A method for planning, designing, and optimizing the operation of a heating system that incorporates a long-distance heat source.

Abstract

This invention discloses a method for planning, designing, and optimizing the operation of a heating system that incorporates a long-distance heat source. The method includes: acquiring basic data on the long-distance heat source to be introduced into the heating system during the heating season, and analyzing the changes in the operation mode and dynamic characteristics of the existing heating network after the introduction of the long-distance heat source; combining external heating industry evolution data during the heating season, quantitatively analyzing and comparing various heating network planning and design schemes to obtain the optimal planning and design scheme; acquiring spatial and temporal characteristic data related to the heating system, establishing a spatiotemporal model of the heating system, and combining multi-dimensional heat load impact data to predict the heat load of each heating network area; based on the predicted heat load values ​​of each heating network area, and considering the multi-dimensional heat loss, heat source output characteristics, and heat distribution characteristics of the long-distance and traditional heat sources, as well as the heat source optimization operation objectives and the heating network control objectives, establishing a multi-timescale optimization operation model to obtain the corresponding optimization operation strategy.

Description

Technical Field

[0001] This invention belongs to the field of smart heating technology, specifically relating to a method for planning, designing and optimizing the operation of a heating system that incorporates a long-distance heat source. Background Technology

[0002] With the acceleration of urbanization and the continuous expansion of centralized heating areas, the capacity of existing local heat sources is becoming insufficient to meet the rapidly growing heat load demand. Against this backdrop, introducing external long-distance heat sources has become a necessary means to provide large-scale and stable heat input. This can not only alleviate the heating pressure on local heat sources, but also improve the stability and reliability of the heating system.

[0003] However, the introduction of external long-distance heat sources into the existing heating network will dynamically change the entire heating system's pipeline layout, operation mode, and coordinated heating mechanism. The current technical challenges mainly include two aspects:

[0004] Firstly, how to measure the impact of the introduction of long-distance heat sources on the operation of the existing heating network during the non-heating season, so as to scientifically guide the planning and design of the heating network and obtain reasonable planning and design schemes for each heat source and pipeline network.

[0005] Secondly, based on the planning and design, the long-distance heat source and local heat source form a multi-heat source joint heating mode during the heating season, which makes the operation and control mechanism of the heating system more complex. It is necessary to comprehensively consider the multi-dimensional influencing factors such as the dynamic changes of each heat source, each heating network area and external time and space, and how to effectively utilize the operating characteristics of each heat source, and explore the multi-source complementary operation mode and equipment control strategy under the heat load demand of different heating periods and different heating network areas.

[0006] To address the aforementioned technical issues, there is an urgent need to design a new method for planning, designing, and optimizing the operation of a heating system that incorporates a long-distance heat source, in order to overcome the shortcomings of existing technologies. Summary of the Invention

[0007] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and provide a method for planning, designing and optimizing the operation of a heating system that introduces a long-distance heat source. This method can effectively analyze the impact of the introduction of the long-distance heat source on the operation of the heating network, quantitatively analyze and obtain the optimal planning and design scheme of the heating network, and based on the optimal planning scheme of the heating network, predict the heat load of each heating network area and establish a multi-time-scale optimized operation model, thereby improving the efficiency of heat source coordination, reducing operating costs, reducing manual intervention and ensuring the quality of heating services.

[0008] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0009] This invention provides a method for planning, designing, and optimizing the operation of a heating system that incorporates a long-distance heat source, comprising:

[0010] S1. Obtain basic data on long-distance heat sources to be introduced into the heating system during the heating season, and analyze the changes in the operation mode and dynamic characteristics of the existing heating system network after the introduction of long-distance heat sources.

[0011] S2. Based on the changes in the operation mode and dynamic characteristics of the heating network, and combined with the evolution data of external heating business during the heating season, quantitative analysis and comparison of various pre-set heating network planning and design schemes are conducted to obtain the heating network planning and design scheme.

[0012] S3. Obtain spatial and temporal characteristic data related to the heating system, establish a spatiotemporal model of the heating system, obtain the heating network structure and operating parameters of the heating system that change over time, and combine the multidimensional influence data of heat load to predict the heat load of each heating network area.

[0013] S4. Based on the predicted heat load values ​​of each heating network area, combined with the multi-dimensional heat loss of long-distance heat sources and traditional heat sources, the output characteristics of heat sources and the heat transmission and distribution characteristics of the heating network, and taking into account the heat source optimization operation objectives and the heating network regulation objectives, establish a multi-time-scale optimization operation model to obtain optimization operation strategies under different time scales.

[0014] Furthermore, S1 specifically includes:

[0015] Obtain basic data on the long-distance heat source to be introduced into the heating system during the heating season, including: heating capacity of the long-distance heat source, outlet temperature, outlet pressure, pipeline length, pipe diameter, insulation layer thickness, burial depth, terrain elevation along the route, heat load adjustment range, connection point location with the existing heating network, and interface pipe diameter.

[0016] Collect existing heating network data, including the topology of the heating network, the pressure and temperature of each node in the heating network, the flow rate of each pipe section, and the parameters of key equipment in the heating network;

[0017] Based on the basic data of the long-distance heat source and the collected data of the existing heating network, a simulation model of the heating system is established. The model analyzes the changes in the heating network zoning, structure, pressure distribution, temperature distribution, flow distribution, and hydraulic stability of the existing heating system after the introduction of the long-distance heat source. It also evaluates the response time and fluctuation range of the heating network pressure and temperature during the commissioning and decommissioning of the long-distance heat source.

