Method and system for improving toughness of power distribution network based on optimal scheduling of integrated energy system

Abstract

The invention discloses a method and a system for improving the toughness of a power distribution network based on optimal scheduling of a comprehensive energy system. The maximum sum of the charge state of electric energy storage and the heat storage state of heat energy storage in a pre-disaster preparation stage is taken as an optimization target; iteratively solving the thermal power balance model and the constraint condition thereof, the gas flow balance model and the constraint condition thereof, and the electric power balance model and the constraint condition thereof to obtain predicted values of output power of various power supplies at different moments before a disaster; the method comprises the following steps of: iteratively solving a thermal power balance model and a constraint condition thereof, a gas flow balance model and a constraint condition thereof, and an electric power balance model and a constraint condition thereof by taking the maximum power sum of loads corresponding to nodes in a power distribution network in a post-disaster defense stage as an optimization target; obtaining predicted values of output power of various power supplies at different moments after the disaster, predicted values of the electric energy storage charge state at different moments after the disaster and predicted values of the heat energy storage state at different moments after the disaster; and carrying out comprehensive energy system optimization scheduling to improve the toughness of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of safe planning and operation of power systems, and in particular relates to a method and system for achieving sustainable recovery by improving the resilience of a distribution network based on optimized scheduling of an integrated energy system. Background Art

[0002] When facing extreme natural disasters, urban distribution networks may suffer serious damage, resulting in power outages and service interruptions. Faulty equipment is difficult to restore in time, highlighting the shortcomings of insufficient resilience of urban distribution networks under extreme natural disasters. Integrated Energy System (IES) is a system that organically combines and optimizes multiple energy forms such as electricity, heat, and gas. By comprehensively utilizing various energy resources, IES can improve energy efficiency, reduce energy consumption and emissions, and provide a variety of energy supply channels for distribution networks, which plays an important role in restoring power supply after disasters.

[0003] The existing literature on improving the resilience of distribution networks under extreme disasters mainly includes three stages: pre-disaster prevention and preparation, disaster adaptation and response, and post-disaster rapid recovery. In the pre-disaster prevention and preparation stage, the distribution of distributed power sources in the distribution network is planned, and the reasonable scheduling and site selection of EVs and mobile vehicle-mounted energy storage equipment are used to reduce load losses and improve the recovery capacity of the distribution network. Some literatures strengthen lines and towers to reduce the probability of transmission line failures. In the disaster adaptation and response stage, flexible resources are combined to reconstruct the topology of the distribution network and optimize the flow to form multi-energy islands to improve the resilience of the disaster stage. In the post-disaster rapid recovery stage, most literature studies combine the transportation network to dynamically dispatch emergency resources such as mobile energy storage and maintenance teams, comprehensively dispatch various flexible resources, and reconstruct the network to divide microgrid islands to achieve rapid recovery of post-disaster loads and maximize load power supply. Existing technical methods do not consider the important supporting role of coordinated scheduling of the electricity-gas-heat integrated energy system in assisting the recovery of urban distribution network power supply. There are complex coupling relationships between various energy resources in IES, and how to effectively coordinate and optimize the scheduling of these resources has become a key issue. Formulating a reasonable and effective integrated energy system optimization and dispatching strategy will greatly enhance the resilience and sustainable recovery capabilities of the distribution network under extreme disasters. Summary of the invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method and system for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system, which comprehensively utilizes the coordinated optimized scheduling of electricity, gas and heat integrated energy to respond to distribution network failures caused by extreme disasters and emergencies, achieve rapid and efficient power supply restoration of the distribution network after a disaster, reduce the losses caused by extreme disasters, and improve the resilience level and sustainable recovery capacity of the distribution network.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a distribution network resilience improvement method based on the optimal dispatch of an integrated energy system. Based on the time of disaster occurrence, the distribution network resilience improvement process is divided into a pre-disaster preparation stage and a post-disaster defense stage, including:

[0007] Obtain the power and power constraints of each distributed generation unit in the distribution network;

[0008] Based on the power and power constraints of distributed power units, the thermal power balance model, gas flow balance model and electric power balance model of the integrated energy system are established respectively, and the constraints of the thermal power balance model, the gas flow balance model and the electric power balance model are established respectively;

[0009] Taking the maximum sum of the state of charge of the electric energy storage and the state of heat storage of the thermal energy storage in the pre-disaster preparation stage as the optimization goal, the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints are iteratively solved to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster.

[0010] Taking the maximum sum of the powers of the corresponding loads of each node in the distribution network in the post-disaster defense stage as the optimization goal, the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints are iteratively solved to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times after the disaster, the predicted values ​​of the state of charge of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster;

[0011] Based on the predicted values ​​of output power of various power sources in the integrated energy system at different times before the disaster, the predicted values ​​of output power of various power sources at different times after the disaster, the predicted values ​​of charge state of electric energy storage at different times after the disaster, and the predicted values ​​of heat storage state of thermal energy storage at different times after the disaster, the integrated energy system is optimized and dispatched as a measure to enhance the resilience of the distribution network.

[0012] Preferably, taking node i as the object, the power constraint condition of the distributed power generation unit satisfies the following relationship:

[0013]

[0014]

[0015] In the formula, are respectively the active power and reactive power of the distributed generation unit corresponding to node i at time t; and are respectively the upper limit of active power and the upper limit of reactive power of the distributed generation unit corresponding to node i at time t; is the lower limit of the active power of the distributed generation unit corresponding to node i at time t; γ i The value of is determined by the type of distributed generation unit. Indicates that node i corresponds to a natural gas-based distributed generation unit. Indicates that node i corresponds to a dual-fuel distributed power generation unit; and are the upper and lower limits of the power factor of the distributed generation unit corresponding to node i respectively; is a 0-1 variable, When it is 1, the upper-level natural gas grid is in a normal supply state at time t. When it is 0, the upper-level natural gas grid is in an abnormal supply state at time t.

[0016] Preferably, taking node i as the object, the thermal power balance model satisfies the following relationship:

[0017]

[0018] In the formula, is the active power of the heat load corresponding to node i at time t; The heat storage power and heat release power of the thermal energy storage corresponding to node i at time t respectively; η g-h is the gas-heat conversion efficiency in the combined heat and power unit, η g-e is the gas-to-electricity conversion efficiency of the distributed power generation unit; η o-h is the oil-heat conversion efficiency in the combined heat and power unit, η o-e The oil-to-electricity conversion efficiency of dual-fuel distributed power units; is the active power of the dual-fuel distributed generation unit corresponding to node i at time t; is the thermal power of the natural gas boiler corresponding to node i at time t, is the thermal power of the dual-fuel boiler corresponding to node i at time t.

