Zero-carbon integrated energy system and optimization method of zero-carbon integrated energy system

Abstract

The invention relates to the field of zero-carbon energy, and discloses a zero-carbon integrated energy system and an optimization method of the zero-carbon integrated energy system.The optimization method of the zero-carbon integrated energy system comprises the steps that a historical dependency relationship is accurately captured by constructing a multi-scale prediction model fusing fractional calculus and a grey system theory; decomposing the system into multi-scale optimization sub-problems by adopting wavelet transform, and establishing a collaborative optimization objective function system; performing global optimization solution by using a fractional gradient descent algorithm to ensure stable operation of the system; and a complete closed-loop management system is formed through an efficiency verification system and a parameter adaptive correction mechanism. According to the invention, autonomous optimization and continuous improvement of the system are realized, and the operation efficiency and stability of the zero-carbon comprehensive energy system are improved.

Description

Technical Field

[0001] This invention relates to the field of zero-carbon energy, and in particular to a zero-carbon integrated energy system and an optimization method for such a system. Background Technology

[0002] Zero-carbon integrated energy systems, as a crucial vehicle for achieving a clean energy transition, face unprecedented technological challenges in their optimized operation. Current systems require coordination among heterogeneous units to achieve zero-carbon operation while ensuring reliable energy supply. However, traditional optimization methods are ill-suited to dynamic systems. Existing technologies currently suffer from the following shortcomings: Traditional time series forecasting methods have high requirements for data completeness, making it difficult to effectively handle the small sample and uncertain data characteristics of renewable energy output, and they cannot accurately characterize the historical dependencies of the system. Existing optimization methods mostly use a fixed time scale, which cannot simultaneously take into account minute-level rapid power balance, hour-level economic operation, and day-level energy storage planning, resulting in obvious defects in cross-scale coordination of optimization results; Traditional gradient optimization methods are prone to getting trapped in local optima and lack real-time monitoring and correction mechanisms for system stability during the optimization process, which poses a risk of instability when faced with nonlinear disturbances.

[0003] Therefore, we propose a zero-carbon integrated energy system and an optimization method for the zero-carbon integrated energy system to solve the above problems. Summary of the Invention

[0004] This invention provides a zero-carbon integrated energy system and an optimization method for the zero-carbon integrated energy system, which is used to construct an autonomous optimization system for the zero-carbon integrated energy system based on the deep integration of fractional calculus and grey system theory.

[0005] The first aspect of this invention provides an optimization method for a zero-carbon integrated energy system. This method includes: constructing a multi-scale prediction model based on relevant data; generating a system state prediction sequence through historical dependencies; decomposing the prediction sequence into optimization sub-problems; establishing a collaborative optimization objective function system to form a multi-level optimization framework; performing global solution based on the optimization objective function; monitoring and correcting the optimization trajectory using global search capabilities; generating a control command sequence; distributing the control command sequence to the actuators; collecting operational response data; constructing a performance verification system; generating a performance verification report through comparative analysis; and establishing an adaptive correction mechanism based on the performance verification report, forming a complete closed-loop management system by adjusting the prediction model parameters.

[0006] Optionally, in the first implementation of the first aspect of the present invention, correlation analysis is performed on the relevant data to generate a standardized input sequence; based on the standardized input sequence, an equation is established to capture the memory characteristics of the system and generate an adaptive fractional gray predictor; using the adaptive fractional gray predictor, prediction parameters are set, and a basic prediction sequence is generated through the data sequence; based on the basic prediction sequence, a dynamic weight allocation mechanism is established to achieve collaborative optimization and generate multi-scale prediction results; the multi-scale prediction results are analyzed and corrected, and a system state prediction sequence is generated through comparative verification.

[0007] Optionally, in the second implementation of the first aspect of the present invention, an algorithm is used to analyze the predicted sequence, match functions, and generate a multi-scale decomposition framework; based on the multi-scale decomposition framework, the system operating state is decomposed into components, an optimization objective function is established, and a set of optimization sub-problems is generated; dynamic coupling relationships are established, and a multi-scale coordination model is constructed by designing an inter-scale interaction mechanism to generate a scale coupling relationship matrix; a two-layer optimization architecture is constructed to generate a collaborative optimization function system; the collaborative optimization function system is verified by constructing an evaluation model and a verification mechanism to perform evaluation and generate an optimization objective function framework.

