CCUS system full life cycle optimization scheduling method for zero-carbon park

Abstract

The invention provides a zero-carbon park-oriented CCUS system full-life-cycle optimal scheduling method, and the method comprises the steps: constructing a standardized data management system and a multi-dimensional model through the whole-process data preparation, dynamic optimization, result application and a long-term iteration mechanism; and full-life-cycle accurate scheduling and parameter self-adaptive updating of the carbon capture, utilization and storage process are realized. According to the method, the coupling capability of the park CCUS system and the energy scheduling platform is enhanced, technical support is provided for real-time monitoring and optimization decision making of park carbon flow, and high efficiency and sustainability of a carbon capture and utilization system in long-term operation are ensured.

Description

Technical Field

[0001] This invention relates to the field of zero-carbon park scheduling technology, and in particular to a method for optimizing the scheduling of the CCUS system throughout its entire lifecycle for zero-carbon parks. Background Technology

[0002] With the deepening implementation of the "dual carbon" goals, zero-carbon industrial parks are gradually becoming important carriers for the green and low-carbon transformation of the industrial sector. Among them, carbon capture, utilization, and storage (CCUS) technology, as a core supporting means to achieve deep carbon reduction, has been extended from traditional single-point governance to an integrated carbon management system at the park level. This system covers key links such as energy production, industrial processes, carbon capture, transportation, storage, and resource reuse, forming a multi-objective optimization structure with emission reduction efficiency, economic feasibility, and system sustainability as its core.

[0003] Existing research largely focuses on optimizing individual stages, such as the selection of capture equipment, transportation route planning, and storage site matching. It also constructs carbon emission reduction scheduling frameworks based on static parameter modeling, with some schemes attempting to introduce renewable energy sources such as wind and solar power to improve the system's greenness. However, these methods generally suffer from problems such as static parameters, insufficient lifecycle modeling, and low cross-stage coupling, making it difficult to meet the collaborative operation needs of multiple energy sources and multiple emission entities in zero-carbon parks.

[0004] Specifically, traditional CCUS scheduling models often use fixed equipment lifecycle and storage capacity parameters, lacking a real-time tracking and updating mechanism for dynamic factors such as equipment degradation and storage capacity decay. This leads to lags or overshoots in system upgrade decisions, resulting in resource waste or deviations from emission reduction targets. Furthermore, the singular goal of minimizing cost ignores the synergistic balance between renewable energy absorption efficiency, carbon emission reduction constraints, and system stability, making it difficult to achieve dynamic optimization and decision support across the entire lifecycle and multiple dimensions. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] Therefore, the first objective of this invention is to propose a full lifecycle optimization scheduling method for CCUS systems in zero-carbon parks.

[0007] The second objective of this invention is to propose a CCUS system full lifecycle optimization scheduling device for zero-carbon parks.

[0008] The third objective of this invention is to provide an electronic device.

[0009] The fourth objective of this invention is to provide a computer-readable storage medium.

[0010] The fifth objective of this invention is to provide a computer program product.

[0011] To achieve the above objectives, the first aspect of this invention proposes a full lifecycle optimization scheduling method for CCUS systems in zero-carbon industrial parks, comprising: S1, constructing a matrix of remaining lifecycles of enterprise equipment and a storage site capacity decay model, and updating the enterprise's active status and the remaining capacity of the storage site in real time based on the physical lifecycle decay curve of equipment and the economic lifecycle changes brought about by industry technological progress; S2, dynamically calculating the remaining capacity of the storage site based on historical injection data and the current year's injection volume, and using it as a constraint parameter for subsequent annual optimization scheduling; S3, determining whether an enterprise has the feasibility for CCUS transformation based on a dynamic ratio threshold model of remaining lifecycle and investment payback period, and triggering a transformation decision when the ratio is greater than a preset threshold; S4, adopting a rolling iteration strategy of annual optimization + quarterly fine-tuning, updating system parameters and performing global optimization every year, and making local corrections every quarter based on actual operating data, forming a data closed-loop update mechanism.

[0012] In one embodiment of the present invention, the construction of the enterprise equipment remaining life cycle matrix and the storage site capacity decay model, based on the equipment physical life cycle decay curve and the economic life changes brought about by industry technological progress, and the real-time updating of the enterprise's active status and the remaining storage site capacity, further includes: S11, calculating the remaining physical life of the enterprise's equipment in the current year through the physical life cycle decay curve and dynamic depreciation rate model in the remaining life cycle matrix; S12, dynamically adjusting the economic life of the equipment using a technology replacement rate model in combination with the economic life changes brought about by industry technological progress, and updating the enterprise's active status for CCUS transformation based on the ratio of remaining physical life to economic life.

[0013] In one embodiment of the present invention, the step of dynamically calculating the remaining capacity of the storage site based on historical injection data and the current year's injection volume, and using it as a constraint parameter for subsequent year's optimized scheduling, further includes: using the storage site capacity decay formula to subtract the cumulative injection volume over the years from the initial storage potential to obtain the remaining receiving capacity of the storage site for the current year; dynamically adjusting the upper limit of injection of the storage site based on the remaining receiving capacity and the geological condition change parameters of the storage site, and using the upper limit as a capacity constraint parameter in the subsequent year's optimization model.

[0014] In one embodiment of the present invention, the dynamic ratio threshold model based on the remaining lifetime and payback period is used to determine whether an enterprise has the feasibility of CCUS retrofitting. When the ratio is greater than a preset threshold, a retrofitting decision is triggered. The method further includes: adaptively adjusting the dynamic ratio threshold according to the technological progress rate of the enterprise's industry to ensure that the retrofitting decision matches the industry development trend; when the ratio of remaining lifetime to payback period is less than the dynamic threshold, the system automatically selects short-term emission reduction measures instead of CCUS retrofitting to avoid wasting investment.

