A carbon benefit and investment cost collaborative optimization analysis system for a regional power grid

Abstract

The application provides a carbon benefit and investment cost collaborative optimization analysis system for a regional power grid, and belongs to the technical field of power grid investment optimization; solves the problems of the prior art in carbon benefit and cost integrated optimization, dynamic response, scene adaptation and the like; the system comprises: an intelligent data acquisition module for acquiring power grid physical data, carbon monitoring data, financial data and dynamic carbon policy data; a multi-source heterogeneous data fusion middle station for performing data cleaning and standardization processing on multi-source heterogeneous data, realizing deep fusion of heterogeneous data, and realizing data storage and scheduling; a dynamic double-objective optimization engine for realizing double-objective optimization of carbon benefit maximization and total investment cost minimization according to basic constraints and dynamic constraints, and solving multiple optimization schemes; a full-scene decision output module for visualizing and displaying the optimization schemes; the application is applied to regional power grid investment considering carbon benefits.

Description

Technical Field

[0001] This application relates to the field of power grid investment optimization technology, and in particular to a system for synergistic optimization analysis of carbon benefits and investment costs for regional power grids. Background Technology

[0002] In existing technologies, regional power grid investment decision-making systems mainly suffer from two types of technical limitations: one is the traditional power grid investment analysis system, which only focuses on financial cost accounting and payback period prediction, without incorporating carbon emission reduction benefits into the core decision-making dimension, and thus cannot meet the low-carbon investment needs under the "dual carbon" objective; the other is the single carbon benefit assessment system, which can only achieve static accounting of carbon emissions, lacks dynamic linkage optimization logic with investment costs, and results in a one-sided decision.

[0003] Meanwhile, existing collaborative analysis technologies only use a simple weighted summation method to handle carbon efficiency and cost targets, without considering the dynamic coupling relationship between the two, and do not cover the full-scenario differences in regional power grid investment, resulting in low optimization accuracy and poor adaptability.

[0004] In summary, the existing technology still has at least the following drawbacks:

[0005] 1. Insufficient integration: Existing technologies have not established a deep coupling mechanism between carbon emission reduction benefits and investment costs. They only achieve independent statistics or simple superposition of the two types of data, which cannot reflect the dynamic correlation and constraint relationship between the two, resulting in a lack of scientific rigor in the decision-making model.

[0006] 2. Lack of dynamic response: The optimized model parameters are mostly fixed settings, which cannot respond in real time to dynamic factors such as carbon trading market price fluctuations, changes in new energy output, and policy adjustments. The output decision-making solutions have poor adaptability and are easily out of touch with actual application scenarios.

[0007] 3. Poor scenario adaptability: Most existing models are general designs and are not customized for the individual needs of different regional power grid scales and different investment types, resulting in insufficient optimization accuracy in specific scenarios and limited decision-making reference value.

[0008] 4. Weak data processing capabilities: It lacks an efficient multi-source heterogeneous data processing mechanism, has poor compatibility with different types of data such as power grid physical data, carbon monitoring data, and financial data, and has low data cleaning and correlation efficiency, making it unable to provide real-time and accurate data support for optimization decisions. Summary of the Invention

[0009] To address the problems existing in current technologies regarding integrated optimization of carbon benefits and costs, dynamic response, and scenario adaptation, this application proposes a collaborative optimization analysis system for carbon benefits and investment costs in regional power grids, which breaks through the limitations of traditional technologies in terms of simplification and staticity.

[0010] The technical solution adopted in this application is: a system for synergistic optimization analysis of carbon benefits and investment costs in regional power grids, comprising:

[0011] Intelligent data acquisition module: used to collect power grid physical data, carbon monitoring data, financial data and dynamic carbon policy data to form multi-source heterogeneous data;

[0012] Multi-source heterogeneous data fusion platform: used to clean and standardize multi-source heterogeneous data, achieve deep fusion of heterogeneous data, and realize data storage and scheduling;

[0013] The data cleaning and standardization process is structured as a three-level data processing workflow, including:

[0014] First-level cleaning: Using a machine learning-based outlier detection algorithm to remove outlier data;

[0015] Secondary conversion: Through a custom standardized protocol, power grid physical data, carbon monitoring data, and financial data are uniformly converted into standardized indicators that can be correlated and calculated.

[0016] The custom standardization protocol adopts a three-part structure of "data identifier header + core parameters + check bit". The data identifier header is used to distinguish data types. The core parameters include the original value, the collection timestamp, and the conversion coefficient ID. The conversion coefficient is used to uniformly convert three types of heterogeneous data with different units and dimensions, namely power grid physical data, carbon monitoring data, and financial data, into two types of standardized indicators: "carbon benefit quantification value" and "equivalent cost value".

[0017] Three-level correlation: Based on the carbon benefit-cost coupling coefficient, establish correlation mapping relationships between different types of data;

[0018] The carbon benefit-cost coupling coefficient is used to establish a strong correlation between power grid physical data, carbon monitoring data, and financial data and “carbon benefit” and “cost” respectively. Then, through the dynamic calibration characteristics of the carbon benefit-cost coupling coefficient, the three types of data can be mapped and calculated together under the same analytical framework.

[0019] Dynamic dual-objective optimization engine: used to achieve dual-objective optimization of maximizing carbon benefits and minimizing total investment costs based on basic constraints and dynamic constraints, and to obtain multiple optimization solutions;

[0020] Full-scenario decision output module: used to visualize the optimization solution.

[0021] Furthermore, the intelligent data acquisition module is equipped with an adaptive communication interface to ensure compatibility with the data output formats of existing power grid monitoring equipment, carbon monitoring instruments, and financial systems, and to support dynamic adjustment of the data acquisition frequency.

[0022] Furthermore, the adaptive communication interface achieves automatic identification and matching of different device interfaces through interface circuits and software algorithms. The interface circuit includes a modular main control unit, a multi-protocol conversion unit, a dynamic adaptation unit, a signal conditioning unit, and a power management unit. The multi-protocol conversion unit includes various conversion chips for different protocols, each connected to the main control unit via an SPI interface. The dynamic adaptation unit includes a programmable clock generator and a voltage adaptive circuit, controlled by the main control unit via an I2C interface. The signal conditioning unit's input port connects to an external terminal, and its output port connects to the multi-protocol conversion unit. Data converted through the protocol is transmitted to the main control unit via an internal bus. The power management unit provides stable power to all units.

[0023] The software algorithm employs an automatic protocol identification algorithm, a dynamic parameter matching algorithm, and an adaptive acquisition frequency adjustment algorithm in conjunction with the interface circuit to achieve adaptive functionality.

[0024] Furthermore, the logic of the automatic protocol identification algorithm is as follows:

[0025] Signal capture: After the interface circuit is started, the main control unit controls the multi-protocol conversion unit to enter "listening mode" to capture the first 3-5 frames of data sent by the external terminal and store them in the buffer;

[0026] Feature extraction: Extracting key features from data frames using algorithms;

[0027] Template matching: The extracted features are compared with the built-in "protocol feature template library". This protocol feature template library contains standard feature parameters of various protocols. The cosine similarity is calculated using a similarity calculation algorithm. When the cosine similarity is ≥90%, it is determined to be a successful match, and the protocol type is output.

