A power self-balancing method and system for promoting green electricity consumption

Abstract

The application provides a power grid power self-balancing method and system for promoting green electricity consumption, and relates to the technical field of power system regulation, wherein the power grid power self-balancing method comprises the following steps: obtaining operation state data and target green electricity demand; calculating the power deviation of each node according to output power and power consumption; determining the carbon emission level of each node; obtaining the green electricity consumption of users; converging the power deviation of each node to a preset range, and taking the total carbon emission level as the optimization target, and constructing an optimization model and outputting an optimization adjustment instruction. The technical problem solved by the application is that the existing power distribution network power regulation only constructs decision logic around the balance of a single physical quantity, active power, and takes power deviation as the only adjustment basis, so that the power regulation result only realizes power balance at the physical layer of the power grid, and cannot meet the low-carbon production and carbon tariff compliance requirements of some key industrial users.

Description

Technical Field

[0001] This invention relates to the field of power system regulation technology, and more specifically, to a method and system for improving grid power self-balancing to enhance the absorption of green electricity. Background Technology

[0002] In traditional power balance control of distribution networks, the dispatching system adjusts power output based on the power deviation between the power output of each node and the power consumption of the load. When a power deficit is detected, energy storage is activated for discharge or thermal power output is increased to fill the gap. When a power surplus is detected, distributed power output is reduced or energy storage is activated for charging. Carbon emission analysis, as an independent module, passively calculates the adjustment results after power regulation is completed and generates carbon emission reports, without participating in real-time decision-making during the power deviation elimination process. Distributed power sources, energy storage devices, smart soft switches, and flexible loads are managed by independent control systems. Energy storage systems execute charging and discharging strategies independently based on local state of charge, and flexible loads are independently controlled by the demand response system.

[0003] However, the relevant technologies have at least one of the following problems: the existing power regulation of the distribution network only builds the decision logic around the balance of a single physical quantity, active power, and takes the power deviation as the only basis for regulation. The power regulation results only achieve power balance at the physical level of the power grid, which cannot meet the low-carbon production and carbon tariff compliance requirements of some key industrial users. Summary of the Invention

[0004] The technical problem solved by this invention is that existing power regulation of distribution networks only builds decision-making logic around the balance of a single physical quantity, active power, and takes power deviation as the only basis for regulation. The power regulation result only achieves power balance at the physical level of the power grid, which cannot meet the low-carbon production and carbon tariff compliance requirements of some key industrial users.

[0005] To address the aforementioned issues, this invention provides a power grid self-balancing method for improving green energy consumption, comprising: acquiring operational status data of each node in the distribution network; acquiring the target green energy demand of each user in each time period and the adjustable power of controllable resources; the operational status data including at least the output power and carbon emission intensity of each node's power source, the power consumption of each node's load, and the power flow distribution of the distribution network; calculating the power deviation of each node based on the output power and power consumption; determining the carbon emission level of each node based on the carbon emission intensity and the power flow distribution of the distribution network; comparing the carbon emission level with a preset carbon intensity threshold to obtain the user's green energy consumption; converging the power deviation of each node to a preset range as a first constraint condition, and ensuring that the green energy consumption is greater than or equal to the corresponding target green energy demand as a second constraint condition; constructing an optimization model and outputting optimization adjustment instructions with the lowest possible sum of carbon emission levels of each node as the optimization objective; and allocating controllable resources according to the optimization adjustment instructions.

[0006] Compared with existing technologies, the technical effects achieved by this solution are as follows: This invention incorporates three different dimensions of indicators—power deviation, target green electricity demand, and carbon emission level—into the same optimization model for collaborative solution. Power deviation convergence serves as the first constraint to ensure the safe and stable operation of the power grid. Green electricity consumption compliance serves as the second constraint to ensure that each user's green electricity usage share meets carbon compliance requirements. Minimizing the total carbon emission level serves as the optimization objective. Under the premise that the above two hard constraints are met, the optimizer is guided to prioritize the adjustment path with the lowest carbon emissions. The final output optimization adjustment command can simultaneously achieve the triple effects of eliminating node power deviation, meeting user green electricity consumption standards, and reducing the carbon emission level of the entire distribution network. This unifies the physical balance of the power grid and the user's low-carbon compliance requirements within the same control framework, solving the problem that existing technologies only use power deviation as the sole adjustment basis, while green electricity consumption and carbon emissions are calculated separately by independent systems after the fact, resulting in the separation of the three.

[0007] In one embodiment of the present invention, obtaining the target green electricity demand of each user in each time period includes: obtaining production process characteristic data, peak and valley electricity consumption characteristic data, carbon emission data, and export carbon tariff traceability demand data for each user; determining a basic electricity consumption curve based on the production process characteristic data and peak and valley electricity consumption characteristic data; determining the mandatory green electricity ratio requirement based on the carbon emission data and export carbon tariff traceability demand data; determining the initial green electricity demand of each user in each time period based on the basic electricity consumption curve and the mandatory green electricity ratio requirement; obtaining regional new energy policy guidance data, determining the green electricity consumption responsibility weight based on the regional new energy policy guidance data; and correcting the initial green electricity demand based on the green electricity consumption responsibility weight to obtain the target green electricity demand of each user in each time period.

[0008] Compared with existing technologies, the technical effects achieved by this solution are as follows: This invention determines the basic electricity consumption curve by acquiring production process characteristic data and peak-valley electricity consumption characteristic data of each user, and determines the mandatory green electricity ratio requirement by acquiring carbon emission data and export carbon tariff traceability demand data of each user. The initial green electricity demand is obtained by multiplying the basic electricity consumption curve by the mandatory green electricity ratio requirement. Then, the green electricity consumption responsibility weight is determined according to the regional new energy policy guidance data, and the initial green electricity demand is corrected. This ensures that the final output of the target green electricity demand of each user in each time period is derived from the user's actual electricity consumption characteristics and is simultaneously driven by carbon tariff constraints and policy guidance. This achieves accurate quantification of users' differentiated green electricity demand, providing a quantifiable benchmark for determining whether the green electricity consumption in the second constraint meets the standard. This enables power regulation to ensure the supply of green electricity in a targeted manner according to the differentiated needs of different users, avoiding the problem of oversupply or undersupply caused by applying a uniform green electricity ratio to all users.

[0009] In one embodiment of the present invention, determining the carbon emission level of each node based on carbon emission intensity and power flow distribution of the distribution network includes: obtaining the carbon emission intensity of each power source; obtaining the transmission power magnitude and transmission direction of each branch; determining the power source received by each node based on the transmission direction; determining the first power ratio received by each node from each power source based on the transmission power magnitude; and multiplying the carbon emission intensity of each power source by the first power ratio to obtain the first carbon emission amount of each node.

[0010] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: This invention obtains the carbon emission intensity of each power source and the transmission power and transmission direction of each branch. Based on the transmission direction, it determines the power source received by each node and determines the first power ratio received by each node from each power source based on the transmission power. By multiplying the carbon emission intensity of each power source by the corresponding power ratio, it obtains the first carbon emission of each node. This achieves accurate traceability of the source of carbon emissions from electricity consumption, enabling nodes to clearly know the specific proportion of their electricity consumption from zero-carbon power sources such as photovoltaic and wind power and from high-carbon power sources such as thermal power. This provides basic data support for the subsequent optimization of carbon emission levels.

