A Remote Terminal Response and Control Method for Thermal Pipeline Network Based on Power Regulation

Abstract

This invention provides a method for remote terminal response and control of a heating network based on power regulation, comprising: acquiring the topology of the heating network, the load of end-user heat users, and the flow rate and temperature data of the heating medium; generating a demand response plan based on a preset production plan combined with peak-shaving and frequency regulation requirements issued by the power system and the flow rate and temperature data of the heating medium; establishing an electro-thermal coordination optimization model based on the demand response plan and preset power regulation assessment indicators, and generating automated commands through the electro-thermal coordination optimization model; during the execution of the automated commands, changing the flow rate of the heating medium through multiple terminals, generating flow regulation results and a desired water supply temperature regulation plan; and dynamically planning and adjusting the demand response plan based on the regulation results using a preset correction model, thereby dynamically adjusting the flow rate of the heating medium. This invention solves the problem of energy loss and waste caused by the unbalanced electro-thermal regulation in existing heating networks.

Description

Technical Field

[0001] This invention relates to the field of power regulation technology, and in particular to a method for remote terminal response and control of thermal pipeline networks based on power regulation. Background Technology

[0002] Currently, the application scenarios for wind and solar power are gradually increasing. However, due to the significant randomness, volatility, and intermittency of renewable energy, traditional power grid technologies for smoothing load fluctuations are no longer sufficient to meet the additional and substantial fluctuations brought about by renewable energy. This poses significant safety and stability challenges for large-scale renewable energy grid integration. In recent years, power system accidents caused by high proportions of renewable energy grid integration have occurred frequently. In the future, new energy will continue its rapid development and become the main source of electricity. The challenges are even more severe, making it imperative to improve the flexibility of the power system and smooth the volatility brought about by renewable energy grid integration.

[0003] In recent years, the proportion of flexible loads with adjustable capabilities, such as building environmental loads, electric vehicle charging piles, and distributed energy storage, on the demand side has been increasing year by year, possessing enormous potential to smooth out fluctuations in renewable energy and provide flexibility for the power system. In addition, distributed photovoltaic power on the distribution network side has also grown rapidly, and the adjustable loads on the distribution network side can provide regulation capabilities for renewable energy on both the distribution and transmission networks. However, due to the large number of entities, small individual capacities, and sheer volume of adjustable loads on the distribution network, centralized control is difficult, and user loads vary significantly with energy consumption behavior, weather changes, and the physical systems to which the loads belong, exhibiting a certain degree of uncertainty. Furthermore, due to the underdeveloped electricity market mechanism, the utilization of adjustable loads on the distribution network is not yet fully realized.

[0004] Compared to source-side power plants and large-scale energy storage, distribution network-side adjustable capacity boasts advantages such as lower cost and higher safety and reliability. However, it faces technical barriers in data communication and optimized operation involving massive amounts of data, as well as management challenges in obtaining control authority over the physical systems to which user loads reside. More importantly, research on demand-side adjustable loads primarily focuses on spontaneous user adjustment under price incentives, with minimal consideration for the boundaries of the adjustable capacity of the physical systems to which loads reside. Correspondingly, the participation of non-electric energy systems from power plants on the transmission network side in power system regulation is a key focus for scholars. However, due to the small scale, difficulty in obtaining control authority, and overall difficulty in regulating non-electric energy systems on the distribution network side, it is often assumed that only the willingness of electricity consumers, primarily human, to participate in regulation needs to be mobilized. However, the question of whether the physical systems to which loads reside can adjust power according to human will, or whether power adjustment can be achieved beyond human will, is rarely addressed. Ultimately, the adjustable capacity inherent in the massive, dispersed physical systems of loads is either underestimated or overestimated.

[0005] In integrated electric-thermal energy systems, the heating network possesses passive heat storage capacity due to its strong thermal inertia, which can provide a certain degree of regulation for the power system and promote the grid integration and consumption of renewable energy. To fully realize the potential of the heating network in this regard, a coordinated and optimized scheduling model that comprehensively considers the dynamic characteristics of the power and heating systems needs to be established. While the heating network in a centralized heating system has an independent operation and regulation control system, current optimization targets and system performance evaluation indicators for coordinated electric-thermal scheduling still primarily focus on the power system, supplemented by other energy systems. In-depth analysis and discussion of the operation and regulation of the heating network itself within the integrated electric-thermal energy system have not been conducted. The potential of the heating network to enhance the regulation capacity of the power system has not been fully explored. Summary of the Invention

[0006] This invention provides a method for remote terminal response and control of a heating network based on electric regulation, which solves the problem of energy loss and waste caused by unbalanced electric and thermal regulation in existing heating networks, achieves reasonable and balanced electric and thermal regulation, rationalizes energy utilization, and reduces energy waste.

