Urban multi-energy system operation reliability evaluation method and system

Abstract

The invention relates to an urban multi-energy system operation reliability evaluation method and system, and the method comprises the steps: building a dynamic energy center model, and constructing a dynamic relation between the operation states of different types of equipment in an urban multi-energy system based on the dynamic energy center model; thermal inertia of the temperature control load in the urban multi-energy system is modeled as equivalent stored energy, and an operation reliability model of the temperature control load and different types of equipment is established; based on the temperature control load and the operation reliability model of the different types of equipment, establishing an emergency state management model of the urban multi-energy system; based on the various constructed models, the operation reliability of the urban multi-energy system is evaluated, and the evaluation process is optimized, so that the efficiency of operation reliability evaluation is improved. According to the method, the operation reliability of the urban multi-energy system can be effectively evaluated on the premise of considering the equivalent energy storage.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability assessment of multi-energy systems, and in particular to a method and system for assessing the operational reliability of a city multi-energy system. Background Art

[0004] Some studies have focused on exploring the flexibility modeling of urban multi-energy systems, which can support reliable operation under emergency fault conditions. Recently, some studies have also been devoted to evaluating the reliability of urban multi-energy systems. However, some key issues have not been well studied. For example, the impact of flexible loads on the reliability of urban multi-energy systems has not been well studied. As a typical flexible load, temperature control loads account for more than 40% of the total urban electricity consumption, which will bring opportunities for improving the reliability of urban multi-energy systems. For example, in winter, if the energy supply to the temperature control load is interrupted (such as the power supply of air conditioners, the hot water supply of heat exchangers, etc.), due to the thermal inertia of the room, the room temperature can still be maintained near the comfortable temperature for a short time. In addition, thermal energy can also be slightly oversupplied in advance, which is equivalent to increasing the thermal energy storage in the room. Through this measure, if the heat supply is interrupted, the indoor temperature can be maintained in the comfortable range for a longer time. Therefore, the temperature control load can be regarded as an equivalent energy storage and a charging and discharging strategy is implemented. These characteristics can be used for the reliable operation of urban multi-energy systems.

[0005] The use of temperature-controlled loads to promote the optimal operation of power systems has been studied in previous literature, but the impact of its equivalent energy storage on the operational reliability of multi-city multi-energy systems has never been evaluated. Introducing the equivalent energy storage of temperature-controlled loads into the operational reliability assessment of urban multi-energy systems is a challenging task that greatly increases its complexity. Temperature-controlled loads are usually large-scale and multi-heterogeneous. The increase in temperature-controlled loads will lead to an exponential increase in the state space. Therefore, there is an urgent need for a general physical model and reliability model, as well as corresponding state space reduction techniques, to reduce the dimensions of control variables and uncertainties. In addition, with the participation of multiple energy storages, the original time-independent optimal operation problem of urban multi-energy systems will be transformed into a time-coupled optimal control problem. The computational burden will increase significantly, especially in reliability analysis, where the optimal control problem will be repeatedly solved many times. In previous studies, there is no method to reduce the uncertainty dimension in this scenario and relax the time coupling characteristics. Summary of the invention

[0006] To this end, the technical problem to be solved by the present invention is to overcome the problem in the prior art that equivalent energy storage is not considered in the operational reliability assessment of a multi-city multi-energy system.

[0007] In order to solve the above technical problems, the present invention provides a method for evaluating the operation reliability of an urban multi-energy system, comprising:

[0008] Step S1: Establishing a dynamic energy hub model, wherein the dynamic energy hub model is used to construct a dynamic relationship between the operating states of different types of equipment in an urban multi-energy system;

[0009] Step S2: for end users connected to different types of equipment in the urban multi-energy system, based on the thermal inertia of the temperature control load in the end users, model them as equivalent energy storage, and establish operation reliability models of the temperature control load and different types of equipment based on the equivalent energy storage;

[0010] Step S3: establishing an emergency management model for the urban multi-energy system based on the temperature control load and the operation reliability models of different types of equipment;

[0011] Step S4: Based on the model constructed in steps S1, S2 and S3, the operational reliability of the urban multi-energy system is evaluated through time-series Monte Carlo simulation, and a simplified Benders decomposition process is introduced in the time-series Monte Carlo simulation to improve the efficiency of operational reliability evaluation.

[0012] In one embodiment of the present invention, the dynamic energy center model is established in step S1, and the formula is:

[0013]

[0014] Where t is time, x, u and y are the sets of state variables, control variables and output variables respectively; α, β, χ, δ are the corresponding coefficient matrices respectively.

[0015] In one embodiment of the present invention, the method for using the dynamic energy hub model in step S1 to construct a dynamic relationship between the operating states of different types of equipment in an urban multi-energy system is:

[0016] Taking the dynamic energy hub model as a city multi-energy system model;

[0017] The location where different energy types are aggregated and distributed in the urban multi-energy system model is taken as the energy bus. The energy bus includes the power bus, natural gas bus, thermal bus, and cooling bus. The energy flows between node i and the power bus, natural gas bus, thermal bus, and cooling bus in the urban multi-energy system model are respectively f i e , f i g , f i h and f i c, where node i is a different type of equipment in the urban multi-energy system; electric energy storage, thermal energy storage, ice storage and temperature control load in the urban multi-energy system model are selected as energy storage equipment and used as state variables, that is, x = [es8, es9, es 10 ,es 11 ,es 12 ] T ; Select partial energy flow as control variable: and the remaining energy flow as output variable

[0018] Assuming the energy flow direction into the energy bus is positive, the elements of each matrix are as follows: α = 0; for β: β 1,5 =β 2,8 =β 3,10 =-1, β 4,2 = -γη4, β 4,7 =β 4,8 =β 4,9 =γ, β 5,7 =(γ-1)η6,β 5,10 =β 5,11 =1-γ, χ=0; for δ: δ 1,1 =-1,δ 1,2 =1, δ 2,3 =δ 2,4 =δ 2,5 =δ 2,6 = -1, where and is the heat generation power and power generation power of the cogeneration unit; η4 is the power of the gas boiler; η6 is the power of the absorption refrigerator; and It is the energy efficiency coefficient of cooling and heating of the electric heat pump.

[0019] In one embodiment of the present invention, in step S2, based on the thermal inertia of the temperature control load, the temperature control load is modeled as equivalent energy storage, and the method of establishing the operating reliability model of the temperature control load and different types of equipment based on the reliability equivalence method is:

[0020] Step S2.1: Establish a thermal inertia model for a single temperature control load:

[0021] The temperature control load is modeled as equivalent energy storage. Since the equivalent energy storage of the temperature control load comes from the thermal inertia of the room, the equivalent energy storage calculation formula of room j is:

[0022]

[0023] Among them, Ca and ρ a is the specific heat capacity and density of air; V j is the spatial volume of the room; and is the upper and lower temperature limits of the comfort range; es 12,j is the capacity of equivalent energy storage;

[0024] During operation, the equivalent energy storage needs to consider the self-dissipation term. The formula is:

[0025]

[0026] in, is the indoor temperature; T a is the outdoor temperature; K j is the thermal conductivity coefficient; The area of ​​the room exposed to the outdoors; is the air exchange frequency; j is the heat dissipation coefficient in the room; A j The activity area of ​​the room;

[0027] Assume that the temperature control load always maintains the indoor temperature at the most comfortable temperature Assuming that the indoor temperature is always maintained in a stable state without sudden changes; when the indoor temperature is at the most comfortable temperature, the corresponding self-dissipation term is the baseline of the temperature control load, and the baseline changes with the change of the outdoor temperature; in addition, assuming that during the charging and discharging process of the equivalent energy storage, the room temperature always needs to be maintained within the comfort range, otherwise it will be regarded as load reduction; the equivalent charging and discharging power of the equivalent energy storage is calculated as the difference between the actual cooling power of the temperature control load and the baseline;