[0011] In one embodiment of the present invention, determining the carbon emission level of each node based on carbon emission intensity and power flow distribution of the distribution network further includes: calculating the active power loss of the corresponding branch based on the real-time power flow and branch impedance of each branch; obtaining the marginal carbon emission intensity of marginal units among the online thermal power units in the current time period; calculating the branch carbon emission amount corresponding to each branch based on the active power loss and marginal carbon emission intensity; determining the proportion of power received by each node from the corresponding branch to the total transmission power of the branch based on the transmission power of each branch, denoted as the second power proportion; allocating the branch carbon emission amount corresponding to each branch to each node receiving power from the branch according to the second power proportion to obtain the second carbon emission amount of each node; and adding the first carbon emission amount and the second carbon emission amount to obtain the carbon emission level of each node.

[0012] Compared with existing technologies, the technical effects achieved by this solution are as follows: This invention incorporates the carbon emissions corresponding to active power losses into the carbon emission accounting of nodes, ensuring that carbon emission levels fully cover the entire process from power generation to consumption. Carbon emissions from power losses are calculated using the carbon emission intensity of marginal units, which more accurately reflects the actual impact of regulation actions on carbon emissions compared to using the average carbon emission intensity of the entire network. Carbon emissions from power losses in each branch are allocated according to the proportion of power received by each node from that branch to the total transmission power of that branch, directly linking the attribution of line loss carbon emissions to the actual electricity consumption behavior of each node. Ultimately, the complete carbon emission level directly participates in the real-time decision-making of power regulation as an optimization target. The generation of optimization regulation commands ensures that the carbon emissions corresponding to transmission losses are fully considered, and carbon compliance is achieved upon completion of regulation.

[0013] In one embodiment of the present invention, the controllable resources include distributed power sources, energy storage devices, smart soft switches, and flexible loads. The optimization model further includes: setting upper and lower output limits for the distributed power sources, where the upper output limit is determined by the rated capacity of the distributed power sources, and the lower output limit is determined by the minimum stable operating output of the distributed power sources; setting upper and lower limits for charging power, discharging power, state of charge (SBC) status, and SBC status for the energy storage devices, where the upper and lower limits are determined by the rated power of the energy storage devices, and the upper and lower limits are determined by the safe operating window of the energy storage devices; and setting upper and lower limits for forward transmission power for the smart soft switches. The upper and lower limits of the reverse transmission power are determined by the rated capacity of the intelligent soft switch. Adjustable upper and lower limits of the power are set for flexible loads. The upper limit of the adjustable power is determined by the maximum interruptible capacity of the flexible load, and the lower limit of the adjustable power is determined by the minimum guaranteed power consumption of the user's production process. The upper and lower limits of output power, charging power, discharging power, state of charge, state of charge, forward transmission power, reverse transmission power, adjustable power, and adjustable power are constructed into a set of equipment operation constraints and input into the optimization model.

[0014] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: This invention sets upper and lower limits for output power determined by rated capacity for distributed power sources and lower and lower limits for output determined by minimum stable operating output; sets upper and lower limits for charging and discharging power determined by rated power and upper and lower limits for state of charge determined by safe operating window for energy storage devices; sets upper and lower limits for forward and reverse transmission power determined by rated capacity for smart soft switches; and sets upper and lower limits for adjustable power determined by maximum interruptible capacity and lower and lower limits for adjustable power determined by minimum guaranteed power consumption during user production processes for flexible loads. All of the above boundary values ​​are uniformly constructed into a set of equipment operation constraints and input into the optimization model. The adjustment amount of each controllable resource is uniformly determined by the optimization model within the same framework based on the direction and magnitude of power deviation, the compliance of green electricity consumption, and the optimization direction of carbon emission levels. This enables distributed power sources, energy storage devices, smart soft switches, and flexible loads to form a coordinated linkage for the same power deviation, solving the problems of contradictory actions such as energy storage charging and distributed power source reduction being executed simultaneously and canceling each other out due to independent control of each resource, or flexible load power reduction and energy storage discharge being executed simultaneously and causing overcompensation.

[0015] In one embodiment of the present invention, before constructing the optimization model, the method further includes: collecting historical photovoltaic power output time series, wind power output time series, and user load electricity consumption time series data; sampling and generating multiple sets of random operating scenarios based on the historical photovoltaic power output time series, wind power output time series, and user load electricity consumption time series data, wherein the random operating scenarios cover four types of operating conditions: cloudy weather, low output without wind, industrial load peak, and distribution network equipment maintenance; statistically obtaining the wind and solar power output fluctuation range and the load electricity consumption fluctuation range based on all random operating scenarios; and constructing a source-load fluctuation constraint set based on the wind and solar power output fluctuation range and the load electricity consumption fluctuation range.

[0016] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: This invention collects historical photovoltaic power output time-series data, wind power output time-series data, and user load electricity consumption time-series data. Based on the above historical data, it samples and generates multiple sets of random operating scenarios covering four extreme conditions: photovoltaic power output fluctuations under cloudy weather, wind power output drops sharply under windless and low-output conditions, electricity demand surges sharply under industrial load peaks, and some branch lines being taken out of operation due to distribution network equipment maintenance. From all random operating scenarios, the fluctuation ranges of wind and solar power output and load electricity consumption are statistically obtained. Based on these two fluctuation ranges, a source-load fluctuation constraint set is constructed. This constraint set is no longer based on a single deterministic scenario as in traditional methods, but rather incorporates the possible changes in wind and solar power output and load electricity consumption under various extreme conditions during the construction stage. This provides a basic constraint framework that includes uncertainties for subsequent optimization models.

[0017] In one embodiment of the present invention, after constructing the source-load fluctuation constraint set, full-scenario verification and constraint correction are performed, including: sequentially traversing all random operating scenarios and verifying the power flow of each branch and the green electricity supply of each user under the source-load fluctuation constraint set; if the power flow of a branch in any random operating scenario exceeds the preset power flow upper limit, the power flow constraint boundary of the corresponding branch in the source-load fluctuation constraint set is tightened; if the green electricity supply in any random operating scenario is lower than the target green electricity demand for the corresponding time period, the minimum green electricity supply constraint boundary in the source-load fluctuation constraint set is increased; the full-scenario verification and constraint correction are repeated until the current correction no longer generates new constraint boundary adjustments, and the current source-load fluctuation constraint set is input into the optimization model.

