Heating pipe network collaborative control system and method considering distributed heat source and flexible load

Abstract

The application discloses a heat supply pipe network coordination control system and method considering distributed heat sources and flexible loads, and the system comprises a central scheduling layer and an edge autonomous layer connected through a communication network; the central scheduling layer is responsible for the optimization control of the global slow time scale of the heat supply system, and comprises a general hydraulic-thermal coupling simulation module and a global collaborative optimization module; the global collaborative optimization module takes the minimum total operation cost of the heat supply system as a core objective function, takes the safe range of the pressure and temperature of key nodes of the main heat supply pipe network as a hard constraint, takes the adjustable interval of the jurisdictional area of each edge autonomous layer as an optimization variable, and generates regional boundary constraint instructions for each edge autonomous layer within a future scheduling period; the edge autonomous layer comprises a plurality of local intelligent control units arranged one-to-one on the side of the distributed heat source cluster or the flexible load cluster, and is responsible for the fast response and local autonomous control of the fast time scale in the jurisdictional area. The clear division of labor between the center responsible for the safety boundary setting and the edge responsible for the optimal boundary is realized.

Description

Technical Field

[0001] This invention relates to the field of heating system technology, and more specifically, to a heating network coordinated control system and method that takes into account distributed heat sources and flexible loads. Background Technology

[0002] With the development of clean heating, the integration of distributed heat sources (such as industrial waste heat, geothermal energy, and solar thermal collectors) and flexible loads (such as electric thermal storage boilers and user-side hot water storage tanks) into heating systems has become a trend. Existing technologies for regulating such systems mainly fall into two categories:

[0003] Category 1: Control methods based on centralized optimization.

[0004] This method establishes a unified, refined mathematical model (usually a hydraulic-thermal coupling model) in the regional heating dispatch center that includes all heat sources, pipe networks, and loads. By solving a large-scale, complex nonlinear optimization problem, it directly calculates the optimal setpoints for all adjustable devices (including distributed heat sources and main heat sources) in the entire network and issues them for execution.

[0005] The second category: control methods based on fully distributed or locally autonomous systems.

[0006] This method emphasizes the autonomy of each distributed unit (heat source or load). Each unit makes independent decisions based on local information (such as its own cost and status) or simple price signals (such as time-of-use heating prices) to achieve its own goals (such as minimizing operating costs), lacking higher-level global coordination.

[0007] Existing technical defects or areas for improvement:

[0008] Addressing the shortcomings of the first type (centralized) method:

[0009] Poor computational complexity and real-time performance: When the system scales up and the number of distributed units surges, the variables and constraints of the centralized optimization model grow exponentially, and the solution takes too long, making it difficult to meet the requirements of real-time control (minute-level).

[0010] The model is rigid and lacks compatibility: its core hydraulic-thermal coupling model is usually designed based on fixed heat source types and load characteristics, resulting in a fixed model structure. When new or heterogeneous heat sources with different characteristics are introduced (such as volatile industrial waste heat or low-grade air source heat pumps), the model needs to be reconstructed. There is a lack of a "plug-and-play" flexible model framework that can uniformly describe the common interface characteristics of multiple heterogeneous heat sources.

[0011] High communication dependency, weak robustness: It relies heavily on reliable, high-speed communication between the central unit and all end units. Any communication interruption may lead to local loss of control or failure of global optimization.

[0012] Difficulty in protecting the privacy and autonomy of distributed units: Requiring all units to upload detailed cost and operational data presents obstacles in practical commercial applications.

[0013] Addressing the shortcomings of the second type (fully distributed) method:

[0014] The lack of global optimization can easily lead to network conflicts: each unit only pursues its own optimality, and its disordered actions may interfere with each other, causing safety problems such as hydraulic imbalance, pressure exceeding limits, and heat backflow in the main heating network, making it impossible to guarantee the overall balance and safety of the hydraulic and thermal systems of the entire network.

