Power distribution network resilience improvement method and system based on integrated energy system optimization scheduling

Abstract

The power distribution network resilience improvement method and system based on integrated energy system optimal scheduling take 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 an optimization target, iteratively solve a thermal power balance model and constraint conditions thereof, a gas flow balance model and constraint conditions thereof, and an electric power balance model and constraint conditions thereof, and obtain predicted values of output powers of various power sources at different times before the disaster; take the maximum sum of powers of corresponding loads of each node in the power distribution network in the post-disaster defense stage as an optimization target, iteratively solve the thermal power balance model and constraint conditions thereof, the gas flow balance model and constraint conditions thereof, and the electric power balance model and constraint conditions thereof, and obtain predicted values of output powers of various power sources at different times after the disaster, predicted values of the state of charge of the electric energy storage at different times after the disaster, and predicted values of the state of heat storage of the thermal energy storage at different times after the disaster; and perform integrated energy system optimal scheduling to improve the resilience of the power distribution network.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of safe planning and operation of power systems, and particularly relates to a method and system for improving the resilience of a distribution network to achieve sustainable recovery based on optimal scheduling of an integrated energy system. BACKGROUND

[0002] City distribution networks may suffer severe damage when facing extreme natural disasters, leading to power outages and service interruptions, and it is difficult to restore the faulty equipment in a timely manner, highlighting the shortcomings of insufficient resilience of city distribution networks under extreme natural disasters. An integrated energy system (IES) is a system that organically combines and optimally configures various energy forms such as electricity, heat, and gas. By comprehensively utilizing various energy resources, IES can improve energy utilization efficiency, reduce energy consumption and emissions, and provide diversified energy supply paths for distribution networks, playing an important role in post-disaster power restoration of distribution networks.

[0003] Existing literature on the resilience improvement of distribution networks under extreme disasters mainly includes the contents of three stages of pre-disaster prevention, disaster response, and post-disaster recovery. In the pre-disaster prevention stage, the distribution of distributed power in the distribution network and the reasonable scheduling and siting of EVs and mobile vehicle energy storage devices are carried out to reduce load loss and improve the recovery ability of the distribution network. Some documents reduce the probability of transmission line failure by reinforcing lines and towers. In the disaster response stage, flexible resources are combined to optimize the topology and flow of the distribution network to form a multi-energy island to improve the flexibility in the disaster stage. In the post-disaster recovery stage, most of the literature studies dynamically schedule emergency resources such as mobile energy storage and repair teams by combining the transportation network, comprehensively schedule various flexible resources, and reconfigure the network to divide micro-grid islands to achieve fast recovery of post-disaster load and maximize power supply. The existing technical method does not consider the important supporting role of coordinated scheduling of electric-gas-heat integrated energy systems to assist in power restoration of city distribution networks. There is a complex coupling relationship between various energy resources in IES, and how to effectively coordinate and optimize the scheduling of these resources becomes a key problem. Developing a reasonable and effective integrated energy system optimization scheduling strategy will greatly improve the resilience and sustainable recovery ability of the distribution network under extreme disasters. SUMMARY

[0004] To solve the problems in the prior art, the present application provides a method and system for improving the resilience of a distribution network based on the optimal scheduling of an integrated energy system, which comprehensively utilizes the coordinated optimization scheduling of electric-gas-heat integrated energy to cope with distribution network failures caused by extreme disasters and emergencies, realizes fast and efficient power restoration of the post-disaster distribution network, reduces the losses caused by extreme disasters, and improves the resilience level and sustainable recovery of the distribution network.

[0005] The application adopts the technical solutions as follows.

[0006] The application provides a power distribution network resilience improvement method based on comprehensive energy system optimization scheduling, divides the power distribution network resilience improvement process into a pre-disaster preparation stage and a post-disaster defense stage based on disaster occurrence time, and includes the following steps:

[0007] Power and power constraint conditions of each distributed power unit in the power distribution network are acquired;

[0008] Based on the power and power constraint conditions of the distributed power unit, a thermal power balance model, a gas flow balance model and an electric power balance model of the comprehensive energy system are respectively established, and constraint conditions of the thermal power balance model, constraint conditions of the gas flow balance model and constraint conditions of the electric power balance model are respectively established;

[0009] Taking the maximum sum of the state of charge of the electric energy storage and the heat storage state of the thermal energy storage in the pre-disaster preparation stage as an optimization target, the thermal power balance model and its constraint conditions, the gas flow balance model and its constraint conditions and the electric power balance model and its constraint conditions are iteratively solved to obtain predicted values of output power of each type of power source in the comprehensive energy system at different times before the disaster;

[0010] Taking the maximum sum of power of each node corresponding load in the power distribution network in the post-disaster defense stage as an optimization target, the thermal power balance model and its constraint conditions, the gas flow balance model and its constraint conditions and the electric power balance model and its constraint conditions are iteratively solved to obtain predicted values of output power of each type of power source in the comprehensive energy system at different times after the disaster, predicted values of the state of charge of the electric energy storage at different times after the disaster and 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 each type of power source in the comprehensive energy system at different times before the disaster, the predicted values of output power of each type of power source 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 comprehensive energy system is optimized and scheduled as a power distribution network resilience improvement measure.