[0018] Furthermore, S2 specifically includes:

[0019] Acquire data on the evolution of external heating services during the heating season, including data on the evolution of heating area during the planning period, data on the growth of heat users, data on energy price fluctuations, and data on the commissioning of new stations;

[0020] Based on the evolution data of external heating services during the heating season, we analyze the changes in heat load demand during the heating season, and conduct a heat supply and demand balance analysis by combining the heating capacity of long-distance heat sources and traditional heat sources.

[0021] Based on the changes in the operation mode and dynamic characteristics of the heating network after the introduction of long-distance heat sources, constraints on the safe and stable operation of each heat source and the heating network are set.

[0022] Based on the results of heat supply and demand balance analysis and the safety operation constraints of each heat source and heating network, multiple heating network planning and design schemes, including heat source combinations and pipeline layouts, are pre-set for different periods of the heating season.

[0023] A multi-indicator comprehensive evaluation system is constructed, and a multi-criteria decision analysis method is used to quantitatively analyze and compare various heat network planning and design schemes to obtain the optimal planning and design scheme, which guides the timing of the withdrawal and commissioning of long-distance heat sources and traditional heat sources, the preliminary output plan of each heat source, the layout of the pipeline network, and the pipeline network routing design scheme.

[0024] Furthermore, the data on the commissioning of the new station includes the heating unit, the area connected to the network, the location of the heating, the heating company to which it belongs, the form of heating, and the type of heating.

[0025] The multi-indicator comprehensive evaluation system includes technical indicators, economic indicators, and environmental indicators.

[0026] Furthermore, in S3, establishing the spatiotemporal model of the heating system specifically includes:

[0027] Acquire spatial and temporal characteristic data related to the heating system; the spatial characteristic data represents the spatial coupling influence between heating networks and heating stations; the temporal characteristic data represents the evolution of the operation and structure of the heating system's heat source, heating network, and heating stations over time.

[0028] The spatial feature data is trained using a first machine learning algorithm to extract spatial features;

[0029] The time feature data is trained using a second machine learning algorithm to extract time features;

[0030] By integrating the extracted spatial and temporal features, a spatiotemporal model of the heating system is established.

[0031] Furthermore, in step S3, the heat load prediction for each heating network area specifically includes:

[0032] Based on the spatiotemporal model of the heating system, the heat network structure and operating parameters of the heating system that change over time are obtained. Combined with meteorological data, building characteristic data of heat users under each heat network area, user behavior data, new station commissioning data and historical heat load data, a heat load impact dataset for each heat network area is formed.

[0033] After preprocessing and feature extraction of the heat load impact datasets of each heating network area, the datasets are input into a hybrid learning algorithm for training and learning, thereby establishing a heat load prediction model for each heating network area and predicting the heat load of each heating network area at different future times.

[0034] Furthermore, S4 specifically includes:

[0035] Based on the heat load forecast values ​​for each heating network area the following day, and combined with the multi-dimensional heat loss and heat source output characteristics of long-distance heat sources and traditional heat sources, an early-day optimization scheduling model is established with the goal of minimizing operating costs and minimizing the heat load supply-demand deviation, to obtain the load allocation strategies for long-distance heat sources and traditional heat sources at each time period of the day.

[0036] Based on the predicted and measured heat load values ​​for each heating network area during the day, and the heat transmission and distribution characteristics from each heat source to each associated heating network area, under the condition of ensuring the heat load demand of each heating network area, the operating parameters of long-distance heat sources and traditional heat sources are adjusted and corrected. With the goal of minimizing adjustment and correction costs and minimizing fluctuations in heating network parameters, an intraday optimization scheduling model is established to obtain the operating guidance curves of long-distance heat sources and traditional heat sources at each time period during the day.

[0037] Based on the operation guidance curves of long-distance heat sources and traditional heat sources at different times of the day, and combined with the preset control temperature targets for each heating network area, a real-time optimization control model is established to generate hourly valve parameter control strategies for each heating network area.

[0038] Furthermore, the multi-dimensional heat loss includes: heat loss caused by heat leakage from the heating network, heat loss from water replenishment caused by water leakage from the heating network, and excessive heat loss in space; the excessive heat loss in space refers to the uneven heating loss between buildings in the heating network and the uneven heating loss among users within the building.

[0039] Furthermore, the analysis of the heat transmission and distribution characteristics from each heat source to each associated heating network area includes: using the heat source temperature rise curve to calculate the transmission delay time from the temperature change of each heat source to the response of the corresponding heating station in each heating network area, and adjusting the operating parameters of each heat source in advance.

[0040] Furthermore, the heating system planning, design, and optimized operation method also includes: analyzing the problems existing in the heating network in response to heat loss caused by heat leakage in the heating network, heat loss due to water replenishment caused by water leakage in the heating network, and excessive heat loss in space, and establishing corresponding heating network renovation strategies.