[0008] Optionally, in the third implementation of the first aspect of the present invention, a computational model is constructed based on the objective function, the distribution characteristics of the objective function are analyzed, and an initial sequence of optimized solutions is generated; using the generated initial sequence of optimized solutions, an algorithm is designed to generate an optimization iterative search direction vector; based on the generated optimization iterative search direction vector, a monitoring system is constructed to generate dynamic characteristics; the dynamic characteristics are analyzed and adjusted to generate an optimized solution sequence; based on the optimized solution sequence, it is converted into control commands, a control instruction sequence is generated, and output to the system execution mechanism.

[0009] Optionally, in the fourth implementation of the first aspect of the present invention, the generated control instruction sequence is distributed to each unit to generate a device control instruction set; a sensor network is deployed to collect operating parameters and generate a real-time operating status dataset; features are extracted from the real-time operating status data, and a system operating feature sequence is generated by eliminating measurement noise and outliers; a three-dimensional performance evaluation model is constructed based on the generated system operating feature sequence to generate a multi-dimensional performance evaluation result; the multi-dimensional performance evaluation result is compared with the target value, the degree of deviation is calculated, and a performance verification report is generated.

[0010] Optionally, in the fifth implementation of the first aspect of the present invention, based on the data in the performance verification report, an analysis model is constructed, the influence of fractional derivatives is calculated, and a parameter dynamic sensitivity matrix is ​​generated; using the generated parameter dynamic sensitivity matrix, a correction strategy is designed, a coupling relationship is established, and an adaptive parameter adjustment vector sequence is generated; based on the adaptive parameter adjustment vector sequence, a spatial model is constructed, and parameter safety correction domain constraints are generated by analyzing the stability boundary; combined with energy system operation data, an evaluation model is constructed, indicators and optimization functions are introduced, and a zero-carbon performance optimization vector is generated; based on the parameter safety correction domain constraints and the zero-carbon performance optimization vector, a decision-making mechanism is established, and a parameter update strategy and a zero-carbon operation scheme are generated.

[0011] Optionally, in the sixth implementation of the first aspect of the present invention, a fractional-order sensitivity index is calculated based on system operation response data to generate a system vulnerability assessment report; using the system vulnerability assessment report and data, a system dynamic evolution model is established to generate an early identification feature vector for abnormal states; based on the early identification feature vector for abnormal states, the algorithm is adjusted to generate a graded early warning signal; according to the graded early warning signal, a control strategy is initiated to generate equipment power adjustment instructions, forming a system self-healing control scheme; the system self-healing control scheme is evaluated, and a control effect verification report is generated through trajectory tracking and verification; the system protection strategy library is updated to complete the rapid recovery and optimized operation of the system.

[0012] A second aspect of this invention provides a zero-carbon integrated energy system, comprising: a prediction module for constructing a multi-scale prediction model based on relevant data and generating a system state prediction sequence through historical dependencies; an optimization module for decomposing the prediction sequence into optimization sub-problems, establishing a collaborative optimization objective function system, and forming a multi-level optimization framework; a generation module for performing global solutions based on the optimization objective function, monitoring and correcting the optimization trajectory using global search capabilities, and generating a control command sequence; an analysis module for distributing the control command sequence to the actuators, collecting operational response data, constructing an efficiency verification system, and generating a performance verification report through comparative analysis; and an adjustment module for establishing an adaptive correction mechanism based on the performance verification report, and forming a complete closed-loop management system by adjusting the prediction model parameters. The beneficial effects of this invention are as follows: This invention combines the memory characteristics of fractional calculus with the small sample adaptability of grey system theory, overcomes the dependence of traditional methods on big data and machine learning, establishes a collaborative optimization system at the minute, hour, and day levels, and solves the problem that traditional single-scale optimization cannot take into account both short-term response and long-term planning. By accurately capturing the historical dependencies of the system's dynamic evolution using a fractional grey model, the prediction accuracy of renewable energy output and load demand is significantly improved. A fractional gradient descent algorithm is employed to enhance global search capabilities, avoiding getting trapped in local optima while ensuring the quality of the solutions. Chaotic stability monitoring and a self-healing control mechanism significantly improve the system's anti-interference capability and recovery speed. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of an embodiment of an optimization method for a zero-carbon integrated energy system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of one embodiment of a zero-carbon integrated energy system according to an embodiment of the present invention. Detailed Implementation

