A new energy high-voltage intelligent power distribution optimization system

Abstract

The application discloses a new energy high-voltage intelligent power distribution optimization system and relates to the technical field of power distribution optimization.The hierarchical digital twin module is used for establishing and synchronizing the topology twin, the equipment twin, the energy twin and the market twin of the high-voltage power distribution network, and the micro-batch flow processing technology is utilized to synchronously update the edge node and the cloud within a time delay of no more than 1 second.The quantum Monte Carlo optimization gateway communicates with the hierarchical digital twin module, is used for mapping the optimal power flow problem into a quantum Monte Carlo sub-problem, and calling quantum acceleration resources to generate a preliminary solution.The local mixed integer programming refiner is used for performing constraint refinement on the preliminary solution output by the quantum Monte Carlo optimization gateway, and ensuring that the preliminary solution conforms to the equipment safety and the power grid constraint.The application realizes the virtualization of the whole network operation state and multi-dimensional situation awareness through the hierarchical digital twin, breaks the data island, eliminates the information perception lag, improves the prediction accuracy, and supports parallel simulation and visualized decision-making.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power distribution optimization, and particularly relates to a new energy high-voltage intelligent power distribution optimization system. BACKGROUND

[0002] With the increasing proportion of wind power, photovoltaic and other new energy installations, the output fluctuation and unpredictability of the new energy bring challenges of voltage fluctuation, overload hidden danger and dispatching response lag to the traditional high-voltage power distribution network. The existing system mainly relies on a single optimization algorithm and manual operation and maintenance, and it is difficult to meet the needs of millisecond-level dynamic reconstruction, adaptive learning and security protection, so there is an urgent need for a high-voltage intelligent power distribution optimization solution integrating digital twin, quantum acceleration, multi-agent collaboration, federated learning and zero-trust security.

[0003] Patent CN117010573B discloses an intelligent power distribution energy-saving optimization method and system, which realizes the technical effects of improving the operation efficiency of the power distribution system and achieving high energy saving.

[0004] The above-mentioned patent solves the technical problems of low efficiency and poor energy saving of the power distribution system in the prior art, but it is difficult to meet the needs of millisecond-level dynamic reconstruction, adaptive learning and security protection.

[0005] Therefore, the application provides a new energy high-voltage intelligent power distribution optimization system for realizing virtualization of the whole network operation state and multi-dimensional situation awareness. SUMMARY

[0006] The application aims to provide a new energy high-voltage intelligent power distribution optimization system to solve the technical problems of output fluctuation and unpredictability bringing voltage fluctuation, overload hidden danger and dispatching response lag to the traditional high-voltage power distribution network.

[0007] To achieve the above-mentioned purpose, the application provides the following technical scheme: a new energy high-voltage intelligent power distribution optimization system, which comprises: a hierarchical digital twin module, a quantum Monte Carlo optimization gateway and a local mixed integer programming refiner.

[0008] The hierarchical digital twin module is used for establishing and synchronizing the topology twin, device twin, energy twin and market twin of the high-voltage power distribution network, and uses the micro-batch flow processing technology to update the edge nodes and the cloud in synchronization with a time delay of not more than 1 second.

[0009] The quantum Monte Carlo optimization gateway communicates with the hierarchical digital twin module and is used for mapping the optimal power flow problem into a quantum Monte Carlo sub-problem and calling quantum acceleration resources to generate a preliminary solution.

[0010] The local mixed integer programming refiner is configured to refine the preliminary solution output by the quantum Monte Carlo optimization gateway to ensure compliance with device safety and power grid constraints.

[0011] Preferably, the system further comprises a multi-agent game scheduling unit, which is configured with a plurality of agents, each agent representing an access-side energy unit, and the agents make power output or operation mode decisions based on a dynamic game model.

[0012] The system further comprises a blockchain power service credit ledger for recording scheduling fulfillment data, response time delay and prediction accuracy of each agent, and dynamically adjusting economic incentives for the agents based on the records.

[0013] Preferably, the system further comprises a federated reinforcement learning controller deployed at each edge node for reinforcement learning training in a local digital twin environment, and aggregating and sharing policy parameters in the cloud through a federated learning protocol.

[0014] The system further comprises a self-healing microgrid management module for automatically generating an emergency island microgrid and operating in a closed loop when the main grid experiences out-of-limit fluctuations or partition faults, and smoothly connecting to the grid after communication recovery.