[0028] The maximum energy release power of equivalent energy storage occurs when the indoor temperature reaches the upper limit of the comfort range. The formula is:

[0029]

[0030] The maximum charging power of equivalent energy storage occurs when the indoor temperature reaches the lower limit of the comfort range. The formula is:

[0031]

[0032] although and Related to the outside temperature, but the difference As well as the equivalent energy storage capacity is only related to the comfort range and the characteristics of the room itself, but has nothing to do with the outside temperature;

[0033] Step S2.2: Establish a reliability model for cluster temperature control load:

[0034] The reliability model of a single temperature control load is described by a two-state Markov process, and the cluster of temperature control loads described by multiple two-state Markov processes is equivalent to a multi-state reliability model to reduce the number of state spaces;

[0035] The state transition rate calculation formula of the temperature control load of the cluster formed by the temperature control load described by multiple two-state Markov processes between the normal state s=1 and the first-order fault state s=j+1 is:

[0036]

[0037] Among them, j represents the cluster, is the state transition rate of the aggregate temperature control load from state s=1 to state s=j+1; is the state transition rate of the aggregate temperature control load from state s=j+1 to s=1; ICS and μ ICS They are the failure rate and repair rate of cyber-physical systems, respectively;

[0038] The calculation formula for the equivalent energy storage capacity and maximum energy release power in the state s=j+1 is:

[0039]

[0040] in, and is the energy storage capacity and maximum charging power of state s; es 12 ,j and 12,j' is the equivalent energy storage capacity of a single temperature control load in clusters j and j';

[0041] Step S2.3: Establish the operation reliability model of different equipment in the urban multi-energy system:

[0042] Step S2.3.1: Establish a multi-state operation reliability model for cogeneration units in urban multi-energy systems:

[0043] The cogeneration unit consists of three subsystems: the prime mover system, the power generation system, and the heat generation system. Each subsystem is represented by a two-state reliability model. With the failure of different subsystems, the cogeneration unit presents four states. The transfer rate calculation formula between the four states is:

[0044]

[0045] in, is the transition rate between state 1 and state 2; e ,λ h ,λp and μ e , μ h , μ p Represent the failure rate and repair rate of power generation system, heat generation system and prime mover system respectively;

[0046] Assume that the feasible region of the cogeneration unit is a quadrilateral, which is defined by four poles, namely (H A ,E A ), (H B ,E B ), (H C ,E C ), (H D ,E D ), representing the coordinates of four groups of heat production and power generation; if any subsystem fails, the feasible domain will degenerate into the projection of the original feasible domain on the corresponding coordinate axis, so the new feasible domain is expressed as:

[0047]

[0048] Among them, when the cogeneration unit is in state 2, formulas (12) and (14) are valid; when the cogeneration unit is in state 3, formulas (13) and (15) are valid; when it is in state 4, formulas (14) and (15) are valid;

[0049] Step S2.3.2: Multi-state operation reliability equivalence of other two-state devices in the urban multi-energy system:

[0050] For other equipment in the urban multi-energy system, including gas boilers, electric heat pumps, absorption refrigerators, distributed generators, electric energy storage, thermal energy storage and ice storage, they are usually composed of multiple devices with the same parameters connected in parallel; assuming that the gas boiler consists of N GB The gas boilers are connected in parallel, and the operating reliability of each gas boiler is represented by a two-state Markov process model. Then a gas boiler equivalent is represented by a multi-state Markov process model. When there is a gas boiler failure, the gas boiler equivalent will not fail completely, but will be transferred to a partial failure state; assuming that the states of the gas boiler equivalents are arranged in descending order according to their available heat production capacity, the calculation formula for the available heat production power of the gas boiler equivalent in state s is:

[0051]

[0052] in, It is the heat generation power of a single gas boiler under normal operating conditions;

[0053] In the Markov process, assuming that only a single state transition occurs in a single time, the state transition of the gas boiler equivalent will only occur between two adjacent states, and the state transition rate between any two adjacent states is and The calculation formula is:

[0054]

[0055] Among them, λ GB and μ GB The failure rate and repair rate of a single gas boiler.

[0056] In one embodiment of the present invention, the method of step S3 is:

[0057] Step S3.1: Construct degradation costs and comfort costs for different types of energy storage:

[0058] During emergency management, the charging and discharging process of power storage will further accelerate the degradation process of power storage, so the degradation cost needs to be considered; cycle depth is one of the biggest factors affecting power storage;

[0059] The degradation cost associated with the cycle depth during a single charge / discharge process within a time step is calculated as:

[0060]

[0061] in, is the degradation cost of electric energy storage j in time step k; is the purchase cost of electric energy storage j; is the cycle depth of the electric energy storage j before charging and discharging in time step k; Δδ j,k is the cycle depth of power storage j increased within time step k; Φ j is the cycle life loss, and the calculation formula is:

[0062]

[0063] Among them, Δt is the time step;

[0064] The degradation cost of thermal energy storage and ice storage is calculated as the average depreciation cost, using the formula:

[0065]

[0066] in, and are the depreciation costs of thermal energy storage and ice storage, respectively; and are the investment costs of thermal energy storage and ice storage respectively; T9 and T 10are the life cycles of thermal energy storage and ice storage, respectively; NK is the number of time steps;

[0067] Equivalent storage comfort cost in summer Evaluated based on predicted dissatisfaction percentage:

[0068]

[0069] Among them, σ is the coefficient that converts PPD into comfort cost; the PPD function is approximated as a quadratic function, and the original form of the PPD function is:

[0070]

[0071] Among them, M is metabolic rate, W is mechanical work, P is a is the water vapor pressure, t cl is the clothing temperature, t r is the mean radiation temperature, f cl is the area coefficient of the clothes, h c is the convective heat transfer coefficient;

[0072] Assuming that the most comfortable temperature is 26°C, the PPD function is fitted as follows:

[0073] PPD(T)=0.3222T 2 -16.7544T+17.8072(24)

[0074] Winter comfort costs Cost of summer comfort The calculation method is the same; therefore, the total operating cost of all energy storage is C ES The calculation formula is:

[0075]

[0076] Where γ is an integer variable, γ = 1 and 0 represent winter and summer respectively. In summer, the electric heat pump only operates in cooling mode, and the temperature control load only has cold energy storage; in winter, the electric heat pump only operates in heating mode, and the temperature control load only has hot energy storage;

[0077] Step S3.2: Emergency management of urban multi-energy systems based on optimal control framework:

[0078] The goal of emergency management of urban multi-energy systems is to minimize external costs, including energy purchase costs, load reduction costs, and energy storage degradation and comfort costs, expressed as:

[0079]

[0080] in, is the operating cost of the urban multi-energy system in time step k; and are the prices of natural gas and electricity at time steps k, respectively; l represents the form of energy, where el, ht and cl represent electricity, heat and cold, respectively; the optimal control framework is subject to the following constraints:

[0081] The system state equation and output equation are formula (1), which satisfies the following constraints:

[0082] Operational constraints for electric energy storage, thermal energy storage, ice storage and equivalent energy storage:

[0083]

[0084] The operating constraints of the cogeneration unit under different states, the operating constraints of the gas boiler, the energy consumption constraints of the power and natural gas transmission system, and the operating constraints of other equipment including electric heat pumps, absorption chillers and distributed generation:

[0085]

[0086] The initial and terminal conditions of energy storage, that is, the amount of energy stored in the energy storage device at the final moment of emergency management needs to be restored to the value before emergency management:

[0087]

[0088] Load reduction upper limit constraint:

[0089]

[0090] Trivial constraints on energy flow:

[0091]

[0092] in, represents the capacity of energy storage i in state s; is the maximum charge and discharge power of the electric energy storage under state s; N EHP and N AB is the number of electric heat pumps and absorption refrigerators; s EHP and AB is the state number of the electric heat pump and absorption refrigerator; and is the heating and cooling power of a single electric heat pump; is the generation capacity of distributed generation in state s; es 8,NK and 8,1 Represents the amount of energy stored in the electric energy storage at time steps k=1 and k=NK.

