Power regulation and control method responding to power grid emergency degree and related equipment

Abstract

The embodiment of the invention discloses a power regulation and control method for responding to the emergency degree of a power grid and related equipment, and the method comprises the steps: collecting the five indexes, i.e., voltage deviation, frequency deviation, line congestion degree, carbon quota tension index and renewable energy fluctuation index in real time, dynamically distributing weights in combination with the temperature and congestion degree, and generating a real-time emergency degree index; the emergency degree of power grid power adjustment is accurately quantified, and the hysteresis of traditional static scheduling and empirical designation is avoided. And obtaining the power adjustment priority of the user according to the emergency degree and the adjustable capability index of the user. The actual power adjustment value of the user is obtained based on the power adjustment priority, the total power adjustment value and the maximum power adjustment capacity of the user, it is ensured that the scheduled power value is matched with the actual adjustment capacity of the user, real-time and accurate coupling matching of power scheduling is achieved, and the adjustment efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, and in particular to a power control method and related equipment that respond to the urgency of the power grid. Background Technology

[0002] With the accelerated construction of new power systems, a high proportion of renewable energy sources such as wind power and photovoltaics are being integrated into the grid. The randomness and volatility of their output pose significant challenges to the real-time power balance and safe and stable operation of the grid. Against this backdrop, high-energy-consuming industrial loads with large scale and high energy consumption characteristics, such as electrolytic aluminum, chlor-alkali chemical industry, and electric arc furnace steelmaking, are transforming from simple electricity consumers into high-quality, flexible regulation resources urgently needed by the grid. Their role in demand response, ancillary services markets, and responding to grid emergencies is becoming increasingly prominent. However, the production processes of high-energy-consuming industrial loads are complex, with strict technological constraints and diverse regulation characteristics. Currently, the grid dispatch center's understanding and management of their regulateability remains relatively rudimentary. Existing dispatch strategies are mostly static or empirically based, lacking a mechanism for real-time and precise coupling and matching of dynamic grid urgency with differentiated user regulation capabilities, leading to supply-demand mismatch and low regulation efficiency. Summary of the Invention

[0003] In view of this, the present invention provides a power regulation method and related equipment in response to the urgency of the power grid.

[0004] The specific technical solution of the first embodiment of the present invention is as follows: a power regulation method in response to grid urgency, the method comprising: acquiring grid operating status indicators, the operating status indicators including voltage deviation index, frequency deviation index, line congestion index, carbon quota tension index, and renewable energy fluctuation index; acquiring a first target weight for each operating status indicator based on the current temperature, the current grid congestion level, and a preset weight setting rule; the preset weight setting rule including setting different weights for the operating status indicators based on different temperatures and grid congestion levels; acquiring the grid urgency index based on the first target weight and the operating status indicator; the urgency index being used to characterize... The urgency of power grid adjustment is determined; based on the urgency and the adjustability indicators of different users in the power grid, the power adjustment priority of each user is obtained; the adjustability indicators include response delay, adjustable power range, minimum production target within the power dispatch cycle, unit power adjustment cost, net benefit to the owner during power dispatch, historical response reliability, and load resilience coefficient; the total power adjustment value of the power grid and the maximum power adjustment capacity of different users are obtained; based on the total power adjustment value, the maximum power adjustment capacity of different users, and the power adjustment priority, the actual power adjustment value of different users is obtained; power regulation is performed based on the actual power adjustment values ​​of different users.

[0005] Preferably, the urgency index is obtained using the following formula:

[0006] in, The voltage deviation index is... The frequency deviation index is... This refers to the carbon quota tension index. The renewable energy volatility index, This refers to the line congestion index. The first target weight of the voltage deviation index is... The first target weight of the frequency deviation index is... This is the first target weight for the carbon quota tension index. The first target weight of the renewable energy volatility index is... This is the first objective weight for the line congestion index. As a preset constant, It is a hyperbolic sine function.

[0007] Preferably, the preset weight setting rule includes setting different weights for the operating status indicators according to different temperatures and grid congestion levels, including: the higher the temperature, the higher the weight of the voltage deviation index and the lower the weight of the frequency deviation index; the greater the congestion level, the greater the weight of the carbon quota tension index, the renewable energy fluctuation index, and the line congestion index.

[0008] Preferably, the step of obtaining the power regulation priority of different users based on the urgency level and the adjustability index of different users in the power grid includes: obtaining a second target weight for each adjustability index based on the urgency level and a preset adjustability attribute index weight determination table, wherein the preset adjustability attribute index weight determination table presets the weights of different adjustability indices for different urgency levels; and calculating the power regulation priority of different users based on the second target weight and the adjustability index.

[0009] Preferably, the step of calculating and obtaining the power adjustment priority of different users based on the second target weight and the adjustable capability index includes: obtaining the priority score of each user based on the second target weight and the adjustable capability index; obtaining the power adjustment priority of different users based on the priority score and a preset priority determination rule; the preset priority determination rule includes that the higher the user's priority score, the higher the user's power adjustment priority.

[0010] Preferably, the user's priority score is obtained using the following formula:

[0011] in, For the i-th user, the priority score is... Let be the response latency for the i-th user. For the adjustable power range of the i-th user, For the i-th user, the minimum production target within the power scheduling cycle is... For the unit power regulation cost of the i-th user, Let i be the net benefit of the i-th user during power scheduling. Let be the historical response reliability rate of the i-th user. Let be the load resilience coefficient for the i-th user. , , , , , and The second objective weights are used for different adjustable capability indicators.

[0012] Preferably, the actual power adjustment value is obtained using the following formula:

[0013] in, This represents the actual power adjustment value for the user prioritized as k. For the first Maximum power regulation capacity per user This represents the total power regulation value of the power grid. This represents the actual power adjustment value for the user ranked j.

