A method for monitoring urban energy supply reliability taking into account heating network delay and residents' tolerance

Abstract

The present invention discloses a method for monitoring the reliability of urban energy supply taking into account the delay of the heating network and the tolerance of residents, including: constructing a quasi-steady-state model of the urban energy system according to the structure and initial operating conditions of the urban energy system; combining the structure of the urban energy system and the state enumeration technology to obtain the system fault state space set required for urban energy supply reliability monitoring; inputting the conditions before and after the failure of the heating system into the quasi-steady-state simulation model of the urban energy system to obtain the state before and after the failure of the electric-gas-heat system, as well as the temperature change process of the heating system and the building; constructing a generalized load loss segmented metering weight according to the human body's comfort and safety constraints on the indoor temperature of the building, and calculating the equivalent load loss that integrates energy supply and human body temperature acceptance; combining different failure probabilities and load losses to calculate the reliability index, that is, the expected shortage of energy service. The present invention improves the reliability monitoring accuracy and the rationality of the results, and fills the lack of consideration of user life safety under the influence of low temperature environment in traditional urban energy supply reliability monitoring.

Description

Technical Field

[0001] The present invention relates to the field of urban energy systems, and in particular to a method for monitoring urban energy supply reliability taking into account heating network delays and residents' tolerance. Background Art

[0002] As energy transformation progresses, the proportion of combined heat and power and electrically driven heat sources (such as electric boilers) in urban energy supply continues to increase, leading to an increase in the integration of different energy systems. While the integration of multiple energy systems represented by electricity / gas / heat energy improves energy efficiency, the interaction between different systems also brings many new problems to urban energy services.

[0003] Changes in energy supply reliability are one of the important issues affected by the integration of the above energy systems. On the one hand, the coordinated operation of multiple energy systems can achieve multi-system mutual assistance and improve the reliability of urban energy supply systems. By using local gas generators or gas boilers, users can meet their own electricity / heat needs without relying on the power grid or heat network, thereby improving the reliability of the urban energy system; on the other hand, emergencies in one system will threaten the operation of all other systems. Grid failures may cause the heat source to lose power supply and affect the energy supply of the thermal system, while gas pipeline failures may cause the connected cogeneration unit to shut down and affect the energy supply of the electricity / heat system. Against the background of the increasing proportion of intermittent renewable energy supply, it is necessary to study the monitoring technology of energy supply reliability to ensure the security of urban energy supply.

[0004] Unlike directly measurable information such as power and pressure, reliability is a systematic feature. Monitoring it requires comprehensive consideration of multiple factors in the urban energy supply system to achieve indirect acquisition of indicators. Traditional urban energy supply reliability monitoring technology focuses on the multi-system cascade effect brought about by coupled units such as cogeneration and heat pumps. Technically, it mainly extracts corresponding reliability data based on load losses calculated by steady-state power flow. Due to the transmission delay of the heat network, the heat supply of the heat source at a certain moment actually meets the heat demand in tens of minutes to several hours, which leads to the inconsistency of the scheduling time scale between the power grid and the heat network. In addition, due to the thermal inertia of the building maintenance structure, the change of indoor temperature needs to go through a period of decline, which makes the building can be regarded as a heat storage device of the urban energy system; the delayed dynamics of the heating network and the user's acceptance range of indoor temperature changes will also affect the operation and scheduling of the heating system, thereby affecting the reliability of the urban energy system. To address this problem, existing studies have introduced gas pipelines, heat networks, and building thermal dynamics into the reliability calculation of the urban energy supply system to improve the accuracy of reliability monitoring results.

[0005] However, there are still challenges in monitoring the reliability of urban energy systems. Unlike power systems, which experience an immediate loss of power load after a fault occurs, for heating systems, it is more common to see a decrease in heating quality (a slow drop in room temperature), which in turn affects human comfort. It is particularly important to note that if the indoor temperature is below the safety lower limit and persists for more than a period of time, it will threaten human life, and its impact is far greater than the cost of the missing energy. This has led to a significant decrease in the potential reliability of the energy supply system during fault recovery. This nonlinear change process has not been effectively considered in existing reliability monitoring technologies.

