Power distribution network carbon emission balance regulation and control method, device, equipment and medium

Abstract

The invention discloses a power distribution network carbon emission balance regulation and control method, device, equipment and medium, and the method comprises the steps: collecting load change data and distributed power supply output data of a boundary power distribution network in a target power distribution network in real time, and calculating the load change data and the distributed power supply output data through a preset continuous power flow calculation method according to the load change data and the distributed power supply output data; determining a regulation and control difference propagation path; if the regulation and control difference propagation path exceeds a preset path threshold value, reading the current transmission power of the cross-regional line, and calculating the transmission load rate of the cross-regional line according to the current transmission power; and according to the cross-regional line transmission load rate, analyzing inter-regional carbon emission level difference to generate a carbon emission adjustment strategy, and according to the carbon emission adjustment strategy, controlling the target power distribution network to carry out load regulation and energy storage scheduling to realize carbon emission regulation balance of the target power distribution network. According to the invention, efficient and accurate carbon emission regulation and control balance of the power distribution network can be realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for power distribution networks, and in particular to a method, device, equipment, and medium for carbon emission balance regulation of power distribution networks. Background Technology

[0002] Under the new multi-level control architecture of the power system, adjacent control areas often experience uneven carbon emission control pressure due to differences in regulatory measures such as carbon tax standards, renewable energy quotas, and carbon emission limits. This pressure can propagate into the control area at varying depths and intensities. The operating status of the distribution network is closely related to its carbon emission level. Real-time load fluctuations, changes in distributed energy output, and dynamic changes in inter-regional power exchange directly affect regional voltage stability, load transfer efficiency, and power adjustment response speed. Therefore, carbon emission balance regulation of the distribution network is necessary to mitigate the boundary effects caused by regulatory differences and ensure the low-carbon and reliable operation of the power grid.

[0003] Existing technologies for managing carbon emission balance in distribution networks largely rely on static regional control strategies. These include pre-setting demand response plans based on historical load data, developing energy storage charging and discharging schedules at fixed intervals, or optimizing local power output allocation only for carbon emission limits in a single control area. They lack dynamic, coordinated responses to differences in control between adjacent control areas. At the data processing level, existing technologies suffer from insufficient dynamic monitoring of the operational status of boundary distribution networks and lack comprehensive analytical capabilities for real-time load, distributed energy output, and inter-regional power exchange. This makes it impossible to accurately assess the scope and extent of the impact of control differences on the system, hindering the rapid adaptation of local control strategies to changes. At the strategy optimization level, the dynamic optimization capabilities of demand response and energy storage scheduling are insufficient. Due to the lag in operational status monitoring, demand response strategies and energy storage charging and discharging plans cannot be flexibly adjusted according to real-time carbon emission intensity, further exacerbating the negative impact of boundary effects. Therefore, how to effectively address the boundary effects caused by differences in control between adjacent control areas by collecting real-time operational data from boundary distribution networks and dynamically optimizing local demand response and energy storage scheduling schemes has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for controlling carbon emission balance in power distribution networks, which can achieve efficient and accurate control and balance of carbon emissions in power distribution networks.

[0005] In a first aspect, embodiments of the present invention provide a method for controlling carbon emission balance in a power distribution network, comprising:

[0006] The load change data and distributed generation output data of the boundary distribution network in the target distribution network are collected in real time, and the control difference propagation path is determined based on the load change data and distributed generation output data through a preset continuous power flow calculation method.

[0007] If the controlled differential propagation path exceeds the preset path threshold, the current transmission power of the cross-regional line is read, and the cross-regional line transmission load rate is calculated based on the current transmission power.

[0008] Based on the cross-regional line transmission load rate, the differences in carbon emission levels between regions are analyzed to generate a carbon emission adjustment strategy. Based on the carbon emission adjustment strategy, the target distribution network is controlled to perform load regulation and energy storage scheduling to achieve carbon emission regulation balance of the target distribution network.

[0009] This invention, through real-time acquisition of load change data and distributed generation output data from the boundary distribution networks within the target distribution network, forms the basis for all subsequent analysis and control. It provides the initial data for accurately understanding the operating status of the distribution network. Furthermore, by employing a pre-defined continuous power flow calculation method, combined with the load change data and distributed generation output data, the propagation path of differences in control strategies between adjacent control areas within the distribution network can be clearly identified, clarifying the node distribution and diffusion direction of the impact of these differences. This provides crucial information for determining whether further control is necessary. By calculating the cross-regional line transmission load rate based on the current transmission power, the load level of cross-regional lines is quantified, providing core data for subsequent carbon emission measurements. By... The cross-regional transmission load rate analysis identifies differences in carbon emission levels between regions and generates adjustment strategies based on these differences. This approach can specifically narrow regional carbon emission gaps, ensuring that the control strategies align with carbon emission control targets. Furthermore, based on these carbon emission adjustment strategies, the target distribution network is controlled for load regulation and energy storage dispatch. Load regulation optimizes the electricity consumption structure by adjusting adjustable loads, reducing electricity demand during high-carbon periods. Energy storage dispatch utilizes energy storage devices to charge during off-peak hours and discharge during peak hours, smoothing load fluctuations and supplementing power shortages. The two work together to execute the adjustment strategy, achieving carbon emission control balance in the target distribution network and resolving the problem of uneven boundary carbon emission pressure caused by inconsistent control strategies in adjacent control areas. Compared with existing technologies, this invention achieves efficient and precise carbon emission control balance in the distribution network.

[0010] Furthermore, the step of determining the propagation path of control differences based on the load change data and distributed power output data using a preset continuous power flow calculation method specifically involves:

[0011] Based on the load change data and distributed power output data, a power transmission matrix between adjacent control areas is constructed, and the characteristic values ​​of the power transmission matrix are calculated to obtain the inter-area electrical coupling strength index based on the characteristic values.

[0012] Based on the inter-regional electrical coupling strength index, the inter-regional influence correlation strength level is determined. Then, using a preset continuous power flow calculation method, power disturbance is added to each node starting from the boundary node of the boundary distribution network according to the inter-regional influence correlation strength level, so as to obtain the voltage deviation value after the power disturbance is added to each node. The power disturbance added to each node is determined according to the corresponding inter-regional influence correlation strength level.

[0013] Nodes whose voltage deviation values ​​exceed a preset deviation threshold are marked as over-limit nodes to obtain an over-limit node set, and the control difference propagation path is determined based on the over-limit node set.

[0014] In this embodiment of the invention, the elements of the power transmission matrix represent the ratio of the actual transmitted power to the rated capacity of the inter-regional interconnection lines, which can intuitively reflect the tightness of power exchange between regions. The calculated eigenvalues ​​can extract the core information of the matrix. The magnitude of the eigenvalue corresponds to the electrical coupling strength. The higher the index, the tighter the power correlation between regions, and the greater the impact of the control differences on each other, providing quantitative support for determining the influence correlation strength level. By determining the influence correlation strength level between regions, the abstract electrical coupling strength index is transformed into a specific level, which facilitates a clear division of the impact degree of control differences. By setting differentiated power disturbances according to the correlation strength level, the stronger the correlation, the closer the disturbance setting is to the actual impact, avoiding calculation deviations caused by uniform disturbances. Furthermore, by adding disturbances one by one from the boundary nodes and calculating the voltage deviation value, the voltage change of each node under the influence of control differences can be accurately captured, providing data for identifying over-limit nodes and ensuring that no key impact nodes are missed. The preset deviation threshold is the standard for judging whether the node voltage is normal. Over-limit nodes that exceed the threshold are key nodes affected by control differences. By analyzing the location and connection relationship of the over-limit node set, the propagation path of control differences in the power grid can be clearly identified, providing clear target nodes for subsequent control.

[0015] Furthermore, before reading the current transmission power of the inter-regional line, the following steps are included:

[0016] If the control difference propagation path exceeds the preset path threshold, the cumulative number of voltage over-limits and the power transmission capacity utilization rate within the most recent preset time period are calculated, and the boundary operating pressure level is determined based on the cumulative number of voltage over-limits and the power transmission capacity utilization rate.

[0017] Based on a preset pressure-state mapping table, state data corresponding to the boundary operating pressure level is extracted from a pre-acquired boundary effect assessment dataset to construct a pressure impact correlation vector. The state data in the boundary effect assessment dataset includes the regional voltage fluctuation impact range, the number of load transfer impact nodes, and the power adjustment response time under different operating conditions. The state data are all determined based on the propagation path of the control differences under different operating conditions.

[0018] The current comprehensive impact factor is obtained by weighted summation of each component in the pressure impact correlation vector.

[0019] This invention determines the boundary operating pressure level based on the cumulative number of voltage overruns and power transmission capacity utilization. These factors comprehensively assess the boundary power grid's operating status from two dimensions: voltage stability and line load, avoiding the bias of a single indicator. By assigning weights to the importance of each state parameter under different pressure levels and obtaining a weighted summation of the comprehensive influence factor, the overall impact of control differences on the boundary power grid is quantified. This provides a dynamic correction basis for subsequent calculations of cross-regional line transmission load rates, improving the accuracy of load rate calculations.