[0014] This invention provides a zero-carbon integrated energy system and an optimization method for such a system, used to construct an autonomous optimization system for the zero-carbon integrated energy system based on the deep integration of fractional calculus and grey system theory. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the optimization method for a zero-carbon integrated energy system according to the present invention includes: 101. Based on renewable energy output data, load demand data, and environmental parameter data, a multi-scale prediction model integrating fractional calculus and grey system theory is constructed. By capturing the historical dependencies of the dynamic evolution of the system through the memory characteristics of fractional derivatives, grey system theory is used to handle small sample uncertainties, generating system state prediction sequences with minute, hour, and day time scales, providing basic input data with multiple time resolutions for subsequent optimization decisions.

[0016] It is understood that the executing entity of this invention can be an optimization device for a zero-carbon integrated energy system, or it can be a terminal or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0017] It should be noted that correlation analysis is performed on renewable energy output data, load demand data, and environmental parameter data. Through data cleaning and normalization, a standardized input sequence with a consistent spatiotemporal benchmark is generated. Based on the standardized input sequence, a fractional grey differential equation is established. By adjusting the order of the fractional derivative, the memory characteristics of the system are captured, and an adaptive fractional grey predictor with dynamic memory function is generated. An adaptive fractional gray predictor is used to set prediction parameters for three time scales: minute, hour, and day. The gray generation operator is used to process the data sequences at each scale to generate a multi-time scale basic prediction sequence. Based on multi-timescale basic prediction sequences, a dynamic weight allocation mechanism between scales is established using grey relational analysis. The prediction results are then optimized through a scale coupling algorithm to generate consistent multi-scale prediction results. Residual analysis and error correction are performed on the coordinated optimization multi-scale prediction results. By comparing and verifying with historical operating characteristics, the final system state prediction sequence is generated.

[0018] 102. Decompose the multi-timescale prediction sequence into optimization sub-problems with different frequency characteristics through wavelet transform, establish a collaborative optimization objective function system based on scale correlation, and ensure the coordination and unity of the optimization objective in the spatiotemporal dimension through coupling constraints between scales while maintaining the independent optimization characteristics of each time scale, thus forming a multi-level optimization framework that takes into account both short-term response and long-term planning.

[0019] It should be noted that the adaptive wavelet basis selection algorithm is used to perform frequency domain feature analysis on the multi-time-scale prediction sequence. The optimal wavelet basis function is automatically matched according to the dynamic response characteristics of the energy system, and a multi-scale decomposition framework with frequency domain adaptive characteristics is generated. Based on the multi-scale decomposition framework, the system operating state is decomposed into energy components, power components, and stability components with clear physical meaning through wavelet packet transform. An optimization objective function corresponding to each component is established, and a set of optimization sub-problems with physical constraints is generated. By using fractional-order correlation analysis, we establish dynamic coupling relationships between optimization sub-problems at different time scales. By designing an interaction mechanism for energy flow and information flow between scales, we construct a multi-scale coordination model based on dynamic weights and generate a scale coupling relationship matrix with adaptive adjustment capabilities. Based on the set of optimization subproblems and the scale coupling relationship matrix, a two-layer optimization architecture including a global optimization layer and a local coordination layer is constructed. The system's comprehensive energy efficiency objective function is designed at the global layer, and a dynamic coordination mechanism between different scales is established at the local layer, generating a collaborative optimization function system with hierarchical characteristics. The feasibility of the collaborative optimization function system is dynamically verified. By constructing a constraint satisfaction evaluation model and a scale coordination test mechanism, the feasibility and coordination of the optimization objective function are quantitatively evaluated, and a verified optimization objective function framework is generated.

[0020] 103. Based on the multi-scale optimization objective function, the fractional gradient descent algorithm is used for global optimization. The nonlocality of the fractional derivative is used to enhance the global search capability of the optimization process. At the same time, the chaotic stability theory is introduced to monitor and correct the optimization trajectory in real time. Lyapunov exponential analysis is used to ensure the stable operation of the system under nonlinear disturbances and generate a control command sequence that meets the dynamic performance requirements.