[0093] In one embodiment of the present invention, the method of step S4 is:

[0094] Step S4.1: Define operational reliability:

[0095] The expected load shortage EDNS and load loss probability LOLP are extended to multiple energy dimensions including electricity, cooling and heat to measure the operational reliability of urban multi-energy systems:

[0096]

[0097] Wherein, NS is the number of sequential Monte Carlo simulations; flag(x) represents the flag function, if x>0, flag(x)=1, otherwise flag(x)=0;

[0098] Step S4.2: Run the reliability assessment process:

[0099] The optimization control problem in the reliability assessment of urban multi-energy systems is divided into two problems: the main problem is the state determination problem of energy storage, and the sub-problem is the state determination problem of energy conversion equipment;

[0100] In order to avoid or reduce load shedding during reliability assessment, a simplified Benders decomposition method is designed, and the computational efficiency is further improved by optimizing the sequential Monte Carlo simulation process.

[0101] In one embodiment of the present invention, the method for improving computational efficiency by optimizing the sequential Monte Carlo simulation process is:

[0102] Step 1: Initialization: input data and set system parameters;

[0103] Step 2: For each sequential Monte Carlo simulation, the initial state of the urban multi-energy system is determined based on the steady-state probability, assuming that the initial temperature of the temperature control load is the most comfortable temperature;

[0104] Step 3: Simulate the state sequence of system components: Generate a random number U that follows the average distribution on (0, 1). Assuming that the current state of the device is s, the calculation formula for the duration τ of the state is:

[0105] τ=min{-λ s,s' / ln(U s' ),s'∈S,s≠s'} (37)

[0106] Where S is the system state set of the component, and the next state will be s';

[0107] Step 4: Repeat step 3 until the duration of the operation period is reached, repeat steps 2-4 for all devices in the urban multi-energy system, aggregate the state sequences of all devices, and generate the state sequence of the system;

[0108] Step 5: For each sequential Monte Carlo simulation, solve the emergency management problem and set the upper and lower bounds of the Benders decomposition, UB (0) =+∞,LB (0) =0, and the convergence criterion ζ;

[0109] Step 6: Solve the main problem:

[0110]

[0111] Subject to the state equation constraint (1), related constraints (28)-(35) and the Benders cuts filled in by the subproblems; u MP =[f 8,k ,f 9,k ,f 10,k ,f 11,k ,f 12,k ], k=1,...,NK are the control variables of the main problem; the cost of the subproblem is φ k Initialized to φ k ≥0; the solution of the main problem in the vth iteration is And the value of the objective function is ψ (v) ; Update lower bound LB (v) =max{LB (v-1) ,ψ (v)}Get u MP The solution is

[0112] Step 7: Solve the subproblem: For each time step k, based on Create the following sub-problems:

[0113]

[0114] Subject to the output equation (1), the relevant constraints are formulas (30)-(32), (34) and (35), and the control variables of the sub-problem are recorded as If the subproblem is not feasible, go to step 8, otherwise go to step 9;

[0115] Step 8: Fill the following feasible cuts back to the main problem:

[0116]

[0117] Among them, κ is the Farkas dual variable of the subproblem; and b are the coefficients of the subproblem constraints in the following compact form:

[0118]

[0119] Step 9: According to the optimization results, check whether there is load reduction. If not, stop the Benders iteration. Otherwise, backfill the following optimal cut to the main problem:

[0120]

[0121] Step 10: Check whether all time steps have been traversed. If so, update the upper bound according to the solution; solve the new main problem after backfilling the new Benders cut in step 1; otherwise, for the next time step k=k+1, start a new cycle from step 3;

[0122] Step 11: Check whether the following convergence criteria are met. If so, output the solution to the optimal control problem. Otherwise, repeat the next Benders iteration from step 5:

[0123] (UB (v) -LB (v) ) / (UB (v) +LB (v) )<ζ (43)

[0124] Step 12: Based on the load reduction amount obtained by Bender iteration calculation, the operation reliability index is calculated, and whether it converges is evaluated based on the calculation reliability. If converged, the final result is output; if not, the next sequential Monte Carlo simulation is repeated from step 2.

[0125] In order to solve the above technical problems, the present invention provides an urban multi-energy system operation reliability assessment system, comprising:

[0126] The first building module is used to establish a dynamic energy hub model, wherein the dynamic energy hub model is used to construct a dynamic relationship between the operating states of different types of equipment in the urban multi-energy system;

[0127] The second building module is used to model the end users connected to different types of equipment in the urban multi-energy system as equivalent energy storage based on the thermal inertia of the temperature control load in the end users, and establish the operation reliability model of the temperature control load and different types of equipment based on the reliability equivalence method;

[0128] The third building module is used to establish an emergency management model for the urban multi-energy system based on the temperature control load and the operation reliability model of different types of equipment;

[0129] Evaluation module: It is used to evaluate the operational reliability of the urban multi-energy system through time-series Monte Carlo simulation based on the model constructed by the first building module, the second building module and the third building module, and introduces a simplified Benders decomposition process in the time-series Monte Carlo simulation to improve the efficiency of operational reliability evaluation.

[0130] In order to solve the above technical problems, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for evaluating the operation reliability of a multi-energy system in a city are implemented.

[0131] In order to solve the above technical problems, the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned urban multi-energy system operation reliability assessment method are implemented.

[0132] The above technical solution of the present invention has the following advantages compared with the prior art:

[0133] The present invention proposes a new concept of dynamic energy hub. Compared with the traditional steady-state energy hub model, the dynamic energy hub model can effectively use state space equations and describe the dynamic characteristics of energy conversion devices and energy storage devices based on a unified and linear mathematical language.

[0134] The present invention proposes a multi-state operation reliability model for large-scale multivariate heterogeneous temperature control loads: the failure of the information communication system and the equivalent energy storage effect are characterized in the model. A reliability equivalence method is also proposed to effectively reduce the number of state spaces and reduce the complexity of the subsequent optimal control problem;

[0135] The present invention proposes an emergency management method for an urban multi-energy system: the method can characterize the incremental degradation cost of physical energy storage equipment caused by high-intensity charging and discharging, as well as the comfort cost of temperature control loads; through this measure, the combined impact of energy storage degradation and temperature control load comfort range on the reliability of urban multi-energy systems can be effectively evaluated;

[0136] The present invention proposes a simplified Benders decomposition process: by decoupling sub-problems and relaxing part of the Benders process, the time coupling characteristics of the energy storage control strategy in most scenarios can be relaxed, thereby effectively improving the computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0138] Figure 1 is a structural diagram of an urban multi-energy system in an embodiment of the present invention;

[0139] Figure 2 is a multi-state operation reliability model diagram of a cogeneration unit in an embodiment of the present invention;

[0140] Figure 3 It is a multi-state operation reliability equivalent diagram of the gas boiler in the embodiment of the present invention. DETAILED DESCRIPTION

[0141] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0142] Embodiment 1

[0143] Reference Figure 1 The present invention relates to a method for evaluating the operation reliability of a city multi-energy system, comprising:

[0144] Step S1: Establishing a dynamic energy hub model, wherein the dynamic energy hub model is used to construct a dynamic relationship between the operating states of different types of equipment in an urban multi-energy system;

[0145] Step S2: for end users connected to different types of equipment in the urban multi-energy system (end users include electric load, heat load and cooling load, wherein the heat load and cooling load are both composed of temperature-controlled load and non-temperature-controlled load), based on the thermal inertia of the temperature-controlled load in the end user, the temperature-controlled load is modeled as an equivalent energy storage, and an operation reliability model of the temperature-controlled load and different types of equipment is established based on the equivalent energy storage;

[0146] Step S3: establishing an emergency management model for the urban multi-energy system based on the temperature control load and the operation reliability models of different types of equipment;

[0147] Step S4: Based on the model constructed in steps S1, S2 and S3, the operational reliability of the urban multi-energy system is evaluated through time-series Monte Carlo simulation, and a simplified Benders decomposition process is introduced in the time-series Monte Carlo simulation to improve the efficiency of operational reliability evaluation.