[0020] Furthermore, the calculation of the cross-regional line transmission load rate based on the current transmission power specifically involves:

[0021] Calculate the percentage of the current transmission power to the line's rated capacity, and calculate the cross-regional line transmission load rate based on the current comprehensive influence factor and the percentage.

[0022] The percentage mentioned in this embodiment of the invention is the basic value of the cross-regional line transmission load rate, which directly reflects the current static load of the line and is the core basic data for evaluating the line's carrying capacity. The comprehensive influence factor reflects the dynamic impact of the overall power grid operating pressure on the line. Combining it with the static percentage to calculate the load rate can dynamically correct the actual carrying pressure of the line, avoid the deviation caused by using only static data to calculate the load rate, and more accurately reflect the actual load level of the line under the current power grid conditions.

[0023] Furthermore, the step of analyzing the differences in carbon emission levels between regions based on the cross-regional line transmission load rate to generate a carbon emission adjustment strategy specifically involves:

[0024] Based on the cross-regional line transmission load rate, highly coupled adjacent regions are determined, and the current carbon emission intensity benchmark value of each highly coupled adjacent region is calculated to construct a carbon emission intensity comparison table of adjacent control areas.

[0025] Based on the carbon emission intensity comparison table of adjacent control areas, the priority direction for carbon emission transfer is determined; wherein, the priority direction for carbon emission transfer is from areas with high carbon emission intensity to areas with low carbon emission intensity.

[0026] Historical load data is acquired, and typical daily load data is extracted from the historical load data using a preset clustering analysis method to identify peak and valley period distributions. Based on the peak and valley period distributions, the time range for which load transfer can be performed is determined.

[0027] Based on the aforementioned carbon emission intensity benchmark value and the pre-stored carbon emission values ​​corresponding to each peak and valley period, carbon emission reduction target values ​​for different periods are determined; wherein, each peak and valley period falls within the time range of the executable load transfer.

[0028] By integrating the carbon emission transfer priority directions, the time range of feasible load transfers, and the carbon emission reduction targets for different time periods, a carbon emission adjustment strategy is obtained.

[0029] This invention improves the efficiency and relevance of carbon emission difference analysis by identifying highly coupled regions to focus on key control areas and avoid over-analysis of low-correlation regions. Furthermore, it provides a clear basis for determining the direction of carbon emission transfer by constructing a comparison table of carbon emission intensity between adjacent control areas. By determining the priority direction of carbon emission transfer and shifting load from high-carbon to low-carbon regions, the overall carbon emission level can be directly reduced, avoiding the increase in carbon emissions caused by load transfer to high-carbon regions and ensuring that load transfer is consistent with carbon emission control targets. Identifying peak and off-peak periods clarifies the optimal time for load transfer, reducing peak-hour pressure in high-carbon regions and absorbing surplus electricity in low-carbon regions during off-peak hours, thus improving load transfer efficiency. Finally, by combining regional carbon emission intensity with time-specific carbon emission values, differentiated reduction targets are formulated to ensure that the reduction targets are both aligned with regional realities and meet overall carbon emission control requirements, avoiding control imbalances caused by uniform targets.

[0030] Furthermore, controlling the target distribution network for load regulation and energy storage dispatch according to the carbon emission adjustment strategy includes:

[0031] Based on the priority direction of carbon emission transfer and the time range of executable load transfer in the carbon emission adjustment strategy, the pre-stored distribution network user data is filtered to obtain a list of adjustable load users;

[0032] Based on the historical response records of each user in the adjustable load user list, calculate the comprehensive response capability value of each user, and determine the user response priority based on the comprehensive response capability value;

[0033] Obtain the cut-off time periods for each user in the adjustable load user list, and determine the load adjustment time window based on the cut-off time periods and the time range for which load transfer can be performed;

[0034] Based on the user response priority and load adjustment time window, the target distribution network is controlled to perform load regulation.

[0035] This invention, through obtaining a list of adjustable load users, can accurately locate users capable of participating in load transfer, avoiding resource waste caused by indiscriminate screening and ensuring that the users on the list have actual control value. By calculating a comprehensive response capability value, the reliability and capability of users participating in control are quantified, and priority ranking ensures that high-capability and high-reliability users participate in control first, improving the efficiency and effectiveness of load control and avoiding control failures due to insufficient user response capability. By determining the load adjustment window, it ensures that load control is executed within the time that users can cooperate with, improving user participation and control feasibility.

[0036] Furthermore, controlling the target distribution network for load regulation and energy storage dispatch according to the carbon emission adjustment strategy includes:

[0037] Based on the adjustable load user list, the current rechargeable capacity status and operating parameters of the corresponding energy storage devices are obtained to determine the dispatchable capacity range of each energy storage device.

[0038] Acquire the predicted load curve data of each energy storage device, and determine the charging and discharging timing sequence of the energy storage device based on the predicted load curve data; wherein, the predicted load curve data is generated based on pre-stored historical load curve data;

[0039] Based on the charging and discharging timing sequence of the energy storage devices and the carbon emission reduction targets for different time periods, the charging and discharging power of each energy storage device is allocated for each time period in order to control the target distribution network for energy storage scheduling.

[0040] This invention, through defining the dispatchable capacity range, clarifies the boundaries of energy storage devices' participation in dispatch, avoiding equipment damage or efficiency degradation caused by dispatching beyond the designated range. By determining the charging and discharging timing sequence, it ensures that energy storage devices provide support when the grid needs it, avoiding resource waste caused by disordered charging and discharging, and improving energy storage utilization efficiency. By combining the timing sequence with carbon emission reduction targets and allocating charging and discharging power, it ensures that energy storage dispatch and load regulation are coordinated, jointly promoting the implementation of carbon emission reduction targets, reducing high-carbon thermal power output, and lowering carbon emissions.

[0041] Secondly, embodiments of the present invention provide a carbon emission balance control device for a power distribution network, comprising a propagation path acquisition module, a cross-regional load rate acquisition module, and a carbon emission balance control module, wherein...

[0042] The propagation path acquisition module is used to collect load change data and distributed power output data of the boundary distribution network in the target distribution network in real time, and determine the control difference propagation path based on the load change data and distributed power output data through a preset continuous power flow calculation method.

[0043] The cross-regional load rate acquisition module is used to read the current transmission power of the cross-regional line and calculate the cross-regional line transmission load rate based on the current transmission power if the control difference propagation path exceeds a preset path threshold.

[0044] The carbon emission balance control module is used to analyze the differences in carbon emission levels between regions based on the cross-regional line transmission load rate, generate a carbon emission adjustment strategy, and control the target distribution network to perform load regulation and energy storage scheduling according to the carbon emission adjustment strategy, so as to achieve carbon emission control balance of the target distribution network.

[0045] This invention, through a propagation path acquisition module, collects real-time load change data and distributed generation output data of the boundary distribution network in the target distribution network. This data forms the basis for all subsequent analysis and control, providing original evidence for accurately understanding the operating status of the distribution network. Furthermore, by using a preset continuous power flow calculation method, combined with the load change data and distributed generation output data, the propagation path of differences in control strategies between adjacent control areas can be clearly identified in the distribution network. The distribution of nodes affected by these differences and their diffusion direction can be clarified, providing crucial evidence for determining whether further control is needed. The cross-regional load rate acquisition module calculates the cross-regional line transmission load rate based on the current transmission power, quantifying the load level of cross-regional lines and providing core data for subsequent carbon emission measurement. The carbon emission balance control module analyzes the differences in carbon emission levels between regions based on the cross-regional line transmission load rate and generates adjustment strategies based on these differences. This approach can specifically narrow the regional carbon emission gap, ensuring that the control strategies are consistent with the carbon emission control targets. Furthermore, based on the carbon emission adjustment strategies, the target distribution network is controlled to perform load regulation and energy storage dispatch. Load regulation can optimize the electricity consumption structure by adjusting adjustable loads and reducing electricity demand during high-carbon periods. Energy storage dispatch can utilize energy storage devices to charge during off-peak hours and discharge during peak hours to smooth load fluctuations and supplement power gaps. The two work together to execute the adjustment strategy, achieving carbon emission control balance in the target distribution network and solving the problem of uneven boundary carbon emission pressure caused by inconsistent control strategies in adjacent control areas.

[0046] Thirdly, embodiments of the present invention provide a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0047] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the power distribution network carbon emission balance control method as described in any of the above.

[0048] Fourthly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus where the computer-readable storage medium is located to perform the power distribution network carbon emission balance control method as described in any of the above.