[0007] This invention provides a method for remote terminal response and control of a thermal pipeline network based on power regulation, comprising:

[0008] Obtain the topology of the heating network, the load of end-user heat users, and the flow rate and temperature of the heating medium;

[0009] A demand response plan is generated based on the preset production plan, combined with the peak shaving and frequency regulation requirements issued by the power system and the heat medium water flow and temperature data.

[0010] An electric-thermal coordination optimization model is established based on the demand response plan and preset power regulation assessment indicators, and automated instructions are generated through the electric-thermal coordination optimization model.

[0011] During the execution of the automated instructions, the flow rate of the heat transfer medium is changed through multiple terminals to generate flow rate regulation results and a desired water supply temperature regulation plan.

[0012] Based on the adjustment results, the demand response plan is dynamically adjusted and modified using a preset correction model, thereby dynamically adjusting the flow rate of the heat transfer medium.

[0013] According to the present invention, a method for remote terminal response and control of a heat pipeline network based on power regulation is provided, wherein generating a demand response plan based on a preset production plan and the heat medium water flow data specifically includes:

[0014] Obtain overall optimized dispatch instructions issued by the superior power grid to the distribution area;

[0015] Distributed control commands are issued from the distribution area to distributed loads at all levels according to the optimized scheduling instructions;

[0016] The distributed load is combined with distributed control commands and heat medium water flow data to generate a demand response plan.

[0017] According to the present invention, a remote terminal response and control method for a heat pipeline network based on power regulation is provided, wherein the distributed load combines distributed control commands and heat medium water flow data to generate a demand response plan, including:

[0018] A dynamic response relationship between heat sources and users in a heating network is established by using a pre-set dynamic model of the heating network.

[0019] Constraints are generated by applying the dynamic response relationship to the demand response plan.

[0020] Generate a new demand response plan based on the constraints.

[0021] According to the present invention, a method for remote terminal response and control of a heating network based on power regulation is provided, wherein the distributed load is combined with distributed control commands and heating medium / water flow data to generate a demand response plan, and further includes:

[0022] The demand response plan is applicable to the hierarchical centralized-distributed optimization of the distribution network in ultra-large-scale power systems. It is a centralized optimization for the upper-level distribution network, while the lower-level load is decomposed into a distributed optimization.

[0023] Distribution network automation adopts a hierarchical distributed optimization approach, where centralized optimization is used for distribution area intelligent terminal equipment and distributed optimization is used for distribution area-flexible load.

[0024] After selecting peak shaving and frequency regulation modes in the heating network, one-click participation in regulation is achieved. This action will be recognized as participation at the load level, i.e., the intelligent equipment in the distribution area or the distribution network automation, providing hardware support for decentralization and enabling the distributed optimization of the distribution network to rely on a complete hardware system.

[0025] According to the present invention, a remote terminal response and control method for a thermal pipeline network based on power regulation is provided. This method establishes a power-thermal coordination optimization model based on the demand response plan and preset power regulation performance indicators, and generates automated commands through the power-thermal coordination optimization model. Specifically, the method includes:

[0026] The preset power regulation assessment indicators include: the regulation capacity and response speed required for power regulation;

[0027] The demand response plan is integrated with the regulation capacity and response speed required by the power regulation to establish an electrothermal coordination optimization model;

[0028] The electrothermal coordination optimization model quantitatively adjusts the operating parameters of heating and generator sets according to the renewable energy consumption target, and generates automated instructions.

[0029] According to the present invention, a remote terminal response and control method for a heat pipeline network based on power regulation is provided. During the execution of the automated command, the flow rate of the heat transfer medium is changed through multiple terminals to generate a regulation result, specifically including:

[0030] The automation command changes at least one of the following: indoor temperature setpoint, frequency of heat medium circulation pump, opening degree of solenoid valves in main and branch pipelines, heating temperature of heat source unit, and total heating volume of heat source unit.

[0031] By changing the flow rate of the heat transfer medium, the temperature is altered, and the regulation result is generated.

[0032] According to the present invention, a method for remote terminal response and control of a heat pipeline network based on power regulation is provided. Based on the regulation results, the method dynamically plans and adjusts the demand response plan using a preset correction model, dynamically adjusting the flow rate of the heat transfer medium. Specifically, the method includes:

[0033] The mediation result is fed back to the preset correction model;

[0034] The correction model generates a dynamic programming algorithm by using a rolling correction plan for peak shaving and frequency regulation and a day-ahead dynamic adjustment based on artificial intelligence for peak shaving and frequency regulation.

[0035] The flow rate of the heat transfer medium is dynamically adjusted according to the dynamic programming algorithm to meet the thermal comfort requirements.