[0019] Preferably, taking node i as the object, the air flow balance model satisfies the following relationship:

[0020]

[0021] In the formula, is the flow rate of the natural gas source corresponding to node i at time t; is the sum of the active powers of the natural gas boiler and the dual-fuel boiler corresponding to node i at time t, is the power of the gas load corresponding to node i at time t, Cg-e is the natural gas consumption rate of the distributed generation unit, C g-h is the natural gas consumption rate of the boiler; is the natural gas flow from node i to node j at time t, is the natural gas flow from node k to node i at time t; N GN A collection of nodes in the natural gas grid.

[0022] Preferably, taking node i as the object, the electric power balance model satisfies the following relationship:

[0023]

[0024] Where P ij,t and Q ij,t are the active power and reactive power flowing from node i to node j at time t, respectively, where δ(i) is the set of nodes from which the power flow comes from node i; P ji,t and Q ji,t are the active power and reactive power flowing from node j to node i at time t, respectively, where γ(i) is the set of nodes whose power flows to node i; R ji , X ji are the resistance and reactance of the transmission line from node j to node i, respectively; is the square value of the current flowing from node j to node i at time t; are the active power and reactive power of the electric load corresponding to node i at time t respectively.

[0025] Preferably, the constraints of the thermal power balance model include: constraints of heat load, constraints of boiler thermal power, and constraints of thermal energy storage;

[0026] Taking node i as the object, the constraint condition of heat load satisfies the following relationship:

[0027]

[0028] In the formula, is the power upper limit of the heat load corresponding to node i;

[0029] Taking node i as the object, the thermal power constraint of the boiler satisfies the following relationship:

[0030]

[0031]

[0032] In the formula, is the upper limit of thermal power of the natural gas boiler corresponding to node i, is the lower limit of thermal power of the natural gas boiler corresponding to node i; is the upper limit of thermal power of the dual-fuel boiler corresponding to node i, is the lower limit of thermal power of the dual-fuel boiler corresponding to node i.

[0033] Taking node i as the object, the constraints of thermal energy storage satisfy the following relationship:

[0034]

[0035] In the formula, is the heat storage state of the thermal energy storage corresponding to node i at time t; η TES The efficiency of storing or releasing heat for thermal energy storage; is the capacity reference value of the thermal energy storage corresponding to node i; Δt is the length of the time period for the thermal energy storage to store or release heat; and are the upper and lower limits of the thermal energy storage state corresponding to the node i, respectively; is the heat storage state of the thermal energy storage corresponding to node i at the initial time t0; is the initial value of the heat storage state of the thermal energy storage corresponding to node i; and All are 0-1 variables. is 1 and When it is 0, the thermal energy storage corresponding to node i is in the thermal storage state at time t. is 1 and When it is 0, the thermal energy storage corresponding to node i is in the heat release state at time t; and are the upper limit of heat storage power and heat release power of the thermal energy storage corresponding to node i respectively.

[0036] Preferably, the constraints of the gas flow balance model include: gas source flow constraint, flow pressure constraint, pressure constraint, and flow constraint;

[0037] Taking node i as the object, the gas source flow constraint condition satisfies the following relationship:

[0038]

[0039] In the formula, and are the upper and lower output limits of the natural gas source corresponding to node i respectively;

[0040] Taking node i as the object, the flow pressure constraint condition satisfies the following relationship:

[0041]

[0042] In the formula are the air pressure at node i and node j at time t respectively; When , the flow direction of natural gas in the pipeline is from node i to node j. The opposite is true; C p It is the pipeline constant in the Weymouth equation that describes the relationship between natural gas pressure and flow. Its value depends on the length, diameter and absolute roughness of the natural gas transmission pipeline. p is the pipeline pressure set, Indicates that the pipeline pressure is normal, and T is the pipeline pressure monitoring time;

[0043] Taking node i as the object, the pressure constraint condition satisfies the following relationship:

[0044]

[0045] In the formula, and are the upper and lower limits of the air pressure at node j respectively;

[0046] Taking node i as the object, the flow constraint condition satisfies the following relationship:

[0047]

[0048] In the formula, and are the upper and lower limits of the flow rate in the pipeline from node i to node j respectively.

[0049] Preferably, the constraints of the electric power balance model include power constraints, voltage constraints, power flow safety constraints, distributed power generation unit power constraints, electric energy storage operation constraints, and load constraints;

[0050] Taking node i as the object, the power constraint condition satisfies the following relationship:

[0051]

[0052] In the formula, are the square values ​​of the voltages at nodes i and j at time t; R ij , X ij are the resistance and reactance of the transmission line from node i to node j respectively; M0 is a very large positive real number; α ij，t is a 0-1 variable, α ij，t =1 means that the transmission line from node i to node j is connected at time t, α ij，t =0, then vice versa;

[0053] Taking node i as the object, the voltage constraint condition satisfies the following relationship:

[0054]

[0055] In the formula, are the lower and upper limits of the square value of the voltage at node i, respectively;

[0056] Specifically, taking node i as the object, the current constraint condition satisfies the following relationship:

[0057]

[0058] In the formula, is the upper limit of the square value of the current flowing from node i to node j;

[0059] Taking node i as the object, the power flow safety constraint condition satisfies the following relationship:

[0060]

[0061] Taking node i as the object, the operation constraints of electric energy storage satisfy the following relationship:

[0062]

[0063] In the formula, and are the discharge power and charging power of the energy storage power station corresponding to node i at time t; and All are 0-1 variables. and The energy storage station corresponding to time node i discharges at time t. and The energy storage station corresponding to time node i is charged at time t; and are the upper limit of discharge power and the upper limit of charge power of the energy storage power station corresponding to node i at time t; is the charge state of the energy storage power station corresponding to node i at time t; and are the charging efficiency and discharging efficiency of the energy storage power station corresponding to node i respectively; Δt is the charging period and discharging period of the energy storage power station; and are the upper and lower limits of the state of charge of the energy storage power station corresponding to node i respectively;

[0064] Taking node i as the object, the load constraint condition satisfies the following relationship:

[0065]

[0066]

[0067] In the formula, and are respectively the upper limit of active power and the upper limit of reactive power of the load corresponding to node i at time t.

