Power grid load prediction and scheduling optimization system based on artificial intelligence

Abstract

The invention discloses a power grid load prediction and scheduling optimization system based on artificial intelligence, particularly relates to the technical field of power system automation, and solves the technical problems of low power grid load prediction precision, poor scheduling strategy robustness and insufficient source grid load storage coordination in the prior art. Multi-source heterogeneous data space-time alignment is realized by constructing a data acquisition layer based on edge calculation, a load prediction result is generated by adopting an AI prediction module fused by a graph convolutional network and an attention mechanism, and a source-network-load-storage collaborative scheduling scheme is generated through a multi-target risk hedging optimization algorithm. And closed-loop optimization is realized by using digital twinborn pre-check and incremental learning. And finally, the load prediction accuracy, the scheduling decision reliability and the system adaptive capability in the new energy access environment are improved.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, and more specifically, to an artificial intelligence-based power grid load forecasting and scheduling optimization system. Background Technology

[0002] With the large-scale grid connection of new energy sources and the deepening of power market reforms, power grid load forecasting and dispatch optimization face unprecedented complexity challenges. Load volatility is significantly amplified by the integration of flexible resources such as distributed photovoltaics and electric vehicles, making it difficult for traditional statistical forecasting methods based on historical data to accurately capture nonlinear influencing factors such as sudden weather changes and shifts in user behavior. Simultaneously, the dispatching system needs to coordinate multiple types of resources, including thermal power, new energy, and energy storage, while ensuring power supply security, to achieve a balance between multiple objectives such as economic efficiency and low carbon emissions. Against this backdrop, there is an urgent need to build a next-generation power grid dispatching system with high-precision forecasting capabilities and intelligent decision-making capabilities. Current technologies have shortcomings: 1. Insufficient utilization of multi-source heterogeneous data Existing systems rely on single historical load data for forecasting, lacking integrated analysis of dynamic factors such as meteorological parameters, user behavior patterns, and equipment status. The differences in collection frequency and spatiotemporal scale among various data sources create "information silos," preventing forecasting models from capturing early warning signs of load shifts (such as the correlation between a surge in heating load before a cold wave and the risk of transformer overload), severely limiting the improvement of forecast accuracy.

[0003] 2. The prediction model has weak spatiotemporal coupling capability. Traditional time series forecasting models (such as ARIMA) can only handle load changes at a single node and cannot model the spatial correlation characteristics of the power grid topology (such as the transmission effect of industrial area production reduction on voltage in neighboring residential areas); while machine learning methods (such as support vector machines) can introduce meteorological factors, but have not established a dynamic mapping mechanism between meteorological spatial distribution and load geographical location, resulting in a significant amplification of errors in inter-regional load transfer scenarios.

[0004] 3. Lack of robustness and coordination in scheduling strategies. Current optimization models mostly employ deterministic constraints, failing to consider the uncertainty range of prediction results (such as the fluctuation range of renewable energy output), which can easily lead to the failure of dispatch instructions under extreme weather conditions. At the same time, the optimization objectives focus on the economics of the generation side, without incorporating flexible resources such as interruptible loads on the demand side and V2G electric vehicles into the coordination framework, resulting in insufficient system regulation elasticity and difficulty in coping with random disturbances on both the source and load sides.

[0005] Therefore, an artificial intelligence-based power grid load forecasting and scheduling optimization system is proposed to address the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an artificial intelligence-based power grid load forecasting and scheduling optimization system to address the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-based power grid load forecasting and scheduling optimization system, comprising: a data acquisition layer that collects multi-source heterogeneous data sets with time granularity ranging from minutes to hours in real time through a distributed sensor network, the set covering meteorological parameter change trends, user group electricity consumption behavior patterns, real-time operating status indicators of power grid equipment, and historical load fluctuation curves; an AI prediction module that uses a spatiotemporal feature deep extraction unit and a dynamic weight allocation mechanism to collaboratively process the input data, generating load forecast results and their confidence intervals covering ultra-short-term to medium-to-long-term time scales; a scheduling optimization module that, based on the prediction results and confidence intervals, uses a multi-objective constraint optimization algorithm to dynamically generate combined start-up and shutdown commands for power generation units, charging and discharging power strategies for energy storage systems, and distributed energy consumption scheduling schemes; and an execution feedback layer that sends optimization commands to the power grid control terminal through a power communication protocol, while simultaneously collecting system dynamic response data in real time and feeding it back to the AI ​​prediction module to achieve autonomous calibration of model parameters.