[0015] Preferably, the multi-level digital twin module specifically comprises:

[0016] A topology twin submodule for updating the network structure diagram in real time based on GIS data and topology change events;

[0017] A device twin submodule for collecting and recording the operating status, maintenance history and manufacturer parameters of switches, transformers and energy storage devices;

[0018] An energy twin submodule for simulating power flow, short-circuit current and voltage distribution, and outputting multi-dimensional situation indicators;

[0019] A market twin submodule for simulating price signals, demand response incentives and auxiliary service transactions.

[0020] Preferably, the Monte Carlo optimization gateway comprises:

[0021] A semi-definite relaxation converter for converting integer constraints and power grid nonlinear equations into relaxation problems suitable for quantum annealing;

[0022] A QMC solving engine for parallel execution of quantum Monte Carlo sampling and generation of a set of feasible preliminary solutions meeting a pre-set confidence level;

[0023] A solution set filter for filtering a number of high-quality preliminary solutions based on device safety margin and voltage stability indicators for use by the refiner.

[0024] Preferably, the multi-agent game scheduling unit comprises:

[0025] An energy agent, configured to generate a candidate strategy after evaluating market incentives, prediction errors and safety constraints locally;

[0026] A game coordinator, configured to collect candidate strategies of all agents, perform Nash equilibrium solving, and output a global scheduling strategy;

[0027] A differentiated incentive module, configured to assign rewards or penalties to each agent according to historical behaviors in a blockchain power service credit ledger.

[0028] Preferably, the federated reinforcement learning controller comprises:

[0029] A local policy trainer, configured to perform model training based on a digital twin environment in each scheduling period, and output a policy gradient;

[0030] A model aggregator, configured to periodically aggregate local policy gradients with a cloud global model through a secure federated learning protocol, and update control strategies of each edge node;

[0031] A meta-learning enhancement module, configured to quickly adjust policy parameters according to a small amount of test data when a new device is connected, and realize rapid convergence within several scheduling periods.

[0032] Preferably, the self-healing microgrid management module comprises:

[0033] A fault detection unit, configured to determine an out-of-limit fluctuation or a partition fault based on a digital twin and real-time monitoring data;

[0034] An island division engine, configured to automatically select island protection devices and generate a closed-loop operation topology;

[0035] A grid-connection smoother, configured to smooth hybrid integrated island power and main grid power based on state estimation after communication recovery and main grid stabilization.

[0036] Preferably, the system further comprises a zero-trust secure communication layer, which performs end-to-end encryption on all data and instruction streams in the system based on a hardware trusted root and a quantum security algorithm, and predicts and isolates potential network attacks in the digital twin layer;

[0037] The zero-trust secure communication layer comprises:

[0038] A hardware trusted root module, configured to provide chain trust booting for key nodes based on a TPM;

[0039] A quantum security encryption unit, configured to sign and encrypt all communication messages using a post-quantum encryption algorithm;

[0040] An attack path simulator constructs a component network attack and defense model in a hierarchical digital twin, and automatically issues a defense strategy according to a simulation result.

[0041] Preferably, the system further comprises a virtual-real fusion operation and maintenance interaction terminal, including a WebXR visualization interface and an AI-assisted maintenance assistant, supporting a three-dimensional holographic operation and maintenance view and a multi-modal natural language and image maintenance dialogue on site or remotely;

[0042] The virtual-real fusion operation and maintenance interaction terminal comprises a WebXR rendering module, configured to display a three-dimensional power distribution network model fused with a point cloud of a real scene in an AR and VR device;

[0043] An AI-assisted maintenance dialogue system automatically identifies a fault scene and generates a maintenance scheme based on multi-modal input;

[0044] A field labeling and instruction module allows an operation and maintenance personnel to directly label a device state, change a parameter or issue an operation instruction in a three-dimensional view.