[0014] The specific technical solution of the second embodiment of the present invention is as follows: a power regulation system responding to the urgency of the power grid, the system comprising: a first data acquisition module, a weight acquisition module, an urgency index calculation module, a priority determination module, a second data acquisition module, a regulation value calculation module, and a regulation module; the first data acquisition module is used to acquire the operating status indicators of the power grid, the operating status indicators including voltage deviation index, frequency deviation index, line congestion index, carbon quota tension index, and renewable energy fluctuation index; the weight acquisition module is used to acquire a first target weight for each operating status indicator based on the current temperature, the current degree of grid congestion, and a preset weight setting rule; the preset weight setting rule includes setting different weights for the operating status indicators based on different temperatures and the degree of grid congestion; the urgency index calculation module is used to acquire the first target weight and the operating status indicators based on the current temperature, the current degree of grid congestion, and a preset weight setting rule; the preset weight setting rule includes setting different weights for the operating status indicators based on different temperatures and the degree of grid congestion; the urgency index calculation module is used to acquire the first target weight and the current degree of grid congestion, the first target weight, and the second target weight. The status index obtains the urgency index of the power grid; the urgency index is used to characterize the urgency of power grid adjustment; the priority determination module is used to obtain the power adjustment priority of different users based on the urgency index and the adjustability index of different users in the power grid; the adjustability index includes response delay, adjustable power range, minimum production target within the power dispatch cycle, unit power adjustment cost, net benefit to the owner during power dispatch, historical response reliability rate, and load resilience coefficient; the second data acquisition module is used to obtain the total power adjustment value of the power grid and the maximum power adjustment capacity of different users; the adjustment value calculation module is used to obtain the actual power adjustment value of different users based on the total power adjustment value, the maximum power adjustment capacity of different users, and the power adjustment priority; the adjustment module is used to perform power regulation based on the actual power adjustment values ​​of different users.

[0015] The specific technical solution of the third embodiment of the present invention is as follows: a power regulation device that responds to the urgency of the power grid, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0016] The specific technical solution of the fourth embodiment of the present invention is as follows: a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor performs the steps of the method as described in any one of the first embodiments of this application.

[0017] Implementing the embodiments of the present invention will have the following beneficial effects: This invention generates a real-time urgency index by collecting five indicators in real time: voltage deviation, frequency deviation, line congestion, carbon quota tension index, and renewable energy fluctuation index. This index is dynamically weighted based on temperature and congestion level, accurately quantifying the urgency of grid power adjustments and avoiding the lag of traditional static dispatching and experience-based assignment. The power adjustment priority of each user is obtained based on the urgency level and their adjustability. The actual power adjustment value for each user is then obtained based on the power adjustment priority, total power adjustment value, and the user's maximum power adjustment capacity. This ensures that the dispatched power value matches the user's actual adjustment capability, achieving real-time and precise coupling and matching of power dispatching, and improving regulation efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 Flowchart of power regulation methods in response to grid urgency; Figure 2 A schematic diagram of a power regulation system designed to respond to grid urgency. Among them, 201 is the first data acquisition module; 202 is the weight acquisition module; 203 is the urgency index calculation module; 204 is the priority determination module; 205 is the second data acquisition module; 206 is the adjustment value calculation module; and 207 is the adjustment module. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0021] The terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such processes, methods, products, or apparatus.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] Please see Figure 1 The above is a flowchart of the steps of the power regulation method in response to grid urgency in the first embodiment of this application, which aims to improve regulation efficiency. The method includes: Step 101: Obtain the power grid's operating status indicators, including voltage deviation index, frequency deviation index, line congestion index, carbon quota shortage index, and renewable energy fluctuation index. Step 102: Obtain the first target weight for each operating status indicator based on the current temperature, the current grid congestion level, and the preset weight setting rules; the preset weight setting rules include setting different weights for the operating status indicators based on different temperatures and grid congestion levels. Step 103: Obtain the urgency index of the power grid based on the first target weight and the operating status index; the urgency index is used to characterize the urgency of power grid adjustment; Step 104: Obtain the power regulation priority of different users based on the urgency level and the adjustability indicators of different users in the power grid; the adjustability indicators include response delay, adjustable power range, minimum production target within the power dispatch cycle, unit power regulation cost, net benefit to the owner during power dispatch, historical response reliability rate, and load resilience coefficient. Step 105: Obtain the total power regulation value of the power grid and the maximum power regulation capacity for different users; Step 106: Obtain the actual power adjustment value for different users based on the total power adjustment value, the maximum power adjustment capacity of different users, and the power adjustment priority; Step 107: Perform power regulation based on the actual power adjustment values ​​of the different users.

[0024] Specifically, firstly, the following indicators of the power grid's operational status in a certain region are obtained: voltage deviation index, frequency deviation index, line congestion index, carbon quota tension index, and renewable energy fluctuation index. The current temperature and grid congestion level are then obtained, and the grid urgency index is calculated based on preset weighting rules. Next, the adjustability indicators for different users in the grid are obtained, such as response delay, adjustable power range, minimum production target, unit power adjustment cost, net revenue, historical response reliability, and load resilience coefficient for users A and B. Combined with the urgency index, user A is determined to have a higher power adjustment priority. Finally, the total grid power adjustment and the maximum power adjustment capacity of 500kW for each user are obtained, and the remaining adjustment values ​​are allocated to users according to priority and capacity.