[0049] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0050] Figure 1 A schematic diagram of a carbon emission balance control method for a power distribution network provided in an embodiment of the present invention;

[0051] Figure 2 A schematic diagram illustrating a carbon emission balance control process for a power distribution network, as exemplified by an embodiment of the present invention;

[0052] Figure 3 This is a structural diagram of a power distribution network carbon emission balance control device provided in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1:

[0055] like Figure 1 As shown in the figure, a carbon emission balance control method for a power distribution network provided by an embodiment of the present invention includes the following steps:

[0056] S11, real-time acquisition of load change data and distributed power output data of the boundary distribution network in the target distribution network, and determination of the control difference propagation path based on the load change data and distributed power output data using a preset continuous power flow calculation method;

[0057] In one specific embodiment, real-time acquisition of load change data and distributed generation output data of the boundary distribution network in the target distribution network includes: obtaining real-time current and voltage sampling values ​​from smart meters and power monitoring devices in the boundary distribution network; removing measurement noise using a Kalman filter; performing time-series alignment of multi-source data based on timestamps; performing interpolation resampling for data sources with inconsistent sampling frequencies to obtain a power time series with a unified sampling rate; based on the power time series, setting the sliding window width to a preset duration, calculating the mean and standard deviation of data points within the window; if a data point deviates from the mean by more than a preset standard deviation multiple, it is marked as an outlier; filling out outliers and missing data segments using linear interpolation to obtain complete and continuous load curve data; performing data format conversion and dimension unification processing on the load curve data; obtaining output records from distributed photovoltaic inverters and performing the same format conversion processing; calculating the load change amplitude by dividing the difference between adjacent sampling points of the load within a preset time period by the time interval; and calculating the distributed generation output level by dividing the difference between the maximum and minimum values ​​of the distributed generation output within the preset time period by the average value.

[0058] For example, data acquisition in the boundary distribution network is achieved through smart meters deployed in the substation outgoing line bays and on the low-voltage side of the distribution transformer. These smart meters record the instantaneous values ​​of three-phase voltage and current at preset sampling intervals. Power monitoring devices are installed at tie lines and sectionalizing switches to monitor the bidirectional flow of active and reactive power in real time. The Kalman filter establishes a state-space model, treating measured values ​​as observations, and recursively calculates using prediction and update equations. The state transition matrix is ​​set according to the physical characteristics of the power grid, and the observation noise covariance matrix is ​​obtained through statistical analysis of historical data, thereby effectively filtering out measurement noise caused by electromagnetic interference and equipment aging.

[0059] Specifically, the timing alignment process addresses the clock skew issue of different acquisition devices. By extracting the timestamp information of each data stream and using the standard clock of the distribution automation master station as a reference, the clock offset of each device is calculated, and then the timestamps of all data points are offset corrected. When the sampling frequencies of some devices are inconsistent, cubic spline interpolation is used to resample on the time axis to ensure that all data sources have the same time resolution.

[0060] For example, the sliding window method sets the window width to include enough data points to reflect the normal fluctuation characteristics of the load, typically covering a complete load variation cycle. The window slides point by point along the time axis, calculating the arithmetic mean and standard deviation of the data within the window each time. When a data point deviates from the window mean by more than a preset multiple of the standard deviation, that point is marked as an outlier. For continuous sequences of outliers or missing data segments, linear interpolation connects the normal data points before and after the outlier segment, distributing intermediate values ​​according to the time ratio to maintain the continuity and smoothness of the load curve.

[0061] In one specific embodiment, the output records uploaded by the distributed photovoltaic inverter via the communication interface include parameters such as DC-side input power, AC-side output power, and power factor. These data, after format conversion, are unified with the load data into per-unit values ​​or kilowatt-hours. The load variation range is obtained by calculating the power difference between adjacent sampling points divided by the sampling time interval, reflecting the load ramp-up rate. The distributed power generation output level is obtained by calculating the ratio of the range to the mean output within a preset time period. This ratio reflects the degree of fluctuation in distributed power generation output, providing a quantitative basis for subsequent assessment of the impact of control differences on the distribution network.

[0062] In this embodiment, determining the propagation path of control differences based on the load change data and distributed generation output data using a preset continuous power flow calculation method specifically involves: constructing a power transmission matrix between adjacent control areas based on the load change data and distributed generation output data, and calculating the eigenvalues ​​of the power transmission matrix to obtain an inter-regional electrical coupling strength index based on the eigenvalues; determining the inter-regional influence correlation strength level based on the inter-regional influence correlation strength level, and increasing the power disturbance amount node by node from the boundary node of the boundary distribution network according to the preset continuous power flow calculation method, based on the inter-regional influence correlation strength level, to obtain the voltage deviation value of each node after increasing the power disturbance amount; wherein the power disturbance amount increased at each node is determined according to the corresponding inter-regional influence correlation strength level; marking nodes whose voltage deviation values ​​exceed a preset deviation threshold as over-limit nodes to obtain an over-limit node set, and determining the propagation path of control differences based on the over-limit node set.

[0063] In one specific embodiment, the step of determining the propagation path of control differences based on the load change data and distributed generation output data using a preset continuous power flow calculation method specifically involves: constructing a power transmission matrix between adjacent control areas based on the load change magnitude and distributed generation output level. Each element in the matrix represents the ratio of the actual transmission power to the rated capacity of the inter-area tie line. By calculating the eigenvalues ​​of the power transmission matrix, an inter-area electrical coupling strength index is obtained. If the coupling strength index exceeds a preset strength threshold, a strong correlation effect is determined, and the inter-area influence correlation strength level is determined. Based on the inter-area influence correlation strength level, the power disturbance is gradually increased starting from the boundary node using a continuous power flow calculation method. Record the voltage change trajectory of each node. When the voltage deviation of a node exceeds a preset percentage of the rated voltage, the node is marked as an over-limit node. Statistically analyze the distribution area of ​​all over-limit nodes to obtain the voltage fluctuation impact range and the propagation path of regulation differences. For each node on the propagation path of regulation differences, establish a Jacobian matrix to describe the relationship between node power and voltage. Obtain the voltage sensitivity coefficient to power by inverting the matrix. Statistically count the number of nodes with sensitivity coefficients greater than a preset threshold to determine the number of nodes affected by load transfer. Based on the number of nodes affected by load transfer and the condition number of the Jacobian matrix, evaluate the convergence speed of the power flow equation iteration. Obtain the power adjustment response time by multiplying the number of iterations required to achieve convergence accuracy by the time of a single iteration.

[0064] For example, the power transmission matrix is ​​constructed based on real-time operating data of the distribution network. By collecting active and reactive power flow values ​​from the interconnecting lines between control zones, a numerical matrix reflecting the tightness of electrical connections between zones is formed. The rows and columns of the matrix correspond to different control zones, and matrix element a... ij This represents the proportion of power transmission from region i to region j relative to the rated capacity of the tie line. When the load variation increases or the output fluctuation of distributed power sources intensifies, the element values ​​of the power transmission matrix will change accordingly. By solving the characteristic equation of the matrix, a set of eigenvalues ​​is obtained. The largest eigenvalue reflects the overall coupling degree of the system. When the largest eigenvalue exceeds a preset threshold, it indicates that there is strong electrical coupling between regions, and the control differences will propagate rapidly between regions.

[0065] Specifically, the physical meaning of eigenvalues ​​lies in revealing the inherent characteristics of power transmission networks. The eigenvectors corresponding to larger eigenvalues ​​indicate the main direction of power flow and the distribution of influence intensity. By analyzing the components of the eigenvectors, it is possible to identify which regions play a dominant role in regulating differential propagation.

[0066] It should be noted that the continuous power flow calculation method tracks the trajectory of system state changes with load through parameterization techniques. Starting from the boundary nodes, the power disturbance is gradually increased. For each small increase in disturbance increment, the power flow equation is solved to obtain the voltage magnitude and phase angle of each node. This method avoids the convergence difficulties near the critical point in conventional power flow calculations and can accurately capture the critical conditions for voltage instability. During the calculation process, a predictive-correction technique is employed. First, the location of the next solution point is estimated using a tangent predictor, and then corrected using the Newton-Raphson method to ensure the accuracy of the solution. When the voltage deviation of a node exceeds a preset percentage of the rated voltage, the location of that node and the corresponding power disturbance are recorded. By statistically analyzing the spatial distribution of all nodes exceeding the limit, the influence range of voltage fluctuations is delineated.

[0067] In one specific embodiment, the determination of the modulation difference propagation path relies on the concept of electrical distance, rather than simple physical distance. The electrical distance between two nodes is determined by their equivalent impedance; the smaller the impedance, the shorter the electrical distance, and the faster the modulation difference propagates.

[0068] Preferably, the Jacobian matrix plays a crucial role in power system analysis, describing the partial derivative relationship between node injected power and node voltage. Each element of the matrix... or This represents the degree to which changes in the voltage magnitude or phase angle at node j affect the active or reactive power at node i. The sensitivity coefficient matrix is ​​obtained by inverting the Jacobian matrix, and its elements... This indicates the sensitivity of the voltage change at node i caused by a power change at node j. A higher sensitivity coefficient indicates a more sensitive node to power disturbances and a more significant impact during load transfer. Statistically counting the number of nodes with sensitivity coefficients exceeding a preset threshold allows for a quantitative assessment of the scope of the load transfer's impact.