[0015] In one embodiment of the present invention, the method further includes: calculating the actual power supply distance between the enterprise and the wind and solar power plant based on the spherical distance formula, and using the distance as a constraint condition for the direct power supply path of wind and solar power, so as to optimize the power supply structure and reduce transmission losses.

[0016] To achieve the above objectives, a second aspect of this invention proposes a CCUS system full lifecycle optimization scheduling device for zero-carbon industrial parks, comprising: an equipment lifecycle modeling module, used to construct a matrix of the remaining lifecycle of enterprise equipment and a storage site capacity decay model, and to update the enterprise's active status and the remaining capacity of the storage site in real time based on the physical lifecycle decay curve of equipment and the economic lifecycle changes brought about by industry technological progress; a storage capacity calculation module, used to dynamically calculate the remaining capacity of the storage site based on historical injection data and the current year's injection volume, and use it as a constraint parameter for subsequent annual optimization scheduling; a modification feasibility judgment module, used to determine whether an enterprise has the feasibility for CCUS modification based on a dynamic ratio threshold model of remaining lifecycle and investment payback period, and to trigger a modification decision when the ratio is greater than a preset threshold; and a rolling optimization scheduling module, used to adopt a rolling iteration strategy of annual optimization + quarterly fine-tuning, to update system parameters and perform global optimization every year, and to make local corrections every quarter based on actual operating data, forming a data closed-loop update mechanism.

[0017] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0018] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0019] To achieve the above objectives, a fifth aspect of the present invention provides a computer program product that, when executed by a processor, implements the method described in any one of the first aspects.

[0020] The technical solutions provided by the embodiments of the present invention bring at least the following beneficial effects: (1) This invention innovatively establishes a coupled decision-making model between the remaining life of enterprise equipment and the payback period of CCUS retrofit investment. By tracking the physical life decay curve of equipment and the changes in economic life brought about by technological progress in real time, the timing window for retrofit is accurately identified. This technology introduces variables such as dynamic depreciation rate and technology substitution rate into the optimization constraints for the first time, forming a three-dimensional decision space: when the ratio of remaining life to payback period is greater than a dynamic threshold (which is adaptively adjusted with the speed of technological progress in the industry), a retrofit suggestion is triggered; otherwise, the status quo is maintained or short-term emission reduction measures are adopted.

[0021] (2) Breaking through the limitations of traditional CCUS systems that only consider a single energy supply, a spatiotemporal coupling model of capture energy consumption and wind and solar power is constructed. This model integrates three core algorithms: a wind and solar resource prediction algorithm based on spatiotemporal distribution, a multi-source power supply optimization algorithm under power flow constraints, and a carbon capture load flexible adjustment algorithm (responding to wind and solar power output fluctuations).

[0022] (3) Establish a cost accounting and optimization system covering the entire life cycle of CCUS to achieve precise cost control in the three stages. In the capture stage, establish a dynamic cost model based on the learning curve theory to quantify the impact of technological progress on equipment investment; in the transportation stage, develop a pipeline-flow-cost nonlinear optimization algorithm to solve the defect of traditional models that ignore the impact of pipeline changes on costs; in the storage stage, innovate a dynamic evaluation model for oil displacement revenue, comprehensively considering oil price fluctuations, CO2 price linkage and geological condition changes.

[0023] (4) Breaking through the limitation of the single constraint of traditional models, a complete system of four-dimensional constraints including material flow, energy flow, cost flow, and time flow is constructed. This system includes 12 types of core constraints: from basic mass conservation and emission source capture constraints to innovative enterprise life cycle constraints and power grid security constraints. Through a hierarchical constraint processing mechanism, rigid constraints are given priority and flexible constraints are dynamically optimized.

[0024] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1This is a flowchart of a method for full lifecycle optimization scheduling of a CCUS system for zero-carbon parks according to an embodiment of the present invention; Figure 2 This is an example diagram of a carbon dioxide flow scheme for 2030 according to an embodiment of the present invention; Figure 3 This is an example diagram of a carbon dioxide flow scheme in 2055 according to an embodiment of the present invention; Figure 4 This is an example diagram illustrating the 2030 sealing site situation according to an embodiment of the present invention; Figure 5 This is an example diagram illustrating the sealing location in 2060 according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a CCUS system full life cycle optimization scheduling device for zero-carbon parks according to an embodiment of the present invention. Detailed Implementation

[0026] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0027] Figure 1 This is a flowchart of a CCUS system full lifecycle optimization scheduling method for zero-carbon parks according to an embodiment of the present invention.

[0028] like Figure 1 As shown, the CCUS system full lifecycle optimization scheduling method for zero-carbon parks includes the following steps: S1 constructs a matrix of the remaining life cycle of enterprise equipment and a model of storage site capacity decay. Based on the physical life decay curve of equipment and the changes in economic life brought about by industry technological progress, it updates the enterprise's active status and the remaining acceptance capacity of the storage site in real time.

[0029] Specifically, the core of this step lies in constructing a remaining lifecycle matrix for enterprise equipment and a storage site capacity decay model to achieve dynamic updates of enterprise activity status and storage site acceptance capacity within the CCUS system. At the technical implementation level, this step integrates the physical lifespan decay curve of equipment with changes in economic lifespan brought about by industry technological advancements, employing a multi-dimensional time series modeling method to construct an enterprise lifecycle tracking matrix. Each element in the matrix represents the remaining physical lifespan (in years) and economic lifespan (considering factors such as technology substitution rate and investment payback period) of a certain type of equipment in a particular enterprise. By setting dynamic depreciation rates (e.g., an average annual depreciation rate of 5%-10%) and technology substitution rates (e.g., an average annual decrease of 2%-5%), intelligent identification of equipment upgrade timing is achieved. Simultaneously, the storage site capacity decay model, based on historical injection volume data, employs a modeling method combining geomechanics and fluid dynamics. It dynamically calculates the remaining acceptance capacity by using the ratio of cumulative injection volume to the maximum storage potential (e.g., setting the storage potential threshold to 90%), ensuring that storage scheduling does not exceed geological safety boundaries.