[0015] The absorption efficiency may not be optimal: Due to the lack of a global perspective, it is impossible to systematically coordinate all resources to maximize the absorption of distributed energy, which may result in the main heat source still needing to operate at a high level at times, while distributed energy is abandoned. Summary of the Invention

[0016] To address the aforementioned shortcomings of existing technologies, the purpose of this invention is to provide a method and system for the coordinated control of intelligent heating networks that considers distributed heat sources and flexible loads. This addresses the industry pain points of "difficulty in centralized control and disorder in decentralized control" when heating systems have multiple sources of access. It achieves the control objectives of universal and compatible models, efficient and real-time calculations, safe and controllable operation, and optimal energy consumption. At the same time, it takes into account the privacy and autonomy of distributed units, improves the overall operating efficiency and economy of the heating system, and provides a core technical solution for building an interactive intelligent heating system integrating "source, network, load, and storage".

[0017] To achieve the above objectives, the present invention provides a heating network collaborative control system that considers distributed heat sources and flexible loads, including a central dispatch layer and an edge autonomous layer connected by a communication network;

[0018] The central dispatch layer is located in the regional heating dispatch center and is responsible for the global slow-time-scale optimization and control of the heating system, including a generalized hydraulic-thermal coupling simulation module and a global collaborative optimization module;

[0019] The generalized hydraulic-thermal coupling simulation module is used to generate a hydraulic-thermal coupling simulation model of the heating system, and uses the finite volume method to complete the power flow calculation and operation safety analysis of the entire network.

[0020] The global collaborative optimization module takes the minimum total operating cost of the heating system as its core objective function, uses the pressure and temperature safety ranges of key nodes in the main heating network as hard constraints, and uses the schedulable ranges of the areas under the jurisdiction of each peripheral autonomous layer as optimization variables. It uses an improved particle swarm optimization algorithm based on the calculation results of the generalized hydraulic-thermal coupling simulation module to perform rolling optimization calculations, generating regional boundary constraint instructions for each peripheral autonomous layer in the next scheduling cycle. The regional boundary constraint instructions are coarse-grained boundary constraint instructions and do not include the operating settings of specific equipment.

[0021] The edge autonomous layer is responsible for rapid response and local autonomous regulation within its jurisdiction and includes several local intelligent control units. Each local intelligent control unit is deployed one-to-one on a distributed heat source cluster or flexible load cluster side. Each local intelligent control unit includes a local sensing and execution module and a local autonomous optimization module.

[0022] The local autonomous optimization module receives the regional boundary constraint instructions issued by the central scheduling layer; with the goal of minimizing local operating costs, it quickly solves the optimal operating settings for each device within its jurisdiction under the constraints of the regional boundary constraint instructions from the central scheduling layer, and sends the settings to the local perception and execution module for execution.

[0023] The local sensing and execution module is used to execute the set values ​​generated by the local autonomous optimization module, collect the operating status of all devices in the jurisdiction in real time, and calculate the schedulable interval for the next scheduling cycle and feed it back to the central scheduling layer.

[0024] Based on the current operating status of the equipment and the schedulable range fed back by the edge autonomous layer, the central scheduling layer updates the system state and boundary conditions of the generalized hydraulic-thermal coupling simulation model and enters the optimization calculation for the next scheduling cycle.

[0025] Furthermore, the local sensing and execution module collects the operating status of all devices within its jurisdiction through various types of sensors and actuators, and collects data using the Modbus-RTU protocol.

[0026] Furthermore, the generalized hydraulic-thermal coupling simulation module incorporates the topology and basic parameters of the heating network and integrates a configurable heterogeneous unit interface library. This library pre-contains common interface models for heating network equipment, including industrial waste heat exchangers, solar collectors, air-source heat pumps, electric thermal storage boilers, and user-side hot water storage tanks. It also supports online customization of interface models for newly added heterogeneous equipment via a visual interface. Based on the actual type of heterogeneous unit connected to the heating system, the corresponding interface model for newly added heterogeneous equipment can be matched from or customized within the heterogeneous unit interface library.

[0027] Based on the topology of the heating network and the basic parameters of the pipe sections, all interface models are dynamically combined to form a hydraulic-thermal coupling simulation model of the heating system.

[0028] This invention provides a method for coordinated control of heating networks considering distributed heat sources and flexible loads, comprising the following steps:

[0029] S1: System Initialization: Configure a generalized hydraulic-thermal coupling simulation module and a global collaborative optimization module in the industrial server of the central scheduling layer; configure local intelligent control units in the embedded controllers of each edge autonomous layer; and build a communication network between the central scheduling layer and the edge autonomous layers.

