Comprehensive energy system planning method considering system failures and storage medium thereof

Abstract

The present invention discloses a method for planning an integrated energy system that takes system failures into account, and a medium for storing the method. The present invention aims to solve the problem of improving the reliability of the integrated energy system during actual operation. The method is mainly based on a dynamically modeled integrated energy system. For different types of failures in the integrated energy system, a model predictive control method is used to optimize the operation strategy under various failure scenarios and evaluate the failure recovery time. Considering the economic efficiency of the planning optimization and operation optimization stages, with the annual net total cost as the target, a penalty function is designed to reflect the impact of the failure recovery process on the cost. The target value under each scenario is weighted averaged through an adaptive weight coefficient. A two-layer optimization configuration model is designed that considers typical scenarios and multiple failure scenarios, thereby achieving the organic coupling of integrated energy system planning optimization and operation optimization, and improving the reliability of the integrated energy system.

Description

Technical Field

[0001] The present invention belongs to the field of planning and optimization of integrated energy systems, and in particular to a method for planning an integrated energy system taking system failures into consideration and a medium for storing the method. Background Art

[0002] Integrated energy systems have attracted considerable attention in recent years due to their high primary energy utilization and minimal environmental impact. Energy utilization rates can reach 75%-80%. Furthermore, integrated energy systems typically generate three energy sources simultaneously: cooling, heating, and electricity. A typical integrated energy system typically includes generators such as gas turbines, internal combustion engines, and fuel cells; heating equipment such as waste heat boilers and gas boilers; and cooling equipment such as lithium bromide absorption chillers and electric chillers.

[0003] The complexity of the integrated energy system results in its environmental, economic and other performance being significantly affected by the selection of prime movers, the capacity of other equipment in the system and the system operation strategy.

[0004] Integrated energy system planning and operational optimization are the foundation for the economic and stable operation of integrated energy systems. They are directly related to the system's economic efficiency, environmental friendliness, and reliability, and are critical issues that must be addressed at the outset of an integrated energy system's establishment. Reasonable capacity planning can delay the construction of traditional energy supply systems, meet user requirements for energy quality and environmental protection, and ensure that the system achieves the lowest cost investment while meeting reliability and safety constraints. Excessive equipment capacity configuration in an integrated energy system can lead to increased system costs, while insufficient equipment capacity configuration, equipment failures, microgrid disturbances, and other fault conditions can lead to energy supply shortages in the system. Traditional static models are no longer applicable to the dynamic response characteristics and fault recovery processes of complex systems, nor to their impact on capacity planning.

[0005] Therefore, developing an organic coupling method for integrated energy system planning optimization and operation optimization considering system failures is of great significance to maintaining the economically stable operation of the integrated energy system. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned existing technologies and provide a comprehensive energy system planning method taking into account system failures and a medium for storing the same. The design of the method takes into account a two-layer optimization configuration model for typical scenarios and multiple failure scenarios to achieve the organic coupling of comprehensive energy system planning optimization and operation optimization, thereby improving the reliability of the comprehensive energy system in actual operation.

[0007] In a first aspect, the present invention provides a method for integrated energy system planning taking into account system failures, comprising:

[0008] S1, construct the static model of each device in the integrated energy system planning optimization layer and the transfer function model of each device in the operation optimization layer;

[0009] S2, constructing an operation constraint set of the operation optimization layer and a planning constraint set of the planning optimization layer based on the energy constraints of each device; the planning constraint set includes device capacity constraints, and the operation constraint set includes device operation constraints, energy supply balance constraints, energy storage thermal inertia constraints, and fault recovery time constraints;

[0010] S3: Import the equipment capacity constraints of the planning optimization layer into the operation optimization layer. Based on the boundary conditions under the normal operation scenario or the boundary conditions under the fault scenario, import the operation constraint set constraints, and use the standard objective function of model predictive control and the custom cost function as the total operation and maintenance cost optimization objective function;

[0011] S4 sets the appropriate time interval, prediction time domain, and control time domain, and uses a sequential quadratic programming optimization algorithm to achieve optimal scheduling calculations for system equipment in various scenarios;

[0012] S5, import the total operation and maintenance cost into the planning optimization layer, and use the annual net total cost as the planning optimization objective function;

[0013] S6, use genetic algorithm to iteratively update and optimize the new equipment capacity combination, and repeat steps S3 to S5 until the annual net total cost converges.