[0045] Compared with the prior art, the present application has the following beneficial effects:

[0046] 1. The present application realizes virtualization of a whole network operation state and multi-dimensional situation awareness through a hierarchical digital twin, breaks data islands, eliminates information perception lag, improves prediction accuracy, supports parallel simulation and visualized decision-making;

[0047] 2. The present application realizes rapid preliminary optimization of a million variables and fine satisfaction of constraints through quantum Monte Carlo optimization and MIP refinement, solves the problems of slow convergence and easy falling into local optimum of traditional optimization, optimizes a cycle of less than 500 ms, reduces a loss rate and improves a new energy consumption rate;

[0048] 3. The present application realizes distributed collaborative scheduling and differentiated economic incentives through multi-agent game and blockchain incentives, solves the problems of disordered response of distributed resources and insufficient incentives, improves response speed and fulfillment rate and reduces scheduling deviation;

[0049] 4. The present application realizes adaptive control and fault self-healing grid connection through federal reinforcement learning and self-healing microgrid, solves the problems of difficult strategy migration and slow fault recovery, realizes rapid strategy convergence, guarantees continuous power supply of key loads and efficiently and smoothly connects to a grid. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 Fig. 1 is a system composition framework schematic diagram of the present application;

[0051] Figure 2 Fig. 2 is an optimization process schematic diagram of the present application. DETAILED DESCRIPTION

[0052] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0053] Please refer to Figure 1 and Figure 2 , an embodiment provided by the present application: a new energy high-voltage intelligent power distribution optimization system, sensor sampling: voltage transformers and current transformers are respectively arranged on the bus and each branch line, and UPS power supply ensures uninterrupted collection within 1s; weather data: local weather stations are collected, and the MQTT protocol is pushed to the edge; micro-batch flow processing: the edge node uses Apache Flink Kafka, and the electrical and weather data streams are packaged in batches of 100ms, and after preliminary cleaning and feature extraction, the gRPC bidirectional stream is uploaded to the cloud digital twin with a delay of <1s;

[0054] The topology twin submodule is based on a GIS database, receives new equipment or network reconstruction events, and automatically updates the substation and line connectivity graph; the network topology is modeled using a Neo4j graph database, and the Dijkstra algorithm is used to calculate the shortest fault isolation path online; the device twin submodule synchronizes device files from the device management system, and implements injection of SCADA operation data, constructs a state machine model for each switch and transformer, and detects abnormal trends in combination with historical maintenance records and operation characteristics; the energy twin submodule is based on a mesh method and a Newton-Raphson power flow solver, and constructs an equivalent node admittance matrix; the power flow and short-circuit current distribution are simulated in the twin body every 500ms, and multi-dimensional situation indicators such as node voltage deviation and line thermal shoe utilization rate are output; the market twin submodule integrates real-time electricity price data, demand response events and auxiliary service bidding information, performs hour-level bidding simulation in parallel in the digital twin, and generates a price signal curve for the next 24 hours;

[0055] The semi-definite relaxation converter takes the voltage amplitude and phase angle variables in the whole network optimal power flow as real variable, and takes the energy storage device state and switch input as integer variable, and constructs a semi-definite relaxation (SDP) relaxation model:

[0056] min Tr(CX) s.t. Tr(A i X) = b i , X ≥ 0;

[0057] X: decision variable matrix; Tr(·): matrix trace operator; C: target matrix; A i : constraint matrix, the coefficient matrix corresponding to the ith equality constraint; b i: The constant term of the i-th equality constraint; X≥0: positive semidefinite constraint;

[0058] The CVX preprocessor solver transforms the SDP problem into a quadratic binary programming form adapted to quantum annealing. The QMC solver engine calls the quantum annealing hardware interface to perform 1000 Monte Carlo samplings in parallel, calculating the energy expectation and constraint violation for each sampling result, and retaining the 50 candidate solutions with the lowest energy and violation rate of <5%. The solution set filter performs a safety margin check on the candidate solutions: checking that all node voltages are between 0.95-1.05 pu and line loads are <80% of rated capacity. Based on the voltage stability index, 10 optimal preliminary solutions are selected and submitted to the local refiner.

[0059] Target:

[0060] α Voltage deviation penalty coefficient, β Energy storage cycle cost weighting coefficient, P 损耗 Active power loss of the line, V i The voltage amplitude at node i, C 循环 Cyclic losses or equivalent operating costs of energy storage devices;

[0061] Constraints: power balance, equipment status continuity, number of switch changes, energy storage charge and discharge rate, etc.