[0068] Preferably, the first optimization goal is to maximize the sum of the state of charge of the electric energy storage and the state of heat storage of the thermal energy storage in the pre-disaster preparation stage, satisfying the following relationship:

[0069]

[0070] Where maxF1 is the first optimization target; N T0 It is the period collection of the pre-disaster preparation stage; N ESS N is the energy storage power station collection in the distribution network; TES It is a collection of thermal energy storage stations in the distribution network; is the charge state of the energy storage power station corresponding to node i at time t; is the heat storage state of the thermal energy storage corresponding to node i at time t.

[0071] Preferably, the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster include: the predicted values ​​of the output power of each distributed power source unit at different times before the disaster, and the predicted values ​​of the output power of the boiler at different times before the disaster; and the predicted values ​​of the output power of various power sources at different times before the disaster will not exceed the corresponding output power upper limit.

[0072] Preferably, the second optimization objective is to maximize the sum of the powers of the loads corresponding to the nodes in the distribution network during the post-disaster defense phase, satisfying the following relationship:

[0073]

[0074] Where maxF2 is the second optimization objective; N T It is a collection of time periods throughout the emergency recovery cycle, both before and after the disaster; and are the electric load weight and thermal load weight corresponding to node i respectively; N E is the set of electric load nodes; N H is the heat load node set; is the active power of the electric load corresponding to node i at time t, is the active power of the heat load corresponding to node i at time t.

[0075] Preferably, the predicted values ​​of output power of various power sources in the integrated energy system at different times after a disaster include: predicted values ​​of output power of each distributed power source unit at different times after a disaster, and predicted values ​​of output power of boilers at different times after a disaster.

[0076] The present invention also proposes a distribution network resilience improvement system based on comprehensive energy system optimization scheduling, comprising:

[0077] Acquisition module, model and constraint condition establishment module, pre-disaster prediction module, post-disaster prediction module, integrated energy system optimization and scheduling module;

[0078] The acquisition module is used to obtain the power and power constraint conditions of each distributed power unit in the distribution network;

[0079] The model and constraint condition establishment module is used to establish the thermal power balance model, gas flow balance model and electric power balance model of the integrated energy system based on the power and power constraint conditions of the distributed power generation units; the constraint conditions of the thermal power balance model, the constraint conditions of the gas flow balance model and the constraint conditions of the electric power balance model are established respectively;

[0080] The pre-disaster prediction module is used to iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints, with the maximum sum of the charge state of the electric energy storage and the heat storage state of the thermal energy storage in the pre-disaster preparation stage as the optimization target, to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster;

[0081] The post-disaster prediction module is used to iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints, with the maximum sum of the powers of the corresponding loads of each node in the distribution network during the post-disaster defense phase as the optimization goal, to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times after the disaster, the predicted values ​​of the state of charge of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster;

[0082] The integrated energy system optimization and scheduling module is used to optimize the scheduling of the integrated energy system based on the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster, the predicted values ​​of the output power of various power sources at different times after the disaster, the predicted values ​​of the charge state of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster, as a measure to improve the resilience of the distribution network.

[0083] A terminal includes a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0084] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0085] The beneficial effect of the present invention is that, compared with the prior art, it at least includes that in the method proposed by the present invention, a comprehensive energy system coupling model of the thermal network, natural gas network and power network is established to construct two objective functions of maximizing the active power of the load and maximizing the electrical and thermal energy storage states in the pre-disaster period, and a multi-objective improved particle swarm algorithm is used to solve the mixed integer programming model to obtain a distribution network resilience improvement strategy based on the optimal scheduling of the comprehensive energy system, which fully utilizes gas and thermal resources for energy storage preparation before extreme disasters occur, and significantly improves the power supply recovery capability of the distribution network after extreme disasters occur.

[0086] Through the coordinated and optimized scheduling of the integrated energy system, the recovery capability and operational stability of the distribution network can be significantly improved in the face of emergencies such as natural disasters and equipment failures.

[0087] By coordinating the use of various energy forms such as electricity, heat, and gas, we can achieve optimal allocation and efficient utilization of energy resources and reduce energy consumption and operating costs.

[0088] By coordinating the use of multiple clean and renewable energy sources, it helps to reduce dependence on traditional fossil energy and reduce overall energy consumption and carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a flow chart of a method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system proposed by the present invention;

[0090] Figure 2 is a structural diagram of an improved 33-node distribution network in an embodiment of the present invention;

[0091] Figure 3 is a diagram of a 14-node natural gas network structure in an embodiment of the present invention;

[0092] Figure 4 It is the 24-hour active power forecast curve of important loads and non-important loads;

[0093] Figure 5 is a total energy storage state diagram of the electric-thermal energy storage device at each moment during the entire emergency defense phase in an embodiment of the present invention;

[0094] Figure 6 It is an active power output diagram of the distributed generation (DG) units, combined heat and power units (CHP) and boilers at all times during the entire emergency defense stage in an embodiment of the present invention. DETAILED DESCRIPTION

[0095] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0096] Provide early warning of the occurrence of extreme disasters, and coordinate and optimize the dispatching of the electricity-gas-heat integrated energy system to charge the electricity-heat energy storage in the pre-disaster stage; construct two objective functions of maximizing the weighted load active power and maximizing the pre-disaster preparedness index, and use a multi-objective optimization algorithm based on the improved particle swarm algorithm to solve the optimization model, and obtain the coordinated optimization dispatching strategy of the integrated energy system for the entire emergency defense stage of pre-disaster preparation and post-disaster recovery, to ensure the sustainable restoration of power supply to important loads, thereby improving the resilience and sustainable recovery of the distribution network.

[0097] The present invention proposes a distribution network resilience improvement method based on the optimal scheduling of an integrated energy system. Based on the time of disaster occurrence, the distribution network resilience improvement process is divided into a pre-disaster preparation stage and a post-disaster defense stage. Figure 1 As shown, the method comprises the following steps:

[0098] Step 1: Obtain the power and power constraints of each distributed generation unit in the distribution network.

[0099] Specifically, taking node i as the object, the power constraint of the distributed generation unit satisfies the following relationship:

[0100]

[0101]

[0102] In the formula, are respectively the active power and reactive power of the distributed generation unit corresponding to node i at time t; and are respectively the upper limit of active power and the upper limit of reactive power of the distributed generation unit corresponding to node i at time t; is the lower limit of the active power of the distributed generation unit corresponding to node i at time t; γ i The value of is determined by the type of distributed generation unit. Indicates that node i corresponds to a natural gas-based distributed generation unit. Indicates that node i corresponds to a dual-fuel distributed power generation unit; and are the upper and lower limits of the power factor of the distributed generation unit corresponding to node i respectively; is a 0-1 variable, When it is 1, the upper-level natural gas grid is in a normal supply state at time t. When it is 0, the upper-level natural gas grid is in an abnormal supply state at time t.

