Multi-energy coupling heat supply control system and intelligent scheduling method

Abstract

The invention discloses a multi-energy coupling heat supply control system and an intelligent scheduling method, and relates to the technical field of heat supply control. The method comprises the following steps: acquiring heat supply related data of a target area; the heat supply related data comprises equipment load data, environment temperature, and outlet water temperature and return water temperature of a heat supply system; the heat supply system adopts multi-source heat supply, and heat supply sources comprise a ground source heat pump, an air source heat pump and a gas-fired boiler. Building a heat supply intelligent scheduling model based on the multi-energy coupling topology, the power following strategy and the optimization model; the multi-energy coupling topology comprises series coupling, parallel coupling and secondary pump coupling; the optimization model comprises a set objective function and corresponding constraint conditions and decision variables; and determining an optimal heat supply mode according to the heat supply related data and the heat supply intelligent scheduling model. The accuracy and flexibility of heat supply control can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heating control, and particularly to a multi - energy coupled heating control system and an intelligent scheduling method. Background Art

[0002] Currently, there are many technical defects in the field of intelligent scheduling of multi - energy coupled heating. Traditional systems have problems such as insufficient regulation means, uneven heating and cooling, and regulation lag. The grid connection of new energy further increases the complexity of regulation. Moreover, the conversion and coupling of heterogeneous energy flows are complex, with strong coupling problems, and there are significant differences in the scheduling time scales of the power, heat, and natural gas systems, making coordination difficult. In addition, the combined heat and power supply system lacks reasonable planning, and optimization models mostly rely on simplified assumptions and do not comprehensively consider the characteristics of multi - energy flows. Finally, due to the fact that current scheduling technologies cannot consider real - time demands and user emergencies, the scheduling scheme is not precise enough. These defects affect the system efficiency, stability, and economy, and there is an urgent need to construct a more perfect scheduling method. Summary of the Invention

[0003] The object of the present invention is to provide a multi - energy coupled heating control system and an intelligent scheduling method, which can improve the accuracy and flexibility of heating control.

[0004] To achieve the above object, the present invention provides the following solution: An intelligent scheduling method for multi - energy coupled heating, comprising: Obtaining heating - related data of a target area; the heating - related data includes equipment load data, ambient temperature, outlet temperature and return water temperature of the heating system; the heating system adopts multi - source heating, and the heat sources include ground - source heat pumps, air - source heat pumps, and gas boilers; Constructing a heating intelligent scheduling model based on multi - energy coupled topology, electric following strategy, and optimization model; the multi - energy coupled topology includes series coupling, parallel coupling, and secondary - pump coupling; the optimization model includes setting an objective function and corresponding constraint conditions and decision variables; Determining the optimal heating method according to the heating - related data and the heating intelligent scheduling model.

[0005] Optionally, the set objective function is constructed by the minimization objectives of annual cost value, primary energy consumption, and carbon dioxide emissions; the constraint conditions include equipment output constraints and power balance constraints; the decision variables include the capacities of internal combustion engines, photovoltaics, solar thermal collectors, storage batteries, water storage tanks, and the heating ratio and cooling ratio of ground - source heat pumps.

[0006] Optionally, before determining the optimal heating method according to the heating - related data and the heating intelligent scheduling model, it further includes: training the heating intelligent scheduling model, and the specific process is: Obtain historical training data; the historical training data includes multi-source data in a historical stage and corresponding heat supply distribution results; Construct a pre-training network based on the multi-energy coupling topology and the optimization model; Input the multi-source data into the pre-training network, aiming to minimize the loss between the network output and the heat supply distribution results, and perform training according to the electric following strategy, and determine the trained network as the final intelligent heat supply scheduling model.

[0007] Optionally, determining the optimal heat supply method according to the heat supply-related data and the intelligent heat supply scheduling model specifically includes: After inputting the heat supply-related data into the intelligent heat supply scheduling model, use Apache Kafka to coordinate cross-node operations, and record the message ID through Redis to avoid duplicate scheduling instructions, and generate the optimal heat supply method.

[0008] Optionally, the key nodes in the multi-energy coupling topology are configured according to the N-1 criterion, and the heat storage system is provided with three-level anti-overheating protection.