[0148] The following is a detailed introduction to this embodiment:

[0149] The urban multi-energy system is the distribution network level of the energy system, which usually includes a variety of energy conversion equipment and energy storage equipment, and can flexibly consume electricity and natural gas from the power and natural gas transmission systems to meet the electrical load, heating load, and cooling load of its own end users. The configuration of a typical urban multi-energy system includes the following: Figure 1 As shown, it includes cogeneration units, gas boilers, electric heat pumps, absorption chillers, distributed generators, electric energy storage, thermal energy storage, and ice storage (i.e., part of the subsequent node i). End users usually have electricity, heat, and cooling loads (i.e., another part of the subsequent node i), where the cooling and heating loads are further composed of temperature-controlled loads (such as heat exchangers) and non-temperature-controlled loads (such as direct use of hot water). Different equipment and loads are usually abstracted into different nodes (i.e., subsequent nodes i), such as Figure 1 The location where different energy types are aggregated and distributed in the urban multi-energy system model is taken as the energy bus, which includes the power bus, natural gas bus, heat bus, and cooling bus (corresponding to Figure 1 The electrical nodes, gas nodes, hot nodes, and cold nodes in it).

[0150] The step 1) specifically comprises the following steps:

[0151] 1.1) Establish a unified mathematical model of the dynamic energy center

[0152] Due to the existence of various energy storage devices and temperature control loads with different dynamic characteristics in the urban multi-energy system, the system becomes a complex dynamic system, and the traditional energy center model is difficult to describe this dynamic relationship. Therefore, this embodiment proposes a model of a dynamic energy center, and its mathematical form is as follows:

[0153]

[0154] The dynamic energy center model consists of a system state equation and an output equation, where x, u, y and are the sets of state variables, control variables and output variables respectively; α, β, χ and δ are the corresponding coefficient matrices respectively.

[0155] 1.2) Establish an urban multi-energy system model based on the dynamic energy hub model

[0156] The model form of the urban multi-energy system is consistent with the model of the dynamic energy hub, that is, The elements inside each matrix are as follows:

[0157] The energy flows between node i (node ​​i is different types of equipment in the urban multi-energy system) and the power bus, natural gas bus, thermal bus, and refrigeration bus are f i e , f i g , f i h and f i cIt is worth noting that the selection of state variables, control variables and output variables is not unique. This embodiment selects power storage, thermal storage, ice storage and temperature control load as energy storage devices and as state variables, that is, x = [es8, es9, es 10 ,es 11 ,es 12 ] T This embodiment selects part of the energy flow as the control variable and the remaining energy flow as output variable Essentially, it consists of three sets of constraints: energy conservation constraints on different energy buses, energy conversion constraints on different devices, and energy charging and discharging constraints on different energy storage devices. In this embodiment, the energy flow direction into the energy bus is set to be positive, and the elements of each matrix are as follows: α = 0; β: β 1,5 =β 2,8 =β 3,10 =-1, β 4,2 = -γη4, β 4,7 =β 4,8 =β 4,9 =γ, β 5,7 =(γ-1)η6,β 5,10 =β 5,11 =1-γ, χ=0; δ in δ 1,1 =-1,δ 1,2 =1, δ 2,3 =δ 2,4 =δ 2,5 =δ 2,6 = -1; where and is the heat generation power and power generation power of the cogeneration unit; η4 is the power of the gas boiler; η6 is the power of the absorption refrigerator; and It is the energy efficiency coefficient of cooling and heating of the electric heat pump.

[0158] The step (2) specifically comprises the following steps:

[0159] 2.1) Establish a thermal inertia model for a single temperature control load

[0160] The equivalent energy storage of the temperature control load comes from the thermal inertia of the room or building. Under normal operation, the indoor temperature of the room is maintained at the most suitable comfort temperature by the temperature control load. When an emergency occurs, assuming that the season is summer, the cooling of the temperature control load may be interrupted for a short time. Thereafter, the indoor temperature will increase, thereby deviating from the most suitable comfort temperature. Nevertheless, before the indoor temperature reaches the upper limit of the comfort temperature range, this embodiment can be considered that the user's comfort is always satisfied. In this process, the temperature control load can be considered to provide equivalent cold energy for the urban multi-energy system. In addition, the cold energy provided to the room can also be oversupplied first, so that the temperature of the room is lower than the most suitable comfort temperature. This can be considered as equivalent charging of the temperature control load. In this case, if an energy supply interruption occurs, the temperature of the room can be maintained in the comfort range for a longer time. In summary, the temperature control load can be charged and discharged at the same time, so it can be regarded as equivalent energy storage.

[0161] The equivalent energy storage of room j can be calculated as follows:

[0162]

[0163] Among them, C a and ρ a is the specific heat capacity and density of air; V j is the spatial volume of the room; and is the upper and lower temperature limits of the comfort range; es 12,j is the capacity of equivalent energy storage.

[0164] Unlike physical energy storage (such as electric energy storage, thermal energy storage and ice storage), the self-dissipation term of equivalent energy storage cannot be ignored during operation. It is also one of the reasons for the rise in room temperature, including the heat dissipated by electrical appliances and people in the room, the heat conducted from the outside to the room, and the heat brought into the room when exchanging air with the outside:

[0165]

[0166] in is the indoor temperature; T a is the outdoor temperature; K j is the thermal conductivity coefficient; The area of ​​the room exposed to the outdoors; is the air exchange frequency; j is the heat dissipation coefficient in the room; A j is the active area of ​​the room. It can be noted that the self-dissipation term is related to the outdoor temperature and therefore varies with time during operation.

[0167] This embodiment assumes that: a) under normal operating conditions, the temperature control load always maintains the indoor temperature at the most comfortable temperature b) The indoor temperature is always maintained in a stable state and will not change suddenly. When the indoor temperature is at the most comfortable temperature, the corresponding self-dissipation term is the baseline of the temperature control load. As the outdoor temperature changes, the baseline also changes. In addition, this embodiment assumes that during the charging and discharging process of the equivalent energy storage, the room temperature always needs to be maintained within the comfort range, otherwise it will be regarded as load reduction. Under these assumptions, the equivalent charging and discharging power of the equivalent energy storage can be calculated as the difference between the actual cooling power of the temperature control load and the baseline. Therefore, the maximum discharge power of the equivalent energy storage is Occurs when the indoor temperature reaches the upper limit of the comfort range:

[0168]

[0169] The maximum charging power occurs when the indoor temperature reaches the lower limit of the comfort range:

[0170]

[0171] Note that although and Related to the outside temperature, but the difference The equivalent energy storage capacity is only related to the comfort range and the characteristics of the room itself, but has nothing to do with the outside temperature. This feature provides predictability and certainty for the system dispatching agency to dispatch the equivalent energy storage.

[0172] 2.2) Establish a reliability model for group temperature control loads

[0173] The reliability model of a single temperature-controlled load is described by a two-state Markov process. Reliability equivalence means aggregating different states with the same performance. For example, the temperature-controlled loads within cluster j have similar physical properties (such as the same maximum charge and discharge power and equivalent energy storage capacity). Then, the failure of any temperature-controlled load within the cluster will result in the same degradation performance of the cluster as a whole. Therefore, all states with only one temperature-controlled load failure can be considered to be the same state for equivalent aggregation. Therefore, a cluster formed by multiple two-state temperature-controlled loads can be reliability-equivalent to a multi-state reliability model, so that the number of state spaces can be greatly reduced. In a cluster, this embodiment considers up to second-order failures. Then, for NJ temperature-controlled load clusters, the number of states is (2+NJ+NJ 2 ), which is better than the reliability level before calibration The number of states should be greatly reduced. j is the number of temperature-controlled loads in cluster j.