[0018] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: Based on the established source-load fluctuation constraint set, this invention performs scenario-by-scenario verification of the constraint set by sequentially traversing all random operating scenarios. If the branch power flow exceeds the preset power flow upper limit in any random operating scenario, the corresponding branch power flow constraint boundary in the source-load fluctuation constraint set is tightened. If the green electricity supply is lower than the target green electricity demand in the corresponding time period in any random operating scenario, the minimum green electricity supply constraint boundary in the source-load fluctuation constraint set is increased. By repeatedly performing full-scenario verification and constraint correction until the current correction no longer generates new constraint boundary adjustments, the final source-load fluctuation constraint set is input into the optimization model. After iterative verification of all random operating scenarios, this constraint set can ensure that the branch power flow does not exceed the limit and the green electricity supply is not short in all random operating scenarios. This solves the problem of static constraint sets failing in extreme scenarios such as sudden drops in wind and solar power output or sudden increases in load, and realizes the enhancement of the constraint set from determinism to robustness.

[0019] In one embodiment of the present invention, if the second constraint has no feasible solution in the optimization model, a green electricity deficit penalty term is introduced to modify the optimization objective, or a portion of the adjustable flexible load is removed to make the second constraint re-established.

[0020] Compared with existing technologies, the technical effects achieved by this technical solution are as follows: When the second constraint has no feasible solution in the optimization model, this invention provides two independent fault-tolerant processing methods. The method of introducing a green energy deficit penalty term to modify the optimization objective allows for a certain deficit in green energy consumption, but the larger the deficit, the worse the objective function value, thereby enabling the solver to minimize the green energy deficit under the premise of power deviation convergence. The method of cutting off part of the adjustable flexible load reduces the corresponding target green energy demand by reducing the total power consumption of the node, thereby making the second constraint condition re-established. The two methods can be used alone or in combination according to the actual operating conditions, ensuring that the optimization model has a feasible solution under any operating condition, and that optimization adjustment commands can be continuously issued without interruption of the control process due to the lack of feasible solutions.

[0021] In one embodiment of the present invention, the present invention also provides a power grid self-balancing system for improving green electricity consumption. The power grid self-balancing system includes: a data acquisition module, used to acquire the operating status data of each node in the distribution network and the target green electricity demand of each user in each time period and the adjustable power of controllable resources. The operating status data includes at least the output power and carbon emission intensity of each node's power source, the power consumption of each node's load, and the power flow distribution of the distribution network; a power deviation calculation module, used to calculate the power deviation of each node based on the output power and power consumption; a carbon emission determination module, used to determine the carbon emission level of each node based on the carbon emission intensity and the power flow distribution of the distribution network; an optimization solution module, used to construct and solve an optimization model with the first constraint that the power deviation of each node converges to a preset range, the second constraint that the green electricity consumption of each user is not lower than the corresponding target green electricity demand, and the optimization objective that the total carbon emission level of each node is minimized, and output optimization adjustment instructions; and an instruction issuance module, used to issue optimization adjustment instructions to controllable resources.

[0022] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: it can achieve the technical effects corresponding to any of the above examples, which will not be elaborated here.

[0023] By adopting the technical solution of the present invention, the following technical effects can be achieved: (1) This invention incorporates power deviation convergence, green electricity consumption compliance, and minimum carbon emission levels into the same optimization model for collaborative solution, so that the final command simultaneously eliminates power deviation, ensures green electricity consumption, and reduces carbon emissions, thus solving the problem of the three being mutually isolated. The target green electricity demand is based on user production processes and carbon tariff requirements and is revised by policy. Carbon emission accounting traces the source of power supply and distributes line loss carbon emissions to each node through marginal units, so that carbon emission data fully covers the entire process from power generation to power consumption and directly participates in real-time decision-making. (2) This invention unifies the operation boundaries of various controllable resources to construct a set of equipment operation constraints, enabling all resources to coordinate and link under the same framework, avoiding action conflicts caused by independent control. At the same time, random operation scenarios covering four extreme working conditions are sampled to build a set of source load fluctuation constraints, and the boundaries are iteratively corrected through traversal verification until convergence, so that the final constraint set can ensure that the branch power flow does not exceed the limit and the green power supply is not short in all extreme scenarios; (3) When the second constraint has no feasible solution, the present invention introduces a green energy deficit penalty term to modify the optimization objective, or cuts off flexible loads to reduce the target green energy demand so that the constraint is re-established. The two methods can be used alone or in combination to ensure that the optimization model has a feasible solution under any operating condition and the control process is not interrupted. The present invention also provides a power self-balancing system, which realizes end-to-end power self-balancing from data acquisition to command issuance through the cooperation of various modules. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of a specific process for a power grid self-balancing method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a power grid self-balancing system provided in an embodiment of the present invention.

[0025] Explanation of reference numerals in the attached figures: 1. Data acquisition module; 2. Power deviation calculation module; 3. Carbon emission determination module; 4. Optimization solution module; 5. Command issuance module. Detailed Implementation

[0026] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] This invention provides a power grid self-balancing method to improve green electricity consumption, comprising: acquiring the operating status data of each node in the distribution network, acquiring the target green electricity demand of each user in each time period and the adjustable power of controllable resources, wherein the operating status data includes at least the output power and carbon emission intensity of each node's power source, the power consumption of each node's load, and the power flow distribution of the distribution network; calculating the power deviation of each node based on the output power and power consumption; determining the carbon emission level of each node based on the carbon emission intensity and the power flow distribution of the distribution network; comparing the carbon emission level with a preset carbon intensity threshold to obtain the green electricity consumption of users; converging the power deviation of each node to a preset range as a first constraint condition, and ensuring that the green electricity consumption is greater than or equal to the corresponding target green electricity demand as a second constraint condition, constructing an optimization model and outputting optimization adjustment instructions with the minimum sum of carbon emission levels of each node as the optimization objective; and allocating controllable resources according to the optimization adjustment instructions.

[0028] Specifically, a distribution network comprises multiple nodes, each of which may be connected to one or more power generation devices or one or more electrical loads. Operating status data is provided in real-time by the distribution network's data acquisition and monitoring control system, specifically including the following: the output power of each node's power source, i.e., the actual power generation of all power generation devices connected to that node at the current moment. For example, if a node is simultaneously connected to distributed photovoltaic (PV) and distributed wind power, then the output power of that node is the sum of the PV and wind power outputs. The carbon emission intensity of each node's power source, i.e., the carbon emissions corresponding to each kilowatt-hour of electricity generated by that power source. The carbon emission intensity of PV and wind power is close to zero, while the carbon emission intensity of thermal power units depends on their fuel type and power generation efficiency. The electrical power consumption of each node's load, i.e., the sum of the actual electrical power consumption of all electrical devices connected to that node at the current moment. Distribution network power flow distribution refers to the magnitude and direction of the power transmitted by each branch in the distribution network at the current moment. This data is provided by the real-time power flow calculation module of the distribution network. The power flow calculation is based on the injected power and load power of each node, combined with the topology of the distribution network and the impedance parameters of each branch to obtain the power distribution of the entire network.

[0029] Furthermore, the adjustable power of controllable resources refers to the range of power that adjustable resources such as distributed power sources, energy storage devices, smart soft switches, and flexible loads can adjust up or down at any given moment. This range is determined by the physical rated parameters of each type of resource and its current operating status.

