Alternating current and direct current flexible interconnection power distribution network optimization scheduling method

Abstract

The invention discloses an optimal scheduling method for an AC / DC flexible interconnection power distribution network. The method comprises the following steps: firstly, deeply analyzing consideration factors such as uncertainty of a distributed power supply, dynamic change of a load and characteristics of flexible DC equipment; then, targets of minimizing the operation cost, maximizing the energy utilization efficiency, optimizing the electric energy quality and achieving the highest reliability are set; in the aspect of a scheduling strategy, intra-regional balance and cross-regional power exchange are realized by adopting layered and partitioned scheduling, intelligent cooperation of all units is realized by applying multi-agent cooperative scheduling, and a scheme is adjusted according to real-time data through real-time optimization; then, a hybrid intelligent algorithm combining a genetic algorithm and a deep learning algorithm is constructed in algorithm selection, and optimization and improvement are continuously carried out; and finally, establishing a coordination control mechanism to ensure effective cooperation of the flexible DC system and the traditional AC system. According to the invention, new requirements and new challenges faced by the AC / DC flexible interconnection power distribution network can be more effectively handled, so that safe, stable and efficient operation of the AC / DC flexible interconnection power distribution network is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of AC / DC flexible interconnected distribution networks, and in particular relates to an optimization scheduling method for AC / DC flexible interconnected distribution networks. Background Art

[0002] The integration of flexible DC technology with existing AC networks to build hybrid AC / DC distribution networks is a new development trend. In recent years, numerous research institutions both domestically and internationally have conducted research on flexible AC / DC interconnected distribution network architectures, achieving technological progress and achieving localized applications. For example, the CPES Center at Virginia Tech in the United States has proposed a hierarchical hybrid AC / DC distribution network structure. The system is divided into picogrids, nanogrids, microgrids, and subgrids based on voltage levels, with each network system connected via flexible interconnection devices. For example, Mitsubishi Corporation in Japan has developed an AC / DC hybrid microgrid system that, in addition to supplying power via a DC bus, also connects to the external AC system via a bypass backup line to improve power supply reliability. The off-grid microgrid system designed by the Zhejiang Electric Power Research Institute utilizes a hybrid AC / DC network structure. Tsinghua University and the Shenzhen Power Supply Bureau have proposed using flexible interconnection devices to form a bipolar ±10kV DC busbar to interconnect two 110kV substations, thereby improving power supply reliability.

[0003] At present, the application of flexible DC technology in urban distribution networks mainly focuses on feasibility studies, operating characteristics analysis and small-capacity prototype development. There is no systematic theoretical and technical system for the practical key equipment for building AC / DC hybrid distribution networks, as well as the resulting key technologies such as planning and design, coordinated control and optimized scheduling. In addition, the growing scale of distributed power sources, electric vehicles and DC loads have also brought new demands and challenges to the optimized scheduling of distribution networks.

[0004] In order to solve the above problems, it is necessary to develop an optimal scheduling method for AC / DC flexible interconnected distribution networks. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide an AC / DC flexible interconnected distribution network optimization scheduling method, which can more effectively respond to the new demands and new challenges faced by the AC / DC flexible interconnected distribution network, thereby achieving safe, stable and efficient operation of the AC / DC flexible interconnected distribution network.

[0006] The object of the present invention is achieved as follows: a method for optimizing the scheduling of an AC / DC flexible interconnected power distribution network, comprising the following steps:

[0007] Step A, Considerations: Detailed analysis of the key characteristics of the AC / DC flexible interconnected distribution network, namely, the uncertainty of distributed generation, dynamic load changes, and the characteristics of flexible DC equipment;

[0008] Step B, target setting: After understanding the actual situation of the power grid, set the target for optimized dispatch based on the above considerations;

[0009] Step C, Scheduling Strategy: Based on the previously set goals and factors, formulate a specific scheduling strategy, including hierarchical partition scheduling and multi-agent collaborative scheduling;

[0010] Step D, algorithm selection: Based on the determined scheduling strategy, a hybrid intelligent algorithm is selected to solve the optimal scheduling solution. The global search capability of the genetic algorithm and the predictive capability of the deep learning algorithm are used to provide computational and data support for the scheduling strategy.