[0069] For example, in actual boundary distribution network operation, when carbon emission control strategies differ between adjacent control areas—such as an increase in carbon tax on one side leading to a reduction in output from high-carbon emission power sources—it will cause changes in cross-regional power flow. This change is first reflected in power transmission through tie lines, and then affects the voltage level at boundary nodes. Furthermore, the condition number of the Jacobian matrix reflects the numerical stability of the power flow equations; a larger condition number indicates that the system is closer to the voltage instability boundary, and the worse the convergence of the power flow calculation. When evaluating the power adjustment response time, the number of iterations required for the power flow calculation to reach a new steady state from the initial state is recorded. Each iteration includes three steps: forming the Jacobian matrix, solving the corrected equations, and updating the state variables. The time for a single iteration depends on the system size and matrix sparsity. The total response time is obtained by multiplying the number of iterations by the average time for a single iteration. This time reflects the adjustment period required for the system to adapt to the differences in regulation, providing a timescale reference for formulating coordinated control strategies.

[0070] Understandably, when the Jacobian matrix approaches singularity, even small power disturbances can cause significant voltage changes. This reduces the system's control margin, necessitating timely preventative control measures. For example, during hot summer months, a surge in air conditioning load leads to heavy-load operation of the distribution network. In this situation, the condition number of the Jacobian matrix increases significantly, reducing the system's tolerance to control discrepancies. By monitoring the trend of the condition number in real time, potential voltage exceedance risks can be predicted in advance, providing dispatchers with valuable decision-making time.

[0071] S12, if the control difference propagation path exceeds the preset path threshold, then read the current transmission power of the cross-regional line and calculate the cross-regional line transmission load rate based on the current transmission power.

[0072] In this embodiment, before reading the current transmission power of the cross-regional line, the process includes: if the control difference propagation path exceeds a preset path threshold, calculating the cumulative number of voltage overruns and power transmission capacity utilization rate within the most recent preset time period, and determining the boundary operating pressure level based on the cumulative number of voltage overruns and power transmission capacity utilization rate; extracting state data corresponding to the boundary operating pressure level from a pre-acquired boundary effect assessment dataset according to a preset pressure-state mapping table to construct a pressure impact correlation vector; wherein, the state data in the boundary effect assessment dataset includes the regional voltage fluctuation impact range, the number of load transfer impact nodes, and the power adjustment response time under different operating conditions; the state data are all determined based on the control difference propagation path under different operating conditions; and weighting and summing the components in the pressure impact correlation vector to obtain the current comprehensive impact factor.

[0073] In this embodiment, calculating the cross-regional line transmission load rate based on the current transmission power specifically involves: calculating the percentage of the current transmission power to the line's rated capacity, and calculating the cross-regional line transmission load rate based on the current comprehensive influence factor and the percentage.

[0074] In one specific embodiment, if the length of the differential propagation path exceeds a preset path threshold, voltage measurement data within the most recent preset time period is extracted from the boundary node real-time monitoring device. The cumulative number of times the voltage at each boundary node exceeds the upper and lower limits of the rated value is counted. Simultaneously, the ratio of the current transmission power to the rated capacity of the cross-regional interconnection line is obtained, and the average value of the ratio within the preset time period is calculated as the power transmission capacity utilization rate. Based on the cumulative number of voltage over-limits and the power transmission capacity utilization rate, when the number of over-limits is less than a first threshold and the utilization rate is lower than a second threshold, it is marked as mild stress; when the number of over-limits exceeds the first threshold but is less than the third threshold, or the utilization rate exceeds the second threshold but is less than the fourth threshold, it is marked as... The pressure level is defined as moderate. When the number of over-limit occurrences exceeds the third threshold and the utilization rate exceeds the fourth threshold, it is marked as severe pressure, thus determining the boundary operating pressure level. Based on the boundary operating pressure level, the values ​​corresponding to the current pressure level are extracted from the previously calculated regional voltage fluctuation impact range, number of load transfer impact nodes, and power adjustment response time to construct a pressure impact correlation vector. The weighted sum of each component of the correlation vector is calculated to obtain the comprehensive impact factor. According to the comprehensive impact factor, the current transmission power of the inter-regional line is read, and its percentage of the line's rated capacity is calculated. The comprehensive impact factor is multiplied by a correction coefficient and then added to the percentage to obtain the inter-regional line transmission load rate.

[0075] For example, when the propagation path length of the regulation difference exceeds a preset path threshold, the distribution network enters a high-risk operating state, at which point the voltage stability of the boundary nodes becomes a critical monitoring target. Real-time monitoring equipment for boundary nodes includes voltage transformers installed on the busbars and synchronous phasor measurement devices configured at both ends of the line. These devices record instantaneous voltage values ​​at millisecond-level sampling frequencies. The preset time period is typically a sliding window of 15 or 30 minutes. Within this period, whenever the voltage value exceeds the allowable positive or negative deviation range of the rated voltage, it is recorded as an over-limit event. The cumulative number of voltage over-limit events reflects the frequency with which the boundary node experiences regulation difference shocks; the more frequent the over-limit events, the more unstable the node's operating state.

[0076] Specifically, the calculation of power transmission capacity utilization involves real-time monitoring of inter-regional tie lines. Power measurement devices configured on the tie lines continuously record the transmission values ​​of active and reactive power. By calculating the apparent power and comparing it with the line's rated capacity, the instantaneous utilization rate is obtained. The arithmetic mean of all instantaneous utilization rate values ​​is calculated over a preset time period to obtain the average utilization rate, which reflects the line's load level under the influence of control differences.

[0077] It should be noted that a two-factor comprehensive evaluation method is used to determine the boundary operating pressure level. The first and second thresholds correspond to the mild warning lines for the number of voltage overruns and capacity utilization, respectively, while the third and fourth thresholds correspond to the severe warning lines. When the number of voltage overruns is less than the first threshold and the capacity utilization is lower than the second threshold, it indicates that the boundary operation is relatively stable and the impact of control differences is small, which is marked as a mild pressure level. When either indicator exceeds the first threshold but does not reach the second threshold, it indicates that stress concentration has begun to occur in the system, which is marked as moderate pressure. Only when both indicators exceed their respective second thresholds is it judged as severe pressure, at which point control measures need to be taken immediately to prevent cascading failures. This classification method avoids the one-sidedness of judging by a single indicator and improves the accuracy of pressure assessment through cross-validation.

[0078] Preferably, the voltage fluctuation range, the number of nodes affected by load transfer, and the power adjustment response time obtained from the preceding calculations constitute the basic dataset for evaluating boundary effects. This data is obtained through continuous power flow calculations and sensitivity analysis, and is categorized and stored according to different operating conditions.

[0079] In one specific embodiment, the construction process of the comprehensive impact factor embodies the integration of multi-dimensional information. The pressure impact correlation vector contains three components, corresponding to the normalized value of the voltage fluctuation range, the relative proportion of the number of affected nodes, and the standardized value of the response time, respectively. The weighting coefficients are set to consider the importance of each factor under different pressure levels; the response time has a larger weight under mild pressure, while the impact range has a larger weight under severe pressure. By calculating the inner product of the correlation vector and the weight vector, the comprehensive impact factor is obtained, which quantifies the degree of impact of regulatory differences on the overall system.

[0080] For example, in actual operation, a boundary distribution network monitored the implementation of differentiated demand response policies by adjacent control areas during the summer peak electricity consumption period. One control area guided users to reduce load by increasing electricity prices, while the other maintained the original electricity price level, causing load to shift to the low-price area and a surge in power on inter-regional interconnection lines. Furthermore, the calculation of the inter-regional line transmission load factor not only considers the current actual transmission power but also introduces a dynamic correction mechanism. The base load factor is obtained by dividing the current transmission power by the line's rated capacity, and a comprehensive influence factor is used as a correction term to reflect the impact of system pressure on the line's carrying capacity. The correction coefficient is determined based on historical operating data and expert experience, typically ranging from 0.8 to 1.2. When the comprehensive influence factor is large, it indicates that the system is under high pressure, the actual carrying capacity of the line will decrease, and a positive correction is needed to increase the load factor value, providing a more conservative reference for dispatching decisions.

[0081] Understandably, this multi-layered stress assessment and load factor calculation method can comprehensively reflect the operating status of the boundary distribution network under the influence of regulatory differences, providing a quantitative basis for formulating cross-regional coordinated control strategies. For example, when the calculated transmission load factor exceeds a preset safety threshold, the dispatch center will activate an emergency response mechanism to reduce the operating pressure in the boundary area and maintain the safe and stable operation of the system by adjusting the output of distributed power sources, activating demand-side response resources, or adjusting the operation mode of tie lines.

[0082] S13. Based on the cross-regional line transmission load rate, analyze the differences in carbon emission levels between regions to generate a carbon emission adjustment strategy, and based on the carbon emission adjustment strategy, control the target distribution network to perform load regulation and energy storage scheduling to achieve carbon emission regulation balance of the target distribution network.

