Method and device for risk assessment and regulation of a multi-energy cooperative heating system

Abstract

The application discloses a kind of multi-energy collaborative heating system risk assessment and regulation method and device, based on improved GNOME model, risk source in multi-energy collaborative heating system is analogized as oil spill source, risk propagation is analogized as oil spill diffusion, by defining risk particle and giving its risk type, risk level, initial position and influence range etc. Property, using similar convection and diffusion mechanism in oil spill model, in combination with energy flow and information transmission in heating system and other factors, the propagation path and change of risk in heating network are simulated. The present application helps to master the risk development trend in real time, early warning potential risks, provides strong support for formulating targeted risk control measures, and ensures the safe, stable and efficient operation of multi-energy collaborative heating system.

Description

Technical Field

[0001] This invention relates to the field of risk assessment technology for multi-energy coordinated heating systems, and specifically to a method and apparatus for risk assessment and control of multi-energy coordinated heating systems. Background Technology

[0002] Traditional energy heating systems are inefficient and carbon emissions are exacerbated by the burning of large amounts of fossil fuels. With the intensification of global climate change and the increasing demand for energy security, renewable energy can reduce carbon emissions at the source, but due to the significant fluctuations and intermittency of single-type energy sources constrained by natural conditions, it is difficult to meet stable heating needs. Therefore, multi-renewable energy collaborative heating systems have emerged. By integrating different types of renewable energy, utilizing their spatiotemporal characteristics for synergy, coupling energy storage technology to smooth out fluctuations, and leveraging smart grids for optimized scheduling, a stable and efficient clean heating network is formed, which retains the advantages of low carbon emissions while breaking through the limitations of single-energy supply.

[0003] However, multi-energy coordinated heating systems are complex in structure, with multiple energy sources coupled together and the operating states of each energy subsystem influencing each other. They are also subject to numerous uncertainties such as weather changes, equipment failures, and energy market fluctuations, exhibiting dynamism and complexity, similar to the spread and evolution of oil spills in the environment. Traditional risk assessment methods for multi-energy coordinated heating systems, such as fault tree analysis, analytic hierarchy process (AHP), and fuzzy comprehensive evaluation, suffer from insufficient description of dynamic risk changes and difficulty in comprehensively considering numerous complex influencing factors. Therefore, this invention proposes a risk assessment and control method for multi-energy coordinated heating systems to provide early warning of potential risks, offering strong support for developing targeted risk control measures and ensuring the safe, stable, and efficient operation of multi-energy coordinated heating systems. Summary of the Invention

[0004] This invention provides a method and apparatus for risk assessment and control of a multi-energy coordinated heating system, in order to solve the problems of insufficient description of dynamic risk changes and difficulty in comprehensively considering numerous complex influencing factors in existing risk assessment methods for multi-energy coordinated heating systems.

[0005] According to the first aspect, one embodiment provides a method for risk assessment and control of a multi-energy coordinated heating system, the method comprising:

[0006] Based on the multi-energy coordinated heating system model, risk sources are identified and risk particles are defined, and attributes are assigned to the risk particles.

[0007] Combining the characteristics of the energy transmission network of the multi-energy coordinated heating system, risk propagation simulation is carried out based on the Lagrange pursuit method. At the same time, the risk level changes under different risk evolution conditions are superimposed to obtain the final risk level update result of the risk particles.

[0008] Based on the final risk level update results of risk particles, the risk level of different risk particles is assessed, and risk response and strategy adjustment are carried out according to different risk levels.

[0009] Furthermore, risk particles are endowed with specific properties, including:

[0010] The risk particle attributes include risk type, risk level, initial location, and scope of influence.

[0011] Furthermore, risk particles are endowed with specific properties, including:

[0012] Risk type definition includes: encoding the risk type of risk particles, using integer codes to represent different risk types, and assuming... T To indicate the risk type, the risk type codes for different scenarios are as follows:

[0013] ;

[0014] Risk level definition includes: setting R This indicates the risk level, with a value range of [0, 100]. The different risk level intervals are as follows:

[0015] ;

[0016] Initial position definition includes: In a multi-energy coordinated heating system, the initial position of the risk particle is the position of the risk source;

[0017] The definition of the scope of impact includes: in a multi-energy coordinated heating system, the scope of impact is used to represent the area affected by the risk.

