Renewable energy penetration power distribution system scheduling method and system based on block chain

Abstract

The invention discloses a renewable energy penetration power distribution system scheduling method and system based on a block chain, and relates to the technical field of power distribution system scheduling, and the method comprises the following steps: constructing a cross-hierarchy time sequence integrity observation layer, introducing a multi-source high-precision reference signal into a synchronous time service link, and carrying out the multi-source high-precision reference signal; the fingerprint acquisition module is used for acquiring external environment electromagnetic disturbance and atmospheric propagation delay data in real time and generating a phase offset fingerprint corresponding to a time service state; and performing causal decomposition operation based on the phase offset fingerprint, performing noise stripping processing on the acquired electromagnetic disturbance signal, and extracting a real time service error trajectory. Through combination of multi-source time service correction, phase fingerprints and virtual impedance time marks, time self-calibration and cross-node synchronization of the block chain power distribution system are realized, account book wrong sequence caused by time service drift is eliminated, power flow reversal is suppressed through time reversal traction and dynamic constraint, scheduling stability and energy flow consistency are ensured, and the power distribution efficiency is improved. The system safety and the clean energy utilization efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution system scheduling, in particular to a renewable energy penetration power distribution system scheduling method and system based on blockchain. BACKGROUND

[0002] The renewable energy penetration power distribution system scheduling based on blockchain aims to improve the access efficiency of distributed energy and the collaborative utilization of power resources through a decentralized and highly transparent mechanism. With the increasing proportion of renewable energy such as wind and photovoltaic, the operation of the power system presents high volatility and complexity. Blockchain technology can build a trusted data interaction and settlement environment among multiple parties, so that the scheduling information between the power generation end, the distribution network and the user side can be shared in real time, the transaction results can be publicly traceable, and the human intervention and trust cost can be reduced. At the same time, the application of smart contract can realize automatic scheduling and settlement, improve the flexibility and accuracy of power distribution. Under this architecture, the volatility of renewable energy output can be effectively coordinated through a transparent distributed scheduling mechanism, which not only guarantees the stability of power supply, but also promotes the improvement of clean energy consumption level, providing a solid foundation for the construction of future smart energy systems.

[0003] The prior art has the following disadvantages:

[0004] Under extreme weather conditions, due to the superimposed effect of external environmental electromagnetic disturbance and atmospheric abnormal propagation effect, the synchronization time link relied on by the power distribution system will have phase drift, resulting in instantaneous imbalance of the time synchronization accuracy of part of the blockchain nodes. In this case, the timestamp sequence generated by the node may be arranged in reverse order, so that the transaction order in the subsequent distributed ledger record is inconsistent with the actual energy flow order. When the smart contract triggers the scheduling logic based on the wrong timestamp, it will backtrack the energy transaction that has not yet occurred to the historical state, thereby causing a large range of mismatch in the power distribution calculation of the scheduling system. This kind of mismatch will not only cause the reversal of cross-regional power flow direction and abnormal fluctuation of voltage frequency, but also may cause the mis-triggering of standby power supply and the failure of main line protection action, thereby causing unstable operation of the large-scale power distribution system and even power failure.

[0005] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a renewable energy penetration power distribution system scheduling method and system based on blockchain to solve the problems in the background.

[0007] In order to achieve the above object, the present application provides the following technical scheme: a renewable energy penetration power distribution system scheduling method based on a blockchain, comprising the following steps:

[0008] A cross-layer timing integrity observation layer is constructed, multi-source high-precision reference signals are introduced into the synchronous time service link, and the external environmental electromagnetic disturbance and atmospheric propagation delay data are collected in real time, and a phase offset fingerprint corresponding to the time service state is generated to represent the real-time phase drift characteristics of the time service link.

[0009] Based on the phase offset fingerprint, a causal decomposition operation is performed, the collected electromagnetic disturbance signals are subjected to noise stripping processing, the real time service error trajectory is extracted, and a dynamic correction budget is generated accordingly, which is used as the real-time compensation input of the time service data.

[0010] According to the dynamic correction budget, a counterfactual playback chain is constructed, a shadow time sequence is generated in the time service link, the distorted time service segment is replaced by the shadow time sequence, the continuous time service record is reconstructed, and the time stamp of the time service is kept monotonically increasing, which provides a basis for cross-node time service unified correction.

[0011] Double-mirror anchor points are embedded in the continuous time service record, and cross-node clock rearrangement is performed based on the anchor points to synchronize and reconstruct the time stamp of each blockchain node, so as to ensure that the transaction order of the distributed ledger is consistent with the actual energy flow order.

[0012] After completing the cross-node clock rearrangement, a time-varying virtual impedance time scale is synthesized according to the unified corrected transaction order, which is used as a power distribution trigger constraint to suppress the cross-regional power flow direction reversal caused by residual time service errors, and forms a stable scheduling boundary condition.

[0013] Time reversal phase traction is performed in combination with the time-varying virtual impedance time scale, inverse phase pulses are injected into the scheduling boundary condition to drive the consistency of the blockchain ledger record and the smart contract execution logic to form a closed loop, thereby ensuring the stable scheduling operation of the renewable energy penetration power distribution system.

[0014] Preferably, the step of constructing the cross-layer timing integrity observation layer comprises:

[0015] The cross-layer timing integrity observation structure is deployed in each node of the renewable energy penetration power distribution network, a multi-path redundant time service reference system is formed by fusing satellite time service signals, ground-based optical fiber synchronization signals and atomic frequency reference signals, and a multi-dimensional timing sampling matrix is generated.

[0016] The multi-dimensional timing sampling matrix is time-aligned and fused with external electromagnetic disturbance and atmospheric propagation delay data to obtain a fused signal set reflecting the environmental coupling state of the time service link.

[0017] Modeling and fingerprinting the phase variation characteristics of multi-source signals based on the fusion signal set, to generate a phase offset fingerprint representing the timekeeping state;

[0018] By comparing the latest phase offset fingerprint with the historical fingerprint sequence, the phase drift rate of the timekeeping link is calculated and the compensation process is triggered to achieve dynamic consistency recovery of the timekeeping link.

[0019] Preferably, the compensation process includes: according to the weight information of each reference signal in the phase offset fingerprint, the delay of the multi-source timekeeping path is inversely fitted and phase aligned, and the compensation is automatically started when the phase drift rate is detected to exceed the preset threshold, to ensure the time continuity and phase consistency of the timekeeping link under the interference of multi-source signals.

[0020] Preferably, the step of performing causal decomposition operation based on the phase offset fingerprint includes:

[0021] The phase offset fingerprint is used as a time feature guide signal to perform timing registration and amplitude normalization on the collected electromagnetic disturbance signals, and a multi-dimensional causal mapping matrix is constructed to identify the effective causal components of the timekeeping error;

[0022] According to the causal mapping matrix, the electromagnetic disturbance signal is subjected to multi-scale adaptive wavelet decomposition and empirical mode decomposition, the random noise is stripped off and the effective components with high coherence with the phase offset fingerprint are retained, to obtain the purified electromagnetic disturbance signal;

[0023] The purified electromagnetic disturbance signal is jointly regressed with the phase offset fingerprint to extract the timekeeping error trajectory to reflect the timekeeping drift trend;

[0024] A dynamic correction budget model is established according to the timekeeping error trajectory to compensate and adaptively correct the timekeeping data in real time.

[0025] Preferably, the process of establishing a dynamic correction budget model includes: based on the short-term variation trend of the timekeeping error trajectory, a weighted least squares method is used to fit a time polynomial model of error variation, to obtain the error derivative and drift acceleration, and the effectiveness of the correction budget is judged through the matching residual of the phase offset fingerprint, when the residual exceeds the preset threshold, the adaptive update of the budget parameters is automatically triggered to improve the accuracy and stability of the timekeeping compensation.

