Plateau new energy project carbon asset and green evidence collaborative financing cost control system

Abstract

The invention discloses a plateau new energy project carbon asset and green certificate collaborative financing cost control system, and the system comprises a data collection module which is used for obtaining the operation data of a plateau new energy project, the carbon asset data of a carbon transaction platform, and the green certificate data of a green certificate transaction platform; the collaborative analysis module is used for analyzing the operation data, the carbon asset data and the green evidence data; a synergistic effect evaluation model is established, and the influence of carbon asset and green evidence collaboration on the overall income and cost of the plateau new energy project is analyzed; the decision support module is used for formulating a cost control strategy of carbon asset and green certificate collaborative financing according to an analysis result of the collaborative analysis module; the execution and monitoring module is used for establishing data interfaces with a carbon trading platform, a green certificate trading platform and a financial institution and automatically executing a cost control strategy; and the execution process of the cost control strategy and key indexes of plateau new energy project operation are monitored and analyzed in real time, so that the cost control strategy is dynamically adjusted and optimized.

Description

Technical Field

[0001] This application relates to the field of new energy technology, and in particular to a cost control system for the coordinated financing of carbon assets and green certificates in plateau new energy projects. Background Technology

[0002] Against the backdrop of a global push for sustainable development, the development and promotion of new energy projects have become increasingly important. Plateau regions, due to their unique geographical environment, possess abundant new energy resources such as solar and wind power, providing unparalleled conditions for the development of new energy projects. However, plateau new energy projects face numerous challenges in their development, among which the issue of controlling financing costs is particularly prominent.

[0003] From a financing perspective, the complex geographical conditions and relatively weak infrastructure in plateau regions lead to higher construction and operation costs for new energy projects, thus increasing the difficulty and cost of financing. Traditional financing models mainly rely on the project's own revenue and asset collateral. However, for plateau new energy projects, the initial investment is large, the payback period is long, and the returns are uncertain due to factors such as the natural environment. This results in financial institutions being cautious about lending to them, leading to higher loan interest rates.

[0004] While relevant market mechanisms exist in the carbon asset and green certificate sector, they suffer from numerous shortcomings. In the carbon trading market, inconsistent trading rules and standards across different regions result in low market activity, making it difficult for carbon assets from plateau renewable energy projects to achieve reasonable market pricing. The green certificate market similarly faces issues with an incomplete system for green certificate issuance, trading, and regulation, hindering the full realization of their value. Furthermore, the lack of an effective coordination mechanism between carbon assets and green certificates prevents them from working together to reduce financing costs.

[0005] Existing financing cost control methods often focus on optimizing a single factor, such as reducing costs by improving project operational efficiency or alleviating financial pressure by securing government subsidies. However, they fail to address the issue of systematic cost control from the perspective of the synergy between carbon assets and green certificates. This one-sided approach cannot fully tap the potential value of carbon assets and green certificates in the financing process and is insufficient to fundamentally solve the problem of high financing costs for plateau new energy projects.

[0006] In summary, existing technologies have many shortcomings in controlling financing costs for new energy projects in plateau regions. There is an urgent need for a method and system that can systematically solve the problem of financing cost control from the perspective of synergy between carbon assets and green certificates. Summary of the Invention

[0007] The main purpose of this application is to provide a cost control system for the coordinated financing of carbon assets and green certificates in plateau new energy projects, so as to solve the problems of high financing costs and ineffective coordination between carbon assets and green certificates in the existing technology.

[0008] To achieve the above objectives, this application provides the following technical solution: A cost control system for coordinated financing of carbon assets and green certificates for plateau new energy projects includes: The data acquisition module is used to acquire operational data of plateau new energy projects, carbon asset data of carbon trading platforms, and green certificate data of green certificate trading platforms. The collaborative analysis module is used to perform correlation analysis and time series prediction analysis on the operational data, carbon asset data, and green certificate data; and to establish a synergy effect assessment model to analyze the impact of carbon asset and green certificate synergy on the overall benefits and costs of the plateau new energy project. The decision support module is used to formulate a cost control strategy for the collaborative financing of carbon assets and green certificates for the plateau new energy project based on the analysis results of the collaborative analysis module. The execution and monitoring module is used to establish data interfaces with the carbon trading platform, green certificate trading platform and financial institutions, automatically execute the cost control strategy, and monitor the execution process of the cost control strategy and the key indicators of the operation of the plateau new energy project in real time, and dynamically adjust and optimize the cost control strategy based on the real-time monitoring results.

[0009] As a further improvement to this application, the collaborative analysis module performs correlation analysis on the operational data, carbon asset data, and green certificate data, including: Set a time unit, and define the operational data, carbon asset data and green certificate data within the time unit as a transaction; Set an appropriate support threshold and confidence threshold, where the support represents the frequency of a certain itemset in a transaction appearing in all transactions, and the confidence represents the frequency of the subsequent transaction appearing in a transaction that includes the predecessor. Based on the support threshold and confidence threshold, the transaction set is scanned multiple times to generate frequently occurring itemsets in the transaction set, thereby uncovering strong correlation rules between the operational data, carbon asset data, and green certificate data.

[0010] As a further improvement to this application, the collaborative analysis module performs time-series predictive analysis on the operational data, carbon asset data, and green certificate data, including: Preprocess the time series data in the operational data; Extract features related to carbon asset and green certificate price predictions from the operational data; The time series data and extracted features are analyzed using a neural network model to output predicted values ​​for the prices of carbon assets and green certificates.

[0011] As a further improvement to this application, the synergistic analysis module establishes a synergistic effect assessment model to analyze the impact of carbon asset and green certificate synergy on the overall benefits and costs of the plateau new energy project, including: Based on a cost-benefit analysis algorithm, the impact of the synergy between carbon assets and green certificates on the overall benefits and costs of the plateau new energy project is evaluated. Quantitative analysis of the risks in the collaborative financing process of carbon assets and green certificates is conducted using financial risk assessment indicators. By integrating the aforementioned cost analysis algorithm and financial risk assessment indicators, a comprehensive synergy effect assessment model is constructed to comprehensively measure the advantages and disadvantages of the carbon asset and green certificate synergy strategy.

[0012] As a further improvement to this application, the decision support module, based on the analysis results of the collaborative analysis module, formulates a cost control strategy for the collaborative financing of carbon assets and green certificates in the plateau new energy project, including: Carbon asset trading strategy development includes: Based on the trend analysis of carbon asset prices by the collaborative analysis module, select an appropriate time to conduct carbon asset trading; Based on the project's own carbon emission reduction reserves and the demand in the carbon emission reduction market, an appropriate carbon emission reduction trading volume should be determined. Evaluate potential trading partners in the carbon trading market and prioritize cooperation with reputable, financially stable partners who have a high level of influence in the market.

[0013] Green certificate trading strategy development includes: Based on the analysis of the demand structure of the green certificate market and the characteristics of the green certificate supply of the project itself, we will conduct precise market demand matching. Based on the price fluctuations in the green certificate market and the project's own funding needs, the green certificate trading rhythm should be reasonably determined. Develop a reasonable green certificate portfolio trading strategy based on the differences in recognition and value in different types of green certificate trading markets.

[0014] As a further improvement to this application, the decision support module, based on the analysis results of the collaborative analysis module, formulates a cost control strategy for the collaborative financing of carbon assets and green certificates for the plateau new energy project, and further includes: The financing plan includes: A comprehensive analysis of the funding requirements and scale of the plateau new energy project at different stages is provided. Based on a comprehensive analysis of the financing costs and risks of the aforementioned plateau new energy projects, suitable financing channels are recommended. Develop financing products that combine carbon assets and green certificates, and assess and design the feasibility, risk and return of such financing products to ensure that they meet the actual needs of the project and market rules.

[0015] Risk response strategy development includes: Based on the price fluctuation risks in the carbon asset and green certificate markets, set reasonable stop-loss and take-profit points; Based on the trend forecast of changes in the supply and demand relationship in the carbon asset and green certificate market, adjust the carbon asset and green certificate trading strategy in advance; Adjust the project's carbon asset development plan or green certificate trading strategy based on the policy adjustment risks related to carbon trading and green certificates.

