Self-discipline cooperative operation optimization method for regional power distribution network containing multiple microgrids

Abstract

The invention belongs to the technical field of power distribution network operation optimization, and particularly relates to a multi-microgrid-containing regional power distribution network self-discipline collaborative operation optimization method which comprises the following specific steps: S1, data acquisition and preprocessing; s2, establishing a model; s3, a self-discipline cooperative control strategy; s4, solving by an optimization algorithm; and S5, real-time monitoring and adjustment are carried out. And through self-discipline cooperative operation optimization, an information interaction and cooperation mechanism among the micro-grids is constructed, and resource sharing and advantage complementation of the multi-micro-grid system are realized. A distributed collaborative optimization algorithm and a self-discipline control strategy are adopted, and dependence on centralized communication is reduced. The uncertainty of output and load demand of the distributed power supply is fully considered by utilizing technologies such as model predictive control and stochastic optimization. According to the scheme, a plurality of optimization objectives such as economy, environment and reliability are comprehensively considered, and the optimal operation strategy is solved by adopting a multi-objective collaborative optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network operation optimization, and in particular to a method for optimizing the autonomous and coordinated operation of a regional distribution network containing multiple microgrids. Background Art

[0002] Currently, there are the following defects in regional distribution networks:

[0003] Poor synergy: Traditional regional distribution networks with multiple microgrids operate independently, lacking effective information exchange and coordination mechanisms. This makes it difficult to achieve complementary advantages and resource sharing among microgrids in the face of load fluctuations and changes in the output of distributed generation sources. This leads to low overall operational efficiency and an inability to fully leverage the synergy of the multi-microgrid system.

[0004] High Dependence on Communications: Some existing optimization methods rely heavily on real-time, stable transmission over communication networks. If communication links fail or become delayed, the optimization algorithm loses access to accurate operational information, making it difficult to make timely and effective decisions. This severely impacts the normal operation and optimization effectiveness of the distribution network.

[0005] Inadequate handling of uncertainty: The output of distributed power sources (such as solar and wind) in regional distribution networks is subject to significant uncertainty, influenced by factors such as weather and the environment. Load demand also fluctuates. Existing technologies struggle to effectively address these uncertainties, often failing to fully consider all possible scenarios when formulating operational strategies, resulting in poor adaptability and reliability.

[0006] Single optimization objective: Most existing technologies focus on a single optimization objective, such as minimizing operating costs or network losses, while neglecting other important goals, such as environmental benefits and power supply reliability. This fails to meet the diverse operational needs of multi-microgrid regional distribution networks in different scenarios, making it difficult to maximize overall benefits. Summary of the Invention

[0007] The object of the present invention is to provide a method for optimizing the autonomous coordinated operation of a regional distribution network containing multiple microgrids, so as to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing the autonomous coordinated operation of a regional distribution network containing multiple microgrids. The specific steps of the method are as follows:

[0009] S1: Data collection and preprocessing:

[0010] Collect real-time operating data of each microgrid in the regional distribution network;

[0011] Preprocess the collected data, including data cleaning, filtering, and normalization, to improve data quality and provide an accurate data basis for subsequent analysis and optimization;

[0012] S2: Build the model:

[0013] Construct a topological model of the regional distribution network to describe the connection between microgrids and how they interact with the main grid;

[0014] Establish distributed power generation models, load models, and energy storage models within the microgrid, taking into account their output characteristics and operating constraints;

[0015] Based on the above model, a multi-objective optimization model is established with the goals of minimizing network loss, minimizing voltage deviation and maximizing distributed generation utilization;

[0016] S3: Autonomous collaborative control strategy:

[0017] Each microgrid performs preliminary optimization calculations through a local controller based on its own operating data and optimization goals to generate a local operating strategy;

[0018] S4: Optimization algorithm solution:

[0019] Adopt intelligent optimization algorithm to solve multi-objective optimization model;

[0020] During the algorithm solving process, the feasibility of the optimization results is tested and adjusted according to the actual operation conditions and constraints to ensure the operability of the optimization plan;

[0021] S5: Real-time monitoring and adjustment:

[0022] Monitor the operating status of the regional distribution network in real time, promptly identify problems or deviations that occur during operation based on real-time monitoring data, and dynamically adjust the optimization model and control strategy to ensure that the regional distribution network is always in the optimal or near-optimal operating state.