[0026] Preferably, the step of constructing an anti-factual playback chain according to the dynamic correction budget includes:

[0027] According to the dynamic correction budget, an anti-factual playback chain is established, by performing multi-dimensional mapping comparison between the actual timekeeping trajectory and the predicted trajectory of the correction budget, the time period when the timekeeping drift exceeds the tolerance threshold is identified and the error residual vector is calculated, to form an ideal time sequence, which is used as a calculation reference template for generating a shadow time sequence;

[0028] Based on the counterfactual replay chain and taking the ideal time sequence as a reference, a shadow time sequence for replacing the distorted time-of-day segment is generated, the shadow time sequence is time-interpolated and smoothed, and the shadow time sequence is time-scale aligned according to the characteristics of the phase offset fingerprint, so that the shadow time sequence is continuous with the normal time period;

[0029] The shadow time sequence is fused with the original time-of-day record in a time matching window, and timestamp rearrangement is performed to maintain monotonicity;

[0030] The inter-node time-of-day deviation matrix is calculated according to the reconstructed time-of-day record, and cross-node time-of-day unified correction is achieved through iterative correction.

[0031] Preferably, the steps of embedding double mirror anchor points in continuous time-of-day records and performing cross-node clock rearrangement include:

[0032] According to the dynamic correction budget, a time consistency interval is determined, a time window with the smallest phase change rate is selected as the center of the mirror anchor point in the continuous time-of-day record, and forward and reverse mirror anchor points are constructed to form a time-symmetric reference coordinate system;

[0033] The mirror anchor points generated by each node are compared through a timestamp matching algorithm, the anchor point pair with the smallest phase deviation is selected as the global reference anchor point, and alignment and segmented inversion operations are performed on the time sequence of each node according to the global reference anchor point, so as to realize mapping regression of time drift;

[0034] The rearranged time sequence is subjected to secondary fitting and smoothing correction to ensure cross-node time continuity, and a time correspondence matrix is established according to the mirror anchor points to perform bidirectional verification and adjustment on the transaction timestamp;

[0035] The phase drift vector of each node is recalculated periodically to trigger dynamic rebalancing, so as to maintain long-term stability of global time consistency and account order.

[0036] Preferably, the steps of synthesizing a time-varying virtual impedance time scale according to the unified corrected transaction order include:

[0037] The unified corrected transaction order is analyzed in the time domain, a high-dimensional time-energy state matrix is established, and a time-driven virtual impedance basis function family is generated according to the covariance function of power change rate and time interval;

[0038] According to the residual time-of-day error trajectory in the dynamic correction budget, weighted smoothing and lag compensation are performed on the virtual impedance time scale to maintain time continuity of the impedance curve and form a time feedback closed loop;

[0039] Introducing a time-varying virtual impedance timescale into the power allocation triggering logic allows for phase verification and dynamic delay adjustment of transaction execution time, thereby suppressing power flow reversal caused by timing errors.

[0040] A stable scheduling boundary region is defined based on the time interval with the minimum rate of change of virtual impedance timescale, and dynamic boundary adaptive correction is achieved through periodic backtracking verification.

[0041] Preferably, the step of performing time-reversal phase pulling in conjunction with time-varying virtual impedance timescales includes:

[0042] Time inversion mapping is performed on the time-varying virtual impedance timescale to establish a phase pulling function and embed it into the timing link feedback loop to compensate for the phase error caused by link delay and disturbance;

[0043] Within the scheduling boundary conditions, an inverse phase injection window is set according to the phase change rate. The amplitude and duration of the inverse phase pulse are calculated, and the asymptotic stability of the traction process is achieved through a dynamic attenuation coefficient.

[0044] The injection time of the reverse phase pulse and the energy distribution event are recorded in the blockchain ledger, and the execution logic of the smart contract is bound to the index table to achieve closed-loop consistency between the ledger and time traction.

[0045] A long-term phase deviation statistical model is established, and the pulse injection frequency and energy ratio are dynamically adjusted based on the operating data. When the deviation exceeds the threshold, a new time-reversal traction process is triggered.

[0046] A blockchain-based renewable energy-integrated power distribution system dispatching system includes a time sequence integrity observation module, a causal decomposition and correction module, a counterfactual time reconstruction module, a cross-node clock rearrangement module, a time-varying impedance dispatching module, and a time inversion traction module.

[0047] The timing integrity observation module constructs a cross-level timing integrity observation layer, introduces multi-source high-precision reference signals into the synchronization timing link, and uses them to collect external environmental electromagnetic disturbances and atmospheric propagation delay data in real time, and generate phase offset fingerprints corresponding to the timing status.

[0048] The causal decomposition and correction module performs causal decomposition operations based on phase offset fingerprints, performs noise stripping on the collected electromagnetic disturbance signals, extracts the true timing error trajectory, and generates a dynamic correction budget accordingly. The dynamic correction budget serves as the real-time compensation input for the timing data.

[0049] The counterfactual time reconstruction module constructs a counterfactual playback chain based on a dynamic correction budget, generates a shadow time series in the time synchronization chain, replaces distorted time synchronization segments with the shadow time series, reconstructs continuous time synchronization records, and maintains the monotonically increasing characteristic of the time synchronization timestamp.

[0050] The cross-node clock reordering module embeds dual mirror anchors in the continuous time synchronization record and performs cross-node clock reordering based on the anchors to synchronously reconstruct the timestamps of each blockchain node.

[0051] After completing the cross-node clock rearrangement, the time-varying impedance scheduling module synthesizes a time-varying virtual impedance time stamp based on the unified and corrected transaction order. The time-varying virtual impedance time stamp is used as a power allocation trigger constraint to suppress the cross-regional power flow direction reversal caused by residual timing errors and form a stable scheduling boundary condition.

[0052] The time-reversal traction module combines time-varying virtual impedance timescales to perform time-reversal phase traction, injecting inverse phase pulses within the scheduling boundary conditions to drive the blockchain ledger records and smart contract execution logic to form a closed-loop consistency.

[0053] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0054] This invention achieves self-correction of time consistency and cross-node synchronous reconstruction in a blockchain power distribution system by introducing a joint mechanism of multi-source high-precision reference signals, phase offset fingerprints, and dynamic correction budgets into the time synchronization link. It can capture and correct time drift in real time under extreme weather conditions, electromagnetic disturbances, and abnormal atmospheric propagation, eliminating the timestamp reversal and ledger misordering problems caused by phase imbalance in traditional time synchronization systems. Through the synergistic effect of the counterfactual replay chain and dual mirror anchors, the system can automatically reconstruct continuous time records and replace distorted time segments with shadow time series, achieving monotonically increasing distributed node times and network-wide synchronization. This ensures that the transaction order in the blockchain ledger strictly corresponds to the actual energy flow order, fundamentally guaranteeing the correct execution of time-driven smart contracts and the causal consistency of the power distribution system.

[0055] This invention establishes a time-feedback-based dynamic power stability constraint and ledger logic closed-loop consistency control through a time-varying virtual impedance timescale and a time-reversal phase traction mechanism. This method utilizes virtual impedance timescales to suppress the amplification effect of residual timing errors in the power allocation process, preventing cross-regional power flow direction reversal and voltage frequency oscillations. Furthermore, it injects inverse-phase pulses within the scheduling boundary to form adaptive time-traction feedback, achieving temporal balance and dynamic stability in the energy scheduling process. Through this mechanism, the system can maintain high-precision synchronization between smart contract triggering, ledger recording, and physical energy flow in a blockchain environment, significantly improving the scheduling stability, security, and clean energy utilization efficiency of renewable energy-integrated power distribution systems. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0057] Figure 1 This is a flowchart of the blockchain-based renewable energy penetration power distribution system scheduling method of the present invention.