[0011] Step E, coordinated control: Based on the previous goal setting, scheduling strategy and algorithm selection, a coordinated control mechanism is established to ensure effective coordination between the flexible DC system and the traditional AC system to ensure stable operation of the power grid.

[0012] Preferably, the step A is specifically:

[0013] (1) Detailed analysis of distributed power uncertainty:

[0014] First, historical meteorological data and distributed power output data are collected to establish a distributed power output forecasting model based on time series analysis and machine learning algorithms. This forecasting model is used to make short-term and medium-term forecasts of distributed power output, with a forecast time span covering the next few hours to days.

[0015] Then, the probability distribution of the distributed power output is determined based on the prediction results. Through multiple simulations and predictions, the probability density function of the distributed power output in different time periods is obtained, so that various possible output conditions can be considered in scheduling.

[0016] (2) In-depth study of load dynamic changes:

[0017] First, smart meters are installed to collect real-time load data. Big data analytics are then used to analyze the time series characteristics, seasonal variations, and user behavior patterns of the load. By analyzing historical load data, patterns of load variation between weekdays and weekends, and in different seasons, as well as the electricity usage characteristics of specific user groups, can be identified.

[0018] Then, combining economic development trends and population growth factors, we predict future load growth and estimate the load growth trend in the next few years to provide a reference for power grid planning and scheduling;

[0019] (3) Comprehensive understanding of the characteristics of flexible DC equipment:

[0020] First, the key equipment in the flexible DC system is tested and modeled. The efficiency, response time, and power regulation range of each device are measured, and an accurate mathematical model of the equipment is established to accurately consider the operating limitations and performance characteristics of the equipment during scheduling.

[0021] Then, the interaction between the flexible DC system and the AC system is studied, and the impact of the reactive power injected into the AC system by the flexible DC system on the AC voltage is analyzed, as well as the impact of AC system faults on the flexible DC system, providing a basis for coordinated control.

[0022] Preferably, the step B is specifically as follows:

[0023] (1) Minimize operating costs:

[0024] First, a power generation cost model was established, taking into account the fuel costs, maintenance costs, and carbon emission costs of different power sources. For traditional thermal power generation, the fuel costs and equipment maintenance costs were calculated; for distributed renewable energy, the equipment investment costs and operation and maintenance costs were considered, as well as the renewable energy subsidy policy factors.

[0025] Then, optimize the equipment operation strategy to reduce equipment loss;

[0026] (2) Maximizing energy efficiency:

[0027] First, establish energy efficiency indicators to comprehensively evaluate the energy efficiency of the entire distribution network;

[0028] Then, optimize the power flow distribution and reduce line losses;

[0029] (3)Optimal power quality:

[0030] First, set voltage fluctuation and frequency stability indicators; through real-time monitoring of the grid voltage and frequency, timely adjust power output and reactive power compensation equipment to ensure that power quality meets requirements;

[0031] Then, use advanced power electronic equipment and control technology to suppress harmonics and voltage flicker and improve power quality;

[0032] (4) Highest reliability:

[0033] First, a reliability index system is established, including power supply reliability, equipment reliability, and system stability, to assess the reliability level of the power grid;

[0034] Then, optimize fault response strategies to improve fault isolation and recovery speed.

[0035] Preferably, the step C is specifically:

[0036] (1) Hierarchical partition scheduling:

[0037] First, the distribution network is divided into several regions based on the topology and load distribution of the power grid. Each region has a regional control center responsible for power dispatching, load management, and equipment monitoring within the region. Each region is equipped with a regional control system.

