A comprehensive energy system operation method considering equipment failure risks

Abstract

The present invention relates to an integrated energy system operation method that takes into account the risk of equipment failure. First, the present invention constructs a corresponding integrated energy system energy flow architecture and various energy supply equipment output models based on the actual energy supply situation; secondly, considering that the energy supply equipment will have an adverse effect on the equipment itself when operating underload or overloaded, an energy supply equipment failure probability model based on the operating state and a system failure probability model are established; then, the planned operating cost and risk value of the day-ahead operation strategy are simultaneously used as optimization targets, and constraints such as energy balance, equipment output and ramp-up are considered to establish a day-ahead optimized operation model of the integrated energy system; finally, by processing and solving the model, the day-ahead operation strategy of the park integrated energy system is generated, and on the premise of ensuring the economic efficiency of the park system operation, the equipment operating conditions are optimized, the system operation risk is reduced, and the system operation safety is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to an integrated energy system operation method taking equipment failure risks into consideration. Background Art

[0002] With the emergence of distributed energy supply systems centered around clean energy and the increasing maturity of energy conversion technologies and energy coupling equipment, the barriers between the production, transmission, conversion, and consumption of different energy sources have been broken down. Integrated energy systems, instead of operating independently of single energy systems, now enable the integrated supply of multiple energy sources, the integration of multiple energy flow networks, and the integration of a large number of distributed devices, becoming the dominant form of future energy system development. The increasing number of energy conversion devices and the enhanced coupling characteristics of integrated energy systems, coupled with the uncertainty of distributed energy output, pose additional challenges to the safe operation of integrated energy systems. Conducting effective integrated energy system security analysis, mitigating potential risks in their operation, and improving their operational reliability are crucial for their implementation.

[0003] Currently, research on the optimized operation of integrated energy systems is still in the theoretical stage, primarily focusing on improving the economic efficiency, environmental protection, and safety of integrated energy systems. Wang Chengshan et al. proposed a multi-timescale optimal scheduling scheme based on model predictive control. Through rolling optimization and dynamic adjustment phases, this scheme reduces forecast errors for renewable energy and load, thereby improving the economic efficiency of system operation. Jia Hongjie's team proposed a multi-objective optimal hybrid power flow algorithm, which, when applied to the optimized operation of integrated energy systems, can simultaneously ensure both economic and environmental performance. Much research on the optimized operation of integrated energy systems has focused on economic efficiency, environmental protection, and overall energy efficiency, but has largely neglected system safety. Current research on the safety of integrated energy systems primarily focuses on safety analysis and reliability assessment. Shao Junyan et al. used the N-1 static safety analysis method for power systems to conduct a static safety analysis of the operating states of integrated energy systems under different coupled component control modes. Zhang Yuying et al. evaluated the reliability, environmental protection, and economic performance of integrated energy systems and proposed an evaluation index for the operational efficiency of integrated energy systems. Yu Juan et al. proposed a P2G-based reliability assessment method for electric-gas interconnected systems, considering multiple uncertainties such as component failures and source-load fluctuations. A large number of studies in safety analysis, reliability assessment and risk assessment focus on the operating status of the system at a certain time section, lacking risk assessment of the system scheduling plan within a certain time period. At the same time, they do not combine the system's operating strategy within a scheduling cycle with the evaluation results. Through feedback from the evaluation results, the operating strategy is adjusted to achieve the best balance between the system's operating economy and safety.

[0004] In summary, existing research or methods have the following shortcomings:

[0005] 1. Current research on operational optimization of integrated energy systems focuses primarily on economic efficiency, environmental protection, and overall energy efficiency, but lacks consideration of system operational risks.

[0006] 2. In some optimization operation studies that consider component failure, the component failure model is relatively simple, and the component failure probability is usually taken as a constant, ignoring the impact of different component operating conditions on the failure probability, which has certain limitations;

[0007] 3. Research on the safety of integrated energy systems often focuses on assessment methods, such as reliability assessment and safety risk assessment. These assessment methods often focus on the system state at a specific time interval, aiming to identify system weaknesses. Few methods assess the system's scheduling and operation strategies over a specific period of time. Summary of the Invention

[0008] The purpose of the present invention is to provide a method for operating an integrated energy system that takes into account the risk of equipment failure in order to address the problems existing in the prior art.

[0009] In order to achieve the above object, the technical solution adopted by the present invention is:

[0010] A method for operating an integrated energy system taking into account equipment failure risks includes the following steps:

[0011] Step 1: Construct the energy flow structure of the electricity-gas-heat integrated energy system. The inputs are electricity purchased from the grid, natural gas purchased from the gas grid, and clean electricity. The intermediate process is the conversion of energy by various functional devices, and the output is electricity and / or heat.