[0103] Step 2: Based on the power and power constraints of the distributed power units, a thermal power balance model, a gas flow balance model and an electric power balance model of the integrated energy system are established respectively;

[0104] Specifically, taking node i as the object, the thermal power balance model satisfies the following relationship:

[0105]

[0106] In the formula, is the active power of the heat load corresponding to node i at time t; The heat storage power and heat release power of the thermal energy storage corresponding to node i at time t respectively; is a 0-1 variable, When it is 1, the upper-level natural gas grid is in a normal supply state at time t. When η is 0, the upper-level natural gas grid is in an abnormal supply state at time t; g-h is the gas-heat conversion efficiency in the combined heat and power unit, η g-e is the gas-to-electricity conversion efficiency of the distributed power generation unit; is the active power of the distributed power generation unit corresponding to the node i at time t. In a non-limiting preferred embodiment, the distributed power generation unit includes but is not limited to a dual-fuel distributed power generation unit and a natural gas distributed power generation unit; η o-h is the oil-heat conversion efficiency in the combined heat and power unit, η o-e The oil-to-electricity conversion efficiency of dual-fuel distributed power units; is the active power of the dual-fuel distributed generation unit corresponding to node i at time t; is the thermal power of the natural gas boiler corresponding to node i at time t, is the thermal power of the dual-fuel boiler corresponding to node i at time t.

[0107] The natural gas network includes gas sources, compressors, natural gas pipelines and natural gas loads. Natural gas loads include distributed power units, boilers, residential loads and industrial loads. Since the natural gas network is connected to the distribution network and is small in scale, it does not contain compressors. The Weymouth equation is used to describe the relationship between gas pressure and flow, and a natural gas network constraint model is established.

[0108] Specifically, taking node i as the object, the air flow balance model satisfies the following relationship:

[0109]

[0110] In the formula, is the flow rate of the natural gas source corresponding to node i at time t; is the active power of the distributed generation unit corresponding to node i at time t; is the sum of the active powers of the natural gas boiler and the dual-fuel boiler corresponding to node i at time t, is the power of the gas load corresponding to node i at time t, C g-e is the natural gas consumption rate of the distributed generation unit, C g-h is the natural gas consumption rate of the boiler; is the natural gas flow from node i to node j at time t, is the natural gas flow from node k to node i at time t; N GN is a set of nodes of the natural gas grid;

[0111] Among them, the active power of the distributed power generation unit corresponding to node i at time t is variable Thermal power of the natural gas boiler corresponding to node i at time t Thermal power of the dual-fuel boiler corresponding to node i at time t It is the associated variable of the thermal power balance model and the gas flow balance model, which fully reflects that in the method proposed in the present invention, due to the energy storage preparation using gas thermal resources before the occurrence of extreme disasters, the power supply recovery capacity of the distribution network is significantly improved after the occurrence of extreme disasters.

[0112] In this embodiment, the linearized DistFlow model is used to constrain the power flow of the distribution network, and the Big-M method is used to relax the node voltage equation. Taking node i as the object, the electric power balance model satisfies the following relationship:

[0113]

[0114] Where P ij,t and Q ij,t are the active power and reactive power flowing from node i to node j at time t, respectively, where δ(i) is the set of nodes from which the power flow comes from node i; P ji,t and Q ji,t are the active power and reactive power flowing from node j to node i at time t, respectively, where γ(i) is the set of nodes whose power flows to node i; R ji , X ji are the resistance and reactance of the transmission line from node j to node i, respectively; is the square value of the current flowing from node j to node i at time t; are respectively the active power and reactive power of the distributed generation unit corresponding to node i at time t; are respectively the active power and reactive power of the electric load corresponding to node i at time t;

[0115] Among them, the active power of the distributed power generation unit corresponding to node i at time t is It is the associated variable of the thermal power balance model and the electric power balance model, which fully reflects that in the method proposed in the present invention, due to the energy storage preparation using gas and thermal resources before the occurrence of extreme disasters, the power supply recovery capacity of the distribution network is significantly improved after the occurrence of extreme disasters.

[0116] Step 3, respectively establish the constraints of the thermal power balance model, the air flow balance model and the electrical power balance model.

[0117] Specifically, the constraints of the thermal power balance model include: thermal load constraints, boiler thermal power constraints, and thermal energy storage constraints.

[0118] Specifically, taking node i as the object, the constraint condition of the heat load satisfies the following relationship:

[0119]

[0120] In the formula, is the power upper limit of the heat load corresponding to node i;

[0121] Specifically, taking node i as the object, the thermal power constraint of the boiler satisfies the following relationship:

[0122]

[0123] In the formula, is the upper limit of thermal power of the natural gas boiler corresponding to node i, is the lower limit of thermal power of the natural gas boiler corresponding to node i; is the upper limit of thermal power of the dual-fuel boiler corresponding to node i, is the lower limit of thermal power of the dual-fuel boiler corresponding to node i.

[0124] Specifically, taking node i as the object, the constraint condition of thermal energy storage satisfies the following relationship:

[0125]

[0126] In the formula, is the heat storage state of the thermal energy storage corresponding to node i at time t; η TES The efficiency of storing or releasing heat for thermal energy storage; is the capacity reference value of the thermal energy storage corresponding to node i; Δt is the length of the time period for the thermal energy storage to store or release heat; and are the upper and lower limits of the thermal energy storage state corresponding to the node i, respectively; is the heat storage state of the thermal energy storage corresponding to node i at the initial time t0; is the initial value of the heat storage state of the thermal energy storage corresponding to node i; and All are 0-1 variables. is 1 and When it is 0, the thermal energy storage corresponding to node i is in the thermal storage state at time t. is 1 and When it is 0, the thermal energy storage corresponding to node i is in the heat release state at time t; and are the upper limit of heat storage power and heat release power of the thermal energy storage corresponding to node i respectively.

[0127] The constraints of the gas flow balance model include: gas source flow constraint, flow pressure constraint, pressure constraint, and flow constraint.