[0058] Figure 2 This is a schematic diagram of the module of the blockchain-based renewable energy penetration power distribution system dispatching system of the present invention. Detailed Implementation

[0059] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0060] This invention provides, for example Figure 1 The blockchain-based renewable energy-integrated power distribution system dispatching method shown includes the following steps:

[0061] A cross-level time sequence integrity observation layer is constructed, and multi-source high-precision reference signals are introduced into the synchronization time link to collect external environmental electromagnetic disturbances and atmospheric propagation delay data in real time, and generate phase offset fingerprints corresponding to the time synchronization status to characterize the real-time phase drift characteristics of the time synchronization link.

[0062] The specific sub-steps for implementing this process are as follows:

[0063] To achieve high-precision monitoring of the timing link in complex environments, a cross-level timing integrity observation structure is deployed at various nodes in the renewable energy-permeable distribution network. This observation structure uses multi-source high-precision reference signals as the timing basis, fusing satellite timing signals, ground-based fiber optic synchronization signals, and local atomic frequency reference signals to form a timing reference system with multi-path redundancy. In this system, satellite signals provide a macroscopic absolute time reference, ground-based fiber optic signals are used to correct propagation path delays, and atomic frequency reference signals provide locally stable phase anchoring. Through parallel sampling and timestamp comparison of multi-source signals, the phase differences of the timing link at both the macroscopic and microscopic levels can be captured at any given moment, thus obtaining a multi-dimensional timing sampling matrix for subsequent processing. This matrix records the delay, jitter, and drift characteristics of each timing path with nanosecond-level precision, laying the foundation for subsequent electromagnetic disturbance identification and decoupling from atmospheric propagation effects.

[0064] After acquiring the multi-dimensional time-series sampling matrix, it is synchronously fused with external environmental data to achieve dynamic tracking of the timing link's environmental coupling characteristics. To this end, electromagnetic disturbance sensing arrays and atmospheric parameter sensing arrays are deployed in the perimeter environment of each timing path node to capture multiple parameters in real time, including electromagnetic radiation intensity, magnetic field change rate, air dielectric constant, humidity gradient, and ionospheric electron concentration. All environmental data are time-aligned with the time-series sampling matrix using a unified timescale and preprocessed using a coherent filter to eliminate random noise and nonlinear distortion. This process yields a fused signal set containing changes in the physical environment and the timing link status, reflecting the instantaneous impact of electromagnetic disturbances and atmospheric propagation delays on timing accuracy. At this point, a time offset verification algorithm can be used to calculate the correlation coefficient between environmental disturbances and timing deviations, identifying environmental disturbance sources that significantly affect timing stability and thus determining the main causes of phase shift.

[0065] After identifying the main disturbance sources affecting the stability of the timing link, the phase change characteristics of the multi-source signals are modeled and fingerprinted to generate a phase offset fingerprint corresponding to the timing state. To ensure the traceability and uniqueness of this fingerprint, the phase change trajectories of the multi-source signals are first mapped to a unified time-frequency domain space and subjected to multi-scale wavelet decomposition to extract the difference features between long-term drift components and short-term disturbance components. Subsequently, the dominant frequency response interval of the phase change is determined through high-order autocorrelation function analysis, and a multi-dimensional feature vector is established based on the phase sensitivity of different signal sources. Then, through feature vector normalization and hash mapping calculation, a phase offset fingerprint with unique identification significance at a specific moment is generated. This phase offset fingerprint not only records the instantaneous phase state of the timing link but also implicitly contains the mapping relationship between environmental disturbances and propagation path characteristics, thus serving as an accurate reference template in subsequent timing error tracking and correction processes.

[0066] After generating the phase offset fingerprint, the real-time phase drift characteristics of the timing link are continuously monitored and dynamically updated based on this fingerprint. Specifically, by comparing the latest phase offset fingerprint with historical fingerprint sequences, the time derivative and offset rate of the link phase drift can be calculated, thereby inferring the dynamic trend of timing accuracy. When the drift rate exceeds a set threshold, a real-time alarm is immediately triggered and the timing link compensation process is initiated. The compensation process utilizes the reference signal weight information contained in the phase offset fingerprint to perform inverse fitting and phase alignment on the delay of each timing path, thereby restoring the timing consistency of the timing link. At the same time, the updated fingerprint is recorded in a local time state storage table to form a traceable time consistency archive. In long-term operation, this archive can identify the stability trend of the timing link and its correlation with climate disturbances through pattern comparison algorithms, providing a key reference for subsequent dynamic correction budgeting and counterfactual time series reconstruction.

[0067] Through the above process, not only is the fusion perception and dynamic tracking of multi-source timing signals realized, but the phase integrity and time continuity of the timing link can still be maintained under the condition of frequent environmental disturbances, ensuring the stability and reliability of the timing consistency of the renewable energy penetration power distribution system under the blockchain architecture.

[0068] Based on phase-off fingerprinting, causal decomposition is performed to remove noise from the collected electromagnetic disturbance signal, extract the true timing error trajectory, and generate a dynamic correction budget accordingly. The dynamic correction budget serves as the real-time compensation input for the timing data.

[0069] The specific sub-steps for implementing this process are as follows:

[0070] After obtaining the phase offset fingerprint corresponding to the timing status, the acquired electromagnetic disturbance signal is decomposed based on this fingerprint to remove nonlinear noise components and identify the true cause of the timing deviation. To achieve this, the phase offset fingerprint is first used as a time feature guiding signal and time-series registered and amplitude-normalized with the original electromagnetic disturbance signal at the corresponding time to ensure comparability in both time axis and amplitude space. Subsequently, a multidimensional causal mapping matrix is ​​constructed, and the causal strength of each disturbance component on the timing drift is evaluated by calculating the time-delay mutual information between different disturbance frequency bands and the phase offset. During this mapping process, a sliding time window mechanism is used to dynamically capture the changing trend of the electromagnetic disturbance, and local energy distribution features are extracted within each time window, making the phase offset fingerprint the primary reference baseline for causal discrimination. When the time-delay mutual information of a specific disturbance frequency band is significantly higher than the background noise level, the disturbance in that frequency band is determined to be an effective causal component of the timing error. Through this process, a preliminary multidimensional causal relationship between the disturbance source and the timing deviation can be established, providing directional constraints for subsequent noise removal and error extraction.

[0071] After obtaining the causal mapping matrix, the electromagnetic disturbance signal undergoes layered noise stripping to filter out random and non-physical components, thus retaining timing error information with temporal correlation. To this end, high-noise intervals in the causal mapping matrix are first marked as invalid time periods, and a multi-scale adaptive wavelet packet decomposition method is used to decompose the electromagnetic disturbance signal into multiple sub-band signals. By comparing the phase difference evolution curves of each sub-band signal with the phase shift fingerprint, the effective frequency band consistent with the timing drift trend can be identified. Subsequently, using the empirical mode decomposition method, the effective frequency band signal is further subdivided into several intrinsic mode components, and the instantaneous phase and amplitude envelope of each component are calculated. By performing coherence analysis on these intrinsic mode components and the phase shift fingerprint, pseudo-signals belonging to the inherent disturbances of the system and effective components that truly reflect the timing drift can be distinguished. Finally, all intrinsic components with high coherence to the phase shift fingerprint are reconstructed to obtain the noise-stripped electromagnetic disturbance signal, thereby achieving a purified representation of the impact of the electromagnetic environment on the timing link.