[0025] In a specific embodiment, the urgency index is obtained using the following formula:

[0026] in, The voltage deviation index is... The frequency deviation index is... This refers to the carbon quota tension index. The renewable energy volatility index, This refers to the line congestion index. The first target weight of the voltage deviation index is... The first target weight of the frequency deviation index is... This is the first target weight for the carbon quota tension index. The first target weight of the renewable energy volatility index is... This is the first objective weight for the line congestion index. As a preset constant, The algorithm employs a hyperbolic sine function. Specifically, by integrating key indicators such as voltage deviation, frequency deviation, carbon quota tension, renewable energy fluctuations, and line congestion, a comprehensive quantitative assessment of the power grid's operational status is achieved. Each indicator covers multiple dimensions, including safety (voltage / frequency deviation), economic efficiency (carbon quota), environmental friendliness (renewable energy fluctuations), and reliability (line congestion), avoiding the limitations of single-indicator assessments. The use of a natural logarithmic function and a hyperbolic sine function for nonlinear processing of key indicators suppresses the excessive influence of extreme values ​​on the assessment results, prevents distortion of the urgency index due to anomalies in a single indicator, and retains sensitivity to key risks. The introduction of a carbon quota tension index and a renewable energy fluctuation index guides low-carbon dispatch while ensuring grid security, promoting the synergistic achievement of renewable energy consumption and carbon emission reduction targets.

[0027] In a specific embodiment, the preset weight setting rule includes setting different weights for the operating status indicators based on different temperatures and grid congestion levels. Specifically, the higher the temperature, the higher the weight of the voltage deviation index and the lower the weight of the frequency deviation index; the greater the congestion level, the greater the weights of the carbon quota tension index, the renewable energy fluctuation index, and the line congestion index. Specifically, increasing the weight of the voltage deviation index in high-temperature scenarios prioritizes ensuring the safe operation boundary of equipment, avoiding the risk of voltage instability caused by decreased insulation performance due to temperature increases; simultaneously reducing the frequency deviation weight reflects the system's increased tolerance to short-term frequency fluctuations during peak load periods at high temperatures, achieving precise response in temperature-sensitive scenarios. When line congestion increases, the weights of the carbon quota tension index, renewable energy fluctuation index, and line congestion index are strengthened to achieve triple synergistic optimization: prioritizing response to line thermal stability constraints to prevent overload tripping; strengthening the low-carbon goal orientation to optimize carbon quota allocation strategies when transmission is limited; and increasing the sensitivity to renewable energy fluctuations to avoid congestion exacerbating power imbalance.

[0028] In a specific embodiment, obtaining the power regulation priority of different users based on the urgency level and the adjustability indicators of different users in the power grid includes: obtaining a second target weight for each adjustability indicator based on the urgency level and a preset adjustability attribute indicator weight determination table, wherein the preset adjustability attribute indicator weight determination table presets the weights of different adjustability indicators for different urgency levels; and calculating the power regulation priority of different users based on the second target weight and the adjustability indicators. Specifically, by pre-setting a multi-urgency level weight matrix, precise matching between regulation strategies and system states is achieved. In high urgency situations, user resources with fast response and large capacity are prioritized, significantly improving emergency regulation efficiency. The weight design of historical response reliability rate and load elasticity coefficient effectively identifies reliable regulation resources and avoids secondary risks caused by user-side execution failures.

[0029] In a specific embodiment, calculating the power adjustment priority of different users based on the second target weight and the adjustable capability index includes: obtaining a priority score for each user based on the second target weight and the adjustable capability index; obtaining the power adjustment priority of different users based on the priority score and a preset priority determination rule; the preset priority determination rule includes that the higher the user's priority score, the higher the user's power adjustment priority. By calculating standardized index values ​​and weighted scores, user adjustment capabilities are transformed into comparable quantitative indicators, eliminating subjective judgment bias and improving the credibility of scheduling decisions.

[0030] In a specific embodiment, the user's priority score is obtained using the following formula:

[0031] in, For the i-th user, the priority score is... Let be the response latency for the i-th user. For the adjustable power range of the i-th user, For the i-th user, the minimum production target within the power scheduling cycle is... For the unit power regulation cost of the i-th user, Let i be the net benefit of the i-th user during power scheduling. Let be the historical response reliability rate of the i-th user. Let be the load resilience coefficient for the i-th user. , , , , , and The second objective weights are used for different adjustable capability indicators.

[0032] In a specific embodiment, the actual power adjustment value is obtained using the following formula:

[0033] in, This represents the actual power adjustment value for the user prioritized as k. For the first Maximum power regulation capacity per user This represents the total power regulation value of the power grid. This represents the actual power adjustment value for the user ranked j.

[0034] In a specific embodiment, the detailed embodiments of the present invention are as follows: To achieve refined scheduling, this invention first decomposes and analyzes the production process of high-energy-consuming industrial users, and then classifies their loads into three basic types: continuous process, intermittent process, and auxiliary utility, laying the foundation for the subsequent construction of a differentiated indicator system.

[0035] (1) Continuous process load modeling Continuous process loads are represented by electrolytic cells in aluminum smelting and chlor-alkali chemical industries. These loads have high power output and operate continuously, requiring strict electrochemical and thermal balance constraints for regulation. The DC-side power of the electrolytic cell can be expressed as:

[0036] In the above formula, This refers to the active power of the electrolytic cell. Slot voltage, It is direct current. Its regulation capability is limited by the electrode reaction rate and the stability of the melt temperature. Small power adjustments can be achieved by dynamically adjusting the firing angle of the rectifier unit, while large adjustments require optimization of the start-stop combination of a series of tanks to complete power ramp-up on a time scale of minutes to hours. This type of load has a slow response speed but strong continuity, making it suitable for participating in the intraday and interday basic peak shaving of the power grid.

