Heat supply pipe network centralized heat storage tank optimal configuration method considering toughness identification

Abstract

The invention discloses a heat supply network centralized heat storage tank optimal configuration method considering toughness identification, which comprises the following steps: acquiring related multi-source data during configuration of a centralized heat storage tank by using a heat supply network digital twin model, performing toughness identification on each node of a heat supply network, and determining the toughness of each node of the heat supply network by combining a heat load rising model of the node of the heat supply network in different time periods in the future. Performing preliminary site selection on the centralized heat storage tank to obtain a plurality of preliminary site selection schemes; establishing a pressure distribution and heat loss simulation model of the heat supply pipe network containing the centralized heat storage tank, and simulating the influence of different site selection of the centralized heat storage tank on the pressure and temperature of the pipe network; the heat loss, delay time and economic cost of the heat supply pipe network after the centralized heat storage tank is configured are minimum as targets, safe operation constraint conditions of the heat supply pipe network are set, an optimal configuration model of the centralized heat storage tank is constructed, and an optimal configuration scheme of the centralized heat storage tank is obtained through solving. And establishing a configuration evaluation system of the centralized heat storage tank, and evaluating the optimal configuration scheme of the centralized heat storage tank.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimal configuration of heating systems, and in particular relates to an optimal configuration method for centralized heat storage tanks in a heating pipe network considering toughness identification. Background Art

[0002] Centralized thermal storage tanks are suitable for large-scale centralized heating systems, such as urban centralized heating and heating in large industrial parks. In these scenarios, large amounts of thermal energy must be stored to meet the heating needs of numerous users. Centralized thermal storage tanks effectively achieve centralized storage and distribution of thermal energy, improving the efficiency and stability of the heating system. Furthermore, centralized thermal storage tanks generally have a large thermal storage capacity and can store large amounts of thermal energy. They can serve as a backup peak-shaving heat source to meet the heating peak demand of large areas or numerous users, providing a stable heat energy supply for the entire heating system.

[0003] However, the construction location and capacity selection of the heat storage tank have a great impact on the heating system. Currently, the main problems are unreasonable location and capacity selection, insufficient coordination with the pipeline network, etc., which lead to excessively long transmission and distribution pipelines, increased heat loss along the way, insufficient heating pressure for end users, and long heat transfer delay time, affecting the stability of heating.

[0004] Based on the above technical problems, it is necessary to design a new optimal configuration method for centralized heat storage tanks in the heating network considering toughness identification. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a method for optimizing the configuration of centralized heat storage tanks in a heating pipe network taking toughness identification into consideration.

[0006] In order to solve the above technical problems, the technical solution of the present invention is:

[0007] The present invention provides a method for optimizing the configuration of centralized heat storage tanks in a heating network considering toughness identification, which includes:

[0008] S1. Utilize the constructed digital twin model of the heating network to obtain multi-source data related to the configuration of centralized heat storage tanks in the heating network and perform resilience identification of each node in the heating network.

[0009] S2. Based on the toughness identification results of each node in the heating network and the heat load increase model of the network nodes in different time periods in the future, preliminary site selection for centralized heat storage tanks is carried out, and multiple preliminary site selection plans are obtained;

[0010] S3. Establish a simulation model for the pressure distribution and heat loss of a heating network containing a centralized heat storage tank, and simulate the impact of different locations of the centralized heat storage tank on the pressure and temperature of the network;

[0011] S4. By setting the goal of minimizing the heat loss, delay time, and economic cost of the heating network after configuring the centralized heat storage tank, setting the safe operation constraints of the heating network, constructing the centralized heat storage tank optimization configuration model, and solving to obtain the optimal configuration scheme of the centralized heat storage tank;

[0012] S5. Establish a configuration evaluation system for centralized heat storage tanks and evaluate the optimal configuration scheme for centralized heat storage tanks.

[0013] Furthermore, the S1 specifically includes:

[0014] Use digital twin technology to virtually map the actual operation of the heating network in a virtual space, build a digital twin model of the heating network, and obtain multi-source data related to the configuration of centralized heat storage tanks in the heating network, including the network topology, heat source distribution, spatiotemporal characteristics of heat load, static properties of the network, historical fault records of the network, operation data of the network in extreme weather scenarios, and changes in network disturbance operation parameters;

[0015] Based on the acquired multi-source data, each resilience identification index is analyzed, and the resilience value of each node in the pipeline network is calculated, which is expressed as:

[0016]

[0017] S i is the toughness value of the i-th pipe network node; w j is the weight of the jth toughness identification index; n is the number of toughness identification indexes; x ij is the normalized value of the jth toughness identification index of the i-th pipeline network node;

[0018] Among them, the resilience identification indicators include the heat load loss rate when failure occurs at each node of the heating pipeline network, the structural strength of the pipeline network, the heat loss area of ​​the pipeline network under extreme weather conditions, and the recovery capacity in the face of disturbances.