[0072] After noise stripping, the true trajectory of the timing error is extracted to reflect the dynamic drift process of the timing link. To achieve this, the stripped electromagnetic disturbance signal is first subjected to joint regression analysis with the previously obtained phase offset fingerprint to determine the phase offset rate and its nonlinear evolution trend in the time series. Subsequently, a time-causal network model is introduced, and the regression results are embedded into the time graph structure, so that each timing event node corresponds to a dynamic phase offset state. By calculating the ratio of the phase difference between adjacent nodes to the time interval, the instantaneous rate of change of the timing error can be obtained. Further, the rate of change sequence is integrated and accumulated to obtain the time-continuous trajectory of the timing error. This trajectory reflects the instantaneous offset process and long-term drift trend of the timing link under the influence of external disturbances. To improve the accuracy of the trajectory, the trajectory can be iteratively fitted using a residual self-correction algorithm to eliminate local discontinuities caused by sampling delay or missing data. The final timing error trajectory has time consistency, phase coherence, and high reconfigurability, and can be directly used for subsequent dynamic correction budget generation.

[0073] After mastering the timing error trajectory, a dynamic correction budget is constructed to achieve real-time compensation and predictive correction of timing data. Specifically, firstly, based on the short-term changing trend of the timing error trajectory, a time polynomial model of error change is fitted using the weighted least squares method to obtain the error derivative and drift acceleration for the current time period. Then, this model is extended to the prediction domain, and the amount of error change within a short future time window is calculated by extrapolation to form a time-recursive dynamic correction budget. To ensure the timeliness and reliability of the correction budget, the latest generated budget is jointly matched with the evolution state of the phase offset fingerprint. If the matching residual exceeds a preset threshold, an adaptive update process of the budget parameters is triggered. At this point, the dynamic correction budget not only serves as a real-time compensation input for timing data to adjust the local timing reference of each node in the link, but also corrects the prediction model of the future budget through correction residual feedback, giving the compensation process continuous self-learning capabilities. As the dynamic correction budget continues to iterate, the phase drift of the timing link is gradually offset, and the timing accuracy remains stable over a long period under complex electromagnetic disturbance conditions.

[0074] Through the aforementioned continuous causal decomposition, noise stripping, error trajectory extraction, and dynamic budget generation process, a complete closed loop from phase offset fingerprint to timing error correction is achieved. This enables the renewable energy-integrated power distribution system to maintain cross-node timing consistency and scheduling accuracy in a blockchain environment, providing a solid time foundation for the subsequent reconstruction of shadow time series and the accurate recovery of ledger transaction order.

[0075] Based on the dynamic correction budget, a counterfactual playback chain is constructed, a shadow time series is generated in the time synchronization link, the shadow time series is used to replace the distorted time synchronization segments, and continuous time synchronization records are reconstructed, so that the time synchronization timestamp maintains the monotonically increasing characteristic, providing a basis for unified time synchronization correction across nodes.

[0076] The specific sub-steps for implementing this process are as follows:

[0077] After obtaining the dynamic correction budget, a counterfactual playback chain is established based on the dynamic correction budget to achieve accurate reconstruction of distorted time segments in the timing link. The construction of the counterfactual playback chain uses the error evolution information contained in the dynamic correction budget as a time reference, performing a multi-dimensional mapping and comparison between the actual timing trajectory and the predicted trajectory of the correction budget. Specifically, firstly, the time series of each node in the timing link is segmented and calibrated to identify time periods where the timing drift exceeds the tolerance threshold of the dynamic correction budget, and error residual vectors are calculated for these time periods. Subsequently, using the time recursion function of the dynamic correction budget as a baseline, a time series backtracking operation is performed on the error residual vector to form an ideal time series that the timing process should present under the assumption of no disturbance. Mathematically, this ideal time series is constrained by the higher-order time derivatives of the dynamic correction budget, ensuring its continuous differentiability in the time and phase domains, and serves as a computational reference template for generating the shadow time series. By comparing the ideal time series with the actual distorted segments point by point, the spatial distribution and temporal response characteristics of the timing offset can be obtained. At this point, the counterfactual replay chain uses the ideal time series as its main structure, connecting the backtracking results of each offset node in chronological order to form a complete time replay path, providing a strict causal logic basis for the generation of the shadow time series.

[0078] After establishing the counterfactual playback chain, a shadow time series is generated using the ideal time series as a reference to replace distorted segments in the timing link and reconstruct continuity. To this end, the output of the counterfactual playback chain is first interpolated in the time domain to ensure its time sampling interval matches the original timing link. Then, the interpolation results are smoothed and weighted according to the offset rate parameters of each time period in the dynamic correction budget to eliminate phase oscillations caused by interpolation errors or noise accumulation. Next, a time mapping function is introduced to align the shadow time series to the reference time system of the dynamic correction budget, ensuring its temporal continuity with the normal time periods in the timing link at the boundaries. To further improve the physical interpretability of the time series, the morphological characteristics of the phase shift fingerprint are also referenced during the shadow time series generation process. The phase drift trend recorded in the fingerprint is used as a constraint for time series adjustment, ensuring that the shadow time series maintains a consistent response ratio between time increments and phase changes. The shadow time series generated through the above process is not only continuous and smooth on the time axis but also accurately reflects the stable state of the timing link under ideal conditions in a physical sense.

[0079] After generating the shadow time series, it is fused and replaced with the original time synchronization record to reconstruct a continuous time synchronization record while maintaining the monotonically increasing characteristic of timestamps. To this end, firstly, all time intervals with distorted segments in the time synchronization link are identified, and time matching windows are established at the start and end boundary points of each interval. Then, the shadow time series undergoes boundary correction within each time matching window. By calculating the phase difference and time difference between the shadow time series and the original time synchronization record at the boundary points, linear weighted fusion is performed to ensure a smooth transition between the two time curves within the boundary region. Next, a timestamp rearrangement process is performed on the fused time series to ensure that all timestamp values ​​are arranged in a strictly monotonically increasing order. This process verifies the order relationship of timestamps one by one using a recursive comparison algorithm and automatically adjusts the time offset when local inversions are detected to prevent order anomalies caused by calculation errors. Finally, the continuity of the reconstructed time synchronization record is verified by calculating the standard deviation of the time interval and the mean square error of the phase difference to confirm that the time synchronization link as a whole has been restored to a continuous, stable, and increasing time state. At this point, the logical timing integrity of the timing link is fully restored, and the accumulated error of time drift is effectively eliminated.

[0080] After reconstruction is completed and continuous time synchronization records are obtained, these records provide the foundation for unified time synchronization correction across nodes. To this end, the reconstructed time series of each node are input into a unified time alignment framework. By comparing the phase offset differences between different nodes at the same reference time, a time synchronization deviation matrix between nodes is calculated. Then, based on the real-time updated parameters in the dynamic correction budget, the deviation matrix is ​​normalized and weighted, allowing the time synchronization correction amount of different nodes to be automatically adjusted according to historical stability and signal reliability. Next, guided by the deviation matrix, node time synchronization consistency iterations are performed, gradually converging the time series of each node to a unified reference time plane. To maintain the dynamic stability of the entire time synchronization link, the system also needs to periodically call the counterfactual playback chain to compare the newly generated time synchronization data. If a new drift trend is detected, the shadow time series regeneration process is triggered immediately to continuously maintain time synchronization consistency.

[0081] Through this counterfactual replay mechanism based on dynamic correction budget, the timing link can continuously maintain the incrementality of timestamps and the consistency of the entire network under complex electromagnetic disturbances and environmental changes, thus providing a solid timing foundation for the subsequent maintenance of the time series consistency of the blockchain ledger and the accuracy of the smart contract triggering order.

[0082] Double mirror anchors are embedded in the continuous time synchronization record, and cross-node clock rearrangement is performed based on the anchors to synchronously reconstruct the timestamps of each blockchain node, so as to ensure that the transaction order of the distributed ledger is consistent with the actual energy flow order.