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

[0013]

[0014]

[0015] In the formula, Pdi(t) and Qdi(t) are respectively the active power and the reactive power of the distributed power unit corresponding to node i at time t; Pdi(t) and Qdi(t) are 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 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 power 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; 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] Where, 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] Where, 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 C is the natural gas consumption rate of the distributed power unit; g-h C is the natural gas consumption rate of the boiler; N is the natural gas flow from node i to node j at time t, N is the natural gas flow from node k to node i at time t; GN N is the set of nodes of the natural gas network.

[0022] Preferably, for node i, the electrical power balance model satisfies the following relationship:

[0023]

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

[0025] Preferably, the constraint conditions of the thermal power balance model include: the constraint condition of the thermal load, the thermal power constraint condition of the boiler, and the constraint condition of the thermal energy storage;

[0026] For node i, the constraint condition of the thermal load satisfies the following relationship:

[0027]

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

[0029] For node i, the thermal power constraint condition of the boiler satisfies the following relationship:

[0030]

[0031]

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

[0033] For the node i, the constraint condition of the thermal storage energy satisfies the following relationship:

[0034]

[0035] In the formula, is the thermal storage state of the thermal storage energy corresponding to the node i at time t; η TES is the efficiency of the thermal storage or heat release of the thermal storage energy; is the capacity reference value of the thermal storage energy corresponding to the node i; Δt is the time length of the thermal storage or heat release of the thermal storage energy; and are respectively an upper limit and a lower limit of the thermal storage state of the thermal storage energy corresponding to the node i; is the thermal storage state of the thermal storage energy corresponding to the node i at the initial time t0; is the initial value of the thermal storage state of the thermal storage energy corresponding to the node i; and are both 0-1 variables, is 1 and is 0, the thermal storage energy corresponding to the node i is in the thermal storage state at time t, is 1 and is 0, the thermal storage energy corresponding to the node i is in the heat release state at time t; and are respectively an upper limit and a lower limit of the thermal storage power of the thermal storage energy corresponding to the node i.

[0036] Preferably, the constraint conditions of the gas flow balance model include: a gas source flow constraint condition, a flow pressure constraint condition, a pressure constraint condition, and a flow constraint condition.

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

[0038]

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

[0040] For the node i, the flow pressure constraint condition satisfies the following relationship:

[0041]

[0042] In the formula are respectively the gas pressures at the nodes i and j at time t; when When , the flow direction of natural gas in the pipeline is from node i to node j. The opposite is true; C p N 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] Where, 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] Where, 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] Where, 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 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, otherwise;

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

[0054]

[0055] wherein, and are the lower and upper bounds of the square value of the voltage of node i, respectively;

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

[0057]

[0058] wherein, is the upper bound 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 energy storage operation constraint condition satisfies the following relationship:

[0062]

[0063] wherein, and are the discharging power and charging power of the energy storage power station corresponding to node i at time t, respectively; and are both 0-1 variables, and the energy storage power station corresponding to node i discharges at time t, and the energy storage power station corresponding to node i charges at time t; and are the upper limit of the discharging power and the upper limit of the charging power of the energy storage power station corresponding to node i at time t, respectively; is the state of charge 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 limit and lower limit 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] wherein, and An active power upper limit and a reactive power upper limit of the load corresponding to the node i at the time t, respectively.

[0068] Preferably, the first optimization target is 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, and the following relationship is met:

[0069]

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

[0071] Preferably, the predicted values of the output powers of various types of power sources in the comprehensive energy system at different times before the disaster include: the predicted values of the output powers of various distributed power source units at different times before the disaster, and the predicted values of the output powers of boilers at different times before the disaster; and the predicted values of the output powers of various types of power sources at different times before the disaster do not exceed the corresponding output power upper limits.

[0072] Preferably, the second optimization target is the maximum sum of the powers of the loads corresponding to various nodes in the power distribution network in the post-disaster defense stage, and the following relationship is met:

[0073]

[0074] In the formula, maxF2 is the second optimization target; N T is a time period set of the entire emergency recovery cycle in the pre-disaster and post-disaster stages; and are an electric load weight and a thermal load weight corresponding to the node i, respectively; N E is a set of electric load nodes; N H is a set of thermal load nodes; is an active power of the electric load corresponding to the node i at the time t, is an active power of the thermal load corresponding to the node i at the time t.