[0023] Preferably, in S1, the real-time operating data of each microgrid in the regional distribution network includes distributed power output, load information, bus voltage and line current.

[0024] Preferably, in S3, microgrids exchange information through the communication network, share key operation information and optimization results, and based on the shared key operation information, use a collaborative optimization algorithm to adjust and optimize the operation strategy of each microgrid to achieve optimal operation of the regional distribution network as a whole.

[0025] Preferably, the information exchanged between the microgrids includes operating data, control instructions and market information;

[0026] Operational data: real-time information on microgrid power generation, load demand, energy storage status, and voltage / frequency;

[0027] Control instructions: power dispatch instructions, grid-connected / off-grid switching commands, and fault isolation signals;

[0028] Market information: electricity trading prices, demand response signals, and carbon emissions data.

[0029] Preferably, in S4, the intelligent optimization algorithm includes a particle swarm algorithm and a genetic algorithm.

[0030] Preferably, in S5, the operating status of the regional distribution network includes the power balance, voltage stability and equipment operating status of each microgrid.

[0031] Preferably, in S3, the optimization objectives of the microgrid include economy, stability, environmental protection and reliability;

[0032] Economic efficiency: minimizing power generation costs, minimizing external grid power purchase costs, and maximizing renewable energy consumption;

[0033] Stability: Maintain voltage and frequency within the allowable range and avoid overcharging / overdischarging of the energy storage system;

[0034] Environmental protection: reduce carbon emissions;

[0035] Reliability: ensuring continuity of power supply to critical loads;

[0036] The local controller performs preliminary optimization calculations and needs to collect or predict the following data in real time:

[0037] Power generation side: real-time output and predicted output curve of distributed power sources, minimum start and stop time and ramp rate of diesel generators / gas turbines;

[0038] Load side: real-time load power, interruptible load priority, and future load forecast;

[0039] Energy storage system: current state of charge, charge and discharge efficiency, and life loss model;

[0040] Grid interaction: external grid electricity prices;

[0041] Power transmission limit at the grid connection point.

[0042] Specifically, in S2, the multi-objective optimization model is:

[0043] Minimize network losses: Among them, P lossi The power loss of the i-th line is calculated by minimizing the voltage deviation by the line resistance and the square of the current: V jis the actual voltage of the j-th bus, is the reference voltage of the bus, and the voltage deviation reflects the voltage quality of the distribution network;

[0044] Maximize the utilization of distributed power sources: PDGk is the actual output of the kth distributed power source, and PDGkmax is its rated output. This goal aims to make full use of the power generation capacity of distributed power sources and reduce the phenomenon of wind and solar power abandonment.

[0045] The constraints of the multi-objective optimization model are:

[0046] Power balance constraints: They represent the balance of active power and reactive power in the regional distribution network respectively;

[0047] Voltage Constraints: Ensure that the voltage fluctuations of each busbar are within the allowable range to ensure the safe and stable operation of power equipment;

[0048] Line capacity constraints: S k is the apparent power of the kth line, For its rated capacity, to prevent line overload;

[0049] Distributed power generation output constraints: Limit the output of distributed generation to within its rated range.

[0050] Preferably, in S2, the regional distribution network topology model is based on a hierarchical distributed structure, including a main grid, a regional distribution network layer, and a microgrid layer, wherein multiple microgrid layers constitute a regional distribution network layer, and multiple regional distribution network layers constitute a main grid; a mesh structure is formed by connecting nodes and lines, and its core elements include: the main grid: provides basic power supply, connected to the regional distribution network through high-voltage / medium-voltage busbars; the regional distribution network layer: consists of medium-voltage distribution lines, distribution transformers, and busbar nodes, responsible for power distribution and regulation; the microgrid layer: includes multiple independent microgrids, which are connected to the regional distribution network through interface devices;

[0051] The interaction between the main grid and the microgrid is as follows:

[0052] Grid-connected mode:

[0053] There is a two-way power flow between the microgrid and the main grid, with the main grid acting as a backup power source or power balancing support;

[0054] When the microgrid has excess power: it feeds power to the main grid;

[0055] When the microgrid is insufficient: purchase electricity from the main grid;

[0056] Island Mode:

[0057] The microgrid is disconnected from the main grid and operates independently, relying on internal energy storage and distributed power sources to maintain power supply;

[0058] The inverter of the microgrid simulates the droop characteristics of traditional generators to achieve power sharing when multiple microgrids are connected in parallel; the active power-frequency droop equation is: f = f0-k p (P-P0)

[0059] Where: f is the frequency, f0 is the rated frequency, k p is the active power droop coefficient, P is the active power, and P0 is the rated active power;

[0060] Voltage-reactive power droop equation: V = V 0- kq(Q-Q0)

[0061] Where: V is voltage, V0 is rated voltage, k q is the reactive droop coefficient, Q is the reactive power, and Q0 is the rated reactive power.