[0009] The present invention also provides a multi-energy coupling heat supply control system, including: A data acquisition unit for obtaining heat supply-related data of a target area; the heat supply-related data includes equipment load data, ambient temperature, outlet temperature and return water temperature of the heat supply system; the heat supply system adopts multi-source heat supply, and the heat supply sources include ground-source heat pumps, air-source heat pumps and gas boilers; A model construction unit for constructing an intelligent heat supply scheduling model based on a multi-energy coupling topology, an electric following strategy and an optimization model; the multi-energy coupling topology includes series coupling, parallel coupling and secondary pump coupling; the optimization model includes setting an objective function and corresponding constraint conditions and decision variables; A heat supply scheduling unit for determining the optimal heat supply method according to the heat supply-related data and the intelligent heat supply scheduling model.

[0010] The present invention also provides an electronic device, including a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the multi-energy coupling heat supply intelligent scheduling method according to the above.

[0011] The present invention also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the multi-energy coupling heat supply intelligent scheduling method as described above is implemented.

[0012] According to the specific embodiments provided by the present invention, the following technical effects are disclosed by the present invention: The present invention discloses a multi - energy coupled heating control system and an intelligent scheduling method. The method includes obtaining heating - related data of a target area; the heating - related data includes equipment load data, ambient temperature, outlet water temperature and return water temperature of the heating system; the heating system adopts multi - source heating, and the heat sources include a ground - source heat pump, an air - source heat pump and a gas boiler; constructing a heating intelligent scheduling model based on a multi - energy coupling topology, an electric following strategy and an optimization model; the multi - energy coupling topology includes series coupling, parallel coupling and secondary - pump coupling; the optimization model includes setting an objective function and corresponding constraint conditions and decision variables; determining the optimal heating method according to the heating - related data and the heating intelligent scheduling model. The present invention can improve the accuracy and flexibility of heating control. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0014] Figure 1 It is a schematic flow chart of the intelligent scheduling method for multi - energy coupled heating control of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0016] The object of the present invention is to provide an intelligent scheduling method for multi - energy coupled heating control, which can improve the accuracy and flexibility of heating control.

[0017] In order to make the above - mentioned objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0018] As Figure 1 shown, the present invention provides an intelligent scheduling method for multi - energy coupled heating, including: Step 100: Obtain heating - related data of a target area; the heating - related data includes equipment load data, ambient temperature, outlet water temperature and return water temperature of the heating system; the heating system adopts multi - source heating, and the heat sources include a ground - source heat pump, an air - source heat pump and a gas boiler.

[0019] Step 200: Construct a heating intelligent scheduling model based on a multi - energy coupling topology, an electric following strategy, and an optimization model; the multi - energy coupling topology includes series coupling, parallel coupling, and secondary - pump coupling; the optimization model includes setting an objective function and corresponding constraint conditions and decision variables.

[0020] Step 300: Determine the optimal heating method according to the heating - related data and the heating intelligent scheduling model.

[0021] As a specific implementation manner of the optimization model, the set objective function is constructed by minimizing the annual cost value, the primary energy consumption, and the carbon dioxide emission; the constraint conditions include equipment output constraints and power balance constraints; the decision variables include the capacities of internal combustion engines, photovoltaics, solar thermal, storage batteries, water storage tanks, and the heating ratio and cooling ratio of ground - source heat pumps.

[0022] As a specific implementation manner, before determining the optimal heating method according to the heating - related data and the heating intelligent scheduling model, it further includes: training the heating intelligent scheduling model, and the specific process is as follows: Obtain historical training data; the historical training data includes multi - source data in historical stages and corresponding heating distribution results; construct a pre - training network based on the multi - energy coupling topology and the optimization model; input the multi - source data into the pre - training network, aiming at minimizing the loss between the network output and the heating distribution results, and perform training according to the electric following strategy, and determine the trained network as the final heating intelligent scheduling model.

[0023] As a specific implementation manner, the determining the optimal heating method according to the heating - related data and the heating intelligent scheduling model specifically includes: After inputting the heating - related data into the heating intelligent scheduling model, use Apache Kafka to coordinate cross - node operations, and record the message ID through Redis to avoid duplicate scheduling instructions, and generate the optimal heating method.

[0024] As a specific implementation manner, the key nodes in the multi - energy coupling topology are configured according to the N - 1 criterion, and the heat storage system is provided with three - level anti - overheating protection.

[0025] Based on the above - mentioned technical solutions, the following embodiments are provided.