[0019] Furthermore, the structural strength of the pipeline network includes the stress level and structural reliability of the pipeline network. This is done by evaluating whether the stress borne by the pipeline network under current operating conditions exceeds its allowable stress range. The lower the stress level, the safer the pipeline structure and the higher the toughness of the node. The failure probability of the pipeline network node structure is also calculated. The lower the failure probability, the stronger the structural toughness of the node, which can withstand greater external forces and environmental changes.

[0020] The recovery capability in the face of disturbances includes: when the heating network faces heat source adjustments and changes in heat load demand, analyzing the changes in operating parameters of each node, and evaluating the ability of the node to return to a thermal equilibrium state when thermal imbalance occurs in the heating network. The stronger the recovery capability, the better the resilience of the node.

[0021] Furthermore, in said S2, establishing the heat load increase model of the pipe network node includes:

[0022] Obtain the heating area, building type, building energy efficiency rating, geographical location and historical meteorological data of new buildings that will be connected to each node of the heating network at different time periods in the future, and obtain the heat load data of existing nodes of the heating network;

[0023] Based on the acquired data, the independent variables and dependent variable heat load data that affect the heat load change are selected to establish a heat load prediction model for each node of the heating network at different time periods in the future, and obtain the heat load prediction value of each node of the heating network at different time periods;

[0024] Based on the predicted heat load values ​​of each node in the heating network at different time periods and the historical heat loads of each node in the heating network at the same time last year, the heat load increase rate is calculated and divided into low increase scenario, medium increase scenario and high increase scenario.

[0025] Furthermore, the S2 specifically includes:

[0026] Based on the resilience identification results of each node in the heating network, the scenarios of heat load increases at the nodes in the network at different time periods in the future, the overall layout of the heating network, and actual geographical conditions, the siting principles for centralized heat storage tanks are determined. This includes prioritizing locations in areas with low resilience values ​​and high heat load increases, while also considering site availability and impacts on the surrounding environment.

[0027] Based on the site selection principles, through geographic information system (GIS) technology, combined with pipeline network layout and geographic data, preliminary identification of eligible areas was carried out, multiple candidate locations were screened out, and multiple preliminary site selection plans for centralized heat storage tanks were formed.

[0028] Furthermore, in S3, a pressure distribution model of the heating network is established, which is expressed as:

[0029]

[0030] ∑m a,t -∑m c,t +∑m d,t -∑m p,t,s -∑m h,t =0;

[0031] Σm p,t,r +Σm h,t +∑m c,t -Σm d,t -Σm a,t =0;

[0032] K p is the flow transmission coefficient of the heating network p; m p,t is the heating flow of the heating network p during period t; are the water pressure at the beginning and end of the heating pipe network p during period t; m a,t is the heat medium flow rate of the heat source during period t; m c,t 、m d,t are the heat storage and heat release flows of the heat storage tank during period t; m p,t,s is the heating flow of the heating network p in period t; m h,t is the heat medium flow rate of heat load h in period t; m p,t,r is the heat recovery flow of the heating network p in period t;

[0033] The heat loss simulation model is established and expressed as:

[0034]

[0035] T p,st,t 、T p,end,t are the starting and ending temperatures of the heating network p during period t; C h is the specific heat capacity; X p is the heat transfer coefficient of the heating network p; L p is the length of the heating network p; H t is the heat load demand; η is the heat loss coefficient; For heat source tt delay Heating power during the period; t delay is the delay time; λ p is the thermal delay coefficient of the heating network p; v p is the heat medium flow rate of the heating network p.

[0036] Furthermore, in S3, the impact of different locations of centralized heat storage tanks on the pressure and temperature of the pipe network is simulated, including:

[0037] For each preliminary site selection plan for the centralized heat storage tank, the heat storage tank is coupled with the heating pipe network model. Based on the set boundary conditions and initial conditions, the heat storage and release process of the heat storage tank at different sites is simulated, and the pressure distribution, temperature distribution and heat loss changes of the pipe network are analyzed.

[0038] Among them, the pressure changes of each node in the pipeline network under different site selection schemes are analyzed: whether the connection of the heat storage tank causes the node pressure to be too high or too low, and the impact on the pressure balance of the entire pipeline network;

[0039] Analyze the temperature changes at each node of the pipeline network under different site selection schemes: analyze the effect of the heat storage and release process of the heat storage tank on the regulation of the pipeline network temperature, and whether it can reduce temperature fluctuations;

[0040] Compare the heat loss of the pipeline network under different site selection schemes: consider the distance between the heat storage tank and the heat source and heat user, as well as the impact of pipeline length and thermal delay time on heat loss.

