Carbon electricity energy storage power control strategy analysis method and system considering multi-source-load cooperation

Abstract

The invention relates to the technical field of power control strategy analysis, and discloses a carbon electricity energy storage power control strategy analysis method and system considering multi-source-load cooperation. The method comprises the following steps: performing power balance analysis processing on a source-load power cooperation characteristic matrix under carbon emission constraint to obtain a power scheduling initial solution of the carbon electricity energy storage system; performing source-load collaborative optimization processing on the power scheduling initial solution to obtain a test power optimization sequence; performing energy storage capacity distribution processing on the power optimization sequence to obtain a charging and discharging power instruction set; and performing power correction processing on the charging and discharging power instruction set under the carbon emission cost constraint to obtain an energy storage scheduling strategy, and performing multi-target collaborative optimization processing to obtain a carbon electricity energy storage power control strategy. According to the method, under the condition of considering the carbon emission constraint, accurate extraction and multi-target collaborative optimization of the source load power characteristics are realized, so that the power control performance of the carbon electricity energy storage system is improved.

Description

Technical Field

[0001] The present application relates to the field of power control strategy analysis, and in particular to a method and system for analyzing power control strategy of carbon-electric energy storage considering multi-source-load coordination. Background Art

[0002] With the large-scale integration of renewable energy, carbon-based energy storage systems are playing an increasingly important role in power systems. Traditional power systems primarily employ centralized control strategies, achieving stable system operation through coordinated control of the generation, storage, and load sides. Currently, scholars at home and abroad have conducted extensive research on power control strategies for carbon-based energy storage systems, including energy storage power optimization methods based on predictive control, distributed control strategies that consider load response, and collaborative scheduling methods based on multi-objective optimization. These methods have significantly improved the operating efficiency of carbon-based energy storage systems.

[0003] However, existing control strategies generally have the following shortcomings: first, the analysis of source-load power characteristics is not in-depth enough, making it difficult to accurately grasp the complementary relationship between source and load; second, when considering carbon emission constraints, static constraints are often adopted, which cannot adapt to the dynamic changes in carbon emission costs; third, the lack of coordination between energy storage capacity allocation and charging and discharging power control leads to low overall system operating efficiency; finally, the trade-off between multiple objectives is relatively simple, making it difficult to achieve balanced optimization of each objective. Summary of the Invention

[0004] The present application provides a carbon-electric energy storage power control strategy analysis method and system considering multi-source-load collaboration, which is used to achieve accurate extraction of source-load power characteristics and multi-objective collaborative optimization under the condition of considering carbon emission constraints, thereby improving the power control performance of the carbon-electric energy storage system.

[0005] In a first aspect, the present application provides a method for analyzing a carbon-based energy storage power control strategy considering multi-source-load coordination, the method comprising: Perform multi-time series feature extraction on the source-load power data of the carbon-based energy storage system to obtain the source-load power collaborative feature matrix; Performing power balance analysis and processing on the source-load power coordination characteristic matrix under carbon emission constraints to obtain an initial power scheduling solution for the carbon-electric energy storage system; Performing source-load collaborative optimization processing on the initial power scheduling solution to obtain a power optimization sequence that takes into account the complementary characteristics of multiple sources and loads; Performing energy storage capacity allocation processing on the power optimization sequence to obtain a charge and discharge power instruction set of the energy storage unit; Performing power correction processing on the charge and discharge power instruction set under the carbon emission cost constraint to obtain a carbon-electricity-linked energy storage scheduling strategy; The carbon-electricity energy storage scheduling strategy is subjected to multi-objective collaborative optimization processing to obtain a carbon-electricity energy storage power control strategy.

[0006] In a second aspect, the present application provides a carbon-based energy storage power control strategy analysis system considering multi-source-load coordination, the carbon-based energy storage power control strategy analysis system considering multi-source-load coordination includes: The acquisition module is used to extract and process the multi-time series features of the source-load power data of the carbon-electric energy storage system to obtain the source-load power collaborative feature matrix; a processing module, configured to perform power balance analysis and processing on the source-load power coordination characteristic matrix under carbon emission constraints to obtain an initial power scheduling solution for the carbon-electric energy storage system; An optimization module, configured to perform source-load collaborative optimization processing on the initial power scheduling solution to obtain a power optimization sequence that takes into account the complementary characteristics of multiple sources and loads; an allocation module, configured to perform energy storage capacity allocation processing on the power optimization sequence to obtain a charge and discharge power instruction set for the energy storage unit; a correction module, configured to perform power correction processing on the charge and discharge power instruction set under the carbon emission cost constraint to obtain a carbon-electricity-linked energy storage scheduling strategy; The collaborative module is used to perform multi-objective collaborative optimization processing on the carbon-electricity linked energy storage scheduling strategy to obtain a carbon-electricity energy storage power control strategy.

[0007] In the technical solution provided by the present application, by performing multi-time series feature extraction processing on the source-load power data of the carbon-electric energy storage system, the dynamic change characteristics of the source-load power can be fully and accurately obtained, providing a reliable data basis for subsequent collaborative optimization. At the same time, by performing power balance analysis processing on the source-load power collaborative feature matrix under carbon emission constraints, the power balance of the system can be ensured while meeting the carbon emission requirements, avoiding system instability problems caused by power imbalance. The source-load collaborative optimization processing of the initial solution of power scheduling fully considers the complementary characteristics between sources and loads, which can maximize the advantages of various sources and loads and improve the overall operating efficiency of the system. In addition, by performing energy storage capacity allocation processing on the power optimization sequence, it can Reasonable allocation of energy storage resources can avoid excessive use or waste of energy storage capacity and ensure the efficient use of energy storage units. Furthermore, power correction processing is performed on the charging and discharging power instruction set under the constraint of carbon emission costs. The charging and discharging strategies can be dynamically adjusted to adapt to changes in carbon emission costs, effectively reducing the operating costs of the system. Finally, through multi-objective collaborative optimization of the carbon-electricity linked energy storage scheduling strategy, balanced optimization of multiple objectives such as economy, environmental protection and reliability is achieved, so that the final generated carbon-electricity energy storage power control strategy has strong practicality and adaptability, which can not only ensure the stable operation of the system, but also maximize economic benefits while reducing carbon emissions, significantly improving the comprehensive performance and control effect of the carbon-electricity energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of a method for analyzing a carbon-based energy storage power control strategy considering multi-source-load coordination in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a carbon-electric energy storage power control strategy analysis system considering multi-source-load coordination in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method and system for analyzing a power control strategy of a carbon-electric energy storage system taking into account multi-source-load coordination. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of a carbon-based energy storage power control strategy analysis method considering multi-source-load coordination includes: Step S101: Perform multi-time series feature extraction processing on the source-load power data of the carbon-electric energy storage system to obtain a source-load power collaborative feature matrix; Step S102: performing power balance analysis and processing on the source-load power coordination characteristic matrix under carbon emission constraints to obtain an initial power scheduling solution for the carbon-electric energy storage system; Step S103: Perform source-load collaborative optimization processing on the initial power scheduling solution to obtain a power optimization sequence that takes into account the complementary characteristics of multiple sources and loads; Step S104: performing energy storage capacity allocation processing on the power optimization sequence to obtain a charge and discharge power instruction set of the energy storage unit; Step S105: performing power correction processing on the charge and discharge power instruction set under the carbon emission cost constraint to obtain a carbon-electricity-linked energy storage scheduling strategy; Step S106: Perform multi-objective collaborative optimization on the carbon-electricity energy storage scheduling strategy to obtain a carbon-electricity energy storage power control strategy.