[0030] S2: Global Information Acquisition: The central dispatch layer receives global basic information of the heating system through the data interface. The global basic information includes at least load forecast data and weather forecast data.

[0031] S3: Edge Status Acquisition: The central scheduling layer obtains the operating status and schedulable range of devices within its jurisdiction from each edge autonomous layer through the communication network;

[0032] S4: Global Cooperative Optimization Calculation: Based on global basic information, operating status and schedulable intervals, the central scheduling layer uses a generalized hydraulic-thermal coupling simulation module to complete the power flow calculation and operation safety analysis of the entire network. The global cooperative optimization module uses an improved particle swarm optimization algorithm to solve the optimization problem and obtain the regional boundary constraint instructions for each edge autonomous layer in the next scheduling cycle.

[0033] S5: Boundary command issuance: The central scheduling layer issues the generated regional boundary constraint commands to the corresponding local intelligent control units through the communication network.

[0034] S6: Local Autonomous Optimization and Execution: Within the received regional boundary constraints, the local autonomous optimization module of each edge autonomous layer uses a linear programming algorithm to solve for the optimal operating settings of each device within its jurisdiction, with the goal of minimizing local operating costs, and executes the settings through the local perception and execution module.

[0035] S7: Operation status feedback: Each edge autonomous layer calculates the schedulable interval for the next scheduling cycle based on the operation status collected by the local sensing and execution module, and feeds back the current operation status and schedulable interval of the device to the central scheduling layer through the communication network;

[0036] S8: Rolling Coordinated Control: Repeatedly execute S2 to S7 to achieve closed-loop rolling coordinated control of the heating system.

[0037] Furthermore, the scheduling period is one hour.

[0038] The beneficial effects of this invention are as follows:

[0039] The core technical solution adopted in this invention is as follows: designing a "center-edge" hierarchical distributed collaborative control architecture and constructing a generalized hydraulic-thermal coupling simulation model. Through the division of labor mode of "the central scheduling layer sets the global boundary and the edge autonomous layer performs local optimization", the intelligent heating network collaborative regulation of distributed heat sources and flexible loads is realized. At the same time, a collaborative regulation method based on the system is proposed, clarifying the regulation process and steps to ensure the operability of the system implementation.

[0040] The "center-edge" layered collaborative architecture design clearly defines the division of labor: the center is responsible for defining the "safety boundary," and the edge is responsible for "optimal performance within the boundary." The abstraction of the "generalized interface model" enables unified management and plug-and-play functionality for heterogeneous devices, forming the foundation for the layered architecture. Interactive information centered on "regional boundary constraint instructions" serves as the key information carrier connecting the two layers and achieving decoupled collaboration, conveying global intent while preserving local freedom.

[0041] By combining the aforementioned technical features, the industry pain points of "difficulty in centralization and disorder in decentralized control" in heating systems with multiple access sources are effectively solved. It not only achieves model compatibility, computational efficiency, and security controllability in terms of technology, but also demonstrates good implementability and scalability in engineering applications, providing a practical core technical solution for building a new generation of interactive smart heating systems integrating "source, grid, load, and storage." It can reduce the overall operating cost of the system, improve the utilization rate of distributed energy, and has significant economic and social benefits. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of the framework structure of the heating network coordinated control system of the present invention;

[0044] Figure 2 This is a schematic diagram of the process structure of the heating network coordinated control method of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0046] It should be noted that similar reference numerals or letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0047] A coordinated control system for heating networks considering distributed heat sources and flexible loads, including a central dispatch layer and an edge autonomous layer connected via a communication network;

[0048] The central dispatch layer is located in the regional heating dispatch center and is responsible for the global slow-time-scale optimization and control of the heating system, including a generalized hydraulic-thermal coupling simulation module and a global collaborative optimization module;

[0049] The generalized hydraulic-thermal coupling simulation module is used to generate a hydraulic-thermal coupling simulation model of the heating system, and uses the finite volume method to complete the power flow calculation and operation safety analysis of the entire network.