[0083] The specific sub-steps for implementing this process are as follows:

[0084] After obtaining a continuous time synchronization record that has been repaired by a counterfactual playback chain and maintains a monotonically increasing timestamp, a dual mirror anchor point is embedded in this continuous time synchronization record to establish a unified reference benchmark for cross-node clocks. To achieve this, firstly, based on the time consistency interval defined by the dynamic correction budget, time periods with stable phase characteristics and no drift interference are selected from the time synchronization record as candidate anchor point intervals. Subsequently, the phase envelope of the time series is extracted from the candidate intervals, and its first and second time derivatives are calculated to identify the time window with the minimum phase change rate and the lowest fluctuation variance as the center of the mirror anchor point. This mirror anchor point consists of a pair of symmetrical time points, which extend forward and backward by fixed lengths of micro-time intervals on the time axis with the center time as the axis of symmetry, forming a "positive mirror anchor point" and a "negative mirror anchor point." This bidirectional mirror construction ensures the formation of a reference coordinate system with reflective symmetry characteristics within a local region of the time axis, allowing time synchronization drift to be automatically canceled out within this region. To ensure global consistency of the mirrored anchor points, the mirrored anchor points generated by each node are compared across nodes using a timestamp matching algorithm, and the anchor point pair with the smallest phase deviation is selected as the reference anchor point for the entire network. Thus, the continuous time synchronization record is calibrated into a time coordinate system with mirror symmetry, providing a unified time reference for subsequent cross-node clock rearrangement.

[0085] After embedding the dual mirror anchor points and determining the network-wide reference anchor point, cross-node clock rearrangement is performed based on these anchor points to achieve synchronous reconstruction of timing data among multiple nodes. Specifically, firstly, the local time series of each node is aligned with the network-wide reference anchor point, and its phase difference curve within the anchor point interval is calculated. Then, based on the symmetry characteristics of the anchor points, the phase difference curve of each node is decomposed into mirror symmetric components and asymmetric perturbation components, where the mirror components represent the compensable part of time drift, and the asymmetric components represent the independent offset of the node's own timing error. Next, a piecewise inversion operation is performed on the asymmetric components, and the phase drift of each node is reverted to the network-wide reference time plane through mirror symmetry mapping, so that the local time series of each node achieves precise overlap at the anchor point center. To further improve the accuracy of clock rearrangement, a second fitting is performed on the rearranged time series, using the minimum mean square error principle to smooth the time transition interval between nodes, so that the time continuity remains seamless at the cross-node boundaries. This cross-node clock reordering process can ensure the continuity of time synchronization data while eliminating time deviations caused by node clock drift and link delay inconsistencies, providing a unified time benchmark for the network-wide time sorting of the blockchain ledger.

[0086] After completing the cross-node clock rearrangement, the timestamps of each node are synchronously reconstructed to ensure that the time order of the distributed ledger records is strictly consistent with the actual energy flow order. To this end, the rearranged time series of each node are first input into the timestamp reconstruction bootstrapping function, and a time correspondence matrix between nodes is established based on the center time and reflection interval of the mirror anchor point. Then, based on this correspondence matrix, the timestamp of each distributed transaction is bidirectionally verified: on the one hand, it verifies whether the time difference before and after the mirror anchor point is consistent with the physical transmission delay of the energy flow path; on the other hand, it verifies whether its relative order on the entire network timeline conforms to causal dependency logic. When it is found that the transaction timestamp of a node is earlier than the corresponding energy transmission completion time, its timestamp is automatically adjusted according to the mirror inversion rules to return it to a reasonable time interval. At the same time, the corrected timestamp is re-embedded into the distributed ledger record, ensuring that the transaction order in the ledger is completely consistent with the actual dynamics of energy flow. To prevent the introduction of new time discontinuities during the reconstruction process, the system also needs to perform time consistency verification after each timestamp update to ensure that the latest timestamp of all nodes is within the unified mirror time window of the entire network, thereby achieving a globally consistent reconstruction of the ledger time series.

[0087] After timestamp synchronization reconstruction is completed, consistency verification and continuous correction are performed across the entire network to ensure the long-term stability of the clock rearrangement results. To this end, starting from the network-wide baseline mirror anchor point, the phase drift vector of each node is periodically recalculated, and the drift results are differentially compared with the reconstructed data of the previous cycle to determine the trend of time synchronization accuracy changes. If a significant increase in the drift rate of some nodes is detected, a dynamic rebalancing process is immediately triggered. The counterfactual playback chain is re-called in the time series of that node to generate an updated shadow time series, and the symmetry parameters of the mirror anchor point are recalculated accordingly to achieve adaptive recovery of the local time synchronization structure. Subsequently, the newly generated mirror anchor point replaces the old anchor point, and incremental clock rearrangement is performed on all nodes to minimize time offset adjustments while maintaining global time consistency.

[0088] Through this dynamic closed-loop rearrangement and correction mechanism, the dual mirror anchors not only exist as static reference benchmarks but also possess self-evolution capabilities, automatically maintaining the phase symmetry and temporal coordination of cross-node time synchronization as the environment changes. Ultimately, the distributed ledger of the entire network, based on the reconstructed timestamps, ensures that its transaction order is completely consistent with the energy flow sequence. This guarantees the absolute consistency and causal order of renewable energy scheduling driven by blockchain in the time dimension, solving the problem of ledger time sequence disorder caused by node drift and latency accumulation in traditional centralized time synchronization architectures, and enabling precise mapping of the spatiotemporal relationship between electricity trading and energy transmission.

[0089] After completing the cross-node clock rearrangement, the time-varying virtual impedance time stamp is synthesized according to the unified and corrected transaction order. The time-varying virtual impedance time stamp is used as a power allocation trigger constraint to suppress the cross-regional power flow direction reversal caused by residual timing error and form a stable scheduling boundary condition.

[0090] The specific sub-steps for implementing this process are as follows:

[0091] After completing the cross-node clock rearrangement and obtaining a unified and corrected transaction sequence, a time-varying virtual impedance timescale is synthesized based on this time consistency result, serving as the core constraint benchmark for subsequent power allocation and power flow regulation. To achieve this goal, the unified and corrected transaction sequence is first analyzed in the time domain, mapping the power exchange node, power flow direction, phase angle difference, and timestamp information corresponding to each transaction event into a high-dimensional time-energy state matrix. Based on this, the energy exchange characteristics of adjacent events in the transaction sequence are extracted, and the covariance function of their time interval and power change rate is calculated to reflect the dynamic coupling relationship of energy flow in the time dimension. Then, a time-driven family of virtual impedance fundamental functions is established based on this covariance result, so that each transaction event corresponds to a virtual impedance response function on the time axis, with its initial amplitude inversely proportional to the energy flow direction, while the phase delay is proportional to the transaction time interval. To ensure the physical consistency of the impedance function, a time-phase locking mechanism is introduced, allowing the virtual impedance to change continuously between adjacent transactions, thereby avoiding discontinuous transitions in time segments. Thus, the time-varying virtual impedance timescale based on the unified and corrected transaction sequence has been initially constructed. It not only reflects the constraint effect of time consistency on power flow, but also provides a basis for a continuously differentiable time impedance function for subsequent power scheduling.

[0092] After obtaining the initial time-varying virtual impedance timescale, it undergoes dynamic weighting and continuity processing to ensure accurate reflection of the impact of cross-regional time synchronization errors on power flow direction. To this end, the virtual impedance timescale is first subdivided into time slices. For each time slice, the coupling strength between the node voltage phase angle difference and the active power fluctuation rate is calculated, and this is used as a time weighting factor, embedded into the time derivative term of the virtual impedance function, thus achieving adaptive correction of the timescale. Next, based on the residual time synchronization error trajectory provided in the dynamic correction budget, a quadratic smoothing fit is performed on the virtual impedance function for each time slice, ensuring that the impedance curve has first-order continuity and second-order differentiability in the time domain, thereby avoiding instantaneous jumps caused by residual noise. Subsequently, a time lag compensation operator is superimposed on the time-series structure of the virtual impedance timescale, enabling the impedance response to automatically compensate for the phase difference when phase shifts caused by time synchronization errors occur, thus forming a time feedback closed loop. Finally, the continuous time-varying virtual impedance timescale is normalized to ensure its amplitude distribution remains within the allowable impedance range of the electrical system. This guarantees that the timescale possesses dynamic response capability during long-term operation without compromising system voltage stability. Through the aforementioned continuousization and feedback correction process, the time-varying virtual impedance timescale can accurately map the impact of time drift on power allocation, providing a precise time-scale control basis for subsequent scheduling constraints.