[0037] (2) Modeling of intermittent process load Continuous process loads, represented by electric arc furnaces, rolling mills, and large crushers, exhibit operating characteristics of periodic high-power impacts. The operating power of a single piece of equipment can be expressed as:

[0038] In the above formula, Power consumption for a single device Rated power, A binary function representing the start / stop state of equipment (1 for running, 0 for stopping) has the potential for adjustment through optimization of the runtime sequence, i.e., load transfer. Assume that within a runtime cycle T, the set of planned runtime segments for the equipment is... By adjusting the timing, the load can be shifted from peak hours to off-peak hours, but the total runtime T must be met. total Unchanged, that is:

[0039] (3) Modeling of auxiliary public loads Auxiliary utility loads are those that provide environmental support for production, such as fans, pumps, and compressors. They are typically equipped with variable frequency drives (VFDs) for continuous adjustability. Based on the similarity law of fans / pumps, their shaft power is approximately proportional to the cube of the rotational speed n. Therefore, continuous and rapid power adjustment can be achieved by regulating the rotational speed. The instantaneous power reduction range... for:

[0040] In the above formula, Current operating power, This represents the power output at the minimum rotational speed n.

[0041] To quantify the dispatchability of high-energy-consuming industrial users, this invention constructs a dispatchability index system based on the established load classification model and multi-source data, from the dimensions of response characteristics, adjustment potential, economic cost and reliability.

[0042] (1) Response speed Indicator 1: Response speed. This characterizes the time from when an industrial user receives a dispatch command to when the actual power change occurs. The smaller the value, the more sensitive the user is to the dispatch command.

[0043]

[0044] In the above formula, Let be the response latency of the i-th device. The smaller the value, the faster the scheduling command is executed. The timestamp for when the actual power change of the equipment reaches the target value. The timestamp for the instructions issued by the dispatch center.

[0045] (2) Adjustable power range Adjustable power range refers to the adjustable capacity of industrial users under normal operating conditions, which can be adjusted upwards or downwards: Indicator 2: Upward adjustment capacity

[0046] Indicator 3: Downward Adjustment Capacity

[0047]

[0048]

[0049] In the above formula, The potential for increasing the load of the i-th device. , The maximum and minimum power of the equipment. This indicates the potential for downgrading of the i-th device. This represents the current operating power of the i-th device.

[0050] (3) Production constraints Indicator 4: Production Constraints. This characterizes the rigid limitations imposed by industrial users' production processes on load regulation and is crucial for load modeling.

[0051] In the above formula, Let be the number of production objects processed by the i-th machine in time period t, and T be the total number of time periods in the scheduling cycle. This represents the minimum production target within the scheduling cycle.

[0052] (4) Economic cost of unit power regulation Indicator 5: Unit Power Regulation Cost. The economic cost of power regulation for industrial users represents the additional cost incurred by users participating in grid regulation. This invention uses unit power regulation cost to characterize this cost.

[0053] In the above formula, For unit power regulation cost, To control production loss costs, To control equipment wear and tear costs, Energy costs associated with equipment restarts during the control process; To adjust the power output, To regulate duration. This indicator quantifies the overall economic loss per unit of regulation energy (kWh).

[0054] (5) Ownership constraints Indicator 6: Owner Revenue Constraint. In the process of industrial users participating in load dispatch, the owner constraint measures the net economic benefit that industrial users can obtain when participating in dispatch or demand response. Only when this benefit is non-negative will the owner accept the dispatch instruction; otherwise, there is a risk of default or refusal to participate.

[0055] In the above formula, Let be the net revenue of the i-th device to the owner during the scheduling period. Net profit during the operating period For equipment depreciation costs, To amortize expenses, For user capital expenditures, such as new construction, renovation, and equipment upgrades, Changes in working capital reflect the occupation or release of operating funds such as inventory, receivables, and payables. In a multi-objective scheduling model, owner constraints, economic costs, and production constraints jointly determine the feasible domain of the scheduling scheme. Through constraint checks, loads that would lead to losses for the owner can be eliminated during the scheduling phase, avoiding the risk of default later.

[0056] (6) Regulation reliability Indicator 7: Reliability of Regulation. Represents the historical rate of compliance with regulatory obligations.

[0057] In the above formula, For historical response reliability, This represents the number of times a historical dispatch command was successfully responded to. This represents the total number of times the command has been invoked throughout history. The higher the value of this metric, the stronger the user's willingness and ability to execute commands.

[0058] (7) Load elasticity coefficient Indicator 8: Load elasticity coefficient. This invention uses the load elasticity coefficient to represent the sensitivity of production efficiency to load changes. It is calculated by the ratio of a small power disturbance to the corresponding efficiency change. The magnitude of the elasticity determines the sensitivity of adjustments to output and cost.

[0059] In the above formula, Let represent the relative impact of power changes in the i-th device on production efficiency. The production efficiency of the load is represented by output per unit of energy consumption. The actual power of the load. This represents an increment within a small range, ensuring that the elasticity approximates a locally linear relationship. Among these, When it is a positive value, A negative value indicates that an increase in power will lead to a decrease in efficiency (e.g., compressors, fans). A positive value indicates that increased power can improve production efficiency (e.g., in an electrolytic cell). Additionally: >0 indicates that the increase in power leads to an increase in efficiency, and the load has a greater potential to adjust the peak load of the power grid, which is suitable for demand response adjustment, such as electrolytic cells and heating furnaces; =0 indicates that power changes have no effect on efficiency, and the load has a weak directionality for peak shaving. This is common in pure load types, such as lighting and air conditioning.

[0060] <0 indicates that the increase in power leads to a decrease in efficiency, and the load has a more significant potential for peak shaving, making it suitable for demand response downsizing, such as compressors and centrifugal fans.

[0061] A comprehensive power grid emergency index model is constructed, which integrates multiple indicators such as frequency deviation, voltage over-limit, line load rate, real-time electricity price deviation, carbon quota surplus, and renewable energy power volatility from the dimensions of physical safety, economic operation, and low-carbon environmental protection. The model is quantified through a weighted model to classify the emergency level of power grid operation.