[0128] Specifically, taking node i as the object, the gas source flow constraint condition satisfies the following relationship:

[0129]

[0130] In the formula, and are the upper and lower output limits of the natural gas source corresponding to node i respectively;

[0131] Specifically, taking node i as the object, the flow pressure constraint condition satisfies the following relationship:

[0132]

[0133] In the formula, are the air pressure at node i and node j at time t respectively; When , the flow direction of natural gas in the pipeline is from node i to node j. The opposite is true; C p It is the pipeline constant in the Weymouth equation that describes the relationship between natural gas pressure and flow rate. Its value depends on the length, diameter and absolute roughness of the natural gas transmission pipeline. p is the pipeline pressure set, Indicates that the pipeline pressure is normal, and T is the pipeline pressure monitoring time;

[0134] Specifically, taking node i as the object, the pressure constraint condition satisfies the following relationship:

[0135]

[0136] In the formula, and are the upper and lower limits of the air pressure at node j respectively;

[0137] Specifically, taking node i as the object, the flow constraint condition satisfies the following relationship:

[0138]

[0139] In the formula, and are the upper and lower limits of the flow rate in the pipeline from node i to node j respectively.

[0140] The constraints of the electric power balance model include power constraints, voltage constraints, power flow safety constraints, distributed power generation unit power constraints, electric energy storage operation constraints, and load constraints;

[0141] Specifically, taking node i as the object, the power constraint condition satisfies the following relationship:

[0142]

[0143] In the formula, are the square values ​​of the voltages at nodes i and j at time t; R ij , X ij are the resistance and reactance of the transmission line from node i to node j respectively; M0 is a very large positive real number; α ij，t is a 0-1 variable, α ij，t =1 means that the transmission line from node i to node j is connected at time t, α ij，t =0, then vice versa;

[0144] Specifically, taking node i as the object, the voltage constraint condition satisfies the following relationship:

[0145]

[0146] In the formula, are the lower and upper limits of the square value of the voltage at node i, respectively;

[0147] Specifically, taking node i as the object, the current constraint condition satisfies the following relationship:

[0148]

[0149] In the formula, is the upper limit of the square value of the current flowing from node i to node j;

[0150] Specifically, taking node i as the object, the power flow safety constraint condition satisfies the following relationship:

[0151]

[0152] Specifically, taking node i as the object, the power constraint of the distributed generation unit satisfies the following relationship:

[0153]

[0154] In the formula, and are respectively the upper limit of active power and the upper limit of reactive power of the distributed generation unit corresponding to node i at time t; is the lower limit of the active power of the distributed generation unit corresponding to node i at time t; γ i The value of is determined by the type of distributed generation unit. Indicates that node i corresponds to a natural gas-based distributed generation unit. Indicates that node i corresponds to a dual-fuel distributed power generation unit; and are the upper and lower limits of the power factor of the distributed generation unit corresponding to node i respectively;

[0155] Based on the output constraints of distributed generation units, constraints are imposed on the thermal power balance model, gas flow balance model and electric power balance model.

[0156] Specifically, taking node i as the object, the electric energy storage operation constraint condition satisfies the following relationship:

[0157]

[0158] In the formula, and are the discharge power and charging power of the energy storage power station corresponding to node i at time t respectively; and All are 0-1 variables. and The energy storage station corresponding to time node i discharges at time t. and The energy storage station corresponding to time node i is charged at time t; and are the upper limit of discharge power and the upper limit of charge power of the energy storage power station corresponding to node i at time t; is the charge state of the energy storage power station corresponding to node i at time t; and are the charging efficiency and discharging efficiency of the energy storage power station corresponding to node i respectively; Δt is the charging period and discharging period of the energy storage power station; and They are respectively the upper and lower limits of the state of charge of the energy storage power station corresponding to node i.

[0159] Specifically, taking node i as the object, the load constraint condition satisfies the following relationship:

[0160]

[0161] In the formula, and are respectively the upper limit of active power and the upper limit of reactive power of the load corresponding to node i at time t.

[0162] Step 4, taking the maximum sum of the charge state of the electric energy storage and the heat storage state of the thermal energy storage in the pre-disaster preparation stage as the optimization goal, iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster.

[0163] The first optimization goal is to maximize the sum of the state of charge of the electric energy storage and the state of heat storage of the thermal energy storage in the pre-disaster preparation stage, satisfying the following relationship:

[0164]

[0165] Where maxF1 is the first optimization target; N T0 It is the period collection of the pre-disaster preparation stage; N ESS N is the energy storage power station collection in the distribution network; TES It is a collection of thermal energy storage stations in the distribution network.

[0166] The predicted values ​​of output power of various power sources in the integrated energy system at different times before the disaster include, but are not limited to: the predicted values ​​of output power of each distributed power source unit at different times before the disaster, and the predicted values ​​of output power of boilers at different times before the disaster; and the predicted values ​​of output power of various power sources at different times before the disaster will not exceed the corresponding output power upper limit.

[0167] Step 5, taking the maximum sum of the powers of the corresponding loads of each node in the distribution network in the post-disaster defense stage as the optimization goal, iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times after the disaster, the predicted values ​​of the charge state of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster.

[0168] The second optimization goal is to maximize the sum of the power of the corresponding loads of each node in the distribution network during the post-disaster defense phase, satisfying the following relationship:

[0169]

[0170] Where maxF2 is the second optimization objective; N T It is a collection of time periods throughout the emergency recovery cycle, both before and after the disaster; and are the electric load weight and thermal load weight corresponding to node i respectively; N E is the set of electric load nodes; N H is the heat load node set; is the active power of the electric load corresponding to node i at time t, is the active power of the heat load corresponding to node i at time t.

[0171] In a non-limiting preferred embodiment, the electrical load weight and the thermal load weight are determined according to the unit load reduction cost corresponding to the node.

[0172] The predicted values ​​of output power of various power sources in the integrated energy system at different times after the disaster, including: the predicted values ​​of output power of each distributed power unit at different times after the disaster, and the predicted values ​​of output power of boilers at different times after the disaster.

[0173] Before an extreme disaster occurs, the integrated energy system optimizes the dispatch of electric, gas and thermal energy to charge electric and thermal energy storage, so as to improve the energy storage preparation for supplying important electric and thermal loads after the disaster. At the same time, considering the process of using gas and thermal resources to prepare for charging energy storage equipment before the disaster, and the cost of load power outages when the upper power grid and natural gas grid are shut down in the post-disaster stage, two objective functions are constructed, namely maximizing the pre-disaster preparation index and maximizing the active output of the load; considering minimizing the total load reduction power during the entire emergency cycle and maximizing the preparation index of electric and thermal energy storage in the pre-disaster stage, in a non-restrictive preferred embodiment, the weighted ideal point method is combined with the commercial solver Gurobi to iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints to obtain the required data.