[0006] With the application and promotion of multi-energy coupling devices such as cogeneration, the interaction level between urban electricity, gas and heating systems is constantly improving. The failure of any system may affect the energy supply of all other systems. The heating system with large inertia and flow-dependent dynamic characteristics has more complex behavior, and the impact of heat load loss on users will change nonlinearly as the temperature drops. Therefore, a new urban energy supply reliability monitoring technology is urgently needed to support the operation scheduling decision-making and safety monitoring of urban energy supply systems. Summary of the invention

[0007] The present invention provides a method for monitoring the reliability of urban energy supply taking into account heating delay and residents' tolerance. The present invention comprehensively considers the transmission delay of the pipeline network, the thermal inertia of the building, and the comfort range and safety range of the indoor temperature in the existing reliability monitoring technology; the present invention takes into account the controllable capacity of different types of resources and the nonlinear impact of different indoor temperatures on users in the optimization and scheduling of the urban energy supply system to improve the reliability monitoring accuracy and the rationality of the results, and fills the gap in the consideration of user life safety under the influence of low temperature environment in the traditional urban energy supply reliability monitoring, as described below for details:

[0008] A method for monitoring urban energy supply reliability taking into account heating network delay and residents' tolerance, the method comprising:

[0009] The energy hub model and the traditional electricity-gas-heat steady-state network model are used to solve the steady-state power flow of the urban energy system and obtain the initial operating conditions of the electricity / gas / heating system coupled by the energy center;

[0010] According to the urban energy system structure and the initial operating conditions, a quasi-steady-state model of the urban energy system is constructed;

[0011] Combining the urban energy system structure and state enumeration technology, the system fault state space set required for urban energy supply reliability monitoring is obtained;

[0012] The conditions before and after the heating system failure are input into the quasi-steady-state simulation model of the urban energy system to obtain the state of the electricity-gas-heat system before and after the failure, as well as the temperature change process of the heating system and the building;

[0013] According to the human body's indoor temperature comfort and safety constraints, the generalized load loss segmentation weight is constructed, and the equivalent load loss integrating energy supply and human body temperature acceptance is calculated;

[0014] Combining different fault probabilities and load losses, the reliability index, i.e. the expected shortfall energy service, is calculated.

[0015] Among them, the quasi-steady-state simulation model of the urban energy system is:

[0016]

[0017] Among them, x e ,x g ,x h Represent the state variables of the electricity, gas and heating systems respectively, y e ,y g ,y h Represent the algebraic variables of electricity, gas and heating systems respectively, u eh represents the control variable of the coupling unit described by the energy hub, f e (·),f g (·) represent the dynamic models of electricity and gas, respectively, and f hh (·) and f ht (·) represents the hydraulic and thermal subsystem models of the district heating system, and Represents the thermal dynamic variables and transmission delay dynamic variables of the heating system.

[0018] Furthermore, the reliability index is:

[0019]

[0020]

[0021] Among them, P(S) is the probability of state S, n s is the number of faulty components in state S, n is the total number of components, U i is the unavailability rate of component i in state S, S x is the system fault state space set, C(S) represents the load loss in state S, T 0 is the time interval, and EEENS is the reliability analysis index of urban integrated energy system.

[0022] The equivalent load loss of the calculation integrating energy supply and human body temperature acceptance is:

[0023]

[0024]

[0025] Among them, Φ i represents the heat load of node i, t fault represents the fault duration, c w represents the specific heat of water, represents the mass flow through node i, and represents the water supply temperature and outlet temperature of node i, R b represents the heat loss coefficient of the building, represents the lower limit of the calculated heat loss of the building, sgn(·) represents the sign function, c heat Represents the segment heat loss coefficient, T b is the temperature of node b before the failure.

[0026] Furthermore, the segmented heat loss coefficient c heat for:

[0027]

[0028] in, and Represent the minimum and maximum values ​​of building comfort temperature, T b and represents the building temperature suitable for residents' living and its safety lower limit, c mior ,c low and c high They represent the heat loss weights under different impacts on the reliability of urban energy supply.