[0012] It is understandable that the execution subject of this application can be a carbon-based energy storage power control strategy analysis system considering multi-source-load coordination, or a terminal or server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0013] Specifically, multi-time series feature extraction is performed on source-load power data. By partitioning historical power data into time windows, for example, dividing a day into 96 time points, with one sampling point every 15 minutes, a month of data yields 2,880 time series data points. After normalizing this data, a wavelet transform is used to decompose the power data into frequency components, thereby identifying the key characteristics of power fluctuations. For example, a wavelet transform can be used to decompose photovoltaic power data into three levels: long-term trends, intraday cyclical variations, and short-term random fluctuations. The periodic characteristics of these components are then analyzed using a Fourier transform to obtain a power fluctuation feature sequence. Once this power fluctuation feature sequence is obtained, time series correlation analysis is used to determine the correlation between different sources and loads, and principal component analysis is used to reduce the data dimensionality. Specifically, by calculating the correlation coefficient matrix, the main eigenvectors are extracted. In one case, principal component analysis reduced the original 100-dimensional power feature data to 20 dimensions while retaining over 85% of the information. The K-means clustering algorithm is then used to classify the features after dimensionality reduction, dividing the power features into different categories such as typical working days and rest days. The power sequence is reconstructed according to these categories, and finally the source-load power synergy feature matrix is obtained.

[0014] For power balance analysis under carbon emission constraints, we first establish a carbon emission factor mapping relationship to convert the power values of different types of power generation sources and loads into corresponding carbon emissions. By performing time series decomposition on carbon emission characteristic data, we obtain a benchmark carbon emission sequence and extract the constraint conditions to form a constraint boundary set. In actual application, the carbon emission benchmark value of a regional power grid is 500g / kWh, it is converted into specific power limit conditions through dynamic threshold calculation, and then the power balance constraint conditions are constructed to achieve the initial allocation of source and load power. In the source-load collaborative optimization stage, the complementary relationship between different sources and loads is analyzed to construct a complementary relationship matrix. For example, in a certain area, the complementarity between photovoltaic power generation and industrial load is 0.75, and the complementarity between wind power and commercial load is 0.68. Based on these complementary characteristics, the matching index of source and load power is calculated to generate the corresponding power adjustment coefficient. The weights of different objectives are calculated through the adaptive weight allocation algorithm to obtain the optimized weight coefficient, and then the power scheduling plan is adjusted to form a source-load collaborative candidate sequence.

[0015] In the energy storage capacity allocation phase, a demand analysis is conducted on the power optimization sequence. A day is divided into 96 time periods, and the energy storage capacity demand for each period is calculated by considering factors such as charge and discharge efficiency and state of charge. For example, if the energy storage capacity demand for a certain period is 2MWh, and after considering a charge and discharge efficiency of 0.9, the actual allocated energy storage capacity is 2.22MWh. Constraint verification is used to ensure that the allocation plan meets the technical limitations of the energy storage system, and ultimately a set of charge and discharge power instructions is generated. For power correction under carbon emission cost constraints, the carbon emission costs for different time periods are first calculated. For example, when the carbon trading price is 50 yuan / ton, the carbon emission cost sequence is calculated in combination with power data. Based on the cost constraints, the power correction amount is calculated, and the characteristics of the corrected carbon-electricity linkage are analyzed. Through multiple rounds of iterative optimization, a scheduling strategy that meets carbon emission constraints and is economically efficient is generated.

[0016] During the multi-objective collaborative optimization phase, the energy storage scheduling strategy for the carbon-electricity linkage is broken down into multiple objectives, including economic efficiency, environmental protection, and reliability. The importance of each objective is determined through objective weighting. For example, the weights for economic efficiency, environmental protection, and reliability are 0.4, 0.3, and 0.3, respectively. Strategy adjustments are made based on these weights, and the optimal control strategy is selected through comprehensive performance evaluation.

[0017] Taking a regional microgrid as an example, the area includes 3MW of photovoltaic power generation, 2MW of wind power generation, 4MWh of energy storage system, and 5MW of electricity load. The above method is used to process data from 96 time periods per day. First, the source-load power characteristics are extracted. It is found that photovoltaic power generation and air conditioning load show a significant negative correlation, with a correlation coefficient of -0.82. Under the carbon emission constraint of 400g / kWh, the initial scheduling plan was obtained through power balance analysis. After considering the complementary characteristics of source and load, the energy storage capacity was allocated in different time periods, such as allocating 1.5MWh for energy storage during the peak period of photovoltaic power generation and 2MWh for discharge during the peak period of electricity consumption. The final control strategy reduced the carbon emission intensity of the system to 385g / kWh, while ensuring a renewable energy consumption rate of more than 95%. The overall operating cost of the system is 15% lower than that of traditional scheduling methods.

[0018] In the embodiment of the present application, by performing multi-time series feature extraction processing on the source-load power data of the carbon-electric energy storage system, the dynamic change characteristics of the source-load power can be fully and accurately obtained, providing a reliable data basis for subsequent collaborative optimization. At the same time, by performing power balance analysis processing on the source-load power collaborative feature matrix under carbon emission constraints, the power balance of the system can be ensured under the premise of meeting the carbon emission requirements, and the system instability problem caused by power imbalance can be avoided. The source-load collaborative optimization processing of the initial solution of power scheduling fully considers the complementary characteristics between sources and loads, which can maximize the advantages of various sources and loads and improve the overall operation efficiency of the system. In addition, by performing energy storage capacity allocation processing on the power optimization sequence, it can reasonably Allocate energy storage resources to avoid excessive use or waste of energy storage capacity and ensure efficient utilization of energy storage units. Furthermore, perform power correction processing on the charge and discharge power instruction set under the constraint of carbon emission cost, and dynamically adjust the charge and discharge strategy to adapt to changes in carbon emission cost, effectively reducing the operating cost of the system. Finally, through multi-objective collaborative optimization of the carbon-electricity linked energy storage scheduling strategy, balanced optimization of multiple objectives such as economy, environmental protection and reliability is achieved, so that the final generated carbon-electricity energy storage power control strategy has strong practicality and adaptability, which can not only ensure the stable operation of the system, but also maximize economic benefits while reducing carbon emissions, significantly improving the comprehensive performance and control effect of the carbon-electricity energy storage system.