[0038] Then, local distributed power sources and energy storage devices are prioritized within the region to meet local load demands. When the output of the local distributed power source exceeds the load demand, the excess energy can be stored in the local energy storage device or transmitted to other regions through the flexible DC system. When the output of the local distributed power source is insufficient, the local energy storage device is prioritized for discharge, or power is imported from other regions.

[0039] Finally, cross-region power exchange is carried out through the flexible DC system to achieve global optimization;

[0040] (2) Multi-agent collaborative scheduling:

[0041] First, the power source, load, energy storage device, and flexible DC system are considered as different intelligent agents. Each intelligent agent has autonomous decision-making and communication capabilities and can make corresponding decisions based on its own status and surrounding environment information.

[0042] Then, a communication mechanism between agents is established to achieve information sharing and collaborative decision-making;

[0043] Finally, a distributed optimization algorithm is used to achieve collaborative scheduling of multiple agents;

[0044] (3) Real-time optimization:

[0045] First, a real-time monitoring system was established to collect and analyze the voltage, current, and power parameters of the power grid in real time, and transmit the power grid operation data to the dispatching center in real time so that dispatchers can promptly understand the operating status of the power grid.

[0046] Then, based on real-time monitoring data, a fast optimization algorithm is used to adjust the dispatch plan in real time. Based on the current grid status and the forecast for a period of time in the future, the optimal dispatch plan is quickly calculated and issued to each intelligent agent for execution in a timely manner.

[0047] Finally, consider response strategies for emergencies and improve the resilience of the power grid.

[0048] Preferably, the step D is specifically:

[0049] (1) Construction of hybrid intelligent algorithm:

[0050] First, we combine the advantages of genetic algorithms and deep learning algorithms to build a hybrid intelligent algorithm;

[0051] Then, the encoding method, fitness function and genetic operation parameters of the genetic algorithm are determined;

[0052] Finally, a deep learning algorithm is used to establish a distributed power output and load forecasting model, which is trained on historical data to predict the distributed power output and load demand in the future.

[0053] (2) Algorithm optimization and improvement:

[0054] First, in order to improve the efficiency and accuracy of the algorithm, the hybrid intelligent algorithm can be optimized and improved;

[0055] Then, simulated annealing and taboo search local search algorithms are introduced and combined with genetic algorithms to improve the local search ability of the algorithm.

[0056] Preferably, the step E is specifically as follows:

[0057] (1) Establish a coordination and control mechanism:

[0058] First, a coordinated control strategy is developed to clarify the coordinated control method between the flexible DC system and the traditional AC system;

[0059] Then, a communication network is established to realize information exchange between the flexible DC system and the traditional AC system;

[0060] Finally, a coordinated controller is designed to achieve unified control of the flexible DC system and the traditional AC system;

[0061] (2) Implementation of coordinated control:

[0062] Under normal operating conditions, the coordination controller automatically adjusts the power output of the flexible DC system and the operating parameters of the traditional AC system according to the load demand of the power grid and the output of distributed power sources, achieving optimal power flow distribution and power quality control.

[0063] In the event of a fault, the coordination controller quickly initiates the fault response strategy to achieve fault isolation and recovery.

[0064] Due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows: the present invention first deeply analyzes factors such as the uncertainty of distributed power sources, dynamic changes in loads and characteristics of flexible DC equipment; then sets the goals of minimizing operating costs, maximizing energy utilization efficiency, optimizing power quality and maximizing reliability; then, in terms of scheduling strategy, hierarchical and partitioned scheduling is adopted to achieve regional balance and cross-regional power exchange, and multi-agent collaborative scheduling is used to allow each unit to collaborate intelligently, and the plan is adjusted according to real-time data through real-time optimization; then, in terms of algorithm selection, a hybrid intelligent algorithm combining genetic algorithm and deep learning algorithm is constructed, and continuously optimized and improved; finally, a coordinated control mechanism is established to ensure the effective cooperation between flexible DC and traditional AC systems; it can more effectively respond to the new demands and challenges faced by AC / DC flexible interconnected distribution networks, thereby achieving safe, stable and efficient operation of AC / DC flexible interconnected distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0066] The technical solution of the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0067] like Figure 1 As shown, the purpose of the present invention is achieved as follows: a method for optimizing the scheduling of an AC / DC flexible interconnected distribution network, comprising the following steps:

[0068] Step A, considerations: Detailed analysis of the key characteristics of the AC / DC flexible interconnected distribution network, namely, the uncertainty of distributed generation, dynamic load changes, and the characteristics of flexible DC equipment.

[0069] (1) Detailed analysis of distributed power uncertainty:

[0070] Collect historical meteorological data and distributed power output data to build a distributed power output forecasting model based on time series analysis and machine learning algorithms. For example, LSTM (Long Short-Term Memory) networks can be used to make short-term and medium-term forecasts of the output of distributed power sources such as solar and wind power, with forecasts covering the next few hours to days.

[0071] Based on the prediction results, the probability distribution of the distributed generation output is determined. For example, multiple simulations can be performed to obtain the probability density function of the distributed generation output in different time periods, so that various possible output conditions can be considered in scheduling.

[0072] (2) In-depth study of load dynamic changes:

[0073] Install smart meters and other equipment to collect real-time load data. Utilize big data analysis techniques to analyze load time series characteristics, seasonal variations, and user behavior patterns. For example, by analyzing historical load data, we can discover patterns in load variations between weekdays and weekends, and in different seasons, as well as the electricity usage characteristics of specific user groups (such as industrial users and commercial congestion).

[0074] Future load growth can be predicted by combining factors such as economic development trends and population growth. For example, based on local economic development plans and population growth forecasts, load growth trends over the next few years can be estimated to provide a reference for grid planning and scheduling.

[0075] (3) Comprehensive understanding of the characteristics of flexible DC equipment:

[0076] Performance testing and modeling of key equipment in flexible DC systems, such as converters and circuit breakers. For example, parameters such as converter efficiency, response time, and power regulation range can be measured to build accurate mathematical models of the equipment, allowing accurate consideration of its operating limitations and performance characteristics during scheduling.

[0077] Study the interaction between the Flexible DC system and the AC system. For example, the impact of reactive power injected into the AC system by the Flexible DC system on the AC voltage, as well as the impact of AC system faults on the Flexible DC system, can be analyzed to provide a basis for coordinated control.

[0078] Step B, target setting: After understanding the actual situation of the power grid, set the target of optimized scheduling based on the above considerations.

[0079] (1) Minimize operating costs:

[0080] Build a power generation cost model that takes into account fuel costs, maintenance costs, and carbon emission costs for different power sources. For example, for traditional thermal power generation, calculate its fuel costs and equipment maintenance costs; for distributed renewable energy, consider its equipment investment costs and operation and maintenance costs, and factor in renewable energy subsidy policies.

[0081] Optimize equipment operation strategies and reduce equipment losses. For example, transformer switching can be properly arranged to avoid transformer operation at low voltage load rates, reducing transformer no-load losses. Flexible DC system power regulation can also be optimized to reduce converter switching losses.

[0082] (2) Maximizing energy efficiency:

[0083] Establish energy efficiency indicators, including energy conversion efficiency, transmission efficiency, and terminal utilization efficiency. For example, the generation efficiency of distributed power sources, the transmission efficiency of the power grid, and the energy utilization efficiency of user terminal devices can be calculated to comprehensively evaluate the energy utilization efficiency of the entire distribution network.

[0084] Optimize power flow distribution and reduce line losses. For example, by adjusting the power flow direction and magnitude of the flexible DC system, power flow can be more rationally distributed in the grid, reducing line resistance losses. Reactive power compensation equipment can also be used to optimize the power factor of the grid, reduce reactive power transmission, and improve energy efficiency.