[0174] The state transition rate of the temperature control load of the aggregation (i.e., a cluster formed by temperature control loads described by multiple two-state Markov processes) between the normal state (s=1) and the first-order fault state (s=j+1, if any temperature control load in cluster j fails) can be calculated as:

[0175]

[0176] in, is the state transition rate of the aggregate temperature control load from state s=1 to state s=j+1. is the state transition rate of the aggregate temperature control load from state s=j+1 to s=1. ICS and μ ICS are the failure rate and repair rate of cyber-physical systems, respectively.

[0177] The equivalent energy storage capacity and maximum energy discharge power in the state s=j+1 can be calculated as:

[0178]

[0179] in and is the energy storage capacity and maximum charging power of state s; es 12,j and 12,j' is the equivalent energy storage capacity of a single temperature control load in clusters j and j'.

[0180] Maximum charging power The state transition rate between the first-order fault and the second-order fault, the maximum and minimum charge and discharge power under the second-order fault state, and the equivalent energy storage capacity can all be calculated in a similar way.

[0181] 2.3) Establish the operation reliability model of other equipment

[0182] 2.3.1) Establish a multi-state operation reliability model for cogeneration units

[0183] The combined heat and power unit consists of three subsystems, namely the prime mover system, the power generation system and the heat generation system. Its topology is as follows: Figure 2 As shown. Each subsystem can be represented as a two-state reliability model. With the failure of different subsystems, the cogeneration unit presents four states. The transition rate between the four states can be calculated by the following formula:

[0184]

[0185] in, is the transition rate between state 1 and state 2; e ,λ h ,λ pand μ e , μ h , μ p They represent the failure rate and repair rate of the power generation system, heat generation system and prime mover system respectively.

[0186] In the perfect working state, the feasible domain of the cogeneration unit is a quadrilateral, which is defined by four poles, namely (H A ,E A ), (H B ,E B ), (H C ,E C ), (H D ,E D )(respectively four groups of coordinates of heat production and power generation). If any subsystem fails, the feasible domain will degenerate into the projection of the original feasible domain on the corresponding coordinate axis. Therefore, the new feasible domain can be expressed as:

[0187]

[0188]

[0189] Among them, when the cogeneration unit is in state 2, formulas (12) and (14) are valid. When the cogeneration unit is in state 3, formulas (13) and (15) are valid. When it is in state 4, formulas (14) and (15) are valid.

[0190] 2.3.2) Multi-state operation reliability of other two-state devices is equivalent

[0191] For other equipment, such as gas boilers, electric heat pumps, absorption chillers, distributed generators, power storage, thermal energy storage and ice storage, they are usually composed of multiple components with the same parameters connected in parallel. GB Gas boilers are connected in parallel. The state space diagram is as follows Figure 3 As shown. The operational reliability of each gas boiler is represented by a two-state Markov process model. A gas boiler equivalent can be represented by a multi-state Markov process model. When some gas boilers fail, the gas boiler equivalent does not fail completely, but will be transferred to a partial failure state. Assume that the states of the gas boiler equivalents are arranged in descending order according to their available heat production capacity. The available heat production power of the gas boiler equivalent in state s can be calculated as:

[0192]

[0193] in, It is the heat production power of a single gas boiler under normal operating conditions.

[0194] In the Markov process, it is assumed that only a single state transition occurs in a single time. Therefore, the state transition of the gas boiler equivalent will only occur between two adjacent states. The state transition rate between any two adjacent states is and It can be calculated as:

[0195]

[0196] Among them, GB and μ GB is the failure rate and repair rate of a single gas boiler. The operational reliability equivalents of other equipment can be determined by similar methods.

[0197] The step (3) specifically comprises the following steps:

[0198] Based on the multi-state operation reliability equivalent established in the previous step, this embodiment can simulate the state sequence of the components during the operation period through the sequential Monte Carlo method. It is specifically described in step (4). In each sequential Monte Carlo simulation, if the supply of electricity and natural gas is interrupted, or any component fails, the city's multi-energy system will be re-dispatched. In more serious cases, some loads will also be interrupted. Therefore, in this step, this embodiment establishes an emergency state management method for an urban multi-energy system to minimize load reduction based on the optimal control framework by making full use of the flexibility of energy conversion, storage equipment, and equivalent energy storage of temperature control loads.

[0199] 3.1) Degradation costs and comfort costs of different energy storage

[0200] In the optimization process of emergency management, it is very important to characterize external costs. In addition to the explicit cost of the consumed energy, another part of the external cost comes from the energy storage equipment. The cost of physical energy storage equipment (including electric energy storage, thermal energy storage and ice storage) mainly comes from its degradation process. For the equivalent energy storage of temperature-controlled loads, the cost mainly comes from the comfort loss due to deviation from the most comfortable temperature.

[0201] The power storage of urban multi-energy systems is mainly composed of lithium batteries. Its lifespan is usually short, so the degradation cost cannot be ignored. During the emergency management process, the charging and discharging process of power storage will become more frequent and more intense, which will further accelerate the degradation process. Among the many factors that affect degradation, the cycle depth is one of the most influential factors, and it is closely affected by the emergency management strategy.

[0202] The degradation cost associated with the cycle depth during a single charge / discharge process within a time step can be calculated as:

[0203]

[0204] in, is the degradation cost of electric energy storage j in time step k; is the purchase cost of electric energy storage j; is the cycle depth of the electric energy storage j before charging and discharging in time step k; Δδ j,k is the cycle depth of the power storage j increased within the time step k. Φ j is the cycle life loss, which can be calculated as:

[0205]

[0206] Among them, Δt is the time step.

[0207] The degradation cost of thermal energy storage and ice storage can be calculated as the average depreciation cost as follows:

[0208]

[0209] in, and are the depreciation costs of thermal energy storage and ice storage, respectively. and are the investment costs of thermal energy storage and ice storage respectively; T9 and T 10 are the life cycles of thermal energy storage and ice storage, respectively. NK is the number of time steps.

[0210] Equivalent storage comfort cost in summer It can be evaluated based on the Predicted Percentage of Dissatisfied (PPD):

[0211]

[0212] Wherein, σ is the coefficient for converting PPD into comfort cost; the PPD function is a complex exponential function, which is difficult to be processed by the solver in the optimization problem. Therefore, this embodiment approximates it as a quadratic function. The original form of the PPD function is:

[0213]

[0214] Among them, M is metabolic rate, W is mechanical work, P is a is the water vapor pressure, t cl is the clothing temperature, t r is the mean radiation temperature, f cl is the area coefficient of the clothes, h c is the convective heat transfer coefficient.

[0215] Based on the above function, assuming that the most comfortable temperature is 26°C, the PPD function can be fitted as:

[0216] PPD(T)=0.3222T 2 -16.7544T+17.8072 (24)

[0217] Winter comfort costs The same method can be used to calculate. Therefore, the total operating cost of all energy storage is C ES It can be calculated as:

[0218]

[0219] Where γ is an integer variable. γ=1 and 0 represent winter and summer respectively. This embodiment assumes that the electric heat pump only operates in cooling mode in summer. The temperature control load only has cold energy storage. For winter, vice versa.

[0220] 3.2) Emergency management of urban multi-energy systems based on optimal control framework.

[0221] The goal of emergency management of urban multi-energy systems is to minimize external costs, including energy purchase costs, load reduction costs, and degradation and comfort costs of energy storage. The control variable is u, as shown in step (1).

[0222]

[0223] in, is the operating cost of the urban multi-energy system in time step k; and are the prices of natural gas and electricity at time step k, respectively. l represents the form of energy, where el, ht and cl represent electricity, heat and cold, respectively. The optimal control framework is subject to the following constraints:

[0224] 1) The state equation and output equation of the urban multi-energy system are shown in formula (1).