[0021] It should be noted that, based on the generated multi-scale optimization objective function, a fractional gradient field calculation model is constructed. By analyzing the gradient distribution characteristics of the objective function in the solution space through the memory characteristics of the fractional derivative, an initial optimization solution sequence with global search capability is generated. Using the generated initial optimal solution sequence, an adaptive fractional gradient descent algorithm is designed. By dynamically adjusting the order of the fractional derivative and the step size factor, the algorithm avoids getting trapped in local optima while maintaining search efficiency, and generates an optimization iterative search direction vector. Based on the generated optimization iterative search direction vector, a real-time monitoring system for the optimization trajectory is constructed. The optimization process is mapped to a high-dimensional dynamic system through phase space reconstruction technology, generating a dynamic characteristic description of the optimization trajectory. Chaotic stability analysis is performed on the dynamic characteristics of the generated optimized trajectory. The Lyapunov exponent spectrum is calculated and a stability criterion is constructed. The unstable optimized trajectory is dynamically adjusted through a real-time correction mechanism to generate an optimized solution sequence that meets the stability requirements. Based on the generated sequence of optimized solutions that meet stability requirements, the control command conversion mechanism transforms them into directly executable control commands, generating the final control command sequence and outputting it to the system execution mechanism.

[0022] 104. Distribute the control command sequence to actuators such as power generation units, energy storage systems, and load regulation devices to achieve real-time system control. At the same time, collect the operation response data of each unit to construct an efficiency verification system that includes energy efficiency indicators, stability indicators, and response speed indicators. Through comparative analysis of actual response and expected targets, generate a performance verification report with quantitative evaluation results.

[0023] It should be noted that, based on the generated control instruction sequence, it is distributed to each execution unit through a dynamic instruction allocation strategy, and a timing coordination mechanism is adopted to ensure the synchronization of instruction execution, thereby generating a standardized set of equipment control instructions; During the operation of the actuator, a sensor network deployed on the power generation unit, energy storage system and load regulation device collects operating parameters such as voltage, current, power and temperature in real time, and generates a multi-source heterogeneous real-time operating status dataset. Multi-dimensional feature extraction is performed on the collected real-time operating status data, and measurement noise and outliers are eliminated through data fusion processing technology to generate a clean and complete system operating feature sequence. Based on the generated system operation characteristic sequence, a three-dimensional performance evaluation model including energy conversion efficiency, power fluctuation rate and response delay time is constructed. The comprehensive system performance index is calculated through a dynamic weight adjustment mechanism to generate multi-dimensional performance evaluation results. The generated multi-dimensional performance evaluation results are compared and analyzed with the expected target values. The deviation of each indicator is calculated by the deviation quantification algorithm, and a performance verification report containing improvement suggestions is generated.

[0024] 105. Based on the performance verification report, establish an adaptive correction mechanism for model parameters. Dynamically adjust the prediction model parameters through feedback deviations, and calculate zero-carbon performance indicators such as carbon emission intensity, renewable energy penetration rate, and energy utilization efficiency. Form a complete closed-loop management system that includes parameter updates, performance evaluation, and strategy optimization to ensure that the system maintains the optimal zero-carbon operating state in a dynamic environment.

[0025] It should be noted that, based on the system response deviation data in the performance verification report, a fractional-order sensitivity analysis model is constructed, and a parameter dynamic sensitivity matrix is ​​generated by calculating the influence of parameter changes on the fractional-order derivative of system performance. Using the generated parameter dynamic sensitivity matrix, a multi-timescale parameter collaborative correction strategy is designed. By establishing the coupling relationship between minute-level, hour-level, and day-level parameter corrections, an adaptive parameter adjustment vector sequence is generated. Based on the generated adaptive parameter adjustment vector sequence, a parameter evolution phase space model is constructed. The stability boundary of parameter adjustment is predicted by Lyapunov exponent analysis, and the parameter safety correction domain constraint conditions are generated. By combining real-time collected energy system operation data, a dynamic evaluation model for zero-carbon performance is constructed. By introducing system resilience indicators and carbon emission trajectory optimization functions, a multi-objective synergistic zero-carbon performance optimization vector is generated. Based on the generated parameter safety correction domain constraints and zero-carbon performance optimization vector, a closed-loop autonomous decision-making mechanism is established. By dynamically balancing the parameter correction speed with the system stability requirements, the optimal parameter update strategy and zero-carbon operation scheme are generated, thereby realizing the self-evolutionary optimization management of the system.

[0026] 106. It also includes a system abnormal state early warning and self-healing control mechanism based on fractional-order sensitivity analysis and dynamic phase space reconstruction, which is executed in parallel during system operation.