[0019] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) The historical power data of the carbon-electric energy storage system is divided into time windows to obtain a power data sequence of length T, and the power data sequence is normalized to obtain standardized power data, where T is a positive integer greater than 1; (2) Perform wavelet transform decomposition on the standardized power data to obtain the multi-scale time-frequency characteristics of the power data, and perform periodic analysis on the multi-scale time-frequency characteristics through Fourier transform to obtain the power fluctuation feature sequence; (3) Perform time series correlation analysis on the power fluctuation feature sequence to obtain the source-load power correlation feature vector, and perform principal component analysis on the correlation feature vector to obtain the feature set after dimensionality reduction; (4) The K-means clustering algorithm is used to perform power feature clustering on the feature set after dimensionality reduction to obtain the typical feature categories of source-load power. The power data is then reconstructed based on the time series features to obtain the reconstructed power sequence. (5) Perform time series feature coding on the reconstructed power sequence to obtain the time series feature coding matrix of the source-load power, and construct the source-load power collaborative feature matrix based on the time series feature coding matrix.

[0020] Specifically, the data is segmented into time windows by selecting an appropriate time window length, T. The choice of time window is based on the data sampling frequency and analysis requirements. For example, for power data sampled every 15 minutes, the number of data points per day is 96. If a 7-day time window is selected, T = 672. The segmented power data series is normalized to a standard normal distribution with a mean of 0 and a variance of 1, eliminating the influence of different dimensions. The normalized power data is then subjected to multi-scale decomposition using a wavelet transform. The wavelet transform allows for localized signal analysis in both the time and frequency domains. By selecting appropriate wavelet basis functions (such as the db4 wavelet), the power data is decomposed in three layers to obtain the time-frequency characteristics of different frequency components. For example, for photovoltaic power data, the first layer decomposition yields a low-frequency component of 0-0.5 Hz and a high-frequency component of 0.5-1 Hz. The second layer further decomposes the low-frequency component into components of 0-0.25 Hz and 0.25-0.5 Hz, and the third layer continues to decompose the lowest frequency component. These multi-scale time-frequency features are periodically analyzed through Fourier transform, and the amplitude and phase characteristics of different frequency components are calculated to form a power fluctuation feature sequence.

[0021] A time-series correlation analysis is performed on the resulting power fluctuation feature sequence, calculating the correlation coefficients between different sources and loads to construct a correlation feature vector. For example, in a certain region's carbon-based energy storage system, four types of loads are included: photovoltaic power generation, wind power generation, industrial load, and commercial load. By calculating the Pearson correlation coefficient between these four types of loads, a six-dimensional correlation feature vector is obtained. Principal component analysis is performed on this feature vector. By calculating the eigenvalues and eigenvectors, the principal components with a cumulative contribution rate of at least 85% are selected as the feature set after dimensionality reduction. Cluster analysis is performed on the reduced feature set using the K-means clustering algorithm. K-means clustering divides the data points into K clusters through iterative optimization. First, K cluster centers are randomly selected. The following steps are then repeated: calculating the distance from each data point to each cluster center, assigning the data point to the cluster with the closest cluster center, and recalculating the center point of each cluster until the cluster center no longer changes significantly. The optimal K value is determined by calculating the silhouette coefficient, resulting in typical feature categories for the source and load power. Based on these categories, the time series features of the original power data are reconstructed to form a reconstructed power sequence.

[0022] The reconstructed power sequence is encoded using time series features. A sliding time window method is used to extract local time series features. One-hot encoding is then used to convert categorical features into numerical features, and a time series feature encoding matrix is constructed. Finally, the time series feature encoding matrix is combined with the source-load power correlation features to generate a source-load power synergy feature matrix.

[0023] Taking a regional carbon-based energy storage system as an example, the system consists of a 2MW photovoltaic power generation system, a 1.5MW wind power generation system, a 3MWh energy storage device, and a 4MW combined load. One month of historical power data (with a 15-minute sampling interval) was processed. The data was first divided into 7-day time windows, resulting in data series for four time windows. The data for each time window was normalized, and then the normalized data was decomposed using the db4 wavelet function at three levels to obtain time-frequency characteristics in seven frequency bands. Fourier transform analysis revealed that photovoltaic power generation exhibited significant periodicity over 24-hour and 12-hour periods, with amplitudes of 1.2 and 0.8, respectively. Wind power generation exhibited strong fluctuations over 6-hour and 3-hour periods, with amplitudes of 0.9 and 0.6, respectively. Time series correlation analysis revealed a correlation coefficient between photovoltaic power generation and load of -0.75, a correlation coefficient between wind power generation and load of -0.45, and a correlation coefficient between photovoltaic power generation and wind power of 0.25. Principal component analysis (PCA) selected the first two principal components (with a cumulative contribution of 87%) as features after dimensionality reduction. These features were clustered using the K-means clustering algorithm. By calculating the silhouette coefficient for different K values (K = 2 to 8), the optimal K value was determined to be 4. Four typical power characteristics were obtained: high photovoltaic power and low load, high wind power and medium load, low power generation and high load, and balanced. These four characteristics were time-series encoded, with each characteristic represented as a 4-dimensional vector. Ultimately, a 96×16 source-load power synergy feature matrix was constructed, where 96 represents the number of time points in a day and 16 represents the feature dimension at each time point.

[0024] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Carry out carbon emission factor mapping processing on the source-load power synergy characteristic matrix to obtain a carbon emission characteristic data set, and perform time series decomposition processing on the carbon emission characteristic data set to obtain a carbon emission benchmark sequence; (2) Extract the constraints of the carbon emission benchmark sequence to obtain the carbon emission constraint boundary set, and perform dynamic threshold calculation on the carbon emission constraint boundary set to obtain the carbon emission constraint threshold; (3) Performing balance constraint construction processing on the carbon emission constraint threshold and power data to obtain power balance constraint conditions, and performing initial allocation processing on source and load power according to the power balance constraint conditions to obtain a power allocation plan; (4) Perform power balance verification according to the power allocation scheme to obtain a set of feasible power scheduling solutions, and then screen the set of feasible power scheduling solutions to obtain the initial power scheduling solution of the carbon-electric energy storage system.

[0025] Specifically, different types of power sources and loads are assigned corresponding carbon emission factors: the carbon emission factor of thermal power units is 920g / kWh, the carbon emission factor of photovoltaic power generation and wind power generation is 0g / kWh, and the indirect carbon emission factor of industrial load is 650g / kWh, the indirect carbon emission factor for commercial load is 450g / kWh. These carbon emission factors are multiplied by the corresponding power values in the source-load power synergy characteristic matrix to generate a data set reflecting the carbon emission characteristics of each time period. The carbon emission characteristic data set is subjected to time series decomposition, using seasonal decomposition methods to decompose the data into three components: trend term, seasonal term, and random term. The trend term reflects the long-term trend of carbon emissions, the seasonal term reflects the cyclical changes within the day and week, and the random term represents short-term fluctuations. By calculating the statistical characteristics of each component, such as the mean, standard deviation, and coefficient of variation, a carbon emission benchmark series is formed.