[0041] The present invention, by adopting the above technical solution, has at least the following beneficial effects:

[0042] (1) This invention obtains basic data of long-distance heat sources to be introduced into the heating system during the heating season and analyzes the changes in the operation mode and dynamic characteristics of the existing heating system network after the introduction of long-distance heat sources; it can clarify the degree of coordination and matching between long-distance heat sources and existing heating networks, and identify changes in the operation mode and dynamic characteristics of the heating network in advance, providing a basis for subsequent planning and control strategies.

[0043] (2) Based on the changes in the operation mode and dynamic characteristics of the heating network, and combined with the evolution data of the external heating industry during the heating season, this invention performs quantitative analysis and comparison on a variety of pre-set heating network planning and design schemes to obtain the optimal heating network planning and design scheme; it can combine external heating industry data to ensure that the planning scheme remains reasonable for a period of time in the future, and obtain the optimal planning and design scheme by comprehensively and quantitatively evaluating a variety of heating network planning and design schemes through multiple indicators.

[0044] (3) This invention obtains spatial and temporal characteristic data related to the heating system, establishes a spatiotemporal model of the heating system, obtains the heat network structure and operating parameters of the heating system that change over time, and combines the multidimensional influence data of heat load to predict the heat load of each heat network area; it can capture spatial coupling characteristics, optimize regional coordinated regulation, characterize the temporal evolution law, adapt to dynamic operating scenarios, and make the heat load prediction results more consistent with the physical laws and temporal dimension changes of the actual operation of the heat network, thereby improving the response capability to complex scenarios;

[0045] (4) Based on the predicted heat load values ​​of each heat network area, this invention combines the multi-dimensional heat loss of long-distance heat sources and traditional heat sources, the output characteristics of heat sources and the heat transmission and distribution characteristics of heat networks, and considers the heat source optimization operation objectives and heat network regulation objectives to establish a multi-time scale optimization operation model and obtain optimization operation strategies under different time scales; it can realize load allocation based on multi-dimensional data, fine regulation to adapt to short-term load changes and hourly valve regulation to achieve precise heating, improve heat source coordination efficiency, reduce operating costs, reduce manual intervention and ensure the quality of heating services.

[0046] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating the planning, design, and optimized operation method of a heating system that incorporates a long-distance heat source, according to the present invention.

[0050] Figure 2 This is a flowchart of the heating network planning and design of the present invention;

[0051] Figure 3 This is a schematic diagram of the hierarchical structure of the multi-index comprehensive evaluation system of the present invention;

[0052] Figure 4 A schematic diagram illustrating the principle of establishing a spatiotemporal model of the heating system for this invention;

[0053] Figure 5 The flowchart illustrates the process of establishing a multi-timescale optimization operation model for this invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, this embodiment provides a method for planning, designing, and optimizing the operation of a heating system that incorporates a long-distance heat source, which includes:

[0056] S1. Obtain basic data on long-distance heat sources to be introduced into the heating system during the heating season, and analyze the changes in the operation mode and dynamic characteristics of the existing heating system network after the introduction of long-distance heat sources.

[0057] S2. Based on the changes in the operation mode and dynamic characteristics of the heating network, and combined with the evolution data of external heating business during the heating season, quantitative analysis and comparison of various preset heating network planning and design schemes are conducted to obtain the optimal heating network planning and design scheme.

[0058] S3. Obtain spatial and temporal characteristic data related to the heating system, establish a spatiotemporal model of the heating system, obtain the heating network structure and operating parameters of the heating system that change over time, and combine the multidimensional influence data of heat load to predict the heat load of each heating network area.

[0059] S4. Based on the predicted heat load values ​​of each heating network area, combined with the multi-dimensional heat loss of long-distance heat sources and traditional heat sources, the output characteristics of heat sources and the heat transmission and distribution characteristics of the heating network, and taking into account the heat source optimization operation objectives and the heating network regulation objectives, establish a multi-time-scale optimization operation model to obtain optimization operation strategies under different time scales.

[0060] In this embodiment, S1 specifically includes:

[0061] Obtain basic data on the long-distance heat source to be introduced into the heating system during the heating season, including: heating capacity of the long-distance heat source, outlet temperature, outlet pressure, pipeline length, pipe diameter, insulation layer thickness, burial depth, terrain elevation along the route, heat load adjustment range, connection point location with the existing heating network, and interface pipe diameter.

[0062] Collect existing heating network data, including the topology of the heating network, the pressure and temperature of each node in the heating network, the flow rate of each pipe section, and the parameters of key equipment in the heating network;

[0063] Based on the basic data of the long-distance heat source and the collected data of the existing heating network, a simulation model of the heating system is established. The model analyzes the changes in the heating network zoning, structure, pressure distribution, temperature distribution, flow distribution, and hydraulic stability of the existing heating system after the introduction of the long-distance heat source. It also evaluates the response time and fluctuation range of the heating network pressure and temperature during the commissioning and decommissioning of the long-distance heat source.

[0064] In one specific embodiment of the present invention, as the heat load demand of a city increases, the heat supply from existing traditional heat sources cannot meet the demand, especially during periods of severe cold when the pipeline network heats up, resulting in a significant supply shortage. Therefore, it is necessary to introduce an external heat source and coordinate it with existing traditional heat sources for heating. The external heat source is transported to the main urban area through a long-distance heat transmission network, and after heat exchange at a pressure-reducing station, it supplies heat to the urban area. The long-distance heat transmission network traverses undulating terrain, and its pipeline route and operating conditions are complex.