[0030] Furthermore, the power deviation is calculated as follows: The total power output of the node is obtained by summing the output power of all power sources at that node; the total load power consumption of the node is obtained by summing the power consumption of all loads at that node; and the power deviation of the node is obtained by subtracting the total load power consumption from the total power output. A positive power deviation indicates that the power output of the node exceeds the load demand, resulting in a power surplus. In this case, the excess power of the node needs to be transmitted to other nodes through branches. A negative power deviation indicates that the power output of the node is insufficient to meet the load demand, resulting in a power deficit. In this case, the node needs to receive power from other nodes to make up the gap. The absolute value of the power deviation directly reflects the severity of the power imbalance at that node and is the direct constraint of the first constraint condition in the subsequent optimization model. When the power deviation of all nodes is controlled within the preset range, the entire distribution network achieves power self-balancing.

[0031] Furthermore, the carbon emission level reflects the carbon emissions per unit of electricity consumption at a given node, and its calculation requires consideration of power flow distribution results. This is because the power consumed at each node typically comes from a mix of multiple power sources, with some directly connected to the node and others received from other nodes via branches. The power flow distribution of the distribution network determines which power sources receive the power and the magnitude of that power, thus allowing the calculation of the overall carbon emission intensity corresponding to the electricity consumption at each node.

[0032] Furthermore, the carbon emission levels of each node calculated above are compared with a preset carbon intensity threshold. The portion of the electricity consumption at each node with a carbon emission intensity lower than the preset carbon intensity threshold is converted into the green electricity consumption of each user. For example, a typical value is 0.3 kg of carbon dioxide equivalent per kilowatt-hour. Electricity consumption below this value is included in the green electricity consumption, while electricity consumption above this value is not included.

[0033] Furthermore, the optimization model includes three core elements. The first constraint is that the power deviation of each node converges to a preset range. The preset range allows for a certain dead zone to prevent the regulating equipment from frequently operating due to small fluctuations in power deviation. The specific value of the preset range can be set according to the importance level of the load connected to each node. For nodes connected to critical production loads, the preset range can be set to one percent of the node's maximum load; for nodes connected to general loads, the preset range can be set to five percent of the node's maximum load. The second constraint is that the green electricity consumption of each user is greater than or equal to the corresponding target green electricity demand. The optimization objective is to minimize the total carbon emission level of all nodes, that is, to minimize the sum of the carbon emission levels of all nodes in the entire network while satisfying the above two hard constraints. This is equivalent to minimizing the weighted average carbon emission intensity of the entire network, which means using as much zero-carbon power source as possible, such as photovoltaic and wind power, to meet the load demand. When constructing the optimization model, the adjustable power of each controllable resource is used as the decision variable. That is, what needs to be solved is the output adjustment of each distributed power source, the charging and discharging power of energy storage devices, the transmission power adjustment of smart soft switches, and the power consumption adjustment of flexible loads.

[0034] Furthermore, the optimization and adjustment instructions are transmitted to the execution terminals of each controllable resource through the distribution network's communication network. Each execution terminal performs the corresponding action according to the adjustment direction and amount specified in the instructions. If the instruction requires an energy storage device to discharge at a power of 200 kilowatts, the inverter of that energy storage device will perform the discharge operation according to that power value. If the instruction requires a distributed photovoltaic system to increase its output from the current 100 kilowatts to 150 kilowatts, the output will be increased by adjusting the control parameters of the photovoltaic inverter. After the execution is completed, the power deviation of each node in the distribution network should converge to the preset range, the green electricity consumption of each user should not be lower than their target green electricity demand, and the overall carbon emission level of the network should be minimized while meeting the above constraints.

[0035] In one embodiment of the present invention, obtaining the target green electricity demand of each user in each time period includes: obtaining production process characteristic data, peak and valley electricity consumption characteristic data, carbon emission data, and export carbon tariff traceability demand data for each user; determining a basic electricity consumption curve based on the production process characteristic data and peak and valley electricity consumption characteristic data; determining the mandatory green electricity ratio requirement based on the carbon emission data and export carbon tariff traceability demand data; determining the initial green electricity demand of each user in each time period based on the basic electricity consumption curve and the mandatory green electricity ratio requirement; obtaining regional new energy policy guidance data, determining the green electricity consumption responsibility weight based on the regional new energy policy guidance data; and correcting the initial green electricity demand based on the green electricity consumption responsibility weight to obtain the target green electricity demand of each user in each time period.

[0036] Specifically, the process of obtaining the target green electricity demand adopts a step-by-step approach, starting from basic electricity consumption data, and then successively adding carbon tariff constraints and policy guidance factors to finally obtain a precise quantitative result.

[0037] Furthermore, production process characteristic data reflects the activation status and corresponding power consumption of user production equipment at different times. Differences in production processes across industries lead to different shapes of power consumption curves. For example, the power consumption curve of the steel smelting industry is relatively stable, the electrolytic aluminum industry exhibits high-energy-consuming continuous operation, and the machinery processing industry shows cyclical fluctuations with daytime production and nighttime shutdown. Peak-valley power consumption characteristic data reflects the fluctuation pattern of user power load throughout the day; some industries concentrate their power consumption during the day, while others still have significant power loads at night. Peak-valley power consumption characteristic data is obtained by collecting hourly power consumption data from users over the past year and statistically averaging it to obtain the shape of the power consumption curve for a typical day. Combining production process characteristic data and peak-valley power consumption characteristic data allows for the determination of the user's basic power consumption curve, which represents the user's baseline power consumption at each time period, i.e., each hour.

[0038] Furthermore, carbon emission data reflects the carbon emission level per unit of product in a user's current production process, and this data is typically provided by the user's carbon emission verification report. Users with higher carbon emission levels face greater pressure for carbon compliance and have a more urgent need for green electricity. Export carbon tariff traceability data reflects which countries or regions a user's products are exported to, and different export destinations have different requirements for the proportion of green electricity used in the product production process. Based on a comprehensive analysis of carbon emission data and export carbon tariff traceability data, a mandatory green electricity ratio requirement is determined. This requirement means that a certain percentage of a user's electricity consumption throughout the year or at different times must come from green electricity.

[0039] Multiplying the baseline electricity consumption curve by the mandatory green electricity ratio requirement yields the initial green electricity demand for each user in each time period. This value is calculated by multiplying the user's hourly baseline electricity consumption by the corresponding mandatory green electricity ratio for that hour; the product is the required green electricity power for that hour. The mandatory green electricity ratio may vary in different time periods because export carbon tariff traceability data may have different requirements for distinguishing between production and non-production periods.

[0040] Furthermore, regional renewable energy policy guidance data reflects local policies encouraging or constraining the consumption of green electricity by key industrial users. The green electricity consumption responsibility weight is typically set annually, but can also be broken down monthly or quarterly. Multiplying this weight by each user's initial green electricity demand for each time period yields the corrected target green electricity demand. The specific method of correction depends on the type of policy guidance data. If the policy guidance represents additional incentives for green electricity consumption, a multiplicative correction is used, multiplying the initial demand by an incentive coefficient. If the policy guidance represents mandatory assessment requirements for green electricity consumption, the initial demand is directly replaced with the green electricity demand corresponding to the assessment indicator. Finally, after correction, the target green electricity demand for each user per hour is obtained.

