Multi-region interconnected power grid agc coordination optimization system

Abstract

This invention relates to the field of power grid operation control technology and discloses a multi-region interconnected power grid AGC coordinated optimization system. The system includes a data acquisition module, a data online analysis module, a virtual inertial control module, a distributed predictive control module, a multi-region collaborative control module, an AGC optimization scheduling module, and an evaluation feedback module. Through a closed-loop architecture encompassing data acquisition, online analysis, multi-module collaborative control, and evaluation feedback, the system achieves the beneficial effects of adaptive and continuous evolution in multi-region interconnected power grid AGC coordinated optimization. The modules cooperate collaboratively, and the evaluation feedback module corrects parameters and thresholds in real time, forming a complete closed loop of perception-analysis-decision-execution-evaluation. This enables the system to achieve adaptive optimization and long-term performance improvement in complex scenarios with high penetration of new energy sources and drastic load fluctuations, ultimately enhancing the safety, economy, and clean energy absorption capacity of the interconnected power grid.

Description

Technical Field

[0001] This invention relates to the field of power grid operation control technology, specifically to a multi-regional interconnected power grid AGC coordination and optimization system. Background Technology

[0002] With the large-scale grid connection of fluctuating renewable energy sources (VREs) such as wind and solar power, the physical characteristics of the power system are undergoing profound changes. On the one hand, synchronous generators are being replaced by power electronic converters, resulting in a significant reduction in the system's equivalent rotating inertia (Dgt), highlighting frequency stability issues and placing higher demands on rapid inertia support. On the other hand, the strong randomness of renewable energy output and the coupling between inter-regional power transmission exacerbate power flow fluctuations on inter-regional interconnection lines. Traditional AGC (Automatic Guided Control) models based on fixed thresholds and single-region optimization are insufficient for proactively preventing section overload risks. Furthermore, primary frequency regulation (fast and short-term) and AGC (slower and continuous) are prone to conflicts in response timing and regulation objectives. Improper coordination may lead to secondary frequency drops or even system accidents. Existing technologies mostly focus on single problems (such as simply enhancing virtual inertia or improving AGC algorithms), lacking an integrated architecture capable of panoramic perception, collaborative decision-making, and closed-loop optimization. This fails to systematically address the complex challenges faced by high-proportion renewable energy grids in terms of inertia support, power flow security, and control coordination. Summary of the Invention

[0003] (a) Technical problems to be solved

[0004] To address the shortcomings of existing technologies, this invention provides a multi-region interconnected power grid AGC coordination and optimization system, which has the advantages of panoramic perception, collaborative control and closed-loop optimization. It solves the problems of reduced frequency security and operational stability caused by insufficient inertia, power flow over-limit risks and multi-timescale control mismatch in power grids with a high proportion of new energy access.

[0005] (II) Technical Solution

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-region interconnected power grid AGC coordination and optimization system, comprising a data acquisition module for data collection stations, an online data analysis module for data collection stations, a virtual inertial control module, a distributed predictive control module, a multi-region collaborative control module, an AGC optimization scheduling module, and an evaluation and feedback module;

[0007] The data acquisition module of the acquisition station is responsible for the real-time acquisition and preprocessing of power grid operation data in multiple regions, providing a data foundation for subsequent analysis.

[0008] The online data analysis module of the acquisition station is responsible for online analysis, calculation and judgment of various types of data transmitted by the data acquisition module of the acquisition station, and outputs the system equivalent inertia constant. Cross-regional power flow dynamic adjustment threshold Primary frequency modulation and AGC coordination conflict risk index With early warning information;

[0009] The virtual inertial control module is based on the output of the system's equivalent inertial constant. Dynamically simulate and supplement the rotational inertia response of the system;

[0010] The distributed predictive control module dynamically adjusts the cross-regional power flow threshold based on the output. Establish a distributed model predictive control framework for power grids in various regions to control power flow at key cross-regional sections within a safe threshold range and prevent overload risks.

[0011] The multi-region collaborative control module is based on the output primary frequency modulation and AGC collaborative conflict risk index. Construct a coordination logic and arbitration mechanism for multi-timescale control instructions, when the risk index... When the set limit is exceeded, the allocation strategy of primary frequency regulation reserve capacity and AGC adjustment command will be automatically adjusted.

[0012] The AGC optimization scheduling module integrates the real-time control results of the virtual inertial control module, the distributed predictive control module, and the multi-regional collaborative control module to perform global rolling optimization of unit combination, load allocation, and cross-regional transaction plans, generating the final AGC base point power command and auxiliary service call scheme.

[0013] The evaluation feedback module monitors and records the action effects of the virtual inertial control module, the distributed predictive control module, and the multi-regional collaborative control module in real time, as well as the overall operating status of the system, and comprehensively evaluates the actual performance indicators of frequency support, power flow control, and collaborative optimization.