[0018] Furthermore, considering the characteristics of the energy transmission network in a multi-energy coordinated heating system, risk propagation simulation is conducted based on the Lagrange pursuit method, specifically including:

[0019] The parameters defining the motion of hazardous particles include convection velocity and diffusion coefficient;

[0020] Convection velocity calculation includes:

[0021] Considering that the velocity of energy flow affects the propagation speed of risk during energy supply interruptions, and that the propagation speed of equipment failure information affects the timeliness of fault handling, let the velocity of energy flow be... The speed at which fault information spreads is The convection speed of risk transmission for:

[0022] ;

[0023] In the formula, k 1 and k2 is a weighting coefficient, determined based on the importance of energy flow and fault information propagation to risk propagation, and k 1+ k 2 = 1; Based on the flow rate of the fluid in the pipe Q and pipe cross-sectional area A The calculation yields, i.e. ; Based on the transmission distance of fault information d and transmission delay time t d Calculation, i.e. ;

[0024] The diffusion coefficient calculation includes:

[0025] Let the risk diffusion coefficient be... Risk diffusion coefficient The complexity of the multi-energy coordinated heating system and the degree of equipment coupling are related. When particles generate convection and diffusion motion, the trajectory of the risk particles will be updated at each time step. The formula for updating the position of risk particles is calculated as follows:

[0026] ;

[0027] in, This refers to the displacement increment of the risk particle due to convective motion. This refers to the displacement increment of risk particles due to diffusion. It is the sum of the two;

[0028] ;

[0029] ;

[0030] In the formula, , Let be a random variable that follows a standard normal distribution; This is the convection proportionality coefficient. This is the diffusion ratio coefficient;

[0031] Risk level increment caused by convection and diffusion for:

[0032] ;

[0033] In the formula, a This represents the risk propagation coefficient.

[0034] Furthermore, the risk level changes under different risk evolution scenarios are considered in combination, specifically including:

[0035] Different risk evolution scenarios include risk decay, risk amplification, and risk transfer.

[0036] Furthermore, the risk level changes under different risk evolution scenarios are considered in combination, specifically including:

[0037] Regarding risk decay:

[0038] Risk attenuates during transmission due to control measures. Therefore, the risk level attenuation at each time step is calculated based on the effectiveness and implementation time of these measures. Let the risk attenuation coefficient be... ,

[0039] Then at each time step Internal risk level attenuation for:

[0040] ;

[0041] Regarding situations where risk is amplified:

[0042] Set risk amplification trigger conditions When the equipment failure risk level exceeds the preset threshold Risk amplification is triggered at certain times; let the risk amplification factor be... When the trigger condition is met At that time, the increase in risk level is:

[0043] ;

[0044] For multi-energy coordinated heating systems, photoelectric conversion efficiency, heat collection efficiency, heat exchange efficiency, or energy storage efficiency are used as risk amplification factors.

[0045] Regarding risk transfer:

[0046] Risk transfer probability is defined based on the system's operating status and energy allocation strategy. It is related to the system's operating status and energy allocation strategy. If a risk transfer occurs, the amount of risk level transferred from energy A to energy B is related to the risk level of the transfer. for:

[0047] .

[0048] Furthermore, the final risk level update result of the risk particles is obtained, specifically including:

[0049] New risk level The formula for calculation is:

[0050] ;

[0051] In the formula, RBased on the initial risk level, This represents the amount of risk level attenuation. This refers to the increase in risk caused by risk amplification. This represents the amount of risk level transfer. The risk level increment is caused by convection and diffusion.

[0052] According to the second aspect, one embodiment provides a risk assessment and control device for a multi-energy coordinated heating system, the device comprising:

[0053] The risk particle definition module is used to determine risk sources and define risk particles based on the multi-energy coordinated heating system model, and to assign attributes to the risk particles.

[0054] The risk level update module is used to combine the characteristics of the energy transmission network of the multi-energy coordinated heating system, perform risk propagation simulation based on the Lagrange pursuit method, and simultaneously consider the risk level changes under different risk evolution conditions to obtain the final risk level update result of the risk particles.

[0055] The risk assessment and control module is used to assess the risk level of different risk particles based on the final risk level update results of the risk particles, and to carry out risk response and strategy control according to different risk levels.

[0056] According to a third aspect, one embodiment provides an electronic device, characterized in that the device includes: a processor and a memory;

[0057] The memory is used to store one or more program instructions;

[0058] The processor is configured to run one or more program instructions to perform the steps of a multi-energy coordinated heating system risk assessment and control method as described in any of the preceding claims.

[0059] According to a fourth aspect, one embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a multi-energy coordinated heating system risk assessment and control method as described in any of the preceding claims.