[0016] As a further improvement to this application, the execution and monitoring module establishes a data interface with the carbon trading platform, green certificate trading platform, and financial institutions to automatically execute the cost control strategy; including: Carbon asset transaction execution includes: Through a deep integration interface with the carbon trading platform, trading instructions are automatically generated and issued based on the carbon asset trading strategy formulated by the decision support module. After the transaction instruction is issued, the entire process of carbon asset trading is continuously tracked to confirm the execution status of each step. Detailed records are kept of key data for each carbon asset transaction, including transaction time, transaction price, transaction quantity, and counterparty information; Execution of green certificate transactions includes: Based on the analysis of green certificate market demand and recommendation of trading targets by the decision support module, potential trading targets are accurately identified; After identifying the trading partner, automated communication tools are used to assist the project team in negotiating the transaction with the counterparty and to help both parties complete the contract signing. After the contract is signed, the green certificate delivery status is monitored in real time. After the green certificate is successfully delivered, a settlement instruction is sent to the financial institution to complete the receipt and payment of funds. At the same time, the flow of funds during the settlement process is recorded and verified. The execution of financing operations includes: Based on the financing plan developed by the decision support module, submit financing application materials to the selected financial institution, follow up on the approval progress of the financing application, and obtain feedback information in a timely manner. For financing involving carbon assets and green certificates as collateral, prepare the materials required for the pledge, and complete the registration and filing of the pledged assets in accordance with relevant regulations and the requirements of financial institutions. During the pledge period, check the status of the pledged assets to ensure their value and integrity. After the financing funds are received, their use will be strictly managed to ensure that they are used in accordance with the financing purpose plan.

[0017] As a further improvement to this application, the execution and monitoring module performs real-time monitoring of the execution process of the cost control strategy and the key indicators of the operation of the plateau new energy project, including: By using the data interface with the carbon trading platform and the green certificate trading platform, the latest trading data, including trading prices, trading volumes and inventory quantities, is obtained and compared with the expected indicators set by the decision support module. By connecting with financial institutions' systems, we can obtain information on the disbursement of financing quotas, monitor changes in financing interest rates in real time, and evaluate the efficiency of fund utilization by connecting with the project's financial management system. By interfacing with the data acquisition module, the operational status indicators of the plateau new energy project are continuously tracked.

[0018] As a further improvement to this application, the execution and monitoring module performs real-time monitoring of the cost control strategy execution process and key indicators of the plateau new energy project operation, and further includes: By setting thresholds for price fluctuations and supply and demand changes, real-time early warning and monitoring can be conducted on price fluctuations and supply and demand changes in the carbon asset and green certificate markets. The policy monitoring system is used to obtain policy adjustment information in a timely manner and analyze the potential impact of policy changes on the plateau new energy project. By analyzing the equipment operation data during the operation of the plateau new energy project, the timing and probability of equipment failures can be predicted, and early warnings of equipment failures can be issued.

[0019] As a further improvement to this application, the execution and monitoring module dynamically adjusts and optimizes the cost control strategy based on real-time monitoring results, including: Based on key indicator data and risk warning information obtained from real-time monitoring, analyze the problems and deviations that occur in the current cost control strategy execution process, and determine whether the current cost control strategy needs to be adjusted. The decision support module feeds back the need to adjust the cost control strategy. Based on the feedback, the decision support module re-analyzes and assesses risks, and formulates a new cost control strategy, which is then fed back to the execution and monitoring module. The execution and monitoring module adjusts the relevant strategies for carbon asset trading, green certificate trading, and financing operations based on the new cost control strategy, ensuring that the adjusted strategies can be executed accurately and effectively.

[0020] The beneficial effects of this application are as follows: First, the system in this application improves the overall profitability of plateau new energy projects by optimizing the pricing mechanism of carbon assets and green certificates. Project owners can obtain additional funds through carbon asset trading and green certificate sales, effectively alleviating the pressure on project operating funds, reducing reliance on traditional high-cost financing, and thus directly reducing financing costs.

[0021] Secondly, the system in this application improves the success rate and amount of project financing by expanding financing channels and developing new financing products. Innovative financing methods such as pledge loans based on a combination of carbon assets and green certificates make it easier for projects to obtain support from financial institutions, and the financing interest rates are more favorable than traditional loans.

[0022] Finally, the collaborative mechanism of this application encourages developers of plateau new energy projects to actively participate in carbon emission reduction and green certificate trading, thereby improving the carbon emission reduction efficiency of plateau new energy projects. To obtain greater carbon asset returns and green certificate rewards, project owners will continuously optimize project operation and management, adopting more advanced energy-saving and emission-reduction technologies, thus further reducing greenhouse gas emissions. This has a positive impact on promoting the achievement of carbon emission reduction targets in plateau regions and even globally. Simultaneously, the active green certificate trading also promotes the consumption of green electricity, increases social acceptance and usage of clean energy, and is conducive to optimizing the energy structure and promoting sustainable energy development. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the cost control system for the carbon asset and green certificate collaborative financing of the plateau new energy project in this application.

[0024] Figure 2 This is a flowchart illustrating the steps of the collaborative analysis module in this application to perform correlation analysis on operational data, carbon asset data, and green certificate data.

[0025] Figure 3 This is a flowchart illustrating the method steps for the collaborative analysis module in this application to perform time-series predictive analysis on operational data, carbon asset data, and green certificate data.

[0026] Figure 4 This document presents a flowchart illustrating the steps involved in establishing a synergy effect assessment model for the synergy analysis module in this application, and analyzing the impact of carbon asset and green certificate synergy on the overall benefits and costs of the plateau new energy project.

[0027] Figure 5 This document presents a flowchart illustrating the steps involved in developing a cost control strategy for the collaborative financing of carbon assets and green certificates for plateau new energy projects, based on the analysis results from the collaborative analysis module within the decision support module of this application.

[0028] Figure 6This is a flowchart illustrating the preferred method for the decision support module in this application to formulate a cost control strategy for the collaborative financing of carbon assets and green certificates for plateau new energy projects based on the analysis results of the collaborative analysis module.

[0029] Figure 7 This document presents a flowchart illustrating the steps involved in establishing data interfaces between the execution and monitoring module and carbon trading platforms, green certificate trading platforms, and financial institutions to automatically execute cost control strategies.

[0030] Figure 8 This is a flowchart illustrating the method steps for the execution and monitoring module in this application to monitor the execution process of cost control strategies and key indicators of the operation of the plateau new energy project in real time.

[0031] Figure 9 This is a flowchart illustrating the preferred method for the execution and monitoring module of this application to monitor the execution process of cost control strategies and key indicators of the operation of plateau new energy projects in real time.

[0032] Figure 10 This is a flowchart illustrating the steps of the execution and monitoring module in this application to dynamically adjust and optimize the cost control strategy based on real-time monitoring results. Detailed Implementation

[0033] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0034] like Figure 1 As shown, a cost control system for collaborative financing of carbon assets and green certificates for plateau new energy projects includes: a data acquisition module 1, a collaborative analysis module 2, a decision support module 3, and an execution and monitoring module 4.

[0035] Among them, the data acquisition module 1 is used to acquire the operation data of the plateau new energy project, the carbon asset data of the carbon trading platform, and the green certificate data of the green certificate trading platform; Collaborative analysis module 2 is used to perform correlation analysis and time series prediction analysis on operational data, carbon asset data and green certificate data, and to establish a synergistic effect assessment model to analyze the impact of carbon asset and green certificate synergy on the overall benefits and costs of the plateau new energy project. Decision support module 3 is used to formulate a cost control strategy for the collaborative financing of carbon assets and green certificates for the plateau new energy project based on the analysis results of the collaborative analysis module. The execution and monitoring module 4 is used to establish data interfaces with the carbon trading platform, green certificate trading platform and financial institutions, automatically execute the cost control strategy, and monitor the execution process of the cost control strategy and the key indicators of the operation of the plateau new energy project in real time, and dynamically adjust and optimize the cost control strategy based on the real-time monitoring results.

[0036] The system in this application achieves efficient collaborative work through the functions of the data acquisition module 1, collaborative analysis module 2, decision support module 3, and execution and monitoring module 4. It establishes an automated control system for the collaborative financing cost of carbon assets and green certificates for plateau new energy projects. By optimizing the pricing mechanism of carbon assets and green certificates, it improves the overall profitability of plateau new energy projects. Project owners can obtain additional funds through carbon asset trading and green certificate sales, effectively alleviating the pressure of project operating funds, reducing dependence on traditional high-cost financing, and thus directly reducing financing costs.

[0037] Furthermore, by expanding financing channels and developing new financing products, the success rate and amount of project financing have been improved. The synergistic innovation of financing methods based on carbon assets and green certificates makes it easier for projects to obtain support from financial institutions, and the financing interest rates are more favorable compared to traditional loans.