[0014] Preferably, the data acquisition module of the acquisition station consists of a frequency acquisition station, a power flow acquisition station, a power supply acquisition station, a load acquisition station, a meteorological acquisition station, and a status acquisition station. After acquiring the data from the acquisition station, the module performs filtering and noise reduction, time stamp synchronization, outlier removal, and data compression on the acquired data.

[0015] Preferably, the frequency acquisition station is deployed at the main grid nodes in each region to collect system frequency and frequency change rate; the power flow acquisition station is deployed at inter-regional tie lines and key transmission sections to collect line power, voltage phase angle difference, line load rate and power oscillation characteristics.

[0016] Preferably, the power acquisition station is deployed at VRE sites, energy storage power stations, and synchronous generator units to collect real-time power output, available adjustable capacity, adjustment rate, start-stop status, and datasets characterizing inertial response capability; the load acquisition station is deployed at regional load centers to collect total load demand, load volatility, load forecast deviation, main load type composition, and the status and capacity of interruptible load / demand-side response resources.

[0017] Preferably, the meteorological data acquisition station is deployed in the VRE concentrated area to collect wind speed, wind direction, irradiance, temperature, extreme weather warning information and forecast data, and connects to the new energy power prediction system through a high-speed communication network to update the latest VRE ultra-short-term and short-term power generation prediction curves; the status acquisition station collects real-time status datasets of primary energy, control equipment and protection actions through the dispatch automation system, wide-area measurement system and power plant monitoring system.

[0018] Preferably, the online data analysis module of the acquisition station calculates the equivalent inertial constant of the system based on the preprocessed data of the acquisition station. Cross-regional power flow dynamic adjustment threshold Primary frequency modulation and AGC coordination conflict risk index The system achieves inertial support, cross-regional power flow control, and coordinated control of control objectives through a virtual inertial control module, a distributed predictive control module, and a multi-regional collaborative control module.

[0019] Preferably, the online data analysis module of the acquisition station calculates the equivalent inertial constant of the system based on the preprocessed data of the acquisition station. The calculation formula is as follows: , In the formula, Represents the system's equivalent inertial constant. Denotes the inertial time constant of the i-th synchronous generator unit. This represents the rated capacity of the i-th synchronous generator unit. This represents the equivalent virtual inertia coefficient of the j-th energy storage system. Let represent the rated capacity of the j-th energy storage system, n represent the number of synchronous generators, m represent the number of energy storage systems, and η represent the energy storage discharge efficiency. This indicates the system's baseline capacity.

[0020] Preferably, the online data analysis module of the acquisition station calculates the cross-regional power flow dynamic adjustment threshold based on the preprocessed data of the acquisition station. The calculation formula is as follows: , In the formula, This indicates the dynamic adjustment threshold for inter-regional connection lines. Indicates the rated transmission power of the tie line. This represents the adjustment coefficient. This indicates the voltage phase angle difference between the two ends of the cross-regional tie line.

[0021] Preferably, the online data analysis module of the acquisition station calculates the risk index of primary frequency modulation and AGC coordination conflict based on the preprocessed data of the acquisition station. The calculation formula is as follows: , In the formula, This indicates the risk index of a primary frequency modulation (FM) and AGC (Automatic Guided Vehicle) coordination conflict. , , These represent the primary frequency regulation deviation weight, the AGC command execution deviation weight, and the response timing deviation weight, respectively. This indicates the actual adjustment amount of a single frequency modulation. This indicates the maximum allowable adjustment amount for a single frequency modulation. This represents the baseline value for AGC coordinated adjustment. This indicates the actual adjustment amount executed by AGC. This indicates the timing deviation between the primary frequency modulation and the AGC response.

[0022] Preferably, the virtual inertial control module is based on the system's equivalent inertial constant. Control process:

[0023] S1.1, Inertial Classification Determination: Receives the output from the online analysis module. Values ​​and warning signals, when ≥8s is sufficient / 5-8s is critical / <5s is insufficient;

[0024] S1.2, Strategy Generation: When When the system is at a critical or insufficient level, dynamic virtual inertia coefficients are issued to m energy storage systems. Adjustment instructions;

[0025] S1.3 Multi-source inertial control: The synchronous generator side compensates for the inertial gap through additional damping control via the speed governor; the energy storage system adopts a virtual synchronous machine mode to output and... Proportional instantaneous power support enables precise spatiotemporal matching of inertia.

[0026] Compared with existing technologies, this invention provides a multi-region interconnected power grid AGC coordination optimization system, which has the following beneficial effects:

[0027] 1. This invention achieves the beneficial effect of millisecond-level panoramic perception of the dynamic process of a high proportion of new energy power grid by constructing multiple types of high-frequency acquisition stations and a real-time data preprocessing system. It integrates PMU / WAMS synchronous phasor measurement (≤100ms sampling) with multi-source data to provide a precise data foundation for virtual inertial rapid response and collaborative control, supporting control closed loop within 200ms.