[0060] This invention provides a method and apparatus for risk assessment and control of a multi-energy coordinated heating system. Based on an improved GNOME (GNU Network Object Model Environment) model, it analogizes risk sources in the multi-energy coordinated heating system to oil spill sources and risk propagation to oil spill diffusion. By defining risk particles and assigning them attributes such as risk type, risk level, initial location, and impact range, and utilizing convection and diffusion mechanisms similar to those in oil spill models, combined with factors such as energy flow and information propagation in the heating system, it simulates the propagation path and changes of risks in the heating network. This invention has the following beneficial effects:

[0061] (1) Considering the dynamic nature of risk: Traditional methods are mostly static assessments, while the improved GNOME model can consider the movement of risk particles in processes such as convection and diffusion, and can better reflect the dynamic changes of risk in multi-energy synergistic heating systems over time and space. For example, it can track the propagation and evolution of energy sources such as solar energy, air energy, natural gas, and electricity in multi-energy synergistic heating systems in real time, update the risk level in a timely manner, and provide more accurate information for risk prevention and control.

[0062] (2) Comprehensive influence of multiple factors: The improved GNOME model comprehensively considers the influence of multiple factors such as energy flow speed, information transmission speed, system complexity, and equipment coupling on risk. Compared with some traditional methods that only focus on a few aspects, it can more comprehensively assess system risk.

[0063] (3) Considering the interaction between risks: In multi-energy coordinated heating systems, there are often interrelationships between different energy supply risks. For example, the risk of natural gas supply may affect the use of electric heating equipment, thereby affecting the heating stability of the entire system. The improved GNOME model can assess the interaction between different risks through risk superposition, which can more realistically reflect the new risk level and improve the accuracy of risk assessment, which is lacking in many traditional methods.

[0064] (4) Strong adaptability: Traditional assessment methods may only be applicable to specific system types. However, the improved GNOME model can flexibly adjust model parameters according to different system structures and operating characteristics, and has strong adaptability. Whether it is a large-scale centralized multi-energy coordinated heating system or a small distributed system, its risk status can be accurately assessed by setting reasonable parameters. Attached Figure Description

[0065] Figure 1 A flowchart illustrating a risk assessment and control method for a multi-energy coordinated heating system, as provided in one embodiment of the present invention;

[0066] Figure 2This is a schematic diagram of the logical structure of a risk assessment and control device for a multi-energy coordinated heating system provided in one embodiment of the present invention. Detailed Implementation

[0067] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0068] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0069] Oil spill models are mathematical models used to simulate the movement, diffusion, drift, and weathering of oil spills in the environment after an oil spill occurs in the ocean or water. Their core purpose is to predict the fate and transmission path of oil spills, providing a scientific basis for emergency response, environmental impact assessment, and the development of preventative measures. The GNOME model, as one of the common oil spill models, can realistically simulate the dynamic changes of oil spills in the marine environment. The traditional GNOME model treats oil spills as a large number of discrete particles and uses the Lagrange multiplication table to track the particle trajectories. It is widely used in emergency response to marine oil spills, quickly providing predictive information on oil spill diffusion to aid in emergency decision-making and help determine the possible destination and impact range of the oil spill.

[0070] Given the similarities between the operational characteristics of multi-energy coordinated heating systems and the diffusion and evolution of oil spills in the environment, this invention, based on multi-energy coordinated heating systems, adaptively modifies the traditional GNOME model to construct an improved GNOME model, aiming to conduct risk assessments of multi-energy coordinated heating systems. Based on the improved GNOME model, this invention analogizes risk sources in the heating system (such as energy supply interruptions and equipment failure points) to oil spill sources, risk propagation to oil spill diffusion, and energy transmission networks (including heating networks, power grids, and natural gas pipelines) to oil spill diffusion risk propagation paths. By defining risk particles and assigning them attributes such as risk type, risk level, initial location, and impact range, and utilizing convection and diffusion mechanisms similar to those in oil spill models, combined with factors such as energy flow and information propagation in the heating system, the invention simulates the propagation path and changes of risks in the heating network.

[0071] An embodiment of the present invention provides a risk assessment and control method for a multi-energy coordinated heating system, which is described below in conjunction with... Figure 1 Please provide a detailed explanation.

[0072] like Figure 1 As shown, in step S100, risk sources are identified and risk particles are defined based on the multi-energy coordinated heating system model, and attributes are assigned to the risk particles.

[0073] Specifically, risk particle definition: Based on the multi-energy coordinated heating system model, risk particles are defined and assigned attributes, such as risk type (including energy supply interruption risk, equipment failure risk, extreme weather impact risk, etc.), risk level (divided into high, medium and low levels), initial location, and scope of impact.