[0030] At the parameter level, the enterprise's remaining lifecycle matrix needs to include key fields such as equipment type, installation year, average annual operating hours, and technology learning rate (e.g., 0.1-0.3). The storage site model requires input parameters such as the storage site's maximum injection capacity (unit: million tons / year), cumulative injection volume (unit: million tons), and injection pressure change rate (unit: MPa / million tons). The model update frequency is set to once a year, supplemented by a quarterly fine-tuning mechanism to cope with sudden equipment decommissioning or abnormal changes in storage sites.

[0031] In practical applications, this step is suitable for long-term carbon emission scheduling planning in industrial settings such as coal-fired power plants and steel mills. By updating the enterprise activity status and storage capacity in real time, the system can dynamically adjust capture, transport, and storage strategies, avoiding scheduling failures caused by equipment decommissioning or storage site saturation. Its technical value lies in significantly improving the dynamic adaptability and scheduling accuracy of the CCUS system, providing a scientific basis for achieving carbon emission reduction targets and optimizing resource allocation.

[0032] Furthermore, S1 includes: S11, using the physical life decay curve in the remaining life matrix and the dynamic depreciation rate model, calculates the remaining physical life of the enterprise's equipment in the current year. Specifically, this step calculates the remaining physical lifespan of the enterprise's equipment in the current year using the physical lifespan decay curve in the remaining lifespan matrix and the dynamic depreciation rate model. This is a crucial step in the dynamic optimization scheduling model of this invention to achieve coordinated optimization of equipment modification decisions and system scheduling. In some implementations, this step first constructs a physical lifespan decay model for the enterprise's equipment based on the initial service life, historical operating data, and industry standard lifespan curves. This model typically uses an exponential decay function or a Weibull lifespan distribution function to quantify the degradation trend of equipment performance over time.

[0033] Furthermore, the system introduces a dynamic depreciation rate model to reflect the changes in the economic value of equipment at different stages of its service life. This model combines accounting standards (such as Accounting Standard No. 6 – Intangible Assets) with industry equipment depreciation standards (such as the average depreciation period for power equipment being 15-25 years), employing accelerated depreciation or straight-line depreciation methods to dynamically calculate the remaining economic life of the equipment. In this invention, the remaining physical life and economic life of the equipment together constitute a three-dimensional decision space used to determine whether CCUS (Consumer-Centered System Upgrade) should be implemented. When the ratio of the remaining physical life to the payback period exceeds a dynamic threshold (e.g., 1.2-1.5, which adaptively adjusts with the pace of technological advancement in the industry), the system will trigger an upgrade recommendation.

[0034] In practical applications, this step requires combining information such as equipment service life, industry average lifespan, and historical maintenance records from the enterprise's data tables, and updating it annually through a data loading module and lifecycle tracking algorithm. Its technical benefits lie in accurately identifying the remaining lifespan of equipment, avoiding ineffective investments when equipment is nearing retirement, and ensuring that CCUS resources are rationally allocated while equipment still has upgrade potential, thereby improving the economy and sustainability of system scheduling.

[0035] S12, taking into account the changes in economic life brought about by industry technological progress, adopts a technology replacement rate model to dynamically adjust the economic life of equipment, and updates the enterprise's active status for CCUS transformation based on the ratio of remaining physical life to economic life.

[0036] Specifically, this step aims to dynamically adjust the economic life of equipment using a technology substitution rate model, and determine whether an enterprise is actively engaged in CCUS (Computer-Oriented System) upgrades based on the ratio of remaining physical life to economic life. This method integrates equipment lifecycle management with the impact of technological progress on economic life, thereby improving the dynamic adaptability of CCUS system scheduling and the scientific nature of investment decisions.

[0037] At the technical implementation level, the system first constructs a remaining physical life matrix for enterprise equipment. This matrix is ​​fitted using a Weibull life distribution model based on parameters such as the equipment's initial design life, years of operation, maintenance records, and failure rate, to quantify the remaining physical life of the equipment in different years. Simultaneously, a technology replacement rate model is introduced. This model is based on industry technology progress curves (such as learning curve theory or empirical curve models) and sets a technology replacement rate parameter (such as an average annual decline rate of 5%-15%) to reflect the trend of shortening or extending the economic life of equipment due to technological iteration. In some implementations, the economic life can be calculated using a dynamic depreciation model, combining equipment upgrade costs, operation and maintenance costs, and carbon emission reduction benefits, using net present value (NPV) as a benchmark to determine the optimal retirement time for equipment at different technological stages.

[0038] Regarding parameters, the system sets key threshold parameters, such as the "remaining physical life / economic life ratio threshold," typically set between 1.2 and 1.5. When this ratio is higher than the threshold, it indicates that although the equipment still has a physical life, its economic life is approaching or exceeding its remaining years, making retrofitting economically feasible; conversely, retrofitting is not recommended if the ratio is lower. Furthermore, the model also needs to consider industry-specific CCUS investment payback periods (e.g., 5-10 years), as well as external economic variables such as carbon prices, electricity prices, and equipment depreciation rates, to enhance the realistic adaptability of the decision-making process.

[0039] In application scenarios, this step is suitable for high-carbon emission industries such as coal-fired power plants, steel mills, and cement plants, and is used as part of the enterprise screening mechanism in annual optimized scheduling. The system dynamically identifies enterprises with CCUS (Carbon Capacity for US Renewal) upgrade potential by updating equipment status in real time, thereby optimizing resource allocation and investment priorities and avoiding ineffective investment in equipment that is about to be decommissioned.