[0038] The following will provide a detailed explanation of the specific technical implementation schemes for each module of the system in this application: When collecting operational data for new energy projects on the plateau, data acquisition module 1 adopts a distributed data acquisition approach, taking into account the complexity and diversity of the plateau region. For example, for the operational data collection of solar photovoltaic power stations, considering the large variations in sunlight intensity and complex terrain in the plateau region, in addition to installing sunlight intensity sensors, temperature sensors, and current and voltage sensors on representative photovoltaic panels in each row of photovoltaic panels, multiple meteorological monitoring stations are also set up in different areas of the power station to collect meteorological data such as wind speed, wind direction, and atmospheric humidity, as these meteorological conditions indirectly affect the power generation efficiency of the photovoltaic panels. Simultaneously, smart meters are used to accurately measure the power generation of each photovoltaic panel group, and the data is transmitted in real time to the system's data center via a wireless communication module. For the operational data collection of wind power farms, vibration sensors are installed inside the tower of each wind turbine to monitor the vibration during wind turbine operation and determine if there are any potential faults. Furthermore, strain gauges are installed on the wind turbine blades to monitor the stress on the blades in real time, ensuring the safe and stable operation of the wind turbine. Conventional data such as wind speed, wind direction, and generator speed are also aggregated to the system's data center via a high-speed data transmission network.

[0039] Regarding the acquisition of carbon asset and green certificate data, data acquisition module 1 can establish standardized data interfaces with carbon trading platforms and green certificate trading platforms to obtain not only conventional trading data such as the trading price and volume of carbon emission reductions, but also macroeconomic data such as policy dynamics and market participant behavior patterns in the carbon trading market. For example, when the carbon trading market introduces new quota allocation policies, it can promptly obtain relevant policy documents and interpretations to analyze the potential impact on the carbon asset value of projects. For green certificate data, in addition to obtaining information such as the legal quantity and trading price from the green certificate trading system, it also conducts in-depth research on the demand preferences of different types of users for green certificates, as well as the application scenarios and acceptance of green certificates in different industries.

[0040] For project management and environmental data acquisition, detailed project plans are obtained from the project management system, including information on project construction progress, equipment procurement plans, and personnel allocation. This data is crucial for analyzing project costs and funding requirements. Simultaneously, geographic information data for the plateau region, such as altitude and topography, is collected, as these factors affect the construction costs and operational efficiency of plateau-based new energy projects. Geographic Information System (GIS) technology is used to match geographic information data with the spatial distribution of plateau-based new energy projects, providing spatial support for subsequent data analysis. For example, this allows for analysis of differences in carbon emission reduction and power generation at different altitudes, as well as the impact of topography on project construction and operating costs.

[0041] like Figure 2 As shown, the collaborative analysis module 2 performs correlation analysis on operational data, carbon asset data, and green certificate data, including: S21. Set a time unit and define the operational data, carbon asset data and green certificate data within a time unit as a transaction; S22. Set an appropriate support threshold and confidence threshold. The support threshold represents the frequency of a certain itemset in a transaction in all transactions. The confidence threshold represents the frequency of the subsequent transaction in a transaction that includes the preceding transaction. S23. Based on the support threshold and confidence threshold, the transaction set is scanned multiple times to generate frequently occurring itemsets in the transaction set, thereby mining the strong correlation rules between the operational data, carbon asset data and green certificate data.

[0042] Specifically, in step S21, a transaction set is constructed from the collected large amount of data to integrate the operational data of the plateau new energy project with carbon asset data and green certificate data, achieving effective correlation between the operational data of the plateau new energy project and the carbon asset and green certificate data. Specifically, a transaction can be defined as the project's power generation, power generation efficiency, meteorological conditions (sunlight intensity, wind speed, temperature, etc.), equipment operating status (fault status, vibration, etc.), and corresponding carbon emission reductions and green certificate transactions within a specific time period (e.g., one day, one week). For example, on a daily basis, the power generation, average sunlight intensity, number of photovoltaic panel faults, carbon emission reductions, and green certificate transactions of the photovoltaic power station are recorded to form a transaction record. For some continuous operational data, such as sunlight intensity and temperature, discretization is required to facilitate processing by the correlation mining algorithm. Continuous data is converted into discrete intervals using equidistant partitioning or partitioning methods based on data distribution characteristics. For example, sunlight intensity is divided into intervals of 100W / m², and temperature is divided into intervals of 5℃. After this processing, the data in each transaction is presented in a discrete form, which better meets the input requirements of association rule mining algorithms (such as the Apriori algorithm).

[0043] In step S22, support represents the frequency with which a set of items (a group of related data items) appears in all transactions. Mathematically, if we have a set of transactions T and an itemset X, the formula for calculating support(X) is: Support(X) = Number of transactions containing itemset X / Total number of transactions in transaction set T. For example, in a transaction set T consisting of operational data from a plateau new energy project, carbon asset data, and green certificate-related data, assuming there are a total of 1000 transaction records (which can be understood as 1000 data combinations for specific time periods), 200 of these transaction records simultaneously contain an itemset X consisting of the items "sunlight intensity within a certain range" and "carbon emission reduction reaching a certain value." Then, the support of the itemset X consisting of these two items is Support(X) = 200 / 1000 = 0.2. Support reflects the prevalence of an itemset in a dataset. Higher support means that the itemset appears frequently in the data, indicating a potentially close relationship between related data items. In the analysis of new energy projects, itemsets with high support can help us discover frequently co-occurring operational data features and indicators related to carbon assets and green certificates. For example, if the itemset consisting of "specific wind speed range" and "high green certificate trading volume" has high support, it indicates that green certificate trading is often more active within that wind speed range. This is of significant guiding importance for project owners to understand the relationship between the green certificate market and the operational status of wind power generation. Simultaneously, support can also serve as a preliminary basis for screening rules. Itemets with low support may lack broad representativeness due to their infrequent occurrence and can be prioritized for exclusion when mining association rules, thereby reducing computational load and improving mining efficiency.

[0044] Confidence is used to measure the frequency of the consequent (the set of itemsets in the "then" part of a rule) occurring in transactions that include the antecedent (the itemset in the "if" part of the rule). Assuming there is an association rule X→Y (meaning that if itemset X occurs, then itemset Y will also occur), the confidence (X→Y) is calculated as: Confidence (X→Y) = Number of transactions containing both itemset X and itemset Y / Number of transactions containing itemset X. Continuing with the example above, if among the 200 transaction records containing "light intensity is within a certain range," 160 of these transactions also contain "carbon emissions reach a certain value," then the confidence of the association rule from "light intensity is within a certain range" to "carbon emissions reach a certain value" is 160 / 200 = 0.8. Confidence reflects the reliability of an association rule. It represents the probability that the consequent is true given that the antecedent is true. In data analysis for plateau new energy projects, high-confidence association rules can provide a more reliable basis for project decision-making. For example, if a rule states that "when the wind turbine blade angle is within a certain range, power generation increases significantly and carbon emission reductions rise," and this rule has a high confidence level, then the project owner can use this rule to adjust the wind turbine blade angle in actual operation to expect to increase power generation and carbon emission reductions. Confidence and support work together; high support indicates that the itemset involved in the rule is widespread, while high confidence indicates that the rule itself has high reliability. Together, they help us to mine truly valuable association rules from massive amounts of data to guide the formulation of carbon asset and green certificate synergy strategies for new energy projects.

[0045] In step S23, the Apriori algorithm can be used to explore the hidden relationships between carbon asset data and green certificate data. Appropriate support and confidence thresholds are set, such as a support of 0.2 and a confidence of 0.8. By scanning the transaction set multiple times, frequent itemsets are generated, and strong association rules are then discovered. For example, it was found that when wind power generation in a certain plateau region increases significantly during a specific season, there is a high correlation between the increase in carbon emission reductions and the rise in green certificate demand in that region; that is, the confidence level of the rule "increased wind power generation → increased carbon emission reductions and increased green certificate demand" reaches the set standard. This association provides key clues for the formulation of subsequent collaborative strategies and helps to grasp the collaborative change patterns of carbon assets and green certificates at different stages of project operation.