[0062] The candidate solution is used as the initial solution of MIP. The Big M technique and the tangent plane method are introduced to quickly prune the infeasible region. The Gurobi iteration is used for 50 times, or the process stops when the GAP < 0.5% is reached within 200ms, and the final scheduling instruction is output. The optimal solution is distributed to each agent for execution via the OPC UA protocol.

[0063] Please see Figure 1 and Figure 2 One embodiment of the present invention is a new energy high-voltage intelligent power distribution optimization system, in which an industrial embedded controller is deployed next to each new energy access point (photovoltaic inverter, wind turbine, centralized or distributed energy storage station, controllable load inlet), running a lightweight real-time operating system, and running an "energy subject intelligent agent" process in each controller, and exchanging market, grid status and prediction data required for game theory with local edge nodes through a dedicated Ethernet.

[0064] Inputs: Output and load forecast errors within the latest 5 minutes, current market incentive price signals, and local equipment safety constraints;

[0065] Strategy generation:

[0066] 1. Construct local utility functions

[0067] ;

[0068] 2. Within its policy space, several candidate policy sets are quickly solved using convex optimization;

[0069] Output: The candidate strategies and their corresponding local utility values ​​are encapsulated into a message and pushed to the "game coordinator" via gRPC;

[0070] The game coordinator is deployed on a cloud-based network optimization platform, responsible for solving Nash equilibrium problems across different sites.

[0071] Receive candidate policies and utility values ​​from N agents in parallel;

[0072] Define the strategy set {Pi} and the utility function {Ui}, and construct a static non-cooperative game.

[0073] A heuristic iterative optimal response algorithm is used:

[0074] Initialization: Randomly select policy pi(0) for each agent;

[0075] Iteration: For k=1,2,..., each i is solved individually with the opponent's strategy fixed.

[0076]

[0077] Output: The final Nash equilibrium policy set is returned to each agent as a global scheduling instruction;

[0078] The differentiated incentive module is located in the cloud and interacts with the "credit ledger" module. It calculates the dynamic incentive coefficient η based on the agent's performance records over the most recent T days. i =η0(1+w1Acc i -w2Delay i ), where Acc i To score the prediction accuracy, Delay i Let w1 and w2 be the average response latency, w0 be the base reward rate, and η be the base reward rate. i The information is sent to the agent and adjusted in the utility function during the next game cycle. Valid value.

[0079] Please see Figure 1 and Figure 2 This invention provides an embodiment of a new energy high-voltage intelligent power distribution optimization system, comprising: a local strategy trainer environment construction: utilizing a local digital twin environment to provide state observations, including the local station node voltage vector V, line power flow vector P, and energy storage SOC vector S; an action space: adjusting the tap positions of each branch and the charging and discharging power of energy storage, with an action dimension of approximately 10 continuous variables; and a reward function. ,in ; Training algorithm: SePPO, collect 200 steps trajectory per cycle, update Actor-Critic network 10 times per batch, learning rate 3x10 -4 , discount factor γ = 0.99; Model Aggregator cycle: every 5 minutes; Protocol: secure federated learning protocol based on gRPC + TLS, using homomorphic encryption to encrypt and transmit gradients; Aggregation rule: server uses FedAvg w weighted average:

[0080]

[0081] wherein is the number of data samples at the kth station, is the strategy parameter of the station; After aggregation is completed, is issued to each edge node to update the local network;

[0082] Meta-learning enhancement module application: scenario: when a new energy storage or flexible DC device is connected: method: based on the MAML framework, a meta-model θmeta is maintained in the cloud; the gradient adaptation is performed once every 10 trial cycles of the new node data to quickly obtain the local initial strategy ; Parameter: meta-learning rate α = 10 -3 , 5 times of inner loop update steps;

[0083] Fault detection unit input: digital twin situation indicators (node voltage fluctuation rate, line current sudden change rate) and SCADA data; Discrimination algorithm: combined wavelet packet decomposition-based fault feature extraction and double threshold detection: voltage sudden change rate > 0.1 p.u. / s and current overrun > 120% rated value, determine "overrun fluctuation"; when multiple device fault state machines enter the "fault" state within 5 consecutive sampling cycles in the same section, determine "partition fault";

[0084] Island division engine protector selection: relying on the shortest fault isolation path in the digital twin, preferentially close 3-5 nearby circuit breakers with tripping logic to form an island; closed-loop topology generation: using a greedy algorithm based on maximum available load: select a load priority list within the fault section to ensure that the total load in the island ≤ internal power generation + maximum output of energy storage; control execution: issue circuit breaker switch commands through IEC 61850 GOOSF messages, and the total operation time ≤ 50 ms;