[0012] Construct output models for each energy supply equipment;

[0013] Step 2: Establish a model for equipment failure probability considering the operating status;

[0014]

[0015] Where: Indicates that device s is at t f The probability of failure at any moment, Indicates that device s is at t f Output value at the moment, p s，0 represents the basic failure probability of device s, p s，low represents the maximum failure probability that may be caused by low-load operation of equipment s, p s，over Indicates the maximum failure probability that may be caused by overload operation of equipment s, and Indicates the minimum and maximum output of the device s in a healthy operating state, and Indicates the minimum and maximum output of device s;

[0016] Establish a failure probability model for the integrated energy system;

[0017]

[0018] Where: Indicates that the device is t f The failure scenario in which failure occurs at any time, Indicates a fault scenario The probability of Ω f Indicates the expected fault device set. Indicates t f The probability of device s failing at time s;

[0019] Step 3: Establish an integrated energy system optimization operation model that considers equipment failure risks, including:

[0020] Establish the optimization objective function,

[0021]

[0022] Where: C represents the target value, χ1, χ2, and χ3 represent three priority coefficients, χ1>χ2>χ3, T represents the operating time of the functional device, c risk represents the risk value of the running strategy, C plan represents the planned operating cost,

[0023] Establishing constraints, including energy balance constraints, equipment output constraints, tie line / pipeline constraints, equipment ramp constraints, and system fault operation constraints;

[0024] Step 4: Use a two-level optimization model to solve the optimal operation model of the integrated energy system considering the risk of equipment failure.

[0025] The present invention first constructs the corresponding energy flow architecture of the integrated energy system and the output models of various energy supply equipment according to the actual energy supply situation; secondly, considering that the energy supply equipment will have an adverse effect on the equipment itself when operating underload or overloaded, an energy supply equipment failure probability model based on the operating status and a system failure probability model are established; then, the planned operating cost and risk value of the day-ahead operation strategy are taken as optimization targets at the same time, and the day-ahead optimization operation model of the integrated energy system is established by considering constraints such as energy balance, equipment output and ramp-up; finally, by processing and solving the model, the day-ahead operation strategy of the park integrated energy system is generated, which optimizes the equipment operating conditions, reduces the system operation risks, and improves the system operation safety while ensuring the economic efficiency of the park system operation.

[0026] As a preferred solution of the present invention, constructing the output model of each energy supply device in step 1 includes:

[0027] Constructing a micro gas turbine output model:

[0028]

[0029] Where: P GT and H GT Represents the electrical power and thermal power output of the gas turbine, η GT，e and η GT，h Indicates the power generation efficiency and heat generation efficiency of the gas turbine, F GT Indicates the natural gas flow rate input to the gas turbine, LHv ng Indicates the calorific value of natural gas, taking 9.7kW·h / m 3 ;

[0030] Constructing a gas boiler output model:

[0031] H GB =η GB F GB LHV ng

[0032] Where: H GB Indicates the thermal power output of the gas boiler, η GB Indicates the heating efficiency of the gas boiler, F GB Indicates the natural gas flow rate input to the gas boiler,

[0033] Constructing a fuel cell output model:

[0034] P FC =η FC F FC LHv ng

[0035] Where: P FC represents the electrical power output of the fuel cell, η FC Indicates the power generation efficiency of the fuel cell, F FC represents the natural gas flow rate input to the fuel cell,

[0036] Constructing a heat pump output model:

[0037] H HP =η HP P HP

[0038] Where: H HP Indicates the thermal power output of the heat pump, η HP Indicates the heat production efficiency of the heat pump, P HPIndicates the electrical power input to the heat pump.

[0039] As a preferred embodiment of the present invention, in step 3, the planned operating cost is expressed as:

[0040]

[0041] Where: represents the natural gas flow purchased by the system from the external gas grid at time t, represents the natural gas price of the external gas grid at time t, represents the amount of electricity purchased by the system from the external power grid at time t, represents the electricity price of the external power grid at time t, Ω represents the set of all energy supply devices, represents the output power of device i at time t, R i represents the operation and maintenance cost coefficient of equipment i, Indicates wasted power. represents the power curtailment penalty coefficient, Indicates the amount of heat wasted. represents the heat abandonment penalty coefficient,

[0042] The risk value of the operation strategy is expressed as:

[0043]

[0044]

[0045] Where: Indicates a fault scenario The corresponding additional loss is Indicates the change in the amount of natural gas purchased by the system from the natural gas grid. Indicates the change in the amount of electricity purchased by the system from the grid. Indicates a fault scenario Under this condition, at time t, the output power adjustment amount of device i is: Indicates the change in the amount of abandoned electricity, represents the change in the amount of heat rejection, and represents the penalty cost coefficient for cutting off electricity and gas loads, and Indicates a fault scenario Under the condition of t, the reduction of electricity and gas load at time t is δ adjust，i Represents the penalty cost coefficient for output adjustment of device i.