[0040] The technical effect of this step is that by introducing a dynamic economic life assessment mechanism, it effectively improves the timeliness and economy of CCUS retrofit decisions, enhances the system's responsiveness to changes in equipment lifecycle, and provides key support for achieving long-term carbon emission reduction targets and optimal resource allocation.

[0041] S2 dynamically calculates the remaining capacity of the storage site based on historical injection data and the current year's injection volume, and uses it as a constraint parameter for subsequent year's optimized scheduling.

[0042] Specifically, the step of "dynamically calculating the remaining capacity of the storage site based on historical injection data and the current year's injection volume, and using it as a constraint parameter for subsequent year's optimized scheduling" is a crucial technical step in achieving long-term adaptability and system stability in the CCUS dynamic optimization scheduling model of this invention. Its core lies in reflecting the available capacity status of the storage site in real time through the accumulation and updating of time-series data, thereby providing dynamic constraints for subsequent year's carbon capture and storage scheduling, ensuring that storage operations are carried out within the scope of geological safety and economic feasibility.

[0043] At the technical implementation level, the system first establishes an initial injection potential database for the storage sites. This potential value is typically derived from geological modeling and reservoir assessment, and is expressed in millions of tons of carbon dioxide (MtCO2). It is then standardized according to the IPCC Guidelines for Carbon Sequestration and the Technical Specifications for CO2 Geological Sequestration. Subsequently, the system reads historical annual injection volume records through the data loading module, combines them with the actual injection volume from the current year's optimization results, and calculates the cumulative total injection volume using a summation method.

[0044] At the parameter level, the system needs to set an upper limit threshold for injection at the storage site (e.g., 80% of the initial capacity) to avoid geological risks caused by excessive injection. Simultaneously, the model incorporates injection rate constraints (e.g., annual injection volume not exceeding 15% of the storage site's maximum throughput capacity) to ensure the controllability and safety of the storage process. Furthermore, the remaining capacity is updated annually, matching the rolling mechanism of "annual optimization + quarterly fine-tuning" to support medium- and long-term scheduling decisions.

[0045] In application scenarios, this step is widely used in the planning and operation management of CCUS systems for industrial carbon emission sources such as coal-fired power plants and steel mills. For example, during the optimization period from 2030 to 2060, the system adjusts its carbon sequestration strategy annually based on the remaining capacity of the sequestration sites, prioritizing carbon injection at sites with sufficient remaining potential and low transportation costs, thereby improving the overall economic efficiency and sustainability of the system.

[0046] The technical value of this step lies in its ability to effectively avoid system scheduling failures caused by premature saturation of storage sites through a dynamic capacity management mechanism. At the same time, it provides real-time and accurate constraint boundaries for the optimization model, improves the model's response to storage site capacity decay, and enhances the robustness and adaptability of the CCUS system in long-term operation.

[0047] Furthermore, S2 includes: S21 uses the storage site capacity decay formula to subtract the cumulative injection amount over the years from the initial storage potential to obtain the remaining receiving capacity of the storage site for the current year.

[0048] Specifically, this step employs a landfill capacity decay formula. By subtracting the cumulative injection volume over the years from the initial landfill potential, the remaining capacity of the landfill for the current year is calculated. This is one of the core components of the dynamic optimization scheduling model of this invention for dynamically tracking landfill capacity. Based on geological landfill theory and carbon sequestration engineering practice, and combining historical injection data with the maximum landfill capacity parameter, this step constructs a landfill status assessment mechanism that can be updated in real time.

[0049] At the technical implementation level, this step first retrieves the initial storage potential from the site parameter table, typically in millions of tons of carbon dioxide. This potential is determined based on parameters such as geological structure assessment, porosity, permeability, and caprock sealing, using internationally accepted IPCC or IEA-recommended storage capacity estimation methods. Subsequently, the system calls the historical injection volume record module to extract the cumulative injection volume from 2030 to the current year. This data originates from the injection logs of the transportation and storage subsystem and features a dual verification mechanism of timestamps and injection volume to ensure data accuracy.

[0050] At the application level, this step is periodically invoked during annual optimization scheduling to update the status of storage sites, providing real-time capacity data for subsequent source-sink matching, transportation route planning, and injection allocation. Especially in regionalized CCUS systems with a large number of storage sites and uneven injection distribution, this step can effectively avoid scheduling failures or waste of storage site resources due to capacity misjudgment.

[0051] In terms of technical effectiveness, this step, by introducing a dynamic capacity decay mechanism, significantly improves the model's adaptability to the long-term operating status of the storage site, solving the problem of fixed storage site capacity and inability to respond to historical changes in traditional static models. By combining dynamic parameters of enterprise lifecycle and wind and solar power consumption, closed-loop optimization of the CCUS system in the time dimension is achieved, providing key support for achieving both carbon emission reduction targets and resource utilization efficiency.

[0052] S22. Based on the remaining receiving capacity and the changing parameters of the geological conditions of the storage site, the upper limit of injection into the storage site is dynamically adjusted, and this upper limit is used as the capacity constraint parameter in the subsequent annual optimization model.

[0053] Specifically, the core of this step lies in dynamically adjusting the injection ceiling of the storage site based on its remaining capacity and geological condition changes, and using this ceiling as a capacity constraint parameter in the subsequent annual optimization model. This process is a crucial link in the dynamic scheduling model of the CCUS system, directly affecting the feasibility and economics of carbon sequestration.

[0054] At the technical implementation level, the adjustment of the injection limit of the storage site depends on two core inputs: one is the remaining storage capacity of the storage site, which is usually calculated by geological modeling and the cumulative historical injection volume.