[0026] First, construct a system architecture for executing the above - mentioned various functions. In this system, the data acquisition unit is responsible for collecting heating - related data of the target area; the intelligent scheduling model is constructed based on a multi - energy coupling topology, an electric following strategy, and an optimization model; the training unit is used to train the heating intelligent scheduling model; the scheduling execution unit is used to execute the optimal heating method according to the output of the intelligent scheduling model.

[0027] Then, detailed implementation steps are provided for each function: Step 100: Obtain heat supply - related data of the target area.

[0028] Equipment load data: Collect the equipment load data of the heat supply system through sensors. Ambient temperature: Collect the ambient temperature of the target area through meteorological sensors. Outlet and return water temperatures of the heat supply system: Collect the outlet and return water temperatures of the heat supply system through temperature sensors. Heat sources: The heat supply system adopts multi - source heating, including ground - source heat pumps, air - source heat pumps, and gas boilers.

[0029] Step 200: Build a heat supply intelligent scheduling model.

[0030] Multi - energy coupling topologies include series coupling, parallel coupling, and secondary - pump coupling; among them, series coupling: Multiple energy devices are connected in series to provide energy step by step; parallel coupling: Multiple energy devices are connected in parallel to provide energy simultaneously; secondary - pump coupling: Adopt a secondary - pump system to improve the flexibility and efficiency of the heat supply system.

[0031] Electricity - following strategy: First satisfy the electricity load, and then satisfy the heat load and cold load.

[0032] The optimization model includes an objective function, constraint conditions, and decision variables; among them, the objective function: It is constructed by the minimization objectives of the annual cost value, primary energy consumption, and carbon dioxide emissions; constraint conditions: Include equipment output constraints and power balance constraints; decision variables: Include the capacities of internal combustion engines, photovoltaics, solar thermal, batteries, water storage tanks, as well as the heat supply ratio and cooling ratio of ground - source heat pumps.

[0033] Step 300: Determine the optimal heat supply method.

[0034] Data input: Input the heat - supply - related data into the heat - supply intelligent scheduling model.

[0035] Scheduling model: Use Apache Kafka to coordinate cross - node operations, and record the message ID through Redis to avoid duplicate scheduling instructions.

[0036] Optimal heat supply method: The model outputs the optimal heat supply method, including the output distribution and operation strategy of each energy device.

[0037] Specific implementation process: Deploy sensors in the target area to collect equipment load data, ambient temperature, outlet and return water temperatures of the heat supply system in real - time, and the collected data is transmitted to the data center through Internet of Things technology.

[0038] Design the topologies of series connection, parallel connection, and secondary pump coupling to ensure the flexibility and efficiency of the system. Construct an optimization model that includes the objectives of minimizing the annual cost value, primary energy consumption, and carbon dioxide emissions. In the optimization model, set the equipment output constraints and power balance constraints, and define the capacities of internal combustion engines, photovoltaics, solar thermal, batteries, water storage tanks, and the heating ratio and cooling ratio of ground source heat pumps as decision variables.

[0039] Train the model: Obtain historical training data: Collect multi-source data in the historical stage and the corresponding heating distribution results.

[0040] Construct a pre-training network: Based on the multi-energy coupling topology and the optimization model, construct a pre-training network.

[0041] Training process: Input the multi-source data into the pre-training network, aiming to minimize the loss between the network output and the heating distribution results, and train according to the power following strategy.

[0042] Model optimization: Through multiple iterative trainings, optimize the model parameters to improve the accuracy and reliability of the model.

[0043] Finally, schedule and execute: Data input: Input the real-time collected heating-related data into the trained intelligent heating scheduling model.

[0044] Generate scheduling instructions: Use Apache Kafka to coordinate cross-node operations, and record the message ID through Redis to avoid duplicate scheduling instructions.

[0045] Optimal heating method: The model outputs the optimal heating method, including the output distribution and operation strategy of each energy equipment.

[0046] Execute the schedule: According to the optimal heating method output by the model, control the operation of each energy equipment to ensure the efficient operation of the heating system.

[0047] Therefore, this solution has the following beneficial effects: High efficiency: Through the intelligent scheduling model, optimize the output distribution of energy equipment to improve the operation efficiency of the heating system. Low cost: Reduce primary energy consumption and carbon dioxide emissions, and lower the operation cost of the heating system. High reliability: Configure key nodes using the N-1 criterion, and set three-level anti-overheating protection for the heat storage system to ensure the stability and safety of the system. Flexibility: Support the coupling of multiple energy equipment to adapt to different heating demands and environmental conditions.