[0026] In the process of constraint extraction, the upper and lower boundaries of carbon emission constraints are extracted based on the carbon emission benchmark sequence. The upper boundary is determined by the carbon emission limit stipulated by the country or region. For example, the upper limit of carbon emission intensity stipulated in a certain region is 550g. / kWh; the lower limit takes into account technical and economic feasibility. For example, considering the instability of photovoltaic power generation, the lower limit of carbon emission intensity is set at 200g / kWh. Dynamic thresholds are calculated for these constraint boundaries, and carbon emission constraint thresholds are dynamically adjusted based on factors such as the current time period's load level, renewable energy output forecasts, and carbon trading prices. When constructing balance constraints, carbon emission constraint thresholds are converted into power balance constraints. Based on the principle of power balance, the total power on the generation side at any given time must equal the sum of the electricity load and the energy storage charge and discharge power. Carbon emission constraint thresholds are also considered to calculate the upper and lower output limits for different power sources. For energy storage devices, technical constraints such as state of charge and charge and discharge efficiency must be considered. For example, a storage device's charge and discharge efficiency must be 90%, and its maximum charge and discharge power must be 80% of its rated power. Based on these constraints, a linear programming method is used to initially allocate source and load power, resulting in a power allocation plan for each time period. Power balance verification is performed based on the power allocation plan to check whether the power balance in each time period meets the constraints, including power balance constraints, carbon emission constraints, and equipment operation constraints. Solutions that meet all constraints are included in the set of feasible power scheduling solutions. Then, the solutions in the feasible solution set are comprehensively evaluated and screened. The evaluation indicators include carbon emissions, economic costs, and system stability. Finally, the solution with the best comprehensive performance is selected as the initial solution for power scheduling of the carbon-electric energy storage system.

[0027] Taking the carbon-electric energy storage system of an industrial park as an example, the system includes 3MW photovoltaic power generation, 2MW wind power generation, 5MWh energy storage system and 6MW comprehensive load. The carbon emission factor is mapped to the source-load power synergy feature matrix to obtain a carbon emission feature data set reflecting 96 time points in 24 hours. Through time series decomposition, it is found that carbon emissions generally show a cyclical change with high on weekdays and low on weekends, with a peak-to-valley difference of 300g per day. / kWh. According to the park's carbon emission management requirements, the upper limit of carbon emission intensity is set at 500g / kWh, the lower limit is 150g / kWh. During the 9:00 period, the load demand is 4MW, the photovoltaic output is predicted to be 2MW, the wind power output is predicted to be 1MW, and the carbon trading price is 45 yuan / ton. Through dynamic threshold calculation, the carbon emission constraint for this period is 400gCO2 / kWh. Taking into account that the actual output fluctuation of photovoltaic power generation is within the range of ±10% of the predicted value, the fluctuation of wind power is within the range of ±15%, and the current state of charge of the energy storage system is 60%, the power distribution plan for this period is calculated as follows: actual photovoltaic output 1.8MW, actual wind power output 0.9MW, energy storage discharge 0.8MW, taking into account 90% charging and discharging efficiency. After power balance verification, the plan meets all operating constraints, and the average carbon emission intensity of the period is 385g / kWh, was included in the feasible solution set. Finally, through comprehensive evaluation, this solution was selected as the initial solution for power scheduling during this period.

[0028] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Analyze the source-load complementary characteristics of the initial power scheduling solution to obtain the source-load complementary relationship matrix, and then segment the source-load complementary relationship matrix into time series to obtain the time-divided complementary feature set; (2) Calculate the source-load power matching degree of the time-divided complementary feature set to obtain the source-load power matching degree index, and generate the power adjustment coefficient based on the source-load power matching degree index to obtain the power adjustment coefficient sequence; (3) Performing source-load collaborative optimization on the power regulation coefficient sequence to obtain the collaborative optimization objective function, and performing weight calculation on the collaborative optimization objective function through an adaptive weight allocation algorithm to obtain the optimized weight coefficient; (4) Coordinate the power scheduling scheme according to the optimized weight coefficient to obtain a source-load coordination candidate sequence, and perform complementary performance evaluation on the source-load coordination candidate sequence to obtain a complementary performance indicator set; (5) Comprehensively evaluate the complementary performance index set to obtain the source-load collaborative optimization results, and construct a power optimization sequence that considers the complementary characteristics of multiple sources and loads based on the source-load collaborative optimization results.

[0029] Specifically, the source-load complementarity characteristics of the initial power scheduling solution are analyzed. A source-load complementarity relationship matrix is constructed by calculating the power complementarity index between different source-load types. The source-load power complementarity index represents the degree of anti-correlation between power fluctuations of different source-load types and ranges from -1 to 1, with values closer to -1 indicating stronger complementarity. For each source-load pair, the inverse of the Pearson correlation coefficient is calculated based on their power time series data as the complementarity index, thereby forming an n×n complementarity relationship matrix, where n is the total number of source-load types. The source-load complementarity relationship matrix is then segmented, dividing the 24-hour period into typical time periods, such as the early morning period (00:00-6:00), the daytime period (6:00-18:00), and the nighttime period (18:00-24:00). Within each time period, complementary features between sources and loads are extracted, including indicators such as power complementarity strength, complementarity duration, and complementarity stability, to form a time-series complementary feature set. Each feature in the complementary feature set is normalized so that its value is unified into the interval [0,1].

[0030] The source-load power matching index is calculated based on the time-based complementary feature set. This index reflects the degree of matching between the source and load in terms of power magnitude and timing characteristics. This matching index consists of two components: power capacity matching and timing characteristics matching. The former considers the matching relationship between the source and load power amplitudes, while the latter focuses on the consistency of power variation trends. Based on the power matching index, a corresponding power adjustment coefficient sequence is generated to adjust the source and load power output. The adjustment coefficient range is [0.8, 1.2]. A value greater than 1 indicates a power increase, while a value less than 1 indicates a power decrease. The power adjustment coefficient sequence is subjected to source-load collaborative optimization, constructing a multi-objective optimization function encompassing economy, environmental protection, and reliability. The economic objective considers operating costs and carbon trading costs, the environmental objective focuses on total carbon emissions, and the reliability objective reflects the stability of the power balance. The weight coefficients of each objective are dynamically adjusted using an adaptive weight allocation algorithm. This algorithm updates the weight allocation ratio in real time based on the gradient information of the objective function value, ensuring that the weight coefficients adapt to changes in the objectives during the optimization process.

[0031] The power scheduling scheme is adjusted based on the obtained optimization weight coefficients to generate multiple candidate sequences for source-load coordination. Each candidate sequence is evaluated for complementary performance, and performance indicators including power complementarity, fluctuation suppression rate, and load following error are calculated. Power complementarity measures the complementary effect between sources and loads, fluctuation suppression rate reflects the ability to smooth power fluctuations, and load following error indicates the accuracy of response to load changes. Finally, a comprehensive evaluation of the complementary performance indicator set is conducted, using the analytic hierarchy process to determine the importance of each indicator. A comprehensive score is calculated, and the scheme with the highest score is selected as the source-load coordination optimization result. Based on this score, a power optimization sequence that considers the complementary characteristics of multiple sources and loads is constructed.