[0008] Furthermore, the data acquisition layer includes an edge intelligent computing device cluster deployed at key nodes of the power distribution network. This cluster uses a parallel computing architecture to perform frequency domain filtering and time domain feature enhancement preprocessing on the raw data. At the same time, it is configured with an adaptive multi-protocol data fusion interface to realize the spatiotemporal alignment and standardization conversion of heterogeneous data from meteorological monitoring stations, user-side intelligent measurement devices, and equipment status sensors, ensuring low-latency and high-reliability transmission of data streams.

[0009] Furthermore, the spatiotemporal feature extraction unit of the AI ​​prediction module uses graph convolutional networks to model the spatial correlation of the power grid topology, combines a multi-head attention mechanism to dynamically capture the nonlinear mapping relationship between meteorological variables and load fluctuations, and analyzes the long-term dependency features of the load time series through bidirectional long short-term memory network branches. It also uses deep convolutional neural network branches to extract the spatial correlation features between regions. Finally, a learnable adaptive weighted fusion unit dynamically adjusts the output weights of each branch and generates a load prediction result with uncertainty assessment.

[0010] Furthermore, the AI ​​prediction module integrates a cross-regional transfer learning mechanism, which transfers the model parameters pre-trained in power grids with similar climate and load characteristics to the target region through a feature distribution alignment algorithm, achieving high-precision load prediction under limited training sample conditions. At the same time, an incremental learning engine is configured to automatically trigger online fine-tuning of model parameters based on the difference in response data from the execution feedback layer. This engine includes a sliding window training mechanism and a gradient pruning strategy to ensure learning stability.

[0011] Furthermore, the scheduling optimization module includes a dynamic constraint generation unit, which dynamically adjusts the upper and lower limits of the power generation unit's output constraints based on real-time equipment health assessment indicators and network topology change information; and through a multi-objective optimization unit, with the objective function of synergistic minimization of power supply economic cost, network transmission loss, and total carbon emissions, it uses an improved Pareto front solution algorithm to generate a set of non-dominated scheduling schemes that include source-grid-load-storage coordination strategies.

[0012] Furthermore, the scheduling optimization module implements a rolling time-domain optimization strategy, periodically replanning the scheduling scheme within a time window ranging from minutes to hours based on the update frequency of the load forecast results; at the same time, it introduces a multi-level risk hedging mechanism to generate robust scheduling plans for the uncertainty range of the forecast results. These plans include a standby unit start-up priority strategy, an emergency power support scheme for the energy storage system, and a graded reduction scheme for interruptible loads.

[0013] Furthermore, the execution feedback layer incorporates a digital twin simulation unit, which simulates the dynamic impact of the scheduling scheme on power flow distribution and node voltage based on the real-time topology of the power grid before the instruction is issued; and performs real-time verification of the scheme based on the allowable range of voltage deviation, the safety threshold of line load rate and the N-1 fault criterion through the safety verification unit. When an over-limit risk is detected, the constraint reconstruction and scheme recalculation process of the optimization module is automatically triggered.

[0014] Furthermore, the incremental learning engine includes a concept drift detection unit and a selective forgetting mechanism. The former identifies drift points based on the deviation distribution characteristics between the prediction results and the actual load, while the latter dynamically adjusts the training weights of historical data based on the feature importance evaluation results. At the same time, the demand-side response signals, interruptible load resources, and electric vehicle cluster regulation capabilities are incorporated into the elastic resource constraints of the scheduling optimization objective function through a resource coordination interface.

[0015] The technical effects and advantages of this invention are as follows: Compared with existing technologies, this invention achieves dynamic fusion and spatiotemporal alignment of multi-source heterogeneous data through edge computing clusters, constructs a spatiotemporal hybrid prediction model that coordinates graph convolutional networks and multi-head attention mechanisms, and accurately captures the coupling relationship between meteorological factors, user behavior, and power grid topology; it adopts an improved Pareto front algorithm combined with a multi-level risk hedging mechanism to generate robust scheduling schemes, and achieves multi-dimensional resource collaborative optimization of source, grid, load, and storage under the premise of ensuring power supply security; it relies on digital twin simulation units to pre-verify the grid adaptability of scheduling instructions, and dynamically corrects the prediction model parameters through an incremental learning engine, forming a closed-loop intelligent control system of "perception-prediction-decision-verification-evolution" to improve the load prediction accuracy, scheduling decision reliability, and system anti-disturbance capability in scenarios with a high proportion of new energy access. Attached Figure Description

[0016] Figure 1 This is a system framework diagram of the present invention.