[0046] As a preferred solution of the present invention, in step 3, the energy balance constraint is expressed as:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] Where: Ω E Represents the power supply equipment collection, Ω H Represents the set of heating equipment, Ω F Indicates a collection of gas-using equipment. represents the output power of power supply device i at time t, represents the output thermal power of heating equipment i at time t, It represents the output power of the gas-consuming equipment at time t, and represents the electrical and thermal loads at time t, and Indicates that the device s is at t f In the fault scenario where a fault occurs at time t, the adjustment amounts of the output power of the power supply equipment, the output power of the heating equipment, and the input natural gas flow of the gas-consuming equipment are calculated at time t.

[0054] As a preferred solution of the present invention, in step 3, the tie line / pipeline constraint is expressed as:

[0055]

[0056]

[0057]

[0058]

[0059] Where: Indicates the minimum and maximum transmission power of the power tie line, Indicates the minimum and maximum flow of the natural gas interconnecting pipeline.

[0060] As a preferred solution of the present invention, in step 3, the device output constraint is expressed as:

[0061]

[0062]

[0063] Where: and Indicates the minimum and maximum output power of device i, and Indicates the start and stop status of device i in the base state and fault state, represents the output power of device i at time t,

[0064] The equipment climbing constraint is expressed as:

[0065]

[0066]

[0067] Where: and Indicates the ramp-down and ramp-up rates of device i.

[0068] As a preferred solution of the present invention, in step 3, the system fault operation constraint is expressed as:

[0069]

[0070]

[0071] As a preferred solution of the present invention, in step 4, the upper-layer system base state optimization operation model is expressed as:

[0072]

[0073] The fault state optimization operation model of the lower-level system is expressed as:

[0074]

[0075] As a preferred solution of the present invention, the upper model uses a particle swarm algorithm to find the optimal base-state unit planned output value, and the lower model uses CPLEX to obtain the actual unit adjustment amount and load shedding amount with the minimum loss cost in each fault state.

[0076] As a preferred solution of the present invention, the result of the optimization operation of the original single-layer model is used as the initial search point for iterative optimization of the upper-layer model.

[0077] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0078] 1. The present invention can generate a day-ahead operation method for an electricity-gas-heat integrated energy system that takes into account both economy and safety. This operation method can effectively reduce the failure probability of energy supply equipment and the operating risk value of the system, which is of great significance for actual production.

[0079] 2. The present invention establishes a failure probability model for energy supply equipment that takes into account the operating status, which can characterize the correlation between the underload and overload operating conditions of the equipment and the failure probability, and is more in line with actual production conditions.

[0080] 3. The present invention uses the particle swarm algorithm to solve the problem and uses the optimization results of the original single-layer model as the initial search point, which can improve the convergence speed and optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a structural diagram of the electricity-gas-heat integrated energy system described in the present invention.

[0082] Figure 2 This is the equipment failure probability model considering operating conditions described in the present invention.

[0083] Figure 3 Schematic diagram of the double-layer model of the present invention.

[0084] Figure 4 It is a solution flow chart of the present invention.

[0085] Figure 5 It is the forecast value of renewable energy output and electric and thermal load.

[0086] Figure 6 It is the iterative convergence situation.

[0087] Figure 7 It is the power balance situation.

[0088] Figure 8 It is a thermal energy balance situation.

[0089] Figure 9 This is the natural gas balance situation.

[0090] Figure 10 It is a comparison of gas turbine output before and after optimization.

[0091] Figure 11 It is the comparison of gas turbine failure probability before and after optimization.

[0092] Figure 12 It is a comparison of the risk values of gas turbine failure at different times before and after optimization. DETAILED DESCRIPTION

[0093] The present invention will be described in detail below with reference to the accompanying drawings.

[0094] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0095] Example 1

[0096] A method for operating an integrated energy system taking into account equipment failure risks includes the following steps:

[0097] Step 1: Construct the energy flow structure and energy supply equipment output model of the electricity-gas-heat integrated energy system.

[0098] 1.1 Construct an energy flow structure for an integrated electricity-gas-heat energy system. Consider that the park's inputs are electricity and natural gas purchased from the power grid and gas network, as well as clean electricity generated by wind power and photovoltaics. The intermediate process involves the conversion of energy by various energy supply equipment. The outputs are electricity and heat, which are used to supply the park's electrical and thermal loads. This energy flow structure and energy supply equipment types can be adjusted based on the specific circumstances of different parks.

[0099] 1.2 Construct output models for each energy supply device

[0100] 1.2.1 Construction of micro gas turbine output model

[0101] Micro gas turbines can convert natural gas into electricity and heat, and are the core equipment in integrated energy systems. Their output model can be expressed as:

[0102]

[0103] Where: P GT and H GT Represents the electrical power and thermal power output of the gas turbine, η GT，e and η GT，h Indicates the power generation efficiency and heat generation efficiency of the gas turbine, F GT Indicates the natural gas flow rate input to the gas turbine, LHv ng Indicates the calorific value of natural gas, taking 9.7kW·h / m 3 .

[0104] 1.2.2 Constructing a gas boiler output model

[0105] Gas boilers can convert natural gas into thermal energy. Its output model can be expressed as:

[0106] H GB =η GB F GB LHv ng (2)

[0107] Where: H GB Indicates the thermal power output of the gas boiler, η GB Indicates the heating efficiency of the gas boiler, F GB Indicates the natural gas flow rate input to the gas boiler.