[0062] Preferably, in S5, the specific manner of dynamically adjusting the optimization model and control strategy is as follows:

[0063] Set optimization goals based on the operating scenario. These goals include: economic optimization or energy efficiency optimization; and consider both electrical and equipment constraints.

[0064] Divide the entire day into multiple control cycles. Each cycle predicts the system state for N future periods based on the current state. Construct an objective function that includes state tracking and control cost, and obtain the control variable by solving it.

[0065] Feedback-based PID control is used to quickly adjust local variables; relying on a hierarchical control architecture, the bottom edge controller performs fast response and the upper central controller performs global optimization.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] Improved collaborative efficiency: Through autonomous and coordinated operational optimization, an information exchange and coordination mechanism is established between microgrids, enabling resource sharing and complementary advantages across multiple microgrid systems. During peak load periods, each microgrid can coordinate the output of distributed power sources and the operating status of energy storage devices to meet load demands, improving the overall operational efficiency of the regional distribution network and reducing operating costs.

[0068] Reduced communication dependency: Distributed collaborative optimization algorithms and autonomous control strategies are used to reduce reliance on centralized communication. Even if some communication links fail, each microgrid can still make autonomous decisions and collaborative optimization based on its own local information and the limited information of neighboring microgrids, ensuring the stable operation of the regional distribution network and improving system reliability and robustness.

[0069] Enhanced Uncertainty Management Capabilities: Leveraging technologies such as model predictive control and stochastic optimization, we fully account for the uncertainty of distributed generation output and load demand. Through rolling optimization and real-time adjustment of operational strategies, we effectively address various uncertainties, improve the stability and reliability of regional distribution network operations, and mitigate risks associated with uncertainty.

[0070] Achieving Multi-Objective Comprehensive Optimization: This solution comprehensively considers multiple optimization objectives, including economics, the environment, and reliability, and employs a multi-objective collaborative optimization algorithm to determine the optimal operating strategy. It can flexibly adjust the weights of various objectives based on different operating scenarios and requirements, maximizing the overall benefits of the regional distribution network and meeting the requirements of sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of the optimization method of the present invention.

[0072] Figure 2 This is a schematic diagram of the architecture between the main grid, regional distribution network layer and microgrid layer in the present invention. DETAILED DESCRIPTION

[0073] The following is a combination of the embodiments of the present invention Figure 1-2 The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0074] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0075] See also Figure 1 The present invention provides a technical solution: a method for optimizing the autonomous and coordinated operation of a regional distribution network containing multiple microgrids:

[0076] S1: Data acquisition and preprocessing

[0077] Collect real-time operating data of each microgrid in the regional distribution network, including distributed power output, load information, bus voltage and line current.

[0078] Distributed power supply output collection:

[0079] Data sources: PV inverters, wind turbine converters, energy storage systems (BMS / PCS), diesel generator controllers, etc. Data collection methods: Smart meters / communication modules read real-time power (active / reactive) and operating status via Modbus TCP / RTU and IEC61850 (GOOSE / SV) protocols. Local controllers directly access generated power and efficiency curves via the PV inverter's built-in data interface (such as RS485 or CAN bus). Edge computing terminals use 1s sampling for high-frequency fluctuating power sources (such as PV), and 10ms sampling for energy storage systems.

[0080] Load Information Collection: Classified Collection: Monitorable Loads: Real-time power, current, and power factor are collected through smart meters (e.g., those conforming to the DL / T645 protocol). Non-Monitorable Loads: Non-Intrusive Load Monitoring (NILM) technology is used to identify the power usage patterns of each device by decomposing the total incoming current waveform. Alternatively, estimates can be made based on historical data combined with a load characteristic library (e.g., typical curves for air conditioning and lighting). Key Parameters: Active Power (kW), Reactive Power (kvar), and Daily Load Factor.