[0028] Protocol activation: Based on the identification result, the main control unit activates the corresponding protocol conversion chip in the multi-protocol conversion unit through the SPI interface, loads the parsing rules of the protocol, and establishes a data parsing link.

[0029] Furthermore, the logic of the dynamic parameter matching algorithm is as follows:

[0030] Baud rate detection: For the identified protocol type, the main control unit controls the programmable clock generator to output the commonly used baud rates in sequence, while listening for data feedback. When a complete and correctly verified data frame is received, it is determined that the current baud rate is successfully matched.

[0031] Data format matching: The system uses a trial-and-error method to automatically match data bits, stop bits, and check bits, combine parameters in sequence, and send test commands. When a correct response frame is received from the terminal, the parameter configuration is locked.

[0032] Dynamic adjustment: Real-time monitoring of data transmission error rate. When the error rate is ≥3%, the parameter matching process is automatically restarted to adapt to dynamic changes in external terminal parameters.

[0033] Furthermore, in the secondary conversion, "carbon benefit quantification value" and "equivalent cost value" are the two core standardized indicators. The mapping relationship between power grid physical data, carbon monitoring data, financial data and standardized indicators is established. Among them, carbon benefit quantification value is used to quantify the direct economic benefits and policy incentive benefits brought about by carbon emission reduction, and equivalent cost value is used to quantify investment costs, operation and maintenance costs, and carbon emission penalty costs.

[0034] For power grid physical data, the conversion factor = loss penalty benchmark factor × measured value of regional carbon intensity in the current month / carbon intensity value in the previous month;

[0035] For carbon monitoring data, the conversion factor = carbon element conversion benchmark factor × current carbon trading price / average carbon price over the past 30 days;

[0036] For financial data, the conversion factor = investment type benchmark factor × current carbon price / historical average carbon price;

[0037] Furthermore, it automatically matches conversion rules for different data types through the protocol's built-in "conversion coefficient library," achieving dynamic adaptation of conversion rules.

[0038] Furthermore, based on the carbon benefit-cost coupling coefficient, the logic for establishing the correlation mapping relationship between different types of data is as follows:

[0039] The correlation mapping between power grid physical data and carbon monitoring data is achieved through the following two formulas:

[0040] Line loss carbon emissions = Line loss power × Operating time × Regional carbon intensity;

[0041] Carbon benefit loss value = line loss carbon emissions × carbon trading price × policy incentive coefficient × K, where K is the carbon benefit-cost coupling coefficient;

[0042] The mapping between power grid physical data and financial data is achieved through the following two formulas:

[0043] Equivalent cost of new energy access = initial investment cost × cost allocation coefficient × (1-K);

[0044] Carbon benefit improvement value = New energy power generation × Regional carbon intensity × Carbon trading price × Policy incentive coefficient × K;

[0045] The correlation mapping between carbon monitoring data and financial data is achieved through the following formula:

[0046] Equivalent cost savings = quantified carbon benefit value × K / (1-K).

[0047] Furthermore, the fundamental constraints include constraints on the safe operation of the regional power grid, carbon emission reduction policies, and investment budgets;

[0048] Dynamic constraints introduce constraints on carbon trading price fluctuations and new energy output fluctuations, and dynamically adjust constraint thresholds based on real-time collected data.

[0049] Furthermore, the objective function of the dual-objective optimization is max(carbon benefit quantification value × carbon benefit weight) - min(total investment cost × cost weight), where the carbon benefit quantification value = carbon emission reduction × carbon trading price × policy incentive coefficient, and the two weight coefficients are adjusted in real time through a dynamic adaptive algorithm.

[0050] Where carbon benefit weight = K × dynamic adjustment factor α, cost weight = (1-K) × dynamic adjustment factor β, and the constraint condition is α+β=2.0, where K is the carbon benefit-cost coupling coefficient;

[0051] The carbon benefit-cost coupling coefficient is based on a multi-factor comprehensive evaluation method, selecting five key influencing factors: carbon trading price volatility, the proportion of renewable energy output, investment budget adequacy ratio, carbon intensity target achievement rate, and grid security margin. The weight of each factor is determined using the analytic hierarchy process (AHP). Carbon trading price volatility, renewable energy output ratio, and carbon intensity target achievement rate are positively correlated with the K value, while investment budget adequacy ratio and grid security margin are negatively correlated with the K value. The calculation formula is as follows:

[0052] ;

[0053] Where wi is the weight of the i-th factor, and fi is the quantized value of the i-th factor.

[0054] Furthermore, the full-scenario decision output module includes:

[0055] Model library matching: The system has a built-in full-scenario decision-making adaptation model library, which includes six special sub-models: provincial power grid new energy access optimization sub-model, municipal power grid upgrading and transformation optimization sub-model, county power grid energy storage supporting optimization sub-model, cross-regional power grid interconnection optimization sub-model, industrial park power grid low-carbon transformation optimization sub-model, and rural power grid green upgrading optimization sub-model. The system can automatically match the optimal sub-model based on the parameters input by the user.

[0056] Results presentation and output include:

[0057] Visual presentation: Through charts and graphs, the carbon emission reduction benefits, total investment cost, investment payback period, carbon benefit-cost coupling coefficient, and key indicators of safety constraint satisfaction of each optimization scheme are presented intuitively.

[0058] Decision support: The built-in solution ranking algorithm sorts the solutions according to the user-defined priorities and generates a decision report that includes indicator interpretation, risk warnings, and implementation suggestions. It supports export in different formats.

[0059] The advantages of this application over the prior art are as follows:

[0060] 1. A deep coupling mechanism between carbon benefits and investment costs was constructed. Through core parameter design and algorithm innovation, the same-dimensional integrated analysis of two types of heterogeneous indicators was achieved, which solved the defect of insufficient integration of existing technologies.

[0061] 2. A dynamic dual-objective optimization engine was developed, which enables real-time response to dynamic factors such as power grid operation status, market prices, and policy adjustments. It can adaptively optimize decision priorities and improve the dynamic adaptability of the solution.

[0062] 3. A full-scenario decision-making adaptation model library has been established, and special sub-models have been designed for different regional scales and different investment types, which has improved the optimization accuracy and decision-making adaptability of the system in diverse application scenarios.

