Power distribution system total factor resource collaborative optimization operation method and system

Abstract

The invention belongs to the technical field of power system optimization operation, and discloses a power distribution system total factor resource collaborative optimization operation method and system, and the method comprises the steps: reflecting a data flow closed loop through a five-layer structure: a resource layer (data source), a sensing layer (data collection), an AI layer (intelligent processing), an execution layer (control feedback), and an evaluation layer (effect verification); the modeling problem of dynamic change of a real-time topological relation of massive distributed resources is solved, and meanwhile, through spatial scale collaboration of micro-grid autonomous optimization and aggregator global scheduling, strategy conflicts of different time scale optimization targets are eliminated, and through value function decomposition, the problem that global sub-optimization is caused by local optimization is solved; according to the method, a source load active power group prediction system considering the source load time sequence and the spatial distribution rule and a resource optimization strategy considering the real-time performance, the accuracy and the high efficiency of the system are considered at the same time, and a theoretical basis and a calculation framework are provided for cooperative operation of total element resources of the power distribution system.

Description

Technical Field

[0001] This invention relates to the field of power system optimization operation technology, specifically to a method and system for coordinated optimization of all elements of a power distribution system. Background Technology

[0002] With the large-scale integration of distributed resources, such as distributed photovoltaics, new energy storage, and charging loads, into the distribution network, the scale continues to expand, significantly increasing the complexity of the power grid. This makes it difficult for traditional distribution network operation and management modes to adapt, and there is an urgent need to enhance the distribution network's flexible capacity to support large-scale distributed resources.

[0003] In new power distribution systems, the coordinated optimization of all resource elements encompasses key aspects such as distributed power sources, energy storage, flexible loads, and grid equipment. These elements are coupled and dynamically interact, constructing a complex and information-intensive system architecture. Peak charging is easy, but peak absorption is difficult; energy storage is not included in the distribution network regulation and control scope, and its enormous power sponge value is not fully realized.

[0004] Due to the complex and diverse dynamic characteristics, operating states, and interactive relationships of various elements, the sheer volume of information, and the high real-time requirements, traditional optimization methods struggle to effectively address the demands of modern power distribution systems for efficient and flexible scheduling. Traditional system situational awareness methods are inadequate for grasping global internal and external factors, failing to achieve dynamic resource balancing and optimal allocation. A key challenge lies in constructing a model representing the regulation characteristics of distributed source-load resources under different time-scenario conditions, integrating deep learning methods with power regulation characteristics, and realizing the dynamic aggregation of distributed resources. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for the collaborative optimization of all elements of a power distribution system. It introduces artificial intelligence technology to address the uncertainties of distributed sources and loads and the complex impacts of diverse resource types on the power distribution network. A five-layer structure embodies a closed-loop data flow: resource layer (data source) → perception layer (data acquisition) → AI layer (intelligent processing) → execution layer (control feedback) → evaluation layer (effect verification). This solves the modeling challenge of dynamic changes in the real-time topological relationships of massive distributed resources. Furthermore, through spatial-scale collaboration between microgrid autonomous optimization and global scheduling by aggregators, it eliminates policy conflicts between optimization objectives at different time scales and addresses the problem of "local optimization leading to global suboptimal" through value function decomposition.