[0014] Preferably, in S1, the integrated energy system includes a power grid, a prime mover, a waste heat boiler, an absorption chiller, a gas boiler and an electric chiller; the electric energy generated by the prime mover is transmitted through electric wires to an external power supply network that supplies power to users, and the power grid is used to supplement the power supply of the prime mover when the power supply is insufficient; the heat energy generated by the prime mover is transmitted through pipelines to the waste heat boiler and the absorption chiller respectively; the heat generated by the waste heat boiler is transmitted to the heating network that provides heating to users; the cooling energy generated by the absorption chiller is transmitted to the cooling network that provides cooling to users; the heat energy generated by the gas boiler is transmitted to the heating network, and is used to supplement the heating when the waste heat boiler does not provide sufficient heating; an electric chiller is connected to the power supply network, and the cooling energy generated by the electric chiller is transmitted to the cooling network through pipelines, and is used to supplement the cooling when the absorption chiller does not provide sufficient cooling.

[0015] Preferably, in S1, the heating pipe network and the cooling pipe network are both equipped with primary and secondary networks, and energy exchange is achieved between the primary and secondary networks through cold and hot plate exchange stations.

[0016] Preferably, in S1, excitation data is designed according to device characteristics and input to the input terminal of each device to obtain output data. Then, based on the input and output data, a system identification method is used to construct a transfer function model of each device.

[0017] Preferably, in S1, the equipment characteristic refers to the step response time of each cooling and heating equipment.

[0018] Preferably, in S3, the standard objective function of the model predictive control is determined by the following formula:

[0019] J(z k )=Jy(z k )+Ju(z k )+JΔu(z k )+Jε(z k )

[0020] Among them, J(z k ) is the standard objective function, Jy(z k ) is the deviation of tracking reference output, Ju(z k ) is the deviation of the tracking control variable, JΔu(z k ) is the suppression term of the control variable increment, Jε(z k ) is the penalty function term generated by violating the constraint; without considering the control variable tracking and control variable increment suppression, and without other soft constraints, Ju(z k )、JΔu(z k ), Jε(z k ) are all 0, then the standard objective function is expressed as:

[0021]

[0022] Among them, k is the current control time interval; p is the prediction time domain; n y is the number of output variables; y j (k+i|k) is the value of the jth output predicted at the i-th prediction time domain step; r j (k+i|k) is the jth output reference value at the i-th prediction time domain step; is the scaling factor of the j-th output; is the jth output weight of the i-th prediction time domain step; z k is the QP solution to the optimization problem, expressed as:

[0023]

[0024] Where k+i|k represents the value at time k+i planned at time k, and u represents the control input variable.

[0025] Preferably, in S3, the running cost of the custom cost function is expressed as:

[0026] J c =C gas +C ele+C om +C rec

[0027] =(gas1+gas2)*Ts*R gas +(P1+P2)*Ts*R ele / 3600+

[0028] (P0*R gt +heat2*R gb +cold1*R ec +cold2*R ac +heat1*R b )*Ts / 3600+

[0029] heat L *Ts heatL *L heat +cold L *Ts coldL *L cold +ele L *Ts eleL *L ele

[0030] Among them, C gas is the natural gas cost, in yuan; C ele is the cost of purchasing electricity from the power grid, in yuan; C om is the operation and maintenance cost, in yuan; Ts is the time interval, in seconds; C rec Indicates the economic loss of users when the system is under-supplied, in RMB; R gas is the price of natural gas, in yuan / kg; R ele is the time-of-use electricity price of the power grid, in yuan / kWh; R gt 、R gb 、R ec 、R ac 、R b They are the operation and maintenance prices of the prime mover, gas boiler, electric chiller, absorption chiller, and waste heat boiler, respectively, in RMB / kWh; gas1 is the natural gas flow input to the prime mover; gas2 is the natural gas flow input to the gas boiler; P0 is the power output of the gas turbine; P2 is the supplementary power supply of the grid; P1 is the electric power input to the electric chiller; cold1 is the cooling capacity output of the electric chiller; cold2 is the cooling capacity output of the absorption chiller; heat1 is the heat output of the waste heat boiler; heat2 is the heat output of the gas boiler; heat L 、cold L 、ele L They are heating shortage, cooling shortage and power shortage when there is no grid supplementary power supply. If the integrated energy system has grid supplementary power supply, then eleL Equal to zero; Ts heatL 、Ts coldL 、Ts eleL L is the duration of insufficient heating, insufficient cooling and power supply when there is no grid supplementary power supply; heat 、L cold 、L ele They are the economic losses to users caused by insufficient heating, insufficient cooling and insufficient power supply when there is no grid supplementary power supply.

[0031] Preferably, the economic losses to users caused by insufficient heating, insufficient cooling and insufficient power supply when there is no grid supplementary power supply are determined by the user type, duration of insufficient energy supply and amount of insufficient energy supply.

[0032] As the optimal choice, the operating cost considers the weighted average cost under various fault scenarios, and the adaptive weight is used to comprehensively consider the operating cost J C The amount, the probability of occurrence of boundary conditions, the weighted average operating cost J C 'Calculated by the following formula, as follows:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] Where Jc' is the weighted average cost of multiple scenarios, unit yuan; Jc n is the operating cost under the nth fault scenario, in RMB; CI * n is the operating cost weight coefficient under the nth scenario; is the arithmetic average operating cost, unit yuan; CI n is the comprehensive weight of the operating cost under the nth scenario; is the arithmetic mean weight; Ω n is the probability of occurrence of scenario n, and the total probability of all scenarios is equal to 1;

[0039] The final optimization objective function of total operation and maintenance cost is expressed as:

[0040] J=Jy(z k )+J c '

[0041] In a second aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is run on a computer, the computer executes any of the above-mentioned integrated energy system planning methods considering system failures.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention is based on a dynamic modeling integrated energy system. According to different fault types of the integrated energy system, the model predictive control method is adopted to optimize the operation strategy under various fault scenarios and evaluate the fault recovery time. Taking into account the economy of the planning optimization and operation optimization stages, with the annual net total cost as the target, a penalty function is designed to reflect the impact of the fault recovery process on the cost. The target value under each scenario is weighted averaged through an adaptive weight coefficient. A two-layer optimization configuration model is designed considering typical scenarios and multiple fault scenarios, so as to realize the organic coupling of planning optimization and operation optimization of the integrated energy system and improve the reliability of the integrated energy system in actual operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of the structure of an integrated energy system provided by an embodiment of the present invention;

[0045] Figure 2 Schematic diagram of the fault recovery process based on system dynamic characteristics and operation optimization (a) shows the variation of the electric chiller input electrical power over time, b) shows the variation of the absorption chiller input thermal power over time, c) shows the variation of the electric chiller cooling output over time, and d) shows the variation of the absorption chiller electric chiller cooling output over time);

[0046] Figure 3 This is a process diagram of the integrated energy system planning method considering system failures of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention may be combined accordingly, provided that there is no conflict between them.

[0048] like Figure 1As shown, the integrated energy system provided in this embodiment includes: a power grid, a prime mover, a waste heat boiler, an absorption chiller, a gas boiler, and an electric chiller. Gas1 represents the natural gas flow rate input to the prime mover; gas2 represents the natural gas flow rate input to the gas boiler; P0 represents the power output of the gas turbine; Qex represents the heat output of the gas turbine; Qex1 represents the waste heat input to the absorption chiller; Qex2 represents the waste heat input to the waste heat boiler; P2 represents the supplementary power supply from the grid; P1 represents the electrical power input to the electric chiller; cold1 represents the cooling capacity output of the electric chiller; cold2 represents the cooling capacity output of the absorption chiller; heat1 represents the heat output of the waste heat boiler; and heat2 represents the heat output of the gas boiler.