[0036] The present invention also provides a remote terminal response and control system for a thermal pipeline network based on power regulation, the system comprising:

[0037] The data acquisition module is used to acquire the flow rate data of the heat transfer medium water in the heating network;

[0038] The planning module is used to generate a demand response plan based on a preset production plan and the heat medium water flow data;

[0039] The instruction generation module is used to establish an electric-thermal coordination optimization model based on the demand response plan and preset power regulation assessment indicators, and to generate automated instructions through the electric-thermal coordination optimization model.

[0040] The execution module is used to change the flow rate of the heat transfer medium through multiple terminals during the execution of the automation instructions and generate adjustment results;

[0041] The correction module is used to dynamically plan and adjust the demand response plan based on the adjustment results using a preset correction model, and dynamically adjust the flow rate of the heat transfer medium.

[0042] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the remote terminal response and control method for power-regulated thermal pipeline networks as described above.

[0043] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the remote terminal response and control method for power-regulated thermal pipeline networks as described above.

[0044] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the remote terminal response and control method for thermal pipeline networks based on power regulation as described above.

[0045] This invention provides a remote terminal response and control method for a power-regulated heating network. After acquiring the flow rate data of the heating medium water, a demand response plan is generated based on a preset production plan and relevant constraints. Automated commands are executed according to the demand response plan and power regulation performance indicators. Multiple components adjust the flow rate of the heating medium water to determine its intrinsic impact on wind power absorption and user comfort, identifying the optimal flow rate value. This value is then dynamically adjusted using a correction model. This achieves rational and balanced power-heat regulation, optimizing energy utilization and reducing energy waste. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is one of the flowcharts of a remote terminal response and control method for a thermal pipeline network based on power regulation provided by the present invention;

[0048] Figure 2 This is the second flowchart of a method for remote terminal response and control of a thermal pipeline network based on power regulation provided by the present invention;

[0049] Figure 3 This is the third flowchart of a method for remote terminal response and control of a thermal pipeline network based on power regulation provided by the present invention;

[0050] Figure 4 This is the fourth flowchart of a method for remote terminal response and control of a thermal pipeline network based on power regulation provided by the present invention;

[0051] Figure 5 This is the fifth flowchart of a method for remote terminal response and control of a thermal pipeline network based on power regulation provided by the present invention;

[0052] Figure 6 This is the sixth flowchart of a method for remote terminal response and control of a thermal pipeline network based on power regulation provided by the present invention;

[0053] Figure 7 This is a schematic diagram of the module connection of a remote terminal response and control system for a thermal pipeline network based on power regulation, provided by the present invention.

[0054] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0055] Figure 9 This is a schematic diagram of the heat transfer medium water circulation within the heat pipe network provided by the present invention.

[0056] Figure label:

[0057] 110: Data acquisition module; 120: Planning module; 130: Instruction generation module; 140: Execution module; 150: Correction module;

[0058] 810: Processor; 820: Communication interface; 830: Memory; 840: Communication bus. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this 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 this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0060] In a heating network, the heating network serves as the medium connecting heat sources and end-users. It includes equipment such as pipes, circulating pumps, valves, and insulation layers. The main controllable parameters of a heating network are the supply water temperature and mass flow rate. Although the supply water temperature is controlled by the heat source, it can be controlled by the heating network itself. The mass flow rate can be directly adjusted by the heating network control center via the water pumps. Furthermore, the valves in the pipelines can be used to fine-tune the flow rate by changing their opening degree.

[0061] The main regulation methods for heating network operation include quality regulation, quantity regulation, combined quality and quantity regulation, and staged variable flow quality regulation. Different regulation methods are selected depending on the heat load. The principle is to adjust the temperature first, then the flow rate. Therefore, in the initial stage of heating, a fixed mass flow rate is generally determined, and the supply water temperature is adjusted and fine-tuned valves are used to meet the changes in heat load at different times. In severe cold periods, the flow rate is usually increased, combined with temperature regulation. Combined quality and quantity regulation and staged variable flow quality regulation are also frequently used. The regulation capability of the heating network lies in the thermal inertia caused by the flow time. Therefore, when the heating network participates in the demand-side response of the power system, the supply water temperature and flow rate need to be adjusted as a whole. In addition, the return water temperature feedback value of heat users serves as the basis for heat source and mass flow rate regulation, which is called the thermal compensation feedback regulation mechanism. The terminal control of the heating network needs to revolve around temperature, flow rate, passive energy storage in the heating network, and user heat load. The operation of the heating network in combined heat and power (CHP) is not directly controlled by the power plant, so the heating network is more focused on the flexibility of the load side.