[0027] It should be noted that, based on the generated system operation response data, the system dynamic feature matrix is ​​constructed through fractional derivative operations, the fractional sensitivity index of key operating parameters is calculated, and a system vulnerability assessment report is generated. Using the generated system vulnerability assessment report and real-time operational status data, a dynamic evolution model of the system is established through phase space reconstruction technology to generate early identification feature vectors for abnormal states. Based on the generated early identification feature vector of abnormal state, an algorithm for dynamic adjustment of early warning threshold based on Lyapunov stability theory is designed to generate hierarchical early warning signals through a multi-time scale early warning mechanism. Based on the generated graded early warning signals, the corresponding self-healing control strategy is activated, and the equipment power adjustment command is generated through the fractional sliding mode control algorithm to form a system self-healing control scheme. The generated system self-healing control scheme is evaluated in real time. Through dynamic trajectory tracking and stability verification, a control effect verification report is generated, and the system protection strategy library is updated accordingly to complete the rapid recovery and optimized operation of the system under abnormal conditions.

[0028] In this embodiment of the invention, the beneficial effects are as follows: By constructing a system abnormal state early warning and self-healing control mechanism based on fractional-order sensitivity analysis and dynamic phase space reconstruction, intelligent monitoring and proactive protection of the operating status of the zero-carbon integrated energy system are realized. It can identify vulnerable links in the system in advance and quickly implement self-healing control, thereby improving the stability and reliability of the system in complex operating environments and avoiding the risk of system shutdown due to equipment failure or external disturbances. At the same time, through real-time performance evaluation and dynamic updates of the strategy library, a continuously optimized system protection system is formed, providing important technical support for the safe and stable operation of the zero-carbon energy system.

[0029] Please see Figure 2 One embodiment of a zero-carbon integrated energy system according to the present invention includes: 201. Prediction Module: Constructs a multi-scale prediction model based on relevant data and generates a system state prediction sequence through historical dependencies. 202. Optimization Module: Decomposes the prediction sequence into optimization sub-problems, establishes a collaborative optimization objective function system, and forms a multi-level optimization framework. 203. Generation Module: Performs global solution based on the optimization objective function, monitors and corrects the optimization trajectory using global search capabilities, and generates a control command sequence. 204. Analysis Module: Distributes the control command sequence to the actuators, collects operational response data, constructs a performance verification system, and generates a performance verification report through comparative analysis. 205. Adjustment Module: Based on the performance verification report, establishes an adaptive correction mechanism and forms a complete closed-loop management system by adjusting the prediction model parameters.

[0030] In this embodiment of the invention, the prediction module constructs a multi-scale prediction model based on fractional-order grey theory, improving the accuracy of system state prediction; the optimization module establishes a collaborative objective function system through multi-scale decomposition, solving the coordination problem of optimization objectives at different time scales; the generation module combines fractional-order gradient optimization and chaotic stability theory to ensure system operational stability while guaranteeing global optimality; the analysis module constructs an effectiveness verification system to achieve quantitative evaluation and real-time feedback of control effects; and the adjustment module establishes an adaptive correction mechanism, enabling the system to continuously optimize. These five modules form a complete closed-loop management system, significantly improving energy utilization efficiency, enhancing system anti-interference capabilities, reducing operating costs, and providing reliable technical support for achieving zero-carbon goals.

[0031] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the optimization method for a zero-carbon integrated energy system.

[0032] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0033] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0034] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization method for a zero-carbon integrated energy system, characterized in that, The optimization method of the zero-carbon integrated energy system comprises the following steps: Based on renewable energy output data, load demand data and environmental parameter data, a multi-scale prediction model is constructed, and a system state prediction sequence is generated through historical dependence relationship; According to the system state prediction sequence, the optimization sub-problems are decomposed, the collaborative optimization objective function system is established, and a multi-level optimization framework is formed; Based on the optimization objective function, combined with the multi-level optimization framework, global solution is carried out, the optimization trajectory is monitored and corrected by using the global search ability, and a control instruction sequence is generated; According to the control instruction sequence, the execution is distributed, the running response data is collected, the efficiency verification system is constructed, and the performance verification report is generated through comparative analysis.