[0028] 2. This invention utilizes the system's equivalent inertia constant. Online calculation and virtual inertia hierarchical control have achieved the beneficial effects of accurately compensating for inertia deficit and suppressing rapid frequency changes. An innovative formula for joint inertia calculation of synchronous generator units and energy storage systems has been proposed. Through inertia hierarchical judgment and multi-source collaborative control, the virtual inertia coefficient of energy storage is dynamically adjusted, effectively reducing the frequency change rate and controlling the maximum frequency deviation within ±0.2Hz. Ultimately, this solves the problems of reduced system inertia and deteriorated frequency stability caused by a high proportion of new energy grid connection.

[0029] 3. This invention dynamically adjusts the threshold through cross-regional power flow. Computational and distributed predictive control achieves the beneficial effects of proactively managing tie-line power flow and preventing section overload. It predicts 5-minute power fluctuations based on an ARIMA-GARCH hybrid model and uses dynamic thresholds. With adaptive power redistribution, multi-region DMPC collaboration is achieved, keeping the power flow at key sections within a safe margin to improve cross-regional power transmission capacity. Ultimately, this solves the problem that traditional AGC, which uses fixed thresholds or single-region optimization, is unable to cope with cross-regional power flow exceeding limits caused by new energy fluctuations.

[0030] 4. This invention addresses the risk of conflict between primary frequency modulation and AGC coordination. Quantitative assessment and tiered correction have achieved the beneficial effects of resolving control conflicts and ensuring the smoothness of frequency recovery, through real-time calculation. By dynamically adjusting the weighting coefficients and hierarchical coordination strategies, precise matching of the primary frequency regulation and AGC response timing and regulation targets is achieved, reducing the risk of coordination conflicts and thus eliminating system failures caused by control mismatch. Ultimately, this solves the risk of frequency collapse caused by improper coordination between primary frequency regulation and AGC in power plant tripping events. Attached Figure Description

[0031] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figure 1 The multi-region interconnected power grid AGC coordination and optimization system includes a data acquisition module for data collection stations, an online data analysis module for data collection stations, a virtual inertial control module, a distributed predictive control module, a multi-region collaborative control module, an AGC optimization scheduling module, and an evaluation and feedback module.

[0034] The data acquisition module of the data acquisition station is responsible for the real-time acquisition and preprocessing of power grid operation data in multiple regions, providing a data foundation for subsequent analysis.

[0035] The online data analysis module of the data acquisition station is responsible for online analysis, calculation, and judgment of various types of data transmitted by the data acquisition module of the data acquisition station, and outputs the system's equivalent inertia constant. Cross-regional power flow dynamic adjustment threshold Primary frequency modulation and AGC coordination conflict risk index Early warning information supports subsequent decision-making by various control and scheduling modules;

[0036] The virtual inertial control module is based on the output of the system's equivalent inertial constant. By rapidly adjusting the output of energy storage devices, flexible loads, and new energy power generation equipment, the rotational inertia response of the system is dynamically simulated and supplemented to suppress the initial rate of change and maximum deviation of the frequency, thereby enhancing the frequency stability of the power grid under a high proportion of new energy access.

[0037] The distributed predictive control module dynamically adjusts the cross-regional power flow threshold based on the output. Establish a distributed model predictive control framework for power grids in various regions. Through rolling optimization and feedback correction, while meeting the internal constraints of each region, proactively adjust generator output and tie line power plans to accurately control the power flow at key cross-regional sections within a safe threshold range and prevent overload risks.

[0038] The multi-region collaborative control module is based on the risk index of primary frequency modulation and AGC collaboration. Construct a coordination logic and arbitration mechanism for control instructions across multiple time scales; when the risk index When the set limit is exceeded, the module dynamically adjusts the allocation strategy of primary frequency regulation reserve capacity and AGC adjustment command, or introduces transient correction signal to resolve the possible conflict between the two in terms of response speed and adjustment target, and ensure the smoothness and stability of the frequency recovery process.

[0039] The AGC optimization scheduling module integrates the real-time control results of the virtual inertial control module, the distributed predictive control module, and the multi-regional collaborative control module. With the comprehensive goal of maximizing system operation economy, safety, and clean energy consumption, it performs global rolling optimization of unit combination, load allocation, and cross-regional trading plans within a longer scheduling cycle (such as 15 minutes to hours), generating the final AGC base point power command and auxiliary service call scheme.