[0062] In integrated electric-thermal energy systems, the heating network possesses passive heat storage capacity due to its strong thermal inertia, which can provide a certain degree of regulation for the power system and promote the grid integration and consumption of renewable energy. To fully realize the potential of the heating network in this regard, a coordinated and optimized scheduling model that comprehensively considers the dynamic characteristics of the power and heating systems needs to be established. While the heating network in a centralized heating system has an independent operation and regulation control system, current optimization targets and system performance evaluation indicators for coordinated electric-thermal scheduling still primarily focus on the power system, supplemented by other energy systems. In-depth analysis and discussion of the operation and regulation of the heating network itself within the integrated electric-thermal energy system have not been conducted. The potential of the heating network to enhance the regulation capacity of the power system has not been fully explored.

[0063] The following is combined with Figures 1-6 This invention describes a method for remote terminal response and control of a thermal pipeline network based on power regulation, comprising:

[0064] S100: Obtain the topology of the heating network, the load of end-user heat users, and the flow rate and temperature of the heating medium.

[0065] S200. Generate a demand response plan based on the preset production plan, combined with the peak shaving and frequency regulation requirements issued by the power system and the heat medium water flow and temperature data.

[0066] S300. Establish an electric-thermal coordination optimization model based on the demand response plan and preset power regulation assessment indicators, and generate automated instructions through the electric-thermal coordination optimization model.

[0067] S400, The automation command changes the flow rate of the heat medium water through multiple terminals during the execution process, and generates the flow rate regulation result and the expected water supply temperature regulation plan;

[0068] S500. Based on the adjustment results, the demand response plan is dynamically adjusted and modified using a preset correction model, and the flow rate of the heat transfer medium is dynamically adjusted.

[0069] In this invention, the following are included: the on / off status identification of the heating network participating in power system regulation with one click; the receiving end of the frequency difference or power regulation demand issued by the upper-level power demand side; the demand response regulation plan of the heating network based on the upper-level demand; the automated command execution mechanism of the heating network taking into account peak shaving, frequency regulation or other regulation needs; the actual operation of each component of the heating network; the feedback value of the actual cooling and heating supply of the heating network; the change of the actual power consumption of the heat source; the exit and recovery mechanism of the heat source participating in ancillary services (grid regulation); and the market settlement mechanism for the regional cooling and heating system participating in ancillary services.

[0070] Heating networks primarily employ five common regulation methods: quality regulation, quantity regulation, phased variable flow quality regulation, combined quality and quantity regulation, and intermittent regulation. Quality regulation only adjusts the supply temperature of the heating medium, quantity regulation only adjusts the flow rate, and the other three methods are subdivided for different types of heating scenarios. Essentially, all these methods involve steady-state regulation of temperature and flow rate. The heating network operation regulation mechanism still plays a role in the coordinated optimization of electro-thermal operation. However, current electro-thermal optimization methods, for the sake of simplicity, mostly consider only the quality regulation mechanism and neglect flow rate regulation. Therefore, this invention combines demand response planning and the performance indicators required for power regulation to automate command execution, facilitating accurate control of the heating medium flow rate to achieve optimal thermal comfort for users, while also maximizing energy utilization and avoiding waste.

[0071] A demand response plan is generated based on a pre-set production plan and the aforementioned heat transfer fluid flow data, specifically including:

[0072] S201. Obtain the overall optimized dispatching instructions issued by the superior transmission network to the distribution area;

[0073] S202. Distributed control commands are issued from the distribution area to the distributed loads at all levels according to the optimized scheduling command;

[0074] S203. The distributed load is combined with distributed control commands and heat medium water flow data to generate a demand response plan.

[0075] Distributed loads are combined with distributed control commands and heat medium / water flow data to generate demand response plans, including:

[0076] S2031. Establish the dynamic response relationship between heat sources and users in the heating network through a preset dynamic model of the heating network;

[0077] S2032. Constrain the demand response plan according to the dynamic response relationship and generate constraint conditions;

[0078] S2033. Generate a new demand response plan based on the constraints.

[0079] In this invention, the heating network, based on the upper-level power system's demand-side regulation plan, depends on the overall optimized operation and control method of the distribution network's power demand response or ancillary services. Possible methods include six approaches: purely distributed optimization considering decentralization, distributed optimization based on blockchain consensus mechanisms, hierarchical centralized-distributed optimization, centralized optimization, decomposition and coordination optimization, and differential regulation decentralized control similar to generator primary frequency regulation. The heating network's regulation plan is obtained through optimization based on the ideas of these six approaches. This invention primarily uses hierarchical centralized-distributed optimization to generate the demand response regulation plan. In ultra-large-scale power systems, hierarchical centralized-distributed optimization of the distribution network involves centralized optimization at the top and distributed optimization at the bottom. This includes hierarchical distributed optimization of distribution network automation-transformer intelligent terminal equipment (centralized optimization) and transformer-flexible load distributed optimization. After determining the total demand transmitted by the upper-level transmission network, each level of transformer area dynamically allocates resources to meet the total demand constraints.