[0065] In addition, the heating system also involves multiple gas-fired boiler rooms; the entire heating system supplies heat to each heating network area based on external heat sources and gas-fired boiler rooms. At the same time, the heating network area is divided according to the heating network pipeline structure layout, the location of heat stations, pressure reducing stations, the location of heat sources, heating capacity and other parameters. The heating area and heat consumption of each heating network area are different.

[0066] When establishing a simulation model of a heating system, the topological connection logic of heat sources, pipe networks, and heat exchange stations is established using graphical configuration. Parameters are used to specifically describe the spatial location and structural information of equipment such as heat sources, boilers, pipes, elbows, pumps, and heat exchange stations. Specifically, a heating network model is established based on the mechanism models of components such as heat sources, heating stations, boilers, valves, pipelines, and buildings, as well as the actual route of the heating network, ensuring consistency with the actual heating network in construction. The simulation model can also simulate changes in supply and return water temperature, pressure, flow rate, specific friction, and heat source parameters, as well as analyze the operating status of the pipe network after the introduction of long-distance heat sources.

[0067] It should be noted that the following aspects are considered: Pressure distribution changes: Compare the pressure distribution of each node in the heating network before and after the introduction of the long-distance heat source to assess whether there are any pressure exceeding or falling short of limits. For example, analyze whether the pressure at certain nodes exceeds the pipeline's design pressure, or whether the pressure at certain user ends is lower than the minimum pressure required to meet heating demand. Flow distribution changes: Analyze the changes in flow distribution across each pipe segment of the heating network after the introduction of the long-distance heat source to determine whether this will lead to hydraulic imbalance. The degree of hydraulic imbalance can be assessed by calculating the flow ratio of each pipe segment. Temperature distribution changes: Compare the temperature distribution of each node in the heating network before and after the introduction of the long-distance heat source to analyze the impact of temperature changes on heating performance.

[0068] Hydraulic stability assessment: Calculate the damping ratio, critical flow rate, and other indicators of the heating network to assess its hydraulic stability after the introduction of a long-distance heat source. If the damping ratio is less than 0.3, it indicates a potential risk of hydraulic oscillation in the heating network, requiring corresponding optimization measures. Response time assessment: Evaluate the response time of the heating network pressure and temperature during dynamic processes such as the connection and switching of long-distance heat sources to determine whether the system's safe operation requirements are met; generally, the response time should not exceed 10 minutes. Fluctuation amplitude analysis: Analyze the fluctuation amplitude of pressure and temperature during dynamic processes to assess whether it will have adverse effects on equipment and users; for example, the pressure fluctuation amplitude should not exceed 20% of the pipeline design pressure.

[0069] like Figure 2 As shown, in this embodiment, S2 specifically includes:

[0070] Acquire external heating service evolution data during the heating season, including heating area evolution data, heat user growth data, energy price fluctuation data, and new station commissioning data; the new station commissioning data includes heating unit, network access area, heating location, affiliated heating company, heating method, and heating type;

[0071] Based on the evolution data of external heating services during the heating season, we analyze the changes in heat load demand during the heating season, and conduct a heat supply and demand balance analysis by combining the heating capacity of long-distance heat sources and traditional heat sources.

[0072] Based on the changes in the operation mode and dynamic characteristics of the heating network after the introduction of long-distance heat sources, constraints on the safe and stable operation of each heat source and the heating network are set.

[0073] Based on the results of heat supply and demand balance analysis and the safety operation constraints of each heat source and heating network, multiple heating network planning and design schemes, including heat source combinations and pipeline layouts, are pre-set for different periods of the heating season.

[0074] A comprehensive evaluation system with multiple indicators is constructed, including technical, economic, and environmental indicators. A multi-criteria decision analysis method is used to quantitatively analyze and compare various heat network planning and design schemes to obtain the optimal planning and design scheme. This will guide the timing of the withdrawal and commissioning of long-distance heat sources and traditional heat sources, the preliminary output plan of each heat source, the layout of the pipeline network, and the pipeline network routing design scheme.

[0075] Set constraints for the safe and stable operation of each heat source and heating network, including:

[0076] Upper limit of heating temperature for heat source equipment: The outlet temperature of the heat source equipment must not exceed the design value, otherwise it may damage equipment components and accelerate pipe aging; Operating temperature range of heat source equipment: During equipment operation, the temperature of key internal components must be maintained within a reasonable range; Upper limit of pressure for heat source equipment: The outlet pressure of the heat source must not exceed the bearing capacity of the equipment and pipelines, as overpressure may cause serious accidents such as explosions; Lower limit of pressure for heat source equipment: Some heat source equipment cannot operate normally when the pressure is below a certain level; Maximum load of heat source equipment: The heating capacity of the heat source is limited and cannot exceed its rated heating load; Minimum load of heat source equipment: Some heat source equipment has a minimum stable operating load, below which the equipment operation becomes unstable;