[0074] In this embodiment, the risk particles used correspond to Lagrange particles. Risk particles correspond to entities in a multi-energy collaborative system, namely risk sources, such as energy supply interruption points and equipment failure points in the system. These risk sources that fail are defined as risk particles.

[0075] In oil spill models, the defined oil spill particles mainly focus on the physicochemical properties of oil, such as density, viscosity, and volatility, to describe the diffusion and weathering processes of oil spills in the marine environment. By analogy with the oil spill particles in oil spill models, the improved GNOME model in this embodiment will use risk particles. Each risk particle is assigned attributes related to the heating system, such as risk type, risk level, initial location, and impact range. These attribute settings enable the model to more accurately simulate the generation and development of various risks in multi-energy coordinated heating systems. Therefore, it is necessary to assign specific attributes to these risk particles, i.e., particle attribute parameterization.

[0076] Particle property definition:

[0077] (1) Risk type

[0078] The risk types of multi-energy coordinated heating systems are coded, with different risk types represented by integer codes. Let... T To indicate the risk type, the risk codes for different types of faults are as follows:

[0079] ;

[0080] (2) Risk level

[0081] Corresponding to indicators of severity such as oil spill concentration or volume, risk levels are set in the improved GNOME model to determine the speed and extent of risk propagation. Let... R This indicates the risk level, with a value range of [0, 100]. In this embodiment, the risk level range is as follows:

[0082] ;

[0083] (3) Initial position

[0084] Analogous to the location of an oil spill leak, in a heating system, the initial location is the location of the risk source, such as energy production equipment, transmission pipeline nodes, etc.

[0085] (4) Scope of impact

[0086] In the context of heating systems, the affected area refers to the region where the risk may be impacted, such as certain heating areas that may be affected by energy supply disruptions or equipment malfunctions.

[0087] like Figure 1 As shown, in step S200, risk propagation simulation is performed based on the Lagrange tracking method, taking into account the characteristics of the energy transmission network of the multi-energy coordinated heating system. At the same time, the risk level changes under different risk evolution conditions are superimposed to obtain the final risk level update result of the risk particle.

[0088] Specifically, risk propagation simulation calculation: Based on the Lagrange tracking method, and closely combined with the characteristics of the energy network of the multi-energy synergistic system, the risk level changes under different risk types and different risk evolutions (risk decay, amplification, and transfer) are calculated.

[0089] 1. Establishment of equations of motion

[0090] In traditional models, particle motion is determined by convection velocity and diffusion coefficient, while in the improved GNOME model, these correspond to risk particle motion parameters.

[0091] (1) Convection velocity

[0092] In traditional models, water and air currents propel oil spill particles, analogous to energy flow and information propagation speeds in heating systems. The velocity of energy flow within the heating network affects the propagation speed of energy supply disruption risks. The "convective velocity" of risk propagation can be calculated based on the characteristics of the energy transmission network (such as pipe diameter and flow rate). Secondly, the propagation speed of equipment fault information within the control system affects the timeliness of fault handling, thus influencing the development of risks. Therefore, information propagation speed is included in the calculation of "convective velocity."

[0093] Specifically, let the speed of energy flow be... The speed at which fault information spreads is The convection speed of risk transmission for:

[0094] ;

[0095] In the formula, k 1 and k 2 is a weighting coefficient, determined based on the importance of energy flow and information dissemination to risk propagation, and k 1+ k 2 = 1; It can be based on the flow rate of the fluid in the pipeline. Q and pipe cross-sectional area A The calculation yields, i.e. ; Based on the transmission distance of fault information d and transmission delay time t d Calculation, i.e. .

[0096] (2) Diffusion coefficient

[0097] In traditional models, the diffusion coefficient reflects the degree of particle diffusion caused by turbulence in the ocean; in heating systems, it can represent the uncertain propagation of risk. A single equipment failure can lead to the random diffusion of risk within a certain range due to the complexity and interconnectedness of the system. The diffusion coefficient can be determined based on factors such as the complexity of the system and the degree of coupling between equipment.

[0098] Specifically, let the risk diffusion coefficient be... This is related to the complexity of the multi-energy cooperative system and the degree of equipment coupling. In summary, when particles generate convection and diffusion motion, the trajectory of the risky particles will be updated at each time step. The calculation method for the risk particle position update formula is as follows:

[0099] ;

[0100] in, This refers to the displacement increment of the risk particle due to convective motion. This refers to the displacement increment of risk particles due to diffusion. It is the sum of the two. Their specific expressions are as follows:

[0101] ;

[0102] ;

[0103] In the formula, , Let be a random variable that follows a standard normal distribution; This is the convection proportionality coefficient. This is the diffusion ratio coefficient, which is related to the system configuration.