[0006] To solve the above-mentioned technical problems, the technical solution provided by the present invention is as follows: This invention provides a method for the coordinated optimization of all resource elements in a power distribution system, comprising the following steps: S1. Construct the full-element resource layer of the power distribution system: Collect the basic data of the full-element distributed resources of the power distribution system, obtain the original real-time status data stream, and form the full-element resource layer of the power distribution system. S2. Construct the IoT edge perception layer of the power distribution system: preprocess and edge control the raw real-time status data stream collected in S1, combine it with protocol conversion and data alignment to obtain a high-value feature dataset, form the IoT edge perception layer of the power distribution system, clean and compress the high-value feature dataset, and realize the cognition of the operating status, security boundary and resource value of the entire power distribution system. S3. Construct the artificial intelligence computing framework layer of the power distribution system: Based on the high-value feature dataset output by S2 and the information data of the external system, the final dataset is obtained. The final dataset is dynamically aggregated in real time, the system situation is recognized, and multi-scale collaborative decision-making is performed. The artificial intelligence computing framework model for the collaborative optimization of all elements of the power distribution system is constructed, forming the artificial intelligence computing framework layer of the power distribution system, and outputting a multi-dimensional optimization control instruction set for different objects and different time scales. S4. Construct the collaborative control and execution layer of the power distribution system: Decompose the multi-dimensional optimization control commands obtained from S3 to obtain optimization decisions, "execute" the optimization decisions to subjects at different levels, form the collaborative control and execution layer of the power distribution system, output control results feedback, and realize resource collaborative control; S5. Construct the effect evaluation and iteration layer of the power distribution system: monitor the control result feedback of S4 and the new all-element distributed resource basic data collected by S1, conduct closed-loop evaluation of the model's operation effect evaluation index and model performance index, and feed the results back to the artificial intelligence computing framework layer described in S3 to trigger the model's iterative update, thus forming the effect evaluation and iteration layer of the power distribution system.

[0007] Preferred, In S3, an artificial intelligence computing framework for real-time aggregation, prediction, and analysis of distributed resources is constructed, as follows: S3.1 Resource Aggregation and Prediction: A spatiotemporal graph neural network is constructed, incorporating meteorological correlation and electrical distance into the graph structure to predict the spatiotemporal evolution of resource output; and a dynamic clustering algorithm is used to aggregate massive resources into virtual power plants that can be uniformly dispatched according to their characteristics, quantifying their adjustable potential and providing a stable and reliable input basis for optimization decisions; S3.2 Situation Awareness and Assessment: Utilizing federated learning and data fusion, a comprehensive understanding of the power distribution system's operating status, security boundaries, and resource value is achieved to support risk assessment and value discovery; Safety margin assessment is performed by defining a mathematical expression for voltage safety constraints: ; In the formula, This refers to the voltage stability coefficient (a core evaluation indicator, typically ranging from [0,1])), while Nodes refers to the "set of nodes" of the research object (such as buses in a power system or nodes in electronic circuits). This represents the actual operating voltage of the i-th node (such as the real-time voltage value of a bus in a power system). This is a preset reference value. This is the voltage limit value; By collecting the voltage of each node in real time Combined with preset reference values With limit The stability margin of the weakest node in the computational system. When the voltage exceeds the limit, the emergency adjustment strategy of the optimization module is triggered. Quantify resource value by defining a resource adjustment potential value function: ; In the formula, Net Flexibility Value (a core performance indicator that measures the net value of the economic benefits of flexibility resources after deducting costs). t 1 and t 2 represents the time interval for the evaluation. P up and P down Indicates the upward / downward adjustment power capability. and for t Adjust service prices up or down at any time. C wear Adjusting equipment wear and tear costs; S3.3 Collaborative Optimization Decision Engine: Integrates reinforcement learning, distributed optimization, and mechanistic models to perform multi-objective optimization decisions on economic, safety, and low-carbon goals across multiple spatiotemporal scales; Define a multi-objective optimization mathematical model for power system operation or energy management optimization, which includes one objective function and three constraints: ; In the formula, For total cost, For the cost of purchasing electricity from the power grid, To mitigate the risk of voltage exceeding limits, Carbon is carbon emissions ω 1 ,ω 2 ,ω 3 Each is assigned a weighting coefficient to control the importance of each objective. The three constraints are as follows: Renewable energy power balance: ; In the formula, P res Contribute to total renewable energy, For photovoltaic and wind power, This refers to the amount of wind and solar power that has been curtailed. Battery energy storage system updates: ; In the formula, The battery is in its state of charge. The charging and discharging power of the battery (negative for charging, positive for discharging). For the battery's rated capacity, For charging and discharging efficiency, For time step; Node voltage constraints: ; In the formula, Let be the voltage magnitude at node i. These are the upper and lower limits of the voltage. We define a mathematical model for reinforcement learning rewards, and by deducting energy storage losses and safety risk costs, we obtain a more realistic net system benefit, which is used to optimize decisions to achieve multi-objective collaborative optimization. ; In the formula, It is a punishment for system security risks. R t It is the real-time target value at time t. r profit It is the direct benefit at time t. β It is the energy storage loss coefficient. It is the change in the state of charge (SOC) of the stored energy at time t. It is a voltage over-limit indicator, when When this occurs, a penalty is triggered; It is the penalty coefficient; Training and updating of the S3.4 distributed artificial intelligence computing framework: Federated learning and transfer learning are used to address the challenges of data privacy and resource dynamism, ensuring that the AI ​​model can be continuously adapted and effectively updated.