[0041] In one embodiment of the present invention, determining the carbon emission level of each node based on carbon emission intensity and power flow distribution of the distribution network includes: obtaining the carbon emission intensity of each power source; obtaining the transmission power magnitude and transmission direction of each branch; determining the power source received by each node based on the transmission direction; determining the first power ratio received by each node from each power source based on the transmission power magnitude; and multiplying the carbon emission intensity of each power source by the first power ratio to obtain the first carbon emission amount of each node.

[0042] Specifically, photovoltaic and wind power generation do not produce direct carbon emissions during operation, and their carbon emission intensity is close to zero. The carbon emission intensity of thermal power units depends on the fuel type and operating efficiency of the unit. The carbon emission intensity of each power source is pre-stored as a known parameter in the distribution network's database.

[0043] Furthermore, in a distribution network, a branch refers to a transmission line or distribution line connecting two adjacent nodes. After the power flow calculation is completed, each branch has a corresponding transmission power value, the sign of which usually indicates the transmission direction.

[0044] Furthermore, the proportion of power received by each node from each power source is determined based on the transmission power. The specific calculation method for this proportion is as follows: First, identify all upstream power source nodes supplying power to the node and their corresponding transmission paths. Then, count the power value received by the node from each power source node. Divide the power value contributed by each power source node by the total power received by the node to obtain the contribution proportion of that power source to the node.

[0045] For example, if a node's total power consumption is 100 kilowatts, with 30 kilowatts from power source A, 50 kilowatts from power source B, and 20 kilowatts from the node's own distributed photovoltaic (PV) system, then power source A contributes 30%, power source B contributes 50%, and distributed PV contributes 20%. The first carbon emission of each node is obtained by multiplying the carbon emission intensity of each power source by the proportion of the first power received by each node from that power source. If the carbon emission intensity of power source A is 0.9 kg per kilowatt-hour, that of power source B is 0.4 kg per kilowatt-hour, and that of distributed PV is 0.05 kg per kilowatt-hour, then the first carbon emission is calculated as 0.9 multiplied by 30%, plus 0.4 multiplied by 50%, plus 0.05 multiplied by 20%, resulting in 0.48 kg of CO2 equivalent per kilowatt-hour. The first carbon emission reflects the carbon emission level corresponding to the portion of the node's electricity directly received from each power source.

[0046] In one embodiment of the present invention, determining the carbon emission level of each node based on carbon emission intensity and power flow distribution of the distribution network further includes: calculating the active power loss of the corresponding branch based on the real-time power flow and branch impedance of each branch; obtaining the marginal carbon emission intensity of marginal units among the online thermal power units in the current time period; calculating the branch carbon emission amount corresponding to each branch based on the active power loss and marginal carbon emission intensity; determining the proportion of power received by each node from the corresponding branch to the total transmission power of the branch based on the transmission power of each branch, denoted as the second power proportion; allocating the branch carbon emission amount corresponding to each branch to each node receiving power from the branch according to the second power proportion to obtain the second carbon emission amount of each node; and adding the first carbon emission amount and the second carbon emission amount to obtain the carbon emission level of each node.

[0047] Specifically, during power transmission, active power losses occur when current passes through branches. These losses increase the total power generation required to meet load demand, and consequently generate additional carbon emissions. In traditional carbon emission accounting, carbon emissions corresponding to line losses are usually attributed to the grid side or the generation side. However, in this embodiment, they are allocated to each node to make the carbon emission accounting for each node more complete and accurate.

[0048] Furthermore, the active power loss is calculated by multiplying the square of the branch current by the branch resistance, where the branch current can be obtained from the transmission power and voltage level of the branch, and the branch resistance is an inherent parameter of the line.

[0049] Furthermore, marginal units refer to the online operating units with the highest marginal cost in the system. In the economic dispatch theory of power systems, the output adjustment of marginal units determines the marginal electricity price of the system, and correspondingly, their carbon emission intensity represents the marginal carbon emission level corresponding to the increased electricity demand in the system. When active power losses exist, the power corresponding to the losses is actually compensated by the additional power generated by the marginal units; therefore, the carbon emissions corresponding to the losses should be calculated using the carbon emission intensity of the marginal units.

[0050] Furthermore, the carbon emission intensity of the marginal unit is obtained, which is determined by the fuel type and operating efficiency of the marginal unit. The second carbon emission amount corresponding to each branch is calculated based on the active power loss and the marginal carbon emission intensity. The calculation method is to multiply the active power loss of the branch by the carbon emission intensity of the marginal unit; the product is the carbon emission amount corresponding to the loss of that branch.

[0051] Furthermore, based on the transmission power of each branch, a second power ratio is determined for the power received by each node from the corresponding branch relative to the total transmission power of that branch. The second power ratio is calculated as follows: For a branch connected to a sending node and a receiving node at its two ends, a portion of the power transmitted by this branch is received and used by the receiving node, while the remaining portion may continue to be transmitted downstream. The second power ratio of the receiving node's power to that branch is obtained by dividing the power directly received by the receiving node from that branch by the total transmission power of that branch.

[0052] Furthermore, the carbon emissions corresponding to the losses of each branch are allocated to each node according to the aforementioned second power ratio to obtain the second carbon emissions of each node. Allocation means that the carbon emissions corresponding to the losses generated during power transmission of a branch are shared by each downstream node of that branch according to the proportion of power transmitted by that branch.

[0053] Furthermore, the first carbon emission level is added to the second carbon emission level to obtain the complete carbon emission level for each node. This carbon emission level comprehensively reflects all carbon emissions corresponding to the electricity consumption of the node, including both carbon emissions from direct power generation and carbon emissions corresponding to transmission losses.

[0054] In one embodiment of the present invention, the controllable resources include distributed power sources, energy storage devices, smart soft switches, and flexible loads. The optimization model further includes: setting upper and lower output limits for the distributed power sources, where the upper output limit is determined by the rated capacity of the distributed power sources, and the lower output limit is determined by the minimum stable operating output of the distributed power sources; setting upper and lower limits for charging power, discharging power, state of charge (SBC) status, and SBC status for the energy storage devices, where the upper and lower limits are determined by the rated power of the energy storage devices, and the upper and lower limits are determined by the safe operating window of the energy storage devices; and setting upper and lower limits for forward transmission power for the smart soft switches. The upper and lower limits of the reverse transmission power are determined by the rated capacity of the intelligent soft switch. Adjustable upper and lower limits of the power are set for flexible loads. The upper limit of the adjustable power is determined by the maximum interruptible capacity of the flexible load, and the lower limit of the adjustable power is determined by the minimum guaranteed power consumption of the user's production process. The upper and lower limits of output power, charging power, discharging power, state of charge, state of charge, forward transmission power, reverse transmission power, adjustable power, and adjustable power are constructed into a set of equipment operation constraints and input into the optimization model.