[0017] Figure 2 This is a flowchart of the prediction-optimization workflow of the present invention.

[0018] Figure 3 This is a flowchart of the incremental learning closed-loop process of the present invention.

[0019] Figure 4 This is the source-grid-load-storage collaborative optimization diagram of the present invention. Detailed Implementation

[0020] The following will refer to the appendices in the embodiments of the present invention. Figure 1-4 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] Implementation Process 1: Prediction and Optimization under Conventional Load Scenarios Step 1 - Dynamic Acquisition and Fusion of Multi-Source Data After the system is started, the edge computing equipment cluster deployed at the distribution network transformer nodes, meteorological monitoring stations, and user meter boxes immediately goes into operation. Among them, the meteorological sensors capture minute-level changes in temperature, humidity, and light intensity in real time, while monitoring potential environmental impacts such as wind speed and precipitation; smart meters not only record user behavior patterns such as the frequency of air conditioner start-stop and charging pile usage, but also collect energy consumption fluctuations of home appliances such as refrigerators and water heaters to build a comprehensive electricity consumption habit map; and the equipment status monitoring device continuously uploads key operating indicators such as transformer oil temperature, line current, and coil vibration, providing basic data for real-time fault early warning. All raw data undergoes efficient spatiotemporal alignment processing via an adaptive multi-protocol fusion interface: First, a high-precision time server synchronizes the timestamps of all devices to the power grid dispatch clock reference, eliminating millisecond-level deviations; second, a Kriging spatial interpolation algorithm is applied to accurately match discrete meteorological station data to the grid coordinates of the distribution area, ensuring data consistency in spatial dimensions; finally, these inputs are integrated, and after noise reduction, normalization, and feature engineering optimization, a standardized data stream containing 128-dimensional feature vectors is generated. These dimensions cover composite features such as meteorology, user behavior, and equipment health, and are periodically transmitted to the AI ​​prediction module every 5 minutes for load demand forecasting, power grid fault diagnosis, or renewable energy dispatch, thereby improving the accuracy and robustness of power grid intelligent decision-making.

[0022] Step 2 - Spatiotemporal Mixed Load Forecasting Model Calculation After receiving the data stream, the AI ​​prediction module initiates a hybrid model processing flow: First, a graph convolutional network deeply analyzes the power grid topology and accurately calculates the spatial correlation weights between substations and load centers, such as quantifying the impact coefficient of a fault in substation A on commercial area B; second, a bidirectional long short-term memory network analyzes historical load curves spanning up to two years, accurately identifying the periodic patterns of morning and evening peak hours and the characteristics of load mutations caused by events such as statutory holidays and extreme weather; simultaneously, a deep convolutional network extracts the deep spatial correlations of loads between regions, such as capturing the dynamic response characteristics of power fluctuations in industrial areas transmitted to adjacent residential areas through the power grid coupling effect; in addition, a multi-head attention mechanism dynamically weights multi-dimensional meteorological influencing factors, not only calculating the growth slope of air conditioning load corresponding to every 1°C increase in temperature, but also associating the combined effects of humidity changes on dehumidification equipment energy consumption and light intensity on the reverse adjustment of photovoltaic output. The outputs of each branch are integrated by an adaptive weighted fusion unit: this unit dynamically configures the model weights according to the prediction time scale—for ultra-short-term (30-minute) predictions, it emphasizes the spatial correlation model; for short-term (24-hour) predictions, it strengthens the time series model; and for medium-term (7-day) predictions, it enhances the contribution of the meteorological coupling module. Finally, it generates load forecast results containing confidence intervals (e.g., 70%-95% probability bandwidth), and verifies its robustness through Monte Carlo simulation, providing key decision support for power grid dynamic dispatch, demand response strategy formulation, and preventive maintenance planning.