[0080] The distributed load, combined with distributed control commands and heat medium / water flow data, generates a demand response plan, which also includes:

[0081] The demand response plan is applicable to the hierarchical centralized-distributed optimization of the distribution network in ultra-large-scale power systems. It is a centralized optimization for the upper-level distribution network, while the lower-level load is decomposed into a distributed optimization.

[0082] Distribution network automation adopts a hierarchical distributed optimization approach, where centralized optimization is used for distribution area intelligent terminal equipment and distributed optimization is used for distribution area-flexible load.

[0083] After selecting peak shaving and frequency regulation modes in the heating network, one-click participation in regulation is achieved. This action will be recognized as participation at the load level, i.e., the intelligent equipment in the distribution area or the distribution network automation, providing hardware support for decentralization and enabling the distributed optimization of the distribution network to rely on a complete hardware system.

[0084] An electricity-heat coordination optimization model is established based on the demand response plan and preset power regulation assessment indicators. Automated instructions are generated through this model, specifically including:

[0085] S301, The preset power regulation assessment indicators include: the regulation capacity and response speed required for power regulation;

[0086] S302. Integrate the demand response plan with the regulation capacity and response speed required by the power regulation to establish an electric-thermal coordination optimization model;

[0087] S303. The electrothermal coordination optimization model quantitatively adjusts the heating and generator set operating parameters according to the renewable energy consumption target, and generates automated instructions.

[0088] The automated command execution mechanism for thermal pipeline networks in this invention, which considers peak shaving, frequency regulation, or other adjustment needs, improves the existing automated control process of thermal pipeline networks based on the adjustment plan and the assessment indicators such as adjustment capacity and response speed required by power regulation. The resulting automated control execution mechanism for thermal pipeline networks, which considers adjustment requirements, may execute processes including: changing indoor temperature setpoints, setting fan speed, starting fixed-frequency compressors, adjusting variable-frequency compressor frequency, adjusting the working fluid pump, setting refrigerant mass flow rate, setting the opening of the thermal expansion valve, and setting the opening of the electromagnetic expansion valve. The state variables affected by the execution process include circulating water mass flow rate, circulating water pump frequency, compressor outlet temperature and pressure, condensing temperature and pressure, and refrigerant mass flow rate. The automated command execution mechanism, considering power regulation assessment indicators, chiller unit safety constraints, and circulating water unit safety constraints, allows controllable quantities such as compressor frequency, circulating water pump, fan speed, working fluid pump, and throttling valve.

[0089] The automated instructions, during execution, change the flow rate of the heat transfer medium through multiple terminals to generate adjustment results, specifically including:

[0090] S401, the automation command performs at least one of the following: indoor temperature setpoint change, heat medium water circulation pump frequency, main and branch solenoid valve opening degree, heat source unit heating temperature, and heat source unit total heating volume.

[0091] S402. By changing the flow rate of the heat transfer medium, the temperature is changed, and the regulation result is generated.

[0092] This invention directly embeds a remote terminal operation control method for locally automated thermal pipeline networks, providing a physical support technology from the load-side terminal of the distribution network to help operators participate in the electricity market. This is because the start-up, shutdown, and power fluctuations of the terminal physical system are not only subject to human will but also depend to a large extent on the operating characteristics of the physical system to which the load belongs and the corresponding optimized operation control methods. Furthermore, this invention sets up a remote terminal control device, embedding a thermal pipeline network control system based on thermal regulation within the remote terminal control device. This enables a mode similar to the peak shaving and frequency regulation of transmission network generators, but also considers the characteristics of the massive distributed loads of the distribution network. Distributed optimization, centralized optimization, and decomposed coordination optimization of the distribution network are all built into the remote terminal control unit, providing diversified control modes for the distribution network's power demand response. The remote terminal refers to distribution network automation and transmission network dispatching. In the process of thermal power participating in power system peak shaving and frequency regulation, it includes power system dispatching, power plant distributed control systems, remote terminal control units, and coordination control systems. The massive and dispersed nature of the distribution network loads makes it impossible to achieve the same mode as the transmission network. However, it is still necessary to regulate the demand for each terminal load through distributed optimization. Since the physical system to which the load belongs is usually relatively simple, the functional modules of the distributed control system (DCS) and the coordinated control system (CCS) can be integrated into the remote terminal control device. The function of the heating network in demand response regulation plan based on the upper-level demand is similar to that of the distributed control system, while the automated command execution mechanism of the heating network that takes into account peak shaving, frequency regulation, or other regulation needs is similar to that of the coordinated control system.