[0008] Preferred, The distributed resources in S1 include distributed wind power, distributed photovoltaic power, clean energy, new energy storage, new loads, microgrids, and aggregators; The basic data of the total resources include power, voltage, SOC, temperature, switching status, and adjustable potential.

[0009] Preferred, The data preprocessing in S2 includes filtering, cleaning, time alignment, and heterogeneous data acquisition to perceive massive distributed resources. The edge control includes energy storage charging and discharging, and emergency load reduction, which are used to achieve local rapid data processing, protocol conversion and emergency control, reduce the pressure on the center and ensure real-time performance; The protocol conversions include Modbus, MQTT, IEC 61850, and OCPP.

[0010] Preferred, In S4, optimization decisions are executed to different levels of entities, including aggregators responsible for multi-microgrid coordination, market interaction, and resource combination scheduling; microgrid EMS responsible for source-grid-load-storage optimization, frequency stability control, and internal autonomous operation during islanded grid-connection switching; load controllers responsible for receiving DR commands and flexible load adjustment; and energy storage management systems responsible for receiving DR commands.

[0011] Preferred, The operational performance evaluation indicators in S5 include economic benefits, voltage qualification rate, control command execution rate, and resource combination scheduling rate; the model performance indicators include model prediction results, optimization result quality, and computational latency. The iteration includes periodically retraining the model, updating performance thresholds, and adapting to and responding to scene changes.

[0012] This invention provides a power distribution system with coordinated optimization of all resource elements, based on the aforementioned method for coordinated optimization of all resource elements in a power distribution system. The system includes a full-element resource layer for the power distribution system, which is used to collect raw real-time status data streams of all distributed resources of the power distribution system. The IoT edge sensing layer is used for preprocessing and edge control of raw real-time status data streams of all-element distributed resources, and for cleaning and compressing high-value feature datasets. The artificial intelligence computing framework layer is used to dynamically aggregate high-value feature datasets and information data from external systems in real time, realize system status, make multi-scale collaborative decisions, and output multi-dimensional optimization control instruction sets for different objects and time scales. The collaborative control and execution layer is used to decompose and execute multi-dimensional optimization control instructions, "execute" optimization decisions to entities at different levels, and collaboratively control resources. The effect evaluation and model iteration layer is used to evaluate the model's performance indicators and drive model iteration updates in a closed loop.

[0013] Preferred, The artificial intelligence computing framework layer comprises four modules: A, B, C, and D. Module A is used for resource aggregation and prediction; module B is used for situational awareness and assessment; module C is used for collaborative optimization decision engine; and module D is used for training and updating the distributed artificial intelligence computing framework. The resource aggregation and prediction in module A, together with the situational awareness and assessment in module B, jointly support the collaborative optimization decision engine in module C. The training and updating of the distributed artificial intelligence computing framework in module D, which receives evaluation feedback, drives the system model iteration.

[0014] The beneficial effects of this invention are: The novel method and system for collaborative optimization of all elements of a power distribution system constructed in this invention has a five-layer closed-loop data flow structure: the resource layer provides the data source, the perception layer is responsible for data acquisition, the AI ​​layer performs intelligent processing, the execution layer implements control and provides feedback, and the evaluation layer verifies the operational effect. This method and system effectively solve the modeling challenge of dynamic changes in the real-time topological relationships of massive distributed resources; it realizes the coordination of microgrid autonomous optimization and aggregator global scheduling at the spatial scale, eliminates the strategy conflict of optimization objectives at different time scales; and based on the value function decomposition technology, it overcomes the problem of global suboptimal caused by local optimization. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the power distribution system's all-element resource collaborative optimization system according to the present invention. Figure 2 This is a flowchart of the method and system for collaborative optimization of all elements of power distribution system according to the present invention. Detailed Implementation

[0016] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the following embodiments are given for illustrative purposes only and are not intended to limit the scope of the present invention. Those skilled in the art can make various modifications and substitutions to the present invention without departing from its spirit and essence.