[0023] Step 3 - Multi-objective rolling optimization decision generation Upon receiving the prediction results, the scheduling optimization module immediately initiates a multi-level decision chain: the dynamic constraint generation unit retrieves the resource status of the entire power grid in real time, including generator health scores (such as boiler efficiency decay rate of coal-fired units and icing coefficient of wind turbine blades), SOC status of energy storage systems and charge / discharge rate limits, and integrates power grid topology constraints to dynamically generate a three-dimensional constraint set including spinning reserve capacity, voltage safety boundary, and branch current carrying capacity limits; based on this, the multi-objective optimization unit constructs a three-dimensional objective function for power supply cost (covering fuel costs, unit start-up and shutdown losses, and maintenance costs), network loss (quantifying non-uniformity through power flow distribution entropy), and carbon emission intensity (converting thermal power output into standard coal consumption), and solves it using an improved Pareto front algorithm: the first round generates the function through chaotic initialization. From 500 solutions, 300 candidate solutions are selected through non-dominated sorting. Solutions that violate the N-1 safety criterion and exceed node voltage limits are eliminated. Then, the TOPSIS method with entropy weight is used to select the top 10 optimal compromise solutions from the 50 retained non-dominated solutions. The rolling optimization engine triggers recalculation every 15 minutes, and dynamically adjusts the strategy based on the confidence interval of the latest load forecast. That is, when the upper limit of the forecast load exceeds the preset threshold, the standby gas turbine unit is automatically activated and the energy storage discharge depth is increased to 85% of the safety limit. At the same time, the demand response system is linked to send peak-shaving signals to high-energy-consuming users. Finally, a time-stamped strategy instruction set is generated to achieve a dynamic balance of economy, low carbon emissions, and reliability while ensuring system safety, forming a closed-loop optimization ecosystem of "prediction-decision-execution-feedback".

[0024] Step 4 - Digital Twin Security Pre-verification and Execution The execution feedback layer performs multi-dimensional pre-verification of the optimal solution: First, a real-time holographic model of the power grid topology is loaded into the digital twin simulation unit. Combined with a historical fault database, the dynamic response under dispatch instructions is simulated to accurately simulate the temperature rise effect caused by the load rate of a 220kV line increasing from 65% to 89%, and simultaneously, the wind resistance margin of the towers in the typhoon warning area is deduced. The safety verification unit implements layered detection based on the IEEE 1547 standard. At the transmission network level, it scans whether the voltage deviation exceeds the ±5% dynamic limit. At the distribution network level, it identifies weak nodes of the line with a load rate exceeding the 90% warning line. Simultaneously, the protection setting coordination logic under the N-2 fault scenario is verified through a topology reconstruction algorithm. Any exceeding of the limit will trigger a real-time alarm and generate a reverse correction vector for the optimization module (such as adding SVG reactive power compensation or reconstructing the load transfer path). The verification is conducted in accordance with IEC standards. The 61850 protocol's GOOSE fast message and MMS manufacturing message coordination mechanism issues multi-objective optimization instructions in a tiered manner: it implements a stepped output reduction control for the main coal-fired units (reducing to 80% of rated output at a rate of 2% per minute), instructs the energy storage cluster to execute a 200MW constant power discharge strategy during peak electricity price periods and dynamically adjusts the PCS converter modulation ratio, and simultaneously issues regional coordinated emission limiting instructions to the distributed photovoltaic monitoring system (adjusting the power factor from 0.95 lagging to 0.98 leading, and uniformly converging the group control emission limiting ratio to 15%). Finally, it forms a closed-loop execution file that includes operation verification reports, safety margin assessments, and carbon emission tracking tags, providing a data-driven optimization benchmark for subsequent strategy iterations.

[0025] Step 5 - Incremental Learning Model Self-Correction After the system is operational, it collects real-time actual load data from the entire network through a Wide Area Measurement System (WAMS) and performs dynamic deviation analysis with the prediction results. The concept drift detection unit uses the Kolmogorov-Smirnov test method to analyze the deviation distribution characteristics (e.g., the predicted value is systematically 3% lower than the actual value for 12 consecutive hours, and the deviation kurtosis continues to increase). Combined with configuration change events in the SCADA data stream of new energy power plants, it accurately determines the model drift phenomenon caused by a "sudden increase in distributed photovoltaic installations". The selective forgetting mechanism then activates an adaptive sample weighting strategy, using an exponential decay function to dynamically filter historical datasets (decreasing the weight of winter load samples from two years ago to 0.3, while retaining key scene fragments of extreme cold wave events), and injects the current quarter's typical data. The model of electricity consumption during industrial transformation is used as a compensation sample; the sliding window training engine simultaneously activates the incremental learning mode, loads the latest 30-day high-precision data stream (including timestamps of load change events), and fine-tunes the hidden layer parameters of the LSTM network through a constrained stochastic gradient descent algorithm: focusing on optimizing the convolution kernel size of the holiday feature extraction channel to enhance the response sensitivity to short-term installed capacity fluctuations; at the same time, the temporal decomposition technique is used to separate the new energy output disturbance component, reconstruct the load fundamental wave prediction model, so that the prediction error of the next cycle can be quickly converged to within 1.5% within 24 hours, and the corrected feature association rules (such as temperature-air conditioning load response coefficient) are fed back to the meteorological influence factor weighting module in real time, forming a triple closed-loop learning ecosystem covering the equipment layer, model layer, and strategy layer.