[0093] After forming a usable time-varying virtual impedance timescale, it is introduced into the power allocation triggering logic to suppress cross-regional power flow direction reversal caused by residual timing errors. To this end, a power triggering constraint based on the time-varying virtual impedance timescale is first established, requiring each transaction to be determined by the timescale to ensure it falls within the allowed time phase interval before execution. Specifically, the timestamp of the transaction event is compared with the instantaneous phase of the virtual impedance timescale. When a transaction event is detected to be at a phase reversal point on the impedance curve, the execution time of the transaction is automatically delayed until the impedance phase returns to a continuous monotonic interval. Subsequently, the delayed transaction time is redistributed with the time intervals of adjacent transactions, ensuring the overall transaction sequence remains time-consistent without disrupting the causal logic of the ledger records. Next, the power allocation rate is controlled by the dynamic derivative of the time-varying virtual impedance timescale, matching the power change rate with the impedance change rate, thereby maintaining the stability of the power flow direction under the influence of timing errors. Meanwhile, a cross-node impedance synchronization mechanism is introduced. When a phase drift of the virtual impedance timescales in different regions is detected on the time axis, the trigger windows of adjacent regions are automatically adjusted to keep them consistent in terms of timescale derivatives, thus preventing power flow reversal caused by local timing offsets. Through this timescale-based power allocation constraint mechanism, the scheduling process is effectively constrained in time, and the system can maintain the consistency of power flow direction under extreme disturbance conditions.

[0094] After completing power allocation control based on time-varying virtual impedance timescales, stable scheduling boundary conditions are formed using these timescales, thus establishing a foundation for long-term stable operation at the system level. To this end, the time interval with the smallest rate of change of the time-varying virtual impedance timescale is first selected across the entire network as the stable scheduling boundary region, and a time stability band is defined centered on this interval. Subsequently, power scheduling behavior within the time stability band is considered the steady-state operating region, while the region outside the band is designated as the dynamic transition region. When performing energy allocation, the scheduling system only allows transaction trigger points to fall within the steady-state region, thereby avoiding nonlinear disturbances caused by timing residuals in the time dimension. Next, to further improve the anti-disturbance capability of the scheduling boundary, a statistical analysis of the long-term evolution trend of the virtual impedance timescale is performed, calculating the mapping function between its time drift variance and power reversal probability. This function is used to dynamically adjust the boundary width, enabling the system to automatically expand the steady-state region to enhance stability under high-disturbance environments and shrink the boundary to improve scheduling accuracy under low-disturbance environments. Finally, through periodic backtracking verification, the latest transaction sequence, timing records and virtual impedance timescales are comprehensively compared. If the time drift of the boundary conditions is found to exceed the allowable threshold, the impedance timescale resynthesis process is immediately initiated to restore the symmetry and continuity of the scheduling boundary.

[0095] Through the dynamic boundary construction of the entire process described above, the time-varying virtual impedance timescale not only becomes the core of the scheduling time constraint, but also evolves into an active control variable for maintaining system stability. Therefore, cross-regional power distribution systems can achieve intelligent scheduling with time order, power balance, and consistent direction in a blockchain-distributed environment, fundamentally eliminating the impact of residual timing errors on power flow direction and scheduling stability.

[0096] By combining time-varying virtual impedance timescale implementation time inversion phase traction, inverse phase pulses are injected within the scheduling boundary conditions to drive the blockchain ledger records and smart contract execution logic to form a closed-loop consistency, thereby ensuring the stable scheduling and operation of renewable energy penetration distribution systems;

[0097] The specific sub-steps for implementing this process are as follows:

[0098] After obtaining the time-varying virtual impedance timescale synthesized based on the unified correction transaction sequence and establishing stable scheduling boundary conditions, time-reversal phase traction is implemented on this timescale to actively eliminate residual phase deviations and restore the temporal coupling relationship between the timing link and power flow. To achieve this, the time-varying virtual impedance timescale is first time-reversed, mathematically reflecting the original forward-growing impedance change sequence along the time axis to obtain a reverse-evolving impedance-time curve. This inversion curve is used to predict the phase change trend of the system in future moments and is superimposed with the impedance phase difference at the current moment to form a phase traction function. This phase traction function uses the instantaneous derivative of the time-varying virtual impedance as the driving quantity and calculates the nonlinear mapping relationship between the time difference and the phase difference through a phase inversion operator, thereby constructing a time-reversible dynamic phase control law. Then, this control law is embedded in the time feedback loop of the timing link, ensuring that the local clock phase of each timing node is synchronously adjusted with the inverted phase change of the time-varying virtual impedance. This time-reversal traction mechanism can compensate for phase errors caused by link delays, environmental disturbances, or algorithm lags in the time dimension, enabling the timing system to regain time consistency and phase coherence.

[0099] After establishing the time-reversal phase-pull mechanism, inverse-phase pulses are injected within the scheduling boundary conditions to dynamically enhance the time-pull effect and achieve local phase locking. To this end, firstly, based on the phase change rate recorded in the time-varying virtual impedance timescale, the intervals where phase drift overshoot may occur during time reversal are determined, and inverse-phase injection windows are set within these intervals. Then, by calculating the synchronization deviation between the phase derivative of the time-varying virtual impedance and the power allocation rate, the amplitude, duration, and phase offset angle of the inverse-phase pulse are determined. The injection direction of the inverse-phase pulse is opposite to the current phase drift direction of the system, and its spectral distribution is conjugate to the virtual impedance change frequency, thus generating a reverse-pull effect on phase drift. When the inverse-phase pulse is injected into the scheduling boundary conditions, the local phase oscillation of the timing link is weakened, and the time offset between nodes tends to converge. Simultaneously, the energy of the inverse-phase pulse is allocated to the low-frequency band of the power scheduling process, forming a dynamic suppression effect on the residual error of the timing link. To avoid system oscillations caused by overcompensation, a dynamic attenuation coefficient is introduced during the reverse-phase pulse injection process, causing the pulse energy to decrease exponentially over time, thereby ensuring the asymptotic stability of the traction process. Through this process, the effect of time-reversal traction is locally enhanced, and the phase stability and time consistency of the timing link are synchronously restored.

[0100] After the reverse-phase pulse injection is completed, closed-loop consistency between time-reversal phase traction and distributed ledger operations is achieved through dynamic reconstruction of blockchain ledger records and smart contract execution logic. To this end, the injection time of each reverse-phase pulse and its corresponding energy allocation event are first marked in the blockchain ledger, establishing a time-reversal record index table. Subsequently, the execution trigger conditions of smart contracts are bound to this index table, so that when determining the transaction execution order, the contract logic not only refers to the traditional timestamp order but also incorporates the time adjustment information caused by the reverse-phase pulse. Thus, when the ledger system detects a slight deviation between the transaction timestamp and the energy flow order, it can perform reverse correction based on the reverse-phase pulse index, ensuring that the transaction settlement order is consistent with the physical energy flow order again. Next, after the smart contract execution is completed, the system updates the time-varying virtual impedance timescale in reverse, synchronizing its phase trajectory with the ledger timeline, forming a two-way feedback between time and ledger state. Simultaneously, to prevent phase traction from introducing new delays at the ledger layer, the system binds ledger writing and timing link updates to the same transaction logic unit, performing time verification immediately after each write to ensure complete consistency in the timing of both operations. Through this closed-loop control, time reversal traction not only becomes the phase correction mechanism within the time synchronization system, but also becomes part of the blockchain ledger's time logic, enabling a highly consistent dynamic synergy between physical energy flow and ledger transaction flow.