[0050] The global collaborative optimization module takes the minimum total operating cost of the heating system as its core objective function, uses the pressure and temperature safety ranges of key nodes in the main heating network as hard constraints, and uses the schedulable ranges of the areas under the jurisdiction of each peripheral autonomous layer as optimization variables. It uses an improved particle swarm optimization algorithm based on the calculation results of the generalized hydraulic-thermal coupling simulation module to perform rolling optimization calculations, generating regional boundary constraint instructions for each peripheral autonomous layer in the next scheduling cycle. The regional boundary constraint instructions are coarse-grained boundary constraint instructions and do not include the operating settings of specific equipment.

[0051] The edge autonomous layer is responsible for rapid response and local autonomous regulation within its jurisdiction and includes several local intelligent control units. Each local intelligent control unit is deployed one-to-one on a distributed heat source cluster or flexible load cluster side. Each local intelligent control unit includes a local sensing and execution module and a local autonomous optimization module.

[0052] The local autonomous optimization module receives the regional boundary constraint instructions issued by the central scheduling layer; with the goal of minimizing local operating costs, it quickly solves the optimal operating settings for each device within its jurisdiction under the constraints of the regional boundary constraint instructions from the central scheduling layer, and sends the settings to the local perception and execution module for execution.

[0053] The local sensing and execution module is used to execute the set values ​​generated by the local autonomous optimization module, collect the operating status of all devices in the jurisdiction in real time, and calculate the schedulable interval for the next scheduling cycle and feed it back to the central scheduling layer.

[0054] Based on the operating status and schedulable intervals fed back by the edge autonomous layers, the central scheduling layer updates the system state and boundary conditions of the generalized hydraulic-thermal coupling simulation model and enters the optimization calculation for the next scheduling cycle.

[0055] Preferably, the local sensing and execution module collects the operating status of all devices within its jurisdiction through various types of sensors and actuators, and collects data using the Modbus-RTU protocol.

[0056] Preferably, the generalized hydraulic-thermal coupling simulation module has a built-in topology of the heating network, basic parameters of pipe sections, and integrates a configurable heterogeneous unit interface library. The heterogeneous unit interface library is pre-loaded with common interface models for heating network equipment, including at least industrial waste heat exchangers, solar collectors, air source heat pumps, electric thermal storage boilers, and user-side hot water storage tanks. It also supports online customization of interface models for adding heterogeneous equipment through a visual interface. Based on the actual equipment and heterogeneous unit types connected to the heating system, the corresponding interface models for adding heterogeneous equipment can be matched from or customized from the heterogeneous unit interface library.

[0057] Based on the topology of the heating network and the basic parameters of the pipe sections, all interface models are dynamically combined to form a hydraulic-thermal coupling simulation model of the heating system.

[0058] Specifically, the communication network adopts wired / wireless communication (industrial Ethernet + 5G wireless communication) to realize bidirectional information interaction between the central scheduling layer and the edge autonomous layer. The composition and functional modules of each layer are as follows:

[0059] The central dispatch layer is responsible for the optimization and control of the heating system at a global slow time scale (15 minutes to 1 hour), and the hardware is mounted on an industrial-grade server.

[0060] The generalized hydraulic-thermal coupling simulation module includes the topology of the heating network, basic parameters of pipe sections (pipe diameter, pipe length, and friction coefficient), and integrates a configurable heterogeneous unit interface library. The heterogeneous unit interface library has pre-set common interface models for industrial waste heat exchangers, solar collectors, air source heat pumps, electric thermal storage boilers, and user-side hot water storage tanks. It also supports adding custom interface models for heterogeneous equipment online through a visual interface.

[0061] The global collaborative optimization module takes the minimum total system operating cost as the core objective function (which can also be switched to the minimum total carbon emissions), uses the pressure and temperature safety range of key nodes in the main heating network as hard constraints, and uses the schedulable range (such as maximum / minimum net heating power) of each peripheral autonomous layer as optimization variables. It adopts an improved particle swarm optimization algorithm (convergence speed is 30% faster than traditional algorithms) and performs rolling optimization calculations based on the calculation results of the generalized hydraulic-thermal coupling simulation module to generate a set of regional target instructions for each peripheral autonomous layer in the next scheduling cycle. The regional target instructions are coarse-grained boundary constraint instructions and do not include the operating settings of specific equipment.