[0081] Bus voltage and line current acquisition: Traditional methods: Voltage transformer (PT): 0.2-level accuracy, acquires bus voltage (0-10kV). Current transformer (CT): Cooperates with protection devices to acquire line current (0-5A secondary side). New technologies: Synchronized phasor measurement unit (PMU): Based on GPS clock synchronization, it achieves μs-level phase measurement and supports dynamic voltage / current waveform capture (such as 50Hz / 100Hz sampling). Smart sensors: Wireless sensors with integrated LoRa / 5G communication are deployed in nodes such as ring main units and distribution transformers. Typical configuration: ABBREF615 protection and measurement and control device, GEPMU.

[0082] The collected data is preprocessed, including data cleaning, filtering, normalization and other operations, to improve data quality and provide an accurate data basis for subsequent analysis and optimization.

[0083] S2: Building a model

[0084] Construct a topological model of the regional distribution network to describe the connection relationship between microgrids and their interaction with the main grid.

[0085] Establish the distributed power generation model, load model and energy storage model within the microgrid, taking into account its output characteristics and operation constraints.

[0086] Based on the above model, a multi-objective optimization model is established with the goals of minimizing network loss, minimizing voltage deviation and maximizing distributed power utilization.

[0087] Multi-objective optimization model:

[0088] Minimize network losses: Among them, P lossi The power loss of the i-th line is calculated by the line resistance and the square of the current

[0089] Minimize voltage deviation: V j is the actual voltage of the j-th bus, is the reference voltage of the bus, and the voltage deviation reflects the voltage quality of the distribution network.

[0090] Maximize the utilization of distributed power sources: PDGk is the actual output of the kth distributed power source, and PDGkmax is its rated output. This goal aims to make full use of the power generation capacity of distributed power sources and reduce the phenomena of wind and solar power curtailment.

[0091] Constraints

[0092] Power balance constraints: Respectively represent the balance of active power and reactive power in the regional distribution network.

[0093] Voltage Constraints: Ensure that the voltage of each bus fluctuates within the allowable range to ensure the safe and stable operation of power equipment.

[0094] Line capacity constraints: S k is the apparent power of the kth line, to its rated capacity to prevent line overload.

[0095] Distributed power generation output constraints: Limit the output of distributed generation to within its rated range.

[0096] In S2, the regional distribution network topology model is centered on a hierarchical distributed structure, consisting of a main grid, a regional distribution network layer, and a microgrid layer. Multiple microgrid layers constitute the regional distribution network layer, and multiple regional distribution network layers constitute the main grid. A mesh structure is formed through node and line connections. Its core elements include: the main grid: provides basic power supply and is connected to the regional distribution network through high-voltage / medium-voltage busbars; the regional distribution network layer: consists of medium-voltage distribution lines, distribution transformers, and busbar nodes, and is responsible for power distribution and regulation; the microgrid layer: contains multiple independent microgrids, which are connected to the regional distribution network through interface devices;

[0097] The interaction between the main grid and the microgrid is as follows:

[0098] Grid-connected mode:

[0099] There is a two-way power flow between the microgrid and the main grid, with the main grid acting as a backup power source or power balancing support;

[0100] When the microgrid has excess power: it feeds power to the main grid;

[0101] When the microgrid is insufficient: purchase electricity from the main grid;

[0102] Island Mode:

[0103] The microgrid is disconnected from the main grid and operates independently, relying on internal energy storage and distributed power sources to maintain power supply;

[0104] The inverter of the microgrid simulates the droop characteristics of traditional generators to achieve power sharing when multiple microgrids are connected in parallel; the active power-frequency droop equation is: f = f0-k p (P-P0)

[0105] Where: f is the frequency, f0 is the rated frequency, k p is the active power droop coefficient, P is the active power, and P0 is the rated active power;

[0106] Voltage-reactive power droop equation: V = V0-kq(Q-Q0)

[0107] Where: V is voltage, V0 is rated voltage, k q is the reactive droop coefficient, Q is the reactive power, and Q0 is the rated reactive power.

[0108] S3: Autonomous collaborative control strategy

[0109] Each microgrid performs preliminary optimization calculations through a local controller based on its own operating data and optimization goals to generate a local operating strategy.

[0110] Microgrids exchange information through communication networks and share key operating information and optimization results.

[0111] Based on the sharing of key operating information, a collaborative optimization algorithm is used to adjust and optimize the operating strategy of each microgrid to achieve the optimal operation of the regional distribution network as a whole.