[0062] (1) Construction of power grid operation status index set Collect real-time monitoring data of voltage U, frequency f, and line power flow from the power grid. The remaining carbon allowances provided by the carbon trading platform And the renewable power fluctuation index provided by the renewable energy forecasting system. Output the power grid state vector:

[0063] Indicator 1: Voltage Deviation Index This reflects the stability of node voltage:

[0064] In the above formula, U is the real-time voltage measurement value of the critical node (unit: kV). The node's rated voltage, The larger the value, the more severe the voltage deviation.

[0065] Indicator 2: Frequency Deviation Index This reflects the active power balance of the system:

[0066] In the above formula, This is the system's real-time frequency measurement value (unit: Hz). The system's rated frequency, The larger the value, the more unstable the frequency.

[0067] Indicator 3: Line Congestion Index This reflects the load level of key transmission sections:

[0068] In the above formula, Real-time active power flow (unit: MW) for critical paths. This is the maximum safe transmission capacity of the line. The closer a value is to 1, the closer the line is to full load, and the higher the risk of congestion.

[0069] Indicator 4: Carbon Quota Tightness Index This reflects the pressure on the regional power grid to operate in a low-carbon manner.

[0070] In the above formula, The remaining free carbon allowances for the regional power grid in the current accounting period (unit: tons). This represents the initial total amount of free carbon credits for that period. The higher the value, the tighter the carbon quota and the greater the pressure on low-carbon scheduling.

[0071] Indicator 5: Renewable Energy Volatility Index This reflects the impact of the uncertainty of new energy output on the power grid.

[0072]

[0073] In the above formula, Short-term projected output of renewable energy power plants (unit: MW) Actual output (unit: MW). Rated installed capacity of the power station (unit: MW). The larger the value, the greater the prediction deviation and the greater the system regulation pressure.

[0074] (2) Construction of a weighted fusion model of power grid operation urgency index Example 1: The standardized indicators are weighted and summed to obtain a comprehensive power grid urgency index. :

[0075] In the above formula, , , , , These are the weight coefficients for the corresponding indicators, satisfying... =1. The entropy weight method is used to determine the weighting coefficients to accurately reflect the contribution of each indicator to the power grid emergency state.

[0076] Weight coefficient determination method based on Bayesian optimization To objectively and adaptively determine the weight coefficients of each indicator in the power grid urgency index, this invention adopts a Bayesian optimization method. Its core is to find the optimal weight combination of the objective function with the fewest number of evaluations by constructing a surrogate model and a data acquisition function.

[0077] Determining the weighting coefficients is transformed into a constrained optimization problem:

[0078]

[0079] In the above formula, =( , , , ), which is the weight vector to be determined; The objective function is defined as the historical misjudgment loss. The number of historical samples. It is the true urgency level of the i-th historical sample. Is it using weights? The urgency index is calculated from the i-th historical sample.

[0080] Bayesian optimization finds the optimal weights by iteratively following these steps. : ① Constructing a proxy model: Using Gaussian processes to simulate the unknown objective function. :

[0081] In the above formula, It is the mean function. It is the covariance function.

[0082] ② Select the acquisition function: Based on the current agent model, select the next most promising evaluation point. Use the expected improvement as the acquisition function.

[0083]

[0084] In the above formula, It is the minimum loss value among the currently evaluated samples. The EI function measures the expected improvement of point w relative to the current optimal solution.

[0085] ③ Iterative Update: Initialization, randomly select a small number of weight combinations to calculate the loss function. Using all current observation data Update the Gaussian process surrogate model to find the point that maximizes the acquisition function; calculate the loss function for the new point, and synchronously add the new data point to the observation dataset. Repeat this process until the maximum number of evaluations is reached.

[0086] ④ Weight constraint processing To satisfy the constraint that the sum of the weights is 1 and non-negative, the following transformation is performed during the optimization process: Randomly generate points in 5-dimensional space By projecting it onto the standard simplex using the Softmax function, we obtain the valid weights:

[0087] This transformation guarantees The sum is 1 and >0, the optimization process actually takes place in the unconstrained v space, and establishes a connection with the constrained w space through the above transformation.

[0088] Example 2: The power grid urgency index is obtained using the following formula. : .

[0089] (3) Classification of urgency of power grid operation status Based on the calculated urgency index The power grid operating status is divided into four levels:

[0090] In the above formula, - The threshold for classifying levels (0 < < < <1), - The settings are based on industry standards such as the "Guidelines for the Safety and Stability of Power Systems", the operating procedures of power grid companies, and the statistical analysis results of historical operating data.

[0091] Power grid-user characteristic coupling matching and scheduling priority determination: (1) Construction of feature-coupled multi-attribute decision model This invention constructs a multi-attribute decision-making model to determine the optimal scheduling order for high-energy-consuming users from among numerous high-energy-consuming users in a specific emergency situation, based on the best overall benefits. Assume...

[0092] In the above formula: ① Each user is assigned a matching score; the higher the score, the higher the priority of regulation. ② - The weights of the industrial user adjustability attribute indicators constructed in Section 5.2 are dynamically adjusted according to the urgency level of the power grid operation. When the urgency level of the power grid operation increases, the weights are increased. , The weighting of adjustable power and owner constraint importance is used to quickly adjust the grid operating status. This invention is based on expert experience. - The weighting rules are as follows: Table 1. Weighting of Adjustable Capability Attribute Indicators

[0093] ③ Explanation of formula terms: : This invention uses the time from when an industrial user receives a scheduling command to when the power actually changes to represent the time. It characterizes the response speed; the larger the value, the stronger the response capability. This is an adjustable power (up / down), and a larger value indicates a stronger response capability. For the normalized production target constraint, the smaller the value, the better. The higher the value, the stronger the response capability; : This indicates the economic cost of regulating power per unit area; similarly, This indicates that the larger the value, the stronger the response capability; As above, the smaller the owner constraint, the stronger the responsiveness. : To regulate the reliability index, the higher the value, the stronger the response capability; : This is the load elasticity coefficient; the larger the value, the more suitable it is to be called upon.