[0046] like Figure 3 As shown, the collaborative analysis module 2 performs time-series predictive analysis on operational data, carbon asset data, and green certificate data, including: S24. Preprocess the time series data in the running data; S25. Extract features from operational data that are relevant to carbon asset and green certificate price forecasts; S26. Analyze the time series data and extracted features using a neural network model, and output predicted values ​​for carbon assets and green certificates.

[0047] Specifically, in step S24, the time series data in the operational data of the plateau new energy project, such as the data on the change of power generation over time, is preprocessed. First, a stationarity test is performed. If the data is not stationary, it is made stationary through differencing. For example, first-order differencing is performed on power generation data with obvious trends to eliminate the influence of trends. Then, the characteristics of the time series are determined based on the autocorrelation function (ACF) and partial autocorrelation function (PACF), providing a basis for selecting a suitable prediction model.

[0048] In step S25, features relevant to carbon asset and green certificate price predictions are extracted from the operational data. Besides power generation itself, indicators such as power generation efficiency and equipment utilization can also be considered. For example, the photovoltaic panel conversion efficiency of a photovoltaic power plant can be calculated and incorporated as a feature into the prediction model. Furthermore, composite features are constructed by combining meteorological data, such as the impact of sunlight intensity and temperature on power generation. For instance, the ratio of sunlight intensity to power generation can be used as a new feature to reflect the efficiency of solar resource utilization. These features can more comprehensively describe the potential relationship between the operational status of new energy projects and carbon asset and green certificate prices, improving the accuracy of the prediction model.

[0049] In step S26, an LSTM neural network model is used to construct a network structure containing an input layer, an LSTM layer, a fully connected layer, and an output layer. Time series data is input into the input layer at fixed time steps. The LSTM layer captures long-term dependencies in the data, the fully connected layer integrates the features, and the output layer obtains the predicted value. During training, the backpropagation algorithm is used to adjust the network weights and biases to minimize the loss function. The trained model is validated using a validation set, and model hyperparameters, such as the number of neurons in the LSTM layer and the learning rate, are adjusted to ensure good generalization ability. Finally, the model's predictive performance is evaluated using a test set, such as calculating the root mean square error (RMSE) and mean absolute error (MAE) of the predictions, to assess the accuracy of the LSTM neural network model in predicting carbon asset and green certificate prices, providing a strong basis for selecting the timing of carbon asset and green certificate trading.

[0050] The above steps, through time-series forecasting analysis of operational data, carbon asset data, and green certificate data, help project owners better understand the changing synergistic relationship between carbon assets and green certificates over time. For example, if the forecast shows that carbon asset prices rise during a certain period, and the demand for green certificates also increases accordingly, project owners can strengthen the coordinated operation of both, develop joint marketing strategies, and improve overall profitability. By grasping the time characteristics of the carbon asset and green certificate markets, project owners can more effectively integrate resources to achieve synergistic development of carbon assets and green certificates, enhancing the overall synergistic effect of the system. Based on the time-series forecast results, project owners can make more rational resource allocations across various stages such as carbon asset development, green certificate trading, new energy project operation, and financing. For example, if the carbon asset market is predicted to be highly profitable in the future, project owners can appropriately increase resource investment in carbon emission reduction technology research and development and carbon asset trading; simultaneously, based on the forecast of the green certificate market, they can rationally allocate human and material resources for green certificate acquisition and trading. By dynamically optimizing resource allocation and improving resource utilization efficiency, project owners can ensure optimal development at each stage, thereby improving the overall benefits of the project.

[0051] like Figure 4 As shown, the synergy analysis module 2 establishes a synergy effect assessment model to analyze the impact of carbon asset and green certificate synergy on the overall benefits and costs of the plateau new energy project, including: S27. Based on the cost-benefit analysis algorithm, evaluate the impact of the synergy between carbon assets and green certificates on the overall benefits and costs of the plateau new energy project. S28. Use financial risk assessment indicators to conduct quantitative analysis of the risks in the process of co-financing carbon assets and green certificates; S29. Integrate cost analysis algorithms and financial risk assessment indicators to construct a comprehensive synergy effect assessment model, and comprehensively measure the advantages and disadvantages of carbon asset and green certificate synergy strategies.

[0052] Specifically, in step S27, based on a cost-benefit analysis algorithm, the impact of carbon asset and green certificate synergy on the overall revenue and cost of the Plateau New Energy Project is assessed. Detailed calculations are performed on all costs incurred by the Plateau New Energy Project in developing carbon assets and participating in green certificate trading, including carbon emission reduction monitoring and accounting costs, green certificate application and trading fees, etc. Simultaneously, the revenue obtained through carbon asset trading and green certificate sales is accurately calculated. By comparing the costs and benefits under different synergy strategies, the project's net revenue is determined. For example, under one synergy strategy, if the project's carbon asset development cost is X10,000 yuan, green certificate trading cost is X20,000 yuan, carbon asset trading revenue is Y10,000 yuan, and green certificate sales revenue is Y20,000 yuan, then the net revenue is (Y1+Y2)-(X1+X2)0,000 yuan. Through cost-benefit analysis of multiple strategies, the optimal synergy economic strategy is determined to maximize the project's economic benefits.

[0053] In step S28, financial risk assessment indicators are used to quantitatively analyze the risks in the collaborative financing process of carbon assets and green certificates. Risk assessment indicators are calculated using data such as power generation fluctuations and equipment failure frequencies from the operational data of plateau new energy projects. For power generation fluctuations, indicators such as the standard deviation and coefficient of variation are calculated to measure the stability of power generation. For example, the standard deviation of monthly power generation of a wind farm over the past year is calculated; a larger standard deviation indicates greater power generation fluctuations and higher uncertainty in project returns. For equipment failure frequencies, the number of equipment failures within a certain period is statistically analyzed, and indicators such as the mean and variance of failure intervals are calculated to assess the reliability risk of equipment operation. For example, the number of photovoltaic panel failures in a photovoltaic power station within a quarter, and the interval between each failure, are statistically analyzed to analyze the potential impact of equipment failures on power generation and project returns.

[0054] The calculated risk indicators are incorporated into the synergy evaluation system, combined with risk factors in the carbon asset and green certificate markets, such as changes in carbon trading policies and fluctuations in green certificate market demand. Financial risk assessment indicators are used to quantitatively analyze the risks in the synergistic financing process. The standard deviation is calculated to measure the volatility of the synergistic returns between carbon assets and green certificates; a larger standard deviation indicates greater volatility and higher risk. For example, after calculating the standard deviation σ of returns under a certain synergistic strategy, it is compared with other strategies to determine the risk level of that strategy. Simultaneously, the Sharpe ratio is used to assess excess returns under risk; the formula is: Where SR is the Sharpe ratio, R p The expected return of a synergistic portfolio, R f The risk-free rate, σ p The standard deviation of the portfolio is used. Sharpe ratio analysis determines whether sufficient returns have been achieved at a given level of risk, providing investors and project owners with a reference for risk-return trade-offs and helping them make more rational financing decisions.

[0055] In step S29, cost analysis algorithms and financial risk assessment indicators are integrated to construct a comprehensive synergy effect assessment model, comprehensively measuring the advantages and disadvantages of the carbon asset and green certificate synergy strategy. Different indicators can be assigned corresponding weights. The rationality of the weights is determined using methods such as the Analytic Hierarchy Process (AHP). First, a hierarchical model is constructed, with the target layer set as the comprehensive synergy effect assessment, the criteria layer divided into economic benefits, financial risks, and environmental benefits, and the indicator layer consisting of the specific assessment indicators mentioned above. Then, a judgment matrix is ​​established through expert scoring or pairwise comparisons. For example, regarding the relative importance of economic benefits and financial risks, experts score based on experience and project characteristics to form a judgment matrix. Next, the eigenvectors and the largest eigenvalue of the judgment matrix are calculated, and a consistency check is performed to ensure the rationality of the judgment matrix, ultimately determining the weights of each indicator.

[0056] For example, the weight of the economic benefit indicator is w1, the weight of the financial risk indicator is w2, and the weight of the environmental benefit indicator is w3, and w1 + w2 + w3 = 1. Within the economic benefit indicator, the weights of net present value (NPV), internal rate of return (IRR), and benefit-cost ratio (BCR) are set as w1, w2, and w3, respectively. 11 w 12 w 13 And w 11 +w 12 +w 13 =1; Within the financial risk indicators, the standard deviation (σ) and Sharpe ratio (SR) have weights of w respectively. 21 w 22 And w 21 +w 22 =1; Within the environmental benefit indicators, the weights of carbon emission reduction (CER) and green certificate consumption (GEC) are w respectively. 31 w 32 And w 31 +w 32 =1. The calculation formula for the comprehensive synergy effect evaluation model is: .