[0063] 4. A multi-source heterogeneous data fusion platform was built, the data processing flow and adaptation algorithm were optimized, and the processing efficiency and quality of cross-type and cross-dimensional data were improved, providing real-time and accurate data support for collaborative optimization decision-making. Attached Figure Description

[0064] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0065] Figure 1 System structure block diagram provided for embodiments of this application;

[0066] Figure 2 This is a flowchart illustrating the implementation of conversion rules for automatically matching different data types using conversion coefficients, as provided in an embodiment of this application. Detailed Implementation

[0067] like Figure 1 and Figure 2 As shown, this application provides a system for the coordinated optimization analysis of carbon benefits and investment costs in regional power grids. It adopts an innovative end-to-end architecture of "data acquisition - fusion processing - dynamic optimization - decision output," and mainly consists of four parts: an intelligent data acquisition module, a multi-source heterogeneous data fusion platform, a dynamic dual-objective optimization engine, and a full-scenario decision output module. These modules are connected in series via a high-speed communication interface. The specific structure is as follows:

[0068] (a) Intelligent data acquisition module:

[0069] 1. Hardware configuration: Equipped with multiple types of data acquisition terminals, including power grid operation parameter acquisition units (current / voltage sensors, power transmitters), carbon emission online monitoring units (carbon emission concentration sensors, fuel consumption meters), financial data acquisition units (equipment price acquisition terminals, operation and maintenance cost statistics modules), and dynamic parameter acquisition units (carbon trading price receivers, policy coefficient update terminals), covering all dimensions of data needs for carbon benefit and cost analysis.

[0070] 2. An adaptive communication interface was designed: An adaptive communication interface was built into the data acquisition terminal, which is compatible with the data output formats of existing power grid monitoring equipment, carbon monitoring instruments and financial systems, and supports dynamic adjustment of data acquisition frequency (1 minute to 1 hour), solving the problems of poor data acquisition compatibility and insufficient real-time performance of existing technologies.

[0071] a. The implementation principle of the adaptive communication interface in this application is as follows:

[0072] The core principle of the adaptive communication interface is "automatic multi-protocol identification + dynamic parameter matching + cross-device compatibility adaptation." Through the collaborative work of hardware-level multi-protocol compatible circuits and software-level intelligent identification algorithms, it achieves automatic adaptation to the data formats, communication protocols, and transmission rates of different types of terminals (power grid monitoring equipment, carbon monitoring instruments, financial systems, etc.). Seamless data acquisition can be completed without manual configuration, and it also supports dynamic adjustment of the acquisition frequency, solving the problems of poor compatibility and insufficient real-time performance of existing interfaces. Its core logic is: the adaptive communication interface uses software algorithms to detect the communication characteristics (protocol type, data frame format, transmission rate) of the access terminal in real time, automatically matching the corresponding communication protocol parsing rules and hardware parameter configurations to establish a stable data transmission link; simultaneously, it dynamically adjusts the acquisition frequency according to the importance of the data and the system's computing load, reducing system resource consumption while ensuring data real-time performance.

[0073] b. Circuit structure design of adaptive communication interface

[0074] The interface circuit adopts a modular structure of "main control unit + multi-protocol conversion unit + dynamic adaptation unit + signal conditioning unit", and the specific design is as follows:

[0075] 1. Core Circuit Composition

[0076] 1) Main control unit: It adopts a high-performance microcontroller (MCU, model STM32H743VI) as the core, which is responsible for coordinating the work of each unit, running adaptive algorithms, storing protocol rule base, having high-speed data processing capability (480MHz main frequency) and rich peripheral interfaces, supporting multi-threaded parallel processing, and ensuring the real-time performance of protocol recognition and data transmission.

[0077] 2) Multi-protocol conversion unit (creative core circuit): This unit incorporates a multi-protocol hardware parsing chip, covering protocols commonly used in power grid equipment such as Modbus-RTU / TCP, IEC 61850, and DL / T 645; the HJ 212-2017 protocol commonly used in carbon monitoring instruments; and RS-232 / 485 and Ethernet protocols commonly used in financial systems. Through hardware-level protocol compatibility design, it avoids the latency issues associated with software protocol conversion. The circuit adopts a "protocol chip array" architecture, including MAX485 (RS-485 conversion chip), W5500 (Ethernet protocol chip), and SD3078 (IEC 61850 protocol dedicated chip). Each chip connects to the MCU via an SPI interface and can activate the corresponding protocol chip based on the identification result, achieving rapid protocol conversion.

[0078] 3) Dynamic Adaptation Unit: This unit includes a programmable clock generator (model CDCE913) and a voltage adaptive circuit. The programmable clock generator supports clock signal output from 1MHz to 100MHz and can dynamically adjust the communication baud rate (1200bps-115200bps) according to software algorithm instructions to adapt to the transmission rate requirements of different terminals. The voltage adaptive circuit adopts a wide voltage input design (3.3V-24V) and automatically adjusts the output voltage through an LDO regulator chip (model AMS1117) to match the power supply requirements of the connected terminal, avoiding communication failures caused by voltage mismatch.

[0079] 4) Signal Conditioning Unit: Composed of a differential amplifier circuit, a filter circuit, and a level conversion circuit. The differential amplifier circuit uses an AD8421 chip to amplify weak differential signals (such as small voltage signals from sensors) output from the terminal to a standard level (0-3.3V), improving signal anti-interference capability. The filter circuit uses a second-order RC low-pass filter, with a cutoff frequency that can be configured via software (1kHz-10kHz), filtering out electromagnetic interference noise in the industrial environment. The level conversion circuit uses a 74HC125 chip to automatically convert signal levels from different terminals (TTL / RS-232 / RS-485), ensuring the stability of signal transmission.

[0080] 5) Power Management Unit: It adopts a dual power supply design with an input voltage of AC 220V or DC 12V. It is converted into three stable voltages of 3.3V, 5V and 12V through a switching power supply module (model MP2307) to power the MCU, protocol conversion unit and signal conditioning unit respectively. It is also equipped with TVS transient suppression diodes and fuses to prevent voltage surges and short circuits from damaging the circuit.

[0081] 2. Circuit connection relationship

[0082] The main control unit (STM32H743VI) connects to the protocol chips of the multi-protocol conversion unit via the SPI interface and controls the programmable clock generator and voltage adaptive circuit via the I2C interface. The input port of the signal conditioning unit is connected to an external terminal, and the output port is connected to the multi-protocol conversion unit. The data after protocol conversion is transmitted to the MCU through the internal bus. The power management unit provides stable power to each unit. The overall circuit is modularly integrated through PCB board wiring. The interface adopts a standard industrial interface (aviation plug) to improve environmental adaptability.

[0083] 3. Software algorithm design for adaptive communication interface

[0084] The software algorithm adopts a three-in-one design of "automatic protocol identification algorithm + dynamic parameter matching algorithm + adaptive acquisition frequency adjustment algorithm", which works in conjunction with the hardware circuit to achieve adaptive function, as detailed below:

[0085] 1) Automatic Protocol Identification Algorithm

[0086] The core logic of the algorithm is to automatically identify the communication protocol type of the access terminal based on data frame feature extraction and template matching, without the need for manual preset. Specific implementation steps:

[0087] Step 1: Signal Acquisition. After the interface is started, the MCU controls the multi-protocol conversion unit to enter "listening mode" to capture the first 3-5 frames of data (data length ≥ 32 bytes) sent by the access terminal and store them in the buffer.

[0088] Step 2: Feature Extraction. Key features of the data frame are extracted using algorithms, including frame header / tail identifiers (such as the 0x01-0x0F address code in the Modbus protocol, and the "##" frame header in the HJ 212 protocol), data length, checksum method (CRC16 / CRC32 / XOR check), and field separators (comma / space / semicolon), etc.