[0085] Grid-connection smoother state estimation: After fault recovery, run WLS state estimation to obtain the phase difference Δφ and frequency deviation Δf between the island and the main grid voltage; merging strategy: phase synchronization: through the fine tuning of the output power of the island energy storage inverter, Δφ < 0.5°, Δf < 0.02Hz is realized; step-by-step closing: the bus circuit breaker is closed in three steps, each step interval is 200ms, and the current impact is verified through online simulation; switching is completed: all grid-connection operations are completed within 2s, and the voltage deviation during smooth transition is less than 0.02p.u.

[0086] Referring to Figure 1 and Figure 2 , the present application provides an embodiment: a new energy high-voltage intelligent power distribution optimization system, the overall deployment of the system: on each edge node (substation micro server) and the key server of the cloud optimization platform, a trusted platform module chip is integrated for storing the root trust of the device firmware and the key, the zero-trust gateway is deployed in a special security subnet, and all external and internal communications must be verified by the gateway and encrypted.

[0087] When starting, the TPM completes PCR scanning, measures and records the hash of BIOS, Bootloader, Kernel and key container image, and generates a chain trust report; the cloud verification server regularly initiates a TPM quote request to each edge node to check whether the PCR value is in the whitelist; once an abnormal hash is found, the node is immediately prohibited from participating in any scheduling or federated learning operation; all long-term symmetric keys and post-quantum public-private key pairs are generated and protected by the TPM internally and are not exposed to the operating system;

[0088] NIST standardized post-quantum algorithms are used: signature: based on CRYSTALS-Dilithium; key exchange: based on Kyber; key encapsulation and encryption: based on FrodoKEM; when establishing a TLS1.3 channel, Kyber is used to complete key negotiation and generate a session key; all gRPC, MQTT, OPC UA messages are encrypted by AES-GCM using the session key at the application layer, and a Dilithium signature is attached to the message tail; after the session ends, the next session key seed is re-encapsulated using FrodoKEM to prevent key replay;

[0089] In the cloud digital twin platform, an attack-defense graph model is constructed for each network node and communication link, the node attributes include hardware model, open port and known vulnerability label, and the edge attributes include protocol type and encryption strength; periodically triggered, the improved A* algorithm is combined with probability theory to search for the shortest possible path from the external intrusion entrance to the key controller, and the cost function considers: vulnerability CVSS score; encryption algorithm security strength; node detection capability; if the simulation path cost is lower than the threshold T alert, automatically issues enhanced policies to relevant edge nodes through the zero-trust gateway, such as enabling deep packet inspection and temporarily isolating high-risk ports; at the same time, it generates security event alerts on the log platform and pushes them to security operation personnel; the simulator updates the detection capability attributes of the graph model nodes based on the feedback from the real intrusion detection system, gradually improving the simulation accuracy;

[0090] All security policy changes and simulation results are written to a dedicated audit blockchain, ensuring tamper resistance; the security operation interface pulls audit records through REST API and combines them with the digital twin three-dimensional visualization scene to visually display network risk areas and defense deployment effectiveness.

[0091] See Figure 1 and Figure 2 , the present application provides an embodiment: a new energy high-voltage intelligent power distribution optimization system, which collects point cloud data from laser radar and structured light camera acquisition equipment on site, and the edge node pre-processes the point cloud (noise filtering, downsampling), then matches it with the CAD model in the cloud digital twin through the ICP algorithm; the matched data is pushed to the WebXR front end in gITF format stream;

[0092] Based on Three.js and WebXR Device API, AR and VR dual-mode rendering is realized; in AR mode, the three-dimensional power distribution network is "attached" to the real scene on site, supporting operation and maintenance personnel to directly observe on site with HoloLens; in VR mode, operation and maintenance personnel can freely roam, zoom, and cut the three-dimensional model in the virtual machine room, and view the real-time running status of each device (temperature, load, current, etc.);

