Urban energy system interaction influence and improvement method based on electricity, gas and heat interdependence

Abstract

The invention provides an electricity, gas and heat interdependence-based urban energy system interaction influence and promotion method, and belongs to the field of energy utilization. Comprising the following steps: establishing a coupling model of an electric power subsystem, a natural gas subsystem and a heat supply subsystem; providing a multi-dimensional toughness evaluation index to evaluate the comprehensive performance under an extreme event; introducing an emergency resource scheduling strategy, including introducing a generator car scheduling model to plan the position deployment of a generator car in an extreme event, and using a maintenance personnel scheduling model to optimize the repair path of the maintenance personnel under the condition that the power network is damaged; under the condition that the power system is damaged, dynamic reconstruction of the topological structure of the power distribution network is carried out; the thermal inertia of the building is utilized, and the temperature of the building is maintained under the condition of insufficient heat supply by optimizing a thermal management strategy; and the resource allocation of the three energy systems is collaboratively optimized. Cooperative operation and emergency recovery of the multi-energy system are achieved, the overall performance, resistance and recovery capacity of the system under extreme conditions are improved, and safety and stability of energy supply are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and particularly to a method for interacting and enhancing the urban energy system based on the interdependence of electricity, heat, and gas. Background Art

[0002] Under the background of global energy transformation, carbon peak, and carbon neutrality, the structure of China's energy system is developing towards a more low-carbon, intelligent, and interconnected direction. As an important part of the energy network, the Urban Energy System (UES) is an important carrier for achieving efficient energy utilization and enhancing energy security. However, the urban energy system involves multiple subsystems such as electricity, natural gas, and heating. There are complex coupling relationships between these energy subsystems. Therefore, when the system faces extreme events such as natural disasters and equipment failures, chain reactions may occur, leading to problems such as large-scale power outages, gas supply interruptions, and insufficient heating, thereby significantly reducing the overall performance of the system.

[0003] Currently, traditional energy system design and planning usually ignore the coupling relationships between subsystems, and each energy subsystem operates independently, resulting in a lack of coordinated response strategies when extreme events occur. At the same time, the existing emergency response mechanisms are not perfect enough, lacking comprehensive emergency dispatching strategies for multi-energy systems. In addition, the physical and network infrastructure of the urban energy system is aging, and the increasing frequency of extreme weather brought about by climate change has posed unprecedented challenges to the resilience of the energy system.

[0004] Therefore, there is an urgent need to provide a resilience improvement method that comprehensively considers the interdependence between multi-energy systems such as electricity, natural gas, and heating to improve the reliability and recoverability of the urban energy system under extreme conditions. Summary of the Invention

[0005] The object of the present invention is to overcome the problems in the prior art that each energy subsystem operates independently, lacks the ability to cooperate in the face of extreme events, and the anti-interference and recovery capabilities of the system are weak, affecting the stability and security of urban energy. A method for interacting and enhancing the urban energy system based on the interdependence of electricity, heat, and gas is provided. This method can achieve the coordinated operation and emergency recovery of multi-energy systems, improve the overall performance, resistance, and resilience of the system under extreme conditions, and ensure the safety and stability of energy supply.

[0006] To achieve the above object, the present invention provides a method for interacting and enhancing the urban energy system based on the interdependence of electricity, heat, and gas, the method comprising:

[0007] Step 1: Establish a coupled model for the three subsystems of electricity, natural gas, and heating: Establish the physical models of the power system, natural gas system, and heating system respectively, and form a unified urban energy system model by combining the coupling relationships among the three;

[0008] Step 2: Propose multi-dimensional resilience evaluation indicators covering the overall system performance, resistance, responsiveness, and recoverability to evaluate the comprehensive performance of the system under extreme events;

[0009] Step 3: Introduce emergency resource scheduling strategies, including introducing a power generation vehicle scheduling model to plan the location deployment of power generation vehicles in extreme events, and using a maintenance personnel scheduling model to optimize the repair path of maintenance personnel in the case of power network damage;

[0010] Step 4: Conduct dynamic reconfiguration of the distribution network topology in the case of power system damage;

[0011] Step 5: Utilize the thermal inertia of buildings to maintain the temperature of buildings in the case of insufficient heating by optimizing the thermal management strategy;

[0012] Step 6: Collaboratively optimize the resource allocation of the three energy systems of electricity, natural gas, and heating.

[0013] Preferably, in Step 1, establishing the mathematical model of the power grid subsystem includes:

[0014] Use the LinDistFlow model to model the power flow of the power grid. This model linearizes the originally complex non-linear power flow equation by ignoring the minor losses on the line and assuming that the node voltage is close to the per-unit value. The way to establish the model is as follows:

[0015] For node i and line ij, the power flow equation is as follows:

[0016]

[0017] Among them, P ij and Q ij respectively represent the active power and reactive power of line ij, P i and Q i represent the active power and reactive power of node i, R ij and X ij respectively represent the resistance and reactance of the line;

[0018] Linearize the node voltage. In the LinDistFlow model, it is assumed that the voltage deviation is small, so the node voltage can be approximated as the per-unit value 1. Therefore, the linearization formula of the voltage drop is as follows:

[0019] V j =V i -2(Rij P ij +X ij Q ij )

[0020] wherein, V j and V i respectively represent the per-unit values of the voltages of nodes j and i;

[0021] In the power subsystem, the LinDistFlow model is used to constrain the flow of power to ensure the balance between power supply and demand at each node, that is, at each node i:

[0022] P in,i -P out,i =P load,i +P loss,i

[0023] Q in,i -Q out,i =Q load,i +Q loss,i

[0024] wherein, P in,i and Q in,i respectively represent the active power and reactive power flowing into node i, P out,i and Q out,i represent the active power and reactive power flowing out of node i, P load,i and Q load,i represent the load demand of node i;

[0025] Set the voltage and current limits of each line and node. The constraints on voltage deviation are as follows:

[0026] V min ≤V i ≤V max

[0027] wherein, V min and V max represent the lower and upper limits of the voltage allowed in the system;

[0028] Optimize the power grid objective function according to the constructed mathematical model. With the goal of minimizing the power system losses, determine the optimal operating state of the distribution network under extreme events by solving the linearized power flow equation. Its objective function is:

[0029]

[0030] wherein, F is the total power loss, R ij represents the resistance of the line, and I ij represents the current flowing through line ij.