[0174] In the embodiment, the ideal point method and the commercial solver Gurobi are used to solve the original multi-objective optimization problem. The specific solution steps are as follows:

[0175] First, the single-objective optimization problem based on the first optimization target F1 and the single-objective optimization problem based on the second optimization target F2 are solved respectively, and the optimal values ​​of the single-objective optimization problems of the first optimization target and the second optimization target are obtained respectively, satisfying the following relationship:

[0176]

[0177] Where x is the decision variable of the original multi-objective optimization problem; Ω is the constraint condition of the original multi-objective optimization problem; and They are the optimal values ​​based on the single-objective optimization problems of F1 and F2, i.e., the ideal points of the original multi-objective optimization problems;

[0178] The next step is to transform the original multi-objective optimization problem into a single-objective optimization problem with the weighted distance between F1 and F2 and the ideal point as the optimization target, satisfying the following relationship:

[0179]

[0180] 0<ω1<1, 0<ω2<1, ω1+ω2=1

[0181] Where ω1 and ω2 are the weight coefficients of the squared distances from F1 and F2 to the ideal point, respectively; D is the weighted distance function from the first optimization objective F1 and the second optimization objective F2 to the ideal point of the original multi-objective optimization problem;

[0182] Multiple groups of ω1 and ω2 are obtained by uniformly dividing the interval [0, 1] and multiple groups of weighted distance functions D. The single-objective optimization problem with D as the optimization target is solved in sequence, and the solution x is near the ideal point. The Pareto dominance judgment is used to obtain the Pareto dominance set;

[0183] Finally, a Pareto optimal solution is selected from the obtained Pareto dominating set as the distribution network resilience improvement strategy. The above single-objective optimization models with Ω as the constraint condition are all mixed integer second-order cone programming problems, which can be solved using the commercial solver Gurobi.

[0184] Step 6: Based on the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster, the predicted values ​​of the output power of various power sources at different times after the disaster, the predicted values ​​of the charge state of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster, optimize the scheduling of the integrated energy system as a measure to improve the resilience of the distribution network.

[0185] In this embodiment, the Figure 2 The improved IEEE 33 distribution network shown is Figure 3 The 14-node natural gas network shown is simulated and analyzed.

[0186] The improved IEEE 33 distribution network includes 7 distributed generation units (such as Figure 2 The specific parameters are shown in Table 1), 3 energy storage power stations (such as Figure 4 The specific parameters are shown in Table 2), 3 charging piles (such as Figure 2 (shown as diamond in the middle).

[0187] Table 1 Parameters of distributed power generation units

[0188]

[0189] Table 2 Parameters of energy storage station (ESS)

[0190]

[0191] The importance parameters of distribution network load are shown in Table 3:

[0192] Table 3 List of distribution network load importance

[0193]

[0194]

[0195] The unit load reduction cost of important loads is 10 yuan / kW, and that of non-important loads is 1 yuan / kW. The 24-hour active power forecast curves of important loads and non-important loads are as follows: Figure 4 shown.

[0196] The six distributed generation units DG1, DG2, DG3, DG4, DG5, and DG6 in the improved IEEE 33 distribution network are connected to the 14-node natural gas network, with node locations of 3, 7, 10, 4, 5, and 11. The natural gas pressure of each node in the 14-node natural gas network is kept constant at 4.13 bar. The gas loads of natural gas nodes 2, 8, 10, and 14 are constant, which are 25 kfc / h, 15 kfc / h, 10 kfc / h, and 10 kfc / h, respectively. The upper and lower limits of the gas pressure of the natural gas network nodes are 4.13 bar and 3.45 bar, respectively.

[0197] In the embodiment, it is defined that there are five hybrid nodes HN1, HN2, HN3, HN4, and HN5 in the electric-gas network. The hybrid nodes include not only the grid nodes and the gas grid nodes, but also the heat load, boiler, thermal energy storage, and CHP unit. The locations of the five hybrid nodes are shown in Table 4. Among them, the relevant parameters of the thermal energy storage are shown in Table 5, and the relevant data of the boiler are shown in Table 6. The natural gas and gasoline consumption rates of the boiler are 4.2kfc / MWh and 120kfc / MWh respectively. It is assumed that 30% of the heat load of each HN node is an important load.

[0198] Table 4. Location table of hybrid nodes

[0199]

[0200] Table 5 Thermal energy storage parameters

[0201]

[0202] is the heat storage state of the thermal energy storage corresponding to node i at the initial time t0.

[0203] Table 6 Boiler parameters

[0204]

[0205] In the embodiment, due to the early warning of the occurrence of extreme events, the urban integrated energy system starts charging the electric-thermal energy storage device at 00:00 using the method proposed in the present invention. The extreme disaster is predicted to occur at 06:00, resulting in the disconnection of the natural gas grid and the distribution network. The upper-level power grid is shut down at 09:00. During this period, the electric-thermal energy storage device uses this strategy to assist the electric-thermal load in resuming operation. The upper-level power grid and the natural gas grid are expected to resume energy supply at 14:00.

[0206] Depend on Figure 5 As shown, due to the early warning of the occurrence of extreme disasters, the control integrated energy system proposed by the present invention starts to store energy for the electric-thermal energy storage device from time 01:00, and the electric-thermal energy storage device is basically full at 06:00. Figure 6 During the period shown, the DG units, boilers, and CHP units consistently maintained maximum active output power; from 06:00 to 09:00, due to the outage of the upper-level natural gas grid, the gas-fired DG units and boilers stopped operating. In order to maintain the normal supply of thermal loads, the thermal energy storage equipment began to slowly output active power. Since the upper-level power grid was still supplying power normally at this time, important electrical loads were able to operate normally, and the energy storage status of the energy storage power station remained at 100%; from 09:00, the upper-level power grid stopped supplying power, and the energy storage power station began to discharge to ensure the operation of important electrical loads. At 14:00, the upper-level power grid and gas grid resumed power supply, and the emergency defense cycle of the integrated energy system ended.