[0108] 1.2.3 Constructing a fuel cell output model

[0109] Fuel cells can convert natural gas into electrical energy. Its output model can be expressed as:

[0110] P FC =η FC F FC LHv ng (3)

[0111] Where: P FC represents the electrical power output of the fuel cell, η FC Indicates the power generation efficiency of the fuel cell, F FC Indicates the natural gas flow rate input to the fuel cell.

[0112] 1.2.4 Constructing a heat pump output model

[0113] H HP =η HP P HP (4)

[0114] Where: H HP Indicates the thermal power output of the heat pump, η HP Indicates the heat production efficiency of the heat pump, P HP Indicates the electrical power input to the heat pump.

[0115] Step 2: Establish a model for equipment failure probability considering the operating status.

[0116] 2.1 Equipment failure probability model considering operating status

[0117] The probability of failure of energy supply equipment is related to its operation time and current operating conditions. The effect of operation time on the probability of equipment failure is usually described by a bathtub curve. In the present invention, the effect of operation time on the probability of equipment failure is ignored, and it is assumed that the basic failure probability of each device is at the bottom of its bathtub curve. In addition, generally speaking, the operating status of most energy supply equipment can be simplified and classified into rated power operating state, low-load operating state and overload operating state. Taking diesel generators as an example, low-load operation will cause poor sealing of the diesel engine piston and cylinder liner, thereby causing the oil to rise to the combustion chamber and burn, causing generator failure; overload operation will cause the cooling system and generator winding to overheat, etc., posing a serious safety hazard. Therefore, the present invention makes the following assumptions about the relationship between the equipment operating status and the probability of failure:

[0118] 1) The energy supply equipment operates near the rated power range and is in a healthy working state. At this time, the probability of equipment failure is kept at a minimum level;

[0119] 2) The output of the energy supply equipment exceeds the rated power range, and the equipment is in an overloaded working state. At this time, the probability of equipment failure increases as the degree of overload increases. When its output reaches the maximum power limit, the protection device is activated and the equipment stops operating;

[0120] 3) The output of the energy supply equipment is lower than the rated power range, and the equipment is in a low-load working state. At this time, the probability of equipment failure increases with the increase in the degree of low load, but the harm is not as serious as overload, that is, the growth rate of the failure probability is slower than that of the overload state.

[0121] Based on the above assumptions, a model of equipment failure probability considering the operating status is established, such as Figure 2 and shown in formula (5).

[0122]

[0123] Where: Indicates that device s is at t f The probability of failure at any moment, Indicates that device s is at t f Output value at the moment, p s，0 represents the basic failure probability of device s, p s，low represents the maximum failure probability that may be caused by low-load operation of equipment s, p s，over Indicates the maximum failure probability that may be caused by overload operation of equipment s, and Indicates the minimum and maximum output of the device s in a healthy operating state, and Indicates the minimum and maximum output of device s.

[0124] 2.2 Establishing a comprehensive energy system failure probability model

[0125] If any one or more components within a system fail during a scheduling cycle, it is considered a system failure. For simplicity, this paper only considers the N-1 failure of the device, and does not consider Nk failures or failures of power supply lines and pipelines. In addition, assuming that the system fails at time t, the failure state will remain throughout the subsequent scheduling cycle. This can be used to give a model for the system's failure probability during a certain period of time:

[0126]

[0127] Where: Indicates that the device is t f The failure scenario in which failure occurs at any time, Indicates a fault scenario The probability of Ω f Indicates the expected fault device set. Indicates t fThe probability that device s fails at time t.

[0128] Step 3: Establish an integrated energy system optimization operation model that takes into account equipment failure risks.

[0129] For the convenience of expression, we first define the system base state to represent the day-ahead phase of the planned operation strategy value, and the system fault state to represent the possible failure scenarios that may occur in the actual operation of the system.

[0130] 3.1 Objective Function

[0131] This paper uses the average failure probability of the system in each time period, the planned operating cost of the day-ahead operation strategy, and the risk value of the day-ahead operation strategy as the three optimization objectives of the model. The planned operating cost refers to the costs incurred by the system to arrange the output of each device according to the day-ahead operation strategy, including the purchase of electricity and gas, equipment operation and maintenance, and the cost of curtailed power and heat. The planned operating cost can be expressed as:

[0132] The planned operating cost is expressed as:

[0133]

[0134] Where: represents the natural gas flow purchased by the system from the external gas grid at time t, represents the natural gas price of the external gas grid at time t, represents the amount of electricity purchased by the system from the external power grid at time t, represents the electricity price of the external power grid at time t, Ω represents the set of all energy supply devices, represents the output power of device i at time t, R i represents the operation and maintenance cost coefficient of equipment i, Indicates wasted power. represents the power curtailment penalty coefficient, Indicates the amount of heat wasted. Represents the heat abandonment penalty coefficient.