[0075] Preferably, the predicted values of the output powers of various types of power sources in the comprehensive energy system at different times after the disaster include: the predicted values of the output powers of various distributed power source units at different times after the disaster, and the predicted values of the output powers of boilers at different times after the disaster.

[0076] The application further provides a power distribution network resilience improvement system based on comprehensive energy system optimization scheduling, which comprises:

[0077] The acquisition module, the model and constraint condition establishment module, the pre-disaster prediction module, the post-disaster prediction module, and the comprehensive energy system optimal scheduling module;

[0078] The acquisition module is configured to acquire power and power constraints of each distributed power unit in the power distribution network.

[0079] The model and constraint condition establishment module is configured to establish a thermal power balance model, a gas flow balance model, and an electric power balance model of the comprehensive energy system based on the power and power constraints of the distributed power unit, and establish constraint conditions of the thermal power balance model, constraint conditions of the gas flow balance model, and constraint conditions of the electric power balance model, respectively.

[0080] The pre-disaster prediction module is configured 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 as an optimization objective, and iteratively solve the thermal power balance model and its constraint conditions, the gas flow balance model and its constraint conditions, and the electric power balance model and its constraint conditions to obtain predicted values of output power of each type of power source in the comprehensive energy system at different times before the disaster.

[0081] The post-disaster prediction module is configured to maximize the sum of power of each node corresponding to the load in the power distribution network in the post-disaster defense stage as an optimization objective, and iteratively solve the thermal power balance model and its constraint conditions, the gas flow balance model and its constraint conditions, and the electric power balance model and its constraint conditions to obtain predicted values of output power of each type of power source in the comprehensive energy system at different times after the disaster, predicted values of the state of charge of the electric energy storage at different times after the disaster, and predicted values of the state of heat storage of the thermal energy storage at different times after the disaster.

[0082] The comprehensive energy system optimal scheduling module is configured to perform optimal scheduling of the comprehensive energy system based on the predicted values of output power of each type of power source in the comprehensive energy system at different times before the disaster, the predicted values of output power of each type of power source 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 state of heat storage of the thermal energy storage at different times after the disaster, as a power distribution network resilience improvement measure.

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

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

[0085] The beneficial effects of the present application at least include that, compared with the prior art, in the method proposed by the present application, a comprehensive energy system coupling model of the heat network, the natural gas network and the power network is established to construct two objective functions of maximizing the active power of the load and maximizing the state of the electric and thermal energy storage before the disaster, a multi-objective improved particle swarm algorithm is used to solve the mixed integer programming model, and a power distribution network resilience improvement strategy based on the optimal scheduling of the comprehensive energy system is obtained.

[0086] Through the coordinated optimal scheduling of the comprehensive energy system, the recovery ability and the operation stability of the power distribution network in the face of sudden events such as natural disasters and equipment failures can be significantly improved.

[0087] Through the coordination of the use of various energy forms such as electricity, heat, gas and the like, the optimal allocation and efficient use of energy resources are realized, and the energy consumption and operation cost are reduced.

[0088] Through the coordination of the use of various clean energy and renewable energy, it is helpful to reduce the dependence on traditional fossil energy, and reduce the overall energy consumption and carbon emissions. BRIEF DESCRIPTION OF DRAWINGS

[0089] Figure 1 is a flow chart of a power distribution network resilience improvement method based on comprehensive energy system optimal scheduling proposed by the present application;

[0090] Figure 2 is an improved 33-node power distribution network structure diagram in the embodiment of the present application;

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

[0092] Figure 4 is a 24-hour active power prediction curve diagram of important loads and non-important loads;

[0093] Figure 5 is a total energy storage state diagram of the electric-thermal energy storage equipment at each time in the entire emergency defense stage in the embodiment of the present application;

[0094] Figure 6 is an active power output diagram of the distributed generator (DG) unit, the combined heat and power unit (CHP) and the boiler at each time in the entire emergency defense stage in the embodiment of the present application. DETAILED DESCRIPTION

[0095] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0096] Provide early warning of the occurrence of extreme disasters, 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 preparation index, use a multi-objective optimization algorithm based on the improved particle swarm algorithm to solve the optimization model, and obtain a coordinated optimization dispatching strategy for the integrated energy system in 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] This paper proposes a distribution network resilience improvement method based on the optimization and scheduling of the integrated energy system. Based on the time of disaster occurrence, the distribution network resilience improvement process is divided into the pre-disaster preparation stage and the post-disaster defense stage; Figure 1 As shown, the method includes the following steps:

[0098] Step 1: Obtain the power and power constraints of each distributed power 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] Where, are the active power and reactive power of the distributed generation unit corresponding to node i at time t; and are 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 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 power 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; is a 0-1 variable, 1, the upper-level natural gas network at time t is in a normal supply state, 0, the upper-level natural gas network at time t is in an abnormal supply state.