[0017] like Figure 1 As shown, the present invention provides a method for the coordinated optimization of all resource elements in a power distribution system based on artificial intelligence, comprising the following steps: S1. Construct the full-element resource layer of the power distribution system: Collect the basic data of the full-element distributed resources of the power distribution system covering six types of resources: distributed wind power and photovoltaic clean energy, new energy storage, new loads, microgrids, and aggregators. Obtain the original real-time status data streams of the full-element resources, such as power / voltage / SOC / temperature / switching status / adjustable potential, to form the full-element resource layer of the power distribution system. S2. Constructing the IoT edge perception layer of the power distribution system: Using federated learning and data fusion, the raw real-time status data stream collected by S1 is preprocessed and edge controlled. Combined with protocol conversion, data alignment is achieved to obtain a high-value feature dataset, forming the IoT edge perception layer of the power distribution system. The high-value feature dataset is cleaned and compressed to realize the cognition of the operating status, security boundary, and resource value of the entire power distribution system. S3. Constructing the AI ​​computing framework layer for the power distribution system: Based on the high-value feature dataset output by S2, and information data from external systems (such as weather forecasts, real-time electricity market prices, power grid operation status data, user-side and load-related data, and traffic data), the final dataset is obtained. In real time, the final dataset is dynamically aggregated for resources, the system situation is recognized, and multi-scale collaborative decision-making is performed. An AI computing framework model for the collaborative optimization of all elements of the power distribution system is constructed, and a multi-dimensional set of optimization control instructions for different objects and different time scales is output. S4. Construct the collaborative control and execution layer of the power distribution system: Decompose the multi-dimensional optimization control commands obtained from S3 to obtain optimization decisions, "execute" the optimization decisions to subjects at different levels, form the collaborative control and execution layer of the power distribution system, output control results feedback, and realize resource collaborative control; S5. Construct the effect evaluation and iteration layer of the power distribution system: monitor the control result feedback of S4 and the new all-element distributed resource basic data collected by S1, evaluate the model operation effect and model performance in a closed loop, including prediction accuracy, optimization degree and calculation speed, and feed the results back to the artificial intelligence computing framework layer described in S3 to trigger the model iterative update, forming the effect evaluation and iteration layer of the power distribution system, realizing the system's self-learning and continuous improvement.

[0018] S1 encompasses all elements of distributed resources, including distributed wind and solar power, various new types of energy storage, flexible new loads such as electric vehicles, smart homes, interruptible loads, independent microgrids, and aggregators responsible for resource aggregation and trading.

[0019] The basic data for all resource elements include power, voltage, SOC, temperature, switching status, and adjustable potential.

[0020] In S2, data preprocessing, including filtering, cleaning, time alignment, and heterogeneous data acquisition, addresses the sensing challenges of massive distributed resources. Edge control includes energy storage charging and discharging, and emergency load reduction, enabling rapid local data processing, protocol conversion, and emergency control to alleviate central pressure and ensure real-time performance. Heterogeneous data acquisition and protocol conversion include Modbus, MQTT, IEC 61850, and OCPP.

[0021] S2 is a key step in achieving risk assessment and value discovery.

[0022] In S3, an artificial intelligence computing framework for real-time aggregation, prediction, and analysis of massive distributed resources is constructed. It includes four core modules: A, B, C, and D. These four modules work collaboratively: resource prediction (A) and situational awareness (B) jointly support optimization decision-making (C); model update (D) receives feedback from the evaluation layer and drives model iteration. Specific functions are as follows: Module A, Resource Aggregation and Prediction: This module applies spatiotemporal graph neural networks and deep learning techniques to solve the problem of high-precision real-time aggregation and prediction of distributed resource output, load, and status, laying the foundation for optimization decision-making. Module B, Situation Awareness and Assessment: Based on federated learning and data fusion technology, it enables a comprehensive understanding of the operating status, safety boundaries, and resource value of new power distribution systems, supporting risk assessment and value mining; The safety margin assessment is performed by defining a mathematical expression for voltage safety constraints: ; By collecting the voltage of each node in real time Combined with preset reference values With limit The stability margin of the weakest node in the computational system. When the voltage exceeds the limit, the emergency adjustment strategy of the optimization module is triggered. The quantification of resource value is achieved by defining a value function for resource adjustment potential. ; In the formula, P up and P down Indicates the upward / downward adjustment power capability. In order to adjust service prices in real time, C wear Adjusting equipment loss costs (such as SOH decay in energy storage).