[0055] At the application level, this step is suitable for industrial carbon emission reduction systems that coordinate the scheduling of multiple storage sites, especially in the context of declining storage site capacity year by year and dynamic changes in enterprise emission sources. The system automatically updates the injection limit of each storage site annually based on the latest geological monitoring data and historical injection records, ensuring that the model does not exceed its physical carrying capacity during subsequent optimizations, thereby avoiding storage failure or geological risks.

[0056] At the technical level, this step, by introducing a dynamic capacity constraint mechanism, significantly improves the adaptability and robustness of the CCUS system scheduling model. It effectively solves the problem of fixed storage site capacity parameters in traditional models, which cannot respond to geological changes, making the optimization results closer to actual engineering operating conditions and ensuring the long-term feasibility of carbon sequestration and the overall economic efficiency of the system. Furthermore, this mechanism provides crucial support for the rolling scheduling strategy of "annual optimization + quarterly fine-tuning," ensuring continuous optimization and precise control of the model during long-term operation.

[0057] S3, based on a dynamic ratio threshold model of remaining useful life and payback period, determines whether a company has the feasibility for CCUS retrofitting. When the ratio exceeds a preset threshold, a retrofitting decision is triggered. Specifically, this step, based on a dynamic ratio threshold model of the remaining lifespan of enterprise equipment and the payback period of CCUS retrofit investment, determines whether the enterprise has the feasibility for CCUS retrofitting. This is one of the key decision-making mechanisms in the dynamic optimization scheduling model of this invention. Its technical implementation principle integrates equipment lifecycle management and investment economics assessment, and by constructing a ratio index, it achieves accurate identification and dynamic response to the timing of retrofitting.

[0058] At the technical implementation level, the model first obtains the remaining lifespan of the enterprise's equipment, typically estimated in years based on equipment aging curves, historical operating data, and predictive maintenance models. Simultaneously, it calculates the payback period for CCUS retrofitting, which is derived by dividing the initial investment cost (including capture equipment, transportation pipelines, control systems, etc.) by the annual net revenue (such as carbon tax exemptions, oil recovery revenue, emission reduction subsidies, etc.). The model incorporates dynamic depreciation rate and technology replacement rate to reflect changes in economic lifespan due to equipment aging and technological advancements. When this ratio exceeds a preset threshold (e.g., 1.2), the system determines that the enterprise has the feasibility for CCUS retrofitting and triggers a retrofit decision.

[0059] Regarding parameter indicators, the calculation of RUL needs to combine industry standards (such as API RP 581 equipment reliability assessment specifications) and equipment health status monitoring data, and is usually predicted using Weibull distribution or exponential decay models. The calculation of PP needs to consider the life cycle cost (LCOE) and revenue model, where capture energy consumption costs, transportation costs (unit distance cost of 0.05~0.15 yuan / ton·km), and storage costs (including geological risk assessment and injection costs) must all be included in the cost function. The revenue component includes oil displacement revenue (calculated based on oil price and CO2 displacement efficiency, such as each ton of CO2 increasing crude oil production by 0.1~0.3 tons) and carbon trading revenue (calculated based on carbon price and capture volume).

[0060] In application scenarios, this model is suitable for high-carbon emission industries such as coal-fired power plants, steel mills, and cement plants. It is particularly effective when equipment is nearing retirement or a technology upgrade cycle is approaching, preventing resource waste and emission reduction delays caused by premature or delayed upgrades. Through a dynamic ratio mechanism, the system can respond in real time to changes in equipment lifespan and fluctuations in the investment environment, enhancing the scientific rigor and foresight of decision-making.

[0061] In terms of technical effectiveness, this step enables dynamic and economically driven CCUS retrofit decisions, effectively balancing short-term costs and long-term emission reduction benefits. Compared to traditional static assessment methods, the introduction of a three-dimensional decision space (remaining lifespan, payback period, and technology substitution rate) significantly enhances the model's adaptability to complex industrial systems, providing crucial support for building a low-carbon industrial system.

[0062] Furthermore, S3 includes: S31, based on the rate of technological progress in the industry in which the enterprise operates, adaptively adjusts the dynamic ratio threshold to ensure that transformation decisions match industry development trends.

[0063] Specifically, the step of "adaptively adjusting the dynamic ratio threshold according to the rate of technological progress in the industry to ensure that the transformation decision matches the industry development trend" is one of the key decision-making mechanisms in the CCUS dynamic optimization scheduling method of this invention. Its technical implementation is based on a coupled analysis model of the remaining life cycle of enterprise equipment and the payback period of CCUS transformation investment. This model constructs a three-dimensional decision space by introducing parameters such as dynamic depreciation rate, technology substitution rate and industry technological progress rate, thereby achieving accurate identification and dynamic control of transformation timing.

[0064] At the technical implementation level, the system first extracts the technology progress curves of various industries (such as power, steel, and cement) from the industry database, typically using a learning curve model. The industry's technology progress rate is quantified by the average annual decline rate of unit cost, a parameter derived from industry expert predictions or historical data fitting. The system dynamically adjusts the threshold of the ratio of a company's remaining life to its investment payback period based on the Accumulated Cost Reduction (ACR), which can be linear or exponential, ensuring that transformation decisions are triggered earlier in industries with faster technological progress.

[0065] Regarding the parameters, the system sets the initial value of the ratio threshold to 1.2, indicating that when the ratio of a company's remaining lifespan to its payback period is greater than this value, it is economically viable for transformation. This threshold is updated annually based on the industry's Average Life Rate (ACR). For example, in industries with an ACR of 5%, the threshold decreases by 0.05 annually; in industries with an ACR of 10%, it decreases by 0.10. Simultaneously, the system incorporates a remaining lifespan matrix and a payback period matrix, and by calculating the ratio in real time, determines whether a company has entered a transformation window.