[0089] Step 3: Template Matching. The extracted features are compared with the built-in "Protocol Feature Template Library". The template library contains standard feature parameters of various protocols (such as the ASN.1 encoding format of the IEC 61850 protocol and the 68H frame header of the DL / T 645 protocol). A similarity calculation algorithm (cosine similarity) is used. When the similarity is ≥90%, it is determined to be a successful match, and the protocol type is output.

[0090] Step 4: Protocol Activation. Based on the identification result, the MCU activates the corresponding protocol chip in the multi-protocol conversion unit through the SPI interface, loads the parsing rules of the protocol (such as data field definitions and byte order conversion methods), and establishes a data parsing link.

[0091] 2) Dynamic parameter matching algorithm

[0092] Based on protocol identification, the system automatically matches parameters such as communication baud rate, data bits, stop bits, and parity bits to ensure accurate data transmission. Specific implementation steps:

[0093] Step 1: Baud Rate Detection. For the identified protocol type, the MCU controls the programmable clock generator to sequentially output common baud rates (1200bps, 2400bps, 9600bps, 19200bps, 115200bps), while simultaneously monitoring data feedback. When a complete and correctly verified data frame is received, it is determined that the current baud rate is successfully matched.

[0094] Step 2: Data format matching. A trial-and-error method is used to automatically match data bits (7 / 8 bits), stop bits (1 / 2 bits), and parity bits (no parity / odd parity / even parity). Parameters are combined sequentially, and test commands (such as Modbus protocol's register read command 0x03) are sent. When a correct response frame is received from the terminal, the parameter configuration is locked.

[0095] Step 3: Dynamic Adjustment. Monitor the data transmission error rate in real time. When the error rate is ≥3% (≥3 erroneous frames out of 100 consecutive data frames), automatically restart the parameter matching process to adapt to dynamic changes in terminal parameters (such as parameter reset after terminal restart).

[0096] 3) Adaptive adjustment algorithm for acquisition frequency

[0097] Based on data importance levels and system computing load, the data acquisition frequency (1 minute to 1 hour) is dynamically adjusted to balance real-time performance and system resource consumption. Specific implementation steps:

[0098] Step 1: Data Classification. The collected data is divided into three levels: Level 1 data (grid operation parameters, carbon trading prices) is core data with the highest priority; Level 2 data (carbon emissions, equipment prices) is important data with medium priority; and Level 3 data (operation and maintenance cost statistics) is general data with the lowest priority.

[0099] Step 2: Load monitoring. The MCU monitors the system's computing load (CPU utilization, memory usage) and data transmission bandwidth in real time. When the load is ≥80%, the frequency adjustment mechanism is triggered.

[0100] Step 3: Frequency Adjustment. Based on the data level and load status, the optimal acquisition frequency is calculated using an algorithm: Level 1 data frequency is maintained at 1-5 minutes / time to ensure real-time performance; Level 2 data frequency is set to 10 minutes / time when the load is low (≤50%) and 30 minutes / time when the load is high (50%-80%); Level 3 data frequency is set to 30 minutes / time when the load is low and 1 hour / time when the load is high. The adjustment command is sent to the acquisition terminal via the MCU, and the terminal responds and updates the acquisition frequency.

[0101] (II) Multi-source heterogeneous data fusion platform (core module), including:

[0102] 1. Data cleaning and standardization:

[0103] Design a three-level data processing flow:

[0104] First, data cleaning and standardization work was carried out, and a three-level data processing workflow was designed:

[0105] First-level cleaning: Employing machine learning-based outlier identification algorithms (such as the Isolation Forest algorithm) to remove abnormal data caused by sensor malfunctions and data transmission errors;

[0106] Secondary conversion: Through a custom standardized protocol, power grid physical data (unit: MW, kV), carbon emission data (unit: tons of CO2), and financial data (unit: RMB 10,000) are uniformly converted into standardized indicators that can be calculated in relation to each other. Its core principle is based on "physical meaning mapping + standardized protocol definition + coupling coefficient calibration", with "carbon benefit quantification value" and "equivalent cost value" as the two core standardized indicators, and establishes a mapping relationship between various raw data and standardized indicators.

[0107] The carbon benefit quantification value (unit: 10,000 yuan) primarily reflects the direct economic benefits (carbon trading revenue) and policy incentive benefits brought about by carbon emission reduction. The formula is as follows:

[0108] Carbon benefit quantification value = carbon emission reduction (tons of CO2) × carbon trading price (ten thousand yuan / ton of CO2) × policy incentive coefficient (no unit, range 1.0-1.8).

[0109] The equivalent cost value (unit: RMB 10,000) covers investment costs, operation and maintenance costs, and carbon emission penalty costs. The formula is as follows:

[0110] Equivalent cost value = investment cost (ten thousand yuan) + operation and maintenance cost (ten thousand yuan) + carbon over-emission penalty cost (ten thousand yuan).

[0111] Calculate the carbon benefit quantification value and carbon benefit quantification value for different types of data respectively.

[0112] For power grid physical data (such as line loss rate), based on the physical correlation between "energy consumption and carbon emissions," line losses correspond to additional electrical energy consumption, which is then converted into carbon emissions and mapped to equivalent costs. The formula is as follows:

[0113] Line loss carbon emissions (tons of CO2) = Line loss power (MW) × Operating time (h) × Average carbon intensity of regional power grid (tons of CO2 / MWh).

[0114] Equivalent cost increment (ten thousand yuan) = line loss carbon emissions × carbon trading price × 1.2 (loss penalty coefficient), and real-time calibrated by retrieving the average carbon intensity data of the regional power grid for the current month, updated once a month;

[0115] Carbon monitoring data (such as carbon emission concentration) is combined with the volumetric flow rate of emission sources to calculate carbon emissions per unit time, which is directly correlated with the quantified value of carbon benefits. The formula is as follows:

[0116] Carbon emissions (tons of CO2) = carbon emission concentration (mg / m³) 3 ) × Emission source flow (m 3 / h) × running time (h) × 10 -9 × Carbon element conversion coefficient (1.96, derived from CO2 molecular weight), and calibrated using the factory calibration coefficient of online monitoring equipment + on-site measurement (once per quarter).

[0117] Financial data (such as equipment prices) are directly incorporated into the equivalent cost value. Cost allocation coefficients are set for different investment types (new construction / renovation), using the following formula:

[0118] Equivalent investment cost (ten thousand yuan) = equipment price × cost allocation coefficient (1.0 for new projects, 0.6 for renovation projects, derived based on depreciation period), and dynamically adjusted in conjunction with carbon trading price fluctuations. The adjustment formula is as follows:

[0119] Allocation factor = Baseline factor × (Current carbon price / Historical average carbon price).

[0120] The custom standardized protocol (named "Carbon Efficiency-Cost Data Unified Protocol V1.0") adopts a three-segment structure of "data identifier header + core parameters + check bit". The data identifier header (8 bytes) is used to distinguish data types (01=power grid physical data, 02=carbon monitoring data, 03=financial data). The core parameters (32 bytes) contain the original values, collection timestamps, and conversion coefficient IDs. The check bit (4 bytes) is calculated based on the CRC32 algorithm to ensure that the data conversion process is tamper-proof. Through the protocol's built-in "conversion coefficient library", it automatically matches the conversion rules for different data types, realizing dynamic adaptation of conversion rules.