[0041] Furthermore, in S4, the goal is to minimize the heat loss, delay time and economic cost of the heating network after configuring the centralized heat storage tank, which is expressed as:

[0042] minf=H k,r,loss +t k,r,delay +C k,r,hs ;

[0043] C k,r,hs =C k,r,sys +C k,r,ope +C k,r,dis -E k,r,expand -E k,r,loss -E k,r,fg ;

[0044] f1 is the minimum target of heat loss and delay in the heating network; H k,r,loss is the heat loss under the kth site selection scheme and the rth heat storage tank capacity; t k,r,delay is the delay time under the kth location option and the rth heat storage tank capacity; C k,r,hs is the economic cost under the kth site selection scheme and the rth heat storage tank capacity; C k,r,sys 、C k,r,ope 、C k,r,dis E are the investment cost, operation and maintenance cost, and disposal cost of the centralized heat storage tank configured with the kth site selection scheme and the rth capacity; k,r,expand 、E k,r,loss 、E k,r,fg These are the cost savings for heating network expansion, heat loss savings, and peak-shaving and valley-filling benefits after configuring the kth site selection scheme and the rth capacity of the centralized heat storage tank.

[0045] Furthermore, in said S4, constraints for safe operation of the heating network are set, including pressure constraints of the heating network, power balance constraints, heat storage and release power constraints and rated capacity constraints of the centralized heat storage tanks, and geographical space restriction constraints.

[0046] Furthermore, in said S5, a configuration evaluation system for the centralized heat storage tank is established, including energy storage technical indicators, energy storage economic indicators, and energy storage environmental protection indicators.

[0047] The beneficial effects of the present invention are:

[0048] (1) The present invention utilizes the constructed digital twin model of the heating network to obtain multi-source data related to the configuration of centralized heat storage tanks in the heating network, and identifies the resilience of each node in the heating network. It accurately reflects the actual operating status of the network, provides rich and accurate data support for node resilience identification, and helps to fully understand the anti-interference ability and recovery ability of each node in the network under different working conditions. In addition, it can discover possible weak links and potential risks in advance during actual operation, provide a basis for subsequent optimization configuration, and thus configure centralized heat storage tanks in a targeted manner to improve the reliability and stability of the network.

[0049] (2) The present invention conducts preliminary site selection for centralized heat storage tanks based on the toughness identification results of each node in the heating network and combines the heat load increase model of the network nodes in different time periods in the future, thereby obtaining multiple preliminary site selection schemes. This scheme can fully take into account the toughness of each node in the network, the trend of heat load changes, and the ability to withstand disturbances, making the site selection layout of the centralized heat storage tank more reasonable, improving the supporting role of the heat storage tank for the heating network, and better meeting the heating demand in different time periods in the future.

[0050] (3) The present invention establishes a pressure distribution and heat loss simulation model for a heating network containing a centralized heat storage tank, thereby simulating the effects of different sitings of the centralized heat storage tank on the pressure and temperature of the network. This allows for an in-depth understanding of the operational characteristics of the network under different siting schemes, ensuring that the heating system can maintain stable and efficient operation under different operating conditions. Furthermore, by understanding the effects of different siting schemes on the pressure and temperature of the network in advance, it helps to avoid problems such as excessively high or low pressure in the network and uneven temperature distribution due to improper siting, thereby reducing operational failures and accidents and improving the safety and reliability of the heating system.

[0051] (4) The present invention sets the safe operation constraint conditions of the heating network by taking the minimum heat loss, delay time and economic cost of the heating network after configuring the centralized heat storage tank as the goal, constructs the centralized heat storage tank optimization configuration model, and solves and obtains the optimal configuration scheme of the centralized heat storage tank; it can comprehensively weigh multiple factors to achieve multi-objective optimization of the heating network in terms of safety, economy and efficiency, find the best centralized heat storage tank configuration scheme, and improve the overall performance of the heating system; and it can reasonably configure the capacity and location parameters of the centralized heat storage tank so that the heat storage tank can minimize heat loss and delay time to the greatest extent while meeting the heating demand, reduce economic costs, and improve the utilization efficiency of energy and resources;

[0052] (5) The present invention establishes a configuration evaluation system for centralized heat storage tanks to evaluate the optimal configuration scheme of centralized heat storage tanks; it can comprehensively and objectively evaluate the optimal configuration scheme of centralized heat storage tanks from multiple dimensions, accurately measure the comprehensive performance of the scheme, and provide a scientific basis for the final decision; and it can timely discover problems and deficiencies in the scheme, further optimize and improve the scheme, and ensure that the selected scheme can maximize economic benefits, environmental benefits and social benefits in the long-term operation, thereby ensuring the sustainable development of the heating system.

[0053] Other features and advantages will be described in the following description, and in part will become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 This is a flow chart of a method for optimizing the configuration of centralized heat storage tanks in a heating network considering toughness identification according to the present invention;

[0057] Figure 2 Schematic diagram of the configuration evaluation system of the centralized heat storage tank of the present invention. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] like Figure 1 As shown, this embodiment provides a method for optimizing the configuration of centralized heat storage tanks in a heating network considering toughness identification, which includes:

[0060] S1. Utilize the constructed digital twin model of the heating network to obtain multi-source data related to the configuration of centralized heat storage tanks in the heating network and perform resilience identification of each node in the heating network.