[0057] The comprehensive synergy assessment score S, calculated using this comprehensive synergy assessment model, comprehensively reflects the overall performance of carbon asset and green certificate synergy in plateau new energy projects across economic, financial, and environmental aspects. A higher score indicates a better synergy effect, providing a quantitative reference for project owners in formulating carbon asset and green certificate synergy strategies, assessing project feasibility, and making resource allocation decisions. Different projects can adjust the indicators and weights appropriately according to their own characteristics and objectives to ensure the model better reflects reality and accurately assesses the synergy effect.

[0058] like Figure 5 As shown, based on the analysis results of the collaborative analysis module 2, the decision support module 3 formulates a cost control strategy for the collaborative financing of carbon assets and green certificates for the plateau new energy project, including: S31. Carbon asset trading strategy development; including: Based on the trend analysis of carbon asset prices by the Collaborative Analysis Module 2, the appropriate timing for carbon asset trading is selected. For example, if carbon asset prices are predicted to rise in the short term, and the increase is expected to exceed a certain percentage (e.g., 10%), the Decision Support Module 3 will advise project owners to temporarily hold their carbon emission reductions and wait for prices to reach a high level before selling to maximize profits. Conversely, if prices are expected to continue to fall, and the decline is significant (e.g., exceeding 15%), the project owner is advised to sell their carbon assets in advance to avoid losses. For instance, by analyzing historical price data and market supply and demand, combined with current policy guidance, if it is predicted that carbon asset prices will rise in the next three months due to the tightening of quotas in a certain region's carbon trading market, the Decision Support Module 3 will provide corresponding holding decision recommendations.

[0059] Based on the project's own carbon emission reduction reserves and market demand, an appropriate carbon emission reduction trading volume is determined. Besides price factors, the project's own carbon emission reduction reserves and market demand must also be considered. If the project has sufficient carbon emission reduction reserves and market demand is strong, Decision Support Module 3 may recommend appropriately increasing the trading volume to fully utilize market opportunities. At the same time, market liquidity will also be considered to avoid a price drop due to large-scale selling. For example, when market demand for carbon emission reductions suddenly increases, and the project has abundant carbon emission reduction reserves, Decision Support Module 3 will provide an appropriate trading volume recommendation after assessing market absorption capacity, ensuring that market demand is met while guaranteeing the project a good return.

[0060] The decision support module 3 assesses potential trading partners in the carbon trading market, prioritizing those with good reputations, financial stability, and significant market influence. This evaluation considers factors such as the partner's creditworthiness, financial status, and market influence. Prioritizing partners with good reputations, financial stability, and significant market influence mitigates trading risks and ensures smooth transactions. For example, by analyzing historical trading records and credit ratings of various partners, the module recommends the most suitable trading partners to project owners, ensuring that carbon asset transactions can be completed in a safe and stable environment.

[0061] S32. Green certificate trading strategy formulation, including: Based on an analysis of the demand structure of the green certificate market and the specific green certificate supply characteristics of each project, precise market demand matching is achieved. For example, some large energy users have a relatively stable and large demand for green certificates to meet their green energy consumption commitments; while some emerging green technology companies may have a phased and innovative demand for green certificates. Decision Support Module 3 will precisely match the project's green certificate supply characteristics, such as the type of green electricity corresponding to the green certificate and the stability of power generation, with market demand. For projects primarily focused on stable wind power generation, Decision Support Module 3 will prioritize recommending transactions with users who prefer wind power green certificates, thereby increasing the success rate of transactions and realizing the value of the green certificates.

[0062] Based on price fluctuations in the green certificate market and the project's own funding needs, a reasonable green certificate trading schedule should be determined. If the green certificate market price is high and the project has significant recent funding needs, Decision Support Module 3 will recommend selling green certificates promptly to obtain funds. Conversely, if the market price is low and the project's funding pressure is not significant, transactions can be appropriately delayed, waiting for prices to rebound. For example, through real-time monitoring and trend analysis of green certificate market prices, if it is found that a market price decline occurs in a certain quarter due to concentrated green certificate issuance, while the project's funding needs are relatively stable in that quarter, Decision Support Module 3 will recommend that the project temporarily refrain from selling green certificates and wait for prices to recover before proceeding with the transaction.

[0063] Based on the differences in recognition and value across different types of green certificate trading markets, a reasonable green certificate portfolio trading strategy should be developed. For example, for projects that possess both wind power and solar power green certificates, the sales ratio should be appropriately allocated based on market demand and price conditions. If the current market demand for wind power green certificates is strong and prices are high, while the market for solar power green certificates is relatively stable, the module would recommend appropriately increasing the sales ratio of wind power green certificates to optimize green certificate asset allocation and improve overall returns.

[0064] like Figure 6 As shown, the decision support module 3, based on the analysis results of the collaborative analysis module 2, formulates a cost control strategy for the collaborative financing of carbon assets and green certificates for plateau new energy projects, and also includes: S33. Financing plan formulation, including: A comprehensive analysis is conducted to determine the characteristics and scale of funding needs for plateau new energy projects at different stages. For example, the construction phase requires consideration of funding needs for land acquisition, equipment procurement, and infrastructure construction; the operation phase involves expenses such as daily maintenance, personnel salaries, and raw material procurement. Through detailed cost accounting and project planning, the specific funding gap for each stage is determined. For instance, when constructing a large-scale solar photovoltaic power station, Decision Support Module 3 calculates land lease fees, photovoltaic panel and related equipment procurement costs, installation and commissioning costs, etc., based on the project plan, to arrive at the total funding requirements for the construction phase.

[0065] This module comprehensively analyzes the financing costs and risks of plateau new energy projects and recommends suitable financing channels. For large-scale plateau new energy projects, due to the large capital requirements and long payback period, equity financing can be considered, such as attracting venture capital institutions or strategic investors to participate in the project. They can not only provide financial support but also potentially bring advanced technology and management experience. Simultaneously, debt financing can be combined, such as applying for long-term loans from banks or issuing green bonds. Decision support module 3 will conduct a detailed analysis and comparison of the interest rates, repayment methods, financing terms, and collateral requirements of different financing channels. For example, it compares the long-term loan interest rates, repayment term flexibility, and acceptance of carbon assets and green certificates as collateral offered by different banks to help project owners select the most suitable bank loan scheme.

[0066] Develop financing products that synergize carbon assets and green certificates, and assess and design the feasibility, risk, and return of these products to ensure they meet the actual needs of projects and market principles. For example, design financing products based on a combination of carbon assets and green certificates as collateral, using the project's carbon emission reductions and green certificates as collateral to increase the financing amount and success rate. Simultaneously, explore cooperation with financial institutions to launch revenue rights transfer financing products, whereby project owners transfer the revenue rights from carbon asset trading and green certificate sales within a certain future period to financial institutions, obtaining financial support in advance. Decision support module 3 will assess and design the feasibility, risk, and return characteristics of these new financing products to ensure they meet the actual needs of project owners and market principles.

[0067] S34. Risk response strategy development, including: Based on the price volatility risks in the carbon asset and green certificate markets, reasonable stop-loss and take-profit points should be set. For example, closely monitor price fluctuations in the carbon trading and green certificate markets, and quantify the impact of price changes on project returns by establishing price volatility models. If the historical volatility of carbon asset prices is high, it indicates a greater price volatility risk, and project owners need to be more cautious when formulating carbon asset trading strategies. Decision support module 3 will provide risk management suggestions to project owners based on the price volatility risk assessment results, such as setting stop-loss and take-profit points. When the carbon asset price falls to the stop-loss point, sell the carbon asset in a timely manner to avoid further losses; when the price rises to the take-profit point, realize profits in a timely manner.

[0068] Based on the predicted trends in the supply and demand relationship of the carbon asset and green certificate markets, trading strategies for carbon assets and green certificates can be adjusted in advance. For example, the impact of changes in the supply and demand relationship in the carbon asset and green certificate markets on projects can be analyzed. If the supply of carbon emission reductions in the market suddenly increases significantly, it may lead to a drop in carbon asset prices; conversely, if the demand in the green certificate market decreases due to policy adjustments or changes in the behavior of market participants, it will also affect the sales price and returns of green certificates. Decision support module 3 will collect market supply and demand data, including information on carbon allowance issuance, new carbon emission reductions from new energy projects, the number of green certificates issued, and changes in green certificate demand in different industries, to build a supply and demand analysis model and predict market supply and demand trends.