[0062] The edge autonomous layer is responsible for rapid response and local autonomous regulation within its jurisdiction on a fast timescale (seconds to minutes). Each local intelligent control unit includes a local perception and execution module and a local autonomous optimization module.

[0063] Local sensing and execution module: Composed of various sensors (temperature sensor, pressure sensor, electromagnetic flow meter, power sensor) and actuators (electric regulating valve, frequency converter, water pump set, boiler control cabinet), it uses Modbus-RTU protocol to collect data at a frequency of 1-5 seconds / time. It is used to collect real-time operating data of all equipment in the jurisdiction (such as heat source output, equipment temperature and operating cost) and execute specific equipment control commands generated by the local autonomous optimization module, with a control response delay of ≤1 second.

[0064] Local autonomous optimization module: It has built-in specific mathematical models and operating cost functions for all devices within the jurisdiction, and uses a linear programming algorithm to solve them, with a solution time of ≤3s; it can receive regional target instructions issued by the central scheduling layer through a 4G / 5G module; with the core objective of minimizing local operating costs, under the constraints of regional target instructions from the central scheduling layer, this module quickly solves for the optimal operating settings (such as output, water flow, start / stop status) of each device (such as waste heat exchangers, air source heat pumps, and heat storage tanks) within the jurisdiction, and sends the settings to the local sensing and execution module for execution via the Profibus-DP protocol.

[0065] The region is divided into multiple jurisdictional areas. Each level of the system achieves bidirectional information exchange through a communication network, operating in a closed-loop mode of "digital twin modeling - global boundary command generation - local autonomous optimization - status feedback - rolling optimization." The specific coordination is as follows: ① Model preparation stage: The central scheduling layer obtains the interface type and key operating parameters (such as maximum output and rated efficiency) of the jurisdictional units from each edge autonomous layer via industrial Ethernet. Interface model matching and parameter configuration are completed in the generalized hydraulic-thermal coupling simulation module, dynamically generating a hydraulic-thermal coupling simulation model of the entire system, forming a usable "digital twin" system for the heating system. Modeling time is ≤10 minutes. ② Command generation and issuance: After the global collaborative optimization module of the central scheduling layer completes the rolling optimization calculation, it converts the optimization results into regional boundary constraint commands for each edge autonomous layer and issues them to the corresponding local intelligent control units via the 5G wireless communication network. The command issuance delay is ≤500ms. This process only issues regional constraints and does not directly interfere with the specific operational decisions of local equipment. ③ Command Execution and Status Feedback: Within the received regional boundary constraints, the local autonomous optimization module of the edge autonomous layer solves and executes the optimal setpoints for local devices. Simultaneously, the local sensing and execution module collects the actual operating results of the devices, calculates the schedulable capacity (adjustable range) for the next scheduling cycle based on the latest device status (such as the thermal storage rate of the thermal storage tank), and feeds it back to the central scheduling layer via the 5G wireless communication network, with a feedback data volume ≤100KB / time. ④ Closed-Loop Rolling Optimization: Based on the actual operating results and schedulable capacity fed back by the edge autonomous layer, the central scheduling layer updates the system state and boundary conditions of the generalized hydraulic-thermal coupling simulation model, enters the optimization calculation for the next scheduling cycle, and realizes dynamic collaborative control of the entire network. The overall control closed-loop cycle is consistent with the time scale of the central scheduling layer (15 minutes to 1 hour).

[0066] The method for coordinated control of heating networks considering distributed heat sources and flexible loads includes the following steps:

[0067] S1: System Initialization: Configure a generalized hydraulic-thermal coupling simulation module and a global collaborative optimization module in the industrial server of the central scheduling layer; configure local intelligent control units in the embedded controllers of each edge autonomous layer; and build a communication network between the central scheduling layer and the edge autonomous layers.

[0068] Configure a generalized hydraulic-thermal coupling simulation module (with a pre-set heterogeneous unit interface library, pipeline topology and parameters) and a global collaborative optimization module (with settings for objective function, constraints, and optimization algorithm parameters) in the central scheduling layer industrial server; configure local intelligent control units (with built-in local device models, cost functions, and sensor / actuator communication protocols) in the embedded controllers of each edge autonomous layer; build a communication network (industrial Ethernet + 5G wireless communication) between the central scheduling layer and the edge autonomous layers, complete the communication connection and debugging of all devices, test the communication success rate ≥99.9%, and ensure that all modules of the system work normally.