[0031] Preferably, in step 1, establishing the mathematical model of the natural gas subsystem includes:

[0032] The natural gas system includes multiple nodes and pipelines. Among them, the nodes represent gas sources, loads or connection points, and the pipelines represent the transmission paths of natural gas;

[0033] For each node i, there is gas inflow and outflow, and a node balance equation is established:

[0034]

[0035] Among them, q ij represents the natural gas flow rate from node i to node j, and d i represents the demand of node i;

[0036] Based on the gas flow characteristics in the pipeline, as well as the frictional losses and temperature changes in the pipeline, a pipeline flow equation is established:

[0037]

[0038] Among them, q ij is the flow rate from node i to node j; p i and p j are the pressures of nodes i and j respectively; K ij is the flow coefficient of the pipeline, which is related to parameters such as pipeline length and diameter; L ij is the pipeline length; based on the non-ideal characteristics of natural gas, Z ij is the compression factor; T ij is the pipeline temperature;

[0039] The pressure of each node needs to meet the following constraints to ensure the safe operation of the system:

[0040] p min ≤p i ≤p max

[0041] Among them, p min and p max are the lowest and highest pressures allowed in the system respectively;

[0042] Then, a non-isothermal flow equation can be established and obtained:

[0043]

[0044] Among them, R is the gas constant; T is the gas temperature; f is the friction coefficient, which is related to the pipeline roughness; D is the pipeline diameter;

[0045] According to the constructed mathematical model, with the goal of minimizing the pressure loss and energy consumption of the entire system, an optimization objective function is established:

[0046]

[0047] Among them, P represents the set of all pipelines in the system; λ is the penalty coefficient used to maintain the stability of the system pressure; p ref is the reference pressure value in the system.

[0048] Preferably, in step 1, establishing the mathematical model of the thermal pipeline subsystem includes:

[0049] The heating system consists of a heat source, heat load, and heat pipe network. Establish the node and pipeline models of the heat pipe network, where the nodes represent heat sources, user loads, or connection points, and the pipelines are used to transfer heat;

[0050] For each node i, establish the heat balance equation:

[0051]

[0052] Among them, h ij represents the temperature flowing from node i to j; L ij is the heat loss of the pipeline; based on the non-linear characteristics of the pipeline, λ ij is the correction coefficient of the pipeline length;

[0053] Based on the heat loss in the pipeline, establish the heat loss model of the heat pipeline:

[0054]

[0055] Among them, H ij is the heat conduction coefficient; T i and T j are the temperatures of node i and node j respectively; L ij is the heat loss of the pipeline; based on the non-linear characteristics of the pipeline, λ ij is the correction coefficient of the pipeline length;

[0056] The temperature of each node needs to satisfy the following temperature constraint conditions to ensure the stability of the heating system:

[0057] T min ≤T i ≤T max

[0058] Among them, T min and T max are the lowest and highest temperatures operating in the system respectively;

[0059] For each heat source node, the heating capacity is restricted by its maximum heating power:

[0060] h i ≤Hmax,i +β i T i

[0061] where H max,i represents the maximum heat supply capacity of heat source i, and β i is a temperature-related adjustment coefficient;

[0062] The model of the heating system is solved by using the non-linear programming NLP or the mixed integer non-linear programming MINLP method to ensure the effective allocation of heat resources in the event of extreme events;

[0063] The system is simulated for different extreme scenarios, and the temperature changes, heat flow distribution and energy consumption of the system are analyzed to verify the reliability and stability of the system.

[0064] Preferably, in step 2, the urban distribution network is used as the core of the UES and is tightly coupled with other energy systems.

[0065] Preferably, in step 2, for the improvement of the distribution network, the goal is to minimize the sum of the weighted losses of electricity, gas and heat loads, and combined with the interdependence and mutual influence relationships between subsystems, the resilience effect improvement of different stages of the system is achieved by implementing various measures; the objective function of this improvement method is:

[0066] minF = minF DS +F GS +F HS

[0067] where the definitions of the loss functions of each subsystem are as follows:

[0068]

[0069] where F DS 、F GS 、F HS are the weighted load loss values of the urban power distribution, natural gas pipeline system, and heat supply and return pipe network subsystems respectively; are the weight coefficients of the power load i, gas load n, and building n' heat load loss amounts respectively; are the loss values of the power load i, gas load n, and room r heat load respectively; ρ i represents the reliability coefficient of node i under extreme conditions; η n is the reliability weight of the natural gas node; λ ij is the pressure difference penalty coefficient; κ h′ is the importance weight of the node; δ r is the penalty coefficient for heat storage utilization; is the heat supply of the energy storage unit at time t; T dis the scheduling period; B is the set of grid nodes; S res,n' is the set of all rooms in building n';