[0121] The conversion coefficient is the core quantitative parameter of the multi-source heterogeneous data fusion platform. It is used to convert heterogeneous data from different units and dimensions, such as power grid physics, carbon monitoring, and finance, into two standardized indicators: "carbon benefit quantification value" and "equivalent cost value".

[0122] Specific type:

[0123] 1. Power grid physical data (related to line losses): Conversion coefficient = 1.2 (loss penalty benchmark coefficient) × measured value of regional carbon intensity in the current month / carbon intensity value in the previous month;

[0124] 2. Carbon monitoring data (related to carbon emission concentration): Conversion coefficient = 1.96 (carbon element conversion benchmark coefficient) × current carbon trading price / average carbon price over the past 30 days;

[0125] 3. Financial data (equipment investment related): Conversion factor = investment type benchmark factor (new construction 1.0 / renovation 0.6) × current carbon price / historical average carbon price.

[0126] The principle behind automatically matching conversion rules for different data types using the protocol's built-in "conversion coefficient library" and achieving dynamic adaptation of conversion rules is as follows:

[0127] By "labeling data with identifier headers, and then the system finding the corresponding transformation rules based on the labels," automatic adaptation is achieved without manual intervention. The specific process is as follows:

[0128] 1. When data is accessed, the "data identifier header" (8 bytes) of the custom standardized protocol will clearly indicate the data type (01=power grid physical data, 02=carbon monitoring data, 03=financial data).

[0129] 2. After reading the identifier header, the system automatically retrieves the corresponding type of preset rule set (including exclusive conversion coefficients, conversion formulas, and calibration logic) from the conversion coefficient library.

[0130] 3. Combining core data parameters (such as collection timestamp and terminal type), the optimal conversion coefficient and conversion logic are matched from the rule set and directly applied to the data conversion process.

[0131] Three-level association: Based on the carbon benefit-cost coupling coefficient, establish the association mapping relationship of different types of data. For example, extra carbon emissions can be estimated by using line loss rate data, and then combined with the coupling coefficient to transform into equivalent cost indicators, so as to achieve deep integration of heterogeneous data.

[0132] The core principle of establishing correlation mapping between different types of data based on the carbon benefit-cost coupling coefficient is to use the coupling coefficient as a "quantitative correlation hub" to establish strong correlations between power grid physical data, carbon monitoring data, and financial data and the two core dimensions of "carbon benefit" and "cost" respectively. Then, through the dynamic calibration characteristics of the coupling coefficient, the mutual mapping and linkage calculation of the three types of data under the same analysis framework can be realized.

[0133] 1. Correlation and mapping between power grid physical data and carbon monitoring data

[0134] Power grid physical data (such as line loss power) is first converted into carbon emissions based on the physical relationship of regional carbon intensity. Then, combined with the coupling coefficient K, which reflects the weight of carbon benefits, a correlation logic of "the greater the line loss → the higher the carbon emissions → the more significant the carbon benefit loss" is established. The quantitative mapping is achieved through two formulas: "Line loss carbon emissions = line loss power × operating time × regional carbon intensity" and "Carbon benefit loss value = line loss carbon emissions × carbon trading price × policy incentive coefficient × K". This achieves a precise mapping between line loss data and carbon benefit loss data, and can also amplify or reduce the correlation strength synchronously through the dynamic adjustment of the coupling coefficient K (such as increasing K when carbon prices rise), ensuring that the mapping relationship is adapted to the external environment.

[0135] 2. Correlation and mapping between power grid physical data and financial data

[0136] The power grid physical data is first used to calculate the initial investment cost based on the access cost model. Then, combined with the carbon benefit-cost coupling coefficient K, which reflects the cost weight (1-K), a correlation logic is established that "the higher the proportion of new energy access → the higher the initial investment cost, but the more significant the improvement in carbon benefits (when K increases, it can offset some of the cost pressure)". Its quantitative mapping is achieved through two formulas: "equivalent cost of new energy access = initial investment cost × cost sharing coefficient × (1-K)" and "carbon benefit improvement value = new energy power generation × regional carbon intensity × carbon trading price × policy incentive coefficient × K". By using the coupling coefficient, the power grid physical data and financial data are closely linked to complete the linkage calculation of "cost input - carbon benefit return".

[0137] 3. Correlation and mapping between carbon monitoring data and financial data

[0138] The coupling coefficient directly serves as a bridge for the same-dimensional conversion between carbon monitoring data and financial data, achieving direct equivalence between "carbon benefits and costs." The mapping path is as follows: carbon monitoring data (such as carbon emission reductions) is first converted into a quantified carbon benefit value (carbon emission reductions × carbon trading price × policy incentive coefficient), and then mapped to "equivalent cost savings" (quantified carbon benefit value × K / (1-K)) using the coupling coefficient K. Conversely, financial data (such as additional investment costs) is mapped to "acceptable upper limit of carbon emission reductions" using the coupling coefficient (1-K) (additional investment cost × (1-K) / (carbon trading price × policy incentive coefficient)). The system uses the quantitative mapping formula "equivalent cost savings = carbon benefit quantification value × K / (1-K)" to achieve a direct mapping between carbon emission reduction data and cost savings data. For example, when K = 0.8 (carbon benefit priority), 1 million yuan of carbon benefit can be mapped to 4 million yuan of equivalent cost savings, clarifying the decision logic that "an additional 4 million yuan of cost can be invested to obtain this carbon benefit". When K = 0.3 (cost priority), 1 million yuan of carbon benefit is only mapped to 428,000 yuan of equivalent cost savings, thereby limiting additional cost investment and ensuring that the mapping relationship is consistent with the decision priority.

[0139] The calibration principle of the carbon benefit-cost coupling coefficient is as follows:

[0140] 1) Data type adaptation calibration

[0141] To address the heterogeneity of power grid physical data, carbon monitoring data, and financial data, a precise mapping relationship is constructed using scenario-based calibration coefficients.

[0142] Example 1: When converting power grid physical data (such as line loss rate), the coupling coefficient is calibrated to the correlation coefficient of "loss-carbon emission-cost" (1.2 × regional carbon intensity calibration value). The regional carbon intensity is updated monthly based on the actual energy consumption data of the power grid to ensure the accuracy of the conversion between line loss and equivalent cost.

[0143] Example 2: When converting carbon monitoring data (such as carbon emission concentration), the coupling coefficient is calibrated to the correlation coefficient of "concentration-emission-carbon benefit" (1.96 × real-time carbon trading price). Combined with the dynamic update of carbon trading price every 15 minutes, the carbon emission amount and the carbon benefit quantification value are synchronized and adapted.