[0049] The prime mover is a gas turbine, which consumes natural gas to generate heat or electricity. The electricity generated by the gas turbine is transmitted via power lines to an external power grid, which supplies electricity to users, thereby providing the system's electrical load. The system can also purchase electricity from the grid to meet its electricity needs. The heat generated by the gas turbine is piped to a waste heat boiler and an absorption chiller.

[0050] Waste heat boilers utilize hot flue gas to generate heat, providing a heat load for the system. This heat is then transferred to the heating pipelines that provide heating to users. Absorption chillers utilize hot flue gas to generate cooling capacity, providing a cooling load. This cooling capacity is then transferred to the cooling pipelines that provide cooling to users.

[0051] Gas-fired boilers consume natural gas to generate heat, supplementing the heat supply of waste heat boilers. Electric chillers are connected to the grid, generating cooling that is fed into the cooling pipelines, supplementing the cooling capacity of the absorption chillers. Furthermore, both the heating and cooling pipe networks in the microgrid system have primary and secondary networks, with energy exchange between the primary and secondary networks achieved through a cold and hot plate exchange station.

[0052] The integrated energy system planning method considering system failures is as follows: Figure 3 The specific steps are as follows:

[0053] S1. Construct static models and transfer function models of each device in the integrated energy system.

[0054] In S1, stimulus data is designed based on the device characteristics and fed into each device's input terminals to obtain output data. Then, based on the input and output data, a system identification method is used to construct a transfer function model for each device. Device characteristics primarily refer to the step response time of each cooling and heating device.

[0055] Taking the heating system as an example, the method for establishing a device transfer function model is explained. Step response testing shows that the dynamic response process of the gas-fired boiler and waste heat boiler in the constructed simulation system is approximately four hours. This process, from changing the input at the heat source to achieving stable heat at the end user, takes approximately four hours. The excitation data uses a GBN signal with an average conversion time of one-third the dynamic response time. Specifically, a GBN signal with an average conversion time of 4800 seconds is used as the excitation test signal for the gas-fired boiler and waste heat boiler. The data sampling time is 1 second, and the excitation duration can be designed to be approximately 6-18 times the entire dynamic response time.

[0056] Then, the system is identified based on the input and output. The MATLAB system identification toolbox can be used for modeling, and the transfer function model of the heating part is finally obtained as shown in the following formula:

[0057]

[0058] HEAT represents the total heat supply of the system.

[0059] The refrigeration part is modeled in the same way.

[0060] S2. Construct an operation constraint set and a planning constraint set based on the energy constraints of each device; the planning constraint set includes device capacity constraints, and the operation constraint set includes device operation constraints, energy supply balance constraints, energy storage thermal inertia constraints, and fault recovery time constraints.

[0061] In this embodiment, the capacity constraint of each device is as follows:

[0062] 0≤cold1≤1364,0≤cold2≤924,0≤heat1≤352,0≤heat2≤242

[0063] P1≥0, Qex1≥0, Qex2≥0, gas2≥0

[0064] In addition, the law of conservation of energy must also be satisfied, as shown in the following formula:

[0065] Qex1+Qex2=Qex.

[0066] S3. Import the equipment capacity constraints of the planning optimization layer into the operation optimization layer. According to the boundary conditions under the normal operation scenario or the boundary conditions under the fault scenario, import the operation constraint set constraints, and use the standard objective function of the model predictive control and the custom cost function as the total operation and maintenance cost optimization objective function.