[0069] S2: Global Information Acquisition: The central dispatch layer receives global basic information of the heating system through the data interface. The global basic information includes at least load forecast data and weather forecast data.

[0070] The central dispatch layer receives global basic information such as load forecast data (heat load demand for the next hour with a forecast accuracy of ≥90%) and weather forecast data (such as solar radiation of 0~1000W / m2 and ambient temperature of -20~35℃, used to predict the output of distributed heat sources) through the data interface. The data is updated every 15 minutes.

[0071] S3: Edge Status Acquisition: The central scheduling layer obtains the operating status and schedulable range of devices within its jurisdiction from each edge autonomous layer through the communication network;

[0072] The central dispatch layer obtains the current operating status of equipment within its jurisdiction (such as real-time output of heat sources, heat storage rate of thermal storage equipment, and equipment operating efficiency) and the forecast of the dispatchable range for the next hour (such as maximum / minimum net heating power) from each edge autonomous layer through the communication network. The data acquisition takes ≤30 seconds.

[0073] S4: Global Cooperative Optimization Calculation: Based on global basic information, operating status and schedulable intervals, the central scheduling layer uses a generalized hydraulic-thermal coupling simulation module to complete the power flow calculation and operation safety analysis of the entire network. The global cooperative optimization module uses an improved particle swarm optimization algorithm to solve the optimization problem and obtain the regional boundary constraint instructions for each edge autonomous layer in the next scheduling cycle.

[0074] S5: Boundary command issuance: The central scheduling layer issues the generated regional boundary constraint commands to the corresponding local intelligent control units through the communication network.

[0075] S6: Local Autonomous Optimization and Execution: Within the received regional boundary constraints, the local autonomous optimization module of each edge autonomous layer uses a linear programming algorithm to solve for the optimal operating settings of each device within its jurisdiction, with the goal of minimizing local operating costs, and executes the settings through the local perception and execution module.

[0076] S7: Operational Status Feedback: Each edge autonomous layer calculates the schedulable range for the next scheduling cycle based on the operational status collected by the local sensing and execution modules, and feeds back the operational status and schedulable range to the central scheduling layer through the communication network; Each edge autonomous layer collects the actual control results of the equipment (such as actual output, water supply temperature, and operating costs) through the local sensing and execution modules, and calculates the schedulable capacity for the next scheduling cycle in combination with the current status of the equipment, and feeds back the above information to the central scheduling layer through the communication network. The feedback completion time is ≤20s.

[0077] S8: Rolling Coordinated Control: Repeat S2 to S7 to achieve closed-loop rolling coordinated control of the heating system. The central scheduling layer updates the global information and system model status based on the information fed back by the edge autonomous layer, and enters the optimization calculation for the next 1-hour scheduling cycle, repeating S2 to S7 to achieve closed-loop rolling coordinated control of the heating system.

[0078] Preferably, the scheduling cycle is one hour.

[0079] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0080] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A heating network control system considering distributed heat sources and flexible loads, characterized in that, This includes a central scheduling layer and an edge autonomous layer connected via a communication network; The central dispatch layer is located in the regional heating dispatch center and is responsible for the global slow-time-scale optimization and control of the heating system, including a generalized hydraulic-thermal coupling simulation module and a global collaborative optimization module; The generalized hydraulic-thermal coupling simulation module is used to generate a hydraulic-thermal coupling simulation model of the heating system, and uses the finite volume method to complete the power flow calculation and operation safety analysis of the entire network. The global collaborative optimization module takes the minimum total operating cost of the heating system as the objective function, the pressure safety range and temperature safety range of the key nodes of the main heating network as hard constraints, and the schedulable range of the jurisdiction of each peripheral autonomous layer as the optimization variable. It uses an improved particle swarm optimization algorithm to perform rolling optimization calculations based on the calculation results of the generalized hydraulic-thermal coupling simulation module, and generates regional boundary constraint instructions for each peripheral autonomous layer in the future scheduling cycle. The regional boundary constraint instructions are coarse-grained boundary constraint instructions and do not include the operating settings of specific equipment. The edge autonomous layer is responsible for rapid response and local autonomous regulation within its jurisdiction and includes several local intelligent control units. Each local intelligent control unit is deployed one-to-one on a distributed heat source cluster or flexible load cluster side. Each local intelligent control unit includes a local sensing and execution module and a local autonomous optimization module. The local autonomous optimization module receives the regional boundary constraint instructions issued by the central scheduling layer; with the goal of minimizing local operating costs, it quickly solves the optimal operating settings for each device within its jurisdiction under the constraints of the regional boundary constraint instructions from the central scheduling layer, and sends the settings to the local perception and execution module for execution. The local sensing and execution module is used to execute the set values ​​generated by the local autonomous optimization module, collect the operating status of all devices in the jurisdiction in real time, and calculate the schedulable interval for the next scheduling cycle and feed it back to the central scheduling layer. Based on the current operating status of the equipment and the schedulable range fed back by the edge autonomous layer, the central scheduling layer updates the system state and boundary conditions of the generalized hydraulic-thermal coupling simulation model and enters the optimization calculation for the next scheduling cycle.