[0055] Specifically, for distributed generation (DG), upper and lower output limits are set. The upper output limit is determined by the rated capacity of the DG, which is the maximum continuous output power specified by the equipment manufacturer. For example, a photovoltaic inverter with a rated capacity of 100 kilowatts cannot exceed 100 kilowatts in its maximum continuous output power, and the DG cannot exceed this value under any operating condition. The lower output limit is determined by the minimum stable operating output of the DG. When the output power of a photovoltaic inverter is less than 10% of its rated capacity, its conversion efficiency drops sharply and there is a risk of shutdown; therefore, this value is used as the lower output limit. When the optimization model's adjustment command requires the DG's output to be lower than the lower output limit, the DG will be directly shut down during actual execution, i.e., its output will drop to zero.

[0056] Furthermore, for energy storage devices, upper limits are set for charging power, discharging power, state of charge (SOC), and SOC limits. The upper and lower limits for charging and discharging power are determined by the rated power of the energy storage converter, which determines the maximum amount of electrical energy the energy storage device can charge or release per unit time. The upper and lower limits for SOC are determined by the safe operating window of the energy storage battery. SOC is usually expressed as a percentage, with 100% indicating a fully charged battery and 0% indicating a completely discharged battery.

[0057] Optionally, the upper limit of the state of charge (SOC) is typically set to 90% to prevent overcharging and potential safety hazards. The lower limit is typically set to 10% to prevent over-discharging and subsequent lifespan degradation. These upper and lower limits together define the usable charge / discharge capacity of the energy storage device at any given time. The optimization model must ensure that the SOC of the energy storage device remains within these upper and lower limits at every time period when deciding on the charge / discharge power.

[0058] Furthermore, a smart soft switch is a power electronic device installed at the feeder connection point of a distribution network. Its core function is to continuously adjust the magnitude and direction of power transmitted between two feeders. The upper limits of forward and reverse power transmission are determined by the rated capacity of the smart soft switch, which in turn determines the maximum power value allowed to pass through the smart soft switch in any direction.

[0059] Furthermore, for flexible loads, upper and lower limits for adjustable power are set. The upper limit is determined by the maximum interruptible capacity of the flexible load, which refers to the total power consumption of the user or node that can be interrupted or transferred without causing a safety incident. The lower limit is determined by the minimum guaranteed power consumption of the user's production process. This power cannot be lower than the minimum power consumption required to ensure the user's basic production and safety, such as the uninterrupted power needs of a factory's ventilation system, security monitoring system, and standby power for critical equipment. The adjustable power range of the flexible load defines the adjustment space for the optimization model when reducing the power of the load.

[0060] Furthermore, since all constraint boundary values ​​are based on explicit equipment parameters, the construction of this constraint set does not rely on manual experience. The adjustment amount of each controllable resource is uniformly determined by the optimization model within the same framework based on the direction and magnitude of power deviation, the compliance of green electricity consumption, and the optimization direction of carbon emission levels, thereby enabling the four types of controllable resources to coordinate and link up for the same power deviation. This coordination and link is specifically manifested in the following way: when a node experiences a power deficit, the optimization model can simultaneously increase the output of distributed power sources, schedule energy storage discharge, transfer power from adjacent feeders through intelligent soft switching, and reduce the electricity consumption of flexible loads. The optimization model uniformly decides the adjustment share that each of the above four adjustment methods should bear according to the goal of minimizing carbon emission levels, thereby avoiding the problems of mutual cancellation caused by the simultaneous execution of energy storage charging and distributed power reduction, or the contradiction of over-compensation caused by the simultaneous execution of flexible load power reduction and energy storage discharge.

[0061] In one embodiment of the present invention, before constructing the optimization model, the method further includes: collecting historical photovoltaic power output time series, wind power output time series, and user load electricity consumption time series data; sampling and generating multiple sets of random operating scenarios based on the historical photovoltaic power output time series, wind power output time series, and user load electricity consumption time series data, wherein the random operating scenarios cover four types of operating conditions: cloudy weather, low output without wind, industrial load peak, and distribution network equipment maintenance; statistically obtaining the wind and solar power output fluctuation range and the load electricity consumption fluctuation range based on all random operating scenarios; and constructing a source-load fluctuation constraint set based on the wind and solar power output fluctuation range and the load electricity consumption fluctuation range.

[0062] Specifically, since photovoltaic and wind power outputs are significantly affected by weather conditions, and user load electricity consumption also varies between weekdays and rest days, and between different times of the weekday, these uncertainties may affect the effectiveness of the optimization model in actual operation. Therefore, it is necessary to quantify this uncertainty as a constraint.

[0063] Furthermore, historical photovoltaic power output time-series data, wind power output time-series data, and user load electricity consumption time-series data are collected first. The time span of these data collections must cover at least one full year to ensure that they can reflect the changing patterns of wind and solar power output and load electricity consumption under different seasons and weather conditions.

[0064] Optionally, time-series data are typically time-resolutiond at fifteen minutes or one hour, with each sampling point corresponding to the output power or power consumption value at a given moment.

[0065] Furthermore, multiple sets of random operating scenarios are generated based on the aforementioned historical time-series data. Specifically, photovoltaic (PV) output curves, wind power output curves, and load consumption curves from different dates are extracted from historical data and combined to form multiple possible operating scenarios. The generated random operating scenarios need to cover four types of extreme operating conditions: PV output fluctuates frequently due to cloud cover during cloudy weather; wind power output is close to zero during windless and low-output conditions; industrial load peak periods result in the highest annual electricity demand; and power flow redistribution occurs when some branch lines are taken out of service during distribution network equipment maintenance.

[0066] Furthermore, the statistical method for determining the fluctuation range involves identifying the minimum and maximum values ​​of photovoltaic power output at each moment from all random operating scenarios to obtain the photovoltaic power output fluctuation range. The same statistical operation is performed on wind power output and load electricity consumption to obtain their respective fluctuation ranges.

[0067] Furthermore, the source-load fluctuation constraint set means that when the optimization model decides on the adjustment amount of each controllable resource, it must ensure that both the first and second constraint conditions are met under any combination of values ​​within the fluctuation range of wind and solar power output and load power consumption. In other words, the source-load fluctuation constraint set transforms the uncertainties of wind and solar power output and load power consumption into robust constraints for the optimization model, ensuring that the solution of the optimization model can guarantee that the branch power flow does not exceed the limit and that there is no shortage of green electricity supply, even when wind and solar power output or load power consumption changes arbitrarily within their fluctuation range. In one embodiment of the present invention, after constructing the source-load fluctuation constraint set, full-scenario verification and constraint correction are performed, including: sequentially traversing all random operating scenarios and verifying the power flow of each branch and the green electricity supply of each user under the source-load fluctuation constraint set; if the power flow of a branch in any random operating scenario exceeds the preset power flow upper limit, the power flow constraint boundary of the corresponding branch in the source-load fluctuation constraint set is tightened; if the green electricity supply in any random operating scenario is lower than the target green electricity demand for the corresponding time period, the minimum green electricity supply constraint boundary in the source-load fluctuation constraint set is increased; the full-scenario verification and constraint correction are repeated until the current correction no longer generates new constraint boundary adjustments, and the current source-load fluctuation constraint set is input into the optimization model.