[0083] In this embodiment, the step of analyzing the differences in carbon emission levels between regions based on the cross-regional line transmission load rate to generate a carbon emission adjustment strategy specifically involves: determining highly coupled adjacent regions based on the cross-regional line transmission load rate, and calculating the current carbon emission intensity benchmark value for each highly coupled adjacent region to construct a carbon emission intensity comparison table for adjacent control areas; determining the priority direction for carbon emission transfer based on the carbon emission intensity comparison table for adjacent control areas, wherein the priority direction for carbon emission transfer is from high carbon emission intensity regions to low carbon emission intensity regions; acquiring historical load data, and extracting typical daily load data from the historical load data using a preset clustering analysis method to identify peak and valley time period distributions, and determining the time period range for executable load transfer based on the peak and valley time period distributions; determining carbon emission reduction target values ​​for different time periods based on the carbon emission intensity benchmark value and the pre-stored carbon emission values ​​corresponding to each peak and valley time period, wherein each peak and valley time period belongs to the time period range for executable load transfer; and integrating the priority direction for carbon emission transfer, the time period range for executable load transfer, and the carbon emission reduction target values ​​for different time periods to obtain the carbon emission adjustment strategy.

[0084] In one specific embodiment, based on the cross-regional transmission load rate, real-time output data of various generator sets, including thermal power, hydropower, wind power, and photovoltaic power generation, are obtained from the energy management system of adjacent control areas. Combined with the carbon emission factor per unit power generation of each power source, a carbon emission intensity benchmark value for each control area at the current moment is calculated, and a carbon emission intensity comparison table of adjacent control areas is constructed. Based on the carbon emission intensity comparison table, the carbon emission intensity difference between adjacent control areas is calculated. When the difference exceeds a preset difference threshold, a priority direction for load transfer from high-carbon emission intensity areas to low-carbon emission intensity areas is determined. Simultaneously, typical daily load curves are extracted from historical load data to identify peak-valley time distributions and determine the time range for executable load transfer. For the priority direction of load transfer and the executable time range, carbon emission reduction target values ​​are set for different time periods, with the reduction ratio during peak periods being higher than that during valley periods. Based on the carbon emission intensity difference and reduction target values, a local adjustment strategy plan is integrated, including the load transfer direction, time adjustment range, and carbon emission control target value.

[0085] For example, the energy management system acquires the operating parameters of various generator units in real time through a data acquisition and monitoring system. The carbon emission factor of thermal power units is determined based on the type of coal and combustion efficiency; typically, bituminous coal units emit approximately 0.9 kg of carbon dioxide per kilowatt-hour, while lignite units can reach 1.1 kg of carbon dioxide per kilowatt-hour. The carbon emission factors of hydropower, wind power, and photovoltaic power are close to zero, considering only indirect emissions from equipment manufacturing and maintenance processes.

[0086] Specifically, the carbon emission intensity benchmark value is calculated using a weighted average method. The real-time output of each type of power source is multiplied by its corresponding carbon emission factor, summed, and then divided by the total power generation to obtain the comprehensive carbon emission intensity of the control area. This indicator reflects the decarbonization level of the region's power structure and is a core parameter for evaluating the effectiveness of regulation. A comparison table of carbon emission intensity between adjacent control areas is presented in matrix form, with rows and columns corresponding to different control areas, and matrix elements representing intensity values, facilitating rapid identification of the distribution patterns of high- and low-carbon regions.

[0087] It should be noted that the determination of load transfer direction follows the principle of prioritizing carbon emission reduction. When the difference in carbon emission intensity between adjacent control zones exceeds a preset threshold, the system automatically marks the high-carbon zone as the load output zone and the low-carbon zone as the load receiving zone. The preset difference threshold is usually set at 0.1 to 0.2 kg of carbon dioxide per kilowatt-hour. This value can trigger effective load transfer while avoiding system disturbances caused by frequent adjustments.

[0088] Preferably, the extraction of typical daily load curves is based on cluster analysis, classifying historical load data according to weekdays, rest days, and holidays to identify representative load patterns. Peak periods typically occur between 10:00 AM and 12:00 PM and between 2:00 PM and 5:00 PM, while trough periods occur between 0:00 AM and 6:00 AM.

[0089] In one specific embodiment, the carbon emission reduction target is set taking into account time-period characteristics and user responsiveness. During peak periods, due to high load demand and difficulty in adjustment, the reduction ratio is set at 5%-8% of the baseline emissions; during off-peak periods, with lower loads, there is more room for adjustment, and the reduction ratio can reach 10%-15%.

[0090] For example, the integration process of local adjustment strategy planning determines the load transfer direction as the decision-making starting point, the time period adjustment range defines the execution boundary, and the carbon emission control target value provides quantitative constraints. Together, these three constitute a complete control scheme framework to guide the low-carbon operation of the regional distribution network.

[0091] In this embodiment, controlling the target distribution network to perform load regulation and energy storage dispatch according to the carbon emission adjustment strategy includes: filtering pre-stored distribution network user data to obtain an adjustable load user list based on the carbon emission transfer priority direction and the time range of executable load transfer in the carbon emission adjustment strategy; calculating the comprehensive response capability value of each user based on the historical response records of each user in the adjustable load user list, and determining the user response priority based on the comprehensive response capability value; obtaining the reducible time period for each user in the adjustable load user list, and determining the load regulation time window based on the reducible time period and the time range of executable load transfer; and controlling the target distribution network to perform load regulation based on the user response priority and the load regulation time window.

[0092] In one specific embodiment, based on the load transfer direction and time period adjustment range in the local adjustment strategy plan, the electricity category identifiers and historical electricity consumption data of all users in the region are read from the distribution network user information management database. Users are grouped into three categories: industrial, commercial, and residential. Typical daily load curves and monthly electricity consumption for each category are extracted, and users participating in demand response are selected to form an adjustable load user list. Based on the adjustable load user list, the maximum reducible load capacity and minimum continuous reduction duration parameters for each user are read. The maximum reducible load capacity is divided by the user's average load to obtain the reduction capacity ratio. The response success rate is calculated based on the ratio of the number of successful responses in the user's historical response records to the total number of responses. The reduction capacity ratio is multiplied by the response success rate to obtain the comprehensive response capability value. For the comprehensive response capability value, different weighting coefficients are assigned according to the production continuity requirements of industrial users, the business hour flexibility of commercial users, and the necessities of life for residential users. Weighted priority scores are calculated and ranked to obtain the user response priority ranking. Simultaneously, the time period adjustment range of the local adjustment strategy plan is intersected with the reducible time periods of each user to determine the load adjustment time window.

[0093] For example, the power distribution network user information management database records the basic information and electricity consumption behavior data of all users within the region. Industrial users include manufacturing enterprises, processing plants, and data centers, whose electricity loads exhibit continuous and regular characteristics; commercial users encompass shopping malls, office buildings, and hotels, whose electricity demand fluctuates significantly with business hours; and residential users show a clear bimodal distribution pattern, with peak demand in the morning and evening. By extracting historical electricity consumption data from these users and analyzing the morphological characteristics of their typical daily load curves, user groups with adjustment potential can be identified.

[0094] Specifically, the reduction capacity ratio reflects the user's load regulation depth. The maximum scalable load capacity is typically determined when the user connects to the grid based on their production processes or equipment characteristics, while the average load is obtained by statistically analyzing the user's recent electricity consumption data. A higher reduction capacity ratio indicates a higher load regulation potential for the user. The response success rate is statistically derived from historical demand response event records, reflecting the user's response reliability. The comprehensive response capability value is obtained by multiplying the two values, taking into account both regulation capacity and execution reliability, providing a quantitative basis for subsequent prioritization.

[0095] It should be noted that the weighting coefficients are set to reflect the characteristics of different types of users. Industrial users have high requirements for production continuity, and production interruptions would cause significant economic losses, so their weighting coefficients are relatively low; commercial users have greater flexibility in adjustment during non-business hours, so their weighting coefficients are moderate; the basic electricity needs of residential users must be guaranteed, but non-essential loads such as air conditioners and water heaters can participate in regulation, and their weighting coefficients are dynamically adjusted according to the season and time of day.

[0096] Preferably, the time window is determined using set operations. The time period adjustment range given by the local adjustment strategy planning represents the time period during which the system needs to adjust its load, while the time periods that each user can reduce are determined by their production plans or business arrangements.

[0097] In one possible implementation, the intersection operation is performed by comparing two time period sets to find the overlapping time intervals.

[0098] For example, if an industrial user's load reduction period is 10:00-14:00 and 20:00-22:00, while the adjustment strategy requires a period of 11:00-15:00, then the user's load adjustment time window is 11:00-14:00.

[0099] For example, this hierarchical and categorized response mechanism can fully tap the adjustment potential of various users and achieve precise scheduling of load resources while meeting users' basic electricity needs.