[0103] Step 2, based on the power and power constraint conditions of the distributed power unit, a thermal power balance model, a gas flow balance model and an electric power balance model of the comprehensive energy system are respectively established;

[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 thermal load corresponding to node i at time t; is the heat storage power and heat release power of the thermal storage corresponding to node i at time t, respectively; is a 0-1 variable, 1, the upper-level natural gas network at time t is in a normal supply state, 0, the upper-level natural gas network at time t is in an abnormal supply state; η g-h is the gas-heat conversion efficiency in the cogeneration unit, η g-e is the gas-electricity conversion efficiency of the distributed power unit; is the active power of the distributed power unit corresponding to node i at time t, and in the non-restrictive preferred embodiment, the distributed power unit includes but is not limited to a dual-fuel type distributed power unit and a natural gas type distributed power unit; η o-h is the oil-heat conversion efficiency in the cogeneration unit, η o-e is the oil-electricity conversion efficiency of the dual-fuel type distributed power unit; is the active power of the dual-fuel type distributed power unit corresponding to node i at time t; is the thermal power of the natural gas type boiler corresponding to node i at time t, is the thermal power of the dual-fuel type boiler corresponding to node i at time t.

[0107] The natural gas network includes a gas source, a compressor, a natural gas pipeline and a natural gas load. The natural gas load includes a distributed power unit, a boiler, a residential load and an industrial load. Since the natural gas network is connected with the power distribution network and has a small scale, the compressor is not included, the relationship between gas pressure and flow is described using the Weymouth equation, and a natural gas network constraint model is established.

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

[0109]

[0110] wherein, is the flow of natural gas corresponding to node i at time t; is the active power of the distributed generator unit corresponding to node i at time t; is the sum of the active power 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 generator unit, C g-h is the natural gas consumption rate of the boiler; is the flow of natural gas from node i to node j at time t, is the flow of natural gas from node k to node i at time t; N GN is the set of nodes of the natural gas network;

[0111] wherein, the active power of the distributed generator unit corresponding to node i at time t 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 is the associated variable of the thermal power balance model and the gas flow balance model, which fully embodies the method proposed in the present application, that is, due to the energy storage preparation by using gas and thermal resources before the occurrence of extreme disasters, the power supply recovery capability of the distribution network is significantly improved after the occurrence of extreme disasters.

[0112] In the present 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 an object, the electric power balance model satisfies the following relationship:

[0113]

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

[0115] wherein, active power of the distributed power unit corresponding to node i at time t is the associated variable of the heat power balance model and the electric power balance model, and fully embodies that, in the method proposed in the application, due to the energy storage preparation by using the gas and heat resources before the occurrence of the extreme disaster, the power supply recovery capability of the distribution network is significantly improved after the occurrence of the extreme disaster.

[0116] Step 3, respectively, the constraint condition of the heat power balance model, the constraint condition of the gas flow balance model and the constraint condition of the electric power balance model are established.

[0117] Specifically, the constraint condition of the heat power balance model includes: the constraint condition of the heat load, the heat power constraint condition of the boiler and the constraint condition of the heat energy storage.

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

[0122]

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

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

[0125]

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

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

[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 limit and the lower limit of the output 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 gas pressures at nodes i and j at time t respectively; when the flow direction of natural gas in the pipeline is from node i to node j, and vice versa; C p is the pipeline constant in the Weymouth equation describing the relationship between natural gas pressure and flow, the value of which depends on the length, diameter and absolute roughness of the natural gas transmission pipeline, etc.; N p is the pipeline pressure set, indicates that the pipeline pressure is normal, and T is the pipeline pressure monitoring time length;

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

[0135]

[0136] Where, 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] Where, 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 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] Where, 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 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, otherwise;

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

[0145]

[0146] Where, 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] Where, 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 security constraint condition satisfies the following relationship:

[0151]

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

[0153]

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

[0155] Based on the distributed power unit output constraint condition, the thermal power balance model, the gas flow balance model and the electric power balance model are all constrained.