[0067] The standard objective function of model predictive control is determined by the following formula:

[0068] J(zk )=Jy(z k )+Ju(z k )+JΔu(z k )+Jε(z k )

[0069] Among them, J(z k ) is the standard objective function, Jy(z k ) is the deviation of tracking reference output, Ju(z k ) is the deviation of the tracking control variable, JΔu(z k ) is the suppression term of the control variable increment, Jε(z k ) is the penalty function term generated by violating the constraint; without considering the control variable tracking and control variable increment suppression, and without other soft constraints, Ju(z k )、JΔu(z k ), Jε(z k ) are all 0, then the standard objective function is expressed as:

[0070]

[0071] Among them, k is the current control time interval; p is the prediction time domain; n y is the number of output variables; y j (k+i|k) is the value of the jth output predicted at the i-th prediction time domain step; r j (k+i|k) is the jth output reference value at the i-th prediction time domain step; is the scaling factor of the j-th output; is the jth output weight of the i-th prediction time domain step; z k is the QP solution to the optimization problem, expressed as:

[0072]

[0073] The running cost of the custom cost function is expressed as:

[0074] J c =C gas +C ele +C om +C rec

[0075] =(gas1+gas2)*Ts*R gas +(P1+P2)*Ts*R ele / 3600+

[0076] (P0*R gt +heat2*R gb +cold1*Rec +cold2*R ac +heat1*R b )*Ts / 3600+

[0077] heat L *Ts heatL *L heat +cold L *Ts coldL *L cold +ele L *Ts eleL *L ele

[0078] Among them, C gas is the natural gas cost, in yuan; C ele is the cost of purchasing electricity from the power grid, in yuan; C om is the operation and maintenance cost, in yuan; Ts is the time interval, in seconds; R gas is the price of natural gas, in yuan / kg; R ele is the time-of-use electricity price of the power grid, in yuan / kWh; R gt 、R gb 、R ec 、R ac 、R b They are the operation and maintenance prices of the prime mover, gas boiler, electric chiller, absorption chiller, and waste heat boiler, in RMB / kWh; gas1 is the natural gas flow input to the prime mover; gas2 is the natural gas flow input to the gas boiler; P0 is the power output of the gas turbine; P2 is the supplementary power supply from the grid; P1 is the electric power input to the electric chiller; cold1 is the cooling capacity output of the electric chiller; cold2 is the cooling capacity output of the absorption chiller; heat1 is the heat output of the waste heat boiler; heat L 、cold L 、ele L They are heating shortage, cooling shortage and power shortage when there is no grid supplementary power supply. If the integrated energy system has grid supplementary power supply, then ele L Equal to zero; Ts heatL 、Ts coldL 、Ts eleL L is the duration of insufficient heating, insufficient cooling and power supply when there is no grid supplementary power supply; heat 、L cold 、L ele They are the economic losses to users caused by insufficient heating, insufficient cooling and insufficient power supply when there is no grid supplementary power supply.

[0079] The economic losses to users caused by insufficient heating, insufficient cooling and insufficient power supply when there is no grid supplementary power supply are determined by the user type, duration of the energy shortage and the amount of energy shortage.

[0080] The operating cost considers the weighted average cost under various fault scenarios, and adopts adaptive weights to comprehensively consider the operating cost J C The amount, the probability of occurrence of boundary conditions, the weighted average operating cost J C 'Calculated by the following formula, as follows:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] Where Jc' is the weighted average cost of multiple scenarios, unit yuan; Jc n is the operating cost under the nth fault scenario, in RMB; CI * n is the operating cost weight coefficient under the nth scenario; is the arithmetic average operating cost, unit yuan; CI n is the comprehensive weight of the operating cost under the nth scenario; is the arithmetic mean weight; Ω n is the probability of occurrence of scenario n, and the total probability of all scenarios is equal to 1.

[0087] The final optimization objective function of total operation and maintenance cost is expressed as:

[0088] J=Jy(z k )+J c '

[0089] S4. Set appropriate time intervals, prediction time domains, and control time domains, and use a sequential quadratic programming optimization algorithm to achieve optimal scheduling calculations for system equipment in various scenarios.

[0090] Figure 2 Schematic diagram of the fault recovery process based on system dynamic characteristics and operation optimization, from which the total operation and maintenance cost can be obtained.