[0040] The evaluation feedback module monitors and records the actions of the virtual inertial control module, the distributed predictive control module, and the multi-regional collaborative control module in real time, as well as the overall operating status of the system. It quantifies and evaluates the actual performance indicators of frequency support, power flow control, and collaborative optimization. The evaluation results are compared and analyzed with historical data to generate closed-loop feedback information. The calculation parameters and early warning thresholds in the online data analysis module of the acquisition station, as well as the strategy parameters of each control module, are dynamically adjusted to achieve adaptive optimization and continuous performance improvement of the system.

[0041] The advantages are: through the closed-loop architecture of data acquisition from collection stations, online analysis, multi-module collaborative control, and evaluation feedback, the system achieves the beneficial effects of adaptive and continuous evolution in the coordinated optimization of AGC in multi-regional interconnected power grids. Data is interconnected and strategies are coordinated among modules, and the evaluation feedback module corrects parameters and thresholds in real time, forming a complete closed loop of perception-analysis-decision-execution-evaluation. This enables the system to achieve adaptive optimization and long-term performance improvement in complex scenarios such as high penetration of new energy sources and drastic load fluctuations, significantly improving the safety, economy, and clean energy absorption capacity of interconnected power grids.

[0042] The data acquisition module is configured with multiple types of acquisition stations, including: frequency acquisition station, power flow acquisition station, power supply acquisition station, load acquisition station, meteorological acquisition station, and status acquisition station. After acquiring the data from the acquisition stations, the acquired data is filtered and denoised, time-stamped, outlier removed, and compressed to ensure that the data quality meets the real-time control requirements (transmission delay ≤200ms).

[0043] Frequency acquisition stations are deployed at the main network nodes in each region to acquire system frequency (Hz) and rate of change of frequency (ROCOF, Hz / s). Their sampling period is ≤100ms, providing millisecond-level dynamic frequency perception for virtual inertial control and collaborative control.

[0044] Power flow acquisition stations are deployed on inter-regional tie lines and key transmission sections to collect line power (active / reactive power flow), voltage phase angle difference, line load rate, and power oscillation characteristics (such as oscillation mode and damping ratio), providing key inter-regional coupled dynamic information for distributed model predictive control (DMPC).

[0045] Power acquisition stations are deployed at VRE (wind power, photovoltaic), energy storage power stations, and synchronous generator units (thermal power, hydropower, gas power) to collect real-time power output (MW), available adjustable capacity (up / down, MW), adjustment rate (MW / min), start-up and shutdown status, and datasets characterizing inertial response capabilities. Specifically, these include: the inertial time constant (H value) of synchronous generator units, the equivalent virtual inertial coefficient (K_vic) and state of charge (SOC) of energy storage systems, and real-time maximum available power output and ultra-short-term power prediction data of VRE stations.

[0046] Load acquisition stations are deployed in regional load centers to collect total load demand (MW), load volatility, load forecasting deviation, composition of major load types (such as industrial, commercial, and residential), and status and capacity of interruptible loads / demand-side response resources, providing a panoramic view of load-side information for refined power balance and control.

[0047] Meteorological data collection stations are deployed in VRE (Vehicle Regeneration and Energy) concentrated areas to collect wind speed, wind direction, irradiance, temperature, extreme weather warning information (typhoon, rainstorm, dust storm) and forecast data. They are connected to the renewable energy power prediction system through a high-speed communication network to update the latest VRE ultra-short-term and short-term power generation prediction curves, supporting inertial early warning and advanced dispatching decisions.

[0048] The status acquisition station collects real-time status datasets of primary energy sources, control equipment, and protection actions through the dispatch automation system, wide area measurement system (WAMS), and power plant monitoring system. These datasets include: synchronous unit start-up and shutdown status, energy storage SOC (state of charge), primary frequency regulation action signals (action flags, regulation amounts, and durations), AGC actuator (unit / energy storage) received instructions and feedback status, and relay protection and stability device action signals. This provides direct evidence for collaborative logic verification, control conflict identification, and system safety assessment.

[0049] The advantages are: by acquiring and preprocessing high-frequency real-time data from multiple types of acquisition stations, a full-scenario data perception system is constructed, providing high-quality data support at the millisecond level (≤100ms) and in multiple dimensions (frequency / power flow / power supply / load / meteorology / status) for subsequent analysis and control, thus meeting the needs of rapid dynamic process monitoring for high-proportion renewable energy power grids.

[0050] The online data analysis module of the data acquisition station calculates the equivalent inertial constant of the system based on the preprocessed data of the data acquisition station. Cross-regional power flow dynamic adjustment threshold Primary frequency modulation and AGC coordination conflict risk index By using a virtual inertial control module, a distributed predictive control module, and a multi-regional collaborative control module, the system achieves inertial support, cross-regional power flow control, and coordinated control of control objectives, thus accurately solving the three core problems of the system.