[0070] In terms of performance maintenance, the UES based on the electrical-gas-thermal interdependence includes the maintenance of the performance of three subsystems, namely, electrical load maintenance, gas load maintenance, and heat load maintenance:

[0071]

[0072]

[0073] Among them, R SPM is the system performance maintenance index; is the performance maintenance index of the urban distribution network DS; is the performance maintenance index of the natural gas pipeline network subsystem GS; is the performance maintenance index of the heat supply and return pipeline network subsystem HS; The coefficient 100 / T d functions to linearly map the index to the interval [0, 100], that is, to evaluate using a "percentage system", and the higher the score, the stronger the resilience; is the probability of the occurrence of the extreme event scenario s; is the electrical load of node i; is the flow rate of natural gas required for the gas load n; is the heat power supplied to room r in building n'; N is the total number of scenarios extracted; are the starting times of the damage stages of the electrical, gas, and heat systems respectively;

[0074] In the resistance index, after the extreme event occurs, the load retention in DS, GS, and HS can reflect the resistance ability of each subsystem to the extreme event, that is:

[0075]

[0076] Among them, R RSS is the system resistance index; are the corresponding indexes of DS, GS, and HS respectively; G n,s is the supply volume of the natural gas storage facility; H r,s is the additional heat support required when the heat supply system is impacted;

[0077] In the restorability index, both the load restoration amount and the restoration speed need to be considered during evaluation:

[0078]

[0079] Among them, R RCV is the system restorability index; are the restorability indexes of DS, GS, and HS respectively; and are the recovery stages of DS, GS, and HS respectively; P i,r , G n,r , H r,t_r are the power recovery compensation amount, natural gas recovery amount, and heat recovery compensation amount respectively.

[0080] Preferably, based on the improved genetic algorithm-based boosting algorithm, the system's recovery ability is maximized by optimizing resource allocation and scheduling strategies to enhance the overall resilience of the electric-gas-heat multi-system in extreme events; among them, the improved genetic algorithm incorporates a variety of optimization means, including adaptive mutation rate, elite retention strategy, and chaotic search, specifically including the following steps:

[0081] Step a, optimization objective. After an extreme event occurs, the recovery indicators of the power, natural gas, and heating systems are optimized through the improved genetic algorithm to enhance the overall resilience. The objective function is:

[0082] maxF = α1R SPM +α2R RSS +α3R RCV

[0083] where R SPM , R RSS , R RCV are the performance evaluation index, resistance ability index, and recovery ability index respectively, and α1, α2, α3 are weight coefficients;

[0084] Step b, selection and crossover operations. A certain number of individuals are randomly selected from the population, and the individual with the highest fitness is selected through competition operations, and several individuals with the highest fitness in the current generation are directly retained to the next generation to ensure the retention of the optimal genes;

[0085] The selected individuals form a new parental generation, and two-point crossover is used to exchange genes between parental individuals; by randomly selecting two crossover points, the gene segments between parents are exchanged to generate new offspring; the mutation probability P m is dynamically adjusted according to the fitness value of the individual and the generation number of the population. The purpose of mutation is to increase the diversity of the population and prevent falling into local optima. The formula is as follows:

[0086]

[0087] where the mutation operation can randomly select some gene positions in the individual for adjustment;

[0088] Step c, search operation. After each generation of genetic operations, chaotic search is introduced to perform chaotic reset on individuals with poor fitness to prevent falling into local optima; specifically, the Logistic map is used to generate a chaotic sequence:

[0089] x t+1 = μx t (1 - x t ), 0 < x t < 1

[0090] where μ is the chaos mapping parameter, which is used to generate chaotic behavior and enhance population diversity;

[0091] Step d: Fitness evaluation and iteration. For the new population after mutation and chaotic reset, calculate the fitness of each individual. Among them, the evaluation of the fitness value is based on the comprehensive score of performance, resistance, and resilience indicators. When reaching the maximum generation N max or the population fitness tends to be stable, stop the iteration and output the optimal solution.

[0092] Preferably, the output optimal individual recovery strategy includes resource allocation of power, natural gas, and heating systems, energy storage scheduling, and node recovery priority.

[0093] According to the above technical solution, the present invention constructs a coupled model of the electric - gas - heat multi - energy system, realizes the collaborative optimization and resource sharing among subsystems, comprehensively improves the performance, resistance, and resilience of the urban energy system through multi - objective optimization, and each system can support each other under normal and extreme conditions to ensure the stability of the energy system. Specifically, by combining tournament selection, adaptive crossover and mutation, elite retention, and chaotic search, the global exploration ability and local convergence ability of the algorithm are effectively improved. By optimizing the system resource allocation and scheduling, the overall performance of the system under normal and extreme conditions is significantly improved. The improved recovery strategy and intelligent scheduling enable the system to have stronger anti - disturbance ability under extreme events. At the same time, after being impacted by extreme events, the optimized recovery ability enables the system to return to the original normal operation state faster.