[0144] Example 3: When converting financial data (such as equipment prices), the coupling coefficient is calibrated to the correlation coefficient of "investment type - cost allocation" (1.0 for new projects / 0.6 for retrofit projects × carbon price fluctuation coefficient), and dynamically adjusted according to the ratio of the current carbon price to the historical average carbon price to ensure the matching degree between investment cost and equivalent cost value.

[0145] 2) Spatiotemporal Dimension Dynamic Calibration

[0146] Time-dimensional calibration: The coupling coefficient is dynamically corrected at fixed intervals. The regional power grid carbon intensity is calibrated monthly, the carbon monitoring equipment accuracy is calibrated quarterly, and the carbon trading price is calibrated every 15 minutes to avoid spatiotemporal deviations caused by long-term fixed coefficients.

[0147] Regional calibration: To address the differences in carbon intensity targets and renewable energy ratios in different regions of the power grid, preset regional calibration coefficients (e.g., the coupling coefficient is increased by 10% in regions with high renewable energy ratios and by 15% in regions with high carbon intensity) to ensure that data conversion is consistent with the characteristics of regional scenarios.

[0148] 3) Error feedback closed-loop calibration

[0149] Error calibration is achieved through cross-validation of multi-source data, forming a closed-loop optimization mechanism: when the verification error after data fusion (such as the deviation between the calculated value of carbon emissions and the online monitoring value) is ≥2%, the coupling coefficient calibration process is automatically initiated to adjust the weight allocation of the correlation coefficient; combined with the outlier removal results after data cleaning, the coupling coefficients corresponding to high-confidence data (error ≤0.5%) are retained, and the coupling coefficients corresponding to low-confidence data (error ≥3%) are corrected to ensure that the coefficients are compatible with the data quality.

[0150] 4) Optimize target collaborative calibration

[0151] The calibration results of the coupling coefficient are synchronously fed back to the dynamic dual-objective optimization engine, realizing the coordinated linkage of "data transformation - objective optimization":

[0152] When the coupling coefficient K in the optimization engine is adjusted to 0.8 (carbon benefit priority) due to the increase in carbon price, the coupling coefficient in the data fusion stage is simultaneously calibrated to "carbon benefit weight tilt type" to increase the weight of carbon-related data in the same dimension conversion;

[0153] When the coupling coefficient K in the optimization engine is adjusted to 0.3 (cost priority) due to tight investment budget, the coupling coefficient in the data fusion stage is simultaneously calibrated to "cost weight tilt type" to prioritize the conversion accuracy of investment cost-related data.

[0154] 2. Data storage and scheduling: A hybrid storage architecture of distributed time-series database (InfluxDB) and relational database (MySQL) is adopted. The time-series database is used to store high-frequency real-time data (such as power grid operation parameters and carbon emissions), and the relational database is used to store structured data (such as financial statements and policy parameters). An intelligent scheduling algorithm is designed to retrieve relevant data in real time according to the optimization decision requirements, with a data query response time of ≤100ms.

[0155] By employing a three-tiered data processing workflow and a hybrid storage architecture, this approach addresses the shortcomings of existing technologies, such as low data processing efficiency, poor compatibility, and insufficient correlation, thereby providing high-quality data support for subsequent optimization decisions.

[0156] The core principle of the intelligent scheduling algorithm, which retrieves relevant data in real time based on optimization decision requirements, is demand-driven multi-database collaboration + priority scheduling + hotspot caching. Leveraging the characteristics of a hybrid storage architecture, it accurately matches and efficiently retrieves time-series / relational database data based on the real-time decision requirements of a dynamic dual-objective optimization engine. Specifically: first, it parses the data requirements for optimization decisions and accurately matches them with labeled data in the database; then, it allocates retrieval priorities according to the data's role in optimization, prioritizing high-frequency real-time data for core operations; finally, it employs multi-database parallel reading + local caching of hotspot data to reduce database access frequency, achieving low-latency, high-precision data scheduling and providing real-time data support for optimization decisions.

[0157] (III) Dynamic dual-objective optimization engine (core module), including:

[0158] 1. Setting constraints:

[0159] First, set the constraints, which include basic constraints and dynamic constraints.

[0160] The basic constraints include regional power grid safety operation constraints (transmission capacity, voltage stability threshold), carbon emission reduction policy constraints (regional carbon intensity target), and investment budget constraints (total investment ceiling, unit carbon emission reduction cost ceiling).

[0161] Dynamic constraints introduce constraints based on carbon trading price fluctuations and renewable energy output fluctuations. These constraints dynamically adjust thresholds based on real-time data collection to ensure the feasibility of optimization solutions. The core principle of dynamic constraints is "real-time data-driven adaptive adjustment of constraint thresholds."

[0162] The basic constraints are fixed boundaries, and the dynamic constraints are variable boundaries. The final constraint conditions are generated by combining the basic constraints with dynamic offsets, as shown in the formula:

[0163] The final constraint threshold = basic constraint threshold × (1 + dynamic offset), where the dynamic offset is calculated from real-time data and ranges from -0.1 to 0.1 (to ensure that the constraints do not exceed the physical and policy allowable range).

[0164] In typical dynamic constraints, the dynamic offset of carbon trading price fluctuation constraints is calculated as (current carbon price - average carbon price over the past 7 days) / average carbon price over the past 7 days × 0.5. When the carbon price rises, the offset is positive, appropriately relaxing the carbon emission reduction cost constraints; when the carbon price falls, the offset is negative, tightening the cost constraints. The dynamic offset of renewable energy output fluctuation constraints is calculated as (standard deviation of renewable energy output over the past hour / rated output) × (-0.3). The greater the output fluctuation, the more negative the offset, tightening the grid transmission capacity constraints and ensuring operational safety.

[0165] 2. Optimize model construction:

[0166] The objective function is max(carbon benefit quantification value × carbon benefit weight) - min(total investment cost × cost weight), where the carbon benefit quantification value = carbon emission reduction × carbon trading price × policy incentive coefficient, and the weight coefficient is adjusted in real time through a dynamic adaptive algorithm (adjustment range 0.3~0.7). The innovative improvement lies in introducing a carbon benefit-cost coupling coefficient to establish a dynamic correlation equation between the two objectives, avoiding the optimization imbalance caused by the traditional weighted summation method. The carbon benefit-cost coupling coefficient (denoted as K, with a value range of 0.1~0.9) is the core parameter for achieving the dynamic correlation between the two objectives. Its design principle is based on "dynamic adaptation of objective priority." The larger the K value, the higher the weight of the carbon benefit objective in the optimization; the smaller the K value, the higher the weight of the cost control objective. The formula is:

[0167] Carbon benefit weight = K × dynamic adjustment factor (α), cost weight = (1-K) × dynamic adjustment factor (β), with the constraint α+β=2.0 (ensuring the dynamic balance of the total weight).