[0051] The online data analysis module of the data acquisition station calculates the equivalent inertial constant of the system based on the preprocessed data of the data acquisition station. The calculation formula is as follows: , In the formula, It represents the system's equivalent inertial constant (s). Let represent the inertial time constant (s) of the i-th synchronous generator unit. This represents the rated capacity (MVA) of the i-th synchronous generator unit. This represents the equivalent virtual inertia coefficient of the j-th energy storage system. Let represent the rated capacity (MVA) of the j-th energy storage system, n represent the number of synchronous generators, m represent the number of energy storage systems, and η represent the energy storage discharge efficiency. Indicates the system baseline capacity (MVA);

[0052] The advantages are: the formula accurately calculates the current inertia level of the system, divides it into three levels: sufficient inertia, critical inertia, and insufficient inertia, and finally outputs an inertia warning signal to the virtual inertia control module and the AGC optimization scheduling module to support the inertia support control of the system.

[0053] The online data analysis module at the data acquisition station calculates the dynamic adjustment threshold for cross-regional power flow based on the preprocessed data from the data acquisition station. The calculation formula is as follows: , In the formula, This indicates the dynamic adjustment threshold (MW) for inter-regional tie lines. This indicates the rated transmission power (MW) of the tie line. This represents the adjustment coefficient (values ​​range from 0.8 to 1.2, dynamically adjusted based on the regional coupling strength). This represents the voltage phase angle difference (rad) between the two ends of the inter-regional tie line; combined with the voltage phase angle difference and line load rate data collected by the power flow acquisition station;

[0054] The advantage is that this formula dynamically calculates the cross-regional power flow regulation threshold. It identifies the risk of cross-regional power flow exceeding limits and outputs cross-regional coupling status data to the distributed predictive control module to support cross-regional power flow management.

[0055] The online data analysis module of the data acquisition station calculates the risk index of primary frequency modulation and AGC coordination conflict based on the preprocessed data of the data acquisition station. The calculation formula is as follows: , In the formula, This represents the risk index of conflict between primary frequency modulation and AGC (dimensionless, ranging from 0 to 1, with values ​​closer to 1 indicating a higher risk of conflict). , , These represent the primary frequency regulation deviation weight, AGC command execution deviation weight, and response timing deviation weight, respectively (the sum of the three is 1, dynamically calibrated according to the actual power grid operation scenario, with default values ​​of 0.4, 0.4, and 0.2, respectively). This represents the actual adjustment amount (MW) of a primary frequency regulation. This represents the maximum permissible adjustment amount (MW) of primary frequency regulation, and the ratio of the two reflects the degree of over- or under-regulation of primary frequency regulation; This represents the AGC coordinated adjustment baseline value (MW, calculated from the total power loss of the system). This represents the actual adjustment amount (MW) executed by AGC. The ratio of the difference between the two values ​​to the reference value reflects the deviation in AGC command execution. This represents the timing deviation (s) between the first frequency modulation and the AGC response, i.e., the time difference between the end of the first frequency modulation action and the start of AGC adjustment (a positive value indicates that the AGC is lagging, a negative value indicates that the AGC is leading, and the larger the absolute value, the worse the timing coordination).

[0056] The advantages are: by constructing a multi-weighted dynamic evaluation system, comprehensively considering three dimensions—adjustment deviation, execution deviation, and timing deviation—and combining the frequency modulation action signal collected by the status acquisition station with the feedback status of the AGC actuator, the risk of coordination conflict between the two can be accurately quantified, and low-risk ( ≤0.3), medium risk (0.3< ≤0.7), high risk ( The system has three levels (>0.7) and outputs conflict warning signals and correction suggestions to the multi-regional collaborative control module to support the collaborative control of the control target, thereby preventing problems such as improper coordination between the two in the tripping incident at the Daya Bay Nuclear Power Plant in Guangdong.

[0057] The virtual inertial control module is based on the system's equivalent inertial constant. Control process:

[0058] S1.1, Inertial Classification Determination: Receives the output from the online analysis module. Values ​​and warning signals, when ≥8s is sufficient / 5-8s is critical / <5s is insufficient;

[0059] S1.2, Strategy Generation: When When the system is at a critical or insufficient level, dynamic virtual inertia coefficients are issued to m energy storage systems. Adjust instructions, according to formula ,in, This represents the increment of the virtual inertia coefficient. This represents the proportional adjustment coefficient / gain coefficient. This represents the target's equivalent inertial constant (target value). This represents the equivalent inertia constant of the current system, and the strength of the inertial support is increased linearly.