[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 the charging power of the energy storage station corresponding to node i at time t; and are both 0-1 variables, and the energy storage station corresponding to node i discharges at time t, and the energy storage station corresponding to node i charges at time t; and are the discharge power upper limit and the charging power upper limit of the energy storage station corresponding to node i at time t; is the state of charge of the energy storage station corresponding to node i at time t; and are the charging efficiency and the discharging efficiency of the energy storage station corresponding to node i; Δt is the charging period and the discharging period of the energy storage station; and SoCmaxand SoCminare the upper limit and lower limit of the state of charge of the energy storage station corresponding to node i, respectively.

[0159] Specifically, for node i, the load constraint condition satisfies the following relationship:

[0160]

[0161] In the formula, and SoCmaxand SoCminare the upper limit and lower limit of the state of charge of the energy storage station corresponding to node i, respectively.

[0162] Step 4, taking the maximum sum of the state of charge of the electric energy storage and the heat storage state of the thermal energy storage in the pre-disaster preparation stage as the optimization objective, iteratively solving the heat power balance model and its constraint conditions, the gas flow balance model and its constraint conditions, and the electric power balance model and its constraint conditions, obtaining the predicted values of the output power of various types of power sources in the integrated energy system at different times before the disaster.

[0163] Taking the maximum sum of the state of charge 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 objective, satisfying the following relationship:

[0164]

[0165] In the formula, maxF1is the first optimization objective; N T0 is the time period set in the pre-disaster preparation stage; N ESS is the set of energy storage stations in the distribution network; N TES is the set of thermal energy storage stations in the distribution network.

[0166] The predicted values of the output power of various types of power sources in the integrated energy system at different times before the disaster include, but are not limited to, the predicted values of the output power of various distributed power units at different times before the disaster, and the predicted values of the output power of boilers at different times before the disaster; and the predicted values of the output power of various types of 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 power of the loads corresponding to each node in the distribution network in the post-disaster defense stage as the optimization objective, iteratively solving the heat power balance model and its constraint conditions, the gas flow balance model and its constraint conditions, and the electric power balance model and its constraint conditions, obtaining the predicted values of the output power of various types of 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.

[0168] Taking the maximum sum of the power of the loads corresponding to each node in the distribution network in the post-disaster defense stage as the second optimization objective, satisfying the following relationship:

[0169]

[0170] maxF2 is a second optimization objective; N T is a time period set of the entire emergency recovery cycle of the pre-disaster and post-disaster stages; and are the electrical load weight and the thermal load weight corresponding to the node i; N E is an electrical load node set; N H is a thermal load node set; is the active power of the electrical load corresponding to the node i at the time t, is the active power of the thermal load corresponding to the node i at the 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 the output powers of various power sources in the integrated energy system at different times after the disaster include: the predicted values of the output powers of various distributed power units at different times after the disaster, and the predicted values of the output powers of boilers at different times after the disaster.

[0173] Before the occurrence of an extreme disaster, the electrical and thermal energy of the integrated energy system is optimized to charge the electrical and thermal energy storage to improve the energy storage preparation for the important electrical and thermal loads after the disaster. At the same time, considering that the process of charging the energy storage device with the gas-thermal resource before the disaster and the cost of the load power loss when the upper-level power grid and the natural gas network are out of operation after the disaster cannot be too large, two objective functions are constructed, which are to maximize the pre-disaster preparation index and to maximize the active power output of the load; considering the minimization of the total load reduction power in the entire emergency cycle and the maximization of the preparation index of the electrical and thermal energy storage before the disaster, in a non-limiting preferred embodiment, the ideal point method is used in combination with the commercial solver Gurobi to iteratively solve the thermal power balance model and its constraint conditions, the gas flow balance model and its constraint conditions, and the electrical power balance model and its constraint conditions 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, and the specific solving steps are as follows:

[0175] First, a single-objective optimization problem based on the first optimization objective F1 and a single-objective optimization problem based on the second optimization objective F2 are solved respectively to obtain the optimal values of the single-objective optimization problems of the first and second optimization objectives, which satisfy 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 are the optimal values ​​of the single-objective optimization problems based on F1 and F2, i.e., the ideal points of the original multi-objective optimization problem;

[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 distance terms of 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 partitioning 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. The solution x is near the ideal point, and the Pareto dominance judgment is used to obtain the Pareto dominating set.

[0183] Finally, a Pareto optimal solution is selected from the Pareto dominating set as the distribution network resilience improvement strategy. The above single-objective optimization models with Ω as the constraint 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] Among them, the improved IEEE 33 distribution network includes 7 distributed power generation units (such as Figure 2 The specific parameters are shown in Table 1), 3 energy storage power stations (such as Figure 4 As shown in the middle circle, the specific parameters are shown in Table 2), 3 charging piles (such as Figure 2 (shown as diamond in the middle).