[0085] (3)Optimal power quality:

[0086] Set voltage fluctuation and frequency stability indicators. For example, you can specify that the voltage fluctuation range is within ±5% and the frequency deviation is within ±0.2Hz. Then, by monitoring the voltage and frequency of the power grid in real time, you can adjust the power output and reactive power compensation equipment in a timely manner to ensure that the power quality meets the requirements.

[0087] Advanced power electronics and control technologies can be used to suppress harmonics and voltage flicker. For example, active filters and dynamic voltage restorers can be installed at distributed power access points and critical loads to effectively eliminate harmonics and voltage flicker and improve power quality.

[0088] (4) Highest reliability:

[0089] Establish a reliability indicator system, including power supply reliability, equipment reliability, and system stability. For example, the average outage duration, average outage frequency, and equipment failure rate can be calculated to assess the reliability level of the power grid.

[0090] Optimize fault response strategies to improve fault isolation and recovery speed. For example, the flexible DC system's rapid control capabilities can be leveraged to quickly isolate the faulty area when a fault occurs, and restore power as quickly as possible by transferring loads or activating backup power sources.

[0091] Step C, Scheduling Strategy: Based on the previously set goals and factors considered, formulate a specific scheduling strategy, including hierarchical partition scheduling and multi-agent collaborative scheduling.

[0092] (1) Hierarchical partition scheduling:

[0093] Based on the grid's topology and load distribution, the distribution network is divided into several regions. Each region has a regional control center responsible for power dispatch, load management, and equipment monitoring within that region. For example, a city's distribution network can be divided into several administrative regions or power supply areas, each equipped with a regional control system.

[0094] Within a region, priority is given to dispatching local distributed power sources and energy storage devices to meet local load demands. For example, when the output of the local distributed power source exceeds the load demand, the excess energy can be stored in the local energy storage device or transmitted to other regions via the flexible DC system. When the output of the local distributed power source is insufficient, priority is given to discharging the local energy storage device or importing power from other regions.

[0095] Cross-region power exchange through the Flexible DC system enables global optimization. For example, if the load in a certain region suddenly increases and the local power supply cannot meet the demand, the Flexible DC system can import power from adjacent regions to provide inter-regional power support.

[0096] (2) Multi-agent collaborative scheduling:

[0097] The power source, load, energy storage device, and flexible DC system are considered as different intelligent agents. Each intelligent agent has autonomous decision-making and communication capabilities, and can make appropriate decisions based on its own status and surrounding environmental information. For example, the distributed energy intelligent agent can decide whether to supply power to the grid based on its own processing status and market price information; the load intelligent agent can adjust its power consumption behavior based on its own needs and the grid supply status.

[0098] Establish communication mechanisms between agents to enable information sharing and collaborative decision-making. For example, through wireless communication networks or power line communication technologies, agents can exchange data such as power demand, pricing information, and equipment status in real time, and jointly negotiate the optimal scheduling plan.

[0099] Distributed optimization algorithms can be used to achieve collaborative scheduling of multiple agents. For example, a distributed particle swarm optimization algorithm or a distributed genetic algorithm can be used to allow each agent to perform optimization calculations locally, and through information exchange and coordination mechanisms, gradually converge to the global optimal solution.

[0100] (3) Real-time optimization:

[0101] Establish a real-time monitoring system to collect and analyze the voltage, current, power and other parameters of the power grid in real time, and transmit the power grid operation data to the dispatching center in real time so that the dispatching personnel can grasp the operation status of the power grid in a timely manner.

[0102] Based on real-time monitoring data, fast optimization algorithms are used to adjust the dispatch plan in real time. For example, model predictive control algorithms or online optimization algorithms can be used to quickly calculate the optimal dispatch plan based on the current grid status and future forecasts, and then issue it to each intelligent agent for execution.

[0103] Consider emergency response strategies to improve the grid's resilience. For example, when a distributed power source suddenly fails or the load suddenly increases, the dispatch system can quickly activate the backup power source or adjust the power output of the flexible DC system to maintain stable grid operation.