[0032] Taking a carbon-based energy storage system in an industrial park as a specific example, the system consists of a 2.5MW photovoltaic power generation system, a 2MW wind power generation system, a 4MWh energy storage system, and a 5MW industrial load. First, the source-load complementarity matrix is calculated. The complementarity index between photovoltaic power generation and industrial load is -0.82, the complementarity index between wind power generation and industrial load is -0.58, and the complementarity index between photovoltaic power generation and wind power generation is 0.35. Dividing a day into three time periods, from 6:00 AM to 6:00 PM, the photovoltaic power generation power varies between 0.5 and 2.3MW, and the industrial load power varies between 2.8 and 4.5MW. The calculated power capacity matching is 0.78, and the timing characteristics matching is 0.85. Based on these matching indicators, power regulation coefficients are generated. For example, at 10:00 AM, the photovoltaic power generation regulation coefficient is 1.15 (increasing generation is required), and the wind power generation regulation coefficient is 0.92 (reducing generation is required). The initial weights of the economic, environmental, and reliability objectives were calculated using an adaptive weight allocation algorithm to be 0.4, 0.35, and 0.25, respectively. During the optimization process, due to increased pressure from carbon emissions, the weight of the environmental objective was dynamically adjusted to 0.42, while the weights of the economic and reliability objectives were adjusted to 0.35 and 0.23, respectively. Based on the adjusted weights, the source and load power were collaboratively optimized to generate five candidate solutions. After complementary performance evaluation, the specific data of the optimal solution at 2:00 PM were: actual photovoltaic output of 2.1MW, actual wind power output of 1.6MW, energy storage charging of 0.5MW, and industrial load of 3.2MW. The power complementarity of this solution reached 0.88, the fluctuation suppression rate was 82%, and the load following error was less than 5%.

[0033] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Perform energy storage capacity demand analysis on the power optimization sequence to obtain an energy storage capacity demand feature set, and then divide the energy storage capacity demand feature set into time periods to obtain a time-divided capacity demand sequence; (2) Perform energy storage unit status evaluation on the time-divided capacity demand sequence to obtain the energy storage status characteristic matrix, and perform charge and discharge efficiency calculation on the energy storage status characteristic matrix to obtain the charge and discharge efficiency coefficient; (3) Perform initial allocation processing on the energy storage capacity according to the charge and discharge efficiency coefficient to obtain the energy storage capacity allocation plan, and perform constraint verification processing on the energy storage capacity allocation plan to obtain a feasible capacity allocation set; (4) The charge and discharge power calculation process is performed on the feasible capacity allocation set to obtain a charge and discharge power sequence, and power instruction generation process is performed based on the charge and discharge power sequence to obtain a charge and discharge power instruction set of the energy storage unit.

[0034] Specifically, the energy storage capacity demand analysis is performed on the power optimization sequence, and the energy storage capacity demand feature set is obtained by calculating the power fluctuation characteristics. The specific analysis process is based on the following equation: Where, represents the energy storage capacity allocation value at time t, is the weight coefficient of the i-th allocation factor, ranging from [0,1], is the power balance coefficient, used to balance the real-time power deviation. is the power change rate coefficient, which reflects the dynamic change characteristics of power. is the historical power integral coefficient, considering the historical power accumulation effect, is the SOC deviation coefficient, reflecting the state of charge constraint, is the net power value, In practical applications, such as the energy storage capacity allocation of an industrial park, when the power fluctuation is ±2MW, it is calculated =0.6, =0.3, =0.1, which effectively suppresses power fluctuations.

[0035] Furthermore, the energy storage capacity demand feature set is divided into time periods, and the 24-hour period is divided into three periods: peak, valley and flat according to the load characteristics. In each period, the correlation between the features is calculated: Where, is the total feature correlation, is the association weight of feature pair (i, j), is the feature covariance, which indicates the correlation between features. is the characteristic standard deviation, is the capacity change correlation function, is the state-of-charge correlation function. Analysis of a photovoltaic power generation and energy storage project showed that the characteristic correlations during peak and valley periods reached 0.85 and 0.72, respectively. Based on this, the time-divided capacity demand sequence was determined. The energy storage unit state was evaluated for the time-divided capacity demand sequence, and a state characteristic matrix was constructed that included the state of charge (SOC), cycle life, and temperature characteristics. The evaluation process used a weighted scoring method, limiting the SOC to a range of 15%-95%. The cycle life was based on 6,000 complete cycles, and the temperature was controlled between -10°C and 45°C. The charge and discharge efficiency was calculated based on the state characteristic matrix. Under normal operating conditions, the charging efficiency was 92% and the discharging efficiency was 95%, forming an efficiency coefficient matrix.

[0036] Energy storage capacity is initially allocated based on the charge and discharge efficiency coefficient, prioritizing capacity allocation to time periods and operating conditions with higher efficiency. For example, with an efficiency coefficient of 0.92 for a 4MWh energy storage system, 1.8MWh is allocated during peak periods, 1.2MWh during normal periods, and 1MWh during off-peak periods. Constraint verification is performed on the allocation scheme to ensure compliance with conditions such as power limits (≤1.2 times the rated power), SOC range (15%-95%), and cycle limit (≤2 cycles per day). Finally, the charge and discharge power of the feasible capacity allocation set is calculated, and power commands are generated based on real-time power balancing principles. For example, when the PV power is 2.5MW and the load demand is 3MW, the calculated energy storage discharge power is 0.5MW for 30 minutes. The power command generation takes into account constraints such as ramp limits (≤20% of the rated power / minute) and minimum start / stop times (≥15 minutes). For a comprehensive case study, an industrial park equipped with 5MWh energy storage systems analyzed data from 96 time points throughout the day using the above method. First, the capacity demand characteristics were calculated, resulting in a maximum power fluctuation of 2.8MW, lasting 45 minutes. After time division, the peak demand (10:00-15:00) was 2.2MWh, the average demand (7:00-10:00, 15:00-18:00) was 1.6MWh, and the off-peak demand (the rest of the time) was 1.2MWh. A state assessment showed that the current SOC was 65%, 1200 charge and discharge cycles had been completed, with a charge efficiency of 0.91 and a discharge efficiency of 0.94. Based on this, the generated charge and discharge power instructions were: a charging power of 1.5MW at 2:00 PM (lasting 20 minutes), with a ramp rate limit of 15% / minute, achieving efficient utilization of the energy storage capacity.