[0093] Multi-modal input: text and voice: obtain the natural language description of the operation and maintenance personnel through WebRTC, and use STS service for real-time transcription; image: take pictures of the fault part of the device through AR and VR devices or tablet cameras; text understanding: identify keywords based on the Fine-tuned BERT model; voice intent: use the end-to-end RNN-Transducer model to analyze intent and slot; image analysis: call YOLOv8 to detect foreign matter, damage, or siphon discharge traces, and combine with thermal imaging to locate hot spots;

[0094] Maintenance scheme generation:

[0095] 1. The system detects the built-in knowledge base (including device manuals and past fault cases) and combines it with the current digital twin state;

[0096] 2. Based on the GPT-4 architecture fine-tuned fault handling large model, automatically generate step-by-step detection process;

[0097] 3. Push the scheme to the operation and maintenance personnel in the form of dialogue, and highlight the relevant components in the three-dimensional view.

[0098] In the WebXR environment, the operation and maintenance personnel can "point" and "draw" on the three-dimensional model for labeling by gestures or VR handles; the labeling information is fed back to the cloud in real time and associated with the corresponding node in the digital twin; the labeling interface integrates a draggable slider and an input box, and the operation and maintenance personnel can directly adjust the parameters; after the adjustment is confirmed, the new running state is fed back in real time in the digital twin through the OPC UA protocol and is safely issued to the edge node for execution; multi-user collaborative dialogue is supported: multiple operation and maintenance personnel can simultaneously enter the same WebXR scene, and their respective perspectives and labels are synchronized in real time; after the session ends, 3D audio and video and operation logs are automatically recorded and can be used for subsequent training and audit playback.

[0099] Working principle: The system first collects key parameters such as voltage, current, temperature, and weather in real time through multi-modal sensors at the high-voltage bus, lines, and energy access points, and synchronously injects the collected micro-batch data into the "digital twin body" at the edge node and the cloud; the digital twin body is simulated in parallel at four levels of topology, equipment, energy, and market, realizing full-dimensional virtual mapping of the distribution network running state;

[0100] Based on the latest digital twin state, the "quantum Monte Carlo optimization gateway" in the cloud combines semi-definite relaxation and quantum Monte Carlo sampling algorithm to quickly generate a preliminary solution of power flow, which is refined by local mixed integer programming to ensure that the grid constraints are met; then, the multi-agent game scheduling unit issues a global scheduling strategy to each unit according to the Nash equilibrium algorithm under the incentive mechanism of the blockchain ledger;

[0101] The federal reinforcement learning controller deployed at each edge node continuously learns and updates strategies in the local digital twin environment, achieving adaptive control of actions such as tap position and energy storage charging and discharging, and when there are over-limit fluctuations or partition faults, the self-healing micro-grid management module automatically forms an island operation, and after communication is restored, it smoothly connects to the grid, and the system security layer and the virtual-real integrated operation and maintenance end cooperate to provide holographic interaction and AI-assisted maintenance support for operation and maintenance personnel.

[0102] It will be obvious to a person skilled in the art that the application is not limited to the details of the above-described exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the application. Therefore, the embodiments should be considered exemplary and non-limiting, and the scope of the application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the application. Any reference signs in the claims should not be considered limiting of the claims involved.

Claims

1. A new energy high-voltage intelligent power distribution optimization system, characterized in that: The system comprises a hierarchical digital twin module, a quantum Monte Carlo optimization gateway and a local mixed integer programming refiner; The hierarchical digital twin module is configured to establish and synchronize a topology twin, a device twin, an energy twin and a market twin of a high-voltage power distribution network, and to update the twins on an edge node and in the cloud within a time delay of no more than 1 second by using micro-batch stream processing technology; The hierarchical digital twin module specifically comprises: a topology twin submodule configured to update a network structure diagram in real time based on GIS data and topology change events; a device twin submodule configured to collect and record the operating status, maintenance history and manufacturer parameters of switches, transformers and energy storage devices; an energy twin submodule configured to simulate power flow, short-circuit current and voltage distribution, and output multi-dimensional situation indicators; a market twin submodule configured to simulate price signals, demand response incentives and auxiliary service transactions; The quantum Monte Carlo optimization gateway is in communication with the hierarchical digital twin module, and is configured to map an optimal power flow problem into a quantum Monte Carlo sub-problem, and to call quantum acceleration resources to generate a preliminary solution; The quantum Monte Carlo optimization gateway comprises: a semi-definite relaxation converter configured to convert integer constraints and nonlinear equations of the power grid into a relaxation problem suitable for quantum annealing; a QMC solving engine configured to perform quantum Monte Carlo sampling in parallel, and to generate a set of feasible preliminary solutions meeting a preset confidence level; a solution set filter configured to filter a number of high-quality preliminary solutions according to device safety margins and voltage stability indicators, for use by the refiner; The local mixed integer programming refiner is configured to perform constraint refinement on the preliminary solutions output by the quantum Monte Carlo optimization gateway, to ensure compliance with device safety and power grid constraints.