[0104] Step D, algorithm selection: Based on the determination of the scheduling strategy, a hybrid intelligent algorithm is selected to solve the optimal scheduling solution, and the global search capability of the genetic algorithm and the predictive capability of the deep learning algorithm are used to provide computational and data support for the scheduling strategy.

[0105] (1) Construction of hybrid intelligent algorithm:

[0106] Combining the advantages of genetic algorithms and deep learning algorithms, a hybrid intelligent algorithm is constructed. Genetic algorithms have strong global search capabilities and good robustness, and can be used to find the optimal scheduling solution. Deep learning, on the other hand, has powerful data analysis and prediction capabilities and can be used to predict the output and load of distributed power sources, providing data support for scheduling.

[0107] Determine the genetic algorithm's encoding method, fitness function, and genetic operations, among other parameters. For example, binary encoding can be used to represent the scheduling solution as a binary string. The fitness function can be constructed based on a comprehensive objective function defined by the target setting to measure the performance of the scheduling solution. Genetic operations can include selection, crossover, and mutation, enabling continuous iterative evolution to find the optimal scheduling solution.

[0108] Use deep learning algorithms to build distributed power output and load forecasting models. For example, deep learning models such as CNN convolutional neural networks or RNN recurrent neural networks can be used to train historical data to predict distributed power output and load demand over a period of time.

[0109] (2) Algorithm optimization and improvement:

[0110] To improve the efficiency and accuracy of the algorithm, hybrid intelligent algorithms can be optimized and improved. For example, parallel computing technology can be used to speed up the search speed of genetic algorithms, and technologies such as adaptive learning rates can be used to improve the training effect of deep learning.

[0111] Introducing simulated annealing and tabu search local search algorithms, combined with genetic algorithms, improves the algorithm's local search capabilities. For example, during the evolution of a genetic algorithm, when the fitness value of an individual is good, simulated annealing or tabu search can be used to locally optimize that individual, further improving the quality of the scheduling solution.

[0112] Step E, coordinated control: Based on the previous goal setting, scheduling strategy and algorithm selection, a coordinated control mechanism is established to ensure effective coordination between the flexible DC system and the traditional AC system to ensure stable operation of the power grid.

[0113] (1) Establish a coordination and control mechanism:

[0114] Develop a coordinated control strategy to clarify the coordinated control method between the flexible DC system and the traditional AC system. For example, master-slave control, peer-to-peer control, or hierarchical control can be adopted. Select the appropriate coordinated control strategy based on the actual situation and operational requirements of the power grid.

[0115] Establish a communication network to enable information exchange between the flexible DC system and the traditional AC system. For example, high-speed fiber-optic communication networks or wireless communication technologies can be used to ensure real-time and reliable information transmission.

[0116] Design a coordinated controller to achieve unified control of the Flexible DC system and the traditional AC system. For example, advanced control technologies such as fuzzy controllers and neural network controllers can be used to automatically adjust the operating parameters of the Flexible DC system and the traditional AC system according to the operating status and target requirements of the power grid to achieve coordinated control.

[0117] (2) Implementation of coordinated control:

[0118] Under normal operating conditions, the coordinated controller automatically adjusts the power output of the Flexible DC system and the operating parameters of the traditional AC system based on the grid's load demand and the output of distributed generation units, achieving optimal power flow distribution and power quality control. For example, when the output of distributed generation units is high, the coordinated controller can transmit excess power to other areas through the Flexible DC system or store it in energy storage devices. When the load demand is high, the coordinated controller can call on the energy storage devices to discharge power or import power from other areas through the Flexible DC system.

[0119] In the event of a fault, the coordinated controller rapidly activates fault response strategies to isolate and restore the fault. For example, if an AC system fault occurs, the coordinated controller can quickly disconnect the faulty area from the flexible DC system to prevent the fault from spreading. Simultaneously, the flexible DC system's rapid control capabilities can restore power to non-faulty areas.