[0069] Based on the policy adjustment risks related to carbon trading and green certificates, the module adjusts the project's carbon asset development plan or green certificate trading strategy. For example, adjustments to the quota allocation policy in the carbon trading market may alter the scarcity of carbon emission reductions, thereby affecting carbon asset prices; changes in green certificate issuance standards may affect the quantity and quality of green certificates obtained by the project. Decision support module 3 interprets and analyzes policies, assessing the potential risks of policy adjustments to the project. By establishing a policy risk matrix, different policy factors are categorized according to their likelihood and degree of impact on the project, providing project owners with intuitive policy risk assessment results. For policy changes that may have a significant impact on the project, the module will issue timely warnings and provide corresponding response strategy suggestions, such as adjusting the project's carbon asset development plan or green certificate trading strategy to adapt to the policy changes.

[0070] By implementing the above steps, a scientific strategy can be developed for carbon asset and green certificate trading, ensuring transaction security and returns, and improving the operational efficiency and profitability of projects in the carbon asset and green certificate market.

[0071] After a comprehensive analysis of the funding needs at different stages of the project, and taking into account factors such as financing costs and risks, we recommend suitable financing channels for project owners and innovatively develop new financing products based on the synergistic advantages of carbon assets and green certificates. This helps project owners obtain sufficient funding while optimizing their financing structure and reducing financing costs. For example, we design financing products based on a combination of carbon assets and green certificates as collateral, increasing the amount of project financing and the success rate, alleviating project funding pressure, and promoting the smooth implementation of the project.

[0072] By comprehensively assessing market risks, policy risks, and project-specific risks, quantifying the degree of risk impact, and providing targeted risk response recommendations, project owners can understand potential risks in advance, formulate risk prevention measures, and reduce the likelihood and impact of risks. For example, setting stop-loss points for carbon asset price volatility risks, adjusting project strategies in a timely manner to address policy risks, ensuring stable project operation, and avoiding significant losses due to risks.

[0073] like Figure 7As shown, the execution and monitoring module 4 establishes data interfaces with the carbon trading platform, green certificate trading platform, and financial institutions to automatically execute cost control strategies; including: S41. Execution of carbon asset transactions, including: Through a deep integration interface with the carbon trading platform, the execution and monitoring module 4 automatically generates and issues trading instructions based on the carbon asset trading strategy formulated by the decision support module 3. The execution and monitoring module 4, based on the carbon asset trading strategy determined by the decision support module 3, automatically generates and issues trading instructions. For example, when the decision support module 3 issues an instruction to sell carbon emission reductions, the execution and monitoring module 4 will quickly create a sell order on the carbon trading platform based on set parameters such as price, quantity, and trading time. For instance, if the strategy suggests selling Y tons of carbon emission reductions when the carbon asset price reaches X yuan per ton, the execution and monitoring module 4 will monitor the carbon trading market price in real time. Once the price reaches the target price, it will immediately send a sell order to the carbon trading platform to ensure timely and accurate execution of the transaction.

[0074] After a trading order is placed, the entire carbon asset trading process is continuously tracked to confirm the execution status of each step. The execution and monitoring module 4 obtains real-time feedback from the carbon trading platform throughout the entire process, from order submission, matching with a counterparty, transaction completion, to final settlement, confirming the execution status of each step. If any abnormal situation occurs during the trading process, such as an order failing to match successfully for an extended period or a counterparty defaulting, the execution and monitoring module 4 will promptly issue an alert and automatically attempt to take corresponding measures, such as resubmitting the order, finding other counterparties, or initiating default handling procedures. For example, if an order fails to match successfully within a specified time, the execution and monitoring module 4 will automatically adjust the selling price or quantity and resubmit the order to improve the success rate of the transaction.

[0075] Detailed records are kept of key data for each carbon asset transaction, including transaction time, price, quantity, and counterparty information. These records are used not only for financial accounting and auditing but also provide crucial information for subsequent strategy evaluation. Simultaneously, the execution and monitoring module 4 automatically generates transaction documents, such as transaction contracts and settlement statements, and archives them according to prescribed formats and storage paths. For example, after each transaction is completed, the execution and monitoring module 4 saves the transaction contract and settlement statement to a dedicated electronic folder and creates an index directory for easy retrieval and access.

[0076] S42. Execution of green certificate transactions, including: Based on the analysis of green certificate market demand and the recommendation of trading partners by Decision Support Module 3, potential trading partners are accurately identified. For example, based on the analysis of green certificate market demand and the recommendation of trading partners by Decision Support Module 3, Execution and Monitoring Module 4 utilizes the search and filtering functions of the green certificate trading platform to accurately identify potential trading partners. The green certificate trading platform automatically compares the type, quantity, quality, and other attributes of the project's green certificates with the needs of potential trading partners to quickly find the most suitable trading partner. For example, if Decision Support Module 3 suggests a green certificate transaction with a large energy company, Execution and Monitoring Module 4 will search for the company's published demand information on the green certificate trading platform and match the project's green certificate information with it to confirm the trading intentions of both parties.

[0077] After identifying a counterparty, automated communication tools assist the project team in negotiating the transaction and facilitating contract signing. For example, after identifying a counterparty, Execution and Monitoring Module 4 assists the project team in negotiating with the counterparty. Automated communication tools, such as email and instant messaging systems, are used to convey the transaction terms and intentions of both parties, facilitating the negotiation process. Once both parties reach an agreement, Execution and Monitoring Module 4 automatically generates a green certificate transaction contract template and fills in the contract terms according to the negotiation results, ensuring the accuracy of the contract content. After the contract is generated, Execution and Monitoring Module 4 reminds the project team and the counterparty to sign the contract and tracks the signing progress. For example, through an electronic signature system, the project team and the counterparty can sign the contract online, and Execution and Monitoring Module 4 monitors the contract signing status in real time. Once both parties have completed the signing, the subsequent transaction process is immediately initiated.

[0078] After contract signing, the execution and monitoring module 4 monitors the green certificate delivery status in real time. Upon confirmation of successful delivery, it sends settlement instructions to financial institutions to complete fund receipt and payment. Simultaneously, it records and verifies the flow of funds during the settlement process. For example, after contract signing, the execution and monitoring module 4 coordinates the delivery and settlement of green certificates. It collaborates with the green certificate trading platform and relevant financial institutions to ensure the smooth transfer of ownership of green certificates to the counterparty and timely fund settlement. The execution and monitoring module 4 monitors the green certificate delivery status in real time and sends settlement instructions to financial institutions upon confirmation of successful delivery, completing fund receipt and payment. Simultaneously, it records and verifies the flow of funds during the settlement process to ensure the safety and accuracy of transaction funds. For example, after green certificate delivery is completed, the execution and monitoring module 4 sends a payment notification to the project party's bank account, records the receipt of funds, and updates the transaction records on the green certificate trading platform.

[0079] S43. Execution of financing operations, including: Based on the financing plan developed by Decision Support Module 4, the Execution and Monitoring Module 4 submits financing application materials to the selected financial institution, tracks the approval progress of the financing application, and obtains timely feedback. For example, based on the financing plan designed by Decision Support Module 3, the Execution and Monitoring Module 4 prepares and submits financing application materials to the selected financial institution. These materials include basic project information, financial statements, carbon asset and green certificate related supporting documents, and a description of the financing purpose. After submitting the application, the Execution and Monitoring Module 4 continuously tracks the approval progress of the financing application, maintains close communication with the financial institution, and obtains timely feedback. For example, through the online application platform provided by the financial institution, the Execution and Monitoring Module 4 can check the approval status of the financing application in real time, such as accepted, under review, or approved, and take corresponding follow-up measures according to different statuses.

[0080] For financing involving carbon assets and green certificates as collateral, the execution and monitoring module 4 prepares the necessary materials for the pledge and completes the registration and filing of the pledged assets in accordance with relevant regulations and financial institution requirements. During the pledge period, the module checks the status of the pledged assets to ensure their value and integrity. For example, for financing involving carbon assets and green certificates as collateral, the execution and monitoring module 4 assists the project owner and financial institution in handling the pledge procedures. It is responsible for preparing various documents required for the pledge, such as carbon asset pledge contracts and green certificate pledge registration certificates, and completing the registration and filing of the pledged assets in accordance with relevant regulations and financial institution requirements. During the pledge period, the execution and monitoring module 4 regularly checks the status of the pledged assets to ensure their value and integrity. For example, the execution and monitoring module 4 will check the pledge status of carbon assets and green certificates with the relevant registration department within the specified time. If any abnormalities are found, such as the pledged assets being frozen or seized, the module will promptly notify the project owner and financial institution and assist in taking remedial measures.