[0225] 2) Operational constraints for electric energy storage, thermal energy storage, ice storage and equivalent energy storage:

[0226]

[0227]

[0228] 3) Operation constraints of cogeneration units under different conditions. Operation constraints of gas boilers. Energy consumption constraints of power and natural gas transmission systems, and operation constraints of other equipment (including electric heat pumps, absorption chillers, and distributed generation).

[0229]

[0230] 4) Initial and terminal conditions of energy storage: The amount of energy stored in the energy storage device at the end of the emergency management needs to be restored to the value before the emergency management.

[0231]

[0232] 5) Load reduction upper limit constraint:

[0233]

[0234] 6) Trivial constraints on energy flow:

[0235]

[0236] in represents the capacity of energy storage i in state s; is the maximum charge and discharge power of the electric energy storage under state s; N EHP and N AB is the number of electric heat pumps and absorption refrigerators; s EHP and AB is the state number of the electric heat pump and absorption refrigerator; and is the heating and cooling power of a single electric heat pump; is the generation capacity of distributed generation in state s; es 8,NK and 8,1 Represents the amount of energy stored in the electric energy storage at time steps k=1 and k=NK.

[0237] The step 4) specifically comprises the following steps:

[0238] 4.1) Definition of operational reliability indicators

[0239] The expected load shortage (EDNS) and loss of load probability (LOLP) are extended to multiple energy dimensions (electricity, cooling, and heat) to measure the operational reliability of urban multi-energy systems:

[0240]

[0241] Wherein, NS is the number of sequential Monte Carlo simulations; flag(x) represents a flag function, where flag(x)=1 when x>0, otherwise flag(x)=0.

[0242] 4.2) Operational reliability assessment process

[0243] The emergency state management in step 3) is a large-scale, time-coupled quadratic programming problem. In the sequential Monte Carlo simulation, the emergency state management will be repeated many times, which will increase the computational burden. By observing the structure of the optimization control problem, it can be found that the state equation of the energy storage is the cause of the time coupling. By fixing the state of the energy storage, the optimal control problem will become a time-independent problem. Therefore, this embodiment can divide the original problem into two problems: the main problem is the state determination problem of the energy storage, and the sub-problem is the state determination problem of the energy conversion equipment.

[0244] In addition, in the process of reliability assessment, the main purpose is to avoid or reduce load shedding, and accurate solution of equipment operating status is not necessary. Based on this, this embodiment designs a method to simplify Benders decomposition, and further improves the calculation efficiency by optimizing the sequential Monte Carlo simulation process. The specific process is as follows:

[0245] Step 1: Initialization. Input data and set system parameters.

[0246] Step 2: For each sequential Monte Carlo simulation, the initial state of the urban multi-energy system is determined based on the steady-state probability. Assume that the initial temperature of the temperature control load is the most comfortable temperature.

[0247] Step 3: Simulate the state sequence of system components. Generate a random number U that follows the average distribution on (0, 1). Assume that the current state of the component is s. Then, the duration τ of this state can be calculated as:

[0248] τ=min{-λ s,s' / ln(U s' ),s'∈S,s≠s'} (37)

[0249] Where S is the system state set of the component. Then, the next state will be s'.

[0250] Step 4: Repeat step 3 until the duration of the runtime is reached. Repeat steps 2-4 for all system components. Aggregate the state sequences of all components to generate the state sequence of the system.

[0251] Step 5: For each sequential Monte Carlo simulation, solve the emergency management problem. Set the upper and lower limits of the Benders decomposition, UB (0) =+∞,LB (0) =0, and the convergence criterion ζ.

[0252] Step 6: Solve the main problem:

[0253]

[0254] It is subject to the state equation constraint (1), the related constraints (28)-(35) and the Benders cuts filled in by the subproblems. MP =[f 8,k ,f 9,k ,f 10,k ,f 11,k ,f 12,k ], k=1,...,NK are the control variables of the main problem. The cost of the subproblem φ k Initialized to φ k ≥0. The solution of the main problem in the vth iteration is And the value of the objective function is ψ (v) . Update the lower bound LB (v) =max{LB (v-1) ,ψ (v)}Get u MP The solution is

[0255] Step 7: Solve the subproblem. For each time step k, based on Create the following sub-problems:

[0256]

[0257] The output equation (1) is obeyed, and the related constraints are formulas (30)-(32), (34) and (35). The control variables of this embodiment are recorded as If this subproblem is not feasible, go to step 8, otherwise go to step 9.

[0258] Step 8: Fill the following feasible cuts back to the main problem:

[0259]

[0260] Where κ is the Farkas dual variable of the subproblem; and b are the coefficients of the subproblem constraints in the following compact form:

[0261]

[0262] Step 9: Check if there is any load shedding based on the optimization results. If not, stop the Benders iteration, even if the optimal solution is not found. Otherwise, backfill the following optimal cut to the main problem:

[0263]

[0264] Step 10: Check if all time steps have been traversed. If so, update the upper bound according to the solution; solve the new master problem after backfilling the new Benders cut as in step 1. Otherwise, for the next time step k = k + 1, start a new cycle from step 3.

[0265] Step 11: Check whether the following convergence criteria are met. If so, output the solution to the optimal control problem. Otherwise, repeat the next Benders iteration from step 5.

[0266] (UB (v) -LB (v) ) / (UB (v) +LB (v) )<ζ (43)

[0267] Step 12: Based on the load reduction calculated by Bender iteration, calculate the operating reliability index. Evaluate whether it converges based on the calculated reliability. If it converges, output the final result; if it does not converge, repeat the next sequential Monte Carlo simulation from step 2.

[0268] Embodiment 2

[0269] This embodiment provides an urban multi-energy system operation reliability assessment system, including:

[0270] The first building module is used to establish a dynamic energy hub model, wherein the dynamic energy hub model is used to construct a dynamic relationship between the operating states of different types of equipment in the urban multi-energy system;

[0271] The second building module is used to model the end users connected to different types of equipment in the urban multi-energy system as equivalent energy storage based on the thermal inertia of the temperature control load in the end users, and establish the operation reliability model of the temperature control load and different types of equipment based on the reliability equivalence method;

[0272] The third building module is used to establish an emergency management model for the urban multi-energy system based on the temperature control load and the operation reliability model of different types of equipment;

[0273] Evaluation module: It is used to evaluate the operational reliability of the urban multi-energy system through time-series Monte Carlo simulation based on the model constructed by the first building module, the second building module and the third building module, and introduces a simplified Benders decomposition process in the time-series Monte Carlo simulation to improve the efficiency of operational reliability evaluation.

[0274] Embodiment 3

[0275] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for evaluating the operation reliability of a multi-energy system in a city described in Embodiment 1 are implemented.

[0276] Embodiment 4

[0277] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for evaluating the operating reliability of an urban multi-energy system described in the first embodiment are implemented.

[0278] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0279] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0280] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0281] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0282] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0283] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A method for evaluating the operational reliability of an urban multi-energy system, characterized in that: include: Step S1: Establishing a dynamic energy hub model, wherein the dynamic energy hub model is used to construct a dynamic relationship between the operating states of different types of equipment in an urban multi-energy system; Step S2: for end users connected to different types of equipment in the urban multi-energy system, based on the thermal inertia of the temperature control load in the end users, model them as equivalent energy storage, and establish operation reliability models of the temperature control load and different types of equipment based on the equivalent energy storage; Step S3: establishing an emergency management model for the urban multi-energy system based on the temperature control load and the operation reliability models of different types of equipment; Step S4: Based on the model constructed in steps S1, S2 and S3, the operational reliability of the urban multi-energy system is evaluated through time-series Monte Carlo simulation, and a simplified Benders decomposition process is introduced in the time-series Monte Carlo simulation to improve the efficiency of operational reliability evaluation.