[0023] Module C, Collaborative Optimization Decision Engine: Integrates reinforcement learning, distributed optimization, and mechanistic models to perform multi-objective optimization decisions for economy, safety, and low carbon at multiple spatiotemporal scales, such as day-ahead / intraday / real-time and resource / microgrid / mainnet interactions; The multi-objective optimization mathematical model is defined for power system operation or energy management optimization, and includes one objective function and three constraints: ; In the formula, For total cost, For the cost of purchasing electricity from the power grid, To mitigate the risk of voltage exceeding limits, Carbon is carbon emissions ω 1 ,ω 2 ,ω 3 Each is assigned a weighting coefficient to control the importance of each objective.

[0024] The three constraints are as follows: 1. Renewable energy power balance: ; In the formula, P res Contribute to total renewable energy, For photovoltaic and wind power, This refers to the amount of wind and solar power that has been curtailed.

[0025] 2. Battery energy storage system dynamics: ; In the formula, The battery is in its state of charge. The charging and discharging power of the battery (negative for charging, positive for discharging). This refers to the battery's rated capacity. For charging and discharging efficiency, For time step.

[0026] 3. Node voltage constraints: ; In the formula, Let be the voltage magnitude at node i. These are the upper and lower limits of the voltage.

[0027] The reinforcement learning reward mathematical model is defined to achieve multi-objective collaborative optimization: ; In the formula, It is a punishment for system security risks. R tIt is the real-time target value at time t. r profit It is the direct benefit at time t. β It is the energy storage loss coefficient. It is the change in the state of charge (SOC) of the stored energy at time t. This is a voltage over-limit indicator. When... At that time, a penalty will be triggered. This is the penalty coefficient. By deducting energy storage losses and safety risk costs, this formula yields a more accurate net system benefit, which can be used to optimize decisions such as energy storage charging and discharging strategies and grid dispatching.

[0028] Module D, Training and Updating the Distributed Artificial Intelligence Computing Framework: This module employs federated learning and transfer learning techniques to address the challenges of data privacy and resource dynamism, ensuring the continuous adaptation and effective updating of the artificial intelligence computing framework model and guaranteeing the long-term stable operation of the framework.

[0029] In S4, optimization decisions are implemented at different levels of entities, including aggregators responsible for coordinating multiple microgrids, market interaction, and resource combination scheduling; microgrid EMS responsible for source-grid-load-storage optimization, frequency stability control, and internal autonomous operation during islanded grid-connection switching; load controllers responsible for receiving DR commands and flexible load adjustment; and energy storage management systems responsible for receiving DR commands.

[0030] In S5, the evaluation metrics for operational performance include economic benefits, voltage qualification rate, control command execution rate, and resource combination scheduling rate; model performance metrics include model prediction results, optimization result quality, and computational latency; iteration includes periodic model retraining, performance threshold updates, scene change adaptation, and feedback to module D. A closed-loop feedback mechanism is established to evaluate operational performance and the performance of the artificial intelligence computing framework model, including prediction accuracy, optimization degree, and computational speed, and feeds the results back to the artificial intelligence computing framework layer to drive model iterative updates, enabling the system to self-learn and continuously improve.