[0066] In application scenarios, this step is widely applicable to the deployment decisions of CCUS systems in various industries, especially in industries with short technology iteration cycles and frequent equipment updates (such as power and chemical industries). It can effectively avoid redundant investment or inappropriate timing of retrofits due to technological lag. For example, in regions with abundant wind and solar resources and rapid development of CCUS technology, the system can identify enterprises with retrofit potential in advance and guide them to prioritize access to the CCUS system, thereby improving overall emission reduction efficiency and economics.

[0067] The technical advantage of this step lies in the fact that, through adaptive adjustment of the dynamic ratio threshold, the system can respond in real time to cost changes brought about by technological advancements in the industry, achieving a balance between forward-looking and economical transformation decisions. Compared to traditional static threshold methods, this invention significantly improves the dynamic adaptability and scientific rigor of the scheduling model, providing solid support for the long-term optimized operation of the CCUS system.

[0068] S32, when the ratio of remaining lifespan to investment payback period is less than the dynamic threshold, the system automatically selects short-term emission reduction measures instead of CCUS retrofitting to avoid wasting investment.

[0069] Specifically, when the system assesses whether a company should undertake CCUS retrofitting, if the ratio of its remaining equipment lifespan to the CCUS investment payback period is less than a dynamic threshold, the system will automatically select short-term emission reduction measures instead of implementing CCUS retrofitting to avoid ineffective investment due to the impending retirement of equipment. This step is one of the key decision-making mechanisms in the dynamic optimization scheduling model of this invention, reflecting a comprehensive consideration of equipment lifecycle and economic feasibility.

[0070] At the technical implementation level, the system first obtains the remaining lifespan of equipment in real time through an enterprise lifecycle tracking matrix. This lifespan is dynamically predicted based on equipment aging models, historical operating data, and industry standards (such as IEC 60034-1's method for assessing motor lifespan). Simultaneously, the system calculates the payback period for CCUS retrofitting based on current CCUS technical parameters (such as capture efficiency, energy consumption, and construction costs) and industry technology learning curves. This payback period is typically measured in years and considers economic factors such as initial investment, operating costs, carbon price benefits, and potential oil recovery benefits. The system calculates the ratio of RUL to PP; if this ratio is less than a set dynamic threshold, the CCUS retrofitting is deemed economically unfeasible.

[0071] The dynamic threshold TR is not a fixed value, but rather an adaptive adjustment based on factors such as the speed of technological progress in the industry, carbon price fluctuations, and policy guidance. For example, in industries with high technological maturity and stable carbon prices (such as the cement industry), TR can be set to 0.8; while in industries where the technology is still in its early stages of development and carbon prices fluctuate greatly (such as the steel industry), TR can be set to 1.2, and dynamically adjusted through Monte Carlo simulation or sensitivity analysis.

[0072] In practical applications, this mechanism is suitable for industrial scenarios such as coal-fired power plants and steel mills, where equipment replacement cycles are short and investment recovery risks are high. When a company has only 5 years of remaining lifespan, while the investment recovery period for CCUS retrofitting is 7 years, the system will automatically exclude the company from CCUS retrofitting and instead recommend emission reduction measures with lower carbon capture efficiency but shorter investment cycles (such as combustion optimization and carbon tax deductions), thereby avoiding resource waste while ensuring emission reduction targets are met.

[0073] The technical value of this step lies in its ability to effectively improve the economy and dynamic adaptability of CCUS system scheduling by introducing a coupled decision-making model that integrates equipment remaining lifespan and investment payback period. Its innovation lies in incorporating physical lifespan and economic lifespan into a unified optimization framework, providing a scientific and executable decision-making basis for long-term scheduling, and significantly enhancing the model's practicality and foresight in complex industrial environments.

[0074] S4 adopts a rolling iteration strategy of annual optimization and quarterly fine-tuning. It updates system parameters and performs global optimization every year, and makes local corrections based on actual operating data every quarter, forming a closed-loop data update mechanism.

[0075] Specifically, the rolling iteration strategy of "annual optimization + quarterly fine-tuning" in this invention is the core mechanism for realizing long-term dynamic scheduling and closed-loop data updates of the CCUS system. This strategy ensures that the model can continuously output scientific and feasible scheduling solutions when facing dynamic factors such as enterprise equipment retirement, storage capacity decay, and wind and solar power fluctuations through a phased and granular optimization process.

[0076] At the technical implementation level, this strategy is based on the periodic solution and parameter update mechanism of a linear programming model. Every December, the system filters active status based on the remaining lifecycle matrix of enterprises (including parameters such as equipment physical life, economic life, and technology replacement cycle), eliminating enterprises with remaining lifecycles shorter than the investment payback period to prevent non-economical modification decisions. Simultaneously, the system calls the dynamic update formula for the storage capacity to ensure that storage scheduling does not exceed the geological safety threshold. During the annual optimization process, the system reloads the latest wind and solar resource forecast data, grid power supply capacity, enterprise emissions, and other parameters, performs global optimization, and outputs the optimal scheduling plan for the year.

[0077] During the quarterly fine-tuning phase, the system collects actual operational data every quarter (e.g., March, June, September, and December), including newly installed wind and solar power capacity, early retirement status of enterprises, actual capture volume, and transportation routes. Through the data fusion module, the actual data is compared with the prediction model to identify sources of deviation and to locally correct key parameters in the model (such as wind and solar power output coefficients, transportation economic distance thresholds, and injection rates at storage sites). During fine-tuning, the system employs an incremental parameter update strategy, remodeling and solving only variables that have changed significantly to improve computational efficiency.

[0078] In practical applications, this strategy can be deployed on provincial or regional CCUS scheduling platforms, supporting the collaborative optimization of thousands of industrial emission sources, hundreds of storage sites, and wind and solar power plants. Its technical value lies in two aspects: firstly, ensuring the system's economic viability and feasibility in long-term planning through annual global optimization; and secondly, enabling rapid response to short-term disturbances through a quarterly fine-tuning mechanism, enhancing the model's real-time adaptability. Combined with dynamic parameter tracking and closed-loop feedback mechanisms, this strategy significantly enhances the robustness and sustainability of the CCUS system, providing solid technical support for deep industrial decarbonization.