[0094] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification, and are used to explain the embodiments of the present invention together with the following specific implementation, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0096] Figure 1 is a diagram of the interdependence relationship of the electric - gas - heat system in the method for interacting and enhancing the urban energy system based on the interdependence of electricity, gas, and heat provided by the present invention;

[0097] Figure 2It is a flowchart of a method for the interaction and improvement of an urban energy system based on the interdependence of electricity and heat according to the present invention. Detailed implementation manners

[0098] The following will detail the specific implementation manners of the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific implementation manners described herein are only for explaining and illustrating the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0099] It should be noted that the acquisition, transmission, storage, use, processing, etc. of data in the technical solution of this application all comply with the relevant regulations of national laws and regulations. In the embodiments of this application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned. They should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0100] See Figure 2 , the present invention provides a method for the interaction and improvement of an urban energy system based on the interdependence of electricity and heat, and the method includes:

[0101] Step 1: Establish a coupling model for the three subsystems of power, natural gas, and heating: respectively establish physical models of the power system, natural gas system, and heating system, and form a unified urban energy system model in combination with the coupling relationship among the three;

[0102] Step 2: Propose multi-dimensional resilience evaluation indicators covering the overall system performance, resistance, responsiveness, and recoverability to evaluate the comprehensive performance of the system under extreme events;

[0103] Step 3: Introduce an emergency resource scheduling strategy, including introducing a power generation vehicle scheduling model to plan the location deployment of power generation vehicles in extreme events, and using a maintenance personnel scheduling model to optimize the repair path of maintenance personnel in the case of power network damage, so as to improve the repair efficiency and response speed of the system;

[0104] Step 4: In the case of damage to the power system, perform dynamic reconfiguration of the distribution network topology to ensure that as much load as possible is restored to power supply in a short time;

[0105] Step 5: Utilize the thermal inertia of buildings to maintain the temperature of buildings in the case of insufficient heating by optimizing the thermal management strategy, so as to improve the resilience of the system;

[0106] Step 6: Coordinate and optimize the resource allocation of the three energy systems of power, natural gas, and heating to ensure the effective utilization of energy in extreme events and minimize the impact of energy supply interruption to the greatest extent.

[0107] In this embodiment, establishing the mathematical model of the power grid subsystem in Step 1 includes:

[0108] The LinDistFlow model is used to model the power flow of the power grid. By ignoring the minor losses on the lines and assuming that the node voltages are close to the per-unit value, the originally complex non-linear power flow equations are linearized, facilitating rapid calculation and optimization. The model is established as follows:

[0109] For node i and line ij, the power flow equations are as follows:

[0110]

[0111] Among them, P ij and Q ij respectively represent the active power and reactive power of line ij, P i and Q i represent the active power and reactive power of node i, R ij and X ij respectively represent the resistance and reactance of the line;

[0112] For the linearization of the node voltage, in the LinDistFlow model, it is assumed that the voltage deviation is small. Therefore, the node voltage can be approximated as the per-unit value 1. Thus, the linearization formula for the voltage drop is as follows:

[0113] V j = V i - 2(R ij P ij + X ij Q ij )

[0114] Among them, V j and V i respectively represent the per-unit values of the voltages of nodes j and i;

[0115] In the power subsystem, the LinDistFlow model is used to constrain the power flow to ensure the power supply and demand balance at each node, that is, at each node i:

[0116] P in,i - P out,i = P load,i + P loss,i

[0117] Q in,i - Q out,i = Q load,i + Q loss,i

[0118] Among them, P in,i and Q in,i respectively represent the active power and reactive power flowing into node i, P out,i and Q out,iDenote the active power and reactive power flowing out of node i, P load,i and Q load,i represent the load demand of node i;

[0119] Set the voltage and current limits of each line and node to ensure the safe operation of the system. The constraints on voltage deviation are as follows:

[0120] V min ≤V i ≤V max

[0121] where, V min and V max represent the lower and upper voltage limits allowed in the system;

[0122] Optimize the power grid objective function according to the constructed mathematical model, with the goal of minimizing the power system loss. By solving the linearized power flow equation, determine the optimal operating state of the distribution network under extreme events. Its objective function is:

[0123]

[0124] where, F is the total power loss, R ij represents the resistance of the line, I ij represents the current flowing through line ij.

[0125] In this embodiment, the establishment of the natural gas subsystem mathematical model in step 1 includes:

[0126] The natural gas system includes multiple nodes and pipelines. Among them, the nodes represent gas sources, loads or connection points, and the pipelines represent the transmission paths of natural gas;

[0127] For each node i, there is gas inflow and outflow, and a node balance equation is established:

[0128]

[0129] where, q ij represents the natural gas flow from node i to node j, d i represents the demand of node i;

[0130] Based on the gas flow characteristics in the pipeline, as well as the friction loss and temperature change in the pipeline, establish a pipeline flow equation:

[0131]

[0132] where, q ij is the flow from node i to node j; p i and p j are the pressures of nodes i and j respectively; K ijis the flow coefficient of the pipeline, which is related to parameters such as pipeline length and diameter; L ij is the pipeline length; Based on the non-ideal characteristics of natural gas, Z ij is the compressibility factor; T ij is the pipeline temperature;

[0133] The pressure at each node needs to satisfy the following constraints to ensure the safe operation of the system:

[0134] p min ≤p i ≤p max

[0135] where p min and p max are the minimum and maximum pressures allowed in the system, respectively;

[0136] Subsequently, the non-isothermal flow equation can be established and obtained:

[0137]

[0138] where R is the gas constant; T is the gas temperature; f is the friction coefficient, which is related to the pipeline roughness; D is the pipeline diameter;

[0139] According to the constructed mathematical model, with the goal of minimizing the pressure loss and energy consumption of the entire system, an optimization objective function is established:

[0140]

[0141] where P represents the set of all pipelines in the system; λ is the penalty coefficient, which is used to maintain the stability of the system pressure; p ref is the reference pressure value in the system.