[0037] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Perform carbon emission cost calculation on the charge and discharge power instruction set to obtain a carbon emission cost sequence, and perform threshold constraint analysis on the carbon emission cost sequence to obtain cost constraint conditions; (2) Based on the cost constraint, the charge and discharge power is corrected and processed to obtain a power correction sequence, and the power correction sequence is analyzed and processed for carbon-electricity linkage to obtain a carbon-electricity linkage feature set; (3) Perform scheduling strategy generation processing on the carbon-electricity linkage feature set to obtain the carbon-electricity linkage scheduling plan, and perform feasibility verification processing on the carbon-electricity linkage scheduling plan to obtain a feasible scheduling plan set; (4) Perform scheduling performance evaluation on the feasible scheduling scheme set to obtain scheduling performance indicators, and screen the scheduling schemes based on the scheduling performance indicators to obtain a carbon-electricity-linked energy storage scheduling strategy.

[0038] Specifically, in the analysis of the carbon-based energy storage power control strategy, the carbon emission cost of the charging and discharging power instruction set is first calculated using the following comprehensive cost calculation model: in: is the comprehensive carbon emission cost at time t; is the carbon price dynamic coefficient in the kth period; is the energy exchange amount; is the carbon emission intensity factor; is the power fluctuation penalty factor; is the real-time power value; is the energy conversion efficiency; is the depth cycle impact function; The data processing based on this model is as follows: First, the carbon emission of the charge and discharge power instructions at 96 time points is calculated, and the carbon emission coefficient of photovoltaic power generation is set to 0.12kg. / kWh, wind power is set to 0.08kg / kWh. For a 5MWh energy storage device, assuming a 70% photovoltaic power generation ratio and a 30% wind power ratio, the carbon emissions for each time period were calculated. A threshold constraint analysis was then performed, setting the upper limit of the carbon emission cost at 500 yuan per hour and the lower limit at 100 yuan per hour. When the carbon trading price was 45 yuan per ton, the carbon emission costs for each time period were calculated, resulting in the cost constraints: carbon emissions during peak periods should not exceed 8 tons / hour, during normal periods should not exceed 6 tons / hour, and during off-peak periods should not exceed 4 tons / hour.

[0039] Based on these constraints, the charging and discharging power is corrected. The corrections are calculated using an iterative optimization method, with a 15-minute calculation cycle, gradually adjusting the charging and discharging power. When constraints are exceeded, power is adjusted in steps of 0.2 MW until the constraints are met. A carbon-electricity linkage analysis is conducted on the corrected power sequence to examine the impact of power adjustment on carbon emissions and form a carbon-electricity linkage feature set. During the scheduling strategy generation phase, the day is divided into 48 half-hour periods, and charging and discharging power is scheduled for each period. For example, during the peak photovoltaic power generation period (10:00-15:00), the energy storage charging power is scheduled to 75% of the rated power; during the peak electricity consumption period (18:00-21:00), the discharge power is scheduled to 85% of the rated power. Technical constraint verification ensures that the strategy meets the operational requirements of the energy storage device.

[0040] Finally, the scheduling scheme's performance was evaluated using metrics including carbon emission reduction (tons / day), economic benefit (yuan / day), and equipment loss rate (% / day). The weighting of each metric was determined using the Analytic Hierarchy Process (AHP), with a weight of 0.4 for carbon emission reduction, 0.35 for economic benefit, and 0.25 for equipment loss. For a 4MWh energy storage system in an industrial park, the specific implementation process during the 9:00-10:00 AM period was as follows: the predicted photovoltaic output was 2.5MW, the predicted wind power output was 1.2MW, and the load demand was 4.5MW. The initial charging power command was 1.8MW, resulting in a calculated carbon emission of 1.2 tons / hour (0.8 tons from photovoltaic power and 0.4 tons from wind power). Considering a carbon price of 45 yuan / ton, the initial carbon emission cost was 54 yuan / hour. Since this did not exceed the cost constraint, no power correction was required. The generated scheduling strategy is: 1.8MW charging power is implemented from 9:00 to 9:30, and it is reduced to 1.5MW from 9:30 to 10:00. The SOC increases from 45% to 58%, achieving a carbon emission reduction of 0.9 tons and a cost saving of 40.5 yuan.

[0041] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Perform multi-objective decomposition processing on the energy storage scheduling strategy of carbon-electricity linkage to obtain a target feature set, and then perform target weight calculation processing on the target feature set to obtain a target weight vector; (2) Perform multi-objective collaborative analysis on the target weight vector to obtain a collaborative optimization index set, and perform strategy adjustment based on the collaborative optimization index set to obtain an adjustment strategy sequence; (3) Perform comprehensive performance evaluation on the adjustment strategy sequence to obtain strategy evaluation indicators, and perform optimal selection based on the strategy evaluation indicators to obtain a set of candidate control strategies; (4) The candidate control strategy set is evaluated by a collaborative evaluation algorithm to obtain the strategy evaluation results, and the carbon-electric energy storage power control strategy is constructed based on the strategy evaluation results.

[0042] Specifically, the energy storage scheduling strategy undergoes a multi-objective decomposition. The resulting objective characteristics include indicators across three dimensions: economic objectives (minimizing operating costs and maximizing carbon trading benefits), environmental objectives (minimizing carbon emissions and maximizing renewable energy absorption), and technical objectives (maximizing equipment lifespan and optimizing system stability). Weights for these objectives are calculated using the Analytic Hierarchy Process (AHP). The specific steps include constructing a judgment matrix, calculating eigenvectors, and performing a consistency check. The initial weights are assigned: 0.35 for economic objectives, 0.4 for environmental objectives, and 0.25 for technical objectives. A multi-objective collaborative analysis is conducted on the objective weight vectors using a Pareto-optimal multi-objective optimization method. First, a mapping relationship is established between the objectives, and the coupling effects between them are analyzed. For example, there is a certain degree of conflict between economic and environmental objectives. When the carbon price is 45 yuan / ton, reducing carbon emissions by one ton increases operating costs by approximately 38 yuan. Based on these analysis results, a collaborative optimization indicator set is generated, including comprehensive cost indicators, carbon emission intensity indicators, and equipment loss indicators.

[0043] The strategy is dynamically adjusted based on the collaborative optimization indicator set. The adjustment process adopts a rolling optimization method with an optimization cycle of 15 minutes. Within each cycle, the optimization target is updated based on real-time data, and the charging and discharging strategy is adjusted. For example, during periods of high carbon prices (greater than 60 yuan / ton), the energy storage discharge power is appropriately increased to improve carbon emission reduction benefits; when equipment losses are large (cycle depth exceeds 20%), the charging and discharging power is reduced to extend equipment life. A comprehensive performance evaluation is conducted on the adjusted strategy sequence, and the evaluation indicators include: average daily carbon emission reduction, economic benefits, equipment life loss rate, etc. The evaluation process adopts a fuzzy comprehensive evaluation method, combining quantitative and qualitative indicators for scoring. Based on the evaluation results, the top five schemes with the highest comprehensive scores are selected as the candidate control strategy set.

[0044] Finally, the candidate strategies are evaluated using a collaborative evaluation algorithm that comprehensively considers multiple evaluation metrics. A comprehensive score is calculated for each candidate strategy, taking into account metric weights and the degree of achievement. The strategy with the highest comprehensive score is selected as the final carbon-based energy storage power control strategy.