[0026] Implementation Process Two: Cold Start Scenario for Small Sample Areas Step 1 - Cross-regional transfer learning initialization When historical data is lacking for the power grid of a newly built industrial park: First, pre-trained model parameters, including the LSTM time weight matrix and the graph convolution spatial correlation map, are retrieved from the power grid of a mature industrial park with a similar climate. Then, the feature distribution alignment algorithm is used to match the load characteristics of the target area and the source area: it is detected that the target area has a high proportion of precision manufacturing industry, so the "machining load-temperature sensitivity coefficient" in the source model is migrated and calibrated from 0.75 to 0.82. Finally, the 168-hour sample collected in the first week of the target area is used for domain adaptive training, and the model prediction error is reduced from the initial 22% to below 10%.

[0027] Step 2 - Robust Risk Hedging Optimization To address the challenges of high uncertainty in small-sample prediction scenarios, the system constructs a multi-layered proactive defense system: when the prediction confidence interval exceeds the 15% warning threshold, a multi-level risk hedging mechanism automatically generates a five-dimensional contingency plan library. For example, it activates the "extreme high temperature contingency plan," reserving 200MW of gas-fired backup capacity while simultaneously coordinating with the water-cooling system to reduce load. At the rolling optimization level, real-time data closed-loop feedback is introduced. When PMU measurement data shows that the actual load exceeds the upper limit of the prediction by 5% for two consecutive hours, an emergency response procedure is immediately triggered: controlling the energy storage system to execute a deep discharge strategy (SOC decreasing from 80% to 40% within 30 minutes, with a discharge power gradient). The power output of the wind farm is controlled at 5MW / minute, and the active power output of the adjacent wind farm is simultaneously increased to the limit. The dynamic interruptible load management system reconstructs the user classification according to the industry sensitivity model: the polishing production line of the semiconductor factory is classified as a level three interruptible load (50% flexible load reduction is achieved within 10 minutes through intelligent circuit breakers), while the chip lithography workshop is equipped with millisecond-level seamless switching protection with dual-path UPS coupling, and voltage sag compensation is implemented through power electronic transformers. Finally, a dynamic resilient grid architecture of "prevention-response-guarantee" is constructed, which can still maintain the continuous and reliable power supply of the core load in high interference scenarios with load fluctuation standard deviation of 12%.

[0028] Implementation Process 3: Flexible Dispatch under Extreme Weather Step 1 - Enhancement of meteorological disaster model data After the typhoon warning triggers a Level III emergency response, the data acquisition layer immediately activates the frequency domain enhancement mode: the raw signals from wind speed sensors deployed on coastal towers are processed by Morlet wavelet transform to extract energy characteristics in the 3-5Hz frequency band as precursor indicators of conductor galloping and low-frequency oscillations in the power grid, simultaneously fusing atmospheric liquid water content data from ground-based microwave radiometers; the historical disaster data engine calls upon typhoon cases with similar landfall paths in the past five years (such as "Lekima" and "Hwamak"), constructing multi-dimensional mapping tables such as "wind speed-tree failure rate" and "rainfall intensity-insulator flashover probability" through spatiotemporal alignment technology, and injecting real-time soil saturation correction coefficients; edge devices switch to anti-interference transmission mode, and key monitoring data are transmitted via the LoRaWAN protocol. The 470MHz band enables triple-redundant transmission (automatically switching to the Mesh self-organizing network when the main path fails), ensuring continuous sampling of the minimum dataset (including tower tilt angle, insulator leakage current, and voltage dip events for important users) for 15 seconds during the passage of the typhoon eye. At the same time, it pre-loads defense strategies: it simulates the movement trajectory of strong wind belts on the digital twin platform, tightens the sag of transmission lines in high-risk areas in advance (reducing the safe current carrying capacity by 20% through the dynamic capacity expansion system), and switches the string inverters of coastal photovoltaic power stations to virtual inertia support mode. Finally, it forms a four-dimensional typhoon response system of "frequency domain early warning - historical simulation - communication guarantee - active defense", enabling the power grid to maintain about 99% power supply reliability in the core load area under the coverage of the 12-level wind circle.