[0048] Through the above embodiments, an efficient, economical, and reliable multi-energy coupling heating intelligent scheduling system can be constructed, providing strong support for energy conservation, emission reduction, and sustainable development in the heating field.

[0049] In addition, the present invention also provides a multi - energy coupled heating control system, including: A data acquisition unit for acquiring heating - related data of a target area; the heating - related data includes equipment load data, ambient temperature, outlet temperature and return water temperature of the heating system; the heating system adopts multi - source heating, and the heat sources include a ground - source heat pump, an air - source heat pump and a gas boiler; A model construction unit for constructing a heating intelligent scheduling model based on a multi - energy coupling topology, an electric following strategy and an optimization model; the multi - energy coupling topology includes series coupling, parallel coupling and secondary - pump coupling; the optimization model includes a set objective function and corresponding constraint conditions and decision variables; A heating scheduling unit for determining the optimal heating method according to the heating - related data and the heating intelligent scheduling model.

[0050] The present invention also provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the multi - energy coupled heating intelligent scheduling method according to the above.

[0051] The present invention also provides a computer - readable storage medium storing a computer program, and when the computer program is executed by a processor, the multi - energy coupled heating intelligent scheduling method as described above is implemented.

[0052] In this specification, each embodiment is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0053] In this article, specific examples are used to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A multi-energy coupled heating intelligent scheduling method, characterized in that: include: Obtaining heating-related data of the target area; the heating-related data includes equipment load data, ambient temperature, outlet water temperature and return water temperature of the heating system; the heating system adopts multi-source heating, and the heating sources include ground source heat pump, air source heat pump and gas boiler; A heating intelligent scheduling model is constructed based on multi-energy coupling topology, electric following strategy and optimization model; the multi-energy coupling topology includes series coupling, parallel coupling and secondary pump coupling; the optimization model includes setting the objective function and the corresponding constraints and decision variables; Determine the optimal heating mode according to the heating-related data and the heating intelligent scheduling model; The set objective function is constructed by minimizing the annual cost value, primary energy consumption and carbon dioxide emissions; the constraints include equipment output constraints and power balance constraints; the decision variables include the capacity of the internal combustion engine, photovoltaic, solar thermal, battery, water tank, and the heating ratio and cooling ratio of the ground source heat pump; Before determining the optimal heating mode according to the heating-related data and the heating intelligent scheduling model, the method further includes: training the heating intelligent scheduling model, and the specific process is as follows: Acquire historical training data; the historical training data includes multi-source data of historical stages and corresponding heat distribution results; Building a pre-trained network based on the multi-energy coupling topology and the optimization model; The multi-source data is input into the pre-trained network, with the goal of minimizing the loss between the network output and the heat distribution result, and training is performed according to the electric following strategy, and the trained network is determined as the final intelligent heating scheduling model.

2. The multi-energy coupling heating intelligent scheduling method according to claim 1 is characterized in that: The determining of the optimal heating mode according to the heating-related data and the heating intelligent scheduling model specifically includes: After the heating-related data is input into the intelligent heating scheduling model, Apache Kafka is used to coordinate cross-node operations, and Redis is used to record message IDs to avoid repeated scheduling instructions and generate the optimal heating method.

3. The multi-energy coupling heating intelligent scheduling method according to claim 1 is characterized in that: The key nodes in the multi-energy coupling topology are configured according to the N-1 criterion, and the heat storage system is provided with three levels of overheating protection.

4. A multi-energy coupling heating control system, based on the intelligent scheduling method according to any one of claims 1 to 3, characterized in that: include: A data acquisition unit is used to obtain heating-related data of a target area; the heating-related data includes equipment load data, ambient temperature, outlet water temperature and return water temperature of a heating system; the heating system adopts multi-source heating, and the heating sources include a ground source heat pump, an air source heat pump and a gas boiler; A model building unit, used to build a heating intelligent scheduling model based on a multi-energy coupling topology, an electric following strategy and an optimization model; the multi-energy coupling topology includes series coupling, parallel coupling and secondary pump coupling; the optimization model includes setting an objective function and corresponding constraints and decision variables; A heat supply scheduling unit is used to determine the optimal heat supply mode according to the heat supply related data and the heat supply intelligent scheduling model.

5. An electronic device, characterized in that: It includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the multi-energy coupling heating intelligent scheduling method according to any one of claims 1-3.

6. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the multi-energy coupling heating intelligent scheduling method as described in any one of claims 1-3.

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