[0104] Therefore, the increase in risk level caused by convection and diffusion for:

[0105] ;

[0106] In the formula, a =0.05, which is the risk propagation coefficient.

[0107] 2. Risk Evolution Calculation

[0108] In traditional oil spill models, the weathering processes of oil spills, such as evaporation, emulsification, and dissolution, alter the physicochemical properties of the oil, leading to other risks. Similarly, in multi-energy synergistic heating system models, the risks change due to factors such as the degradation of solar cell conversion efficiency, the reduced heat collection efficiency of solar thermal and photovoltaic equipment, and the decreased efficiency of energy storage and heat exchange devices. Here, risks are categorized into several evolutionary scenarios, including risk attenuation, risk amplification, and risk transfer.

[0109] (1) Risk decay

[0110] Risks may attenuate during propagation due to some prevention and control measures (such as backup energy, activation of backup equipment, timely maintenance of equipment, etc.). The amount of risk attenuation in each time step can be calculated based on the effectiveness of the prevention and control measures and the implementation time.

[0111] Let the risk attenuation coefficient be... It is related to the effectiveness of prevention and control measures and the timing of their implementation, and therefore varies at each time step. Internal risk level attenuation It can be represented as:

[0112] ;

[0113] (2) Risk amplification

[0114] In some cases, risks can be amplified due to the chain reaction within the system. Therefore, a risk amplification factor is defined, which increases the risk level under certain conditions. For multi-energy coordinated heating systems, photoelectric conversion efficiency, heat collection efficiency, heat exchange efficiency, and energy storage efficiency can be used as risk amplification factors. Furthermore, risk triggering conditions can be set, such as the collapse of the entire energy supply system triggered by the failure of critical equipment.

[0115] Let the risk amplification trigger condition be: For example, when the risk level of equipment failure exceeds a certain threshold. Time-triggered amplification. Let the risk amplification factor be... When the trigger condition is met At that time, the increase in risk level becomes:

[0116] ;

[0117] (3) Risk transfer

[0118] Furthermore, risks can shift from one energy source to another. For example, in a solar heating system, the risk of a sudden interruption of solar power could lead to an increase in the need for electricity or natural gas.

[0119] Therefore, the risk transfer probability is defined based on the system's operating status and energy allocation strategy. It is related to the system's operating status and energy allocation strategy. If a risk transfer occurs, the amount of risk level transferred from energy A to energy B is expressed by the following function:

[0120] .

[0121] In summary, the new risk level The calculation formula is

[0122] ;

[0123] In the formula, R Based on the initial risk level, This represents the amount of risk level attenuation. This refers to the increase in risk caused by risk amplification. This represents the amount of risk level transfer. The risk level increment is caused by convection and diffusion.

[0124] like Figure 1 As shown, in step S300, based on the final risk level update result of the risk particles, the risk level of different risk particles is evaluated, and risk response and strategy adjustment are carried out according to different risk levels.

[0125] Specifically, risk assessment and strategy formulation: Based on the above risk propagation results, comprehensively assess the risk levels of different areas and equipment in the multi-energy coordinated heating system, and carry out risk response and strategy adjustment according to different risk levels.

[0126] In this embodiment, the specific control strategy includes three levels of risk: low, medium, and high. Control can be implemented according to the different levels of risk, combined with the characteristics of the multi-energy coordinated heating system, from multiple aspects such as energy dispatch, equipment management, and emergency response. The technical means adopted for different risk levels are as follows:

[0127] (1) Low risk

[0128] When the risk level is assessed as low, the focus is primarily on prevention and monitoring to maintain stable system operation and prevent the risk level from escalating. Specifically, this includes:

[0129] a. Continuous monitoring. Strengthen real-time monitoring of key system parameters (such as energy supply flow, equipment operating temperature and pressure, etc.), collect data using sensors and monitoring equipment, and promptly identify potential risks through data analysis models.

[0130] b. Equipment maintenance. Perform regular maintenance and inspections of the system equipment according to the established maintenance plan, promptly replacing aging or worn parts to ensure the equipment is in good operating condition.

[0131] c. Energy optimization. Based on system operation data, further optimize the energy allocation plan to improve energy utilization efficiency.

[0132] (2) Medium risk

[0133] When the risk level is assessed as medium risk, more proactive measures are needed to control the risk and prevent it from further expanding and worsening. Specifically, this includes:

[0134] a. Energy Adjustment. Adjustments are made to energy supplies at risk, energy transmission routes are optimized, and the impact of unstable energy supply on the system is reduced.