2. The heating network coordinated control system considering distributed heat sources and flexible loads according to claim 1, characterized in that, The local sensing and execution module collects the operating status of all devices within its jurisdiction through various types of sensors and actuators, and uses the Modbus-RTU protocol to collect data.

3. The heating network coordinated control system considering distributed heat sources and flexible loads according to claim 1, characterized in that, The generalized hydraulic-thermal coupling simulation module has a built-in topology of the heating network, basic parameters of pipe sections, and integrates a configurable heterogeneous unit interface library. The heterogeneous unit interface library is pre-loaded with common interface models for heating network equipment, including industrial waste heat exchangers, solar collectors, air source heat pumps, electric thermal storage boilers, and user-side hot water storage tanks. It also supports online customization of interface models for adding heterogeneous equipment through a visual interface. Based on the actual type of equipment connected to the heating system, the corresponding interface model for adding heterogeneous equipment can be matched from or customized from the heterogeneous unit interface library. Based on the topology of the heating network and the basic parameters of the pipe sections, all interface models are dynamically combined to form a hydraulic-thermal coupling simulation model of the heating system.

4. A method for coordinated control of heating networks considering distributed heat sources and flexible loads, characterized in that, Includes the following steps: S1: System Initialization: Configure a generalized hydraulic-thermal coupling simulation module and a global collaborative optimization module in the industrial server of the central scheduling layer; configure local intelligent control units in the embedded controllers of each edge autonomous layer; and build a communication network between the central scheduling layer and the edge autonomous layers. S2: Global Information Acquisition: The central dispatch layer receives global basic information of the heating system through the data interface. The global basic information includes at least load forecast data and weather forecast data. S3: Edge Status Acquisition: The central scheduling layer obtains the operating status and schedulable range of devices within its jurisdiction from each edge autonomous layer through the communication network; S4: Global Cooperative Optimization Calculation: Based on global basic information, operating status and schedulable intervals, the central scheduling layer uses a generalized hydraulic-thermal coupling simulation module to complete the power flow calculation and operation safety analysis of the entire network. The global cooperative optimization module uses an improved particle swarm optimization algorithm to solve the optimization problem and obtain the regional boundary constraint instructions for each edge autonomous layer in the next scheduling cycle. S5: Boundary command issuance: The central scheduling layer issues the generated regional boundary constraint commands to the corresponding local intelligent control units through the communication network. S6: Local Autonomous Optimization and Execution: Within the received regional boundary constraints, the local autonomous optimization module of each edge autonomous layer uses a linear programming algorithm to solve for the optimal operating settings of each device within its jurisdiction, with the goal of minimizing local operating costs, and executes the settings through the local perception and execution module. S7: Operation status feedback: Each edge autonomous layer calculates the schedulable interval for the next scheduling cycle based on the operation status collected by the local sensing and execution module, and feeds back the current operation status and schedulable interval of the device to the central scheduling layer through the communication network; S8: Rolling Coordinated Control: Repeatedly execute S2 to S7 to achieve closed-loop rolling coordinated control of the heating system.

5. The heating network collaborative control method considering distributed heat sources and flexible loads according to claim 4, characterized in that, The scheduling period is one hour.

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