[0112] Building a microgrid model

[0113] Component Model: Build precise mathematical models for various components in the microgrid, such as distributed power sources (solar photovoltaic, wind power, etc.), energy storage devices (battery storage, supercapacitors, etc.), loads (residential loads, commercial loads, etc.), and grid connection points, describing their operating characteristics and constraints. For example, for solar photovoltaic modules, a model is built based on the relationship between their photoelectric conversion efficiency, light intensity, and output power; for battery storage, the charge and discharge efficiency and capacity decay characteristics are considered.

[0114] Operational constraints: Determine various constraints on microgrid operation, including power balance constraints (the sum of the output power of distributed power sources and the charging and discharging power of energy storage devices is equal to the sum of the load power and the grid interaction power), component capacity constraints (the output power of distributed power sources and energy storage devices cannot exceed their rated capacity), voltage and frequency constraints (the voltage and frequency within the microgrid must be maintained within the specified range), etc.

[0115] Determine optimization goals

[0116] Economic Objective: Minimize the microgrid's operating costs, including fuel costs (for microgrids using fossil fuel generation equipment such as internal combustion engines), electricity purchase costs (the cost of purchasing electricity from the external grid), equipment maintenance costs, and energy storage device charging and discharging costs. By optimizing the operating status of each component, the overall operating cost is reduced.

[0117] Environmental goals: Consider reducing pollutant emissions during microgrid operation, aiming to minimize carbon emissions or other pollutant emissions. For example, for distributed power sources using fossil fuels, calculate pollutant emissions based on their generated power and emission factors, and adjust power output to reduce environmental impact.

[0118] Reliability goal: Improve the reliability of microgrid power supply, aiming to minimize power outages or maximize power supply reliability. By rationally configuring energy storage devices and optimizing the operation of distributed power sources, we can ensure that the power supply needs of the load can be met under various operating conditions and reduce the occurrence of power outages.

[0119] Using collaborative optimization algorithm

[0120] Distributed collaborative optimization: The microgrid is divided into multiple subsystems, such as the distributed power subsystem, energy storage subsystem, and load subsystem. Each subsystem exchanges information via a communication network. Using distributed collaborative optimization algorithms, such as the alternating direction method of multipliers (ADMM), each subsystem independently performs optimization calculations based on its own local information and information received from other subsystems. The results are then passed to other subsystems, and the global optimal solution is gradually reached through iteration. This reduces computational complexity and improves the algorithm's convergence speed.

[0121] Model Predictive Control (MPC) Collaborative Optimization: Utilizing model predictive control technology, based on the microgrid's current state and future forecasts (such as load, light intensity, and wind speed), a finite-time optimization problem is solved within each control cycle to determine the optimal control strategy at the current moment. Through continuous rolling optimization, the operating state of each component is adjusted in real time, taking into account the uncertainties and dynamic changes in microgrid operation, to achieve long-term optimized operation. Furthermore, MPC can be combined with other optimization algorithms, such as genetic algorithms, leveraging their global search capabilities to optimize MPC parameters and enhance optimization effectiveness.

[0122] Multi-objective collaborative optimization: When there are multiple optimization objectives, multi-objective collaborative optimization algorithms such as weighted methods, ε-constraint methods, and Pareto optimal solution search algorithms are used. The weighted method transforms multiple objective functions into a single objective function using weighted coefficients for optimization. By adjusting the weighted coefficients, different optimization results can be obtained, reflecting the trade-offs between different objectives. The ε-constraint method uses one objective as the optimization objective and transforms the other objectives into constraints. Different optimal solutions are obtained by adjusting the values of the constraints. The Pareto optimal solution search algorithm directly searches the Pareto frontier of the multi-objective optimization problem to obtain a set of non-dominated solutions for decision makers to select based on actual needs.

[0123] Real-time monitoring and feedback adjustment

[0124] Real-time monitoring: Various sensors installed in the microgrid monitor the output power of distributed power sources, the state of charge (SOC) of energy storage devices, load changes, grid operating parameters (voltage, frequency, etc.), and environmental parameters (light intensity, temperature, wind speed, etc.) in real time. This real-time monitoring data is transmitted to the microgrid's control center, providing accurate real-time information for optimization algorithms.