[0135] b. Equipment Management Risk Warning. For equipment involving risks, arrange professional technicians to conduct comprehensive inspections and assessments, strengthen the risk warning mechanism, prepare spare parts in advance, and conduct shutdown maintenance when necessary to prevent the failure from escalating.

[0136] (3) High risk

[0137] When the risk level reaches high risk, emergency response measures must be initiated immediately to minimize the losses caused by the risk and ensure system safety and the normal heating needs of users. Specifically, this includes:

[0138] a. Equipment repair and isolation. Emergency repairs will be carried out on faulty equipment. A professional repair team will be organized, and sufficient resources and equipment will be allocated to restore the equipment to normal operation as quickly as possible.

[0139] b. Emergency Energy Allocation. Switch energy supply routes, adjust equipment operation modes, prioritize heating needs of key areas and important users, and quickly activate backup or emergency energy supply plans.

[0140] Application example:

[0141] A multi-energy coordinated heating system at a railway transportation hub in a frigid region experiences a significant increase in heat load demand during the winter heating season due to extreme outdoor temperatures. Simultaneously, solar thermal collectors suffer reduced capacity due to insufficient sunlight, natural gas supply pipelines are at risk of partial blockage due to low temperatures, resulting in insufficient output from gas-fired boilers, and the power supply is unstable due to aging lines. This section will use the above scenario as an example to provide an implementation example of risk level assessment for the multi-energy coordinated system in this situation.

[0142] (1) Initial state of risk

[0143] Solar thermal collector risk: Let this risk type be defined. Initial risk level (Medium risk) The initial location is at the solar collector of the solar thermal equipment, and the affected area is the area that is heated by solar energy;

[0144] Natural gas supply risk: Let this risk type be defined. Initial risk level (Medium risk) The initial location is the pipeline where there is a risk of blockage, and the affected area is the heating area downstream of the pipeline;

[0145] Power supply risk: Let this risk type be defined. Initial risk level (Low risk), the initial location is the aging line, and the affected area is the area where the relevant electric heating equipment is located.

[0146] (2) Risk dynamic assessment process

[0147] Impact of Convection: Regarding energy flow velocity, due to extreme weather and increased heat load, it is necessary to adjust the hot water flow rate while maintaining a constant hot water temperature. This means increasing the hot water velocity in the pipes. Assuming the increased hot water velocity is 2 m / s, the corresponding energy flow velocity... The direction and size are determined by factors such as pipeline layout. Information transmission speed. If the transmission delay of equipment fault information in the control system is 0.5 seconds and the transmission distance is 100 meters, then According to the entropy method, the following is calculated: k1 =0.6, k 2 =0.4, then the convection velocity of risk propagation At time step Inside, the displacement increment of the risk particle due to convective motion is: This caused the risk to spread to surrounding areas.

[0148] Influence of diffusion motion: By assessing the complexity and equipment coupling of this multi-energy coordinated heating system, calculations were performed. Let random variable Convection proportionality coefficient k c =0.001, diffusion ratio coefficient k d =0.1, at time step Within this multi-energy coordinated heating system, the risk increment of convection and diffusion motion is... and The calculation formula is as follows:

[0149] ;

[0150] ;

[0151] Risk level change :

[0152] .

[0153] (3) Impact of risk evolution

[0154] Risk attenuation: When maintenance personnel discover a blockage in the natural gas pipeline, they activate the backup gas source plan and obtain the risk attenuation coefficient through assessment. At time step Internally, the decline in the risk level of natural gas supply .

[0155] Risk amplification: Due to risks in solar heating and natural gas supply, some areas are experiencing insufficient heating, leading users to turn on more electric heating devices, thus amplifying the power supply risk. The power supply risk level exceeds the threshold. Magnification factor The increase in the power supply risk level is then... .

[0156] Risk transfer: probability of risk transfer This is related to the system's operating status and energy allocation strategies. For example, due to natural gas supply risks, energy allocation strategies might switch some natural gas heating areas to electric heating, causing a transfer of natural gas supply risk to electricity supply risk. If the probability of this risk transfer is calculated... Natural gas supply risk transfer volume This means that some of the risks associated with natural gas supply have been transferred to risks associated with electricity supply.

[0157] (4) Risk level update

[0158] After a time step Taking natural gas supply risk as an example, the updated risk level is: Since the calculated risk is medium, but lower than the initial risk, the response strategy for medium risk can be adopted to further adjust the energy supply at risk, optimize energy transmission paths, and reduce the impact of unstable energy supply on the system. Simultaneously, based on system operation data, the energy allocation plan can be further optimized to improve energy utilization efficiency. Similarly, the updated risk levels for other risks can be calculated, enabling dynamic risk assessment and providing a basis for risk response strategies.