[0029] Step 2 - Uncertainty-aware prediction In the typhoon disaster response mode, the AI ​​prediction module implements three dynamic strategy upgrades: First, the graph convolutional network injects wind resistance attribute dimensions into the power grid topology model, marking the design wind resistance level for each tower (e.g., marking 110kV towers using Q345 steel in coastal areas as level IV), and calculates the load loss probability tree centered on the substation under strong winds through the graph propagation algorithm; the multi-head attention mechanism particularly strengthens the weight allocation channel of the rainstorm factor, and establishes a bivariate coupled model of "rainfall intensity-duration" (triggers the substation when the hourly rainfall exceeds 50mm). The system generates a leakage risk coefficient, combined with a historical flooding event database to determine the probability of regional power outages. The prediction engine ultimately outputs risk labeling results with 3D geographic information, such as labeling "Predicted load of 120MW in Zone C at 23:00 (when wind speed > 14m / s, the load may drop by 35% due to 35kV line vibration, corresponding to confidence intervals [28%, 42%])". Simultaneously, it associates defense suggestions: overlaying and displaying geographic information tags of weak points on the prediction cross-section, and automatically pushing reinforcement priority rankings (e.g., prompting "Prioritize tightening the drain clamp of tower #7 on line B362"). This intelligent early warning system, integrating physical characteristics, reduces the load prediction error rate in typhoon scenarios to 60% of the conventional model and provides a quantitative decision-making benchmark for disaster relief resource allocation.

[0030] Step 3 - Elastic Resource Collaborative Optimization In typhoon disaster response mode, the dispatch optimization module activates a multi-resource collaborative defense system: It connects to the city's electric bus cluster in real time via a resource collaboration interface, dynamically deploying 500 vehicles with V2G capabilities (total regulation capacity 60MWh). A two-layer response mechanism based on electricity price fluctuations is set up: when the real-time electricity price exceeds the 0.8 yuan / kWh threshold, approximately 30% of the energy storage capacity is automatically released, and all standby capacity is forcibly unlocked when the wind speed warning reaches orange level. The multi-objective optimization engine introduces disaster prevention weight factors, quantifying the "risk of power outage for critical loads" into a power outage probability function for key nodes such as hospitals and flood control pumping stations (forcibly unlocking the 10kV lines around hospitals). With a load factor of <70% and voltage deviation compressed to ±3%, the system dynamically adjusts safety margins based on typhoon path probability models. The disaster simulation unit generates a "golden 72 hours for isolated operation" plan: when an N-2 fault occurs in the 220kV main grid, it switches to microgrid mode via millisecond-level static switching. An off-grid system consisting of a pre-installed 25MW rooftop photovoltaic array (configured with typhoon-resistant tilt angle) and 60MWh containerized energy storage is used. This system independently supports the core load of the fire command center (including the operating room life support system) using VSG virtual synchronous machine technology, and maintains the microgrid frequency deviation within ±0.2Hz using hydrogen fuel cells as a black-start power source. This collaborative mechanism was validated during Typhoon Muifa, successfully ensuring a 99% power availability rate in key areas and reducing load loss by more than 20% compared to historical disasters.

[0031] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power grid load forecasting and dispatch optimization system based on artificial intelligence, characterized in that... include: The data acquisition layer collects multi-source heterogeneous data sets with time granularity ranging from minutes to hours in real time through a distributed sensor network. This set covers the changing trends of meteorological parameters, the electricity consumption behavior patterns of user groups, the real-time operating status indicators of power grid equipment, and historical load fluctuation curves. The AI ​​prediction module uses a spatiotemporal feature deep extraction unit and a dynamic weight allocation mechanism to collaboratively process the input data and generate load prediction results and their confidence intervals covering ultra-short-term to medium- and long-term time scales. Based on the prediction results and confidence intervals, the scheduling optimization module uses a multi-objective constraint optimization algorithm to dynamically generate start-stop commands for combined power generation units, charging and discharging power strategies for energy storage systems, and distributed energy consumption scheduling schemes. The execution feedback layer sends optimization commands to the grid control terminal through the power communication protocol, and at the same time collects dynamic response data of the system in real time and feeds it back to the AI ​​prediction module to realize autonomous calibration of model parameters.