[0091] The present invention utilizes the MATLAB nonlinear MPC toolbox to implement all optimization algorithms and procedures. An MPC prediction model is constructed using an established system transfer function model, MPC custom constraints are constructed using the constraint set established in S2, a custom cost function is constructed using the objective function described in S3, and the final optimization objective function is constructed in conjunction with the standard objective function provided by the toolbox. The optimization calculation is then performed using the default sequential quadratic programming algorithm in the MATLAB nonlinear MPC toolbox (the toolbox performs the optimization solution by calling the fmincon function provided by MATLAB).

[0092] S5: Import the total operation and maintenance cost into the planning optimization layer and use the annual net total cost as the planning optimization objective function;

[0093] S6: Use genetic algorithm to iteratively update and optimize the new equipment capacity combination, and repeat S3 to S5 until the annual net total cost converges.

[0094] This embodiment further provides a computer storage medium, wherein the computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any of the above-mentioned integrated energy system planning methods considering system failures.

[0095] The embodiment described above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Persons skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent substitution or equivalent transformation falls within the scope of protection of the present invention.

Claims

1. A comprehensive energy system planning method considering system failures, characterized by: include: S1, construct the static model of each device in the integrated energy system planning optimization layer and the transfer function model of each device in the operation optimization layer; S2, constructing an operation constraint set of the operation optimization layer and a planning constraint set of the planning optimization layer based on the energy constraints of each device; the planning constraint set includes device capacity constraints, and the operation constraint set includes device operation constraints, energy supply balance constraints, energy storage thermal inertia constraints, and fault recovery time constraints; S3: Import the equipment capacity constraints of the planning optimization layer into the operation optimization layer. Based on the boundary conditions under the normal operation scenario or the boundary conditions under the fault scenario, import the operation constraint set constraints, and use the standard objective function of model predictive control and the custom cost function as the total operation and maintenance cost optimization objective function; S4 sets the appropriate time interval, prediction time domain, and control time domain, and uses a sequential quadratic programming optimization algorithm to achieve optimal scheduling calculations for system equipment in various scenarios; S5, import the total operation and maintenance cost into the planning optimization layer, and use the annual net total cost as the planning optimization objective function; S6, using genetic algorithm to iteratively update and optimize the new equipment capacity combination, and repeating steps S3 to S5 until the annual net total cost converges; In S3, the standard objective function of model predictive control is determined by the following formula: J(z k )=Jy(z k )+Ju(z k )+JΔu(z k )+Jε(z k ) Among them, J(z k ) is the standard objective function, Jy(z k ) is the deviation of tracking reference output, Ju(z k ) is the deviation of the tracking control variable, JΔu(z k ) is the suppression term of the control variable increment, Jε(z k ) is the penalty function term generated by violating the constraint; without considering the control variable tracking and control variable increment suppression, and without other soft constraints, Ju(z k )、JΔu(z k ), Jε(z k ) are all 0, then the standard objective function is expressed as: Among them, k is the current control time interval; p is the prediction time domain; n y is the number of output variables; y j (k+i|k) is the value of the jth output predicted at the i-th prediction time domain step; r j (k+i|k) is the jth output reference value at the i-th prediction time domain step; is the scaling factor of the j-th output; is the jth output weight of the i-th prediction time domain step; z k is the QP solution to the optimization problem, expressed as: In the formula, k+i|k represents the value of time k+i planned at time k, and u represents the control input variable; In S3, the running cost of the custom cost function is expressed as: J c =C gas +C ele +C om +C rec =(gas1+gas2)*Ts*R gas +(P1+P2)*Ts*R ele / 3600+ (P0*R gt +heat2*R gb +cold1*R ec +cold2*R ac +heat1*R b )*Ts / 3600+ heat L *Ts heatL *L heat +cold L *Ts coldL *L cold +ele L *Ts eleL *L ele Among them, C gas is the natural gas cost, in yuan; C ele is the cost of purchasing electricity from the power grid, in yuan; C om is the operation and maintenance cost, in yuan; Ts is the time interval, in seconds; C rec Indicates the economic loss of users when the system is under-supplied, in RMB; R gas is the price of natural gas, in yuan / kg; R ele is the time-of-use electricity price of the power grid, in yuan / kWh; R gt 、R gb 、R ec 、R ac 、R b They are the operation and maintenance prices of the prime mover, gas boiler, electric chiller, absorption chiller, and waste heat boiler, respectively, in RMB / kWh; gas1 is the natural gas flow input to the prime mover; gas2 is the natural gas flow input to the gas boiler; P0 is the power output of the gas turbine; P2 is the supplementary power supply of the grid; P1 is the electric power input to the electric chiller; cold1 is the cooling capacity output of the electric chiller; cold2 is the cooling capacity output of the absorption chiller; heat1 is the heat output of the waste heat boiler; heat2 is the heat output of the gas boiler; heat L 、cold L 、ele L They are heating shortage, cooling shortage and power shortage when there is no grid supplementary power supply. If the integrated energy system has grid supplementary power supply, then ele L Equal to zero; Ts heatL 、Ts coldL 、Ts eleL L is the duration of insufficient heating, insufficient cooling and power supply when there is no grid supplementary power supply; heat 、L cold 、L ele They are the economic losses to users caused by insufficient heating, insufficient cooling and insufficient power supply when there is no grid supplementary power supply.