[0061] S2. Based on the toughness identification results of each node in the heating network and the heat load increase model of the network nodes in different time periods in the future, preliminary site selection for centralized heat storage tanks is carried out, and multiple preliminary site selection plans are obtained;

[0062] S3. Establish a simulation model for the pressure distribution and heat loss of a heating network containing a centralized heat storage tank, and simulate the impact of different locations of the centralized heat storage tank on the pressure and temperature of the network;

[0063] S4. By setting the goal of minimizing the heat loss, delay time, and economic cost of the heating network after configuring the centralized heat storage tank, setting the safe operation constraints of the heating network, constructing the centralized heat storage tank optimization configuration model, and solving to obtain the optimal configuration scheme of the centralized heat storage tank;

[0064] S5. Establish a configuration evaluation system for centralized heat storage tanks and evaluate the optimal configuration scheme for centralized heat storage tanks.

[0065] In this embodiment, the S1 specifically includes:

[0066] Use digital twin technology to virtually map the actual operation of the heating network in a virtual space, build a digital twin model of the heating network, and obtain multi-source data related to the configuration of centralized heat storage tanks in the heating network, including the network topology, heat source distribution, spatiotemporal characteristics of heat load, static properties of the network, historical fault records of the network, operation data of the network in extreme weather scenarios, and changes in network disturbance operation parameters;

[0067] Based on the acquired multi-source data, each resilience identification index is analyzed, and the resilience value of each node in the pipeline network is calculated, which is expressed as:

[0068]

[0069] S i is the toughness value of the i-th pipe network node; w j is the weight of the jth toughness identification index; n is the number of toughness identification indexes; x ij is the normalized value of the jth toughness identification index of the i-th pipeline network node;

[0070] Among them, the resilience identification indicators include the heat load loss rate when failure occurs at each node of the heating pipeline network, the structural strength of the pipeline network, the heat loss area of ​​the pipeline network under extreme weather conditions, and the recovery capacity in the face of disturbances.

[0071] In this embodiment, the pipe network structural strength includes the pipe network stress level and structural reliability. This is done by evaluating whether the stress borne by the pipe network under current operating conditions exceeds its allowable stress range. A lower stress level indicates a safer pipe structure and higher node toughness. Furthermore, the failure probability of the pipe network node structure is calculated. A lower failure probability indicates a stronger node structural toughness and the ability to withstand greater external forces and environmental changes.

[0072] The recovery capability in the face of disturbances includes: when the heating network faces heat source adjustments and changes in heat load demand, analyzing the changes in operating parameters of each node, and evaluating the ability of the node to return to a thermal equilibrium state when thermal imbalance occurs in the heating network. The stronger the recovery capability, the better the resilience of the node.

[0073] It should be noted that toughness identification indicators also include:

[0074] 1) Pressure stability index:

[0075] Pressure Fluctuation Coefficient: Calculates the ratio of the standard deviation of the pressure at a node to the mean value, reflecting the degree of pressure fluctuation. The smaller the fluctuation coefficient, the more stable the node pressure and the higher the toughness.

[0076] Pressure recovery time: The time it takes for the node pressure to return to a stable state after a disturbance in the heating network. The shorter the recovery time, the greater the node's resilience in terms of pressure.

[0077] 2) Temperature uniformity index:

[0078] Temperature Deviation Rate: This is the ratio of the difference between the actual temperature at a node and the set temperature to the set temperature, reflecting the degree of deviation of the node temperature. The smaller the deviation rate, the closer the node temperature is to the ideal state, the higher the heating quality, and the better the toughness.

[0079] Temperature field uniformity index: This is evaluated by calculating the uniformity of the temperature distribution within a certain range around the node. For example, statistics such as standard deviation or coefficient of variation are used to describe the degree of dispersion of the temperature field. A smaller index indicates a more uniform temperature field and a stronger temperature resilience of the node.

[0080] 3) Traffic reliability indicators:

[0081] Flow Variation Coefficient: Calculates the ratio of the standard deviation of the flow at a node to the mean value, reflecting the degree of flow variability. The smaller the coefficient of variation, the more stable the flow, the node can reliably supply heat, and the higher the resilience;

[0082] Traffic recovery capability: The ability of a node to restore traffic to normal levels after a traffic disturbance. This can be assessed by calculating the speed of traffic recovery or the ratio of recovered traffic to initial traffic. The stronger the recovery capability, the greater the node's resilience.

[0083] In this embodiment, in S2, establishing the heat load increase model of the pipe network node includes:

[0084] Obtain the heating area, building type, building energy efficiency rating, geographical location and historical meteorological data of new buildings that will be connected to each node of the heating network at different time periods in the future, and obtain the heat load data of existing nodes of the heating network;

[0085] Based on the acquired data, the independent variables and dependent variable heat load data that affect the heat load change are selected to establish a heat load prediction model for each node of the heating network at different time periods in the future, and obtain the heat load prediction value of each node of the heating network at different time periods;

[0086] Based on the predicted heat load values ​​of each node in the heating network at different time periods and the historical heat loads of each node in the heating network at the same time last year, the heat load increase rate is calculated and divided into low increase scenario, medium increase scenario and high increase scenario.