[0120] In summary, the present invention can more effectively respond to the new demands and challenges faced by the AC / DC flexible interconnected distribution network through the above optimization scheduling method, and realize the safe, stable and efficient operation of the AC / DC flexible interconnected distribution network.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the dispatching of an AC / DC flexible interconnected distribution network, characterized in that: The steps include: Step A, factors to be considered: Detailed analysis of the key characteristics of the AC / DC flexible interconnected distribution network, namely, the uncertainty of distributed generation, dynamic changes in loads, and characteristics of flexible DC equipment; Step B, target setting: After understanding the actual situation of the power grid, set the target of optimized dispatch according to the above considerations; Step C, Scheduling Strategy: Based on the previously set goals and factors, formulate specific scheduling strategies, including hierarchical partition scheduling and multi-agent collaborative scheduling; Step D, algorithm selection: On the basis of determining the scheduling strategy, a hybrid intelligent algorithm is selected to solve the optimal scheduling solution, and the global search capability of the genetic algorithm and the prediction capability of the deep learning algorithm are used to provide calculation and data support for the scheduling strategy; Step E, coordinated control: Based on the previous target setting, dispatching strategy and algorithm selection, a coordinated control mechanism is established to ensure effective coordination between the flexible DC system and the traditional AC system to ensure stable operation of the power grid.

2. The AC / DC flexible interconnection distribution network optimization scheduling method according to claim 1 is characterized in that: The step A is specifically as follows: (1) Detailed analysis of distributed power uncertainty: First, collect historical meteorological data and distributed power output data, establish a distributed power output prediction model based on time series analysis and machine learning algorithms, and use the prediction model to make short-term and medium-term predictions of distributed power output. The prediction time span can cover the next few hours to days. Then, the probability distribution of the distributed power output is determined based on the prediction results. Through multiple simulations and predictions, the probability density function of the distributed power output in different time periods is obtained, so that various possible output conditions can be considered in scheduling. (2) In-depth study of load dynamic changes: First, install smart meters to collect load data in real time, and use big data analysis technology to analyze the time series characteristics, seasonal changes and user behavior patterns of load. By analyzing historical load data, we can find the load change patterns on weekdays and weekends, in different seasons, and the electricity consumption characteristics of certain specific user groups. Then, combined with economic development trends and population growth factors, the future load growth is predicted, and the load growth trend in the next few years is estimated to provide a reference for power grid planning and dispatching; (3) Comprehensive understanding of the characteristics of flexible DC equipment: First, the key equipment of the flexible DC system is tested and modeled. The efficiency, response time, and power regulation range parameters of each equipment are measured, and an accurate mathematical model of the equipment is established so that the operating limitations and performance characteristics of the equipment can be accurately considered in scheduling. Then, the interaction between the flexible DC system and the AC system is studied, and the impact of the reactive power injected into the AC system by the flexible DC system on the AC voltage and the impact of AC system faults on the flexible DC system are analyzed to provide a basis for coordinated control.

3. The AC / DC flexible interconnected distribution network optimization scheduling method according to claim 1 is characterized in that: The step B is specifically as follows: (1) Minimization of operating costs: First, a power generation cost model is established, taking into account the fuel cost, maintenance cost and carbon emission cost of different power sources. For traditional thermal power generation, its fuel cost and equipment maintenance cost are calculated; for distributed renewable energy, its equipment investment cost and operation and maintenance cost are considered, and renewable energy subsidy policy factors are taken into account; Then, optimize the equipment operation strategy to reduce equipment loss; (2) Maximizing energy efficiency: First, establish energy efficiency indicators to comprehensively evaluate the energy efficiency of the entire distribution network; Then, optimize the power flow distribution and reduce line losses; (3) Optimal power quality: First, set the voltage fluctuation and frequency stability indicators; through real-time monitoring of the voltage and frequency of the power grid, timely adjust the power output and reactive power compensation equipment to ensure that the power quality meets the requirements; Then, advanced power electronic equipment and control technology are used to suppress harmonics and voltage flicker and improve power quality; (4) Highest reliability: First, a reliability index system is established, including power supply reliability, equipment reliability and system stability, to evaluate the reliability level of the power grid; Then, optimize fault response strategies to improve fault isolation and recovery speed.