[0100] In this embodiment, controlling the target distribution network to perform load regulation and energy storage scheduling according to the carbon emission adjustment strategy includes: obtaining the current rechargeable capacity status and operating parameters of the corresponding energy storage devices based on the adjustable load user list to determine the dispatchable capacity range of each energy storage device; obtaining the predicted load curve data of each energy storage device, and determining the charging and discharging timing sequence of the energy storage devices based on the predicted load curve data; wherein the predicted load curve data is generated based on pre-stored historical load curve data; and allocating the charging and discharging power of each energy storage device in each time period according to the charging and discharging timing sequence of the energy storage devices and the carbon emission reduction target values ​​for different time periods to control the target distribution network to perform energy storage scheduling.

[0101] In one specific embodiment, based on user response priority ranking and load adjustment time window, the current state of charge percentage and rated capacity uploaded by the energy storage device's battery management system are read. The current stored capacity is obtained by multiplying the state of charge percentage by the rated capacity. The remaining rechargeable capacity is obtained by subtracting the current stored capacity from the rated capacity. The dischargeable capacity is obtained by subtracting the minimum reserve capacity from the current stored capacity. Simultaneously, the maximum charge / discharge power and conversion efficiency coefficient of the energy storage device are obtained to determine the dispatchable capacity range of each energy storage unit. Based on the dispatchable capacity range, predicted load curve data is obtained from the distribution network dispatch system to identify the valley periods below the average load and the peak periods above the average load in the load curve. For each time period, when a valley period intersects with the load adjustment time window, the intersecting period is marked as a charging opportunity; when a peak period intersects with the load adjustment time window, the intersecting period is marked as a discharging opportunity, thus obtaining a charging and discharging opportunity sequence for the energy storage device. For the charging and discharging opportunity sequence of the energy storage device, the difference between the load and the reference load in each time period is calculated as the power adjustment demand. The power adjustment demand is allocated according to the proportion of the dispatchable capacity of each energy storage device to the total dispatchable capacity. If the allocated power exceeds the maximum charging and discharging power of the device, the maximum power is used. The charging and discharging time points and corresponding power values ​​of each energy storage device are combined to obtain the charging and discharging time sequence planning and power level allocation of the energy storage device.

[0102] For example, the battery management system of an energy storage device monitors the voltage, current, and temperature parameters of each battery cell in real time, and calculates the state of charge (SOC) percentage using a combination of coulomb counting and open-circuit voltage methods. SOC reflects the proportion of currently stored energy relative to the total capacity and is a fundamental indicator for assessing the dispatchability of energy storage. The product of rated capacity and SOC is the actual amount of energy currently stored, and this value determines the discharge potential of the energy storage device.

[0103] Specifically, the calculation of rechargeable and dischargeable capacity needs to consider the battery's safe operating boundaries. The minimum reserve capacity is typically set at 20% to 30% of the rated capacity to prevent over-discharge from damaging battery life. Dischargeable capacity equals the current stored capacity minus the minimum reserve capacity, ensuring that the energy storage device maintains basic backup capability after participating in dispatch. The conversion efficiency coefficient reflects the energy loss during charging and discharging; the bidirectional conversion efficiency of lithium battery energy storage systems is typically between 90% and 95%.

[0104] It is important to note that identifying peak and valley load curves is crucial for determining charging and discharging timing. The distribution network dispatching system predicts load changes over the next 24 hours based on historical data and meteorological information, calculating a baseline load level using a moving average method. When the load is below the baseline value for a period exceeding a preset threshold, it is identified as a valley period; conversely, it is identified as a peak period. This identification method avoids frequent charging and discharging switching caused by short-term load fluctuations, improving the operational stability of energy storage devices.

[0105] Preferably, the timing sequence of charging and discharging is determined using set intersection operations. The load regulation time window is derived from the user response priority ranking result, representing the schedulable period of demand-side resources. When the off-peak period overlaps with the regulation window, the energy storage device charges during the overlapping period to absorb surplus power from the grid; when the peak period overlaps with the regulation window, the energy storage device discharges to supplement the power deficit of the grid.

[0106] In one specific embodiment, power allocation follows the capacity ratio principle. The proportion of dispatchable capacity of each energy storage device to the total dispatchable capacity determines its share of regulation tasks.

[0107] For example, if the dispatchable capacities of three energy storage devices are 100 kWh, 150 kWh and 250 kWh respectively, and the total power regulation demand is 200 kW, then the power allocated to the three devices will be 40 kW, 60 kW and 100 kW respectively.

[0108] For example, when the allocated power exceeds the device's maximum charging and discharging power, power limiting is used to prevent the device from operating under overload. At the same time, the unallocated power is redistributed to other energy storage devices with spare capacity, thus achieving the complete execution of the power regulation task.

[0109] Preferably, the power output of each region of the distribution network is allocated to achieve optimized control of carbon emissions of the overall distribution network. Specifically, this involves: integrating the energy storage output arrangement with the distributed power generation arrangement based on the charging and discharging sequence planning and power level allocation of energy storage devices; reassessing the regional supply and demand balance; updating the power output allocation for each time period within the region; and obtaining the load supply sufficiency and power shortage time period distribution. Based on the load supply sufficiency and power shortage time period distribution, identifying the regional carbon emission control effect; assessing the need for cross-regional power exchange adjustment; extracting carbon emission reduction data; and adjusting the cross-regional power exchange protocol to obtain an optimized control scheme for carbon emissions of the distribution network.

[0110] In one specific embodiment, the step of integrating energy storage output arrangement and distributed power generation arrangement based on the energy storage device charging and discharging sequence planning and power level allocation, reassessing the regional supply and demand balance, updating the power output allocation for each time period in the region, and obtaining the load supply sufficiency and power shortage time period distribution are as follows: Based on the energy storage device charging and discharging sequence planning and power level allocation, the predicted power generation curves of wind power and photovoltaic power are read from the distributed power control system. At each moment, the energy storage discharge power is taken as a positive value and the charging power is taken as a negative value, and algebraically added with the distributed power generation power to obtain the total power output value for each time period. At the same time, the predicted load demand value for the corresponding time period is obtained from the load forecasting system. Based on the total power output value and the predicted load demand value, the difference between the total power output value and the load demand value for each time period is calculated. If the difference is greater than zero, the power supply for that time period is sufficient. If the difference is less than zero, it is marked as a power shortage time period and the shortage amount is recorded. The proportion of sufficient time periods to the total number of time periods is counted as the load supply sufficiency. All shortage time periods and corresponding shortage amounts are summarized to obtain the power shortage time period distribution.

[0111] For example, the distributed power control system integrates power generation forecasting functions for wind farms and photovoltaic power plants. Wind power forecasting is based on wind speed, wind direction data, and unit operating status, while photovoltaic power forecasting considers changes in solar irradiance, temperature, and cloud cover. These forecast data are updated with a 15-minute time resolution, providing fundamental data support for supply and demand balance assessment.

[0112] Specifically, power superposition calculations employ algebraic rules. Power injected into the grid during energy storage device discharge is denoted as a positive value; power absorbed from the grid during charging is denoted as a negative value. This notation convention ensures the consistency of the power balance equations.

[0113] For example, if energy storage discharges 100 kW, wind power outputs 200 kW, and photovoltaic power outputs 150 kW during a certain period, then the total power output is 450 kW.

[0114] It should be noted that load supply adequacy, as an evaluation indicator, reflects the reliability level of regional power supply. By statistically analyzing the proportion of periods with sufficient power outages within a 24-hour period, the effectiveness of control strategies can be directly assessed. The distribution of power shortage periods identifies time periods requiring special attention, providing a basis for further optimization of dispatching.

[0115] In one specific embodiment, the process of identifying the regional carbon emission control effect based on load supply adequacy and power shortage period distribution, assessing the inter-regional power exchange adjustment needs, extracting carbon emission reduction data, adjusting the inter-regional power exchange protocol, and obtaining an optimized control scheme for distribution network carbon emissions specifically involves: Based on load supply adequacy and power shortage period distribution, reading the carbon emission factor data of thermal and gas-fired power generation units for each period, calculating the actual carbon emissions by multiplying the standby power generation during the shortage period by the corresponding carbon emission factor, comparing it with the preset regional carbon emission control target value, and calculating the difference as the carbon emission reduction amount to be reduced if the actual value exceeds the target value. Based on the carbon emission reduction amount, obtaining the predicted output data and current absorption status of low-carbon power sources such as wind power and photovoltaic power from the dispatch center of the adjacent control area, calculating the surplus capacity of low-carbon power sources, and dividing the surplus capacity by the high-carbon power generation corresponding to the carbon emission reduction amount to obtain the inter-regional power exchange adjustment value. Regarding the aforementioned inter-regional power exchange adjustment value, based on the existing inter-regional power exchange protocol, the adjustment value is added to the upper limit parameter of the exchange power, the exchange period is extended to the power shortage period, and the modified protocol parameters are merged with the regional energy storage and demand response control parameters to obtain an optimized control scheme for the carbon emissions of the distribution network.