[0077] Upper limit of pressure at each node in the heating network: The pressure at each node in the heating network must not exceed the pressure-bearing capacity of the pipelines and equipment. Excessive pressure may cause pipeline rupture. Lower limit of pressure at each node in the heating network: To ensure normal circulation of the heating network and the heating effect at the user end, the pressure at each node must be higher than a certain value. Maximum flow rate of the heating network: The heating network pipelines have a maximum allowable flow rate limit. Exceeding this flow rate will increase the pipeline resistance and may cause hydraulic imbalance. Flow distribution of the heating network: Ensure reasonable flow distribution among the branches of the heating network to avoid hydraulic imbalance such as overheating at the near end and undercooling at the far end. Upper limit of heating network supply water temperature: Excessive heating network supply water temperature may damage the pipeline insulation layer and user-end equipment, and also waste energy. Lower limit of heating network return water temperature: Too low a return water temperature indicates that the heat is not being fully utilized, which will reduce heating efficiency. Too high a return water temperature may affect the operation of the heat source equipment, so the return water temperature must be controlled within a suitable range.

[0078] Construct a comprehensive evaluation system with multiple indicators: technical indicators, economic indicators, and environmental indicators; technical indicators include hydraulic stability, supply and demand balance, response speed, and pipeline transportation efficiency; economic indicators include investment and construction costs and operating costs; and environmental indicators include carbon emission intensity.

[0079] A multi-criteria decision analysis method is used to quantitatively analyze and compare various heating network planning and design schemes to obtain the optimal planning and design scheme. Specifically, the analytic hierarchy process (AHP) is employed, including:

[0080] 1) such as Figure 3 As shown, a hierarchical structure for establishing a multi-indicator comprehensive evaluation system is presented.

[0081] Target layer: The ultimate goal is to "select the optimal heating network planning and design scheme";

[0082] Criterion layer: Technical indicators include hydraulic stability, supply and demand balance, response speed, and pipeline transportation efficiency; economic indicators include investment and construction costs and operating costs; environmental indicators include carbon emission intensity.

[0083] Solution layer: Different heating network planning and design schemes;

[0084] 2) Quantitative Calculation of Indicators

[0085] For each heating network planning and design scheme, calculate its index values ​​in dimensions such as technical, economic, environmental, and social benefits, among which, The m-th index value of the n-th option is represented by the decision matrix, which is as follows: ;

[0086] 3) Weight Determination

[0087] For k possible solutions, construct a judgment matrix. , This represents the importance comparison result of scheme i relative to scheme j, and calculates the relative importance weight of each scheme in the scheme layer relative to the target layer;

[0088] 4) After ranking the various heating network planning and design schemes according to their relative importance weights, the optimal design scheme is selected.

[0089] like Figure 4 As shown, in this embodiment, step S3, establishing the spatiotemporal model of the heating system, specifically includes:

[0090] Acquire spatial and temporal characteristic data related to the heating system; the spatial characteristic data represents the spatial coupling influence between heating networks and heating stations; the temporal characteristic data represents the evolution of the operation and structure of the heating system's heat source, heating network, and heating stations over time.

[0091] The spatial feature data is trained using a first machine learning algorithm to extract spatial features;

[0092] The time feature data is trained using a second machine learning algorithm to extract time features;

[0093] By integrating the extracted spatial and temporal features, a spatiotemporal model of the heating system is established.

[0094] In practical applications, due to the spatial coupling and correlation between different heating network areas and heating stations, changes in the operating status of one heating network area will affect other structurally coupled heating network areas. Similarly, within a heating network area, there are multiple heating stations; adjusting the heat distribution at one station will affect the heat values ​​at other stations. Furthermore, over time, weather changes, heating demand changes, and the pipeline structure changes due to external influencing factors (abnormal fault information, heat source introduction, etc.), thus requiring the adaptive changes in the operation of heat sources, heating networks, and heating stations.

[0095] The spatial feature data is trained using a first machine learning algorithm to extract spatial features, including:

[0096] 1) Algorithm selection: Graph neural network algorithm was chosen. This is because the spatial structure of heating networks and heating stations can be viewed as a graph structure (nodes represent heating network nodes or heating stations, and edges represent the connections between them). Graph neural network algorithms are well-suited for processing this type of topologically structured data and uncovering spatial coupling relationships.

[0097] 2) Model Building and Training: The preprocessed spatial feature data is organized into a graph structure and input into the selected algorithm model. A suitable loss function and optimizer are defined, and the model is trained using the backpropagation algorithm. The model parameters are adjusted to enable the model to learn the spatial coupling characteristics between the heating network and the heating stations.

[0098] 3) Feature Extraction and Selection: After training, spatial feature vectors that have been learned and abstracted are extracted from the model. These feature vectors represent information such as the spatial coupling relationship between the heating network and heating stations, and the impact of spatial layout on operational status. Feature selection methods (such as variance selection method, correlation analysis, etc.) can be further used to select spatial features that contribute significantly to the model and are representative, thereby reducing feature dimensionality and improving the model's efficiency and generalization ability.

[0099] The time feature data is trained using a second machine learning algorithm to extract time features, including:

[0100] 1) Algorithm selection: Recurrent neural network algorithm is selected, which is suitable for processing time series data and can capture long-term dependencies in time series.