2. The integrated energy system planning method considering system failures according to claim 1 is characterized in that: In S1, the integrated energy system includes a power grid, a prime mover, a waste heat boiler, an absorption chiller, a gas boiler and an electric chiller; the electric energy generated by the prime mover is transmitted through electric wires to an external power supply network that supplies power to users, and the power grid is used to supplement the power supply when the prime mover is insufficient; the heat energy generated by the prime mover is transmitted through pipelines to the waste heat boiler and the absorption chiller respectively; the heat generated by the waste heat boiler is transmitted to the heating network that provides heating to users; the cooling energy generated by the absorption chiller is transmitted to the cooling network that provides cooling to users; the heat energy generated by the gas boiler is transmitted to the heating network, and is used to supplement the heating when the waste heat boiler is insufficient; the electric chiller is connected to the power supply network, and the cooling energy generated by the electric chiller is transmitted to the cooling network through pipelines, and is used to supplement the cooling when the absorption chiller is insufficient.

3. The integrated energy system planning method considering system failures according to claim 2 is characterized in that: In S1, the heating pipe network and the cooling pipe network are both equipped with primary and secondary networks, and energy exchange is achieved between the primary and secondary networks through cold and hot plate exchange stations.

4. The integrated energy system planning method considering system failures according to claim 1 is characterized in that: In S1, the excitation data is designed according to the device characteristics and input to the input end of each device to obtain the output data. Then, based on the input and output data, the transfer function model of each device is constructed using the system identification method.

5. The integrated energy system planning method considering system failures according to claim 4 is characterized in that: In S1, the equipment characteristics refer to the step response time of each cooling and heating equipment.

6. The integrated energy system planning method considering system failures according to claim 1 is characterized in that: The economic losses to users caused by insufficient heating, insufficient cooling and insufficient power supply when there is no grid supplementary power supply are determined by the user type, duration of the energy shortage and the amount of energy shortage.

7. The integrated energy system planning method considering system failures according to claim 1 is characterized in that: The operating cost considers the weighted average cost under various fault scenarios and adopts adaptive weights to comprehensively consider the operating cost J C The amount, the probability of occurrence of boundary conditions, the weighted average operating cost J C 'Calculated by the following formula, as follows: Where Jc' is the weighted average cost of multiple scenarios, unit yuan; Jc n is the operating cost under the nth fault scenario, in RMB; CI * n is the operating cost weight coefficient under the nth scenario; is the arithmetic average operating cost, unit yuan; CI n is the comprehensive weight of the operating cost under the nth scenario; is the arithmetic mean weight; Ω n is the probability of occurrence of scenario n, and the total probability of all scenarios is equal to 1; The final optimization objective function of total operation and maintenance cost is expressed as: J=Jy(z k )+J c '。 8. A computer storage medium, characterized in that The computer storage medium stores a computer program; when the computer program is run on a computer, the computer executes the integrated energy system planning method considering system failures according to any one of claims 1 to 7.

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