[0031] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0032] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 method for coordinated optimization of all resource elements in a power distribution system, characterized in that, Includes the following steps: S1. Construct the full-element resource layer of the power distribution system: Collect the basic data of the full-element distributed resources of the power distribution system, obtain the original real-time status data stream, and form the full-element resource layer of the power distribution system. S2. Construct the IoT edge perception layer of the power distribution system: preprocess and edge control the raw real-time status data stream collected in S1, combine it with protocol conversion and data alignment to obtain a high-value feature dataset, form the IoT edge perception layer of the power distribution system, clean and compress the high-value feature dataset, and realize the cognition of the operating status, security boundary and resource value of the entire power distribution system. S3. Construct the artificial intelligence computing framework layer of the power distribution system: Based on the high-value feature dataset output by S2 and the information data of the external system, the final dataset is obtained. The final dataset is dynamically aggregated in real time, the system situation is recognized, and multi-scale collaborative decision-making is performed. The artificial intelligence computing framework model for the collaborative optimization of all elements of the power distribution system is constructed, forming the artificial intelligence computing framework layer of the power distribution system, and outputting a multi-dimensional optimization control instruction set for different objects and different time scales. S4. Construct the collaborative control and execution layer of the power distribution system: Decompose the multi-dimensional optimization control commands obtained from S3 to obtain optimization decisions, "execute" the optimization decisions to subjects at different levels, form the collaborative control and execution layer of the power distribution system, output control results feedback, and realize resource collaborative control; S5. Construct the effect evaluation and iteration layer of the power distribution system: monitor the control result feedback of S4 and the new all-element distributed resource basic data collected by S1, conduct closed-loop evaluation of the model's operation effect evaluation index and model performance index, and feed the results back to the artificial intelligence computing framework layer described in S3 to trigger the model's iterative update, thus forming the effect evaluation and iteration layer of the power distribution system.

2. The method for coordinated optimization of all elements of a power distribution system according to claim 1, characterized in that, In S3, an artificial intelligence computing framework for real-time aggregation, prediction, and analysis of distributed resources is constructed, as follows: S3.1 Resource Aggregation and Prediction: A spatiotemporal graph neural network is constructed, incorporating meteorological correlation and electrical distance into the graph structure to predict the spatiotemporal evolution of resource output; and a dynamic clustering algorithm is used to aggregate massive resources into virtual power plants that can be uniformly dispatched according to their characteristics, quantifying their adjustable potential and providing a stable and reliable input basis for optimization decisions; S3.2 Situation Awareness and Assessment: Utilizing federated learning and data fusion, a comprehensive understanding of the power distribution system's operating status, security boundaries, and resource value is achieved to support risk assessment and value discovery; Safety margin assessment is performed by defining a mathematical expression for voltage safety constraints: ； In the formula, This refers to the voltage stability coefficient (a core evaluation indicator, typically ranging from [0,1])), while Nodes refers to the "set of nodes" of the research object (such as buses in a power system or nodes in electronic circuits). This represents the actual operating voltage of the i-th node (such as the real-time voltage value of a bus in a power system). This is a preset reference value. This is the voltage limit value; By collecting the voltage of each node in real time Combined with preset reference values With limit The stability margin of the weakest node in the computational system. When the voltage exceeds the limit, the emergency adjustment strategy of the optimization module is triggered. Quantify resource value by defining a resource adjustment potential value function: ； In the formula, Net Flexibility Value (a core performance indicator that measures the net value of the economic benefits of flexibility resources after deducting costs). t 1 and t 2 represents the time interval for the evaluation. P up and P down Indicates the upward / downward adjustment power capability. and for t Adjust service prices up or down at any time. C wear Adjusting equipment wear and tear costs; S3.3 Collaborative Optimization Decision Engine: Integrates reinforcement learning, distributed optimization, and mechanistic models to perform multi-objective optimization decisions on economic, safety, and low-carbon goals across multiple spatiotemporal scales; Define a multi-objective optimization mathematical model for power system operation or energy management optimization, which includes one objective function and three constraints: ； In the formula, For total cost, For the cost of purchasing electricity from the power grid, To mitigate the risk of voltage exceeding limits, Carbon is carbon emissions ω 1 ,ω 2 ,ω 3 Each is assigned a weighting coefficient to control the importance of each objective. The three constraints are as follows: Renewable energy power balance: ； In the formula, P res Contribute to total renewable energy, For photovoltaic and wind power, This refers to the amount of wind and solar power that has been curtailed. Battery energy storage system updates: ； In the formula, The battery is in its state of charge. The charging and discharging power of the battery (negative for charging, positive for discharging). For the battery's rated capacity, For charging and discharging efficiency, For time step; Node voltage constraints: ； In the formula, Let be the voltage magnitude at node i. These are the upper and lower limits of the voltage. We define a mathematical model for reinforcement learning rewards, and by deducting energy storage losses and safety risk costs, we obtain a more realistic net system benefit, which is used to optimize decisions to achieve multi-objective collaborative optimization. ； In the formula, It is a punishment for system security risks. R t It is the real-time target value at time t. r profit It is the direct benefit at time t. β It is the energy storage loss coefficient. It is the change in the state of charge (SOC) of the stored energy at time t. It is a voltage over-limit indicator, when When this occurs, a penalty is triggered; It is the penalty coefficient; Training and updating of the S3.4 distributed artificial intelligence computing framework: Federated learning and transfer learning are used to address the challenges of data privacy and resource dynamism, ensuring that the AI ​​model can be continuously adapted and effectively updated.

