A multi-energy coupling heating control system and an intelligent scheduling method

Abstract

The present invention discloses a multi - energy coupled heating control system and an intelligent scheduling method, which relates to the technical field of heating control. The method includes: 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; 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 mode according to the heating - related data and the heating intelligent scheduling model. The present invention can improve the accuracy and flexibility of heating control.

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 deficiencies 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 without comprehensively considering the characteristics of multi - energy flows. Finally, due to the current scheduling technology being unable to consider real - time demand and sudden user situations, the scheduling plan is not precise enough. These deficiencies 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 solutions:

[0005] A multi - energy coupled heating intelligent scheduling method includes:

[0006] 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 uses multi - source heating, and the heat sources include ground - source heat pumps, air - source heat pumps, and gas boilers;

[0007] 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;

[0008] Determining the optimal heating method according to the heating - related data and the heating intelligent scheduling model.

[0009] Optionally, the set objective function is constructed by minimizing the 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, and water storage tanks, as well as the heating ratio and cooling ratio of the ground - source heat pump.

[0010] 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 as follows:

[0011] Obtain historical training data; the historical training data includes multi-source data in a historical stage and corresponding heating distribution results;

[0012] Construct a pre-training network based on the multi-energy coupling topology and the optimization model;

[0013] 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 perform training according to the electric following strategy, and determine the trained network as the final heating intelligent scheduling model.

[0014] Optionally, the determining the optimal heating method according to the heating-related data and the heating intelligent scheduling model specifically includes:

[0015] 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.

[0016] 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.

[0017] The present invention also provides a multi-energy coupling heating control system, including:

[0018] 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 uses multi-source heating, and the heat sources include a ground-source heat pump, an air-source heat pump and a gas boiler;

[0019] 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 setting an objective function and corresponding constraint conditions and decision variables;

[0020] A heating scheduling unit for determining the optimal heating method according to the heating-related data and the heating intelligent scheduling model.

[0021] 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 heating intelligent scheduling method according to the above.

[0022] The present invention also provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the multi-energy coupled heating intelligent scheduling method as described above.

[0023] According to the specific embodiments provided by the present invention, the following technical effects are disclosed:

[0024] 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 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 an 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

[0025] 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 for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a schematic flowchart of the intelligent scheduling method for multi-energy coupled heating control of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] 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 of the embodiments of the present invention, rather than all of them. 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.

[0028] The purpose 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.

[0029] In order to make the above 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.

[0030] Such as Figure 1As shown in the figure, the present invention provides a multi - energy coupling heating intelligent scheduling method, including:

[0031] Step 100: Obtain 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 heat sources include ground - source heat pumps, air - source heat pumps and gas boilers.

[0032] 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.

[0033] Step 300: Determine the optimal heating method according to the heating - related data and the heating intelligent scheduling model.

[0034] As a specific implementation manner of the optimization model, the set objective function is constructed by the minimization objectives of the annual cost value, the primary energy consumption and the 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, storage batteries, water storage tanks, as well as the heating ratio and cooling ratio of the ground - source heat pump.

[0035] 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:

[0036] 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, aim 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.

[0037] 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:

[0038] 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.

[0039] 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.

[0040] Based on the above technical solutions, the following embodiments are provided.

[0041] First, construct a system architecture for executing the above various functions. In this system, the data acquisition unit is responsible for collecting heat supply-related data of the target area; the intelligent scheduling model is constructed based on multi-energy coupling topology, the electric following strategy, and an optimization model; the training unit is used to train the heat supply intelligent scheduling model; the scheduling execution unit is used to execute the optimal heat supply method according to the output of the intelligent scheduling model.

[0042] Then, provide detailed implementation steps for each function:

[0043] Step 100: Obtain heat supply-related data of the target area.

[0044] 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 supply sources: The heat supply system adopts multi-source heat supply, including ground-source heat pumps, air-source heat pumps, and gas boilers.

[0045] Step 200: Construct a heat supply intelligent scheduling model.

[0046] The multi-energy coupling topology includes 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.

[0047] Electric following strategy: Give priority to meeting the electric load, and then meet the heat load and cold load.

[0048] The optimization model includes an objective function, constraint conditions, and decision variables; among them, the objective function: 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.

[0049] Step 300: Determine the optimal heat supply method.

[0050] Data input: Input the heat supply-related data into the heat supply intelligent scheduling model.