[0045] For example, a carbon-based energy storage system in an industrial park consists of a 3MW photovoltaic power generation system, a 2MW wind power generation system, and a 4MWh energy storage device. When analyzing multiple objectives, the economic goals included: operating costs kept below 2,000 yuan / day and carbon trading revenue of at least 500 yuan / day; environmental goals included: a 15% reduction in carbon emission intensity and a 90% renewable energy consumption rate; and technical goals included: a storage lifespan loss rate of no more than 0.2% / day and power fluctuations within 5%. Through a multi-objective collaborative analysis, the specific implementation process during the 2:00 PM to 3:00 PM period was as follows: the actual photovoltaic output was 2.8MW, the actual wind power output was 1.5MW, and the load demand was 5MW. Calculations determined an initial charging power of 1.2MW. Considering the carbon price of 55 yuan / ton at the time, the economic benefit was 66 yuan / hour. After adjusting the strategy, the charging power was reduced to 1.0MW. Although the economic benefit decreased slightly (by 8 yuan / hour), the equipment lifespan was better protected (the cycle depth decreased from 22% to 18%). The resulting control strategy: 1.0 MW charging power from 2:00 PM to 2:30 PM, and 0.8 MW from 2:30 PM to 3:00 PM. Key performance indicators during strategy execution included: 0.85 tons / hour of carbon emissions reduction, 58 yuan / hour of economic benefit, and 0.15% of equipment life loss per day. This strategy achieved significant carbon reduction while ensuring economic efficiency and safe and stable equipment operation.

[0046] The above describes the carbon-based energy storage power control strategy analysis method considering multi-source-load coordination in the embodiment of the present application. The following describes the carbon-based energy storage power control strategy analysis system considering multi-source-load coordination in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of a carbon-based energy storage power control strategy analysis system considering multi-source-load coordination includes: The acquisition module 201 is used to perform multi-time series feature extraction processing on the source-load power data of the carbon-electric energy storage system to obtain a source-load power collaborative feature matrix; Processing module 202 is used to perform power balance analysis and processing on the source-load power coordination characteristic matrix under carbon emission constraints to obtain an initial power scheduling solution for the carbon-electric energy storage system; An optimization module 203 is configured to perform source-load collaborative optimization processing on the initial power scheduling solution to obtain a power optimization sequence that takes into account the complementary characteristics of multiple sources and loads; An allocation module 204 is configured to perform energy storage capacity allocation processing on the power optimization sequence to obtain a charge and discharge power instruction set for the energy storage unit; A correction module 205 is configured to perform power correction processing on the charge and discharge power instruction set under the carbon emission cost constraint to obtain a carbon-electricity-linked energy storage scheduling strategy; The coordination module 206 is used to perform multi-objective coordinated optimization processing on the carbon-electricity linked energy storage scheduling strategy to obtain a carbon-electricity energy storage power control strategy.

[0047] Through the collaborative cooperation of the above components, by performing multi-time series feature extraction and processing on the source-load power data of the carbon-electric energy storage system, the dynamic change characteristics of the source-load power can be fully and accurately obtained, providing a reliable data basis for subsequent collaborative optimization. At the same time, by performing power balance analysis and processing on the source-load power collaborative feature matrix under carbon emission constraints, the power balance of the system can be ensured while meeting the carbon emission requirements, avoiding system instability problems caused by power imbalance. The source-load collaborative optimization processing of the initial power scheduling solution fully considers the complementary characteristics between sources and loads, which can maximize the advantages of various sources and loads and improve the overall operation efficiency of the system. In addition, by performing energy storage capacity allocation processing on the power optimization sequence, It can reasonably allocate energy storage resources, avoid excessive use or waste of energy storage capacity, and ensure the efficient use of energy storage units. Furthermore, the charging and discharging power instruction set is subjected to power correction processing under the constraint of carbon emission costs, and the charging and discharging strategy can be dynamically adjusted to adapt to changes in carbon emission costs, effectively reducing the operating cost of the system. Finally, through multi-objective collaborative optimization of the carbon-electricity linked energy storage scheduling strategy, balanced optimization of multiple objectives such as economy, environmental protection and reliability is achieved, so that the final generated carbon-electricity energy storage power control strategy has strong practicality and adaptability, which can not only ensure the stable operation of the system, but also maximize economic benefits while reducing carbon emissions, significantly improving the comprehensive performance and control effect of the carbon-electricity energy storage system.

[0048] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing carbon-based energy storage power control strategy considering multi-source-load coordination, characterized in that: The carbon-based energy storage power control strategy analysis method considering multi-source-load coordination includes: Perform multi-time series feature extraction on the source-load power data of the carbon-based energy storage system to obtain the source-load power collaborative feature matrix; Performing power balance analysis and processing on the source-load power coordination characteristic matrix under carbon emission constraints to obtain an initial power scheduling solution for the carbon-electric energy storage system; Performing source-load collaborative optimization processing on the initial power scheduling solution to obtain a power optimization sequence that takes into account the complementary characteristics of multiple sources and loads; Performing energy storage capacity allocation processing on the power optimization sequence to obtain a charge and discharge power instruction set of the energy storage unit; Performing power correction processing on the charge and discharge power instruction set under the constraint of carbon emission cost to obtain a carbon-electricity-linked energy storage scheduling strategy; The carbon-electricity energy storage scheduling strategy is subjected to multi-objective collaborative optimization processing to obtain a carbon-electricity energy storage power control strategy.

2. The carbon-based energy storage power control strategy analysis method considering multi-source-load coordination according to claim 1 is characterized in that: The multi-time series feature extraction process is performed on the source-load power data of the carbon-electric energy storage system to obtain a source-load power collaborative feature matrix, including: Performing time window processing on historical power data of the carbon-electric energy storage system to obtain a power data sequence of length T, and performing data normalization processing on the power data sequence to obtain standardized power data, wherein T is a positive integer greater than 1; Performing wavelet transform decomposition processing on the standardized power data to obtain multi-scale time-frequency characteristics of the power data, and performing periodic analysis processing on the multi-scale time-frequency characteristics through Fourier transform to obtain a power fluctuation feature sequence; Performing time series correlation analysis on the power fluctuation feature sequence to obtain a source-load power correlation feature vector, and performing principal component analysis on the correlation feature vector to obtain a feature set after dimensionality reduction; Performing power feature clustering processing on the feature set after dimensionality reduction by using a K-means clustering algorithm to obtain typical feature categories of source-load power, and performing time series feature reconstruction processing on the power data according to the typical feature categories to obtain a reconstructed power sequence; The reconstructed power sequence is subjected to time series characteristic coding processing to obtain a time series characteristic coding matrix of source-load power, and a source-load power coordination characteristic matrix is constructed according to the time series characteristic coding matrix.