[0207] The present invention also proposes a distribution network resilience improvement system based on integrated energy system optimization and dispatching, including: a collection module, a model and constraint condition establishment module, a pre-disaster prediction module, a post-disaster prediction module, and an integrated energy system optimization and dispatching module;

[0208] The acquisition module is used to obtain the power and constraints of each distributed power unit in the distribution network;

[0209] The model and constraint condition establishment module is used to establish the thermal power balance model, gas flow balance model and electric power balance model of the integrated energy system based on the power and constraint conditions of the distributed power generation units; the constraint conditions of the thermal power balance model, the constraint conditions of the gas flow balance model and the constraint conditions of the electric power balance model are established respectively;

[0210] The pre-disaster prediction module is used to iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints, with the maximum sum of the charge state of the electric energy storage and the heat storage state of the thermal energy storage in the pre-disaster preparation stage as the first optimization goal, to obtain the first predicted value of the output power of various power sources in the integrated energy system at different times before the disaster;

[0211] The post-disaster prediction module is used to iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints, with the maximum sum of the powers of the corresponding loads of each node in the distribution network in the post-disaster defense stage as the second optimization goal, to obtain the second predicted values ​​of the output power of various power sources in the integrated energy system at different times after the disaster, the predicted values ​​of the state of charge of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster;

[0212] The integrated energy system optimization and scheduling module is used to optimize the scheduling of the integrated energy system based on the first predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster, the second predicted values ​​of the output power of various power sources at different times after the disaster, the predicted values ​​of the charge state of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster, as a measure to improve the resilience of the distribution network.

[0213] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0214] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0215] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0216] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distribution network resilience improvement method based on integrated energy system optimization and scheduling, which divides the distribution network resilience improvement process into a pre-disaster preparation stage and a post-disaster defense stage based on the time of disaster occurrence; characterized in that: include: Obtain the power and power constraints of each distributed generation unit in the distribution network; Based on the power and power constraints of distributed power units, the thermal power balance model, gas flow balance model and electric power balance model of the integrated energy system are established respectively, and the constraints of the thermal power balance model, the gas flow balance model and the electric power balance model are established respectively; Taking the maximum sum of the state of charge of the electric energy storage and the state of heat storage of the thermal energy storage in the pre-disaster preparation stage as the optimization goal, the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints are iteratively solved to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster. Taking the maximum sum of the powers of the corresponding loads of each node in the distribution network in the post-disaster defense stage as the optimization goal, the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints are iteratively solved to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times after the disaster, the predicted values ​​of the state of charge of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster; Based on the predicted values ​​of output power of various power sources in the integrated energy system at different times before the disaster, the predicted values ​​of output power of various power sources at different times after the disaster, the predicted values ​​of charge state of electric energy storage at different times after the disaster, and the predicted values ​​of heat storage state of thermal energy storage at different times after the disaster, the integrated energy system is optimized and dispatched as a measure to enhance the resilience of the distribution network.

2. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 1 is characterized in that: Taking node i as the object, the power constraint of distributed generation units satisfies the following relationship: In the formula, are respectively the active power and reactive power of the distributed generation unit corresponding to node i at time t; and are respectively the upper limit of active power and the upper limit of reactive power of the distributed generation unit corresponding to node i at time t; is the lower limit of the active power of the distributed generation unit corresponding to node i at time t; γ i The value of is determined by the type of distributed generation unit. Indicates that node i corresponds to a natural gas-based distributed generation unit. Indicates that node i corresponds to a dual-fuel distributed power generation unit; and are the upper and lower limits of the power factor of the distributed generation unit corresponding to node i respectively; is a 0-1 variable, When it is 1, the upper-level natural gas grid is in a normal supply state at time t. When it is 0, the upper-level natural gas grid is in an abnormal supply state at time t.

3. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 2 is characterized in that: Taking node i as the object, the thermal power balance model satisfies the following relationship: In the formula, is the active power of the heat load corresponding to node i at time t; The heat storage power and heat release power of the thermal energy storage corresponding to node i at time t respectively; η g-h is the gas-heat conversion efficiency in the cogeneration unit, η g-e The gas-to-electricity conversion efficiency of the distributed power generation unit; η o-h is the oil-heat conversion efficiency in the combined heat and power unit, η o-e The oil-to-electricity conversion efficiency of dual-fuel distributed power units; is the active power of the dual-fuel distributed generation unit corresponding to node i at time t; is the thermal power of the natural gas boiler corresponding to node i at time t, is the thermal power of the dual-fuel boiler corresponding to node i at time t.

4. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 3 is characterized in that: Taking node i as the object, the air flow balance model satisfies the following relationship: In the formula, is the flow rate of the natural gas source corresponding to node i at time t; is the sum of the active powers of the natural gas boiler and the dual-fuel boiler corresponding to node i at time t, is the power of the gas load corresponding to node i at time t, C g-e is the natural gas consumption rate of the distributed generation unit, C g-h is the natural gas consumption rate of the boiler; is the natural gas flow from node i to node j at time t, is the natural gas flow from node k to node i at time t; N GN A collection of nodes in the natural gas grid.

5. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 4 is characterized in that: Taking node i as the object, the electric power balance model satisfies the following relationship: Where P ij,t and Q ij,t are the active power and reactive power flowing from node i to node j at time t, respectively, where δ(i) is the set of nodes from which the power flow comes from node i; P ji,t and Q ji,t are the active power and reactive power flowing from node j to node i at time t, respectively, where γ(i) is the set of nodes whose power flows to node i; R ji , X ji are the resistance and reactance of the transmission line from node j to node i, respectively; is the square value of the current flowing from node j to node i at time t; are the active power and reactive power of the electric load corresponding to node i at time t respectively.

6. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 3 is characterized in that: The constraints of the thermal power balance model include: thermal load constraints, boiler thermal power constraints, and thermal energy storage constraints; Taking node i as the object, the constraint condition of heat load satisfies the following relationship: In the formula, is the power upper limit of the heat load corresponding to node i; Taking node i as the object, the thermal power constraint of the boiler satisfies the following relationship: In the formula, is the upper limit of thermal power of the natural gas boiler corresponding to node i, is the lower limit of thermal power of the natural gas boiler corresponding to node i; is the upper limit of thermal power of the dual-fuel boiler corresponding to node i, is the lower limit of thermal power of the dual-fuel boiler corresponding to node i. Taking node i as the object, the constraints of thermal energy storage satisfy the following relationship: In the formula, is the heat storage state of the thermal energy storage corresponding to node i at time t; η TES The efficiency of storing or releasing heat for thermal energy storage; is the capacity reference value of the thermal energy storage corresponding to node i; Δt is the length of the time period for the thermal energy storage to store or release heat; and are the upper and lower limits of the thermal energy storage state corresponding to the node i, respectively; is the heat storage state of the thermal energy storage corresponding to node i at the initial time t0; is the initial value of the heat storage state of the thermal energy storage corresponding to node i; and All are 0-1 variables. is 1 and When it is 0, the thermal energy storage corresponding to node i is in the thermal storage state at time t. is 1 and When it is 0, the thermal energy storage corresponding to node i is in the heat release state at time t; and are the upper limit of heat storage power and heat release power of the thermal energy storage corresponding to node i respectively.

7. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 4 is characterized in that: The constraints of the gas flow balance model include: gas source flow constraint, flow pressure constraint, pressure constraint, and flow constraint; Taking node i as the object, the gas source flow constraint condition satisfies the following relationship: In the formula, and are the upper and lower output limits of the natural gas source corresponding to node i respectively; Taking node i as the object, the flow pressure constraint condition satisfies the following relationship: In the formula, are the air pressure at node i and node j at time t respectively; When , the flow direction of natural gas in the pipeline is from node i to node j. The opposite is true; C p It is the pipeline constant in the Weymouth equation that describes the relationship between natural gas pressure and flow rate. Its value depends on the length, diameter and absolute roughness of the natural gas transmission pipeline. p is the pipeline pressure set, Indicates that the pipeline pressure is normal, and T is the pipeline pressure monitoring time; Taking node i as the object, the pressure constraint condition satisfies the following relationship: In the formula, and are the upper and lower limits of the air pressure at node j, respectively; Taking node i as the object, the flow constraint condition satisfies the following relationship: In the formula, and are the upper and lower limits of the flow rate in the pipeline from node i to node j respectively.

8. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 5 is characterized in that: The constraints of the electric power balance model include power constraints, voltage constraints, power flow safety constraints, distributed power generation unit power constraints, electric energy storage operation constraints, and load constraints; Taking node i as the object, the power constraint condition satisfies the following relationship: In the formula, are the square values ​​of the voltages at nodes i and j at time t; R ij , X ij are the resistance and reactance of the transmission line from node i to node j respectively; M0 is a very large positive real number; α ij，t is a 0-1 variable, α ij，t =1 means that the transmission line from node i to node j is connected at time t, α ij，t =0, then vice versa; Taking node i as the object, the voltage constraint condition satisfies the following relationship: In the formula, are the lower and upper limits of the square value of the voltage at node i, respectively; Specifically, taking node i as the object, the current constraint condition satisfies the following relationship: In the formula, is the upper limit of the square value of the current flowing from node i to node j; Taking node i as the object, the power flow safety constraint condition satisfies the following relationship: Taking node i as the object, the operation constraints of electric energy storage satisfy the following relationship: In the formula, and are the discharge power and charging power of the energy storage power station corresponding to node i at time t; and All are 0-1 variables. and The energy storage station corresponding to time node i discharges at time t. and The energy storage station corresponding to time node i is charged at time t; and are the upper limit of discharge power and the upper limit of charge power of the energy storage power station corresponding to node i at time t; is the charge state of the energy storage power station corresponding to node i at time t; and are the charging efficiency and discharging efficiency of the energy storage power station corresponding to node i respectively; Δt is the charging period and discharging period of the energy storage power station; and are the upper and lower limits of the state of charge of the energy storage power station corresponding to node i respectively; Taking node i as the object, the load constraint condition satisfies the following relationship: In the formula, and are respectively the upper limit of active power and the upper limit of reactive power of the load corresponding to node i at time t.

9. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 1, characterized in that: The first optimization goal is to maximize the sum of the state of charge of the electric energy storage and the state of heat storage of the thermal energy storage in the pre-disaster preparation stage, satisfying the following relationship: Where maxF1 is the first optimization target; N T0 It is the period collection of the pre-disaster preparation stage; N ESS N is the energy storage power station collection in the distribution network; TES It is a collection of thermal energy storage stations in the distribution network; is the charge state of the energy storage power station corresponding to node i at time t; is the heat storage state of the thermal energy storage corresponding to node i at time t.

10. The method for improving the resilience of a distribution network based on optimized dispatching of an integrated energy system according to claim 9, characterized in that: The predicted values ​​of output power of various power sources in the integrated energy system at different times before the disaster include: the predicted values ​​of output power of each distributed power source unit at different times before the disaster, and the predicted values ​​of output power of boilers at different times before the disaster; and the predicted values ​​of output power of various power sources at different times before the disaster will not exceed the corresponding output power upper limit.

11. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 1, characterized in that: The second optimization goal is to maximize the sum of the power of the corresponding loads of each node in the distribution network during the post-disaster defense phase, satisfying the following relationship: Where maxF2 is the second optimization objective; N T It is a collection of time periods throughout the emergency recovery cycle, both before and after the disaster; and are the electric load weight and thermal load weight corresponding to node i respectively; N E is the set of electric load nodes; N H is the heat load node set; is the active power of the electric load corresponding to node i at time t, is the active power of the heat load corresponding to node i at time t.

12. The method for improving the resilience of a distribution network based on optimized scheduling of an integrated energy system according to claim 11, characterized in that: The predicted values ​​of output power of various power sources in the integrated energy system at different times after the disaster, including: the predicted values ​​of output power of each distributed power unit at different times after the disaster, and the predicted values ​​of output power of boilers at different times after the disaster.

13. A distribution network resilience improvement system based on integrated energy system optimization and dispatching, characterized in that: include: Acquisition module, model and constraint condition establishment module, pre-disaster prediction module, post-disaster prediction module, integrated energy system optimization and scheduling module; The acquisition module is used to obtain the power and power constraint conditions of each distributed power unit in the distribution network; The model and constraint condition establishment module is used to establish the thermal power balance model, gas flow balance model and electric power balance model of the integrated energy system based on the power and power constraint conditions of the distributed power generation units; the constraint conditions of the thermal power balance model, the constraint conditions of the gas flow balance model and the constraint conditions of the electric power balance model are established respectively; The pre-disaster prediction module is used to iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints, with the maximum sum of the charge state of the electric energy storage and the heat storage state of the thermal energy storage in the pre-disaster preparation stage as the optimization target, to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster; The post-disaster prediction module is used to iteratively solve the thermal power balance model and its constraints, the gas flow balance model and its constraints, and the electric power balance model and its constraints, with the maximum sum of the powers of the corresponding loads of each node in the distribution network during the post-disaster defense phase as the optimization goal, to obtain the predicted values ​​of the output power of various power sources in the integrated energy system at different times after the disaster, the predicted values ​​of the state of charge of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster; The integrated energy system optimization and scheduling module is used to optimize the scheduling of the integrated energy system based on the predicted values ​​of the output power of various power sources in the integrated energy system at different times before the disaster, the predicted values ​​of the output power of various power sources at different times after the disaster, the predicted values ​​of the charge state of the electric energy storage at different times after the disaster, and the predicted values ​​of the heat storage state of the thermal energy storage at different times after the disaster, as a measure to improve the resilience of the distribution network.

14. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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