[0029] The beneficial effects of the technical solution provided by the present invention are:

[0030] 1. This invention develops a quasi-steady-state model for the urban energy system to analyze the impact of pipeline transmission delay and building thermal inertia on the long-term dynamic behavior of the energy system. On this basis, the nonlinear changes of the adverse effects of indoor ambient temperature on the human body are introduced into the traditional urban energy supply reliability monitoring technology based on energy loss;

[0031] 2. This method improves the monitoring accuracy and comprehensiveness of indicators of urban energy supply reliability, and fills the gap in the consideration of user life safety under the influence of low temperature environment in traditional urban energy supply reliability monitoring;

[0032] 3. This method takes into account the large transmission delay of the heat network and the thermal inertia of buildings in the urban energy system model, which is closer to the actual situation from the perspective of energy supply monitoring;

[0033] 4. This method adds the temperature comfort zone and safety zone of residents in the building to the traditional reliability calculation model. The results are closer to people's demand for energy services and solve the key technical problems of expanding power reliability monitoring technology to urban energy reliability monitoring technology.

[0034] 5. This method belongs to the application of quasi-steady-state model in the field of integrated energy system monitoring. It has a certain perception and learning ability of the impact of heating network failures. Compared with traditional technologies, the results obtained can ensure the safe operation of the monitoring system during the coordinated regulation of multiple energy sources and can more effectively support the decision-making of operation and dispatching personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is an example diagram of multi-channel dynamics;

[0036] Among them, (a) is the flow state of the pipeline; (b) is the dynamic change of the temperature of node a.

[0037] Figure 2 Schematic diagram of multi-pipeline collaborative simulation method for quasi-steady-state simulation of district heating system;

[0038] Figure 3 is the segment heat loss coefficient c heat Schematic diagram;

[0039] Figure 4 It is a schematic diagram of the comprehensive reliability evaluation algorithm;

[0040] Figure 5 This is a schematic diagram of the topological structure of the urban energy system;

[0041] Figure 6 This is a schematic diagram of the change in building temperature before and after the heat network failure;

[0042] Figure 7 This is a schematic diagram of the change in heat supply to buildings before and after the heat network failure;

[0043] Figure 8 Schematic diagram of the detailed changes in heat supplied to buildings before and after the heating network failure. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0045] Example 1

[0046] An embodiment of the present invention provides a method for monitoring urban energy supply reliability taking into account heating delay and residents' tolerance, the method taking into account the dynamic interaction between different energy systems, buildings and residents, and the method comprises the following steps:

[0047] 101: A quasi-steady-state model of the urban energy system considering the variable flow pipeline delay of the heating system and the thermal inertia of the building is proposed to describe the impact of the quality regulation and quantity regulation of the heating system on the urban energy system;

[0048] 102: A reliability index system including load loss, resident comfort and safety level is proposed to reflect the significant reduction effect of low temperature small load loss on energy supply;

[0049] 103: A reliability monitoring method based on a quasi-steady-state urban energy system model is proposed, and the corresponding relationship between pipeline transmission delay, thermal inertia, user temperature level and urban energy supply reliability index is clarified;

[0050] 104: This paper integrates different types of resources into the regulation of energy infrastructure, improves the monitoring accuracy of urban energy supply reliability, and fills the gap in the traditional urban energy supply reliability monitoring in considering the impact of low temperature environment on human body;

[0051] 105: According to the steady-state energy flow analysis of the urban energy system, the initial temperature and flow rate of the entire system are obtained, and the energy flow and water flow of the urban energy system after the fault are decomposed and calculated to obtain the models of each subsystem after the fault. Then, considering the human comfort and safety interval under different room temperatures, key indicators reflecting the heating loss and temperature change impact of the building are formulated;

[0052] 106: Use the state enumeration method to determine the fault set, analyze the heat source temperature, network topology and flow rate after the fault to monitor the heat transfer process, and use the expected shortage of energy services to reflect the reliability of urban energy supply.