2. The method for evaluating the operational reliability of an urban multi-energy system according to claim 1 is characterized in that: In step S1, a dynamic energy center model is established, and the formula is: Where t is time, x, u and y are the sets of state variables, control variables and output variables respectively; α, β, χ, δ are the corresponding coefficient matrices respectively.

3. The method for evaluating the operational reliability of a city multi-energy system according to claim 2 is characterized in that: The method for using the dynamic energy hub model in step S1 to construct the dynamic relationship between the operating states of different types of equipment in the urban multi-energy system is: Taking the dynamic energy hub model as a city multi-energy system model; The location where different energy types are aggregated and distributed in the urban multi-energy system model is taken as the energy bus. The energy bus includes the power bus, natural gas bus, thermal bus, and cooling bus. The energy flows between node i and the power bus, natural gas bus, thermal bus, and cooling bus in the urban multi-energy system model are respectively f i e , f i g , f i h and f i c , where node i is a different type of equipment in the urban multi-energy system; electric energy storage, thermal energy storage, ice storage and temperature control load in the urban multi-energy system model are selected as energy storage equipment and used as state variables, that is, x = [es8, es9, es 10 ,es 11 ,es 12 ] T ; Select partial energy flow as control variable: and the remaining energy flow as output variable Assuming the energy flow direction into the energy bus is positive, the elements of each matrix are as follows: α = 0; for β: β 1,5 =β 2,8 =β 3,10 =-1, β 4,2 = -γη4, β 4,7 =β 4,8 =β 4,9 =γ, β 5,7 =(γ-1)η6,β 5,10 =β 5,11 =1-γ, χ=0; for δ: δ 1,1 =-1,δ 1,2 =1, δ 2,3 =δ 2,4 =δ 2,5 =δ 2,6 = -1, where and is the heat generation power and power generation power of the cogeneration unit; η4 is the power of the gas boiler; η6 is the power of the absorption refrigerator; and It is the energy efficiency coefficient of cooling and heating of the electric heat pump.

4. The method for evaluating the operational reliability of a city multi-energy system according to claim 1 is characterized in that: In step S2, based on the thermal inertia of the temperature control load, the temperature control load is modeled as equivalent energy storage, and the method of establishing the operating reliability model of the temperature control load and different types of equipment based on the reliability equivalence method is as follows: Step S2.1: Establish a thermal inertia model for a single temperature control load: The temperature control load is modeled as equivalent energy storage. Since the equivalent energy storage of the temperature control load comes from the thermal inertia of the room, the equivalent energy storage calculation formula of room j is: Among them, C a and ρ a is the specific heat capacity and density of air; V j is the spatial volume of the room; and is the upper and lower temperature limits of the comfort range; es 12,j is the capacity of equivalent energy storage; During operation, the equivalent energy storage needs to consider the self-dissipation term. The formula is: in, is the indoor temperature; T a is the outdoor temperature; K j is the thermal conductivity coefficient; The area of ​​the room exposed to the outdoors; is the air exchange frequency; j is the heat dissipation coefficient in the room; A j The activity area of ​​the room; Assume that the temperature control load always maintains the indoor temperature at the most comfortable temperature Assuming that the indoor temperature is always maintained in a stable state without sudden changes; when the indoor temperature is at the most comfortable temperature, the corresponding self-dissipation term is the baseline of the temperature control load, and the baseline changes with the change of the outdoor temperature; in addition, assuming that during the charging and discharging process of the equivalent energy storage, the room temperature always needs to be maintained within the comfort range, otherwise it will be regarded as load reduction; the equivalent charging and discharging power of the equivalent energy storage is calculated as the difference between the actual cooling power of the temperature control load and the baseline; The maximum energy release power of equivalent energy storage occurs when the indoor temperature reaches the upper limit of the comfort range. The formula is: The maximum charging power of equivalent energy storage occurs when the indoor temperature reaches the lower limit of the comfort range. The formula is: although and Related to the outside temperature, but the difference As well as the equivalent energy storage capacity is only related to the comfort range and the characteristics of the room itself, but has nothing to do with the outside temperature; Step S2.2: Establish a reliability model for cluster temperature control load: The reliability model of a single temperature control load is described by a two-state Markov process, and the cluster of temperature control loads described by multiple two-state Markov processes is equivalent to a multi-state reliability model to reduce the number of state spaces; The state transition rate calculation formula of the temperature control load of the cluster formed by the temperature control load described by multiple two-state Markov processes between the normal state s=1 and the first-order fault state s=j+1 is: Among them, j represents the cluster, is the state transition rate of the aggregate temperature control load from state s=1 to state s=j+1; is the state transition rate of the aggregate temperature control load from state s=j+1 to s=1; ICS and μ ICS They are the failure rate and repair rate of cyber-physical systems, respectively; The calculation formula for the equivalent energy storage capacity and maximum energy release power in the state s=j+1 is: in, and is the energy storage capacity and maximum charging power of state s; es 12,j and 12,j' is the equivalent energy storage capacity of a single temperature control load in clusters j and j'; Step S2.3: Establish the operation reliability model of different equipment in the urban multi-energy system: Step S2.3.1: Establish a multi-state operation reliability model for cogeneration units in urban multi-energy systems: The cogeneration unit consists of three subsystems: the prime mover system, the power generation system, and the heat generation system. Each subsystem is represented by a two-state reliability model. With the failure of different subsystems, the cogeneration unit presents four states. The transfer rate calculation formula between the four states is: in, is the transition rate between state 1 and state 2; e ,λ h ,λ p and μ e , μ h , μ p Represent the failure rate and repair rate of power generation system, heat generation system and prime mover system respectively; Assume that the feasible region of the cogeneration unit is a quadrilateral, which is defined by four poles, namely (H A ,E A ), (H B ,E B ), (H C ,E C ), (H D ,E D ), representing the coordinates of four groups of heat production and power generation; if any subsystem fails, the feasible domain will degenerate into the projection of the original feasible domain on the corresponding coordinate axis, so the new feasible domain is expressed as: Among them, when the cogeneration unit is in state 2, formulas (12) and (14) are valid; when the cogeneration unit is in state 3, formulas (13) and (15) are valid; when it is in state 4, formulas (14) and (15) are valid; Step S2.3.2: Multi-state operation reliability equivalence of other two-state devices in the urban multi-energy system: For other equipment in the urban multi-energy system, including gas boilers, electric heat pumps, absorption refrigerators, distributed generators, electric energy storage, thermal energy storage and ice storage, they are usually composed of multiple devices with the same parameters connected in parallel; assuming that the gas boiler consists of N GB The gas boilers are connected in parallel, and the operating reliability of each gas boiler is represented by a two-state Markov process model. Then a gas boiler equivalent is represented by a multi-state Markov process model. When there is a gas boiler failure, the gas boiler equivalent will not fail completely, but will be transferred to a partial failure state; assuming that the states of the gas boiler equivalents are arranged in descending order according to their available heat production capacity, the calculation formula for the available heat production power of the gas boiler equivalent in state s is: in, It is the heat generation power of a single gas boiler under normal operation; In the Markov process, assuming that only a single state transition occurs in a single time, the state transition of the gas boiler equivalent will only occur between two adjacent states, and the state transition rate between any two adjacent states is and The calculation formula is: Among them, λ GB and μ GB The failure rate and repair rate of a single gas boiler.