[0187] Table 1 Parameter table of distributed power units

[0188]

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

[0190]

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

[0192] Table 3 Load importance table of power distribution network

[0193]

[0194]

[0195] The unit load reduction cost of important load is 10 yuan / kw, and that of non-important load is 1 yuan / kw; the 24-hour active power prediction curves of important load and non-important load are shown in Figure 4

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

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

[0198] Table 4 Position table of hybrid nodes

[0199]

[0200] Table 5 Parameters of heat storage

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

[0206] Depend on Figure 5 As shown, due to the early warning of the occurrence of extreme disasters, the integrated energy control system proposed by the present invention starts to store energy for the electric-thermal energy storage device from time 01:00. By 06:00, the electric-thermal energy storage device is basically full. 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 upstream natural gas grid, the gas-fired DG units and boilers ceased operation. To maintain the normal supply of thermal loads, the thermal energy storage equipment began to slowly output active power. Because the upstream grid was still supplying power at this time, important electrical loads were able to operate normally, and the energy storage power station's energy storage status remained at 100%. From 09:00, the upstream grid stopped supplying power, and the energy storage power station began discharging to ensure the operation of important electrical loads. At 14:00, the upstream grid and gas grid resumed supply, and the emergency defense cycle of the integrated energy system ended.

[0207] The present invention also proposes a distribution network resilience enhancement system based on integrated energy system optimization and scheduling, including: an acquisition 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 scheduling 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 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 configured for solving iteratively the heat power balance model and its constraint condition, the gas flow balance model and its constraint condition, and the electric power balance model and its constraint condition, with 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 a first optimization target, to obtain the first predicted value of the output power of each type of power source in the integrated energy system at different time points before the disaster.

[0211] The post-disaster prediction module is configured for solving iteratively the heat power balance model and its constraint condition, the gas flow balance model and its constraint condition, and the electric power balance model and its constraint condition, with the maximum sum of the power of each node corresponding to the load in the power distribution network in the post-disaster defense stage as a second optimization target, to obtain the second predicted value of the output power of each type of power source in the integrated energy system at different time points after the disaster, the predicted value of the state of charge of the electric energy storage at different time points after the disaster, and the predicted value of the state of heat storage of the thermal energy storage at different time points after the disaster.

[0212] The integrated energy system optimization and dispatching module is configured for performing the optimization and dispatching of the integrated energy system based on the first predicted value of the output power of each type of power source in the integrated energy system at different time points before the disaster, the second predicted value of the output power of each type of power source at different time points after the disaster, the predicted value of the state of charge of the electric energy storage at different time points after the disaster, and the predicted value of the state of heat storage of the thermal energy storage at different time points after the disaster, as a power distribution network resilience improvement measure.

[0213] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0214] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic 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 (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, 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 disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a magnetically encoded device such as magnetic strip cards, an optically encoded device such as a compact disc (CD) or DVD, and / or any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0215] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0216] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0217] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing and illustrating, not limiting the technical solutions of the present application. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application. Any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered within the protection scope of the claims of the present application.

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 constraints of each distributed power unit in the distribution network; Based on the power and constraints of the distributed power units, a thermal power balance model, a gas flow balance model, and an electric power balance model for the integrated energy system are established. The active power of the distributed power units corresponding to the nodes at each moment, the variable representing the natural gas supply status, and the thermal power of the natural gas boilers and dual-fuel boilers corresponding to the nodes at each moment serve as associated variables for the thermal power balance model and the gas flow balance model. The active power of the distributed power units corresponding to the nodes at each moment serves as an associated variable for the thermal power balance model and the electric power balance model. Constraints for the thermal power balance model, the gas flow balance model, and the electric power balance model are established separately. Node For the object, the thermal power balance model satisfies the following relationship: Where, For nodes The corresponding heat load at time Active power; 、 Separate nodes The corresponding thermal energy storage is at Heat storage power and heat release power; is the gas-heat conversion efficiency in the cogeneration unit, is the gas-to-electricity conversion efficiency of the distributed power generation unit; is the oil-heat conversion efficiency in the combined heat and power unit, The oil-to-electricity conversion efficiency of dual-fuel distributed power units; For nodes The corresponding dual-fuel distributed power generation unit is at time Active power; For nodes The corresponding natural gas boiler is The thermal power, For nodes The corresponding dual fuel boiler is Thermal power; Node As the object, the air flow balance model satisfies the following relationship: Where, For nodes The corresponding natural gas source at time Traffic volume; For nodes The corresponding natural gas boiler and dual fuel boiler are The sum of the active powers of ; For nodes The corresponding gas load at time The power, is the natural gas consumption rate of the distributed generation units, is the natural gas consumption rate of the boiler; For the moment Slave nodes Inflow Node of natural gas flow, For the moment Slave nodes Inflow Node Natural gas flow; is the node set of the natural gas network; Node As the object, the electric power balance model satisfies the following relationship: Where, and Separate moments Slave nodes Flow Node The active power and reactive power of Flow slave node The node set that comes from it; and Separate moments Slave nodes Flow Node The active power and reactive power of Flow direction node The set of nodes that go; 、 Node To Node The resistance and reactance of the transmission line; For the moment Slave nodes Flow Node The square value of the current; 、 Node The corresponding electrical load at time Active power and reactive power; Taking the maximization of the sum of the state of charge of the electric energy storage and the state of heat of the thermal energy storage during the pre-disaster preparation phase 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 output power of various power sources in the integrated energy system at different times before the disaster. Taking the maximum sum of the power corresponding to the loads of each node in the distribution network during the post-disaster defense phase 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 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. 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, optimized scheduling of the integrated energy system is carried out as a measure to enhance the resilience of the distribution network.