[0081] After the financing funds are received, their use is strictly managed to ensure they are used according to the planned purpose. For example, by connecting with the project's financial management system, the flow of funds is monitored in real time to prevent misappropriation or abuse. Simultaneously, a detailed repayment plan is developed based on the repayment method and term stipulated in the financing contract, and the project owner is reminded to repay on time. For instance, the execution and monitoring module 4 sends a repayment reminder to the project owner before the monthly repayment date, informing them of the amount due, the repayment date, and the repayment account. If the project owner experiences repayment difficulties, the execution and monitoring module 4 assists them in communicating with financial institutions to negotiate adjustments to the repayment plan or to adopt other solutions.

[0082] Through the implementation of the above steps, the strategies formulated in Decision Support Module 3 are automatically and accurately executed in carbon asset trading, green certificate trading, and financing operations. By interfacing with various trading platforms and financial institutions, functions such as automatic issuance of trading orders, tracking of trading processes, assistance with contract signing, and fund management are achieved, ensuring the accuracy and timeliness of strategy execution. For example, in carbon asset trading, market prices are monitored in real time, and automatic selling occurs immediately once the set price is reached, avoiding delays and errors caused by manual operation and ensuring that transactions are completed as planned.

[0083] like Figure 8 As shown, the execution and monitoring module 4 performs real-time monitoring of the cost control strategy execution process and key indicators of the plateau new energy project operation, including: S44. Through data interfaces with carbon trading platforms and green certificate trading platforms, obtain the latest trading data, including trading prices, trading volumes, and inventory quantities, and compare and analyze this data with the expected indicators set by the decision support module. For example, if the actual trading price of carbon assets is lower than the expected price, the execution and monitoring module 4 will immediately issue an early warning signal and analyze the reasons for the price deviation, such as changes in market supply and demand, or competitors selling at low prices. Simultaneously, it will track the inventory quantities of carbon assets and green certificates in real time to ensure that inventory levels meet the project's operational plan and trading strategy.

[0084] S45. By integrating with financial institutions' systems, the system obtains information on the disbursement of financing quotas and monitors changes in financing interest rates in real time. It also integrates with the project's financial management system to assess the efficiency of fund utilization. For example, by integrating with financial institutions' systems, the system obtains information on the disbursement and use of financing quotas and monitors changes in financing interest rates in real time. Regarding repayment, it not only monitors whether repayments are made on time and in full but also analyzes the source and stability of repayment funds. Through data interaction with the project's financial management system, it assesses the efficiency of fund utilization, such as calculating the ratio of capital input to project output. For example, if a sudden increase in financing interest rates is detected, the execution and monitoring module will promptly analyze the reasons, which may include market interest rate fluctuations, changes in the project's credit rating, etc., and provide the project owner with corresponding suggestions, such as negotiating interest rate adjustments or seeking alternative financing channels.

[0085] S46. By interfacing with data acquisition module 1, continuously track the operational status indicators of plateau new energy projects. For example, continuously track operational status indicators such as power generation, power generation efficiency, and equipment failure rate. Power generation and power generation efficiency directly affect carbon emission reductions and the generation of green certificates, while equipment failure rate relates to the stable operation and cost control of the project. For example, if the power generation of a wind farm continuously declines, the execution and monitoring module will combine power generation efficiency and equipment operation data to analyze possible causes such as aging wind turbine blades, changes in weather conditions, or equipment failure, and promptly notify the project operation and maintenance department for investigation and handling.

[0086] By implementing the above steps, real-time monitoring of carbon asset and green certificate trading indicators, financing-related indicators, and operational indicators of plateau new energy projects can be achieved, providing real-time early warnings of market risks, policy risks, and project-specific risks. Project owners can obtain risk information promptly, respond quickly, and take measures to mitigate risks, minimizing potential losses. For example, when carbon asset prices experience a sharp, short-term decline or significant policy adjustments occur, timely warnings can be issued, allowing project owners to quickly adjust their trading strategies or financing plans to avoid risks.

[0087] like Figure 9 As shown, the execution and monitoring module 4 performs real-time monitoring of the cost control strategy execution process and key indicators of the plateau new energy project operation, and also includes: S47. By setting thresholds for price fluctuations and supply-demand changes, real-time early warning and monitoring are conducted on price fluctuations and supply-demand changes in the carbon asset and green certificate markets. For example, if the price of carbon assets falls by more than 10% in a short period of time, or if the demand in the green certificate market suddenly drops by more than 20%, the execution and monitoring module 4 will issue an early warning notification to the project owner and provide a risk analysis report, including the causes of the risk, its potential impact on the project, and corresponding response suggestions. Simultaneously, it continuously monitors market dynamics, updates risk assessment results in a timely manner, and provides the project owner with the latest risk information.

[0088] S48. The policy monitoring system will promptly acquire information on policy adjustments and analyze the potential impact of policy changes on plateau new energy projects. If policy changes that may have a significant impact on the project are discovered, such as tightening of carbon trading policies or changes in green certificate issuance standards, the execution and monitoring module 4 will quickly issue a policy risk warning, provide a detailed interpretation of the policy changes, analyze the impact on the cost control of the project's carbon assets and green certificates collaborative financing, and provide corresponding response strategy suggestions.

[0089] S49. Analyze equipment operation data during the operation of plateau new energy projects to predict the timing and probability of potential equipment failures and issue early warnings. By analyzing the equipment operation data of plateau new energy projects, predict the timing and probability of potential equipment failures and issue early warnings to remind project operation and maintenance personnel to perform preventative maintenance. Monitor the technological development trends in the new energy industry; when new technologies emerge that may affect the project's technological competitiveness, issue warnings of technological update risks and assist the project owner in assessing the necessity and feasibility of technological updates. Simultaneously, monitor risks in the project management process, such as project delays and cost overruns, promptly identify and warn of project management risks, and provide improvement suggestions to the project owner.

[0090] like Figure 10 As shown, the execution and monitoring module 4 dynamically adjusts and optimizes the cost control strategy based on real-time monitoring results, including: S51. Based on key indicator data and risk warning information obtained from real-time monitoring, analyze the problems and deviations that occur during the execution of the current cost control strategy, and determine whether the current cost control strategy needs to be adjusted. For example, if the carbon asset trading price does not meet expectations, it may be due to changes in the market environment, such as competitors' strategy adjustments or the entry of new market participants; if the power generation of new energy projects declines, it may be due to aging equipment, outdated technology, or inadequate maintenance. The execution and monitoring module 4 conducts in-depth analysis of these issues to find the root causes and provides a basis for adjusting the cost control strategy. For example, through detailed analysis of market data, it is found that the decline in carbon asset prices is due to a large enterprise selling off a large amount of carbon emission reductions, leading to an oversupply in the market. This is an important basis for adjusting the carbon asset trading strategy.

[0091] S52. The need to adjust the cost control strategy is fed back to the decision support module 3. The decision support module 3 then conducts a new collaborative analysis and risk assessment based on the feedback, and formulates a new cost control strategy, which is then fed back to the execution and monitoring module 5. For example, if carbon asset prices fall, the decision support module 3 will reassess the market supply and demand situation, price trends, and the project's own carbon asset status to analyze whether the trading strategy needs to be adjusted, such as changing the timing of sales or adjusting the trading volume. After the reassessment, the decision support module 3 formulates new cost control strategy recommendations and returns them to the execution and monitoring module 5.

[0092] S53. The execution and monitoring module 4 adjusts the relevant strategies for carbon asset trading, green certificate trading, and financing operations based on the new cost control strategy to ensure that the adjusted strategies can be executed accurately and effectively. For example, if the new strategy suggests delaying the carbon asset sale time and lowering the expected sale price, the execution and monitoring module 4 will modify the trading instruction parameters and reset the sale order on the carbon trading platform.

[0093] By implementing the above steps, deviations in strategy execution can be analyzed based on real-time monitoring data and risk warning information. Adjustment needs can then be fed back to decision support module 3, enabling dynamic adjustment and optimization of the strategy. Regular evaluation of strategy execution effectiveness allows for continuous improvement, ensuring the project adapts to market changes and maintains optimal cost control and profitability. For example, quarterly evaluations of strategy execution effectiveness allow for adjustments to carbon asset trading strategies or financing plans based on market changes and the project's actual situation, enhancing the project's operational flexibility and competitiveness.