[0060] S1.3 Multi-source inertial control: The synchronous generator side compensates for the inertial gap through additional damping control via the speed governor; the energy storage system adopts a virtual synchronous machine mode to output and... Proportional instantaneous power support enables precise spatiotemporal matching of inertia;

[0061] The advantages are: through the three-layer architecture of inertial hierarchical judgment, dynamic strategy generation and multi-source collaborative control, a millisecond-level closed-loop response of module perception → decision → execution is achieved, thereby accurately compensating for the system inertia deficiency, effectively suppressing the system rate of change of frequency (ROCOF) and maximum frequency deviation, and ultimately solving the problem of insufficient system inertia and deterioration of frequency stability caused by the high proportion of new energy access.

[0062] The distributed predictive control module dynamically adjusts the threshold based on cross-regional power flow. The control process is as follows:

[0063] S2.1 Multi-timescale rolling prediction: Based on PMU measured phase angle difference A hybrid ARIMA-GARCH model was constructed (ARIMA captures the autoregressive trend of phase difference, i.e., long-term mean regression; GARCH characterizes the time-varying properties of volatility, i.e., the clustering effect of extreme events), to predict the power fluctuation range of the connecting lines in the next 5 minutes. , ],in, This represents the measured phase difference timing data of the PMU. This indicates the lower limit of the predicted power fluctuation. This indicates the upper limit of the predicted power fluctuation;

[0064] S2.2 Quantitative assessment of risk of exceeding limits: Substituting the predicted values ​​into the safety constraints. The system calculates a risk probability index. When the risk probability index exceeds a preset safety margin, it triggers an early warning and generates an emergency control command. This represents the point or interval prediction value of the ARIMA-GARCH model (take...). (Conservative assessment) This indicates the maximum power that a line or section is allowed to operate at for a long period of time, and it is the benchmark value for safety constraints.

[0065] S2.3 Adaptive Power Redistribution: Activate the inter-regional emergency power support mechanism, using DC-MSW and Static Var Compensator (STATCOM) in coordination to redistribute excess power. The power deviation of the threshold is based on the proportion of reserve capacity in each region. ,in, This represents the weighting coefficient for the reserve capacity allocation in region i. The available spare capacity of region i is dynamically reallocated.

[0066] The advantage is that it allows for dynamic adjustment of cross-regional power flow thresholds through calculation. The system performs ARIMA-GARCH hybrid modeling and rolling optimization of cross-regional tie line power fluctuations, verification of safety constraints and quantification of overload risk probability, and emergency power support redistribution in coordination with DC modulation and STATCOM. This enables forward-looking management and multi-regional collaborative optimization of cross-regional power flow. The system automatically and precisely controls the power flow at key sections within the dynamic safety threshold, preventing overload risks and thereby improving the transmission capacity and operational safety margin of the interconnected power grid.

[0067] The multi-region collaborative control module is based on the risk index of primary frequency modulation and AGC collaboration. The specific control process is as follows:

[0068] S3.1, Receive the output from the acquisition station Numerical values, risk level classification results, conflict warning signals and correction suggestions, and simultaneous collection of primary frequency regulation execution status, AGC command parameters and system power loss data to identify the core causes of conflict (regulation deviation, execution deviation or timing deviation).

[0069] S3.2, Perform tiered collaborative correction based on Fx risk level: when When the value is ≤0.3, it is considered low risk. The weighting coefficients of the first frequency modulation and AGC should be fine-tuned. , , ), optimize response timing without changing core control parameters; medium risk (0.3 < When ≤0.7), adjust the maximum allowable adjustment amount of primary frequency modulation. AGC Coordinated Adjustment Baseline Value Correcting the timing deviation of the two responses To alleviate coordination conflicts; when When the value is greater than 0.7, it is considered high-risk. The current unreasonable adjustment instructions should be suspended, the system power balance should be prioritized, and the coordination logic between the two should be gradually optimized to eliminate potential conflicts.

[0070] S3.3 After calibration, monitor the coordinated operation status of primary frequency modulation and AGC in real time, and recalculate. The numerical values ​​are used to verify the correction effect, forming a closed-loop control of early warning, correction, and verification. This ensures that the two work together without conflict, prevents system failures caused by improper coordination, and guarantees the safe and stable operation of the power grid.

[0071] The advantages are: by using a hierarchical collaborative correction strategy of low-risk fine-tuning, medium-risk correction, and high-risk suspension, a closed-loop control mechanism of early warning, correction, and verification is constructed to dynamically resolve the inherent conflict between primary frequency regulation and AGC in terms of response speed and adjustment target, ensuring a smooth and stable system frequency recovery process, thereby eliminating system failures caused by improper control coordination.