[0093] Based on the adjustment results, the demand response plan is dynamically adjusted using a preset correction model, specifically including:

[0094] S501. Feedback the mediation result to the preset correction model;

[0095] S502, The correction model generates a dynamic programming algorithm by using a rolling correction plan for peak shaving and frequency regulation and a day-ahead dynamic adjustment based on artificial intelligence for peak shaving and frequency regulation.

[0096] S503. Dynamically adjust the flow rate of the heat transfer medium according to the dynamic programming algorithm to meet the thermal comfort requirements.

[0097] This invention proposes a dynamic programming algorithm for heat sources oriented towards ancillary services. The power market on the distribution network side consists of massive, dispersed, adjustable resources, making it difficult to obtain the optimal operating mode. This can be achieved through dynamic programming of hourly adjustments to the heat supply network using artificial intelligence algorithms, yielding a suboptimal dynamic programming strategy. The aim is to reconcile the contradiction between releasing regulatory capacity at different times, proposing a dynamic programming method that ensures the participation of the heat supply network in regulation with relatively optimal overall regulatory capacity.

[0098] In terms of final implementation, each heating network operation mode should include "peak shaving," "frequency regulation," and "voltage protection" modes. These modes are parallel to "dehumidification," "heating," "cooling," and "air supply" modes. The difference is that these modes are local responses to the grid regulation needs. Grid information is downloaded to the local area through technologies such as the Internet, information communication, and distributed control of intelligent terminal equipment in the distribution area. An automated control method for the heating network that takes into account power regulation needs is proposed and implemented through the regulation and control of working fluid pumps, fans, and compressors.

[0099] This can be implemented through district cooling and heating system operators or third-party retrofitters who can guarantee safety. Because the ports for cooling automation are typically not open to ordinary users, and various adjustment modes need to consider the safety and reliability of the heating network itself, adjustment requirements are embedded as an optimization target for local heating network automation, but still executed under the safety constraints of the heating network. This method is a key link in achieving bottom-up optimized operation of the distribution network. The remote terminal control mode proposed in this invention also incorporates power system adjustment actions while ensuring the safe operation of the heating network. Furthermore, it can achieve interconnection and interoperability with distribution network cluster edge computing and distribution network automation, enabling relatively accurate perception of response, control, and the distribution network's ability to adjust massive loads.

[0100] refer to Figure 7 The present invention also discloses a remote terminal response and control system for a thermal pipeline network based on power regulation, the system comprising:

[0101] Data acquisition module 110 is used to acquire heat medium water flow data of the heating network;

[0102] Planning module 120 is used to generate a demand response plan based on a preset production plan and the heat medium water flow data;

[0103] The instruction generation module 130 is used to establish an electric-thermal coordination optimization model based on the demand response plan and preset power regulation assessment indicators, and to generate automated instructions through the electric-thermal coordination optimization model.

[0104] Execution module 140 is used to change the flow rate of the heat transfer medium through multiple terminals during the execution of the automation instructions and generate adjustment results;

[0105] The correction module 150 is used to dynamically plan and adjust the demand response plan according to the adjustment result through a preset correction model, and dynamically adjust the flow rate of the heat medium water.

[0106] Among them, the planning module 120 obtains the overall optimization scheduling instructions issued by the superior transmission network to the distribution area;

[0107] Distributed control commands are issued from the distribution area to distributed loads at all levels according to the optimized scheduling instructions;

[0108] The distributed load is combined with distributed control commands and heat medium water flow data to generate a demand response plan.

[0109] A dynamic response relationship between heat sources and users in a heating network is established by using a pre-set dynamic model of the heating network.

[0110] Constraints are generated by applying the dynamic response relationship to the demand response plan.

[0111] Generate a new demand response plan based on the constraints.

[0112] The instruction generation module 130 includes the preset power regulation assessment indicators, which include the regulation capacity and response speed required for power regulation.

[0113] The demand response plan is integrated with the regulation capacity and response speed required by the power regulation to establish an electrothermal coordination optimization model;

[0114] The electrothermal coordination optimization model quantitatively adjusts the operating parameters of heating and generator sets according to the renewable energy consumption target, and generates automated instructions.

[0115] The execution module 140 performs at least one of the following actions according to the automation instructions: changing the indoor temperature setpoint, setting the fan speed, starting the fixed-frequency compressor, adjusting the frequency of the variable-frequency compressor, adjusting the working fluid pump, setting the refrigerant mass flow rate, setting the opening degree of the thermal expansion valve, and setting the opening degree of the electromagnetic expansion valve.

[0116] By changing the flow rate of the heat transfer medium, the temperature is altered, and the regulation result is generated.

[0117] The correction module 150 feeds back the mediation result to the preset correction model;

[0118] The correction model generates a dynamic programming algorithm by using a rolling correction plan for peak shaving and frequency regulation and a day-ahead dynamic adjustment based on artificial intelligence for peak shaving and frequency regulation.