[0135] The risk value of an operation strategy is the sum of the risk values of all possible failure states that may occur when the system is operated according to a certain strategy. The risk value of a failure state is the product of the additional loss caused by equipment failure during the actual operation of the system and the probability of the failure occurring. The risk value of the operation strategy and the additional loss of the failure state can be expressed as:

[0136]

[0137]

[0138] Where: Indicates a fault scenario The corresponding additional loss is Indicates the change in the amount of natural gas purchased by the system from the natural gas grid. Indicates the change in the amount of electricity purchased by the system from the grid. Indicates a fault scenario Under this condition, at time t, the output power adjustment amount of device i is: Indicates the change in the amount of abandoned electricity, represents the change in the amount of heat rejection, and represents the penalty cost coefficient for cutting off electricity and gas loads, and Indicates a fault scenario Under the condition of t, the reduction of electricity and gas load at time t is δ adjust，i Represents the penalty cost coefficient for output adjustment of device i.

[0139] Considering that the main purpose of the present invention is to reduce the probability of system failure and thus reduce the system's operating risk, it is considered to prioritize the three optimization objectives mentioned above. Among them, reducing the probability of failure is the first-level optimization objective; reducing the system's operating risk value is the second-level optimization objective; and reducing the system's operating cost is the third-level optimization objective. Therefore, three priority factors χ1, χ2, and χ3 are set, with χ1 = 10000 (due to the small probability value, this factor takes a larger value), χ2 = 10, and χ3 = 1. The objective function is expressed as:

[0140]

[0141] Where: C represents the target value, T represents the operating time of the functional equipment, C risk represents the risk value of the running strategy, C plan Indicates the planned operating cost.

[0142] 3.2 Constraints

[0143] The constraints include energy balance constraints, equipment output constraints, interconnection line / pipeline constraints, equipment ramp constraints, and system fault operation constraints.

[0144] 3.2.1 Energy balance constraints

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] Equations (11) to (16) represent the energy balance constraints of the system base state and fault state. E Represents the power supply equipment collection, Ω H Represents the set of heating equipment, Ω F Indicates a collection of gas-using equipment. represents the output power of power supply device i at time t, represents the output thermal power of heating equipment i at time t, It represents the output power of the gas-consuming equipment at time t, and represents the electrical and thermal loads at time t, and Indicates that the device s is at t f In the fault scenario where a fault occurs at time t, the adjustment amounts of the output power of the power supply equipment, the output power of the heating equipment, and the input natural gas flow of the gas-consuming equipment are calculated at time t.

[0152] 3.2.2 Equipment output constraints

[0153]

[0154]

[0155] Formulas (17) and (18) represent the output constraints of each device in the base state and fault state respectively. and Indicates the minimum and maximum output power of device i, and Indicates the start and stop status of device i in the base state and fault state, represents the output power of device i at time t.

[0156] 3.2.3 Tie Line / Pipeline Constraints

[0157]

[0158]

[0159]

[0160]

[0161] Equations (19) to (22) represent the transmission constraints of the power interconnection lines and natural gas interconnection pipelines between the park and the external system in the base state and fault state. Indicates the minimum and maximum transmission power of the power tie line, Indicates the minimum and maximum flow of the natural gas interconnecting pipeline.

[0162] 3.2.4 Equipment climbing constraints

[0163]

[0164]

[0165] Formulas (23) and (24) represent the climbing constraints of each device in the base state and fault state respectively. It is worth noting that the climbing constraints of the faulty device need to be removed from the constraints of the faulty state. and Indicates the ramp-down and ramp-up rates of device i.

[0166] 3.2.5 System Failure Operation Constraints

[0167]

[0168]

[0169] Formula (25) shows that under the fault state, at the fault time t f Before the fault, each device maintains the base state planned output value; Formula (26) shows that under the fault state, at the fault time t f After that, the output of the faulty device is 0.

[0170] Step 4: Model processing and solution.

[0171] 4.1 Model Processing

[0172] Because the proposed model involves multiple fault scenarios, the number of variables and constraints is large, making the solution complex. Furthermore, the proposed model consists of two parts: a base state and a fault state, exhibiting a distinct hierarchical structure. Therefore, the original single-layer model is converted into a two-layer model for solution, reducing the complexity of the solution.

[0173] First, the output value of each unit in the system base state irrelevant to the fault scenario is set to u, and the output value of each unit in the system fault state related to the fault scenario is set to u s Then the model mentioned in Section 3 can be simplified to the following form:

[0174] Where: C plan (u) represents the planned cost directly related to the base output value u; C risk (u,u s ) represents the difference between the base state output value u and the fault state output value u s The relevant risk value; g(u) = 0 and h(u) ≤ 0 represent the equality and inequality constraints in 3.2 that are only related to the base state output value; g s (u,u s)=0 and h s (u,u s )≤0 represents the equality and inequality constraints related to both the base state output value and the fault state output value in 3.2.