3. The carbon-based energy storage power control strategy analysis method considering multi-source-load coordination according to claim 1 is characterized in that: The power balance analysis and processing under the carbon emission constraint on the source-load power coordination characteristic matrix to obtain the initial power scheduling solution of the carbon-electric energy storage system includes: Performing carbon emission factor mapping processing on the source-load power synergy characteristic matrix to obtain a carbon emission characteristic data set, and performing time series decomposition processing on the carbon emission characteristic data set to obtain a carbon emission benchmark sequence; performing constraint extraction processing on the carbon emission benchmark sequence to obtain a carbon emission constraint boundary set, and performing dynamic threshold calculation processing on the carbon emission constraint boundary set to obtain a carbon emission constraint threshold; Performing a balance constraint construction process on the carbon emission constraint threshold and the power data to obtain a power balance constraint condition, and performing an initial allocation process on the source-load power according to the power balance constraint condition to obtain a power allocation plan; A power balance check process is performed according to the power allocation scheme to obtain a set of power scheduling feasible solutions, and the set of power scheduling feasible solutions is screened to obtain an initial power scheduling solution for the carbon-electric energy storage system.

4. The carbon-based energy storage power control strategy analysis method considering multi-source-load coordination according to claim 1 is characterized in that: The performing source-load collaborative optimization processing on the initial power scheduling solution to obtain a power optimization sequence considering the complementary characteristics of multiple sources and loads includes: Performing source-load complementary characteristic analysis on the initial power scheduling solution to obtain a source-load complementary relationship matrix, and performing time series segmentation processing on the source-load complementary relationship matrix to obtain a time-interval complementary feature set; performing source-load power matching calculation processing on the time-divided complementary feature set to obtain a source-load power matching index, and performing power adjustment coefficient generation processing based on the source-load power matching index to obtain a power adjustment coefficient sequence; Performing source-load collaborative optimization processing on the power regulation coefficient sequence to obtain a collaborative optimization objective function, and performing weight calculation processing on the collaborative optimization objective function through an adaptive weight allocation algorithm to obtain an optimized weight coefficient; Performing collaborative adjustment processing on the power scheduling scheme according to the optimized weight coefficient to obtain a source-load collaborative candidate sequence, and performing complementary performance evaluation processing on the source-load collaborative candidate sequence to obtain a complementary performance indicator set; A comprehensive evaluation process is performed on the complementary performance indicator set to obtain a source-load collaborative optimization result, and a power optimization sequence considering the complementary characteristics of multiple sources and loads is constructed based on the source-load collaborative optimization result.

5. The carbon-based energy storage power control strategy analysis method considering multi-source-load coordination according to claim 1 is characterized in that: The performing energy storage capacity allocation processing on the power optimization sequence to obtain a charge and discharge power instruction set of the energy storage unit includes: Performing energy storage capacity demand analysis on the power optimization sequence to obtain an energy storage capacity demand feature set, and dividing the energy storage capacity demand feature set into time periods to obtain a time-divided capacity demand sequence; Performing energy storage unit state evaluation processing on the time-divided capacity demand sequence to obtain an energy storage state characteristic matrix, and performing charge and discharge efficiency calculation processing on the energy storage state characteristic matrix to obtain a charge and discharge efficiency coefficient; Performing an initial allocation process on the energy storage capacity according to the charge-discharge efficiency coefficient to obtain an energy storage capacity allocation plan, and performing a constraint verification process on the energy storage capacity allocation plan to obtain a feasible capacity allocation set; The feasible capacity allocation set is subjected to charge and discharge power calculation processing to obtain a charge and discharge power sequence, and a power instruction generation processing is performed according to the charge and discharge power sequence to obtain a charge and discharge power instruction set of the energy storage unit.

6. The carbon-based energy storage power control strategy analysis method considering multi-source-load coordination according to claim 1 is characterized in that: The power correction processing of the charge and discharge power instruction set under the carbon emission cost constraint to obtain a carbon-electricity-linked energy storage scheduling strategy includes: Performing carbon emission cost calculation processing on the charge and discharge power instruction set to obtain a carbon emission cost sequence, and performing threshold constraint analysis processing on the carbon emission cost sequence to obtain a cost constraint condition; performing a correction calculation process on the charge and discharge power according to the cost constraint condition to obtain a power correction sequence, and performing a carbon-electricity linkage analysis process on the power correction sequence to obtain a carbon-electricity linkage feature set; Performing a scheduling strategy generation process on the carbon-electricity linkage feature set to obtain a carbon-electricity linkage scheduling plan, and performing a feasibility verification process on the carbon-electricity linkage scheduling plan to obtain a feasible scheduling plan set; The feasible scheduling scheme set is subjected to scheduling performance evaluation to obtain scheduling performance indicators, and the scheduling schemes are screened according to the scheduling performance indicators to obtain a carbon-electricity-linked energy storage scheduling strategy.

7. The carbon-based energy storage power control strategy analysis method considering multi-source-load coordination according to claim 1 is characterized in that: The multi-objective collaborative optimization process of the carbon-electricity linked energy storage scheduling strategy is performed to obtain a carbon-electricity energy storage power control strategy, including: Performing multi-objective decomposition processing on the carbon-electricity linked energy storage scheduling strategy to obtain a target feature set, and performing target weight calculation processing on the target feature set to obtain a target weight vector; Performing multi-objective collaborative analysis on the target weight vector to obtain a collaborative optimization index set, and performing strategy adjustment processing according to the collaborative optimization index set to obtain an adjustment strategy sequence; Performing a comprehensive performance evaluation on the adjustment strategy sequence to obtain a strategy evaluation index, and performing a scheme optimization process based on the strategy evaluation index to obtain a candidate control strategy set; The candidate control strategy set is subjected to strategy evaluation processing by a collaborative evaluation algorithm to obtain a strategy evaluation result, and a carbon-electric energy storage power control strategy is constructed according to the strategy evaluation result.

8. A carbon-electric energy storage power control strategy analysis system considering multi-source-load coordination, used to implement the carbon-electric energy storage power control strategy analysis method considering multi-source-load coordination as described in any one of claims 1-7, characterized in that: The carbon-electric energy storage power control strategy analysis system considering multi-source-load coordination includes: The acquisition module is used to extract and process the multi-time series features of the source-load power data of the carbon-electric energy storage system to obtain the source-load power collaborative feature matrix; a processing module, configured to perform power balance analysis and processing on the source-load power coordination characteristic matrix under carbon emission constraints to obtain an initial power scheduling solution for the carbon-electric energy storage system; An optimization module, configured to perform source-load collaborative optimization processing on the initial power scheduling solution to obtain a power optimization sequence that takes into account the complementary characteristics of multiple sources and loads; an allocation module, configured to perform energy storage capacity allocation processing on the power optimization sequence to obtain a charge and discharge power instruction set for the energy storage unit; a correction module, configured to perform power correction processing on the charge and discharge power instruction set under the carbon emission cost constraint to obtain a carbon-electricity-linked energy storage scheduling strategy; The collaborative module is used to perform multi-objective collaborative optimization processing on the carbon-electricity linked energy storage scheduling strategy to obtain a carbon-electricity energy storage power control strategy.

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