user-declared power generation plan, the ratio of the minimum delivered power cumulative value to the declared power cumulative value is calculated to generate the contract completion index; Based on the user consensus participation records, the ratio of the number of times a user successfully participated in block verification to the total number of calls in the on-chain data is calculated to generate the consensus participation rate metric. Extract the historical system status flags within the preset number of transaction periods from the on-chain data, calculate the ratio of the number of historical system status flags in the active state to the total number, and generate the dynamic weight coefficient based on the ratio. Based on the dynamic weighting coefficient, the consensus participation rate indicator and the contract completion rate indicator are weighted and summed to generate the dynamic reputation value.

5. The method according to claim 1, characterized in that, The process of determining an initial incentive price based on the dynamic reputation value in the matching transaction pair set, setting a target response amount based on the photovoltaic power fluctuation forecast, optimizing and adjusting the initial incentive price based on the target response amount, and applying compensation based on the system status flags to generate a final incentive price table and demand response instructions includes: The initial incentive price is generated by dividing the dynamic credit value of each user in the matching transaction pair set by the maximum dynamic credit value and then multiplying it by the preset base electricity price. The target response quantity is generated by multiplying the predicted photovoltaic power fluctuation value by the absorption coefficient. Based on the user's historical response records in the on-chain data, fit the price sensitivity, and use the target response volume as a benchmark to perform gradient descent iterative optimization on the initial incentive price to generate an optimized incentive price; When the system status flag is active, the optimized incentive price is multiplied by the voltage stabilization compensation coefficient to generate the final incentive price table; Based on the final incentive price list and the target response amount, the demand response instruction is generated, which is used to instruct the adjustment of power output or consumption according to the target response amount.

6. The method according to claim 1, characterized in that, The method further includes: Calculate the voltage stability index based on the actual value of the DC bus voltage fed back by the power converter; When the voltage stability index is greater than the preset stability threshold, the proportional coefficient of the proportional-integral operation in the system status flag is adjusted according to the deviation ratio between the voltage stability index and the preset stability threshold, so as to obtain the adjusted proportional coefficient. The transaction fulfillment rate is calculated based on the actual electricity consumption of the user in response to the demand response instruction. When the transaction fulfillment rate is less than the preset fulfillment rate threshold, the dynamic weight coefficient is adjusted according to the deviation between the transaction fulfillment rate and the preset fulfillment rate threshold to obtain the updated dynamic weight coefficient. Integrate the adjusted proportional coefficient and the updated dynamic weight coefficient to generate a parameter update instruction.

7. The method according to claim 4, characterized in that, The dynamic weighting coefficient is calculated using the following formula: in, The dynamic weighting coefficients are... It is the balance coefficient, and , The ratio of the number of active states in the historical system status flags to the total number. The number of active states in the historical system status flags. For the first The voltage deviation during the next activated state. For the first The frequency deviation during the next activation state. The nonlinear coefficient is the deviation coefficient. , For the first The time interval from the current activation state to the present state. The time decay coefficient, , and These are the historical maximum voltage deviation and the historical maximum frequency deviation, respectively.

8. A new energy power generation technology supervision and management device, characterized in that, The device includes: The data synchronization acquisition module is used to acquire a synchronized dataset including the actual value of DC bus voltage, the actual value of AC grid frequency, the actual value of photovoltaic output current, and the value of user-submitted electricity plans. The status monitoring and signal generation module is used to calculate the difference between the actual value of the DC bus voltage and the actual value of the AC grid frequency and the corresponding rated value to obtain the voltage deviation and frequency deviation. When the voltage deviation or the frequency deviation meets the preset activation condition, the system status flag is set to the active state, and proportional integral calculation is performed based on the voltage deviation to generate a pulse width modulation signal. The pulse width modulation signal is used to control the power converter. The data verification and on-chain module is used to generate actual power generation based on the actual value of photovoltaic output current, obtain data to be verified based on the actual power generation, the user's declared power plan value and the system status flag, and verify the data to be verified through a consensus mechanism to generate on-chain data. The reputation assessment and transaction matching module is used to calculate the contract completion rate index and the consensus participation rate index based on the historical electricity data and user consensus participation records in the on-chain data, determine the dynamic weight coefficient by combining the system status flag and the historical system status flag, generate a dynamic reputation value based on the contract completion index, the consensus participation rate index and the dynamic weight coefficient, and filter users to obtain a set of matching transaction pairs based on the dynamic reputation value. The price optimization and instruction generation module is used to obtain the photovoltaic power fluctuation forecast value, determine the initial incentive price based on the dynamic reputation value in the matching trading pair set, set the target response amount in combination with the photovoltaic power fluctuation forecast value, optimize and adjust the initial incentive price based on the target response amount, apply compensation in combination with the system status flag, and generate the final incentive price table and demand response instructions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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