[0079] The CCUS system full life cycle optimization scheduling method for zero-carbon parks in this invention effectively improves the dynamic collaborative optimization capability of each link of the CCUS system, and achieves comprehensive cost reduction and maximizes the wind and solar power consumption ratio under the rigid constraint of emission reduction targets.

[0080] In this embodiment of the invention, the objective function and constraints are as follows:

[0081] Capture cost function:

[0082] Transportation cost function:

[0083] Storage cost and benefit function:

[0084]

[0085] Electricity cost function:

[0086] Constraints: 1. Mass conservation constraint:

[0087]

[0088] 2. Capture quantity constraint:

[0089] 3. Storage capacity constraints:

[0090]

[0091] 4. Electricity demand balance constraints:

[0092] 5. Direct power supply distance constraint:

[0093] 6. Capacity constraints of wind and solar power plants:

[0094] 7. Remaining life cycle constraint of the enterprise:

[0095] 8. Industry emission reduction target constraints:

[0096] 9. Decision variable type constraints:

[0097]

[0098] See Table 1 for symbol explanations.

[0099] Table 1: Symbols and Meanings

[0100] Furthermore, embodiments of the present invention also calculate the actual power supply distance between the enterprise and the wind and solar power plant based on the spherical distance formula, and use this distance as a constraint condition for the direct power supply path of wind and solar power, so as to optimize the power supply structure and reduce transmission losses.

[0101] Specifically, this step calculates the actual power supply distance between the enterprise and the wind and solar power plant based on the spherical distance formula, and uses this distance as a constraint condition for the direct power supply path of wind and solar power, thereby optimizing the power supply structure and reducing transmission losses. This technical implementation method integrates Geographic Information System (GIS) and power system optimization modeling, and is a key link in the spatiotemporal coupling modeling of wind and solar power and carbon capture system in this invention.

[0102] At the technical implementation level, the system first acquires the latitude and longitude coordinates of the enterprise and the wind and solar power plant, and uses the spherical distance formula for precise calculation. In this way, the system can calculate the straight-line distance between any enterprise and the wind and solar power plant, which serves as the input parameter for subsequent power path planning.

[0103] At the parameter level, the system sets a maximum economic power supply distance threshold (e.g., 500 km). Direct power supply paths from wind and solar power exceeding this distance will be excluded to avoid impacting economic efficiency due to excessive transmission losses. Simultaneously, the model introduces a unit-distance transmission loss coefficient (e.g., 0.005% / km) to quantify energy losses during transmission. Furthermore, parameters such as the installed capacity of wind and solar power plants and the power demand of enterprise capture equipment (e.g., 100 MW·h / year) are also included in the constraints to ensure that power supply capacity matches demand.

[0104] At the application level, this step is widely used in the optimization of CCUS systems in industrial enterprises such as coal-fired power plants and steel mills. For example, in the scenario of abundant wind and solar resources but concentrated industrial loads in Northwest China, the system uses distance constraints to screen out wind and solar power stations that can directly supply electricity, prioritizes matching low-loss paths, improves the utilization rate of renewable energy, and reduces dependence on coal power.

[0105] The technical benefits of this step lie in its ability to effectively reduce transmission losses and improve the economics and feasibility of wind and solar power by introducing geographical constraints. Simultaneously, it provides crucial inputs for the coordinated optimization of power consumption and carbon capture, enhancing the model's engineering adaptability and decision-making rigor, and serving as a vital technical support for realizing multi-energy coordinated dispatch in the CCUS system.

[0106] The CCUS system full life cycle optimization scheduling method for zero-carbon parks in this invention, while achieving comprehensive cost reduction and maximizing the wind and solar power consumption ratio under the rigid constraint of emission reduction targets, further optimizes the configuration of direct wind and solar power supply paths by introducing power supply distance constraints based on the spherical distance formula, thereby improving the rationality of the power supply structure and transmission efficiency, effectively reducing transmission losses and enhancing the collaborative operation capability of the CCUS system and renewable energy.

[0107] To facilitate the implementation of this case study, some data is provided for testing and demonstration. The proposed data is shown in Tables 2 to 5: Table 2 Global Parameters

[0108] Table 3 Example of data on sealed locations

[0109] Table 4 Example of Carbon Source Data

[0110] Table 5 Industry Parameter Settings

[0111] Once standardized data is prepared, the program can be started. The calculation results are divided into five core modules according to function, and iterative calculations are performed within a set period from 2030 to 2060. The first part is the total annual capture volume, which visually presents the total carbon emission reductions generated by the CCUS project in that year; the second part is the carbon dioxide flow scheme, which uses a visual map to show the spatial distribution path of CO2 from enterprises to storage sites. Comparing the schemes of different years can clearly reflect the process of enterprises dynamically joining or leaving CCUS projects due to changes in their life cycle, such as... Figure 2 Figure 3 The comparison shows that the third part is the wind and solar power consumption scheme, which quantifies the green electricity supply from each wind and solar power plant to heavy industrial enterprises, demonstrating the energy-saving and emission-reduction benefits of clean energy replacing traditional coal power; the fourth part is the storage site status tracking, which records the dynamic changes in the storage volume of each storage site in real time during iterative optimization. Taking the comparison of the storage volume in 2030 and 2060 as an example, the capacity decay trend can be intuitively presented, such as... Figure 4 Figure 5 As shown in the comparison, Part Five represents the total annual cost of the entire process, comprehensively reflecting the optimized costs of capture, transportation, storage, and power supply. These five modules form a data loop through annual iterative calculations, fully revealing the dynamic evolution of the CCUS system during long-term operation.