[0094] (2) Generation of scheduling priority list All industrial users according to the calculation Sort the values ​​from largest to smallest, and the resulting list It refers to the scheduling priority order, among which They are the users with the highest matching degree.

[0095] (3) Construction of the regulation target allocation model The total power regulation demand of the power grid is determined based on the power grid's operating status, and regulation tasks are assigned to users according to the regulation priority list.

[0096]

[0097] In the above formula, For the first Control tasks assigned to each user For users The maximum adjustment capacity currently available, To meet the total power regulation needs of the power grid, This represents the control tasks already assigned to the k-th user. This formula indicates that the control power assigned to the current user neither exceeds its own adjustable capacity limit nor the system's remaining control demand.

[0098] Two-layer adaptive optimization scheduling model and real-time iteration: This invention constructs a master-slave game-theoretic two-layer optimization model to simulate the intelligent interaction between the power grid dispatch center and high-energy-consuming users. The upper-layer model considers the power grid's operating status and formulates incentive strategies with the goal of safe and economical power grid operation; the lower-layer users adjust their electricity consumption behavior accordingly to minimize their own costs. Based on the real-time urgency perception results of the power grid, this model integrates the load characteristic index system into the optimization constraints on the user side, and uses priority information generated by coupling matching to initialize the dispatch strategy. Through iterative feedback between the upper and lower layers, it adaptively seeks the optimal equilibrium of the system, and finally outputs the optimal dispatch command that takes into account both system safety and individual economic interests, realizing a closed loop from situational awareness to precise control.

[0099] (1) Construction of upper-level model The upper-level model aims to maximize the overall benefits of the power grid, and the decision variables are: Unit compensation price (RMB / MWh) Let be the penalty coefficient for user default. The objective function is:

[0100] In the above formula, For the overall control target of the power grid, The compensation price is per unit (RMB / MWh). The penalty coefficient for user default. The percentage of the control task not completed for the i-th user. Adjust the task load for the i-th user.

[0101] The constraints are: ①The overall control target equals the sum of the control tasks of each user:

[0102] In the above formula, The total control target quantity, Adjust the task load for the i-th user.

[0103] ② Overall control target The minimum reduction targets required by the current grid emergency level must be met. :

[0104] ③Unit compensation price and penalty coefficient Nonnegativity constraint:

[0105] (2) Construction of the lower-level model The lower-level model aims to minimize the user's overall cost, with the user's actual control power as the decision variable. The objective function is:

[0106] In the above formula, This indicates the economic cost of regulating power per unit area. For the user's actual control power, This indicates that users can adjust their own earnings. This represents the penalty coefficient specific to user i.

[0107] The constraints are: ① User-adjustable power It cannot exceed the user's maximum adjustable capacity. :

[0108] ② Production adjustment time T must be within the order delivery deadline Previous completed:

[0109] ③ User reliability index It cannot be lower than a certain threshold This ensures that scheduling instructions can be executed reliably.

[0110]

[0111] (3) Model solution strategy based on alternating direction iteration For this bi-level optimization model, this invention employs an alternating direction iterative method for solution. The core idea is to decompose the complex bi-level problem into two relatively simple single-level problems. By fixing the decision variables of one side and optimizing the decision variables of the other side, and performing iterative iterations, until the decisions of both sides no longer change significantly, a satisfactory approximate optimal solution is found.

[0112] ① Algorithm Initialization The convergence criteria for the algorithm are defined. Subsequently, the power grid side initializes its decision variables based on the current power grid urgency level and the user priority list obtained through the coupling matching model, that is, sets an initial incentive price and penalty coefficient.

[0113] Based on the current power grid urgency level g and the priority list obtained through coupling matching, the initial incentive price and penalty coefficient are set:

[0114] In the above formula, and This is a base value set based on historical data or market rules. The higher the urgency, the higher the initial incentive. The higher the price, the faster it can attract user responses.

[0115] ② Iterative solution loop Repeat the following process until convergence or the maximum number of iterations is reached: 1) Fix the upper-level variables and solve the lower-level problem in parallel: Input: The current stimulus signal published by the upper layer. , (K is the number of iterations) Process: Each high-energy-consuming user i independently and in parallel solves its own cost minimization problem. The output yields a set of optimal user response decision power reduction amounts. And calculate the current total adjustment. .

[0116] 2) Fix the lower-level variables and solve the upper-level problem: Input: Power reduction amount reported by all users on the grid side. To obtain the current total power reduction of the system. .

[0117] Process: The power grid thus reduces total power. and user response volume Based on this, and with the goal of maximizing the overall benefits of the power grid, the lower-level variables are now fixed, and the upper-level problem is simplified to a linear programming problem. A new round of excitation signals is then obtained through optimization. With penalty coefficient .

[0118] ③ Convergence judgment Calculate the change in the key decision variables at the upper level between two consecutive iterations:

[0119] like If the value is less than the set convergence tolerance, the algorithm is considered to have converged, and the loop is exited; otherwise, k = k + 1, and the loop iteration continues.

[0120] ④ Output the optimal solution Once the algorithm converges, it outputs the final optimal solution. This solution is the adaptive dispatch instruction that achieves optimal synergy between the interests of the power grid and users under the current power grid emergency state. Upper-level model: Optimal incentive price and penalty coefficient

[0121] Lower-level model: Optimal power adjustment

[0122] (4) Model adaptive iterative closed loop This invention establishes an adaptive real-time iterative learning closed loop to ensure that the scheduling strategy can dynamically track the time-varying nature of the power grid and user states, thereby achieving continuously optimized closed-loop control.