[0142] In this embodiment, the establishment of the mathematical model of the thermal pipeline subsystem in step 1 includes:

[0143] The heating system consists of a heat source, a heat load, and a heat pipe network. The node and pipeline models of the heat pipe network are established, where the nodes represent heat sources, user loads, or connection points, and the pipelines are used to transfer heat;

[0144] For each node i, a heat balance equation is established:

[0145]

[0146] where h ij represents the temperature flowing from node i to j; L ij is the heat loss of the pipeline; Based on the non-linear characteristics of the pipeline, λ ij is the correction coefficient of the pipeline length;

[0147] Based on the heat loss in the pipeline, a heat loss model of the hot pipeline is established:

[0148]

[0149] Among them, H ij is the heat conduction coefficient; T i and T j are the temperatures of node i and node j respectively; L ij is the heat loss of the pipeline; Based on the non-linear characteristics of the pipeline, λ ij is the correction coefficient of the pipeline length;

[0150] The temperature of each node needs to satisfy the following temperature constraint conditions to ensure the stability of the heating system:

[0151] T min ≤T i ≤T max

[0152] Among them, T min and T max are the lowest and highest temperatures operating in the system respectively;

[0153] For each heat source node, the heating capacity is restricted by its maximum heating power:

[0154] h i ≤H max,i +β i T i

[0155] Among them, H max,i represents the maximum heating capacity of heat source i, and β i is the temperature-related adjustment coefficient;

[0156] The model of the heating system is solved by using the non-linear programming NLP or the mixed-integer non-linear programming MINLP method to ensure the effective allocation of heat resources in the event of extreme events;

[0157] The system is simulated for different extreme scenarios, and the temperature changes, heat flow distribution and energy consumption of the system are analyzed to verify the reliability and stability of the system.

[0158] In this embodiment, it is preferred that in step 2, the urban distribution network is used as the core of the UES and is closely coupled with other energy systems to provide important support for energy supply. The interdependence between systems is as Figure 1 shown.

[0159] Meanwhile, in Step 2, for the improvement of the distribution network, aiming to minimize the sum of weighted electrical, gas, and heat load losses, and considering the interdependence and mutual influence relationships among subsystems, the resilience effect of the system at different stages is improved by implementing various measures; the objective function of this improvement method is:

[0160] minF = minF DS + F GS + F HS

[0161] Among them, the definitions of the loss functions of each subsystem are as follows:

[0162]

[0163] Among them, F DS 、F GS 、F HS are the weighted load loss values of the urban power distribution, natural gas pipeline system, and heat supply and return pipeline network subsystems, respectively; are the weight coefficients of the electrical load i, gas load n, and heat load loss of building n', respectively; are the loss values of the electrical load i, gas load n, and heat load of room r, respectively; ρ i represents the reliability coefficient of node i under extreme conditions; η n is the reliability weight of the natural gas node; λ ij is the pressure difference penalty coefficient; κ h′ is the importance weight of the node; δ r is the penalty coefficient for the utilization of thermal energy storage; is the heat supply of the energy storage unit at time t; T d is the scheduling period; B is the set of grid nodes; S res,n' is the set of all rooms in building n';

[0164] In terms of performance maintenance, the UES based on the electrical-gas-heat interdependence includes the maintenance of the performance of three subsystems, namely, electrical load maintenance, gas load maintenance, and heat load maintenance:

[0165]

[0166] Among them, R SPM is the system performance maintenance index; is the performance maintenance index of the urban power distribution network (urban DistributionSystem, DS); is the performance maintenance index of the natural gas pipeline network subsystem (natural Gas System, GS); is the performance maintenance index for the Heating supply and return System (HS); the coefficient 100 / T d is used to linearly map the index to the interval [0, 100], that is, the "percentage system" is adopted for evaluation, and the higher the score, the stronger the resilience; is the probability of the occurrence of the extreme event scenario s; is the electrical load of node i; is the flow rate of natural gas required for the gas load n; is the heat power supplied to the room r in building n'; N is the total number of scenarios extracted; are the starting times of the damage stages of the electrical, gas, and heat systems respectively;

[0167] In the resistance index, after the extreme event occurs, the load retention in DS, GS, and HS can reflect the resistance ability of each subsystem to the extreme event, that is:

[0168]

[0169] where, R RSS is the system resistance index; are the corresponding indexes of DS, GS, and HS respectively; G n,s is the supply volume of the natural gas storage facility; H r,s is the additional heat support required when the heating system is impacted; Through the above optimization and improvement method based on the anti-disturbance index, the anti-disturbance ability of the electrical-gas-heat system can be significantly improved, ensuring the coordinated operation and rapid recovery of each system in the face of extreme events, and guaranteeing the stability and resilience of the urban energy system.

[0170] In the recovery index, both the load recovery volume and the recovery speed need to be considered during evaluation:

[0171]

[0172] where, R RCV is the system recovery index; are the recovery indexes of DS, GS, and HS respectively; and are the recovery stages of DS, GS, and HS respectively; P i,r 、G n,r 、H r,t_r are the power recovery compensation volume, the natural gas recovery volume, and the heat recovery compensation volume respectively.