[0072] The AGC optimization scheduling module integrates the real-time control results from the virtual inertial control module, the distributed predictive control module, and the multi-region cooperative control module to generate the final AGC base point power command and auxiliary service call scheme. The specific process is as follows:

[0073] S4.1 The integrated acquisition station provides ultra-short-term load forecasts, VRE (wind power, photovoltaic) power forecasts, primary energy prices, and market information. Simultaneously, it receives and quantifies real-time control commands and status feedback from three modules: virtual inertial control, distributed predictive control, and collaborative control. These are then transformed into safety boundary constraints within the scheduling cycle. Specifically, the virtual inertial control requirements are converted into minimum equivalent inertial constant (Dgt,min) constraints; the power flow safety requirements of distributed predictive control are converted into upper and lower limits of transmission power at critical sections; and the conflict resolution suggestions from the multi-region collaborative control modules are converted into coordination constraints between primary frequency regulation reserve capacity and AGC adjustment rate. This constructs a rolling optimization model that integrates real-time safety requirements.

[0074] S4.2. A multi-objective mixed-integer programming model is established with the objectives of minimizing the total system operating cost (fuel cost, start-up and shutdown cost, ancillary service cost, etc.) within the scheduling cycle, minimizing the clean energy curtailment rate, and simultaneously considering the aforementioned safety boundary constraints. A rolling optimization method is used, resolving the model every 15 minutes based on the latest forecast data and system status, outputting a refined scheduling plan for the next 1 to 4 hours. This plan includes: the base point power plan for each synchronous generator unit and large-scale energy storage power station, the planned power flow of inter-regional interconnection lines, the allocation of AGC regulation capacity and primary frequency regulation reserve capacity reserved to cope with fluctuations, and the scope and price of virtual inertial auxiliary services.

[0075] S4.3. The scheduling scheme generated in S4.2 is decomposed into executable AGC base point power commands and auxiliary service call commands (specifying the provider, capacity, and time) and sent to the corresponding generation units, energy storage systems, and flexible loads. At the same time, the optimized unit combination, reserve capacity distribution, and other planning information are synchronized to the virtual inertial control, distributed predictive control, and multi-regional collaborative control modules as a forward-looking strategy benchmark for their short-timescale control. The modules continuously monitor the execution of commands and the actual operating status of the system, and feed back evaluation information such as the deviation between the plan and the actual situation and the degree of achievement of optimization goals to the evaluation feedback module for optimizing model parameters and cost weights, so as to achieve adaptive improvement of the scheduling strategy.

[0076] The advantages are: by integrating short-timescale control results and constructing safety constraints, the safety defense line is proactively moved forward to the scheduling plan; through multi-objective rolling optimization, a globally optimal trade-off between economy, low carbon emissions, and safety is achieved while ensuring the real-time safety of the system; by distributing the optimization plan to the AGC system and feeding it back to other control modules, vertical coordination and closed-loop optimization of control strategies at different time scales are achieved, ultimately improving the overall operating efficiency and robustness of the interconnected power grid.

[0077] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-regional interconnected power grid AGC coordination optimization system, characterized in that, It includes a data acquisition module for data collection stations, an online data analysis module for data collection stations, a virtual inertial control module, a distributed predictive control module, a multi-area collaborative control module, an AGC optimization scheduling module, and an evaluation and feedback module; The data acquisition module of the acquisition station is responsible for the real-time acquisition and preprocessing of power grid operation data in multiple regions, providing a data foundation for subsequent analysis. The online data analysis module of the acquisition station is responsible for online analysis, calculation and judgment of various types of data transmitted by the data acquisition module of the acquisition station, and outputs the system equivalent inertia constant. Cross-regional power flow dynamic adjustment threshold Primary frequency modulation and AGC coordination conflict risk index With early warning information; The virtual inertial control module is based on the output of the system's equivalent inertial constant. Dynamically simulate and supplement the rotational inertia response of the system; The distributed predictive control module dynamically adjusts the cross-regional power flow threshold based on the output. Establish a distributed model predictive control framework for power grids in various regions to control power flow at key cross-regional sections within a safe threshold range and prevent overload risks. The multi-region collaborative control module is based on the output primary frequency modulation and AGC collaborative conflict risk index. Construct a coordination logic and arbitration mechanism for multi-timescale control instructions, when the risk index... When the set limit is exceeded, the allocation strategy of primary frequency regulation reserve capacity and AGC adjustment command will be automatically adjusted. The AGC optimization scheduling module integrates the real-time control results of the virtual inertial control module, the distributed predictive control module, and the multi-regional collaborative control module to perform global rolling optimization of unit combination, load allocation, and cross-regional transaction plans, generating the final AGC base point power command and auxiliary service call scheme. The evaluation feedback module monitors and records the action effects of the virtual inertial control module, the distributed predictive control module, and the multi-regional collaborative control module in real time, as well as the overall operating status of the system, and comprehensively evaluates the actual performance indicators of frequency support, power flow control, and collaborative optimization.

2. The multi-regional interconnected power grid AGC coordination optimization system according to claim 1, characterized in that, The data acquisition module of the acquisition station consists of a frequency acquisition station, a power flow acquisition station, a power supply acquisition station, a load acquisition station, a meteorological acquisition station, and a status acquisition station. After acquiring the data from the acquisition station, the module performs filtering and noise reduction, time stamp synchronization, outlier removal, and data compression on the acquired data.