[0068] Specifically, for each scenario, two indicators are verified under the current source-load fluctuation constraint set: first, whether the power flow of each branch exceeds the preset power flow limit; and second, whether the green electricity supply of each user is lower than the target green electricity demand for the corresponding time period. The purpose of branch power flow verification is to ensure the safe operation of the power grid and avoid equipment damage or power outages caused by branch overload. The purpose of green electricity supply verification is to ensure that users' green electricity consumption meets carbon compliance requirements.

[0069] Furthermore, if the power flow of a branch exceeds the preset power flow upper limit in any random operating scenario, the power flow constraint boundary of the branch corresponding to that scenario will be tightened. Specifically, the upper limit value of the power flow for that branch in the source-load fluctuation constraint set will be adjusted to the actual power flow value in that scenario, thereby excluding that scenario from the feasible region. If the green electricity supply is found to be lower than the target green electricity demand for the corresponding time period in any random operating scenario, the minimum green electricity supply constraint boundary within the source-load fluctuation constraint set will be adjusted upwards by the minimum adjustment required to ensure that the green electricity supply in that scenario is not lower than the target value.

[0070] Furthermore, after completing a round of traversal and boundary correction of all scenarios, some constraint boundaries may be tightened or increased, causing previously validated scenarios to re-emerge with exceeding limits under the new constraint boundaries. Therefore, the traversal validation and boundary correction steps need to be repeated until no new constraint boundary adjustments are generated in a certain round of traversal. This iterative convergence condition ensures that the final source-load fluctuation constraint set can pass the validation of all randomly run scenarios, and all randomly run scenarios meet the requirements of no branch power flow exceeding limits and no green electricity supply shortage under this constraint set.

[0071] Furthermore, the source-load fluctuation constraint set, after verification and correction, is input into the optimization model. At this point, the constraint set can guarantee that the branch power flow does not exceed the limit and that the green electricity supply is not insufficient under all random operating scenarios, thus strengthening the robustness of the constraint set and solving the problem of static constraint sets failing under extreme scenarios.

[0072] In one embodiment of the present invention, if the second constraint has no feasible solution in the optimization model, a green electricity deficit penalty term is introduced to modify the optimization objective, or a portion of the adjustable flexible load is removed to make the second constraint re-established.

[0073] Specifically, the second constraint is that the green electricity consumption of each user is not less than the corresponding target green electricity demand. An infeasible solution means that, within the current resource allocation scheme and the adjustable power range of each controllable resource, there is no single power adjustment that can simultaneously ensure that the green electricity consumption of all users meets the target. This situation typically occurs under extreme weather conditions when wind and solar power output is severely insufficient, energy storage equipment has no available capacity, and user green electricity demand is at its peak. In this case, the second constraint conflicts with the first constraint. If the green electricity consumption target is forcibly met, the power deviation cannot converge; if the power deviation is forcibly eliminated, green electricity consumption will inevitably fall short.

[0074] When the above-mentioned infeasible solution situation occurs, it is handled in the following two ways.

[0075] The first approach is to introduce a green energy deficit penalty term to adjust the optimization objective. Specifically, this involves calculating the difference between each user's actual green energy consumption and the corresponding target green energy demand, multiplying this difference by a preset penalty coefficient, and adding this penalty term to the optimization objective function. The value of the penalty coefficient determines the degree to which the deficit is tolerated; a larger penalty coefficient makes the deficit more difficult to tolerate, and the solver will try to minimize the green energy deficit while ensuring power deviation convergence. The specific value of the penalty coefficient can be set tiered according to the user's importance level and the stringency of carbon compliance requirements.

[0076] The second approach is to disconnect some adjustable flexible loads. Specifically, this involves disconnecting some of the connected adjustable flexible loads to reduce the total power consumption of that node, thereby reducing the corresponding target green electricity demand and allowing the second constraint to be met again. The disconnection of flexible loads should be carried out under the premise of ensuring the user's basic production safety, and the disconnection order can be preset by the user. Generally, loads with less impact on production, such as air conditioning and lighting systems, are disconnected first, while loads on critical production equipment are disconnected last.

[0077] The two methods described above can be used individually or in combination, depending on the actual operating conditions. The first method is preferred when the green electricity deficit is small, as introducing a penalty term can achieve a suboptimal solution without interrupting user production. The second method is used when the green electricity deficit is large and there is sufficient capacity for flexible load shelving, as shelving flexible loads can fundamentally reduce green electricity demand and allow the constraints to be re-established. This fault-tolerance mechanism ensures that a feasible solution exists at the lower-level operation and control layer under any operating condition, enabling continuous issuance of optimization and control commands without interruption of the control process due to the lack of feasible solutions.

[0078] See Figure 1 In one embodiment, the specific process of a power grid self-balancing method is as follows: S1. Obtain the operating status data of each node in the distribution network, obtain the target green electricity demand of each user in each time period and the adjustable power of controllable resources. The operating status data shall include at least the output power and carbon emission intensity of each node's power source, the power consumption of each node's load, and the power flow distribution of the distribution network. S2. Calculate the power deviation of each node based on the output power and the power consumption; S3. Determine the carbon emission level of each node based on carbon emission intensity and power flow distribution of the distribution network; S4. Compare the carbon emission level with the preset carbon intensity threshold to obtain the user's green electricity consumption; take the convergence of the power deviation of each node to the preset range as the first constraint condition, take the green electricity consumption greater than or equal to the corresponding target green electricity demand as the second constraint condition, take the minimum total carbon emission level of each node as the optimization objective, construct the optimization model and output the optimization adjustment command. S5. Allocate controllable resources according to the optimization and adjustment instructions.

[0079] See Figure 2The present invention also provides a power grid self-balancing system for improving green electricity consumption. The power grid self-balancing system includes: a data acquisition module 1, used to acquire the operating status data of each node in the distribution network and the target green electricity demand of each user in each time period and the adjustable power of controllable resources. The operating status data includes at least the output power and carbon emission intensity of each node's power source, the power consumption of each node's load, and the power flow distribution of the distribution network; a power deviation calculation module 2, used to calculate the power deviation of each node based on the output power and power consumption; a carbon emission determination module 3, used to determine the carbon emission level of each node based on the carbon emission intensity and the power flow distribution of the distribution network; an optimization solution module 4, used to construct and solve an optimization model with the first constraint that the power deviation of each node converges to a preset range, the second constraint that the green electricity consumption of each user is not lower than the corresponding target green electricity demand, and the optimization objective that the total carbon emission level of each node is minimized, and output optimization adjustment instructions; and an instruction issuance module 5, used to issue optimization adjustment instructions to controllable resources.