[0159] Corresponding to the aforementioned method for risk assessment and control of a multi-energy coordinated heating system, this invention also discloses a device for risk assessment and control of a multi-energy coordinated heating system, such as... Figure 2 As shown, it specifically includes:

[0160] The risk particle definition module is used to determine risk sources and define risk particles based on the multi-energy coordinated heating system model, and to assign attributes to the risk particles.

[0161] The risk level update module is used to combine the characteristics of the energy transmission network of the multi-energy coordinated heating system, perform risk propagation simulation based on the Lagrange pursuit method, and simultaneously consider the risk level changes under different risk evolution conditions to obtain the final risk level update result of the risk particles.

[0162] The risk assessment and control module is used to assess the risk level of different risk particles based on the final risk level update results of the risk particles, and to carry out risk response and strategy control according to different risk levels.

[0163] It should be noted that for a detailed description of the risk assessment and control device for a multi-energy coordinated heating system provided in the embodiments of the present invention, please refer to the relevant description of the risk assessment and control method for a multi-energy coordinated heating system provided in the embodiments of the present invention, which will not be repeated here.

[0164] The above examples illustrate the present invention only to aid in understanding it and are not intended to limit the scope of the invention. Those skilled in the art can make various simple deductions, modifications, or substitutions based on the principles of this invention.

Claims

1. A method for risk assessment and control of a multi-energy coordinated heating system, characterized in that, The method includes: Based on the multi-energy coordinated heating system model, risk sources are identified and risk particles are defined, and attributes are assigned to the risk particles. Combining the characteristics of the energy transmission network of the multi-energy coordinated heating system, risk propagation simulation is carried out based on the Lagrange pursuit method. At the same time, the risk level changes under different risk evolution conditions are superimposed to obtain the final risk level update result of the risk particles. Based on the final risk level update results of risk particles, the risk level of different risk particles is assessed, and risk response and strategy adjustment are carried out according to different risk levels. in: Assigning risk particles properties, specifically including: The risk particle attributes include risk type, risk level, initial location, and scope of influence; Assigning risk particles properties, specifically including: Risk type definition includes: encoding the risk type of risk particles, using integer codes to represent different risk types. Let T represent the risk type, then the risk type codes under different scenarios are: Risk level definition includes: Let R represent the risk level, with a value range of [0, 100]. The intervals between different risk levels are as follows: Initial position definition includes: In a multi-energy coordinated heating system, the initial position of the risk particle is the position of the risk source; The definition of the scope of impact includes: in a multi-energy coordinated heating system, the scope of impact is used to represent the area affected by the risk; Based on the characteristics of the energy transmission network of a multi-energy coordinated heating system, risk propagation simulation is conducted using the Lagrange pursuit method, specifically including: The parameters defining the motion of hazardous particles include convection velocity and diffusion coefficient; Convection velocity calculation includes: Considering that the velocity of energy flow affects the propagation speed of risk during energy supply interruptions, and that the propagation speed of equipment failure information affects the timeliness of fault handling, let the velocity of energy flow be... The speed at which fault information spreads is The convection speed of risk transmission for: In the formula, k1 and k2 are weighting coefficients, which are determined according to the importance of energy flow and fault information propagation to risk propagation, and k1+k2=1; The result is calculated based on the flow rate Q of the fluid in the pipe and the cross-sectional area A of the pipe. ; Based on the transmission distance of fault information and transmission delay time Calculation, i.e. ; The diffusion coefficient calculation includes: Let the risk diffusion coefficient be... Risk diffusion coefficient The complexity of the multi-energy coordinated heating system and the degree of equipment coupling are related. When particles generate convection and diffusion motion, the trajectory of the risk particles will be updated at each time step. The formula for updating the position of risk particles is calculated as follows: in, The displacement increment of the risk particle due to convective motion. This refers to the displacement increment of risk particles due to diffusion. It is the sum of the two; In the formula, , Let be a random variable that follows a standard normal distribution; This is the convection proportionality coefficient. This is the diffusion ratio coefficient; Risk level increment caused by convection and diffusion for: In the formula, a is the risk propagation coefficient.

2. The method for risk assessment and control of a multi-energy coordinated heating system as described in claim 1, characterized in that, The risk level changes under different risk evolution scenarios are considered in combination, specifically including: Different risk evolution scenarios include risk decay, risk amplification, and risk transfer.