[0112] To achieve the above embodiments, the present invention also proposes a CCUS system full life cycle optimization scheduling device for zero-carbon parks. Figure 6This is a schematic diagram of a CCUS system lifecycle optimization scheduling device for zero-carbon industrial parks, provided as an embodiment of the present invention. Figure 6 As shown, the device includes: The Equipment Lifecycle Modeling Module 100 is used to construct the remaining lifecycle matrix of enterprise equipment and the storage site capacity decay model. Based on the physical lifecycle decay curve of equipment and the economic lifecycle changes brought about by industry technological progress, it updates the enterprise's active status and the remaining acceptance capacity of the storage site in real time. The sealed capacity calculation module 200 is used to dynamically calculate the remaining capacity of the sealed site based on historical injection data and the current year's injection volume, and use it as a constraint parameter for subsequent year's optimized scheduling. The 300 module for assessing the feasibility of retrofitting is used to determine whether an enterprise has the feasibility for CCUS retrofitting based on a dynamic ratio threshold model of remaining useful life and investment payback period. When the ratio is greater than a preset threshold, a retrofitting decision is triggered. The rolling optimization scheduling module 400 is used to adopt a rolling iteration strategy of annual optimization + quarterly fine-tuning. It updates system parameters and performs global optimization every year, and makes local corrections based on actual operating data every quarter, forming a data closed-loop update mechanism.

[0113] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0114] To implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0115] To implement the above embodiments, the present invention also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0116] To implement the above embodiments, the present invention also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0117] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0118] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0119] This invention is intended to provide implementation schemes for users to selectively prevent the use or access to personal information data. That is, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0120] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0121] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0122] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0124] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0126] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0127] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0128] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0129] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for full lifecycle optimization scheduling of CCUS systems for zero-carbon industrial parks, characterized in that, include: Construct a matrix of remaining lifecycle of enterprise equipment and a model of storage site capacity decay. Based on the physical life decay curve of equipment and the changes in economic life brought about by industry technological progress, update the enterprise's active status and the remaining acceptance capacity of storage sites in real time. Based on historical injection data and the current year's injection volume, the remaining capacity of the sealed land is dynamically calculated and used as a constraint parameter for subsequent year's optimized scheduling. Based on a dynamic ratio threshold model of remaining useful life and payback period, the feasibility of CCUS transformation for enterprises is determined. When the ratio is greater than a preset threshold, a transformation decision is triggered. We adopt a rolling iteration strategy of annual optimization and quarterly fine-tuning. The system parameters are updated and global optimization is performed every year, and local corrections are made every quarter based on actual operating data, forming a closed-loop data update mechanism.

2. The method as described in claim 1, characterized in that, The construction of the enterprise equipment remaining lifecycle matrix and the storage site capacity decay model, based on the equipment physical lifecycle decay curve and the economic lifecycle changes brought about by industry technological progress, also includes real-time updates of enterprise activity status and remaining storage site capacity: The remaining physical life of the enterprise's equipment in the current year is calculated using the physical life decay curve in the remaining life matrix and the dynamic depreciation rate model. By combining the changes in economic life brought about by industry technological progress, the economic life of equipment is dynamically adjusted using a technology substitution rate model, and the enterprise's active status for CCUS transformation is updated based on the ratio of remaining physical life to economic life.

3. The method as described in claim 1, characterized in that, The method of dynamically calculating the remaining capacity of the storage site based on historical injection data and the current year's injection volume, and using this as a constraint parameter for subsequent year-on-year optimized scheduling, also includes: The remaining receiving capacity of the storage site in the current year is obtained by subtracting the cumulative injection amount over the years from the initial storage potential using the storage site capacity decay formula. Based on the remaining receiving capacity and the changing parameters of the geological conditions of the storage site, the injection limit of the storage site is dynamically adjusted, and this limit is used as the capacity constraint parameter in the subsequent annual optimization model.

4. The method as described in claim 1, characterized in that, The dynamic ratio threshold model based on the remaining useful life and payback period is used to determine whether an enterprise has the feasibility for CCUS retrofitting. When the ratio is greater than a preset threshold, a retrofitting decision is triggered. This also includes: Based on the rate of technological progress in the industry in which the enterprise operates, the dynamic ratio threshold is adaptively adjusted to ensure that transformation decisions are in line with industry development trends. When the ratio of remaining lifetime to investment payback period is less than a dynamic threshold, the system automatically selects short-term emission reduction measures instead of CCUS retrofitting to avoid wasting investment.

5. The method as described in claim 1, characterized in that, Also includes: The actual power supply distance between the enterprise and the wind and solar power plant is calculated based on the spherical distance formula, and this distance is used as a constraint condition for the direct power supply path of wind and solar power to optimize the power supply structure and reduce transmission losses.

6. A CCUS system full lifecycle optimization scheduling device for zero-carbon industrial parks, characterized in that, include: The equipment lifecycle modeling module is used to build the remaining lifecycle matrix of enterprise equipment and the storage site capacity decay model. Based on the physical life decay curve of equipment and the changes in economic life brought about by industry technological progress, it updates the enterprise's active status and the remaining acceptance capacity of the storage site in real time. The sealed capacity calculation module is used to dynamically calculate the remaining capacity of the sealed site based on historical injection data and the current year's injection volume, and use it as a constraint parameter for subsequent year's optimized scheduling. The feasibility assessment module is used to determine whether an enterprise has the feasibility for CCUS retrofitting based on a dynamic ratio threshold model of remaining useful life and investment payback period. When the ratio is greater than a preset threshold, a retrofitting decision is triggered. The rolling optimization scheduling module is used to adopt a rolling iteration strategy of annual optimization and quarterly fine-tuning. It updates system parameters and performs global optimization every year, and makes local corrections based on actual operating data every quarter, forming a data closed-loop update mechanism.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-5.

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