[0123] ① Data Update: The system periodically re-collects two types of data: power grid operating status and user operating status. ② Reassessment of urgency: Input the data collected by Shanshan into the power grid operation status perception model constructed in Section 5.3 to calculate the latest index values. Determine the current power grid operating status level.

[0124] ③ Adaptive learning of weights and parameters: The Actor-Critic reinforcement learning algorithm is used to fine-tune the key parameters in the system online.

[0125] State space: This refers to quantities that characterize the system's state, such as the current urgency level, urgency index, and total load.

[0126] Action space: That is, the weights of the coupling matching model constructed in Section 5.4. The excitation coefficient λ in the two-layer model is slightly adjusted.

[0127] ④ Set a reward function: The reward function guides the agent to learn to achieve the power grid peak shaving target as accurately as possible while minimizing the economic impact on user production. The reward function is as follows:

[0128] In the above formula, This represents the total adjustment power actually completed in the previous cycle. The reduction target expected to be achieved in the previous cycle, This represents the total economic cost incurred by all users due to scheduling issues in the previous cycle. This is an economic penalty cost coefficient used to balance the two objectives of peak shaving effect and user economy.

[0129] ⑤ Learning process: The Critic (evaluator) assesses the long-term value of the current strategy based on the actual rewards received; the Actor (implementer) updates their own strategy based on the Critic's evaluation results.

[0130] ⑥ Strategy Update: Update in real time based on the learning results of the reward function. The command results ensure that the scheduling effect continuously meets the urgency requirements.

[0131] This invention achieves precise matching and dynamic optimization of the grid's emergency status and the regulation capabilities of high-energy-consuming industrial users through a combination of technologies including a user feature tag library, multi-dimensional grid urgency perception, coupling matching, and a two-layer adaptive model. This addresses three major pain points in traditional dispatching: ambiguity in industrial user characteristics, mismatch between grid demand and user capabilities, and static strategies. Each module in the technical solution is implemented based on existing smart grid data interfaces and mature algorithms, providing a clear path for engineering implementation.

[0132] This invention constructs a closed-loop control technology system encompassing "load classification modeling, multi-dimensional grid perception, feature coupling matching, and two-layer adaptive optimization," enabling precise profiling of the adjustability of high-energy-consuming users, comprehensive perception of grid emergency states, and dynamic iterative optimization of dispatch strategies.

[0133] The main technical highlights of this invention are as follows: First, a refined classification and characteristic model of high energy-consuming loads was established, and key indicators such as load elasticity coefficient and owner revenue constraint were innovatively introduced. From the perspectives of physical mechanism and economic motivation, the user's adjustability and willingness were accurately characterized, laying a solid foundation for precise scheduling. Second, a multi-dimensional power grid urgency perception model integrating physical security, economic operation, and low-carbon environmental protection was proposed, and the Bayesian optimization method was used to adaptively determine the index weights, thereby achieving accurate and dynamic assessment of the power grid operation status. Third, a power grid-user feature coupling matching model based on dynamic weight rules was constructed, which enables the scheduling priority to be dynamically adjusted according to the urgency of the power grid, ensuring that the most appropriate resources are called when they are most needed. Fourth, a two-layer optimization framework based on master-slave game theory was designed and a reinforcement learning closed loop was introduced to achieve the synergistic optimality between power grid dispatching objectives and individual user interests, and to ensure the continuous adaptive capability of the strategy.

[0134] Using the above method, taking a scenario where a power grid in a certain region includes three types of high-energy-consuming users (two electrolytic aluminum plants, one electric arc furnace workshop in a steel plant, and one set of variable frequency fans in an industrial park) as an example, the scheduling results of applying this method are as follows: Table 2. Simulation results of adaptive regulation under different grid urgency levels

[0135] The results show that the adaptive control method proposed in this invention can dynamically adjust the dispatch strategy according to the real-time urgency of the power grid, achieving a comprehensive improvement in control accuracy, response speed, and overall economic benefits while ensuring power grid security. The priority score, as the direct output of the coupled matching model, is key quantitative evidence connecting power grid demand and user capabilities, proving the scientific validity and effectiveness of the decision-making process in this invention.

[0136] In a specific embodiment, please refer to Figure 2This is a schematic diagram of the power regulation system responding to grid urgency in the second embodiment of this application. The system includes: a first data acquisition module 201, a weight acquisition module 202, an urgency index calculation module 203, a priority determination module 204, a second data acquisition module 205, a regulation value calculation module 206, and a regulation module 207. The first data acquisition module 201 is used to acquire grid operation status indicators, including voltage deviation index, frequency deviation index, line congestion index, carbon quota tension index, and renewable energy fluctuation index. The weight acquisition module 202 is used to acquire a first target weight for each operation status indicator based on the current temperature, the current grid congestion level, and a preset weight setting rule. The preset weight setting rule includes setting different weights for the operation status indicators based on different temperatures and grid congestion levels. The urgency index calculation module 203 is used to acquire a first target weight for each operation status indicator based on the first target weight. The urgency index of the power grid is obtained by weighting the grid and the operating status indicators. The urgency index is used to characterize the urgency of power grid adjustment. The priority determination module 204 is used to obtain the power adjustment priority of different users according to the urgency and the adjustability indicators of different users in the power grid. The adjustability indicators include response delay, adjustable power range, minimum production target within the power dispatch cycle, unit power adjustment cost, net benefit to the owner during power dispatch, historical response reliability rate, and load resilience coefficient. The second data acquisition module 205 is used to obtain the total power adjustment value of the power grid and the maximum power adjustment capacity of different users. The adjustment value calculation module 206 is used to obtain the actual power adjustment value of different users according to the total power adjustment value, the maximum power adjustment capacity of different users, and the power adjustment priority. The adjustment module 207 is used to perform power regulation according to the actual power adjustment values ​​of different users.