[0101] 2) Model Construction and Training: The preprocessed temporal feature data is organized into a sequence according to time order, and divided into training, validation, and test sets. A recurrent neural network algorithm model is constructed, and appropriate hyperparameters such as the number of network layers and hidden units are set. The training set data is input into the model, and the model parameters are updated using a stochastic gradient descent optimization algorithm by minimizing the loss function, enabling the model to learn the evolution of the operation and structure of the heating system's heat source, heating network, and heating stations over time.

[0102] 3) Feature Extraction and Dimensionality Reduction: After training, time feature vectors are extracted from the algorithm model. These vectors contain dynamic change information of the time series. To reduce feature dimensionality, dimensionality reduction methods such as principal component analysis and linear discriminant analysis can be used to reduce the number of features and improve computational efficiency while retaining the main information.

[0103] The process of establishing a spatiotemporal model includes:

[0104] 1) Feature fusion

[0105] The extracted spatial and temporal feature vectors are then fused. A weighted fusion method can be used, assigning different weights to the spatial and temporal features based on their relative importance, to obtain a comprehensive feature vector containing spatiotemporal information.

[0106] 2) Model Building

[0107] Choose a suitable model architecture: Select a spatiotemporal convolutional network, which can process spatiotemporal feature data simultaneously and explore the interaction between spatial and temporal dimensions in the heating system;

[0108] Model training and optimization: Using the fused spatiotemporal feature data as input, we define the corresponding objective function (mean squared error between predicted and true values) and optimization algorithm (particle swarm optimization) to train the model. By continuously adjusting the model parameters, the model can accurately learn the spatiotemporal characteristics of the heating system, improving its predictive and analytical capabilities in the spatiotemporal dimensions.

[0109] Model evaluation and validation: Evaluation metrics (such as root mean square error, mean absolute error, etc.) are used to measure the model's performance. Based on the evaluation results, the model is optimized and adjusted to ensure that it has good generalization ability and can accurately reflect the spatiotemporal characteristics of the heating system.

[0110] In this embodiment, step S3, predicting the heat load for each heating network area, specifically includes:

[0111] Based on the spatiotemporal model of the heating system, the heat network structure and operating parameters of the heating system that change over time are obtained. Combined with meteorological data, building characteristic data of heat users under each heat network area, user behavior data, new station commissioning data and historical heat load data, a heat load impact dataset for each heat network area is formed.

[0112] After preprocessing and feature extraction of the heat load impact datasets of each heating network area, the datasets are input into a hybrid learning algorithm for training and learning, thereby establishing a heat load prediction model for each heating network area and predicting the heat load of each heating network area at different future times.

[0113] In practical applications, when using hybrid learning algorithms to train and learn and establish heat load prediction models for each heating network area, the XGBoost extreme gradient boosting algorithm and the SVM support vector machine algorithm are used for training and learning respectively to obtain the first and second prediction results of heat load. After setting the weights of the first and second prediction results according to the deviation range between the predicted and actual heat load values, the final heat load prediction value is calculated based on the first and second prediction results of heat load and the corresponding weights.

[0114] like Figure 5 As shown, in this embodiment, S4 specifically includes:

[0115] Based on the heat load forecast values ​​for each heating network area the following day, and combined with the multi-dimensional heat loss and heat source output characteristics of long-distance heat sources and traditional heat sources, an early-day optimization scheduling model is established with the goal of minimizing operating costs and minimizing the heat load supply-demand deviation, to obtain the load allocation strategies for long-distance heat sources and traditional heat sources at each time period of the day.

[0116] Based on the predicted and measured heat load values ​​for each heating network area during the day, and the heat transmission and distribution characteristics from each heat source to each associated heating network area, under the condition of ensuring the heat load demand of each heating network area, the operating parameters of long-distance heat sources and traditional heat sources are adjusted and corrected. With the goal of minimizing adjustment and correction costs and minimizing fluctuations in heating network parameters, an intraday optimization scheduling model is established to obtain the operating guidance curves of long-distance heat sources and traditional heat sources at each time period during the day.

[0117] Based on the operation guidance curves of long-distance heat sources and traditional heat sources at different times of the day, and combined with the preset control temperature targets for each heating network area, a real-time optimization control model is established to generate hourly valve parameter control strategies for each heating network area.

[0118] In practical applications, with the objectives of minimizing operating costs and minimizing the deviation between heat load supply and demand, a day-ahead optimization scheduling model is established, expressed as: ;

[0119] , These are the weighting coefficients for the operating cost target and the heat load supply-demand deviation target, respectively; T is the scheduling cycle; S is the number of heat sources; The unit operating cost of heat source s is related to the fuel cost and heat transmission cost of the heat source. Let be the heat supplied by heat source s at time t; Let t be the heat load supply-demand deviation value at time t. , The heating efficiency of heat source s; denoted as heat loss from heat source s; N represents the number of heating network areas. This represents the predicted heat load for heating network area n at time t.