[0101] After achieving closed-loop consistency between the blockchain ledger and smart contract logic, stability maintenance and long-term correction are performed on the entire time-reversal phase traction process to ensure the continuous and stable operation of the renewable energy-integrated power distribution system. To this end, a long-term operational phase deviation statistical model is first established, and time-series analysis is performed on historical phase reversal traction data to extract key indicators such as the mean phase drift, drift rate, and system recovery time. Then, based on these statistical parameters, the injection frequency and energy distribution ratio of the reverse phase pulse are dynamically adjusted, enabling the system to maintain phase balance under different load fluctuations and climatic conditions. Next, a time redundancy verification mechanism is introduced, recalculating the phase consistency between the time-varying virtual impedance timescale and the ledger time index every preset period, and automatically triggering a new time-reversal traction process when a deviation exceeds a threshold. To further improve the adaptive capability of scheduling, a recursive phase prediction algorithm is also introduced into the timing link, enabling the system to identify potential phase drift trends in advance and proactively generate pre-compensation pulses, thereby completing time correction before disturbances occur.

[0102] Ultimately, through the continuous coupling of time-reversal phase traction, reverse-phase pulse injection, ledger logic correction, and dynamic prediction compensation, the entire renewable energy-integrated power distribution system achieves dual closed-loop stability in both the time and energy domains. At this point, the transaction records in the blockchain ledger are completely consistent with the actual energy transmission sequence, the execution logic of smart contracts is synchronized with the phase changes of the timing link, and the dispatch system can maintain a complete timing sequence, ordered power, and stable operation under any external disturbances or extreme weather conditions. This demonstrates the innovative characteristics and highly robust technical effects of the deep integration of time control and energy dispatch.

[0103] This invention achieves self-correction of time consistency and cross-node synchronous reconstruction in a blockchain power distribution system by introducing a joint mechanism of multi-source high-precision reference signals, phase offset fingerprints, and dynamic correction budgets into the time synchronization link. It can capture and correct time drift in real time under extreme weather conditions, electromagnetic disturbances, and abnormal atmospheric propagation, eliminating the timestamp reversal and ledger misordering problems caused by phase imbalance in traditional time synchronization systems. Through the synergistic effect of the counterfactual replay chain and dual mirror anchors, the system can automatically reconstruct continuous time records and replace distorted time segments with shadow time series, achieving monotonically increasing distributed node times and network-wide synchronization. This ensures that the transaction order in the blockchain ledger strictly corresponds to the actual energy flow order, fundamentally guaranteeing the correct execution of time-driven smart contracts and the causal consistency of the power distribution system.

[0104] This invention establishes a time-feedback-based dynamic power stability constraint and ledger logic closed-loop consistency control through a time-varying virtual impedance timescale and a time-reversal phase traction mechanism. This method utilizes virtual impedance timescales to suppress the amplification effect of residual timing errors in the power allocation process, preventing cross-regional power flow direction reversal and voltage frequency oscillations. Furthermore, it injects inverse-phase pulses within the scheduling boundary to form adaptive time-traction feedback, achieving temporal balance and dynamic stability in the energy scheduling process. Through this mechanism, the system can maintain high-precision synchronization between smart contract triggering, ledger recording, and physical energy flow in a blockchain environment, significantly improving the scheduling stability, security, and clean energy utilization efficiency of renewable energy-integrated power distribution systems.

[0105] This invention provides, for example Figure 2 The blockchain-based renewable energy distribution system scheduling system shown includes a time sequence integrity observation module, a causal decomposition and correction module, a counterfactual time reconstruction module, a cross-node clock rearrangement module, a time-varying impedance scheduling module, and a time inversion traction module.

[0106] The timing integrity observation module constructs a cross-level timing integrity observation layer, introduces multi-source high-precision reference signals into the synchronization timing link, and uses them to collect external environmental electromagnetic disturbances and atmospheric propagation delay data in real time, and generate phase offset fingerprints corresponding to the timing status.

[0107] The causal decomposition and correction module performs causal decomposition operations based on phase offset fingerprints, performs noise stripping on the collected electromagnetic disturbance signals, extracts the true timing error trajectory, and generates a dynamic correction budget accordingly. The dynamic correction budget serves as the real-time compensation input for the timing data.

[0108] The counterfactual time reconstruction module constructs a counterfactual playback chain based on a dynamic correction budget, generates a shadow time series in the time synchronization chain, replaces distorted time synchronization segments with the shadow time series, reconstructs continuous time synchronization records, and maintains the monotonically increasing characteristic of the time synchronization timestamp.

[0109] The cross-node clock reordering module embeds dual mirror anchors in the continuous time synchronization record and performs cross-node clock reordering based on the anchors to synchronously reconstruct the timestamps of each blockchain node.

[0110] After completing the cross-node clock rearrangement, the time-varying impedance scheduling module synthesizes a time-varying virtual impedance time stamp based on the unified and corrected transaction order. The time-varying virtual impedance time stamp is used as a power allocation trigger constraint to suppress the cross-regional power flow direction reversal caused by residual timing errors and form a stable scheduling boundary condition.

[0111] The time-reversal traction module combines time-varying virtual impedance timescales to perform time-reversal phase traction, injecting inverse phase pulses within the scheduling boundary conditions to drive the blockchain ledger records and smart contract execution logic to form a closed-loop consistency.

[0112] The blockchain-based renewable energy penetration distribution system scheduling method provided in this embodiment of the invention is implemented through the aforementioned blockchain-based renewable energy penetration distribution system scheduling system. For details of the specific methods and processes of the blockchain-based renewable energy penetration distribution system scheduling system, please refer to the embodiments of the aforementioned blockchain-based renewable energy penetration distribution system scheduling method, which will not be repeated here.

[0113] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A blockchain-based method for dispatching a renewable energy penetration power distribution system, characterized in that, The method comprises the following steps: A cross-level time sequence integrity observation layer is constructed, multi-source high-precision reference signals are introduced into a synchronous time service link, external environmental electromagnetic disturbance and atmospheric propagation delay data are collected in real time, and a phase offset fingerprint corresponding to a time service state is generated; A causal decomposition operation is performed based on the phase offset fingerprint, noise stripping processing is performed on the collected electromagnetic disturbance signal, a real time service error trajectory is extracted, and a dynamic correction budget is generated accordingly, which is used as a real-time compensation input of the time service data; A counterfactual playback chain is constructed according to the dynamic correction budget, a shadow time sequence is generated in the time service link, a distorted time service segment is replaced by the shadow time sequence, a continuous time service record is reconstructed, and the time service timestamp remains monotonically increasing; Double-mirror anchor points are embedded in the continuous time service record, and cross-node clock rearrangement is performed based on the anchor points to synchronize and reconstruct the timestamps of each blockchain node; After the cross-node clock rearrangement is completed, a time-varying virtual impedance time tag is synthesized according to the uniformly corrected transaction sequence, and the time-varying virtual impedance time tag is used as a power distribution trigger constraint to suppress the cross-regional power flow direction reversal caused by residual time service errors, thereby forming a stable scheduling boundary condition; Time reversal phase traction is performed in combination with the time-varying virtual impedance time tag, and inverse phase pulses are injected into the scheduling boundary condition to drive the blockchain ledger record and the smart contract execution logic to form a closed-loop consistency.

2. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 1, wherein, The steps of constructing the cross-level time sequence integrity observation layer include: A cross-level time sequence integrity observation structure is deployed in each node of a renewable energy penetration power distribution network, a multi-path redundant time service reference system is formed by fusing satellite time service signals, ground-based optical fiber synchronization signals and atomic frequency reference signals, and a multi-dimensional time sequence sampling matrix is generated; The multi-dimensional time sequence sampling matrix is time-aligned and fused with external environmental electromagnetic disturbance and atmospheric propagation delay data to obtain a fused signal set reflecting the environmental coupling state of the time service link; The phase change characteristics of the multi-source signals are modeled and fingerprinted based on the fused signal set to generate a phase offset fingerprint representing the time service state; By comparing the latest phase offset fingerprint with a historical fingerprint sequence, the phase drift rate of the time service link is calculated and a compensation process is triggered to restore the dynamic consistency of the time service link.

3. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 2, wherein, The compensation process includes: according to the weight information of each reference signal in the phase offset fingerprint, the delay of the multi-source time service path is inversely fitted and phase-aligned, and the compensation is automatically started when the phase drift rate exceeds a preset threshold.

4. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 1, wherein, The steps of performing a causal decomposition operation based on the phase offset fingerprint include: The phase offset fingerprint is used as a time feature guide signal to perform time sequence registration and amplitude normalization on the collected electromagnetic disturbance signal, and a multi-dimensional causal mapping matrix is constructed to identify the effective causal components of the time service error; The electromagnetic disturbance signal is subjected to multi-scale adaptive wavelet decomposition and empirical mode decomposition according to the causal mapping matrix, random noise is stripped and effective components with high coherence with the phase offset fingerprint are retained, and a purified electromagnetic disturbance signal is obtained; The purified electromagnetic disturbance signal is jointly regressed with the phase offset fingerprint to extract a time service error trajectory to reflect the time service drift trend; A dynamic correction budget model is established according to the time service error trajectory, and real-time compensation and adaptive correction are performed on the time service data.

5. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 4, wherein, The process of establishing the dynamic correction budget model includes: based on the short-term change trend of the time service error trajectory, a time polynomial model of error change is fitted by using the weighted least squares method, the error derivative and drift acceleration are obtained, and the effectiveness of the correction budget is judged by matching the residual error of the phase offset fingerprint; when the residual error exceeds the preset threshold, the adaptive update of the budget parameters is automatically triggered.

6. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 1, wherein, The steps of constructing the counterfactual playback chain according to the dynamic correction budget include: According to the dynamic correction budget, the counterfactual playback chain is established, the actual time service trajectory is compared with the predicted trajectory of the correction budget through multi-dimensional mapping, the time period in which the time service drift exceeds the tolerance threshold is identified, and the error residual vector is calculated, forming an ideal time sequence, which is used as a calculation reference template for generating a shadow time sequence; Based on the counterfactual playback chain and taking the ideal time sequence as a reference, a shadow time sequence is generated to replace the distorted time service segment, the shadow time sequence is subjected to time interpolation and smooth weighting, and the time scale is aligned according to the characteristics of the phase offset fingerprint, so that the shadow time sequence is continuous with the normal time period; The shadow time sequence and the original time service record are fused in the time matching window, and the timestamp rearrangement is performed to maintain the monotonicity; According to the reconstructed time service record, the inter-node time service deviation matrix is calculated, and the cross-node time service unified correction is realized through iterative correction.

7. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 6, wherein, The steps of embedding double mirror anchor points in continuous time service records and performing cross-node clock rearrangement include: According to the dynamic correction budget, the time consistency interval is determined, the time window with the smallest phase change rate is selected as the center of the mirror anchor point in the continuous time service record, and the forward and reverse mirror anchor points are constructed to form a time-symmetric reference coordinate system; The mirror anchor points generated by each node are compared through the timestamp matching algorithm, the anchor point pair with the smallest phase deviation is selected as the global reference anchor point, and the alignment and segmentation inversion operation is performed on the time sequence of each node according to the anchor point; The time sequence after rearrangement is subjected to secondary fitting and smooth correction to ensure the cross-node time continuity, and the time correspondence matrix is established according to the mirror anchor points to perform bidirectional verification and adjustment on the transaction timestamp; Periodically recalculate the phase drift vector of each node and trigger dynamic rebalancing to maintain the long-term stability of the global time consistency and the order of the account book.

8. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 7, wherein, The steps of synthesizing the time-varying virtual impedance time scale according to the unified corrected transaction sequence include: The time domain of the unified corrected transaction sequence is analyzed, a high-dimensional time-energy state matrix is established, and a time-driven virtual impedance basis function family is generated according to the covariance function of the power change rate and the time interval; According to the residual time service error trajectory in the dynamic correction budget, weighted smoothing and lag compensation are performed on the virtual impedance time scale to maintain the time continuity of the impedance curve and form a time feedback closed loop; The time-varying virtual impedance time scale is introduced into the power distribution trigger logic to perform phase verification and dynamic delay adjustment on the transaction time, and to suppress the power flow reversal caused by time service error; The stable scheduling boundary is defined based on the time interval with the minimum rate of change of the virtual impedance time scale, and dynamic boundary adaptive correction is achieved through periodic backtracking verification.

9. The blockchain-based renewable energy penetration power distribution system dispatching method of claim 8, wherein, The steps of performing time reversal phase traction combined with the time-varying virtual impedance time scale include: Time reversal mapping is performed on the time-varying virtual impedance time scale, a phase traction function is established, and is embedded in the timekeeping link feedback loop to compensate for the phase error caused by link delay and disturbance; An inverse phase injection window is set according to the phase change rate within the scheduling boundary condition, the amplitude and duration of the inverse phase pulse are calculated, and a dynamic attenuation coefficient is used to realize the stability of the traction process; The injection time of the inverse phase pulse and the energy distribution event are recorded in the blockchain ledger, and the execution logic of the smart contract is bound to the index table; A long-term phase deviation statistical model is established, the pulse injection frequency and energy ratio are dynamically adjusted according to the operation data, and a new time reversal traction process is triggered when the deviation exceeds the threshold.

10. A blockchain-based renewable energy penetration power distribution system dispatching system for implementing the blockchain-based renewable energy penetration power distribution system dispatching method of any one of claims 1-9, characterized in that, It includes a time sequence integrity observation module, a causal decomposition correction module, a counterfactual time reconstruction module, a cross-node clock rearrangement module, a time-varying impedance scheduling module, and a time reversal traction module. The time sequence integrity observation module constructs a cross-level time sequence integrity observation layer, introduces multi-source high-precision reference signals in the synchronous timekeeping link, and is used to collect external electromagnetic disturbance and atmospheric propagation delay data in real time, and generates a phase offset fingerprint corresponding to the timekeeping state; The causal decomposition correction module performs causal decomposition operation based on the phase offset fingerprint, performs noise stripping processing on the collected electromagnetic disturbance signal, extracts the real timekeeping error trajectory, and generates a dynamic correction budget accordingly, which is used as the real-time compensation input of the timekeeping data; The counterfactual time reconstruction module constructs a counterfactual playback chain according to the dynamic correction budget, generates a shadow time sequence in the timekeeping link, replaces the distorted timekeeping segment with the shadow time sequence, and reconstructs the continuous timekeeping record to maintain the monotonic increasing property of the timekeeping timestamp; The cross-node clock rearrangement module embeds double-mirror anchor points in the continuous timekeeping record, and performs cross-node clock rearrangement based on the anchor points to synchronize and reconstruct the timestamps of each blockchain node; The time-varying impedance scheduling module synthesizes the time-varying virtual impedance time scale according to the unified corrected transaction sequence after completing the cross-node clock rearrangement, uses the time-varying virtual impedance time scale as the power distribution trigger constraint to suppress the cross-regional power flow direction reversal caused by residual timekeeping error, and forms a stable scheduling boundary condition; The time reversal traction module performs time reversal phase traction combined with the time-varying virtual impedance time scale, and injects inverse phase pulses within the scheduling boundary condition to drive the closed-loop consistency of the blockchain ledger record and the smart contract execution logic.

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