5. The method for evaluating the operational reliability of a city multi-energy system according to claim 1 is characterized in that: The method of step S3 is: Step S3.1: Construct degradation costs and comfort costs for different types of energy storage: During emergency management, the charging and discharging process of power storage will further accelerate the degradation process of power storage, so the degradation cost needs to be considered; cycle depth is one of the biggest factors affecting power storage; The degradation cost associated with the cycle depth during a single charge / discharge process within a time step is calculated as: in, is the degradation cost of electric energy storage j in time step k; is the purchase cost of electric energy storage j; is the cycle depth of the electric energy storage j before charging and discharging in time step k; Δδ j,k is the cycle depth of power storage j increased within time step k; Φ j is the cycle life loss, and the calculation formula is: Among them, Δt is the time step; The degradation cost of thermal energy storage and ice storage is calculated as the average depreciation cost, using the formula: in, and are the depreciation costs of thermal energy storage and ice storage, respectively; and are the investment costs of thermal energy storage and ice storage respectively; T9 and T 10 are the life cycles of thermal energy storage and ice storage, respectively; NK is the number of time steps; Equivalent storage comfort cost in summer Evaluated based on predicted dissatisfaction percentage: Among them, σ is the coefficient that converts PPD into comfort cost; the PPD function is approximated as a quadratic function, and the original form of the PPD function is: Among them, M is metabolic rate, W is mechanical work, P is a is the water vapor pressure, t cl is the clothing temperature, t r is the mean radiation temperature, f cl is the area coefficient of the clothes, h c is the convective heat transfer coefficient; Assuming that the most comfortable temperature is 26°C, the PPD function is fitted as follows: PPD(T)=0.3222T 2 -16.7544T+17.8072(24) Winter comfort costs Cost of summer comfort The calculation method is the same; therefore, the total operating cost of all energy storage is C ES The calculation formula is: Where γ is an integer variable, γ = 1 and 0 represent winter and summer respectively. In summer, the electric heat pump only operates in cooling mode, and the temperature control load only has cold energy storage; in winter, the electric heat pump only operates in heating mode, and the temperature control load only has hot energy storage; Step S3.2: Emergency management of urban multi-energy systems based on optimal control framework: The goal of emergency management of urban multi-energy systems is to minimize external costs, including energy purchase costs, load reduction costs, and energy storage degradation and comfort costs, expressed as: in, is the operating cost of the urban multi-energy system in time step k; and are the prices of natural gas and electricity at time steps k, respectively; l represents the form of energy, where el, ht and cl represent electricity, heat and cold, respectively; the optimal control framework is subject to the following constraints: The system state equation and output equation are formula (1), which satisfies the following constraints: Operational constraints for electric energy storage, thermal energy storage, ice storage and equivalent energy storage: The operating constraints of the cogeneration unit under different states, the operating constraints of the gas boiler, the energy consumption constraints of the power and natural gas transmission system, and the operating constraints of other equipment including electric heat pumps, absorption chillers and distributed generation: The initial and terminal conditions of energy storage, that is, the amount of energy stored in the energy storage device at the final moment of emergency management needs to be restored to the value before emergency management: Load reduction upper limit constraint: Trivial constraints on energy flow: in, represents the capacity of energy storage i in state s; is the maximum charge and discharge power of the electric energy storage under state s; N EHP and N AB is the number of electric heat pumps and absorption refrigerators; s EHP and AB is the state number of the electric heat pump and absorption refrigerator; and is the heating and cooling power of a single electric heat pump; is the generation capacity of distributed generation in state s; es 8,NK and 8,1 Represents the amount of energy stored in the electric energy storage at time steps k=1 and k=NK.

6. The method for evaluating the operational reliability of a city multi-energy system according to claim 1 is characterized in that: The method of step S4 is: Step S4.1: Define operational reliability: The expected load shortage EDNS and load loss probability LOLP are extended to multiple energy dimensions including electricity, cooling and heat to measure the operational reliability of urban multi-energy systems: Wherein, NS is the number of sequential Monte Carlo simulations; flag(x) represents the flag function, if x>0, flag(x)=1, otherwise flag(x)=0; Step S4.2: Run the reliability assessment process: The optimization control problem in the reliability assessment of urban multi-energy systems is divided into two problems: the main problem is the state determination problem of energy storage, and the sub-problem is the state determination problem of energy conversion equipment; In order to avoid or reduce load shedding during reliability assessment, a simplified Benders decomposition method is designed, and the computational efficiency is further improved by optimizing the sequential Monte Carlo simulation process.

7. The method for evaluating the operation reliability of a city multi-energy system according to claim 6 is characterized in that: The method for improving computational efficiency by optimizing the sequential Monte Carlo simulation process is as follows: Step 1: Initialization: input data and set system parameters; Step 2: For each sequential Monte Carlo simulation, the initial state of the urban multi-energy system is determined based on the steady-state probability, assuming that the initial temperature of the temperature control load is the most comfortable temperature; Step 3: Simulate the state sequence of system components: Generate a random number U that follows the average distribution on (0, 1). Assuming that the current state of the device is s, the calculation formula for the duration τ of the state is: τ=min{-λ s,s' / ln(U s' ),s'∈S,s≠s'} (37) Where S is the system state set of the component, and the next state will be s'; Step 4: Repeat step 3 until the duration of the operation period is reached, repeat steps 2-4 for all devices in the urban multi-energy system, aggregate the state sequences of all devices, and generate the state sequence of the system; Step 5: For each sequential Monte Carlo simulation, solve the emergency management problem and set the upper and lower bounds of the Benders decomposition, UB (0) =+∞,LB (0) =0, and the convergence criterion ζ; Step 6: Solve the main problem: Subject to the state equation constraint (1), related constraints (28)-(35) and the Benders cuts filled in by the subproblems; u MP =[f 8,k ,f 9,k ,f 10,k ,f 11,k ,f 12,k ], k=1,...,NK are the control variables of the main problem; the cost of the subproblem φ k Initialized to φ k ≥0; the solution of the main problem in the vth iteration is And the value of the objective function is ψ (v) ; Update lower bound LB (v) =max{LB (v-1) ,ψ (v) }Get u MP The solution is Step 7: Solve the subproblem: For each time step k, based on Create the following sub-problems: Subject to the output equation (1), the relevant constraints are formulas (30)-(32), (34) and (35), and the control variables of the sub-problem are recorded as If the subproblem is not feasible, go to step 8, otherwise go to step 9; Step 8: Fill the following feasible cuts back to the main problem: Among them, κ is the Farkas dual variable of the subproblem; and b are the coefficients of the subproblem constraints in the following compact form: Step 9: According to the optimization results, check whether there is load reduction. If not, stop the Benders iteration. Otherwise, backfill the following optimal cut to the main problem: Step 10: Check whether all time steps have been traversed. If so, update the upper bound according to the solution; solve the new main problem after backfilling the new Benders cut in step 1; otherwise, for the next time step k=k+1, start a new cycle from step 3; Step 11: Check whether the following convergence criteria are met. If so, output the solution to the optimal control problem. Otherwise, repeat the next Benders iteration from step 5: (UB (v) -LB (v) ) / (UB (v) +LB (v) )<ζ(43) Step 12: Based on the load reduction calculated by Bender iteration, the operation reliability index is calculated and whether it converges is evaluated based on the calculation reliability. If converged, the final result is output; if not, the next sequential Monte Carlo simulation is repeated from step 2.

8. An urban multi-energy system operation reliability assessment system, characterized by: include: The first building module is used to establish a dynamic energy hub model, wherein the dynamic energy hub model is used to construct a dynamic relationship between the operating states of different types of equipment in the urban multi-energy system; The second building module is used to model the end users connected to different types of equipment in the urban multi-energy system as equivalent energy storage based on the thermal inertia of the temperature control load in the end users, and establish the operation reliability model of the temperature control load and different types of equipment based on the reliability equivalence method; The third building module is used to establish an emergency management model for the urban multi-energy system based on the temperature control load and the operation reliability model of different types of equipment; Evaluation module: It is used to evaluate the operational reliability of the urban multi-energy system through time-series Monte Carlo simulation based on the model constructed by the first building module, the second building module and the third building module, and introduces a simplified Benders decomposition process in the time-series Monte Carlo simulation to improve the efficiency of operational reliability evaluation.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for evaluating the operation reliability of an urban multi-energy system as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the operation reliability of an urban multi-energy system as claimed in any one of claims 1 to 7 are implemented.

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