[0051] Scheduling model: Use Apache Kafka to coordinate cross-node operations, and record the message ID through Redis to avoid duplicate scheduling instructions.

[0052] Optimal heating method: The model outputs the optimal heating method, including the output distribution and operation strategy of each energy device.

[0053] Specific implementation process:

[0054] Deploy sensors in the target area to collect equipment load data, ambient temperature, outlet water temperature and return water temperature of the heating system in real time. The collected data is transmitted to the data center through Internet of Things technology.

[0055] Design the topological structures of series connection, parallel connection and secondary pump coupling to ensure the flexibility and efficiency of the system. Build an optimization model including the objectives of minimizing the annual value of cost, 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 collectors, storage batteries, water storage tanks, and the heating ratio and cooling ratio of ground source heat pumps as decision variables.

[0056] Train the model:

[0057] Obtain historical training data: Collect multi-source data in the historical stage and the corresponding heating distribution results.

[0058] Build a pre-training network: Build a pre-training network based on the multi-energy coupling topology and the optimization model.

[0059] 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.

[0060] Model optimization: Optimize the model parameters through multiple iterative trainings to improve the accuracy and reliability of the model.

[0061] Finally, schedule and execute:

[0062] Data input: Input the heating-related data collected in real time into the trained intelligent heating scheduling model.

[0063] Generate scheduling instructions: Use Apache Kafka to coordinate cross-node operations, and record the message ID through Redis to avoid duplicate scheduling instructions.

[0064] Optimal heating method: The model outputs the optimal heating method, including the output distribution and operation strategy of each energy device.

[0065] Execute the scheduling: Control the operation of each energy device according to the optimal heating method output by the model to ensure the efficient operation of the heating system.

[0066] Therefore, this solution has the following beneficial effects:

[0067] High efficiency: By means of an intelligent scheduling model, optimize the output distribution of energy equipment to improve the operating efficiency of the heating system. Low cost: Reduce the primary energy consumption and carbon dioxide emissions to lower the operating cost of the heating system. High reliability: Configure key nodes according to the N-1 criterion, and the heat storage system is equipped with three-level anti-overheating protection to ensure the stability and security of the system. Flexibility: Support the coupling of multiple energy equipment to adapt to different heating demands and environmental conditions.

[0068] Through the above embodiments, an efficient, economical and reliable multi-energy coupled heating intelligent scheduling system can be constructed, providing strong support for energy conservation, emission reduction and sustainable development in the heating field.

[0069] In addition, the present invention also provides a multi-energy coupled heating control system, including:

[0070] A data acquisition unit for 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 heat sources include ground-source heat pumps, air-source heat pumps and gas boilers;

[0071] A model construction unit for constructing a heating intelligent scheduling model 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 an objective function and corresponding constraint conditions and decision variables;

[0072] A heating scheduling unit for determining the optimal heating method according to the heating-related data and the heating intelligent scheduling model.

[0073] 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 coupled heating intelligent scheduling method according to the above.

[0074] 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 coupled heating intelligent scheduling method as described above is implemented.

[0075] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts among the various embodiments can be referred to each other.

[0076] In this article, specific examples are used to illustrate the principles and implementation manners 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 manners and application scopes. To sum up, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A multi - energy coupled heating intelligent scheduling method, characterized in that Including: Obtain 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 heat sources include ground-source heat pumps, air-source heat pumps and gas boilers; Construct a heating intelligent scheduling model based on the multi-energy coupling topology, the electric following strategy and the 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 constraint conditions and decision variables; Determine the optimal heating method according to the heating-related data and the heating intelligent scheduling model; The set objective function is constructed by the minimization objectives of the annual cost value, the primary energy consumption and the 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 the ground-source heat pump; 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 the historical stage and the 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, with the goal of minimizing the loss between the network output and the heating distribution results, and train according to the electric following strategy, and determine the trained network as the final heating intelligent scheduling model; 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; 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 overheat protection.

2. A multi-energy coupled heating control system, based on the intelligent scheduling method described in any one of claims 1, characterized in that, Including: A data acquisition unit for 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 heat sources include ground-source heat pumps, air-source heat pumps and gas boilers; A model construction unit for constructing a heating intelligent scheduling model based on the multi-energy coupling topology, the electric following strategy and the 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 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.

3. An electronic device, characterized in that, Including 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.

4. A computer-readable storage medium, characterized in that, It stores a computer program, and when the computer program is executed by a processor, it implements the multi-energy coupling heating intelligent scheduling method described in any one of claims 1.

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