[0087] In this embodiment, S2 specifically includes:

[0088] Based on the resilience identification results of each node in the heating network, the scenarios of heat load increases at the nodes in the network at different time periods in the future, the overall layout of the heating network, and actual geographical conditions, the siting principles for centralized heat storage tanks are determined. This includes prioritizing locations in areas with low resilience values ​​and high heat load increases, while also considering site availability and impacts on the surrounding environment.

[0089] Based on the site selection principles, through geographic information system (GIS) technology, combined with pipeline network layout and geographic data, preliminary identification of eligible areas was carried out, multiple candidate locations were screened out, and multiple preliminary site selection plans for centralized heat storage tanks were formed.

[0090] It should be noted that the preliminary site selection and scheme generation for centralized heat storage tanks include:

[0091] 1) Determination of site selection principles

[0092] The site selection principles for centralized heat storage tanks should be determined based on factors such as the overall layout of the heating network, the comprehensive resilience value of the nodes, the growth of heat load, and actual geographical conditions. For example, priority should be given to areas with low comprehensive resilience values ​​and high heat load growth potential. Factors such as site availability, transportation convenience, and impact on the surrounding environment should also be considered.

[0093] 2) Candidate location screening

[0094] Based on the site selection principle, several possible candidate locations are screened in the heating network. Geographic Information System (GIS) technology can be used to combine the network map and relevant geographic data to preliminarily identify eligible areas. For example, in areas with rapid heat load growth, look for locations with sufficient space for heat storage tanks and relatively complete surrounding infrastructure.

[0095] 3) Generate preliminary site selection plan

[0096] Conduct a detailed assessment of each candidate site, including factors such as the site's effect on improving the resilience of surrounding nodes, distance from heat sources and heat users, and construction costs. Based on the assessment results, generate multiple preliminary site selection plans for centralized heat storage tanks. Each plan should include the specific location and scale of the heat storage tank, as well as the expected effect on improving the overall resilience of the heating network and the ability to ensure heat supply. For example, plan one may be to build a larger-scale heat storage tank near a certain heat load growth center, which is expected to significantly improve the resilience of surrounding low-resilience nodes; plan two may be to build a medium-sized heat storage tank at a key node in the network to balance the heat supply in the area and enhance the stability of the overall network.

[0097] In this embodiment, in S3, a heat supply network pressure distribution model is established, which is expressed as:

[0098]

[0099] ∑m a,t -∑m c,t +∑m d,t -∑m p,t,s -∑m h,t =0;

[0100] ∑m p,t,r +∑m h,t +∑m c,t -∑m d,t -∑m a,t =0;

[0101] K p is the flow transmission coefficient of the heating network p; m p,t is the heating flow of the heating network p during period t; are the water pressure at the beginning and end of the heating pipe network p during period t; m a,t is the heat medium flow rate of the heat source during period t; m c,t 、m d,t are the heat storage and heat release flows of the heat storage tank during period t; m p,t,s is the heating flow of the heating network p in period t; m h,t is the heat medium flow rate of heat load h in period t; m p,t,r is the heat recovery flow of the heating network p in period t;

[0102] The heat loss simulation model is established and expressed as:

[0103]

[0104]

[0105] T p,st,t 、T p,end,t are the starting and ending temperatures of the heating network p during period t; C h is the specific heat capacity; p is the heat transfer coefficient of the heating network p; L p is the length of the heating network p; H t is the heat load demand; η is the heat loss coefficient; Heat source tt delay Heating power during the period; t delay is the delay time; λ p is the thermal delay coefficient of the heating network p; v p is the heat medium flow rate of the heating network p.

[0106] In this embodiment, in S3, the simulation of the impact of different locations of centralized heat storage tanks on the pressure and temperature of the pipe network includes:

[0107] For each preliminary site selection plan for the centralized heat storage tank, the heat storage tank is coupled with the heating pipe network model. Based on the set boundary conditions and initial conditions, the heat storage and release process of the heat storage tank at different sites is simulated, and the pressure distribution, temperature distribution and heat loss changes of the pipe network are analyzed.

[0108] Among them, the pressure changes of each node in the pipeline network under different site selection schemes are analyzed: whether the connection of the heat storage tank causes the node pressure to be too high or too low, and the impact on the pressure balance of the entire pipeline network;

[0109] Analyze the temperature changes at each node of the pipeline network under different site selection schemes: analyze the effect of the heat storage and release process of the heat storage tank on the regulation of the pipeline network temperature, and whether it can reduce temperature fluctuations;

[0110] Compare the heat loss of the pipeline network under different site selection schemes: consider the distance between the heat storage tank and the heat source and heat user, as well as the impact of pipeline length and thermal delay time on heat loss.

[0111] It's important to note that boundary conditions include the temperature and flow rate of the heat source, the heat load demand of the heat user, and the ambient temperature. The heat source typically supplies thermal fluid to the network at a given temperature and flow rate, while the heat user extracts a certain flow rate of thermal fluid from the network based on actual demand. The ambient temperature serves as an external condition for heat loss calculations.