[0173] In this embodiment, the method for interacting and enhancing the urban energy system based on the interdependence of electricity and heat also includes improving the algorithm. Specifically, an improved algorithm based on the improved genetic algorithm is adopted to maximize the system's recovery ability by optimizing the resource allocation and scheduling strategy, so as to enhance the overall resilience of the electricity-gas-heat multi-system in extreme events. Among them, the improved genetic algorithm integrates a variety of optimization methods, including adaptive mutation rate, elite retention strategy, and chaotic search, which can improve the global optimization ability and convergence speed of the algorithm. The specific steps are as follows:

[0174] Step a, optimization objective: After an extreme event occurs, optimize the recovery indicators of the power, natural gas, and heating systems through the improved genetic algorithm to enhance the overall resilience. The objective function is:

[0175] maxF = α1R SPM +α2R RSS +α3R RCV

[0176] Where, R SPM , R RSS , R RCV Are the performance evaluation index, resistance ability index, and recovery ability index respectively, and α1, α2, α3 are weight coefficients;

[0177] Step b, selection and crossover operations: Randomly select a certain number of individuals from the population, select the individual with the highest fitness through competition operations, and ensure that several individuals with the highest fitness in the current generation are directly retained to the next generation to ensure the retention of the best genes;

[0178] Form the selected individuals into a new parent generation, and use two-point crossover to exchange genes of the parent generation individuals; by randomly selecting two crossover points, exchange gene segments between the parent generations to generate new offspring; the mutation probability P m Is dynamically adjusted according to the fitness value of the individual and the generation number of the population. The purpose of mutation is to increase the diversity of the population and prevent falling into local optimum. The formula is as follows:

[0179]

[0180] Where, the mutation operation can randomly select some gene positions in the individual for adjustment;

[0181] Step c, search operation: After each generation of genetic operations, introduce chaotic search to perform chaotic reset on individuals with poor fitness to prevent falling into local optimum; specifically, use the Logistic map to generate a chaotic sequence:

[0182] x t+1 = μx t (1 - x t ), 0 < xt <1

[0183] Among them, μ is the chaos mapping parameter, which is used to generate chaotic behavior and enhance population diversity;

[0184] Step d, fitness evaluation and iteration. For the new population after mutation and chaotic reset, calculate the fitness of each individual; among them, the evaluation of the fitness value is based on the comprehensive score of performance, resistance and resilience indicators. When the maximum generation N max or the population fitness tends to be stable, the iteration stops and the optimal solution is output.

[0185] The above-mentioned output optimal individual recovery strategy includes resource allocation, energy storage scheduling and node recovery priority of power, natural gas and heating systems.

[0186] Through the above technical solutions, the present invention realizes the collaborative optimization of the multi-energy system. Specifically, by constructing a coupling model of the electric-gas-heat multi-energy system, the collaborative optimization and resource sharing between subsystems can be realized, and the energy utilization efficiency can be improved. Each system supports each other under normal and extreme conditions to ensure the stability of the energy system. At the same time, the present invention also improves the risk resistance ability. By introducing a multi-objective optimal scheduling strategy and comprehensively considering the coupling relationship of power, natural gas and heating systems, in extreme events, each subsystem can flexibly schedule resources to reduce the impact of energy supply interruption. The resistance of the system is significantly enhanced, and it can better cope with extreme situations such as natural disasters or equipment failures. And, through the combination of the energy storage system and various emergency response measures, the present invention can quickly restore the normal operation state of the system after extreme events and shorten the recovery time. By introducing intelligent algorithms to optimize the scheduling of resources, the system can quickly allocate reserve resources after being impacted and improve the recovery efficiency.

[0187] In addition, the present invention uses an improved genetic algorithm to optimize the scheduling of the energy system, which improves the emergency response ability and scheduling intelligence level of the system. The system can dynamically adjust the recovery strategy according to the real-time state to ensure the continuity and efficiency of energy supply.

[0188] Furthermore, the present invention also improves the resilience of the energy system. Through the collaborative operation of the multi-energy system and the emergency recovery strategy, the system has higher resilience when facing extreme events, can effectively reduce the risk of energy supply interruption, and ensure the restoration of energy services in the shortest time to guarantee the energy security of the city.

[0189] In addition, when this method is actually used, other intelligent optimization algorithms can be adopted to replace the improved genetic algorithm in the above embodiments, such as the particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), or ant colony algorithm (ACO). These algorithms can also intelligently optimize the scheduling strategy of the multi-energy system to achieve efficient scheduling and emergency recovery of the energy system.

[0190] Similarly, when this method is actually used, a modular multi-system collaborative model can also be adopted to replace the unified coupling model constructed in the present invention. Each subsystem is independently modeled and coupled through interfaces, or the scheduling and resource sharing of the system can be achieved through a collaborative model based on distributed control, which can achieve a similar collaborative effect and has higher flexibility in implementation.

[0191] Furthermore, when this method is actually used, an emergency response strategy based on a multi-energy microgrid can also be adopted to replace the emergency response achieved by the combination of energy storage and intelligent algorithms in the present invention. It uses distributed renewable energy (such as solar energy and wind energy) and mobile emergency energy stations to provide emergency power supply services for the system, thereby improving the resilience and response ability of the system.

[0192] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0193] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0194] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction means that implements the functions specified in one or more of the processes Figure 1 one or more of the processes and / or blocks Figure 1 specified in one or more of the blocks or blocks.

[0195] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes Figure 1 one or more of the processes and / or blocks Figure 1 specified in one or more of the blocks or blocks.

[0196] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0197] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0198] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0199] It should also be noted that the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0200] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for the interaction and improvement of an urban energy system based on the interdependence of electricity and heat, characterized in that The method includes: Step 1: Establish a coupled model of the three subsystems of electricity, natural gas, and heating. Respectively establish the physical models of the power system, natural gas system, and heating system, and combine the coupling relationships among the three to form a unified urban energy system model. Step 2: Propose multi-dimensional resilience evaluation indicators covering the overall system performance, resistance, responsiveness, and recoverability to evaluate the comprehensive performance of the system under extreme events. Step 3: Introduce emergency resource scheduling strategies, including introducing a power generation vehicle scheduling model to plan the location deployment of power generation vehicles in extreme events, and using a maintenance personnel scheduling model to optimize the repair path of maintenance personnel in the case of power network damage. Step 4: When the power system is damaged, perform dynamic reconstruction of the distribution network topology. Step 5: Utilize the thermal inertia of buildings and maintain the temperature of buildings in the case of insufficient heating by optimizing the thermal management strategy. Step 6: Collaboratively optimize the resource allocation of the three energy systems of electricity, natural gas, and heating.