[0175] In the simplified model, the value of u is C plan (u) and C risk (u,u s ) will have an impact, and u s The value of only directly affects C risk (u,u s ). In addition, it is obvious that C plan (u) and the objective function C(u,u s ) is positively correlated. Therefore, the objective function C(u, u s ), then C risk (u,u s ) obtains the minimum value when u is determined. In addition, when the base state planned output value u of each unit is determined, the system failure probability matrix Then it is determined that each failure scenario is independent of each other and does not affect each other. Therefore, to obtain the total risk value C risk (u,u s ), we need to find the minimum loss cost under each failure scenario Based on the above analysis, the original single-layer model can be transformed into a double-layer model, such as Figure 3 shown.

[0176] The upper-level system base state optimization operation model is expressed as:

[0177]

[0178] The fault state optimization operation model of the lower-level system is expressed as:

[0179]

[0180] st formula (12)(14)(16)(18)(20)(22) and (24)~(26).

[0181] 4.2 Model Solution

[0182] For the two-layer optimization model in this paper, the intelligent algorithm nested solver method is used for solving. The upper model uses the particle swarm algorithm to find the optimal base state unit planned output value, and the lower model uses CPLEX to find the actual adjustment amount and load shedding amount of the unit with the minimum loss cost of each fault state. In addition, considering that the proposed model is complex and has many variables, it is difficult for the particle swarm algorithm to find the global optimal solution in this case. Therefore, the result of the traditional optimization operation is used as the initial search point for the iterative optimization of the upper model, in the hope of searching for a better solution near the result of the traditional optimization operation. The calculation process is as follows Figure 4 shown.

[0183] Through the above method, a day-ahead operation strategy for the campus-level electricity-gas-heat integrated energy system that takes into account both economy and safety can be generated. This strategy can effectively reduce the failure probability of energy supply equipment and the operating risk cost value of the system, which is of great significance for actual production.

[0184] In order to verify the effectiveness of the proposed method for generating the day-ahead operation strategy for an electric-gas-heat integrated energy system that considers the risks of underload and overload failures of energy supply equipment, a simulation is performed using an electric-gas-heat park integrated energy system as an example.

[0185] 1. Case description

[0186] Consider the energy flow structure of the example park as follows Figure 1 As shown in the figure, it includes a gas turbine (GT), a gas boiler (GB), a fuel cell (FC), a ground source heat pump (HP), a wind turbine (WT), and a photovoltaic (PV) system. Considering the failure risks of the gas turbine under overload and underload, its output range in healthy operation is [40, 325], the basic failure probability is 0.01, the maximum failure rate under underload is 0.05, and the maximum failure rate under overload is 0.2. The parameters of each device are shown in Table 1. The natural gas price is 2.51 yuan / m 3 The electricity prices are shown in Table 2. The forecast output of wind power and photovoltaic power as well as the electricity and heat load are shown in Table 2. Figure 5 As shown, the fluctuation of renewable energy and load is not considered. In addition, the quality requirements of different types of heat loads are not considered, and the heat load in the example can be supplied by all heating equipment. The penalty cost coefficient for heat load shedding is 0.97 yuan / kW, the penalty cost coefficient for power load shedding is 1.02 yuan / kW, the penalty cost coefficient for power abandonment is 0.2 yuan / kW, the penalty cost coefficient for heat abandonment is 0.15 yuan / kW, and the calorific value of natural gas is 9.7kW / m 3 .

[0187] Table 1 Parameters of energy supply equipment and interconnecting lines / pipelines

[0188]

[0189]

[0190] Table 2 Electricity price data

[0191]

[0192] 2. Optimization results

[0193] 2.1 Iteration Convergence

[0194] When the number of particles is set to 50 and the number of iterations is set to 300, the above optimization problem is calculated and the optimization convergence is obtained. Figure 6 The black curve in Figure 3 shows that the risk value converges to 68 around the 60th iteration. Secondly, comparing the black and gray curves shows that when using the particle swarm algorithm to solve this optimization problem, specifying an initial search point allows the algorithm to converge faster and achieve better results. This demonstrates the effectiveness of the present invention's initial search point for the particle swarm algorithm, using the day-ahead operating strategy, which disregards faults, as the initial search point.

[0195] 2.2 Analysis of Operational Strategies Ago

[0196] Figures 7 to 9 The optimized system day-ahead operation strategy is given, including the balance of electricity, heat and natural gas. Figure 7 , it can be seen that the system's electrical load is primarily borne by wind power. During the 24-7 hours, wind power output is high and the load is light. Even though the ground-source heat pump increases the electrical load, it is still unable to absorb all the wind power during this period, resulting in a significant amount of power curtailment. During the 15-22 hours, the electrical load is heavy, and wind power and photovoltaic power output cannot meet all the load demand. Therefore, the system activates the gas turbine and fuel cell to increase power supply during this period. Furthermore, in this example, the cost of power generation from the gas turbine and fuel cell is less than the cost of purchasing electricity from the grid during this period, so the system purchases less electricity from the grid.