[0168] The carbon benefit-cost coupling coefficient is based on a multi-factor comprehensive evaluation method, selecting five key influencing factors: carbon trading price volatility (factor weight 0.35), renewable energy output ratio (factor weight 0.25), investment budget adequacy ratio (factor weight 0.20), carbon intensity target achievement rate (factor weight 0.15), and grid security margin (factor weight 0.05). The weight of each factor is determined using the analytic hierarchy process (AHP). Carbon trading price volatility, renewable energy output ratio, and carbon intensity target achievement rate are positively correlated with the K value, while investment budget adequacy ratio and grid security margin are negatively correlated with the K value. The calculation formula is as follows:

[0169] ;

[0170] Where wi is the weight of the i-th factor, and fi is the quantified value of the i-th factor (normalized to the range of 0 to 1). Based on real-time data from the intelligent data acquisition module, the K value is recalculated every 15 minutes to ensure that the dual-objective weights are synchronized with external dynamic factors. For example, when carbon trading prices rise, the carbon benefit-cost coupling coefficient automatically increases, increasing the weight of the carbon benefit objective; when the investment budget is tight, the carbon benefit-cost coupling coefficient is adjusted in the opposite direction to prioritize cost control.

[0171] 3. Model Solving Algorithm:

[0172] An improved multi-objective particle swarm optimization algorithm is adopted, which integrates the crossover and mutation mechanism of genetic algorithm to improve the algorithm's global search capability and convergence speed, and avoid getting trapped in local optima.

[0173] The core framework of the algorithm is as follows: Initialize the particle swarm size to 100, with particle dimension equal to the number of optimization variables (such as the length of new lines, energy storage capacity, and the proportion of new energy access), and each particle represents one optimization scheme; calculate the fitness function based on the objective function, where a higher fitness value indicates a better scheme; particle updates combine the "individual optimal + global optimal" update strategy of the particle swarm algorithm with the crossover and mutation operations of the genetic algorithm, and the update speed formula is:

[0174] ;

[0175] in The inertial weight is dynamically adjusted to 0.4~0.8. , The learning factor (all values ​​are 2.0). The crossover operation uses random numbers (0-1) to cross over particles with the top 50% fitness values, employing a single-point crossover strategy to exchange key variables (such as carbon benefit weight and investment allocation ratio) in the particle dimension, generating new particles and improving population diversity. The mutation operation randomly adjusts the values ​​in the particle dimension of the crossover particles with a 5% mutation probability (mutation amplitude ≤10%) to avoid the algorithm getting trapped in local optima. When the change in the global optimal fitness value of particles over 20 consecutive generations is ≤0.1%, or the number of iterations reaches 500, the algorithm stops solving and outputs the global optimal solution. This algorithm improves population diversity by more than 40% through the crossover and mutation mechanism, significantly enhances global search capability, and the dynamic inertia weight design improves the convergence speed by 30%, ensuring that the accuracy error is ≤3% within 500 iterations.

[0176] By setting dynamic constraints, associating coupling coefficients, and improving the algorithm, the shortcomings of existing optimization models, such as staticity, poor adaptability, and insufficient accuracy, are solved, and dynamic balance optimization with dual objectives is achieved.

[0177] (iv) Full-scenario decision output module, including:

[0178] 1. Model Library Matching: The system has a built-in full-scenario decision-making adaptation model library, which includes six types of special sub-models: provincial power grid new energy access optimization sub-model, municipal power grid upgrading and transformation optimization sub-model, county power grid energy storage supporting optimization sub-model, cross-regional power grid interconnection optimization sub-model, industrial park power grid low-carbon transformation optimization sub-model, and rural power grid green upgrading optimization sub-model. The system can automatically match the optimal sub-model based on parameters such as regional scale and investment type input by the user.

[0179] 2. Results Display and Output:

[0180] Visual presentation: Through radar charts, line graphs, heat maps and other forms, the key indicators such as carbon emission reduction benefits, total investment cost, investment payback period, carbon benefit-cost coupling coefficient, and safety constraint satisfaction of each optimization scheme are presented intuitively.

[0181] Decision support: The built-in scheme ranking algorithm sorts the schemes according to the user-defined priorities (carbon efficiency priority, cost priority, balance priority) and generates a decision report that includes indicator interpretation, risk warning and implementation suggestions. It supports export in PDF and Excel formats.

[0182] By using a full-scenario model library and intelligent output design, the shortcomings of existing technologies, such as poor adaptability to different scenarios and limited decision-making reference value, are addressed, thereby improving the system's practicality and feasibility.

[0183] This application can achieve the following effects:

[0184] 1. Achieve deep integration and optimization of carbon efficiency and cost. Through the carbon efficiency-cost coupling coefficient and dynamic dual-objective optimization engine, it breaks through the limitations of the simple superposition of traditional technologies and achieves dynamic balance of two heterogeneous indicators in the same dimension, which significantly improves the scientific nature of decision-making.

[0185] 2. It has outstanding dynamic response capabilities and can adapt to dynamic factors such as carbon market price fluctuations, changes in power grid operation status, and policy adjustments in real time. The output decision-making solutions are highly adaptable and effectively reduce investment risks caused by changes in the external environment.

[0186] 3. Strong adaptability to all scenarios: The full-scenario decision-making adaptation model library can cover the application needs of different regional scales and different investment types. It can be quickly deployed without additional secondary development, which greatly reduces the user's usage cost.

[0187] 4. Improved data processing efficiency and accuracy: The three-level processing flow and hybrid storage architecture of the multi-source heterogeneous data fusion platform provide efficient and accurate data support for optimized decision-making.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A system for synergistic optimization analysis of carbon benefits and investment costs in regional power grids, characterized in that: include: Intelligent data acquisition module: used to collect power grid physical data, carbon monitoring data, financial data and dynamic carbon policy data to form multi-source heterogeneous data; Multi-source heterogeneous data fusion platform: used to clean and standardize multi-source heterogeneous data, achieve deep fusion of heterogeneous data, and realize data storage and scheduling; The data cleaning and standardization process is structured as a three-level data processing workflow, including: First-level cleaning: Using a machine learning-based outlier detection algorithm to remove outlier data; Secondary conversion: Through a custom standardized protocol, power grid physical data, carbon monitoring data, and financial data are uniformly converted into standardized indicators that can be correlated and calculated. The custom standardization protocol adopts a three-part structure of "data identifier header + core parameters + check bit". The data identifier header is used to distinguish data types. The core parameters include the original value, the collection timestamp, and the conversion coefficient ID. The conversion coefficient is used to uniformly convert three types of heterogeneous data with different units and dimensions, namely power grid physical data, carbon monitoring data, and financial data, into two types of standardized indicators: "carbon benefit quantification value" and "equivalent cost value". Three-level correlation: Based on the carbon benefit-cost coupling coefficient, establish correlation mapping relationships between different types of data; The carbon benefit-cost coupling coefficient is used to establish a strong correlation between power grid physical data, carbon monitoring data, and financial data and "carbon benefit" and "cost" respectively. Then, through the dynamic calibration characteristics of the carbon benefit-cost coupling coefficient, the three types of data can be mapped and calculated together under the same analytical framework. Dynamic dual-objective optimization engine: used to achieve dual-objective optimization of maximizing carbon benefits and minimizing total investment costs based on basic constraints and dynamic constraints, and to obtain multiple optimization solutions; Full-scenario decision output module: used to visualize the optimization solution.

2. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 1, characterized in that: The intelligent data acquisition module is equipped with an adaptive communication interface to ensure compatibility with the data output formats of existing power grid monitoring equipment, carbon monitoring instruments, and financial systems, and supports dynamic adjustment of the data acquisition frequency.

3. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 2, characterized in that: The adaptive communication interface achieves automatic identification and matching of different device interfaces through interface circuits and software algorithms. The interface circuit includes a modular main control unit, a multi-protocol conversion unit, a dynamic adaptation unit, a signal conditioning unit, and a power management unit. The multi-protocol conversion unit includes various conversion chips for different protocols, and each conversion chip is connected to the main control unit through an SPI interface. The dynamic adaptation unit includes a programmable clock generator and a voltage adaptive circuit. The main control unit controls the programmable clock generator and the voltage adaptive circuit through an I2C interface. The input port of the signal conditioning unit is connected to an external terminal, and the output port is connected to the multi-protocol conversion unit. The data after protocol conversion is transmitted to the main control unit through an internal bus. The power management unit provides a stable power supply to each unit; The software algorithm employs an automatic protocol identification algorithm, a dynamic parameter matching algorithm, and an adaptive acquisition frequency adjustment algorithm in conjunction with the interface circuit to achieve adaptive functionality.

4. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 3, characterized in that: The logic of the automatic protocol identification algorithm is as follows: Signal capture: After the interface circuit is started, the main control unit controls the multi-protocol conversion unit to enter "listening mode" to capture the first 3-5 frames of data sent by the external terminal and store them in the buffer; Feature extraction: Extracting key features from data frames using algorithms; Template matching: The extracted features are compared with the built-in "protocol feature template library", which contains standard feature parameters of various protocols. The cosine similarity is calculated using a similarity calculation algorithm. When the cosine similarity is ≥90%, it is determined to be a successful match, and the protocol type is output. Protocol activation: Based on the identification result, the main control unit activates the corresponding protocol conversion chip in the multi-protocol conversion unit through the SPI interface, loads the parsing rules of the protocol, and establishes a data parsing link.

5. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 3, characterized in that: The logic of the dynamic parameter matching algorithm is as follows: Baud rate detection: For the identified protocol type, the main control unit controls the programmable clock generator to output the commonly used baud rates in sequence, while listening for data feedback. When a complete and correctly verified data frame is received, it is determined that the current baud rate is successfully matched. Data format matching: The "trial and error method" is used to automatically match data bits, stop bits, and check bits, combine parameters in sequence, and send test commands. When a correct response frame is received from the terminal, the parameter configuration is locked. Dynamic adjustment: Real-time monitoring of data transmission error rate. When the error rate is ≥3%, the parameter matching process is automatically restarted to adapt to dynamic changes in external terminal parameters.

6. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 1, characterized in that: In the secondary conversion, "carbon benefit quantification value" and "equivalent cost value" are the two core standardized indicators. The mapping relationship between power grid physical data, carbon monitoring data, financial data and standardized indicators is established. Among them, carbon benefit quantification value is used to quantify the direct economic benefits and policy incentive benefits brought about by carbon emission reduction, and equivalent cost value is used to quantify investment costs, operation and maintenance costs and carbon emission penalty costs. For power grid physical data, the conversion factor = loss penalty benchmark factor × measured value of regional carbon intensity in the current month / carbon intensity value in the previous month; For carbon monitoring data, the conversion factor = carbon element conversion benchmark factor × current carbon trading price / average carbon price over the past 30 days; For financial data, the conversion factor = investment type benchmark factor × current carbon price / historical average carbon price; Furthermore, it automatically matches conversion rules for different data types through the protocol's built-in "conversion coefficient library," achieving dynamic adaptation of conversion rules.

7. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 1, characterized in that: Based on the carbon benefit-cost coupling coefficient, the logic for establishing the correlation mapping relationship between different types of data is as follows: The correlation mapping between power grid physical data and carbon monitoring data is achieved through the following two formulas: Line loss carbon emissions = Line loss power × Operating time × Regional carbon intensity; Carbon benefit loss value = line loss carbon emissions × carbon trading price × policy incentive coefficient × K, where K is the carbon benefit-cost coupling coefficient; The mapping between power grid physical data and financial data is achieved through the following two formulas: Equivalent cost of new energy access = initial investment cost × cost allocation coefficient × (1-K); Carbon benefit improvement value = New energy power generation × Regional carbon intensity × Carbon trading price × Policy incentive coefficient × K; The correlation mapping between carbon monitoring data and financial data is achieved through the following formula: Equivalent cost savings = quantified carbon benefit value × K / (1-K).

8. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 7, characterized in that: The fundamental constraints include regional power grid safety operation constraints, carbon emission reduction policy constraints, and investment budget constraints; Dynamic constraints introduce constraints on carbon trading price fluctuations and new energy output fluctuations, and dynamically adjust constraint thresholds based on real-time collected data.

9. A carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 8, characterized in that: The objective function for the dual-objective optimization is max(carbon benefit quantification value × carbon benefit weight) - min(total investment cost × cost weight), where the carbon benefit quantification value = carbon emission reduction × carbon trading price × policy incentive coefficient, and the two weight coefficients are adjusted in real time through a dynamic adaptive algorithm. Where carbon benefit weight = K × dynamic adjustment factor α, cost weight = (1-K) × dynamic adjustment factor β, and the constraint condition is α+β=2.0, where K is the carbon benefit-cost coupling coefficient; The carbon benefit-cost coupling coefficient is based on a multi-factor comprehensive evaluation method, selecting five key influencing factors: carbon trading price volatility, the proportion of renewable energy output, investment budget adequacy ratio, carbon intensity target achievement rate, and grid security margin. The weight of each factor is determined using the analytic hierarchy process (AHP). Carbon trading price volatility, renewable energy output ratio, and carbon intensity target achievement rate are positively correlated with the K value, while investment budget adequacy ratio and grid security margin are negatively correlated with the K value. The calculation formula is as follows: ； Where wi is the weight of the i-th factor, and fi is the quantized value of the i-th factor.

10. The carbon benefit and investment cost synergistic optimization analysis system for regional power grids according to claim 1, characterized in that: The full-scenario decision output module includes: Model library matching: The system has a built-in full-scenario decision-making adaptation model library, which includes six special sub-models: provincial power grid new energy access optimization sub-model, municipal power grid upgrading and transformation optimization sub-model, county power grid energy storage supporting optimization sub-model, cross-regional power grid interconnection optimization sub-model, industrial park power grid low-carbon transformation optimization sub-model, and rural power grid green upgrading optimization sub-model. The system can automatically match the optimal sub-model based on the parameters input by the user. Results presentation and output include: Visual presentation: Through charts and graphs, the carbon emission reduction benefits, total investment cost, investment payback period, carbon benefit-cost coupling coefficient, and key indicators of safety constraint satisfaction of each optimization scheme are presented intuitively. Decision support: The built-in solution ranking algorithm sorts the solutions according to the user-defined priorities and generates a decision report that includes indicator interpretation, risk warnings, and implementation suggestions. It supports export in different formats.

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