2. The method for interacting and enhancing the urban energy system based on the electrical-thermal interdependence according to claim 1, wherein In Step 1, establishing the mathematical model of the power grid subsystem includes: Use the LinDistFlow model to model the power flow of the power grid. This model linearizes the originally complex non-linear power flow equation by ignoring the minor losses on the line and assuming that the node voltage is close to the per-unit value. The way to establish the model is as follows: For node i and line ij, the power flow equation is as follows: Among them, P ij and Q ij respectively represent the active power and reactive power of line ij, and P i and Q i represent the active power and reactive power of node i, and R ij and X ij respectively represent the resistance and reactance of the line; Linearize the node voltage. In the LinDistFlow model, it is assumed that the voltage deviation is small, so the node voltage can be approximated as the per-unit value 1. Therefore, the linearization formula of the voltage drop is as follows: V j = V i - 2(R ij P ij + X ij Q ij ) Among them, V j and V i respectively represent the per-unit values of the voltages of nodes j and i; In the power subsystem, the flow of power is constrained by the LinDistFlow model to ensure the power supply and demand balance at each node, that is, at each node i: P in,i -P out,i =P load,i +P loss,i Q in,i -Q out,i = Q load,i +Q loss,i Among them, P in,i and Q in,i respectively represent the active power and reactive power flowing into node i, P out,i and Q out,i represent the active power and reactive power flowing out of node i, P load,i and Q load,i represent the load demand of node i; Set the voltage and current limits of each line and node. The constraint of the voltage deviation is as follows: V min ≤V i ≤V max where V min and V max represent the lower and upper voltage limits allowed in the system; According to the constructed mathematical model, optimize the power grid objective function. With the goal of minimizing the power system loss, determine the optimal operating state of the distribution network under extreme events by solving the linearized power flow equation. Its objective function is: Among them, F is the total power loss, and R ij represents the resistance of the line, and I ij represents the current passing through line ij.

3. The method for interaction and improvement of urban energy systems based on electrical-thermal interdependence according to claim 1, wherein In Step 1, establishing the mathematical model of the natural gas subsystem includes: The natural gas system includes multiple nodes and pipelines. Among them, the nodes represent gas sources, loads, or connection points, and the pipelines represent the transmission paths of natural gas. For each node i, there is gas inflow and outflow. Establish a node balance equation: Among them, q ij represents the natural gas flow from node i to node j, and d i represents the demand of node i; Based on the gas flow characteristics in the pipeline, as well as the friction loss and temperature transformation in the pipeline, establish a pipeline flow equation: where q ij is the flow rate from node i to node j; p i and p j are the pressures of nodes i and j respectively; K ij is the flow coefficient of the pipeline, which is related to parameters such as pipeline length and diameter; L ij is the pipeline length; based on the non-ideal characteristics of natural gas, Z ij is the compressibility factor; T ij is the pipeline temperature. The pressure of each node needs to meet the following constraint conditions to ensure the safe operation of the system: p min ≤ p i ≤ p max where p min and p max are the lowest and highest pressures allowed in the system, respectively; Then, the non-isothermal flow equation can be established and obtained: Among them, R is the gas constant; T is the gas temperature; f is the friction coefficient, related to the pipeline roughness; D is the pipeline diameter; According to the constructed mathematical model, with the goal of minimizing the pressure loss and energy consumption of the entire system, establish an optimization objective function: Among them, P represents the set of all pipelines in the system; λ is the penalty coefficient used to maintain the stability of the system pressure; p ref is the reference pressure value in the system.

4. The method for interaction and improvement of an urban energy system based on electrical-thermal interdependence according to any one of claims 1 to 3, characterized in that In Step 1, establishing the mathematical model of the heat pipeline subsystem includes: The heating system consists of a heat source, heat load, and heat pipe network. Establish the node and pipeline models of the heat pipe network. Among them, the nodes represent heat sources, user loads, or connection points, and the pipelines are used to transmit heat. For each node i, establish a heat balance equation: Among them, h ij represents the temperature flowing from node i to j; L ij is the heat loss of the pipeline; based on the non-linear characteristics of the pipeline, λ ij is the correction coefficient of the pipeline length; Based on the heat loss in the pipeline, establish a heat loss model for the hot pipeline: Among them, H ij is the heat conduction coefficient; T i and T j are the temperatures of node i and node j respectively; L ij is the heat loss of the pipeline; based on the non-linear characteristics of the pipeline, λ ij is the correction coefficient of the pipeline length; The temperature of each node needs to satisfy the following temperature constraint conditions to ensure the stability of the heating system: T min ≤T i ≤T max where T min and T max are the lowest and highest temperatures in the system, respectively; For each heat source node, the heating capacity is constrained by its maximum heating power: h i ≤H max,i +β i T i Among them, H max,i represents the maximum heat supply capacity of heat source i, and β i is a temperature-related adjustment coefficient; Use the nonlinear programming NLP or mixed-integer nonlinear programming MINLP method to solve the model of the heating system to ensure the effective allocation of heat resources in the event of extreme events; Simulate the system under different extreme scenarios, and analyze the temperature changes, heat flow distribution and energy consumption of the system to verify the reliability and stability of the system.