[0197] observe Figure 8 , it can be seen that the heat load of the system is mainly supplied by the gas boiler. No heat abandonment occurs during the entire period. In the 20th period, the ground source heat pump shuts down because the electricity load is heavy during this period and the wind power output is small, resulting in insufficient electricity supply to support the operation of the ground source heat pump. Figure 9 It can be seen that gas boilers and gas turbines are the main natural gas consumption units. Fuel cells, due to their small capacity and high cost, only consume a small amount of natural gas overall.

[0198] In summary, the example uses the method proposed in this invention to generate a day-ahead operation strategy for an integrated electricity-gas-heat energy system, achieving multi-energy coupling and complementarity among "electricity-gas-heat", improving the comprehensive utilization rate of energy, meeting the energy supply and demand conditions of the system, and verifying the rationality of the method described in this invention.

[0199] 2.3 Comparative analysis before and after optimization

[0200] 2.3.1 Comparison of gas turbine operating conditions

[0201] Figure 10 and Figure 11 The results show the gas turbine output and failure probability before and after optimization. Before optimization, the gas turbine operated in an underloaded state during periods 8, 10, and 12, and in an overloaded state during periods 16-22. After optimization, the system shut down the gas turbine during underload periods and reduced its output during overload conditions, ensuring that the turbine operated in a healthy state or shut down throughout the entire period, minimizing the probability of unit failure. This validates the effectiveness of the method described in this invention.

[0202] 2.3.2 Comparison of operating costs and risk value

[0203] Figure 12 The risk values for gas turbine failure scenarios at different times are shown. It can be seen that before optimization, the risk values for gas turbine failure at times 8, 10, and 12 were slightly higher than after optimization. This is because the gas turbine is operating at low load during these times, and the probability of failure is slightly higher than in a healthy operating state. Furthermore, since the unit is operating at low load and output, the losses caused by a failure are relatively small. On the other hand, before optimization, the risk values for gas turbine failure at times 15-22 were very high. This is because the gas turbine is operating at overload during these times, resulting in a high probability of failure. Furthermore, the high load level and high output of the gas turbine during these times mean that a failure would have a significant impact on the system, resulting in relatively high losses and costs.

[0204] Table 3 Comparison of results before and after optimization

[0205]

[0206] Table 3 shows the system costs before and after optimization. It can be seen that the planned cost of the optimized system increased by 0.57% compared to the pre-optimization level, while the risk value decreased by 95.14% and the failure rate decreased by 95.21%. This demonstrates that the proposed method significantly reduces the system's failure rate and operational risks, while sacrificing a small amount of planned operating costs, thereby ensuring safe and stable system operation. This demonstrates the superiority of the proposed method.

[0207] 3. Conclusion

[0208] The following conclusions can be drawn from the calculation and analysis of this example.

[0209] 1) The proposed method was used to generate a day-ahead operating strategy for an integrated electricity-gas-heat energy system. Analysis showed that the strategy was consistent with the energy supply and demand sides of the system and achieved multi-energy coupling and complementarity. This validated the proposed method.

[0210] 2) The analysis of gas turbine output and risk values in various scenarios demonstrates that the proposed method can effectively improve the underload and overload operating conditions of energy supply equipment and reduce the risk of unit failure in various scenarios. This verifies the effectiveness of the proposed method.

[0211] 3) The analysis of various costs in the example shows that the proposed method significantly reduces the system risk value by sacrificing a small amount of operation plan cost, verifying the superiority of the method described in the present invention.

[0212] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for operating an integrated energy system taking into account the risk of equipment failure, characterized in that: The following steps are involved: Step 1: Construct the energy flow structure of the electricity-gas-heat integrated energy system. The inputs are electricity purchased from the grid, natural gas purchased from the gas grid, and clean electricity. The intermediate process is the conversion of energy by various functional devices, and the output is electricity and / or heat. Construct output models for each energy supply equipment; Step 2: Establish a model for equipment failure probability considering the operating status; Where: Indicates that device s is at t f The probability of failure at any moment, Indicates that device s is at t f Output value at the moment, p s，0 represents the basic failure probability of device s, p s，low represents the maximum failure probability that may be caused by low-load operation of equipment s, p s，over Indicates the maximum failure probability that may be caused by overload operation of equipment s, and Indicates the minimum and maximum output of the device s in a healthy operating state, and Indicates the minimum and maximum output of device s; Establish a failure probability model for the integrated energy system; Where: Indicates that the device is t f The failure scenario in which failure occurs at any time, Indicates a fault scenario The probability of Ω f Indicates the expected fault device set. Indicates t f The probability of device s failing at time s; Step 3: Establish an integrated energy system optimization operation model that considers equipment failure risks, including: Establish the optimization objective function, Where: C represents the target value, χ1, χ2, and χ3 represent three priority coefficients, χ1>x2>χ3, T represents the operating time of the functional device, and C risk represents the risk value of the running strategy, c plan represents the planned operating cost, Establishing constraints, including energy balance constraints, equipment output constraints, tie line / pipeline constraints, equipment ramp constraints, and system fault operation constraints; Step 4: Use a two-level optimization model to solve the optimal operation model of the integrated energy system considering the risk of equipment failure.