3. The method for coordinated optimization of all elements of a power distribution system according to claim 1, characterized in that, The distributed resources in S1 include distributed wind power, distributed photovoltaic power, clean energy, new energy storage, new loads, microgrids, and aggregators; The basic data of the total resources include power, voltage, SOC, temperature, switching status, and adjustable potential.

4. The method for coordinated optimization of all resource elements in a power distribution system according to claim 1, characterized in that, The data preprocessing in S2 includes filtering, cleaning, time alignment, and heterogeneous data acquisition to perceive massive distributed resources. The edge control includes energy storage charging and discharging, and emergency load reduction, which are used to achieve local rapid data processing, protocol conversion and emergency control, reduce the pressure on the center and ensure real-time performance; The protocol conversions include Modbus, MQTT, IEC 61850, and OCPP.

5. The method for coordinated optimization of all elements of a power distribution system according to claim 1, characterized in that, In S4, optimization decisions are executed to different levels of entities, including aggregators responsible for multi-microgrid coordination, market interaction, and resource combination scheduling; microgrid EMS responsible for source-grid-load-storage optimization, frequency stability control, and internal autonomous operation during islanded grid-connection switching; load controllers responsible for receiving DR commands and flexible load adjustment; and energy storage management systems responsible for receiving DR commands.

6. The method for coordinated optimization of all elements of a power distribution system according to claim 1, characterized in that, The operational performance evaluation indicators in S5 include economic benefits, voltage qualification rate, control command execution rate, and resource combination scheduling rate; the model performance indicators include model prediction results, optimization result quality, and computational latency. The iteration includes periodically retraining the model, updating performance thresholds, and adapting to and responding to scene changes.

7. A power distribution system with coordinated optimization of all resource elements, characterized in that, Based on the power distribution system's all-element resource collaborative optimization operation method as described in claim 1 The system includes a full-element resource layer for the power distribution system, which is used to collect raw real-time status data streams of all distributed resources of the power distribution system. The IoT edge sensing layer is used for preprocessing and edge control of raw real-time status data streams of all-element distributed resources, and for cleaning and compressing high-value feature datasets. The artificial intelligence computing framework layer is used to dynamically aggregate high-value feature datasets and information data from external systems in real time, realize system status, make multi-scale collaborative decisions, and output multi-dimensional optimization control instruction sets for different objects and time scales. The collaborative control and execution layer is used to decompose and execute multi-dimensional optimization control instructions, "execute" optimization decisions to entities at different levels, and collaboratively control resources. The effect evaluation and model iteration layer is used to evaluate the model's performance indicators and drive model iteration updates in a closed loop.

8. The power distribution system's all-element resource collaborative optimization operation system according to claim 7, characterized in that, The artificial intelligence computing framework layer comprises four modules: A, B, C, and D. Module A is used for resource aggregation and prediction; module B is used for situational awareness and assessment; module C is used for collaborative optimization decision engine; and module D is used for training and updating the distributed artificial intelligence computing framework. The resource aggregation and prediction in module A, together with the situational awareness and assessment in module B, jointly support the collaborative optimization decision engine in module C. The training and updating of the distributed artificial intelligence computing framework in module D, which receives evaluation feedback, drives the system model iteration.

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