[0116] For example, the carbon emission factor, as a key parameter for measuring the carbon emission levels of different types of generating units, typically ranges from 0.8 to 1.0 kg CO2 per kilowatt-hour for thermal power units, approximately 0.4 to 0.5 for gas turbine units, while the carbon emission factors for renewable energy sources such as wind power and solar power are close to zero. Regional carbon emission control targets are formulated based on national carbon peaking and carbon neutrality targets, combined with the current state of the regional energy structure and emission reduction potential.

[0117] Specifically, carbon emission calculations need to consider changes in the power structure during different periods. During periods of power shortage, the system typically activates fast-response gas turbine units or calls upon the spinning reserve capacity of thermal power units. The input of these high-carbon-emission power sources leads to a sharp increase in regional carbon emissions. By recording the actual power generation of various backup power sources and multiplying it by the corresponding carbon emission factor, the additional carbon emissions can be accurately quantified. When the actual carbon emissions exceed the control target, the difference between the two is the amount of carbon emissions that must be reduced. This value guides subsequent cross-regional coordinated dispatch decisions.

[0118] It should be noted that the assessment of surplus low-carbon power capacity in adjacent control areas involves data analysis across multiple dimensions. The projected output of wind and solar power is based on weather forecasts and historical power generation patterns, while current absorption reflects the local load's capacity to absorb renewable energy. Surplus capacity equals projected output minus local absorption; this portion of electricity can be transmitted across regions to replace high-carbon power sources.

[0119] Preferably, the calculation of the inter-regional power exchange adjustment value adopts the principle of equivalent substitution. The amount of carbon emissions to be reduced is divided by the difference in carbon emission factors between high-carbon and low-carbon power sources to obtain the amount of high-carbon power generation that needs to be replaced. This amount of electricity is the integral value of the inter-regional power exchange in the corresponding time period.

[0120] In one specific embodiment, the modification of the cross-regional power exchange protocol mainly involves two aspects: first, increasing the upper limit of the exchange power, increasing the amount of power exchange within the capacity allowed by the original transmission channel; and second, extending the exchange period, extending the power support originally only during peak load periods to all power shortage periods.

[0121] For example, the formation of a carbon emission optimization and control scheme for the distribution network is a parameter integration process that includes the coordinated use of multiple control measures such as energy storage charging and discharging power, demand response reduction, and inter-regional exchange power to achieve the regional total carbon emission control target.

[0122] To better illustrate the working principle and steps of this method, see [link / reference]. Figure 2 One example, Figure 2 This is a schematic diagram of a carbon emission balance control process for a power distribution network, provided as an embodiment of the present invention.

[0123] This invention, through real-time acquisition of load change data and distributed generation output data from the boundary distribution networks within the target distribution network, forms the basis for all subsequent analysis and control. It provides the initial data for accurately understanding the operating status of the distribution network. Furthermore, by employing a pre-defined continuous power flow calculation method, combined with the load change data and distributed generation output data, the propagation path of differences in control strategies between adjacent control areas within the distribution network can be clearly identified, clarifying the node distribution and diffusion direction of the impact of these differences. This provides crucial information for determining whether further control is necessary. By calculating the cross-regional line transmission load rate based on the current transmission power, the load level of cross-regional lines is quantified, providing core data for subsequent carbon emission measurements. By... The cross-regional transmission load rate analysis identifies differences in carbon emission levels between regions and generates adjustment strategies based on these differences. This approach can specifically narrow regional carbon emission gaps, ensuring that the control strategies align with carbon emission control targets. Furthermore, based on these carbon emission adjustment strategies, the target distribution network is controlled for load regulation and energy storage dispatch. Load regulation optimizes the electricity consumption structure by adjusting adjustable loads, reducing electricity demand during high-carbon periods. Energy storage dispatch utilizes energy storage devices to charge during off-peak hours and discharge during peak hours, smoothing load fluctuations and supplementing power shortages. The two work together to execute the adjustment strategy, achieving carbon emission control balance in the target distribution network and resolving the problem of uneven boundary carbon emission pressure caused by inconsistent control strategies in adjacent control areas. Compared with existing technologies, this invention achieves efficient and precise carbon emission control balance in the distribution network.

[0124] Example 2:

[0125] like Figure 3 As shown, this embodiment provides a carbon emission balance control device for a power distribution network, including a propagation path acquisition module 201, a cross-regional load rate acquisition module 202, and a carbon emission balance control module 203, wherein...

[0126] The propagation path acquisition module 201 is used to collect load change data and distributed power output data of the boundary distribution network in the target distribution network in real time, and determine the control difference propagation path based on the load change data and distributed power output data through a preset continuous power flow calculation method.

[0127] In this embodiment, the propagation path acquisition module 201 determines the propagation path of the control difference based on the load change data and distributed power output data using a preset continuous power flow calculation method. Specifically, the propagation path acquisition module 201 constructs a power transmission matrix between adjacent control areas based on the load change data and distributed power output data, and calculates the characteristic values ​​of the power transmission matrix to obtain an inter-regional electrical coupling strength index based on the characteristic values. Based on the inter-regional electrical coupling strength index, the inter-regional influence correlation strength level is determined, and based on the inter-regional influence correlation strength level, the power disturbance is increased node by node starting from the boundary node of the boundary distribution network using the preset continuous power flow calculation method to obtain the voltage deviation value after the power disturbance is increased at each node. The power disturbance increased at each node is determined according to the corresponding inter-regional influence correlation strength level. Nodes whose voltage deviation value exceeds a preset deviation threshold are marked as over-limit nodes to obtain an over-limit node set, and the control difference propagation path is determined based on the over-limit node set.

[0128] The cross-regional load rate acquisition module 202 is used to read the current transmission power of the cross-regional line and calculate the cross-regional line transmission load rate based on the current transmission power if the control difference propagation path exceeds a preset path threshold.

[0129] In this embodiment, the cross-regional load rate acquisition module 202 calculates the cross-regional line transmission load rate based on the current transmission power. Specifically, the cross-regional load rate acquisition module 202 calculates the percentage of the current transmission power to the line's rated capacity, and calculates the cross-regional line transmission load rate based on the current comprehensive influence factor and the percentage.

[0130] The carbon emission balance control module 203 is used to analyze the differences in carbon emission levels between regions based on the cross-regional line transmission load rate, generate a carbon emission adjustment strategy, and control the target distribution network to perform load regulation and energy storage scheduling according to the carbon emission adjustment strategy, so as to achieve carbon emission control balance of the target distribution network.

[0131] In this embodiment, the carbon emission balance control module 203 analyzes the differences in carbon emission levels between regions based on the cross-regional line transmission load rate to generate a carbon emission adjustment strategy. Specifically, the carbon emission balance control module 203 determines highly coupled adjacent regions based on the cross-regional line transmission load rate and calculates the current carbon emission intensity benchmark value for each highly coupled adjacent region to construct a carbon emission intensity comparison table for adjacent control areas. Based on the carbon emission intensity comparison table for adjacent control areas, it determines the priority direction for carbon emission transfer; wherein, the priority direction for carbon emission transfer is from high carbon emission intensity regions to low carbon emission intensity regions. Historical load data is acquired, and typical daily load data is extracted from the historical load data using a preset clustering analysis method to identify peak and valley period distributions. Based on the peak and valley period distributions, the time range for executable load transfer is determined. Based on the carbon emission intensity benchmark value and the pre-stored carbon emission values ​​corresponding to each peak and valley period, carbon emission reduction target values ​​for different time periods are determined. Each peak and valley period belongs to the time range for executable load transfer. The carbon emission transfer priority direction, the time range for executable load transfer, and the carbon emission reduction target values ​​for different time periods are integrated to obtain a carbon emission adjustment strategy.

[0132] In this embodiment, the carbon emission balance control module 203 controls the target distribution network to perform load regulation and energy storage scheduling according to the carbon emission adjustment strategy, including: the carbon emission balance control module 203 filters pre-stored distribution network user data according to the priority direction of carbon emission transfer and the time range of executable load transfer in the carbon emission adjustment strategy to obtain an adjustable load user list; calculates the comprehensive response capability value of each user according to the historical response records of each user in the adjustable load user list, and determines the user response priority according to the comprehensive response capability value; obtains the reducible time period of each user in the adjustable load user list, and determines the load regulation time window according to the reducible time period and the time range of executable load transfer; and controls the target distribution network to perform load regulation according to the user response priority and the load regulation time window.

[0133] In this embodiment, the carbon emission balance control module 203 controls the target distribution network to perform load regulation and energy storage scheduling according to the carbon emission adjustment strategy, including: the carbon emission balance control module 203 obtains the current rechargeable capacity status and operating parameters of the corresponding energy storage devices according to the adjustable load user list to determine the dispatchable capacity range of each energy storage device; obtains the predicted load curve data of each energy storage device, and determines the charging and discharging timing sequence of the energy storage devices according to the predicted load curve data; wherein, the predicted load curve data is generated based on pre-stored historical load curve data; and allocates the charging and discharging power of each energy storage device in each time period according to the charging and discharging timing sequence of the energy storage devices and the carbon emission reduction target values ​​for different time periods to control the target distribution network to perform energy storage scheduling.