2. A method for operating an integrated energy system considering equipment failure risks according to claim 1, characterized in that: Constructing the output model of each energy supply device in step 1 includes: Constructing a micro gas turbine output model: Where: P GT and H GT Represents the electrical power and thermal power output of the gas turbine, η GT，e and η GT，h Indicates the power generation efficiency and heat generation efficiency of the gas turbine, F GT Indicates the natural gas flow rate input to the gas turbine, LHV ng Indicates the calorific value of natural gas, taking 9.7kW·h / m 3 ; Constructing a gas boiler output model: H GB =η GB F GB LHV ng Where: H GB Indicates the thermal power output of the gas boiler, η GB Indicates the heating efficiency of the gas boiler, F GB Indicates the natural gas flow rate input to the gas boiler, Constructing a fuel cell output model: P FC =η FC F FC LHV ng Where: P FC represents the electrical power output of the fuel cell, η FC Indicates the power generation efficiency of the fuel cell, F FC represents the natural gas flow rate input to the fuel cell, Constructing a heat pump output model: H HP =the HP P HP Where: H HP Indicates the thermal power output of the heat pump, η HP Indicates the heat production efficiency of the heat pump, P HP Indicates the electrical power input to the heat pump.

3. The method for operating an integrated energy system considering equipment failure risks according to claim 1, characterized in that: In step 3, the planned operating cost is expressed as: Where: represents the natural gas flow purchased by the system from the external gas grid at time t, represents the natural gas price of the external gas grid at time t, represents the amount of electricity purchased by the system from the external power grid at time t, represents the electricity price of the external power grid at time t, Ω represents the set of all energy supply devices, represents the output power of device i at time t, R i represents the operation and maintenance cost coefficient of equipment i, Indicates wasted power. represents the power curtailment penalty coefficient, Indicates the amount of heat wasted. represents the heat abandonment penalty coefficient, The risk value of the operation strategy is expressed as: Where: Indicates a fault scenario The corresponding additional loss is Indicates the change in the amount of natural gas purchased by the system from the natural gas grid. Indicates the change in the amount of electricity purchased by the system from the grid. Indicates a fault scenario Under this condition, at time t, the output power adjustment amount of device i is: Indicates the change in the amount of abandoned electricity, represents the change in the amount of heat rejection, and represents the penalty cost coefficient for cutting off electricity and gas loads, and Indicates a fault scenario Under the condition of t, the reduction of electricity and gas load at time t is δ adjust，i Represents the penalty cost coefficient for output adjustment of device i.

4. A method for operating an integrated energy system considering equipment failure risks according to claim 3, characterized in that: In step 3, the energy balance constraint is expressed as: Where: Ω E Represents the power supply equipment collection, Ω H Represents the set of heating equipment, Ω F Indicates a collection of gas-using equipment. represents the output power of power supply device i at time t, represents the output thermal power of heating equipment i at time t, It represents the output power of the gas-consuming equipment at time t, and represents the electrical and thermal loads at time t, and Indicates that the device s is at t f In the fault scenario where a fault occurs at time t, the adjustment amounts of the output power of the power supply equipment, the output power of the heating equipment, and the input natural gas flow of the gas-consuming equipment are calculated at time t.

5. The method for operating an integrated energy system considering equipment failure risks according to claim 3, characterized in that: In step 3, the tie line / pipeline constraint is expressed as: Where: Indicates the minimum and maximum transmission power of the power tie line, Indicates the minimum and maximum flow rate of the natural gas interconnecting pipeline.

6. The method for operating an integrated energy system considering equipment failure risks according to claim 3, characterized in that: In step 3, the device output constraint is expressed as: Where: and Indicates the minimum and maximum output power of device i, and Indicates the start and stop status of device i in the base state and fault state, represents the output power of device i at time t, The equipment climbing constraint is expressed as: Where: and Indicates the ramp-down and ramp-up rates of device i.

7. A method for operating an integrated energy system considering equipment failure risks according to claim 6, characterized in that: In step 3, the system failure operation constraint is expressed as:

8. A method for operating an integrated energy system considering equipment failure risks according to any one of claims 1 to 7, characterized in that: In step 4, the upper-layer system base state optimization operation model is expressed as: The fault state optimization operation model of the lower-level system is expressed as:

9. The method for operating an integrated energy system considering equipment failure risks according to claim 8, characterized in that: The upper model uses the particle swarm optimization algorithm to find the optimal base-state planned output value of the unit, and the lower model uses CPLEX to obtain the actual adjustment amount and load shedding amount of the unit with the minimum loss cost in each fault state.

10. The method for operating an integrated energy system considering equipment failure risks according to claim 9, characterized in that: The results of the optimization run of the original single-layer model are used as the initial search point for the iterative optimization of the upper-layer model.

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