[0112] Initial conditions are the initial values ​​of parameters such as pressure, temperature, and flow at each node in the network at the start of the simulation. These values ​​can be set based on actual conditions, for example, initially assuming the fluid in the network is at rest and that pressure and temperature are uniformly distributed at each node.

[0113] In this embodiment, in S4, the goal is to minimize the heat loss, delay time and economic cost of the heating network after configuring the centralized heat storage tank, which is expressed as:

[0114] minf=H k,r,loss +t k,r,delay +C k,r,hs ;

[0115] C k,r,hs =C k,r,sys +C k,r,ope +C k,r,dis -E k,r,expand -E k,r,loss -E k,r,fg ;

[0116] f1 is the minimum target of heat loss and delay in the heating network; H k,r,loss is the heat loss under the kth site selection scheme and the rth heat storage tank capacity; t k,r,delay is the delay time under the kth location option and the rth heat storage tank capacity; C k,r,hs is the economic cost under the kth site selection scheme and the rth heat storage tank capacity; C k,r,sys 、C k,r,ope 、C k,r,dis E are the investment cost, operation and maintenance cost, and disposal cost of the centralized heat storage tank configured with the kth site selection scheme and the rth capacity; k,r,expand 、E k,r,loss 、E k,r,fg These are the cost savings for heating network expansion, heat loss savings, and peak-shaving and valley-filling benefits after configuring the kth site selection scheme and the rth capacity of the centralized heat storage tank.

[0117] In this embodiment, in S4, safe operation constraints of the heating network are set, including pressure constraints of the heating network, power balance constraints, heat storage and release power constraints and rated capacity constraints of the centralized heat storage tank, and geographical space restriction constraints.

[0118] In actual applications, the constructed centralized heat storage tank optimization configuration model is solved through an intelligent optimization algorithm to obtain the optimal configuration scheme of the centralized heat storage tank, including the site selection and capacity configuration scheme of the centralized heat storage tank.

[0119] In this embodiment, in S5, a configuration evaluation system for a centralized heat storage tank is established, including energy storage technical indicators, energy storage economic indicators, and energy storage environmental indicators.

[0120] like Figure 2 As shown in the figure, in actual applications, energy storage technical indicators, energy storage economic indicators, and energy storage environmental protection indicators are used as first-level evaluation indicators. Each first-level evaluation indicator is also equipped with multiple second-level evaluation indicators, including:

[0121] 1) Energy storage technical indicators

[0122] Thermal storage capacity: reflects the amount of heat that a thermal storage tank can store, usually measured in joules (J) or gigajoules (GJ). The larger the thermal storage capacity, the more it can meet the needs of the heating system under different operating conditions;

[0123] Charging and discharging efficiency: refers to the energy conversion efficiency of the heat storage tank during the charging and discharging process. High charging efficiency means that energy loss can be reduced when storing heat, and high discharging efficiency means that the stored heat energy can be more effectively transferred to the heating network when releasing heat;

[0124] Response time: refers to the time it takes for the heat storage tank to receive a charge or discharge instruction, actually start charging or discharging, and reach a stable state. A shorter response time helps to quickly adjust the heat supply of the heating system, improving the flexibility and stability of the system;

[0125] Temperature uniformity: This describes the uniformity of the temperature distribution within the thermal storage tank. Good temperature uniformity can prevent local overheating or overcooling, extend the service life of the thermal storage tank, and also help improve the quality of heating.

[0126] 2) Energy storage economic indicators

[0127] Investment cost: This includes the equipment purchase, installation, civil engineering, and related supporting facilities costs of the heat storage tank. Investment cost is one of the important indicators for evaluating the economic feasibility of energy storage solutions and directly affects the initial investment scale of the project.

[0128] Operation and maintenance costs: Covers the energy consumption costs of the heat storage tank during operation, equipment maintenance costs, personnel management costs, etc. Lower operation and maintenance costs help reduce the long-term operating costs of the energy storage system and improve the economic benefits of the project;

[0129] Lifecycle cost: This method takes into account both investment and operation and maintenance costs, and calculates the total cost of an energy storage system over its entire lifecycle using a specific discount rate. Lifecycle cost can more comprehensively assess the economic viability of energy storage solutions and provide a more accurate basis for project decision-making.

[0130] Revenue analysis: Analyze the benefits that thermal storage tanks can gain by participating in peak-shaving and off-peak electricity utilization in the heating system. For example, they can be charged with low-priced electricity during off-peak hours and released during peak hours, thereby reducing heating costs or generating additional revenue by providing ancillary services to the grid.