[0134] For a more detailed explanation of the working principle and procedures of this embodiment, please refer to the relevant description in Embodiment 1.

[0135] This invention, through the propagation path acquisition module 201, collects real-time load change data and distributed generation output data of the boundary distribution network, which forms the basis for all subsequent analysis and control. It provides the original basis for accurately understanding the operating status of the distribution network. By using a preset continuous power flow calculation method, combined with the load change data and distributed generation output data, the propagation path of differences in control strategies between adjacent control areas in the distribution network can be clearly identified, clarifying the node distribution and diffusion direction of the differences, providing a key basis for subsequent judgment on whether further control is needed. Through the cross-regional load rate acquisition module 202, the cross-regional line transmission load rate is calculated based on the current transmission power, quantifying the load level of the cross-regional lines, providing core data for subsequent carbon emission measurement. The carbon emission balance control module 203 analyzes the differences in carbon emission levels between regions based on the cross-regional line transmission load rate, and generates adjustment strategies based on these differences. This allows for targeted reduction of regional carbon emission gaps, ensuring that the control strategies are consistent with carbon emission control targets. Furthermore, based on the carbon emission adjustment strategies, the module controls the target distribution network to perform load regulation and energy storage scheduling. Load regulation optimizes the electricity consumption structure by adjusting adjustable loads, reducing electricity demand during high-carbon periods. Energy storage scheduling utilizes energy storage devices to charge during off-peak hours and discharge during peak hours, smoothing load fluctuations and supplementing power gaps. The two work together to execute the adjustment strategy, achieving carbon emission control balance in the target distribution network and resolving the problem of uneven boundary carbon emission pressure caused by inconsistent control strategies in adjacent control areas.

[0136] Example 3:

[0137] This embodiment provides a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0138] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the power distribution network carbon emission balance control method as described in any of the above.

[0139] Example 4:

[0140] This invention provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when running, controls the device or apparatus containing the computer-readable storage medium to perform the power distribution network carbon emission balance control method as described above.

[0141] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0142] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for carbon emission balance control in a power distribution network, characterized in that, include: The load change data and distributed generation output data of the boundary distribution network in the target distribution network are collected in real time, and the control difference propagation path is determined based on the load change data and distributed generation output data through a preset continuous power flow calculation method. If the controlled differential propagation path exceeds the preset path threshold, the current transmission power of the cross-regional line is read, and the cross-regional line transmission load rate is calculated based on the current transmission power. Based on the cross-regional line transmission load rate, the differences in carbon emission levels between regions are analyzed to generate a carbon emission adjustment strategy. Based on the carbon emission adjustment strategy, the target distribution network is controlled to perform load regulation and energy storage scheduling to achieve carbon emission regulation balance of the target distribution network.

2. The method for carbon emission balance control in a power distribution network as described in claim 1, characterized in that, The method of determining the propagation path of control differences based on the load change data and distributed power output data using a preset continuous power flow calculation method is as follows: Based on the load change data and distributed power output data, a power transmission matrix between adjacent control areas is constructed, and the characteristic values ​​of the power transmission matrix are calculated to obtain the inter-area electrical coupling strength index based on the characteristic values. Based on the inter-regional electrical coupling strength index, the inter-regional influence correlation strength level is determined. Then, using a preset continuous power flow calculation method, power disturbance is added to each node starting from the boundary node of the boundary distribution network according to the inter-regional influence correlation strength level, so as to obtain the voltage deviation value after the power disturbance is added to each node. The power disturbance added to each node is determined according to the corresponding inter-regional influence correlation strength level. Nodes whose voltage deviation values ​​exceed a preset deviation threshold are marked as over-limit nodes to obtain an over-limit node set, and the control difference propagation path is determined based on the over-limit node set.

3. The method for carbon emission balance control in a power distribution network as described in claim 1, characterized in that, Before reading the current transmission power of the inter-regional line, the following is included: If the control difference propagation path exceeds the preset path threshold, the cumulative number of voltage over-limits and the power transmission capacity utilization rate within the most recent preset time period are calculated, and the boundary operating pressure level is determined based on the cumulative number of voltage over-limits and the power transmission capacity utilization rate. Based on a preset pressure-state mapping table, state data corresponding to the boundary operating pressure level is extracted from a pre-acquired boundary effect assessment dataset to construct a pressure impact correlation vector. The state data in the boundary effect assessment dataset includes the regional voltage fluctuation impact range, the number of load transfer impact nodes, and the power adjustment response time under different operating conditions. The state data are all determined based on the propagation path of the control differences under different operating conditions. The current comprehensive impact factor is obtained by weighted summation of each component in the pressure impact correlation vector.

4. The method for carbon emission balance control in a power distribution network as described in claim 3, characterized in that, The calculation of the cross-regional line transmission load rate based on the current transmission power is specifically as follows: Calculate the percentage of the current transmission power to the line's rated capacity, and calculate the cross-regional line transmission load rate based on the current comprehensive influence factor and the percentage.

5. The method for carbon emission balance control in a power distribution network as described in claim 1, characterized in that, The step of analyzing the differences in carbon emission levels between regions based on the cross-regional line transmission load rate to generate a carbon emission adjustment strategy is as follows: Based on the cross-regional line transmission load rate, highly coupled adjacent regions are determined, and the current carbon emission intensity benchmark value of each highly coupled adjacent region is calculated to construct a carbon emission intensity comparison table of adjacent control areas. Based on the carbon emission intensity comparison table of adjacent control areas, the priority direction for carbon emission transfer is determined; wherein, the priority direction for carbon emission transfer is from areas with high carbon emission intensity to areas with low carbon emission intensity. Historical load data is acquired, and typical daily load data is extracted from the historical load data using a preset clustering analysis method to identify peak and valley period distributions. Based on the peak and valley period distributions, the time range for which load transfer can be performed is determined. Based on the carbon emission intensity benchmark value and the pre-stored carbon emission values ​​corresponding to each peak and valley period, carbon emission reduction target values ​​for different periods are determined; wherein, each peak and valley period belongs to the period range of the executable load transfer. By integrating the carbon emission transfer priority directions, the time range of feasible load transfers, and the carbon emission reduction targets for different time periods, a carbon emission adjustment strategy is obtained.

6. The method for carbon emission balance control in a power distribution network as described in claim 5, characterized in that, The step of controlling the target distribution network to perform load regulation and energy storage dispatch according to the carbon emission adjustment strategy includes: Based on the priority direction of carbon emission transfer and the time range of executable load transfer in the carbon emission adjustment strategy, the pre-stored distribution network user data is filtered to obtain a list of adjustable load users; Based on the historical response records of each user in the adjustable load user list, calculate the comprehensive response capability value of each user, and determine the user response priority based on the comprehensive response capability value; Obtain the cut-off time periods for each user in the adjustable load user list, and determine the load adjustment time window based on the cut-off time periods and the time range for which load transfer can be performed; Based on the user response priority and load adjustment time window, the target distribution network is controlled to perform load regulation.

7. The method for carbon emission balance control in a power distribution network as described in claim 6, characterized in that, The step of controlling the target distribution network to perform load regulation and energy storage dispatch according to the carbon emission adjustment strategy includes: Based on the adjustable load user list, the current rechargeable capacity status and operating parameters of the corresponding energy storage devices are obtained to determine the dispatchable capacity range of each energy storage device. Acquire the predicted load curve data of each energy storage device, and determine the charging and discharging timing sequence of the energy storage device based on the predicted load curve data; wherein, the predicted load curve data is generated based on pre-stored historical load curve data; Based on the charging and discharging timing sequence of the energy storage devices and the carbon emission reduction targets for different time periods, the charging and discharging power of each energy storage device is allocated for each time period in order to control the target distribution network for energy storage scheduling.

8. A carbon emission balance control device for a power distribution network, characterized in that, It includes a propagation path acquisition module, a cross-regional load rate acquisition module, and a carbon emission balance control module, among which, The propagation path acquisition module is used to collect load change data and distributed power output data of the boundary distribution network in the target distribution network in real time, and determine the control difference propagation path based on the load change data and distributed power output data through a preset continuous power flow calculation method. The cross-regional load rate acquisition module is used to read the current transmission power of the cross-regional line and calculate the cross-regional line transmission load rate based on the current transmission power if the control difference propagation path exceeds a preset path threshold. The carbon emission balance control module is used to analyze the differences in carbon emission levels between regions based on the cross-regional line transmission load rate, generate a carbon emission adjustment strategy, and control the target distribution network to perform load regulation and energy storage scheduling according to the carbon emission adjustment strategy, so as to achieve carbon emission control balance of the target distribution network.

9. A terminal device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation of the power distribution network carbon emission balance control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the power distribution network carbon emission balance control method as described in any one of claims 1 to 7.

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