4. The AC / DC flexible interconnected distribution network optimization scheduling method according to claim 1 is characterized in that: The step C is specifically as follows: (1) Hierarchical partition scheduling: First, the distribution network is divided into several areas according to the topological structure and load distribution of the power grid. A regional control center is set up in each area to be responsible for power dispatching, load management and equipment monitoring in the area. Each area is equipped with a regional control system. Then, the local distributed power sources and energy storage devices are dispatched preferentially in the region to meet the local load demand. When the output of the local distributed power source is greater than the load demand, the excess electric energy can be stored in the local energy storage device or transmitted to other regions through the flexible DC system. When the output of the local distributed power source is insufficient, the local energy storage device is preferentially called to discharge or electric energy is introduced from other regions. Finally, power exchange is carried out across regions through flexible DC systems to achieve global optimization; (2) Multi-agent collaborative scheduling: First, the power source, load, energy storage device and flexible DC system are regarded as different intelligent agents. Each intelligent agent has autonomous decision-making and communication capabilities and can make corresponding decisions based on its own status and surrounding environment information. Then, a communication mechanism between agents is established to achieve information sharing and collaborative decision-making; Finally, the distributed optimization algorithm is used to achieve the coordinated scheduling of multiple agents; (3) Real-time optimization: First, a real-time monitoring system is established to collect and analyze the voltage, current, and power parameters of the power grid in real time, and transmit the operation data of the power grid to the dispatching center in real time so that the dispatching personnel can grasp the operation status of the power grid in a timely manner; Then, based on the real-time monitoring data, the dispatching plan is adjusted in real time using a fast optimization algorithm. According to the current power grid status and the forecast for a period of time in the future, the optimal dispatching plan is quickly calculated and issued to each intelligent agent for execution in a timely manner; Finally, consider response strategies for emergencies and improve the resilience of the power grid.

5. The AC / DC flexible interconnected distribution network optimization scheduling method according to claim 1 is characterized in that: The step D is specifically as follows: (1) Construction of hybrid intelligent algorithm: First, we combine the advantages of genetic algorithms and deep learning algorithms to build a hybrid intelligent algorithm; Then, the encoding method, fitness function and genetic operation parameters of the genetic algorithm are determined; Finally, a distributed power output and load forecasting model is established using a deep learning algorithm, and historical data is trained to predict the distributed power output and load demand in the future. (2) Algorithm optimization and improvement: First, in order to improve the efficiency and accuracy of the algorithm, the hybrid intelligent algorithm can be optimized and improved; Then, simulated annealing and taboo search local search algorithms are introduced and combined with genetic algorithms to improve the local search ability of the algorithm.

6. The AC / DC flexible interconnected distribution network optimization scheduling method according to claim 1 is characterized in that: The step E is specifically as follows: (1) Establish a coordination and control mechanism: First, formulate a coordinated control strategy to clarify the coordinated control method between the flexible DC system and the traditional AC system; Then, a communication network is established to realize information exchange between the flexible DC system and the traditional AC system; Finally, a coordinated controller is designed to achieve unified control of the flexible DC system and the traditional AC system; (2) Implementation of coordinated control: Under normal operation, the coordination controller automatically adjusts the power output of the flexible DC system and the operating parameters of the traditional AC system according to the load demand of the power grid and the output of distributed power sources, so as to achieve optimal power flow distribution and power quality control; In the event of a fault, the coordination controller quickly initiates the fault response strategy to achieve fault isolation and recovery.

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