3. The method for risk assessment and control of a multi-energy coordinated heating system as described in claim 2, characterized in that, The risk level changes under different risk evolution scenarios are considered in combination, specifically including: Regarding risk decay: Risk attenuates during transmission due to control measures. Therefore, the risk level attenuation at each time step is calculated based on the effectiveness and implementation time of these measures. Let the risk attenuation coefficient be... , Then at each time step Internal risk level attenuation for: Regarding situations where risk is amplified: Set risk amplification trigger conditions When the equipment failure risk level exceeds the preset threshold Risk amplification is triggered at certain times; let the risk amplification factor be... When the trigger condition is met At that time, the increase in risk level is: For multi-energy coordinated heating systems, photoelectric conversion efficiency, heat collection efficiency, heat exchange efficiency, or energy storage efficiency are used as risk amplification factors. Regarding risk transfer: Risk transfer probability is defined based on the system's operating status and energy allocation strategy. It is related to the system's operating status and energy allocation strategy. If a risk transfer occurs, the amount of risk level transferred from energy A to energy B is related to the risk level of the transfer. for: 。 4. The method for risk assessment and control of a multi-energy coordinated heating system as described in claim 1, characterized in that, Obtain the final risk level update result for risk particles, specifically including: New risk level The formula for calculation is: In the formula, R represents the initial risk level. This represents the amount of risk level attenuation. This refers to the increase in risk caused by risk amplification. This represents the amount of risk level transfer. The risk level increment is caused by convection and diffusion.

5. A risk assessment and control device for a multi-energy coordinated heating system, characterized in that, The device includes: The risk particle definition module is used to determine risk sources and define risk particles based on the multi-energy coordinated heating system model, and to assign attributes to the risk particles. The risk level update module is used to combine the characteristics of the energy transmission network of the multi-energy coordinated heating system, perform risk propagation simulation based on the Lagrange pursuit method, and simultaneously consider the risk level changes under different risk evolution conditions to obtain the final risk level update result of the risk particles. The risk assessment and control module is used to assess the risk level of different risk particles based on the final risk level update results of the risk particles, and to carry out risk response and strategy control according to different risk levels. in: Assigning risk particles properties, specifically including: The risk particle attributes include risk type, risk level, initial location, and scope of influence; Assigning risk particles properties, specifically including: Risk type definition includes: encoding the risk type of risk particles, using integer codes to represent different risk types. Let T represent the risk type, then the risk type codes under different scenarios are: Risk level definition includes: Let R represent the risk level, with a value range of [0, 100]. The intervals between different risk levels are as follows: Initial position definition includes: In a multi-energy coordinated heating system, the initial position of the risk particle is the position of the risk source; The definition of the scope of impact includes: in a multi-energy coordinated heating system, the scope of impact is used to represent the area affected by the risk; Based on the characteristics of the energy transmission network of a multi-energy coordinated heating system, risk propagation simulation is conducted using the Lagrange pursuit method, specifically including: The parameters defining the motion of hazardous particles include convection velocity and diffusion coefficient; Convection velocity calculation includes: Considering that the velocity of energy flow affects the propagation speed of risk during energy supply interruptions, and that the propagation speed of equipment failure information affects the timeliness of fault handling, let the velocity of energy flow be... The speed at which fault information spreads is The convection speed of risk transmission for: In the formula, k1 and k2 are weighting coefficients, which are determined according to the importance of energy flow and fault information propagation to risk propagation, and k1+k2=1; The result is calculated based on the flow rate Q of the fluid in the pipe and the cross-sectional area A of the pipe. ; Based on the transmission distance of fault information and transmission delay time Calculation, i.e. ; The diffusion coefficient calculation includes: Let the risk diffusion coefficient be... Risk diffusion coefficient The complexity of the multi-energy coordinated heating system and the degree of equipment coupling are related. When particles generate convection and diffusion motion, the trajectory of the risk particles will be updated at each time step. The formula for updating the position of risk particles is calculated as follows: in, The displacement increment of the risk particle due to convective motion. This refers to the displacement increment of the risk particle due to its diffusion motion; In the formula, , Let be a random variable that follows a standard normal distribution; This is the convection proportionality coefficient. This is the diffusion ratio coefficient; Risk level increment caused by convection and diffusion for: In the formula, a is the risk propagation coefficient.

6. An electronic device, characterized in that, The device includes: a processor and a memory; The memory is used to store one or more program instructions; The processor is configured to run one or more program instructions to perform the steps of a risk assessment and control method for a multi-energy coordinated heating system as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a risk assessment and control method for a multi-energy coordinated heating system as described in any one of claims 1 to 4.

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