[0119] The flow rate of the heat transfer medium is dynamically adjusted according to the dynamic programming algorithm to meet the thermal comfort requirements.

[0120] This invention provides a remote terminal response and control method for a power-regulated heating network. After acquiring the flow rate data of the heating medium water, a demand response plan is generated based on a preset production plan and relevant constraints. Automated commands are executed according to the demand response plan and power regulation performance indicators. Multiple components adjust the flow rate of the heating medium water to determine its intrinsic impact on wind power absorption and user comfort, identifying the optimal flow rate value. This value is then dynamically adjusted using a correction model. This achieves rational and balanced power-heat regulation, optimizing energy utilization and reducing energy waste.

[0121] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8 As shown, the electronic device may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute a remote terminal response and control method for a power-regulated heating network. This method includes acquiring the topology of the heating network, the load of terminal heat users, and the flow rate and temperature data of the heating medium.

[0122] A demand response plan is generated based on the preset production plan, combined with the peak shaving and frequency regulation requirements issued by the power system and the heat medium water flow and temperature data.

[0123] An electric-thermal coordination optimization model is established based on the demand response plan and preset power regulation assessment indicators, and automated instructions are generated through the electric-thermal coordination optimization model.

[0124] During the execution of the automated instructions, the flow rate of the heat transfer medium is changed through multiple terminals to generate flow rate regulation results and a desired water supply temperature regulation plan.

[0125] Based on the adjustment results, the demand response plan is dynamically adjusted and modified using a preset correction model, thereby dynamically adjusting the flow rate of the heat transfer medium.

[0126] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] On the other hand, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being able to be stored on a non-transitory computer-readable storage medium, the computer program being executed by a processor, the computer being able to execute the power-regulated remote terminal response and control method for heat pipe network provided by the above methods, the method including: acquiring the topology of the heat pipe network, the load of the terminal heat user, and the flow rate and temperature data of the heat medium water;

[0128] A demand response plan is generated based on the preset production plan, combined with the peak shaving and frequency regulation requirements issued by the power system and the heat medium water flow and temperature data.

[0129] An electric-thermal coordination optimization model is established based on the demand response plan and preset power regulation assessment indicators, and automated instructions are generated through the electric-thermal coordination optimization model.

[0130] During the execution of the automated instructions, the flow rate of the heat transfer medium is changed through multiple terminals to generate flow rate regulation results and a desired water supply temperature regulation plan.

[0131] Based on the adjustment results, the demand response plan is dynamically adjusted and modified using a preset correction model, thereby dynamically adjusting the flow rate of the heat transfer medium.

[0132] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power-regulated remote terminal response and control method for a heating network provided by the above methods, the method comprising: acquiring the topological relationship of the heating network, the load of the terminal heat user, and the flow rate and temperature data of the heat medium water;

[0133] A demand response plan is generated based on the preset production plan, combined with the peak shaving and frequency regulation requirements issued by the power system and the heat medium water flow and temperature data.

[0134] An electric-thermal coordination optimization model is established based on the demand response plan and preset power regulation assessment indicators, and automated instructions are generated through the electric-thermal coordination optimization model.

[0135] During the execution of the automated instructions, the flow rate of the heat transfer medium is changed through multiple terminals to generate flow rate regulation results and a desired water supply temperature regulation plan.

[0136] Based on the adjustment results, the demand response plan is dynamically adjusted and modified using a preset correction model, thereby dynamically adjusting the flow rate of the heat transfer medium.

[0137] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0138] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for remote terminal response and control of a thermal pipeline network based on power regulation, characterized in that, include: Obtain the topology of the heating network, the load of end-user heat users, and the flow rate and temperature of the heating medium; A demand response plan is generated based on the preset production plan, combined with the peak shaving and frequency regulation requirements issued by the power system and the heat medium water flow and temperature data. An electric-thermal coordination optimization model is established based on the demand response plan and preset power regulation assessment indicators, and automated instructions are generated through the electric-thermal coordination optimization model. The step of establishing an electricity-heat coordination optimization model based on the demand response plan and preset power regulation assessment indicators, and generating automation instructions through the electricity-heat coordination optimization model, includes: The preset power regulation assessment indicators include: the regulation capacity and response speed required for power regulation; The demand response plan is integrated with the regulation capacity and response speed required by the power regulation to establish an electrothermal coordination optimization model; The electrothermal coordination optimization model quantitatively adjusts the heating and generator set operating parameters according to the renewable energy consumption target, and generates automated instructions; During the execution of the automated instructions, the flow rate of the heat transfer medium is changed through multiple terminals to generate flow rate regulation results and a desired water supply temperature regulation plan. Based on the adjustment results, the demand response plan is dynamically adjusted and modified using a preset correction model, thereby dynamically adjusting the flow rate of the heat transfer medium.