2. The method for improving the resilience of distribution networks based on optimized scheduling of integrated energy systems according to claim 1 is characterized in that: Node For the object, the power constraint of the distributed generation unit satisfies the following relationship: Where, 、 Node The corresponding distributed power generation unit is at time Active power and reactive power; and Node The corresponding distributed power generation unit is at time Active power upper limit and reactive power upper limit; For nodes The corresponding distributed power generation unit is at time The lower limit of active power; The value of is determined by the type of distributed generation unit. Representation node The corresponding one is a natural gas type distributed power generation unit. Representation node The corresponding is a dual-fuel distributed power unit; and Node The upper and lower limits of the corresponding distributed power generation unit power factor; is a 0-1 variable, When it is 1, at time The upper-level natural gas grid is in normal supply. When it is 0, at time The upper-level natural gas grid is in an abnormal supply state.

3. The method for improving distribution network resilience based on integrated energy system optimization and scheduling according to claim 1 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; Node For the object, the heat load constraint condition satisfies the following relationship: Where, For nodes The power upper limit of the corresponding heat load; Node For the object, the thermal power constraint of the boiler satisfies the following relationship: Where, For nodes The corresponding upper limit of thermal power of natural gas boiler is: For nodes The corresponding lower limit of thermal power of natural gas boiler; For nodes The corresponding thermal power upper limit of the dual-fuel boiler is For nodes The corresponding lower limit of thermal power of dual-fuel boiler; Node For the object, the constraints of thermal energy storage satisfy the following relationship: Where, For nodes The corresponding thermal energy storage is at The heat storage state; the efficiency of storing or releasing heat for thermal energy storage; For nodes The corresponding thermal energy storage capacity benchmark value; The length of the period for storing or releasing heat for thermal energy storage; and Node The upper and lower limits of the corresponding thermal energy storage state; For nodes The corresponding thermal energy storage at the initial moment The heat storage state; For nodes The initial value of the heat storage state of the corresponding thermal energy storage; and All are 0-1 variables. is 1 and When it is 0, the node The corresponding thermal energy storage is at In heat storage state, is 1 and When it is 0, the node The corresponding thermal energy storage is at in an exothermic state; and Node The corresponding upper limit of heat storage power and heat release power of thermal energy storage.

4. The method for improving distribution network resilience based on integrated energy system optimization and scheduling according to claim 1 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; Node For the object, the gas source flow constraint condition satisfies the following relationship: Where, and Node The upper and lower output limits of the corresponding natural gas source; Node For the object, the flow pressure constraint condition satisfies the following relationship: Where, 、 At the time node and nodes The air pressure at The flow direction of natural gas in the pipeline is from node To Node , When the opposite is true; The pipeline constant in the Weymouth equation that describes the relationship between natural gas pressure and flow rate depends on the length, diameter, and absolute roughness of the natural gas transmission pipeline. is the pipeline pressure set, Indicates that the pipeline pressure is normal. The duration of pipeline pressure monitoring; Node For the object, the pressure constraint condition satisfies the following relationship: Where, and Node The upper and lower limits of atmospheric pressure; Node For the object, the flow constraint condition satisfies the following relationship: Where, and Node To Node The upper and lower limits of the flow rate in the pipeline.