[0053] To summarize, the embodiments of the present invention realize, through the above steps, taking into account the adjustable capabilities of different types of resources and the nonlinear effects of different indoor temperatures on users in the optimization scheduling of the urban energy supply system, so as to improve the reliability monitoring accuracy and the rationality of the results, and fill the lack of consideration for user life safety under the influence of low temperature environment in traditional urban energy supply reliability monitoring.

[0054] Example 2

[0055] The scheme in Example 1 is further introduced below in combination with specific calculation steps and examples, as described below for details:

[0056] The embodiment of the present invention proposes a novel urban energy supply reliability monitoring method, which takes into account the dynamic interaction between different energy systems, buildings and residents, including: transmission delay of heat network pipelines and thermal inertia of buildings.

[0057] 1. Quasi-steady-state modeling of urban energy systems

[0058] 1.1 System Model

[0059] A typical urban energy system consists of electricity, gas and heating systems, and its coupling units include cogeneration, electric boilers, etc. The typical model of an urban energy system can be described as:

[0060]

[0061] in, Represent the power, gas and heating system models respectively, x e ,x g ,x h Represent the state variables of the electricity, gas and heating systems respectively, y e ,y g ,y h Represent the algebraic variables of electricity, gas and heating systems respectively, u eh represents the control variables of the coupling units described by the energy hub. e (·),f g (·),f h (·) represent the dynamic models of electricity, gas and heating systems respectively.

[0062] Urban energy systems span multiple timescales, with hydraulic dynamics in electricity, gas, and heating systems reaching the entire system in seconds. However, temperature changes flow as water flows, and it may take minutes or hours to reach a steady state. After a fault occurs, electricity users are affected within milliseconds, while heating users can still maintain their own temperature within a comfortable level for hours. This delay is caused by the hot water in the pipes and the residual heat in the building itself.

[0063] 1.2 Quasi-steady-state model

[0064] In order to highlight the delayed effects, the model of the heating system is further divided into hydraulic and thermal models with different time scales. When a fault occurs, the indoor temperature will remain at the value before the fault until water with a different temperature is delivered to the building. These characteristics can be described by using the following quasi-steady-state model:

[0065]

[0066] Among them, f hh (·) and f ht (·) Represents the hydraulic and thermal subsystem model of the district heating system. and Represents the thermal dynamic variables and transmission delay dynamic variables of the heating system.

[0067] Ignoring the heat absorbed by the pipe wall, the transport delay effect of the pipeline can be approximated as follows:

[0068]

[0069] Among them, T ab (x) represents the water temperature along the length of the pipe starting from node a. a Represents the inlet temperature. T env represents the ambient temperature. x represents the distance from the inlet to the considered section. represents the pre-fault flow rate in pipe ab. λ represents the heat transfer coefficient. c w Represents the specific heat of water.

[0070] A sudden change in the water flow rate will result in varying degrees of heat loss along the pipeline. In addition, if the temperature of the water in the heat source or other pipelines changes, the water temperature at the pipeline inlet may also change. Figure 1 The three-pipeline network in (a) achieves Figure 1 (b) The post-fault stable state T post Previously, the change in outlet temperature after a fault would go through a multi-stage process.

[0071] In the first stage, t∈[t 0 ,t 1 ], the post-fault outlet temperature of node a is T a ′(t) is mainly determined by the water temperature before the fault and the flow rate after the fault in the pipeline ab Then x in formula (3) can be described as A represents the cross-sectional area. ρ represents the density of water.

[0072] In the second stage, t∈[t 1 ,∞),T a ′(t) is determined by the temperature before the fault and the flow rate injected into node b after the fault. The temperature change process of node a is as follows:

[0073]

[0074] Among them, L ab represents the length of the pipe, h(·) represents the pipeline model, T b is the temperature of node b before the failure.

[0075] For a node with multiple injected flows, the thermodynamics of heat transfer overlap, resulting in more flow segments with different dynamic behaviors. A temperature change at any one node will cause a sudden increase in the outlet temperature of node a until all flows with updated temperatures are delivered to the load. The temperature of a node b with multiple injected flows can be evaluated as follows:

[0076]

[0077] Among them, Ω b represents the set of pipes that flow into node a. ib Represents the temperature of the water flowing from node i to node b. Represents the mass flow rate from node b to node i.