5. The method for interactive influence and improvement of an urban energy system based on electrical-thermal interdependence according to claim 1, wherein In step 2, use the urban distribution network as the core of the UES and closely couple it with other energy systems.

6. The method for interacting and enhancing the urban energy system based on the interdependence of electricity and heat according to claim 5, characterized in that, In step 2, aiming at the improvement of the distribution network, with the goal of minimizing the sum of weighted electricity, gas and heat load losses, and combining the interdependence and mutual influence relationships between subsystems, implement various measures to improve the resilience effect of the system at different stages; the objective function of this improvement method is: minF = minF DS + F GS + F HS Among them, the definitions of the loss functions of each subsystem are as follows: Among them, F DS , F GS , F HS are the weighted load loss values of the urban power distribution, natural gas pipeline system, and heat supply and return pipe network subsystem, respectively; are the weight coefficients of the power load i, gas load n, and heat load loss of the building n', respectively; are the loss values of the power load i, gas load n, and heat load of the room r, respectively; ρ i represents the reliability coefficient of node i under extreme conditions; η n is the reliability weight of the natural gas node; λ ij is the pressure difference penalty coefficient; κ h′ is the importance weight of the node; δ r is the penalty coefficient for heat energy storage utilization; is the heat supply of the energy storage unit at time t; T d is the scheduling period; B is the set of grid nodes; S res,n' is the set of all rooms in building n'; In terms of performance maintenance, the UES based on the interdependence of electricity-gas-heat includes the maintenance of the performance of three subsystems, namely, electricity load maintenance, gas load maintenance and heat load maintenance: Among them, R SPM is the system performance maintenance index; is the performance maintenance index of the urban distribution network DS; is the performance maintenance index of the natural gas pipeline subsystem GS; is the performance maintenance index of the heat supply and return pipeline subsystem HS; The coefficient 100 / T d is used to linearly map the index to the interval [0, 100], that is, the "percentage system" is adopted for evaluation, and the higher the score, the stronger the resilience; is the probability of the occurrence of the extreme event scenario s; is the electrical load of node i; is the flow rate of natural gas required for the gas load n; is the heat power supplied to the room r in the building n'; N is the total number of scenarios extracted; are the starting times of the damage stages of the electrical, gas, and heat systems respectively; In the resistance index, after an extreme event occurs, the load retention in DS, GS, and HS can reflect the resistance ability of each subsystem to extreme events, that is: Among them, R RSS is the system resistance index; are the corresponding indexes of DS, GS, and HS respectively; G n,s is the supply of natural gas storage facilities; H r,s is the additional heat support required when the heating system is under impact; In the recovery index, both the load recovery amount and the recovery speed need to be considered during the evaluation: Among them, R RCV is the system restoration index; are the restoration indices of DS, GS, and HS respectively; and are the restoration phases of DS, GS, and HS respectively; P i,r , G n,r , H r,t_r are the power restoration compensation amount, the natural gas restoration amount, and the heat restoration compensation amount respectively.

7. The method for interaction and improvement of an urban energy system based on electrical-thermal interdependence according to claim 6, characterized in that Based on the improved genetic algorithm-based improvement algorithm, maximize the recovery ability of the system by optimizing the resource allocation and scheduling strategy to enhance the overall resilience of the electricity-gas-heat multi-system in extreme events; among them, the improved genetic algorithm integrates a variety of optimization means, including adaptive mutation rate, elite retention strategy and chaotic search, which specifically include the following steps: Step a, optimization objective. After an extreme event occurs, optimize the recovery indexes of the power, natural gas and heating systems through the improved genetic algorithm to enhance the overall resilience. The objective function is: maxF = α1R SPM + α2R RSS + α3R RCV Among them, R SPM , R RSS , R RCV are the performance evaluation index, the resistance index, and the recovery index respectively, and α1, α2, and α3 are the weight coefficients; Step b, selection and crossover operations. Randomly select a certain number of individuals from the population, select the individual with the highest fitness through competitive operations, and ensure that several individuals with the highest fitness in the current generation are directly retained in the next generation to ensure the retention of the optimal genes; The selected individuals are formed into a new parental generation, and gene exchange is performed on the parental individuals using double-point crossover; by randomly selecting two crossover points, the gene segments between the parents are exchanged to generate new offspring; the mutation probability P m is dynamically adjusted according to the fitness value of the individual and the generation number of the population. The purpose of mutation is to increase the diversity of the population and prevent it from falling into a local optimum. The formula is as follows: Among them, the mutation operation can randomly select some gene positions in the individual for adjustment; Step c, search operation. After each generation of genetic operations, introduce chaotic search to perform chaotic reset on individuals with poor fitness to prevent falling into local optima; specifically, use the Logistic map to generate a chaotic sequence: x t+1 = μx t (1 - x t ), 0 < x t < 1 Among them, μ is the chaotic mapping parameter, which is used to generate chaotic behavior and enhance the diversity of the population; Step d, fitness evaluation and iteration. For the new population after mutation and chaotic reset, calculate the fitness of each individual; among them, the evaluation of the fitness value is based on the comprehensive score of performance, resistance and resilience indicators. When the maximum number of generations N max or the population fitness tends to be stable, stop the iteration and output the optimal solution.

8. The method for interaction and improvement of urban energy systems based on electrical-thermal interdependence according to claim 7, characterized in that The output optimal individual recovery strategy includes the resource allocation of the power, natural gas and heating systems, energy storage scheduling, and node recovery priority.

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