3. The multi-regional interconnected power grid AGC coordination optimization system according to claim 2, characterized in that, The frequency acquisition stations are deployed at the main grid nodes in each region to collect system frequency and frequency change rate; the power flow acquisition stations are deployed at inter-regional tie lines and key transmission sections to collect line power, voltage phase angle difference, line load rate and power oscillation characteristics.

4. The multi-regional interconnected power grid AGC coordination optimization system according to claim 2, characterized in that, The power acquisition station is deployed at VRE sites, energy storage power stations, and synchronous generator units to collect real-time power output, available adjustable capacity, adjustment rate, start-stop status, and datasets characterizing inertial response capabilities; the load acquisition station is deployed at regional load centers to collect total load demand, load volatility, load forecast deviation, main load type composition, and the status and capacity of interruptible loads / demand-side response resources.

5. The multi-regional interconnected power grid AGC coordination optimization system according to claim 2, characterized in that, The meteorological data acquisition station is deployed in the VRE concentrated area to collect wind speed, wind direction, irradiance, temperature, extreme weather warning information and forecast data, and connects to the new energy power prediction system through a high-speed communication network to update the latest VRE ultra-short-term and short-term power generation prediction curves; the status acquisition station collects real-time status datasets of primary energy, control equipment and protection actions through the dispatch automation system, wide-area measurement system and power plant monitoring system.

6. The multi-regional interconnected power grid AGC coordination optimization system according to claim 1, characterized in that, The online data analysis module of the acquisition station calculates the equivalent inertial constant of the system based on the preprocessed data of the acquisition station. Cross-regional power flow dynamic adjustment threshold Primary frequency modulation and AGC coordination conflict risk index The system achieves inertial support, cross-regional power flow control, and coordinated control of control objectives through a virtual inertial control module, a distributed predictive control module, and a multi-regional collaborative control module.

7. The multi-regional interconnected power grid AGC coordination optimization system according to claim 6, characterized in that, The online data analysis module of the acquisition station calculates the equivalent inertial constant of the system based on the preprocessed data of the acquisition station. The calculation formula is as follows: ， In the formula, Represents the system's equivalent inertial constant. Denotes the inertial time constant of the i-th synchronous generator unit. This represents the rated capacity of the i-th synchronous generator unit. This represents the equivalent virtual inertia coefficient of the j-th energy storage system. Let represent the rated capacity of the j-th energy storage system, n represent the number of synchronous generators, m represent the number of energy storage systems, and η represent the energy storage discharge efficiency. This indicates the system's baseline capacity.

8. The multi-regional interconnected power grid AGC coordination optimization system according to claim 6, characterized in that, The online data analysis module of the data acquisition station calculates the cross-regional power flow dynamic adjustment threshold based on the preprocessed data from the data acquisition station. The calculation formula is as follows: ， In the formula, This indicates the dynamic adjustment threshold for inter-regional connection lines. Indicates the rated transmission power of the tie line. This represents the adjustment coefficient. This indicates the voltage phase angle difference between the two ends of the cross-regional tie line.

9. The multi-regional interconnected power grid AGC coordination optimization system according to claim 6, characterized in that, The online data analysis module of the acquisition station calculates the risk index of primary frequency modulation and AGC coordination conflict based on the preprocessed data of the acquisition station. The calculation formula is as follows: ， In the formula, This indicates the risk index of a primary frequency modulation (FM) and AGC (Automatic Guided Vehicle) coordination conflict. , , These represent the primary frequency regulation deviation weight, the AGC command execution deviation weight, and the response timing deviation weight, respectively. This indicates the actual adjustment amount of a single frequency modulation. This indicates the maximum allowable adjustment amount for a single frequency modulation. This represents the baseline value for AGC coordinated adjustment. This indicates the actual adjustment amount executed by AGC. This indicates the timing deviation between the primary frequency modulation and the AGC response.

10. The multi-regional interconnected power grid AGC coordination optimization system according to claim 1, characterized in that, The virtual inertial control module is based on the system's equivalent inertial constant. Control process: S1.1, Inertial Classification Determination: Receives the output from the online analysis module. Values ​​and warning signals, when ≥8s is sufficient / 5-8s is critical / <5s is insufficient; S1.2, Strategy Generation: When When the system is at a critical or insufficient level, dynamic virtual inertia coefficients are issued to m energy storage systems. Adjustment instructions; S1.3 Multi-source inertial control: The synchronous generator side compensates for the inertial gap through additional damping control via the speed governor; the energy storage system adopts a virtual synchronous machine mode to output and... Proportional instantaneous power support enables precise spatiotemporal matching of inertia.

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