[0131] 3) Energy storage environmental performance indicators

[0132] Greenhouse gas emissions: Evaluate the greenhouse gas emissions generated directly or indirectly during the operation of the thermal storage tank. For example, if the heat source of the thermal storage tank comes from a coal-fired boiler, greenhouse gas emissions such as carbon dioxide generated by coal combustion need to be considered; if clean energy is used as the heat source, greenhouse gas emissions are relatively low;

[0133] Environmental pollution: Consider the impact of the heat storage tank on the surrounding environment during construction and operation, such as noise pollution, water pollution, soil pollution, etc. For example, the heat storage tank's charging and discharging equipment may generate a certain amount of noise, so corresponding noise reduction measures need to be taken; leakage of the heat storage medium may cause pollution to the soil and water, so effective leakage prevention measures need to be taken;

[0134] Resource consumption: Analyze the resource consumption of the heat storage tank during operation, such as water resources and land resources. For example, some heat storage technologies may require a large amount of water resources for cooling, and the construction of the heat storage tank also requires a certain amount of land area.

[0135] The weights of each evaluation indicator are determined using methods such as the Analytic Hierarchy Process (AHP) and the Entropy Weight Method. The AHP method establishes a hierarchical model, constructs a judgment matrix, calculates weight vectors, and performs consistency checks to determine indicator weights. The Entropy Weight Method determines weights based on the variability of indicator data; greater variability results in higher weights. For each configuration option, a comprehensive evaluation score is calculated based on the determined indicator weights and the collected data. The different configuration options are ranked based on the comprehensive evaluation score, and the option with the highest score is selected as the preferred option. Furthermore, a sensitivity analysis can be conducted on each option to analyze the impact of changes in indicator weights on the evaluation results, thereby improving the reliability and stability of the evaluation results.

[0136] 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 show the possible architectures, functions, and operations of the systems, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a portion of code, and the module, program segment, or a portion of code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or can be implemented using a combination of dedicated hardware and computer instructions.

[0137] In addition, the functional modules in each embodiment of the present invention can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part. If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk, or an optical disk.

[0138] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A method for optimizing the configuration of centralized heat storage tanks in a heating network considering toughness identification, characterized in that: It includes: S1. Utilize the constructed digital twin model of the heating network to obtain multi-source data related to the configuration of centralized heat storage tanks in the heating network and perform resilience identification of each node in the heating network. S2. Based on the toughness identification results of each node in the heating network and the heat load increase model of the network nodes in different time periods in the future, preliminary site selection for centralized heat storage tanks is carried out, and multiple preliminary site selection plans are obtained; S3. Establish a simulation model for the pressure distribution and heat loss of a heating network containing a centralized heat storage tank, and simulate the impact of different locations of the centralized heat storage tank on the pressure and temperature of the network; S4. By setting the goal of minimizing the heat loss, delay time, and economic cost of the heating network after configuring the centralized heat storage tank, setting the safe operation constraints of the heating network, constructing the centralized heat storage tank optimization configuration model, and solving the optimal configuration scheme of the centralized heat storage tank; S5. Establish a configuration evaluation system for centralized heat storage tanks and evaluate the optimal configuration scheme for centralized heat storage tanks.

2. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 1, characterized in that: Said S1 specifically includes: Use digital twin technology to virtually map the actual operation of the heating network in a virtual space, build a digital twin model of the heating network, and obtain multi-source data related to the configuration of centralized heat storage tanks in the heating network, including the network topology, heat source distribution, spatiotemporal characteristics of heat load, static properties of the network, historical fault records of the network, operation data of the network in extreme weather scenarios, and changes in network disturbance operation parameters; Based on the acquired multi-source data, each resilience identification index is analyzed, and the resilience value of each node in the pipeline network is calculated, which is expressed as: S i is the toughness value of the i-th pipe network node; w j is the weight of the jth toughness identification index; n is the number of toughness identification indexes; x ij is the normalized value of the jth toughness identification index of the i-th pipeline network node; Among them, the resilience identification indicators include the heat load loss rate when failure occurs at each node of the heating pipeline network, the structural strength of the pipeline network, the heat loss area of ​​the pipeline network under extreme weather conditions, and the recovery capacity in the face of disturbances.

3. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 2, characterized in that: The structural strength of the pipeline network includes the stress level and structural reliability of the pipeline network. This is done by assessing whether the stress borne by the pipeline network under current operating conditions exceeds its allowable stress range. The lower the stress level, the safer the pipeline structure and the higher the toughness of the node. The failure probability of the pipeline network node structure is also calculated. The lower the failure probability, the stronger the structural toughness of the node and the ability to withstand greater external forces and environmental changes. The recovery capability in the face of disturbances includes: when the heating network faces heat source adjustments and changes in heat load demand, analyzing the changes in operating parameters of each node, and evaluating the ability of the node to return to a thermal equilibrium state when thermal imbalance occurs in the heating network. The stronger the recovery capability, the better the resilience of the node.

4. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 1, characterized in that: In S2, the establishment of the heat load increase model of the pipe network node includes: Obtain the heating area, building type, building energy efficiency rating, geographical location and historical meteorological data of new buildings that will be connected to each node of the heating network at different time periods in the future, and obtain the heat load data of existing nodes of the heating network; Based on the acquired data, select the independent variables and dependent variable heat load data that affect the heat load change, establish the heat load prediction model for each node of the heating network at different time periods in the future, and obtain the heat load prediction value of each node of the heating network at different time periods; Based on the predicted heat load values ​​of each node in the heating network at different time periods and the historical heat loads of each node in the heating network at the same time last year, the heat load increase rate is calculated and divided into low increase scenario, medium increase scenario and high increase scenario.

5. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claims 1 and 4, characterized in that: The S2 specifically includes: Based on the resilience identification results of each node in the heating network, the scenarios of heat load increases at the nodes in the network at different time periods in the future, the overall layout of the heating network, and actual geographical conditions, the siting principles for centralized heat storage tanks are determined. This includes prioritizing locations in areas with low resilience values ​​and high heat load increases, while also considering site availability and impacts on the surrounding environment. Based on the site selection principles, through geographic information system (GIS) technology, combined with pipeline network layout and geographic data, preliminary identification of eligible areas was carried out, multiple candidate locations were screened out, and multiple preliminary site selection plans for centralized heat storage tanks were formed.

6. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 1, characterized in that: In S3, a pressure distribution model of the heating network is established, which is expressed as: ∑m a,t -∑m c,t +∑m d,t -∑m p,t,s -∑m h,t =0; ∑m p,t,r +∑m h,t +∑m c,t -∑m d,t -∑m a,t =0; K p is the flow transmission coefficient of the heating network p; m p,t is the heating flow of the heating network p during period t; are the water pressure at the beginning and end of the heating network p during period t; m a,t is the heat medium flow rate of the heat source during period t; m c,t 、m d,t are the heat storage and heat release flows of the heat storage tank during period t; m p,t,s is the heating flow of the heating network p in period t; m h,t is the heat medium flow rate of heat load h in period t; m p,t,r is the heat recovery flow of the heating network p in period t; The heat loss simulation model is established and expressed as: T p,st,t 、T p,end,t are the starting and ending temperatures of the heating network p during period t; C h is the specific heat capacity; p is the heat transfer coefficient of the heating network p; L p is the length of the heating network p; H t is the heat load demand; η is the heat loss coefficient; For heat source tt delay Heating power during the period; t delay is the delay time; λ p is the thermal delay coefficient of the heating network p; v p is the heat medium flow rate of the heating network p.

7. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 1, characterized in that: In S3, the impact of different locations of centralized heat storage tanks on the pressure and temperature of the pipe network is simulated, including: For each preliminary site selection plan for the centralized heat storage tank, the heat storage tank is coupled with the heating pipe network model. Based on the set boundary conditions and initial conditions, the heat storage and release process of the heat storage tank at different sites is simulated, and the pressure distribution, temperature distribution and heat loss changes of the pipe network are analyzed. Among them, the pressure changes of each node in the pipeline network under different site selection schemes are analyzed: whether the connection of the heat storage tank causes the node pressure to be too high or too low, and the impact on the pressure balance of the entire pipeline network; Analyze the temperature changes at each node of the pipeline network under different site selection schemes: analyze the effect of the heat storage and release process of the heat storage tank on the regulation of the pipeline network temperature, and whether it can reduce temperature fluctuations; Compare the heat loss of the pipeline network under different site selection schemes: consider the distance between the heat storage tank and the heat source and heat user, as well as the impact of pipeline length and thermal delay time on heat loss.

8. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 1, characterized in that: In S4, the goal is to minimize the heat loss, delay time and economic cost of the heating network after configuring the centralized heat storage tank, which is expressed as: minf=H k,r,loss +t k,r,delay +C k,r,hs ; C k,r,hs =C k,r,sys +C k,r,ope +C k,r,dis -E k,r,expand -E k,r,loss -E k,r,fg ; f1 is the minimum target of heat loss and delay in the heating network; H k,r,loss is the heat loss under the kth site selection scheme and the rth heat storage tank capacity; t k,r,delay is the delay time under the kth location option and the rth heat storage tank capacity; C k,r,hs is the economic cost under the kth site selection scheme and the rth heat storage tank capacity; C k,r,sys 、C k,r,ope 、C k,r,dis E are the investment cost, operation and maintenance cost, and disposal cost of the centralized heat storage tank configured with the kth site selection scheme and the rth capacity; k,r,expand 、E k,r,loss 、E k,r,fg These are the cost savings for heating network expansion, heat loss savings, and peak-shaving and valley-filling benefits after configuring the kth site selection scheme and the rth capacity of the centralized heat storage tank.

9. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 1, characterized in that: In the above-mentioned S4, the safe operation constraints of the heating network are set, including the pressure constraint of the heating network, the power balance constraint, the heat storage and release power constraint and rated capacity constraint of the centralized heat storage tank, and the geographical space restriction constraint.

10. The method for optimizing the configuration of centralized heat storage tanks in a heating network according to claim 1, characterized in that: In the above-mentioned S5, a configuration evaluation system for centralized heat storage tanks is established, including energy storage technical indicators, energy storage economic indicators, and energy storage environmental protection indicators.

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