[0094] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0095] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0096] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A highland new energy project carbon asset and green certificate collaborative financing cost control system, characterized in that, The method comprises the following steps: a data collection module is used to collect operation data of a plateau new energy project, carbon asset data of a carbon trading platform, and green certificate data of a green certificate trading platform; a collaborative analysis module is used to perform correlation analysis and time series prediction analysis on the operation data, carbon asset data, and green certificate data, and to establish a synergistic effect evaluation model to analyze the influence of carbon asset and green certificate synergy on the overall revenue and cost of the plateau new energy project; a decision support module is used to develop a cost control strategy for carbon asset and green certificate synergy financing of the plateau new energy project based on the analysis results of the collaborative analysis module; an execution and monitoring module is used to establish a data interface with the carbon trading platform, the green certificate trading platform, and financial institutions, automatically execute the cost control strategy, and real-time monitor the cost control strategy execution process and key indicators of the plateau new energy project operation, and dynamically adjust and optimize the cost control strategy based on real-time monitoring results.

2. The carbon asset and green certificate co-financing cost control system for highland new energy projects according to claim 1, characterized in that, The collaborative analysis module performs correlation analysis on the operation data, carbon asset data, and green certificate data, including: setting a time unit, defining the operation data, carbon asset data, and green certificate data in one time unit as a transaction; setting a suitable support threshold and confidence threshold, the support representing the frequency of a certain item set in all transactions, and the confidence representing the frequency of the occurrence of the consequent transaction in the transaction containing the antecedent; generating item sets frequently appearing in the transaction set by scanning the transaction set multiple times according to the support threshold and confidence threshold, and further mining strong correlation rules between the operation data, carbon asset data, and green certificate data.

3. The system according to claim 1 or 2, characterized in that, The collaborative analysis module performs time series prediction analysis on the operation data, carbon asset data, and green certificate data, including: preprocessing time series data in the operation data; extracting features related to carbon asset and green certificate price prediction from the operation data; analyzing the time series data and extracted features through a neural network model to output predicted values of the carbon asset and green certificate prices.

4. The system according to claim 1, wherein, The collaborative analysis module establishes a synergistic effect evaluation model to analyze the influence of carbon asset and green certificate synergy on the overall revenue and cost of the plateau new energy project, including: based on a cost-benefit analysis algorithm, evaluating the influence of carbon asset and green certificate synergy on the overall revenue and cost of the plateau new energy project; using financial risk assessment indicators to quantitatively analyze the risks in the process of carbon asset and green certificate synergy financing; integrating the cost analysis algorithm and financial risk assessment indicators to build a comprehensive synergistic effect evaluation model to comprehensively measure the pros and cons of the carbon asset and green certificate synergy strategy.

5. The carbon asset and green certificate co-financing cost control system for high plateau new energy projects according to claim 1 or 4, characterized in that, The decision support module develops a cost control strategy for carbon asset and green certificate synergy financing of the plateau new energy project based on the analysis results of the collaborative analysis module, including: carbon asset trading strategy development, including: selecting a suitable timing for carbon asset trading based on trend analysis of carbon asset prices by the collaborative analysis module; developing a suitable carbon emission reduction trading volume based on the project's own carbon emission reduction reserves and market demand for carbon emission reduction; Evaluate potential trading partners in the carbon trading market, and prefer to cooperate with trading partners with good reputation, financial stability and high influence in the market; Green certificate transaction strategy development, including: According to the demand structure analysis of the green certificate market and the green certificate supply characteristics of the project itself, accurate market demand matching is carried out; According to the price fluctuation of the green certificate market and the fund demand of the project itself, the green certificate transaction rhythm is reasonably developed; According to the recognition and value difference of different types of green certificate trading market, the reasonable green certificate portfolio transaction strategy is developed.

6. The system according to claim 5, wherein, The decision support module develops the cost control strategy of carbon asset and green certificate collaborative financing of the plateau new energy project according to the analysis result of the collaborative analysis module, and further comprises: Financing scheme development, including: Comprehensively analyze the fund demand characteristics and scale of the plateau new energy project at different stages; Comprehensively analyze the financing cost and financing risk of the plateau new energy project, and recommend appropriate financing channels; Develop carbon asset and green certificate collaborative financing products, and evaluate and design the feasibility and risk and return of the financing products to ensure that they meet the actual needs of the project and market rules; Risk response strategy development, including: According to the price fluctuation risk of carbon asset and green certificate market, set reasonable stop loss point and stop profit point; According to the trend prediction of the change of supply and demand relationship of carbon asset and green certificate market, adjust the carbon asset and green certificate transaction strategy in advance; According to the policy adjustment risk related to carbon trading and green certificate, adjust the carbon asset development plan or green certificate transaction strategy of the project.

7. The system according to claim 1, wherein, The execution and monitoring module establishes data interface with the carbon trading platform, green certificate trading platform and financial institutions, and automatically executes the cost control strategy; including: Carbon asset transaction execution, including: Through the deep integration interface with the carbon trading platform, according to the carbon asset transaction strategy developed by the decision support module, automatically generate and issue transaction instructions; After the transaction instruction is issued, continuously track the whole process of carbon asset transaction, and confirm the execution status of each operation; Record the key data of each carbon asset transaction in detail, including transaction time, transaction price, transaction quantity and transaction counterparty information; Green certificate transaction execution, including: According to the analysis of green certificate market demand and transaction object recommendation of the decision support module, accurately locate the potential transaction object; After determining the transaction object, assist the project party and the transaction counterparty in transaction negotiation through the automatic communication tool, and assist both parties to complete contract signing; After completing the contract signing, real-time monitor the green certificate delivery status, confirm the successful delivery of the green certificate, send settlement instruction to the financial institution to complete the fund collection and payment, and record and check the fund flow in the settlement process; Financing operation execution, including: According to the financing scheme developed by the decision support module, submit financing application materials to the selected financial institution, and follow up the financing application approval progress to obtain feedback information in time; Prepare the materials required for the financing involving carbon assets and green certificate pledge, and complete the registration and recordation of the pledge in accordance with relevant regulations and financial institution requirements, and check the status of the pledge during the pledge period to ensure the value and integrity of the pledge; After the financing funds are credited, strictly manage the use of the financing funds to ensure that the financing funds are used in accordance with the financing purpose plan.

8. The system according to claim 1 or 7, characterized in that, The execution and monitoring module monitors the cost control strategy execution process and the key indicators of the operation of the highland new energy project in real time, including: Through the data interface with the carbon trading platform and the green certificate trading platform, the latest transaction data including transaction price, transaction volume and inventory quantity are obtained, and compared with the expected indicators set by the decision support module for comparative analysis; Through the system interface with the financial institutions, the issuance of the financing amount is obtained, the change of the financing interest rate is monitored in real time, and through the interface with the project financial management system, the use efficiency of the funds is evaluated; Through the interface with the data collection module, the running state indicators of the highland new energy project are continuously tracked.

9. The system according to claim 1, wherein, The execution and monitoring module monitors the cost control strategy execution process and the key indicators of the operation of the highland new energy project in real time, further including: By setting price fluctuation threshold and supply and demand change threshold, the price fluctuation and supply and demand change of carbon assets and green certificate market are monitored in real time; Through the policy monitoring system, policy adjustment information is obtained in time, and the potential impact of policy change on the highland new energy project is analyzed; The device running data in the operation process of the highland new energy project is analyzed to predict the time and probability of possible device failure and issue device failure warning in advance.

10. The system according to claim 1 or 9, wherein, The execution and monitoring module dynamically adjusts and optimizes the cost control strategy according to the real-time monitoring results, including: According to the key indicator data and risk warning information obtained by real-time monitoring, analyze the problems and deviations in the current cost control strategy execution process, and determine whether the current cost control strategy needs to be adjusted; The demand for adjusting the cost control strategy is fed back to the decision support module, which reanalyzes and evaluates the risks according to the feedback information, and formulates a new cost control strategy, which is fed back to the execution and monitoring module; The execution and monitoring module adjusts the related strategies of carbon asset trading, green certificate trading and financing operation according to the new cost control strategy to ensure that the adjusted strategy can be accurately and effectively executed.

Images