2. The new energy high-voltage intelligent power distribution optimization system according to claim 1, characterized in that: The system further comprises a multi-agent game scheduling unit, which is configured with a plurality of agents, each representing an access-side energy unit, and the agents make power output or operating mode decisions based on a dynamic game model; The system further comprises a blockchain power service credit ledger configured to record scheduling compliance data, response time and prediction accuracy of each agent, and dynamically adjust economic incentives for the agents based on the records.

3. The new energy high-voltage intelligent power distribution optimization system according to claim 2, characterized in that: The system further comprises a federal reinforcement learning controller deployed at each edge node, configured to perform reinforcement learning training in a local digital twin environment, and to aggregate and share policy parameters in the cloud through a federal learning protocol; The system further comprises a self-healing microgrid management module configured to automatically generate an emergency island microgrid and operate in a closed loop when the main grid experiences an out-of-limit fluctuation or a partition fault, and to smoothly connect to the grid after communication recovery.

4. The new energy high-voltage intelligent power distribution optimization system according to claim 2, characterized in that: The multi-agent game scheduling unit comprises: an energy agent configured to generate a candidate strategy after locally evaluating market incentives, prediction errors and safety constraints; a game coordinator configured to collect candidate strategies of all agents, perform Nash equilibrium solving, and output a global scheduling strategy; a differentiated incentive module configured to allocate rewards or penalties to each agent according to historical behavior in the blockchain power service credit ledger.

5. The new energy high-voltage intelligent power distribution optimization system according to claim 3, characterized in that: The federal reinforcement learning controller comprises: a local policy trainer configured to perform model training based on the digital twin environment in each scheduling period, and to output a policy gradient; A model aggregator that periodically aggregates local policy gradient with cloud global model through secure federated learning protocol to update control policy of each edge node; A meta-learning enhancement module that quickly adjusts policy parameters based on a small amount of experimental data when a new device is connected, achieving rapid convergence within several scheduling cycles.

6. The new energy high-voltage intelligent power distribution optimization system according to claim 3, characterized in that: The self-healing microgrid management module comprises: A fault detection unit for determining over-limit fluctuations or partition faults based on digital twins and real-time monitoring data; An island division engine for automatically selecting island protection devices and generating a closed-loop operation topology; A grid connection smoother for smoothing hybrid integrated island power and main grid power based on state estimation after communication is restored and the main grid is stable.

7. The new energy high-voltage intelligent power distribution optimization system according to claim 1, characterized in that: The system also includes a zero-trust secure communication layer that encrypts all data and instruction streams end-to-end based on hardware trusted roots and quantum security algorithms, and predicts and isolates potential network attacks in the digital twin layer; The zero-trust secure communication layer comprises: A hardware trusted root module that provides chain-of-trust booting for key nodes based on TPM; A quantum security encryption unit that uses post-quantum encryption algorithms to sign and encrypt all communication messages; An attack path simulator that constructs a network attack and defense model in the multi-layer digital twin, and automatically issues defense strategies based on simulation results.

8. The new energy high-voltage intelligent power distribution optimization system according to claim 1, characterized in that: The system also includes a virtual-real integrated operation and maintenance interaction terminal, including a WebXR visualization interface and an AI-assisted maintenance assistant, which supports three-dimensional holographic operation and maintenance views and multi-modal natural language and image maintenance conversations in the field or remotely; The virtual-real integrated operation and maintenance interaction terminal comprises a WebXR rendering module for displaying a three-dimensional power distribution network model fused with on-site real scene point clouds in AR and VR devices; An AI-assisted maintenance conversation system that automatically identifies fault scenarios and generates maintenance solutions based on multi-modal input; A field labeling and instruction module that allows operation and maintenance personnel to directly label device status, change parameters, or issue operation instructions in a three-dimensional view.

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