2. The power grid load forecasting and dispatch optimization system based on artificial intelligence according to claim 1, characterized in that... The data acquisition layer includes an edge intelligent computing device cluster deployed at key nodes of the power distribution network. This cluster uses a parallel computing architecture to perform frequency domain filtering and time domain feature enhancement preprocessing on the raw data. At the same time, it is configured with an adaptive multi-protocol data fusion interface to realize the spatiotemporal alignment and standardization conversion of heterogeneous data from meteorological monitoring stations, user-side intelligent measurement devices and equipment status sensors, ensuring low-latency and high-reliability transmission of data streams.

3. The power grid load forecasting and dispatch optimization system based on artificial intelligence according to claim 1, characterized in that... The spatiotemporal feature extraction unit of the AI ​​prediction module uses graph convolutional networks to model the spatial correlation of the power grid topology, combines a multi-head attention mechanism to dynamically capture the nonlinear mapping relationship between meteorological variables and load fluctuations, and analyzes the long-term dependency features of load time series through bidirectional long short-term memory network branches. It also uses deep convolutional neural network branches to extract inter-regional spatial correlation features. Finally, a learnable adaptive weighted fusion unit dynamically adjusts the output weights of each branch and generates load prediction results with uncertainty assessment.

4. The power grid load forecasting and dispatch optimization system based on artificial intelligence according to claim 3, characterized in that... The AI ​​prediction module integrates a cross-regional transfer learning mechanism, which transfers the model parameters pre-trained in power grids with similar climate and load characteristics to the target region through a feature distribution alignment algorithm, achieving high-precision load prediction under limited training sample conditions. At the same time, an incremental learning engine is configured to automatically trigger online fine-tuning of model parameters based on the difference in response data from the execution feedback layer. This engine includes a sliding window training mechanism and a gradient pruning strategy to ensure learning stability.

5. The power grid load forecasting and dispatch optimization system based on artificial intelligence according to claim 1, characterized in that... The scheduling optimization module includes a dynamic constraint generation unit, which dynamically adjusts the upper and lower limits of the power generation unit's output constraints based on real-time equipment health assessment indicators and network topology change information; and through a multi-objective optimization unit, it generates a set of non-dominated scheduling schemes that include source-grid-load-storage coordination strategies by using an improved Pareto front algorithm to minimize the economic cost of power supply, network transmission loss, and total carbon emissions as the objective function.

6. The power grid load forecasting and dispatch optimization system based on artificial intelligence according to claim 5, characterized in that... The scheduling optimization module implements a rolling time-domain optimization strategy, periodically replanning the scheduling scheme within a time window ranging from minutes to hours based on the update frequency of the load forecast results. At the same time, a multi-level risk hedging mechanism is introduced to generate robust scheduling plans for the uncertainty range of the forecast results. These plans include a standby unit start-up priority strategy, an emergency power support scheme for the energy storage system, and a graded reduction plan for interruptible loads.

7. The power grid load forecasting and dispatch optimization system based on artificial intelligence according to claim 1, characterized in that... The execution feedback layer has a built-in digital twin simulation unit that simulates the dynamic impact of the scheduling scheme on power flow distribution and node voltage based on the real-time topology of the power grid before the instruction is issued. The solution is verified in real time by a safety verification unit based on the allowable range of voltage deviation, the safety threshold of line load rate, and the N-1 fault criterion. When an over-limit risk is detected, the constraint reconstruction and solution recalculation process of the optimization module is automatically triggered.

8. The power grid load forecasting and dispatch optimization system based on artificial intelligence according to claim 4, characterized in that... The incremental learning engine includes a concept drift detection unit and a selective forgetting mechanism. The former identifies drift points based on the distribution characteristics of the deviation between the prediction results and the actual load, while the latter dynamically adjusts the training weights of historical data according to the feature importance evaluation results. Meanwhile, the resource coordination interface incorporates demand-side response signals, interruptible load resources, and electric vehicle cluster control capabilities into the elastic resource constraint term of the scheduling optimization objective function.

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