[0120] With the objectives of minimizing adjustment and correction costs and minimizing fluctuations in heating network parameters, an intraday optimal scheduling model is established, expressed as: ;

[0121] To adjust the weighting coefficient of the repair cost target; Let be the adjustment cost coefficient of heat source s; Let be the output power of heat source s at time t; This represents the fluctuation value of the water supply temperature of the heating network at time t. This represents the return water temperature fluctuation value at time t in the heating network;

[0122] Based on the operational guidance curves of long-distance heat sources and traditional heat sources at different times of the day, and combined with the preset control temperature targets for each heating network area, a real-time optimization control model is established. This includes: based on the operational guidance curves of long-distance heat sources and traditional heat sources at different times of the day, analyzing the target values ​​of secondary water supply temperature or average secondary supply and return temperature at different times in each heating network area; combining the heat transfer characteristics of plate heat exchangers and valve regulation characteristics of heat stations in each heating network area (the heat exchanger heat transfer is related to the primary side flow rate, secondary side flow rate, and primary / secondary side inlet and outlet temperatures, and the valve opening is related to the flow rate), analyzing the identification relationship between valve opening and heating network temperature target values, obtaining the valve opening values ​​for each heating network area, minimizing the deviation between the measured temperature and the target temperature, and issuing the execution to achieve real-time optimization control of the system.

[0123] The required flow rate and temperature for each heating network area are calculated based on the total heat supply of the heat source and the heat load of each heating network area, and are expressed as follows: ;

[0124] For the flow rate of each heating network area; This refers to the specific heat capacity of hot water. , These are the secondary water supply temperature and return water temperature for each heating network area; The target temperature value for each heating network area at time t; Reference temperature; This is the temperature adjustment correction factor; To design outdoor temperature; Let t be the outdoor temperature at time t.

[0125] In this embodiment, the multi-dimensional heat loss includes: heat loss caused by heat leakage from the heating network, heat loss from water replenishment caused by water leakage from the heating network, and excessive heat loss in space; the excessive heat loss in space refers to the uneven heating loss between buildings in the heating network and the uneven heating loss among users within the building.

[0126] In this embodiment, the analysis of the heat transmission and distribution characteristics from each heat source to each associated heating network area includes: using the heat source temperature rise curve to calculate the transmission delay time from the temperature change of each heat source to the response of the corresponding heating station in each heating network area, and adjusting the operating parameters of each heat source in advance.

[0127] In this embodiment, the heating system planning, design and optimized operation method further includes: analyzing the problems existing in the heating network in response to heat loss caused by heat leakage in the heating network, heat loss due to water replenishment caused by water leakage in the heating network and excessive heat loss in space, and establishing corresponding heating network renovation strategies.

[0128] It should be noted that heat source heat loss includes heat loss caused by pipe leakage and heat loss due to water replenishment caused by pipe leakage. Factors significantly impacting heat loss include pipe insulation peeling, pipe aging, manhole seepage, flooding, and shallow pipe burial. Strengthening the management of heating pipelines, gradually phasing out old pipelines, and addressing problematic pipelines will reduce heat loss. Excessive spatial heat loss is due to uneven flow distribution among users caused by the layout and resistance of the pipe network. This mainly includes uneven losses between buildings and uneven losses among users within a building. The primary cause is the lack of effective balancing regulation. Advanced control devices can be installed to achieve automated regulation, such as self-regulating balancing valves and pressure equalization tanks. Strengthening balancing regulation will reduce uneven heat loss and lower the energy consumption of the transmission and distribution system.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0130] Furthermore, the functional modules in the various embodiments of this invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, 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 in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0131] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for planning, designing, and optimizing the operation of a heating system that incorporates a long-distance heat source, characterized in that, It includes: S1. Obtain basic data on long-distance heat sources to be introduced into the heating system during the heating season, and analyze the changes in the operation mode and dynamic characteristics of the existing heating system network after the introduction of long-distance heat sources. S2. Based on the changes in the operation mode and dynamic characteristics of the heating network, and combined with the evolution data of external heating business during the heating season, quantitative analysis and comparison of various pre-set heating network planning and design schemes are conducted to obtain the heating network planning and design scheme. S3. Obtain spatial and temporal characteristic data related to the heating system, establish a spatiotemporal model of the heating system, obtain the heating network structure and operating parameters of the heating system that change over time, and combine the multidimensional influence data of heat load to predict the heat load of each heating network area. S4. Based on the predicted heat load values ​​of each heating network area, combined with the multi-dimensional heat loss of long-distance heat sources and traditional heat sources, the output characteristics of heat sources and the heat transmission and distribution characteristics of the heating network, and taking into account the heat source optimization operation objectives and the heating network regulation objectives, establish a multi-time-scale optimization operation model to obtain optimization operation strategies under different time scales.

2. The method for planning, designing, and optimizing the operation of a heating system according to claim 1, characterized in that, S1 specifically includes: Obtain basic data on the long-distance heat source to be introduced into the heating system during the heating season, including: heating capacity of the long-distance heat source, outlet temperature, outlet pressure, pipeline length, pipe diameter, insulation layer thickness, burial depth, terrain elevation along the route, heat load adjustment range, connection point location with the existing heating network, and interface pipe diameter. Collect existing heating network data, including the topology of the heating network, the pressure and temperature of each node in the heating network, the flow rate of each pipe section, and the parameters of key equipment in the heating network; Based on the basic data of the long-distance heat source and the collected data of the existing heating network, a simulation model of the heating system is established. The model analyzes the changes in the heating network zoning, structure, pressure distribution, temperature distribution, flow distribution, and hydraulic stability of the existing heating system after the introduction of the long-distance heat source. It also evaluates the response time and fluctuation range of the heating network pressure and temperature during the commissioning / exit of the long-distance heat source.