[0137] In a specific embodiment, the third embodiment of this application provides a power regulation device that responds to the urgency of the power grid, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method as described in any of the first embodiments of this application.

[0138] In a specific embodiment, the fourth embodiment of this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method as described in any one of the first embodiments of this application.

[0139] The above embodiments merely illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0140] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A power regulation method in response to grid urgency, characterized in that, The method includes: The operation status indicators of the power grid are obtained, including voltage deviation index, frequency deviation index, line congestion index, carbon quota shortage index, and renewable energy fluctuation index. The first target weight of each operating status indicator is obtained based on the current temperature, the current grid congestion level, and a preset weight setting rule; the preset weight setting rule includes setting different weights for the operating status indicators according to different temperatures and grid congestion levels. The urgency index of the power grid is obtained based on the first target weight and the operating status index; the urgency index is used to characterize the urgency of power grid adjustment. The power regulation priority of different users is obtained based on the urgency level and the adjustability indicators of different users in the power grid; the adjustability indicators include response delay, adjustable power range, minimum production target within the power dispatch cycle, unit power regulation cost, net benefit to the owner during power dispatch, historical response reliability rate, and load resilience coefficient. Obtain the total power regulation value of the power grid and the maximum power regulation capacity for different users; The actual power regulation value for each user is obtained based on the total power regulation value, the maximum power regulation capacity of each user, and the power regulation priority. Power regulation is performed based on the actual power adjustment values ​​of the different users.

2. The power regulation method for responding to grid urgency as described in claim 1, characterized in that, The urgency index is obtained using the following formula: in, The voltage deviation index is... The frequency deviation index is... This refers to the carbon quota tension index. The renewable energy volatility index, This refers to the line congestion index. The first target weight of the voltage deviation index is... The first target weight of the frequency deviation index is... This is the first target weight for the carbon quota tension index. The first target weight of the renewable energy volatility index is... This is the first objective weight for the line congestion index. As a preset constant, It is a hyperbolic sine function.

3. The power regulation method for responding to grid urgency as described in claim 1, characterized in that, The preset weight setting rules include assigning different weights to the operating status indicators based on different temperatures and the degree of grid congestion, including: The higher the temperature, the higher the weight of the voltage deviation index, and the lower the weight of the frequency deviation index. The greater the congestion level, the greater the weight of the carbon quota tension index, the renewable energy volatility index, and the line congestion index.

4. The power regulation method for responding to grid urgency as described in claim 1, characterized in that, The step of obtaining the power regulation priority of different users based on the urgency level and the adjustability index of different users in the power grid includes: The second target weight of each adjustable capability indicator is obtained based on the urgency level and the preset adjustable capability attribute indicator weight determination table. The preset adjustable capability attribute indicator weight determination table presets the weights of different adjustable capability indicators for different urgency levels. The power adjustment priority of different users is calculated based on the second target weight and the adjustable capability index.

5. The power regulation method for responding to grid urgency as described in claim 1, characterized in that, The step of calculating and obtaining the power adjustment priority for different users based on the second target weight and the adjustable capability index includes: Each user's priority score is obtained based on the second objective weight and the adjustable capability index; The power adjustment priority of different users is obtained based on the priority score and the preset priority determination rule; the preset priority determination rule includes that the higher the priority score of a user, the higher the power adjustment priority of the user.

6. The power regulation method for responding to grid urgency as described in claim 5, characterized in that, The user's priority score is obtained using the following formula: in, The priority score for the i-th user. Let be the response latency for the i-th user. For the adjustable power range of the i-th user, For the i-th user, the minimum production target within the power scheduling cycle is... For the unit power regulation cost of the i-th user, Let i be the net benefit of the i-th user during power scheduling. Let be the historical response reliability rate of the i-th user. Let be the load resilience coefficient for the i-th user. , , , , , and The second objective weights are used for different adjustable capability indicators.

7. The power regulation method for responding to grid urgency as described in claim 1, characterized in that, The actual power adjustment value is obtained using the following formula: in, This represents the actual power adjustment value for the user prioritized as k. For the first Maximum power regulation capacity per user This represents the total power regulation value of the power grid. This represents the actual power adjustment value for the user ranked j.

8. A power regulation system responding to grid urgency, characterized in that, The system includes: a first data acquisition module, a weight acquisition module, an urgency index calculation module, a priority determination module, a second data acquisition module, an adjustment value calculation module, and an adjustment module; The first data acquisition module is used to acquire the operating status indicators of the power grid, including voltage deviation index, frequency deviation index, line congestion index, carbon quota shortage index, and renewable energy fluctuation index. The weight acquisition module is used to acquire the first target weight of each operating status indicator based on the current temperature, the current grid congestion level, and the preset weight setting rules; the preset weight setting rules include setting different weights for the operating status indicators based on different temperatures and grid congestion levels. The urgency index calculation module is used to obtain the urgency index of the power grid based on the first target weight and the operating status index; the urgency index is used to characterize the urgency of power grid adjustment. The priority determination module is used to obtain the power regulation priority of different users based on the urgency level and the adjustability indicators of different users in the power grid; the adjustability indicators include response delay, adjustable power range, minimum production target within the power scheduling cycle, unit power regulation cost, net benefit to the owner during power scheduling, historical response reliability rate, and load resilience coefficient. The second data acquisition module is used to acquire the total power regulation value of the power grid and the maximum power regulation capacity of different users; The adjustment value calculation module is used to obtain the actual power adjustment value of different users based on the total power adjustment value, the maximum power adjustment capacity of different users, and the power adjustment priority. The adjustment module is used to regulate power according to the actual power adjustment values ​​of different users.

9. A power regulation device responding to grid urgency, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

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