5. The method for improving distribution network resilience based on integrated energy system optimization and scheduling according to claim 1 is characterized in that: The constraints of the electric power balance model include power constraints, voltage constraints, power flow safety constraints, distributed generation unit power constraints, electric energy storage operation constraints, and load constraints. Node For the object, the power constraint satisfies the following relationship: Where, 、 Separate moments node and nodes The square of the voltage; 、 Node To Node The resistance and reactance of the transmission line; is a very large positive real number; is a 0-1 variable, For the moment Slave nodes To Node The transmission line is connected. On the contrary; Node For the object, the voltage constraint condition satisfies the following relationship: Where, 、 Node The lower and upper limits of the square value of the voltage; Node For the object, the current constraint condition satisfies the following relationship: Where, For slave nodes Flow Node The upper limit of the square value of the current; Node For the object, the power flow safety constraint condition satisfies the following relationship: Node As the object, the electric energy storage operation constraints satisfy the following relationship: Where, and Node The corresponding energy storage power station is at time Discharge power and charging power; and All are 0-1 variables. =1 and =0 when node The corresponding energy storage power station is at time discharge, =0 and =1 when the node The corresponding energy storage power station is at time Charge; and Node The corresponding energy storage power station is at time The upper limit of discharge power and charging power; For nodes The corresponding energy storage power station is at time State of charge; and Node The corresponding charging and discharging efficiency of the energy storage power station; Charging and discharging periods for energy storage power stations; and Node The upper and lower state of charge of the corresponding energy storage power station; Node For the object, the load constraint condition satisfies the following relationship: Where, and Node The corresponding load at time The upper limit of active power and reactive power.

6. The method for improving distribution network resilience based on integrated energy system optimization and scheduling according to claim 1 is 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 during the pre-disaster preparation phase, satisfying the following relationship: Where, is the first optimization goal; A collection of time periods for the pre-disaster preparation phase; It is a collection of energy storage power stations in the distribution network; It is a collection of thermal energy storage stations in the distribution network; For nodes The corresponding energy storage power station is at time State of charge; For nodes The corresponding thermal energy storage is at heat storage state.

7. The method for improving distribution network resilience based on integrated energy system optimization and scheduling according to claim 6 is characterized in that: The predicted values ​​of output power of various power sources in the integrated energy system at different times before the disaster, including: 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.

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

9. The method for improving distribution network resilience based on integrated energy system optimization and scheduling according to claim 8 is 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.

10. A distribution network resilience improvement system based on integrated energy system optimization and scheduling, 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 constraints 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 constraint conditions of the distributed power generation units; wherein, the active power of the distributed power generation units corresponding to the nodes at each moment, the variable characterizing the natural gas supply status, the thermal power of the natural gas boiler and the dual-fuel boiler corresponding to the nodes at each moment, are used as the associated variables of the thermal power balance model and the gas flow balance model; the active power of the distributed power generation units corresponding to the nodes at each moment is the associated variable of the thermal power balance model and the electric power balance model; 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; wherein, the node is used as the associated variable of the thermal power balance model and the gas flow balance model; For the object, the thermal power balance model satisfies the following relationship: Where, For nodes The corresponding heat load at time Active power; 、 Separate nodes The corresponding thermal energy storage is at Heat storage power and heat release power; is the gas-heat conversion efficiency in the cogeneration unit, is the gas-to-electricity conversion efficiency of the distributed power generation unit; is the oil-heat conversion efficiency in the combined heat and power unit, The oil-to-electricity conversion efficiency of dual-fuel distributed power units; For nodes The corresponding dual-fuel distributed power generation unit is at time Active power; For nodes The corresponding natural gas boiler is The thermal power, For nodes The corresponding dual fuel boiler is Thermal power; Node As the object, the air flow balance model satisfies the following relationship: Where, For nodes The corresponding natural gas source at time Traffic volume; For nodes The corresponding natural gas boiler and dual fuel boiler are The sum of the active powers of ; For nodes The corresponding gas load at time The power, is the natural gas consumption rate of the distributed generation units, is the natural gas consumption rate of the boiler; For the moment Slave nodes Inflow Node of natural gas flow, For the moment Slave nodes Inflow Node Natural gas flow; is the node set of the natural gas network; Node As the object, the electric power balance model satisfies the following relationship: Where, and Separate moments Slave nodes Flow Node The active power and reactive power of Flow slave node The node set that comes from it; and Separate moments Slave nodes Flow Node The active power and reactive power of Flow direction node The set of nodes that go; 、 Node To Node The resistance and reactance of the transmission line; For the moment Slave nodes Flow Node The square value of the current; 、 Node The corresponding electrical load at time Active power and reactive power; 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 optimization objective of maximizing the sum of the state of charge of the electric energy storage and the state of heat of the thermal energy storage during the pre-disaster preparation phase. This module obtains the predicted output power values ​​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 optimization goal of maximizing the sum of the power corresponding to the loads at each node in the distribution network during the post-disaster defense phase. This module obtains 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.

11. 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 9.

12. 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 9 are implemented.

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