3. The method for planning, designing, and optimizing the operation of a heating system according to claim 1, characterized in that, S2 specifically includes: Acquire data on the evolution of external heating services during the heating season, including data on the evolution of heating area during the planning period, data on the growth of heat users, data on energy price fluctuations, and data on the commissioning of new stations; Based on the evolution data of external heating services during the heating season, we analyze the changes in heat load demand during the heating season, and conduct a heat supply and demand balance analysis by combining the heating capacity of long-distance heat sources and traditional heat sources. Based on the changes in the operation mode and dynamic characteristics of the heating network after the introduction of long-distance heat sources, constraints on the safe and stable operation of each heat source and the heating network are set. Based on the results of heat supply and demand balance analysis and the safety operation constraints of each heat source and heating network, multiple heating network planning and design schemes, including heat source combinations and pipeline layouts, are pre-set for different periods of the heating season. A multi-indicator comprehensive evaluation system is constructed, and a multi-criteria decision analysis method is used to quantitatively analyze and compare various heat network planning and design schemes to obtain the optimal planning and design scheme, which guides the timing of the withdrawal and commissioning of long-distance heat sources and traditional heat sources, the preliminary output plan of each heat source, the layout of the pipeline network, and the pipeline network routing design scheme.

4. The method for planning, designing, and optimizing the operation of a heating system according to claim 3, characterized in that, The data for the new station's operation includes the heating unit, the area connected to the network, the location of the heating use, the heating company to which it belongs, the form of heating supply, and the type of heating use. The multi-indicator comprehensive evaluation system includes technical indicators, economic indicators, and environmental indicators.

5. The method for planning, designing, and optimizing the operation of a heating system according to claim 1, characterized in that, In S3, establishing the spatiotemporal model of the heating system specifically includes: Acquire spatial and temporal characteristic data related to the heating system; the spatial characteristic data represents the spatial coupling influence between heating networks and heating stations; the temporal characteristic data represents the evolution of the operation and structure of the heating system's heat source, heating network, and heating stations over time. The spatial feature data is trained using a first machine learning algorithm to extract spatial features; The time feature data is trained using a second machine learning algorithm to extract time features; By integrating the extracted spatial and temporal features, a spatiotemporal model of the heating system is established.

6. The method for planning, designing, and optimizing the operation of a heating system according to claim 1, characterized in that, In step S3, the heat load prediction for each heating network area specifically includes: Based on the spatiotemporal model of the heating system, the heat network structure and operating parameters of the heating system that change over time are obtained. Combined with meteorological data, building characteristic data of heat users under each heat network area, user behavior data, new station commissioning data and historical heat load data, a heat load impact dataset for each heat network area is formed. After preprocessing and feature extraction of the heat load impact datasets of each heating network area, the datasets are input into a hybrid learning algorithm for training and learning, thereby establishing a heat load prediction model for each heating network area and predicting the heat load of each heating network area at different future times.

7. The method for planning, designing, and optimizing the operation of a heating system according to claim 1, characterized in that, S4 specifically includes: Based on the heat load forecast values ​​for each heating network area the following day, and combined with the multi-dimensional heat loss and heat source output characteristics of long-distance heat sources and traditional heat sources, an early-day optimization scheduling model is established with the goal of minimizing operating costs and minimizing the heat load supply-demand deviation, to obtain the load allocation strategies for long-distance heat sources and traditional heat sources at each time period of the day. Based on the predicted and measured heat load values ​​for each heating network area during the day, and the heat transmission and distribution characteristics from each heat source to each associated heating network area, under the condition of ensuring the heat load demand of each heating network area, the operating parameters of long-distance heat sources and traditional heat sources are adjusted and corrected. With the goal of minimizing adjustment and correction costs and minimizing fluctuations in heating network parameters, an intraday optimization scheduling model is established to obtain the operating guidance curves of long-distance heat sources and traditional heat sources at each time period during the day. Based on the operation guidance curves of long-distance heat sources and traditional heat sources at different times of the day, and combined with the preset control temperature targets for each heating network area, a real-time optimization control model is established to generate hourly valve parameter control strategies for each heating network area.

8. The method for planning, designing, and optimizing the operation of a heating system according to claim 7, characterized in that, The multi-dimensional heat loss includes: heat loss caused by heat leakage from the heating network, heat loss from water replenishment caused by water leakage from the heating network, and excessive heat loss in space; the excessive heat loss in space refers to the uneven heat supply loss between buildings in the heating network and the uneven heat supply loss among users within the building.

9. The method for planning, designing, and optimizing the operation of a heating system according to claim 7, characterized in that, The analysis of the heat transmission and distribution characteristics from each heat source to each associated heating network area includes: using the heat source temperature rise curve to calculate the transmission delay time from the temperature change of each heat source to the response of the corresponding heating station in each heating network area, and adjusting the operating parameters of each heat source in advance.

10. The method for planning, designing, and optimizing the operation of a heating system according to claim 8, characterized in that, The heating system planning, design, and optimized operation method also includes: analyzing the problems existing in the heating network and establishing corresponding heating network renovation strategies in response to heat loss caused by heat leakage in the heating network, heat loss caused by water leakage in the heating network, and excessive heat loss in space.

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