2. The method for remote terminal response and control of a thermal pipeline network based on power regulation according to claim 1, characterized in that, The process of generating a demand response plan based on a preset production plan, combined with peak-shaving and frequency regulation requirements issued by the power system and the heat transfer fluid flow and temperature data, specifically includes: Obtain overall optimized dispatch instructions issued by the superior power grid to the distribution area; Distributed control commands are issued from the distribution area to distributed loads at all levels according to the optimized scheduling instructions; The distributed load is combined with distributed control commands and heat medium water flow data to generate a demand response plan.

3. The method for remote terminal response and control of a thermal pipeline network based on power regulation according to claim 2, characterized in that, The distributed load, combined with distributed control commands and heat medium water flow data, generates a demand response plan, including: The demand response plan is applicable to the hierarchical centralized-distributed optimization of the distribution network in ultra-large-scale power systems. It is a centralized optimization for the upper-level distribution network, while the lower-level load is decomposed into a distributed optimization. Distribution network automation adopts a hierarchical distributed optimization approach, where centralized optimization is used for distribution area intelligent terminal equipment and distributed optimization is used for distribution area-flexible load. After selecting peak shaving and frequency regulation modes in the heating network, one-click participation in regulation is achieved. This action will be recognized as participation at the load level, i.e., the intelligent equipment in the distribution area or the distribution network automation, providing hardware support for decentralization and enabling the distributed optimization of the distribution network to rely on a complete hardware system.

4. The method for remote terminal response and control of a thermal pipeline network based on power regulation according to claim 2, characterized in that, The distributed load, combined with distributed control commands and heat medium water flow data, generates a demand response plan, including: A dynamic response relationship between heat sources and users in a heating network is established by using a pre-set dynamic model of the heating network. Constraints are generated by applying the dynamic response relationship to the demand response plan. Generate a new demand response plan based on the constraints.

5. The method for remote terminal response and control of a thermal pipeline network based on power regulation according to claim 1, characterized in that, The automated instructions, during execution, change the flow rate of the heat transfer medium through multiple terminals, generating flow rate regulation results and a desired water supply temperature regulation plan, specifically including: The automation command changes at least one of the following: indoor temperature setpoint, frequency of heat medium circulation pump, opening degree of solenoid valves in main and branch pipelines, heating temperature of heat source unit, and total heating volume of heat source unit. By changing the flow rate of the heat transfer medium, the temperature is altered, and the regulation result is generated.

6. The method for remote terminal response and control of a thermal pipeline network based on power regulation according to claim 1, characterized in that, Based on the adjustment results, the demand response plan is dynamically adjusted using a preset correction model, specifically including: The adjustment results are fed back to the preset correction model; The correction model generates a dynamic programming algorithm by using a rolling correction plan for peak shaving and frequency regulation and a day-ahead dynamic adjustment based on artificial intelligence for peak shaving and frequency regulation. The flow rate of the heat transfer medium is dynamically adjusted according to the dynamic programming algorithm to meet the thermal comfort requirements.

7. A remote terminal response and control system for a thermal pipeline network based on power regulation, characterized in that, The system includes: The data acquisition module is used to acquire the topology of the heating network, the load of end-user heat users, and the flow rate and temperature of the heating medium. The planning module is used to generate a demand response plan based on the preset production plan, the peak shaving and frequency regulation requirements issued by the power system, and the flow and temperature data of the heat medium water. The instruction generation module is used to establish an electricity-heat coordination optimization model based on the demand response plan and preset power regulation assessment indicators, and to generate automated instructions through the electricity-heat coordination optimization model. The process of establishing the electricity-heat coordination optimization model based on the demand response plan and preset power regulation assessment indicators, and generating automated instructions through the electricity-heat coordination optimization model, includes: the preset power regulation assessment indicators include: the regulation capacity and response speed required for power regulation; integrating the demand response plan with the regulation capacity and response speed required for power regulation to establish the electricity-heat coordination optimization model; and the electricity-heat coordination optimization model quantitatively adjusts the heating and generator unit operating parameters according to the renewable energy consumption target to generate automated instructions. The execution module is used to change the flow rate of the heat medium water through multiple terminals during the execution of the automation instructions, and generate the flow rate adjustment result and the expected adjustment plan of the water supply temperature. The correction module is used to dynamically plan and adjust the demand response plan based on the adjustment results using a preset correction model, and dynamically adjust the flow rate of the heat transfer medium.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the remote terminal response and control method for thermal pipeline networks based on power regulation as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the remote terminal operation control method for thermal pipeline networks based on power regulation as described in any one of claims 1 to 6.

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