[0080] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for improving grid power self-balancing to enhance green energy absorption, characterized in that, include: The system acquires the operating status data of each node in the distribution network, the target green electricity demand of each user in each time period, and the adjustable power of controllable resources. The operating status data includes at least the output power and carbon emission intensity of each node's power source, the power consumption of each node's load, and the power flow distribution of the distribution network. Calculate the power deviation of each node based on the output power and the power consumption; The carbon emission level of each node is determined based on the carbon emission intensity and the power flow distribution of the power distribution network; The carbon emission level is compared with a preset carbon intensity threshold to obtain the user's green electricity consumption. The first constraint is to converge the power deviation of each node to a preset range. The second constraint is to ensure that the green electricity consumption is greater than or equal to the corresponding target green electricity demand. The optimization objective is to minimize the total carbon emission level of each node. An optimization model is constructed and optimization adjustment instructions are output. The controllable resources are allocated according to the optimization and adjustment instructions.

2. The grid power self-balancing method for improving green electricity consumption according to claim 1, characterized in that, The acquisition of each user's target green electricity demand for each time period includes: Acquire data on each user's production process characteristics, peak and off-peak electricity consumption characteristics, carbon emission data, and export carbon tariff traceability requirements. The basic electricity consumption curve is determined based on the production process characteristic data and the peak-valley electricity consumption characteristic data. The mandatory green electricity ratio requirement is determined based on the carbon emission data and the export carbon tariff traceability requirement data. Based on the basic electricity consumption curve and the mandatory green electricity ratio requirement, determine the initial green electricity demand of each user in each time period; Obtain regional new energy policy guidance data, and determine the green electricity consumption responsibility weight based on the regional new energy policy guidance data; The initial green energy demand is adjusted based on the green energy consumption responsibility weight to obtain the target green energy demand for each user in each time period.

3. The grid power self-balancing method for improving green electricity consumption according to claim 1, characterized in that, The step of determining the carbon emission level of each node based on the carbon emission intensity and the power flow distribution of the distribution network includes: Obtain the carbon emission intensity of each power source; Obtain the transmission power and transmission direction of each branch; The power source of each node is determined according to the transmission direction, and the first power ratio of each node from each power source is determined according to the transmission power magnitude. The carbon emission intensity of each power source is multiplied by the first power ratio to obtain the first carbon emission of each node.

4. The grid power self-balancing method for improving green electricity consumption according to claim 3, characterized in that, The step of determining the carbon emission level of each node based on the carbon emission intensity and the power flow distribution of the distribution network also includes: The active power loss of each branch is calculated based on the real-time power flow and branch impedance of each branch. Obtain the marginal carbon emission intensity of marginal units among all online thermal power units in the current time period; Calculate the carbon emissions of each branch based on the active power loss and the marginal carbon emission intensity. Based on the transmission power of each branch, determine the proportion of power received by each node from the corresponding branch to the total transmission power of that branch, and denot it as the second power proportion; The carbon emissions of each branch are allocated to each node receiving power from that branch according to the second power ratio, thus obtaining the second carbon emissions of each node. The carbon emission level of each node is obtained by adding the first carbon emission level to the second carbon emission level.

5. The grid power self-balancing method for improving green electricity consumption according to claim 1, characterized in that, The controllable resources include distributed power sources, energy storage devices, smart soft switches, and flexible loads. The optimization model also includes: An upper limit and a lower limit of output are set for the distributed power source. The upper limit of output is determined by the rated capacity of the distributed power source, and the lower limit of output is determined by the minimum stable operating output of the distributed power source. The energy storage device is provided with an upper limit value for charging power, an upper limit value for discharging power, an upper limit value for state of charge, and a lower limit value for state of charge. The upper limit value for charging power and the upper limit value for discharging power are determined by the rated power of the energy storage device, and the upper limit value for state of charge and the lower limit value for state of charge are determined by the safe operation window of the energy storage device. The intelligent soft switch is provided with a forward transmission power limit value and a reverse transmission power limit value, which are determined by the rated capacity of the intelligent soft switch. An adjustable power upper limit and an adjustable power lower limit are set for the flexible load. The adjustable power upper limit is determined by the maximum interruptible capacity of the flexible load, and the adjustable power lower limit is determined by the minimum guaranteed power consumption of the user's production process. The upper limit of output power, the lower limit of output power, the upper limit of charging power, the upper limit of discharging power, the upper limit of state of charge, the lower limit of state of charge, the upper limit of forward transmission power, the upper limit of reverse transmission power, the upper limit of adjustable power, and the lower limit of adjustable power are constructed into a set of equipment operation constraints and input into the optimization model.

6. The grid power self-balancing method for improving green electricity consumption according to claim 1, characterized in that, Before constructing the optimization model, the following is also included: Collect historical photovoltaic power output time series, wind power output time series, and user load electricity consumption time series data; Based on the historical photovoltaic power output time series, the wind power output time series, and the user load electricity consumption time series data, multiple sets of random operating scenarios are sampled and generated. The random operating scenarios cover four types of operating conditions: cloudy weather, low power output without wind, industrial load peak, and distribution network equipment maintenance. Based on statistics of all the aforementioned random operating scenarios, the fluctuation ranges of wind and solar power output and load power consumption were obtained. A source-load fluctuation constraint set is constructed based on the wind and solar power output fluctuation range and the load power consumption fluctuation range.

7. The grid power self-balancing method for improving green electricity consumption according to claim 6, characterized in that, After establishing the source load fluctuation constraint set, perform full-scenario verification and constraint correction, including: The random operation scenarios are traversed sequentially, and the power flow of each branch and the green electricity supply of each user are verified under the source load fluctuation constraint set. If the branch power flow in any of the random operation scenarios exceeds the preset power flow limit, then the corresponding branch power flow constraint boundary within the source load fluctuation constraint set is tightened. If the green electricity supply in any of the random operation scenarios is lower than the target green electricity demand for the corresponding time period, then the minimum green electricity supply constraint boundary within the source-load fluctuation constraint set is raised. Repeat the full-scenario verification and constraint correction until the current correction no longer generates new constraint boundary adjustments, and input the current source load fluctuation constraint set into the optimization model.

8. The grid power self-balancing method for improving green electricity consumption according to claim 1, characterized in that, If the second constraint has no feasible solution in the optimization model, a green electricity deficit penalty term is introduced to modify the optimization objective, or a portion of the adjustable flexible load is removed to make the second constraint re-establish itself.

9. A grid power self-balancing system for improving green electricity absorption, characterized in that, For implementing the power grid self-balancing method as described in any one of claims 1-8, the power grid self-balancing system comprises: The data acquisition module is used to acquire the operating status data of each node of the distribution network, the target green electricity demand of each user in each time period, and the adjustable power of the controllable resources. The operating status data includes at least the output power and carbon emission intensity of each node's power source, the power consumption of each node's load, and the power flow distribution of the distribution network. The power deviation calculation module is used to calculate the power deviation of each node based on the output power and the power consumption. A carbon emission determination module is used to determine the carbon emission level of each node based on the carbon emission intensity and the power flow distribution of the distribution network; The optimization solution module is used to construct and solve the optimization model with the first constraint being that the power deviation of each node converges to the preset range, the second constraint being that the green electricity consumption of each user is not lower than the corresponding target green electricity demand, and the optimization objective being that the total carbon emission level of each node is minimized, and outputs the optimization adjustment command. The instruction issuing module is used to issue the optimization and adjustment instructions to the controllable resources.

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