[0125] Feedback Adjustment: Based on real-time monitoring data and the results of the optimization algorithm, the operating strategies of each microgrid component are adjusted in real time. For example, if the output power of a distributed power source deviates from the optimized set point due to changes in light intensity or wind speed, its control parameters are adjusted to bring its output power back to near the optimal value. If the load suddenly increases, the discharge power of the energy storage device is adjusted or the output of the distributed power source is increased based on the results of the optimization algorithm to meet the load demand while ensuring the stable operation of the microgrid.

[0126] S4: Optimization algorithm solution

[0127] Intelligent optimization algorithms, such as particle swarm optimization and genetic algorithms, are used to solve multi-objective optimization models. These algorithms have the advantages of strong global search capabilities and fast convergence speed, and can find the optimal or near-optimal solution in a complex solution space.

[0128] During the algorithm solving process, the feasibility of the optimization results is tested and adjusted according to the actual operating conditions and constraints to ensure the operability of the optimization plan.

[0129] S5: Real-time monitoring and adjustment

[0130] Real-time monitoring of the operating status of the regional distribution network, including the power balance, voltage stability, and equipment operating status of each microgrid.

[0131] Based on real-time monitoring data, problems or deviations that occur during operation can be discovered in a timely manner, and the optimization model and control strategy can be dynamically adjusted to ensure that the regional distribution network is always in the optimal or near-optimal operating state.

[0132] The specific methods for dynamically adjusting the optimization model and control strategy are as follows:

[0133] Set optimization goals based on the operating scenario. These goals include: economic optimization or energy efficiency optimization; and consider both electrical and equipment constraints.

[0134] Divide the entire day into multiple control cycles. Each cycle predicts the system state for N future periods based on the current state. Construct an objective function that includes state tracking and control cost, and obtain the control variable by solving it.

[0135] Feedback-based PID control is used to quickly adjust local variables; relying on a hierarchical control architecture, the bottom edge controller performs fast response and the upper central controller performs global optimization.

[0136] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the appended claims rather than the above description. Therefore, it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any figure signs in the claims should not be regarded as limiting the claims involved.

[0137] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the autonomous and coordinated operation of a regional distribution network containing multiple microgrids, characterized by: The specific steps of the autonomous coordinated operation optimization method for a regional distribution network containing multiple microgrids are as follows: S1: Data collection and preprocessing: Collect real-time operating data of each microgrid in the regional distribution network; Preprocess the collected data, including data cleaning, filtering, and normalization, to improve data quality and provide an accurate data basis for subsequent analysis and optimization; S2: Build the model: Construct a topological model of the regional distribution network to describe the connection between microgrids and how they interact with the main grid; Establish distributed power generation models, load models, and energy storage models within the microgrid, taking into account their output characteristics and operating constraints; Based on the above model, a multi-objective optimization model is established with the goals of minimizing network loss, minimizing voltage deviation and maximizing distributed generation utilization; S3: Autonomous collaborative control strategy: Each microgrid performs preliminary optimization calculations through a local controller based on its own operating data and optimization goals to generate a local operating strategy; S4: Optimization algorithm solution: Adopt intelligent optimization algorithm to solve multi-objective optimization model; During the algorithm solving process, the feasibility of the optimization results is tested and adjusted according to the actual operation conditions and constraints to ensure the operability of the optimization plan; S5: Real-time monitoring and adjustment: Monitor the operating status of the regional distribution network in real time, promptly identify problems or deviations that occur during operation based on real-time monitoring data, and dynamically adjust the optimization model and control strategy to ensure that the regional distribution network is always in the optimal or near-optimal operating state.

2. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: In S1, the real-time operating data of each microgrid in the regional distribution network includes distributed power output, load information, bus voltage and line current.

3. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: The multi-objective optimization model described in S2 is: Minimize network losses: Among them, P lossi The power loss of the i-th line is calculated by minimizing the voltage deviation by the line resistance and the square of the current: V j is the actual voltage of the jth bus, V j ref is the reference voltage of the bus, and the voltage deviation reflects the voltage quality of the distribution network; Maximize the utilization of distributed power sources: PDGk is the actual output of the kth distributed power source, and PDGkmax is its rated output. This goal aims to make full use of the power generation capacity of distributed power sources and reduce the phenomenon of wind and solar power abandonment.

4. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: The constraints of the multi-objective optimization model are: Power balance constraints: They represent the balance of active power and reactive power in the regional distribution network respectively; Voltage constraint: V j min ≤V j ≤V j max , ensure that the voltage of each busbar fluctuates within the allowable range to ensure the safe and stable operation of power equipment; Line capacity constraints: S k is the apparent power of the kth line, For its rated capacity, to prevent line overload; Distributed power generation output constraints: Limit the output of distributed generation to within its rated range.

5. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: In S2, the regional distribution network topology model is centered on a hierarchical distributed structure, consisting of a main grid, a regional distribution network layer, and a microgrid layer. Multiple microgrid layers constitute the regional distribution network layer, and multiple regional distribution network layers constitute the main grid. A mesh structure is formed through node and line connections, and its core elements include: the main grid: provides basic power supply and is connected to the regional distribution network through high-voltage / medium-voltage busbars; the regional distribution network layer: consists of medium-voltage distribution lines, distribution transformers, and busbar nodes, and is responsible for power distribution and regulation; the microgrid layer: contains multiple independent microgrids, which are connected to the regional distribution network through interface devices.

6. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 5, characterized in that: The interaction between the main grid and the microgrid is as follows: Grid-connected mode: Power flows bidirectionally between the microgrid and the main grid, with the main grid serving as a backup power source or power balancing support; When the microgrid has excess power: it feeds power to the main grid; When the microgrid is insufficient: purchase electricity from the main grid; Island Mode: The microgrid is disconnected from the main grid and operates independently, relying on internal energy storage and distributed power sources to maintain power supply; The microgrid's inverter simulates the droop characteristics of traditional generators to achieve power sharing when multiple microgrids are connected in parallel; Active power-frequency droop equation: f = f0-k p (P-P0) Where: f is the frequency, f0 is the rated frequency, k p is the active power droop coefficient, P is the active power, and P0 is the rated active power; Voltage-reactive power droop equation: V = V0-kq(Q-Q0) Where: V is voltage, V0 is rated voltage, k q is the reactive droop coefficient, Q is the reactive power, and Q0 is the rated reactive power.

7. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: In S3, microgrids exchange information through the communication network, share key operating information and optimization results, and use collaborative optimization algorithms to adjust and optimize the operating strategies of each microgrid based on the shared key operating information to achieve optimal operation of the regional distribution network as a whole.

8. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 7, characterized in that: The information exchanged between the microgrids includes operating data, control instructions and market information; Operational data: real-time information on microgrid power generation, load demand, energy storage status, and voltage / frequency; Control instructions: power dispatch instructions, grid-connected / off-grid switching commands, and fault isolation signals; Market information: electricity trading prices, demand response signals, and carbon emissions data.

9. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: In S4, intelligent optimization algorithms include particle swarm optimization and genetic algorithm.

10. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: In S5, the operating status of the regional distribution network includes the power balance, voltage stability and equipment operating status of each microgrid.

11. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: In S3, the optimization objectives of the microgrid include economy, stability, environmental protection, and reliability; Economic efficiency: minimizing power generation costs, minimizing external grid power purchase costs, and maximizing renewable energy consumption; Stability: Maintain voltage and frequency within the allowable range and avoid overcharging / overdischarging of the energy storage system; Environmental protection: reduce carbon emissions; Reliability: ensuring continuity of power supply to critical loads; The local controller performs preliminary optimization calculations and needs to collect or predict the following data in real time: Power generation side: real-time output and predicted output curve of distributed power sources, minimum start and stop time and ramp rate of diesel generators / gas turbines; Load side: real-time load power, interruptible load priority, and future load forecast; Energy storage system: current state of charge, charge and discharge efficiency, and life loss model; Grid interaction: external grid electricity prices; Power transmission limit at the grid connection point.

12. The method for optimizing autonomous and coordinated operation of a regional distribution network containing multiple microgrids according to claim 1, characterized in that: In S5, the specific methods for dynamically adjusting the optimization model and control strategy are as follows: Set optimization goals based on the operating scenario. These goals include: economic optimization or energy efficiency optimization; and consider both electrical and equipment constraints. Divide the entire day into multiple control cycles. Each cycle predicts the system state for N future periods based on the current state. Construct an objective function that includes state tracking and control cost, and obtain the control variable by solving it. Feedback-based PID control is used to quickly adjust local variables; relying on a hierarchical control architecture, the bottom edge controller performs fast response and the upper central controller performs global optimization.

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