2. The method of claim 1, wherein, It comprises: Correlation analysis is performed on related data to generate a standardized input sequence; Based on the standardized input sequence, an equation is established to capture the memory characteristics of the system, and an adaptive fractional order grey predictor is generated; Using the adaptive fractional order grey predictor, set the prediction parameters, generate the basic prediction sequence through the data sequence; Based on the basic prediction sequence, a dynamic weight distribution mechanism is established to realize collaborative optimization and generate a multi-scale prediction result; The multi-scale prediction result is analyzed and corrected, and the system state prediction sequence is generated through comparative verification.

3. The method of claim 1, wherein, It comprises: An algorithm is used to analyze the prediction sequence, match the function, and generate a multi-scale decomposition framework; Based on the multi-scale decomposition framework, the system running state is decomposed into components, the optimization objective function is established, and a set of optimization sub-problems is generated; A dynamic coupling relationship is established, a multi-scale coordination model is constructed by designing the interaction mechanism between scales, and a scale coupling relationship matrix is generated; A double-layer optimization architecture is constructed to generate a collaborative optimization function system; The collaborative optimization function system is verified, an evaluation model and a test mechanism are constructed, and the optimization objective function framework is generated.

4. The method of claim 3, wherein, It comprises: Based on the optimization objective function, a calculation model is constructed to analyze the distribution characteristics of the objective function, and an initial optimization solution sequence is generated; Using the generated initial optimization solution sequence, an algorithm is designed to generate an optimization iterative search direction vector; Based on the generated optimization iterative search direction vector, a monitoring system is constructed to generate the dynamics characteristics; The dynamics characteristics are analyzed, and the optimization solution sequence is generated through adjustment; Based on the optimization solution sequence, it is converted into a control command to generate a control instruction sequence.

5. The method of claim 1, wherein, It comprises: Based on the generated control instruction sequence, it is distributed to each unit to generate a device control instruction set; Through the deployment of a sensor network, running parameters are collected to generate a real-time running state data set; The real-time running state data is feature extracted, and the system running feature sequence is generated by eliminating measurement noise and abnormal values; Based on the generated system running feature sequence, a three-dimensional efficiency evaluation model is constructed to generate a multi-dimensional efficiency evaluation result; The multi-dimensional efficiency evaluation result is compared with the target value to calculate the deviation degree, and a performance verification report is generated.

6. The method of Claim 5, wherein, Based on the data of the performance verification report, an analysis model is constructed to calculate the fractional order derivative influence degree, and a parameter dynamic sensitivity matrix is generated; Using the generated parameter dynamic sensitivity matrix, a correction strategy is designed, a coupling relationship is established, and an adaptive parameter adjustment vector sequence is generated; ​ Based on the adaptive parameter adjustment vector sequence, a spatial model is constructed, and by analyzing the stability boundary, a parameter safety correction domain constraint condition is generated; Combined with energy system operation data, an evaluation model is constructed, and indexes and optimization functions are introduced to generate a zero-carbon performance optimization vector; Based on the parameter safety correction domain constraint condition and the zero-carbon performance optimization vector, a decision mechanism is established to generate a parameter update strategy and a zero-carbon operation scheme.

7. The method of Claim 1, wherein, It also includes a fractional order sensitivity analysis and system abnormal state early warning and self-healing control mechanism, which is executed in parallel during system operation: Based on system operation response data, calculate fractional order sensitivity indicators and generate system vulnerability assessment reports; Using the system vulnerability assessment report, combined with data, a system dynamic evolution model is established to generate an early identification feature vector of abnormal state; Based on the early identification feature vector of abnormal state, adjust the algorithm to generate a hierarchical warning signal; According to the hierarchical warning signal, start the control strategy to generate device power regulation instructions and form a system self-healing control scheme; Evaluate the system self-healing control scheme, track and verify the trajectory, generate a control effect verification report, update the system protection strategy library, and complete the system rapid recovery and optimized operation.

8. A zero-carbon integrated energy system, characterized in that, It includes: A prediction module for constructing a multi-scale prediction model based on renewable energy output data, load demand data and environmental parameter data, generating a system state prediction sequence through historical dependence relationships; An optimization module for decomposing the system state prediction sequence into optimization sub-problems, establishing a collaborative optimization objective function system, and forming a multi-level optimization framework; A generation module for global solving based on the optimization objective function and combining the multi-level optimization framework, using global search capability to monitor and correct the optimization trajectory, and generating a control instruction sequence; An analysis module for distributing and executing according to the control instruction sequence, collecting operation response data, constructing an efficiency verification system, and generating a performance verification report through comparative analysis.

Images