[0078] 1.2 Numerical implementation:

[0079] The simulation is achieved through the following steps.

[0080] 1) According to the calculation results of the energy flow of the steady-state urban energy system, the temperature and flow rate of the entire system are initialized. The temperature of different sections along the pipeline can be obtained according to formula (2);

[0081] 2) Adopting optimal energy flows or predetermined dispatching rules to indirectly reflect the status of the urban energy system after the fault, including: the temperature of the heat source, the mass flow rate of the district heating network, and the energy exchange with the electricity and gas networks;

[0082] 3) Decompose the system after the failure;

[0083] Since nodes with multiple branch injections have a significant impact on the dynamic characteristics of the heating system, the system is first decomposed into multiple subsystems. In each subsystem, there is only one heat source or the start node of a pipeline with multiple injection flows. For each subsystem, if the end node of the pipeline is a leaf node or is connected to a node with multiple injections, the search process will proceed to the next node. This decomposition can be achieved by analyzing the node incidence matrix.

[0084] 4) Evaluate the heat transfer process with updated heat source temperatures, network topology, and flow rates.

[0085] Starting from the blade subsystem, a multi-pipeline co-simulation method is proposed to describe the overlapping transmission delays along the same time axis, e.g. Figure 2 As shown. For source pipes, such as cb and db, the temperature can be calculated by equations (3) and (4). For pipes with multiple flow injections, their dynamics will be determined by the injection flow at different times in the simulation, calculated according to equation (5). During the initialization process, the temperature of the node will have three other states. Therefore, pipes with more medium pipes connected to the heat source will have more complex transmission delay behavior.

[0086] 5) Evaluate the building's heat loss by taking into account human comfort at different temperatures. Changes in indoor temperature are accompanied by changes in heat delivery to the building, including delayed heat interruption during the fault and delayed heat restoration after the fault is cleared.

[0087] 2. Reliability monitoring considering residents’ comfort

[0088] 2.1 Reliability indicators

[0089] Expected Energy Not Supply (EENS) and the possibility of load reduction are usually used to reflect the reliability of urban energy supply for monitoring purposes. Reliability indicators are related to the load loss of each system state. Unlike power and gas systems, the calculation of thermal load loss is more complicated due to pipeline transmission delays and building thermal inertia.

[0090] Users will go through two primary stages before being affected. In the first stage, the hot water stored in the pipes continues to heat the building, and the user's indoor temperature is not affected. In the second stage, the user's heating is lost, but due to the thermal inertia of the building, the building's temperature is still maintained within the residents' comfort range. In these two stages, the impact of heating loss can be ignored in the reliability assessment. In the third stage, the building temperature drops to the minimum comfort temperature T min Below this, the heat load loss begins to increase. It should be noted that as the fault duration increases, the building temperature may drop to the fourth stage, which threatens human health or life. When this happens, the reliability level of the system will drop significantly. Figure 3 shown.

[0091] In order to describe the influence of the energy supply change, the present invention proposes a segmented heat loss coefficient c heat .

[0092]

[0093] in, and Represent the minimum and maximum values ​​of building comfort temperature respectively. b and Represents the building temperature suitable for residents' lives and its safety lower limit. mior ,c low and c high They represent the heat loss weights under different impacts on the reliability of urban energy supply.

[0094] Referring to the existing power system reliability analysis index EENS, the embodiment of the present invention proposes the expected energy service not supplied (EESNS) to quantify the reliability of the urban energy system. Unlike EENS, EESNS evaluates the corrected load reduction considering the impact of low temperature on the human body and the load loss of the central heating system as follows:

[0095]

[0096]

[0097] Where P(S) is the probability of state S. s is the number of faulty components in state S, and n is the total number of components. i is the unavailability rate of component i in state S. x represents the system fault state space set. C(S) represents the load loss in state S. T 0 is the time interval.

[0098] Heat loss caused by heating pipeline transmission delay and indoor temperature C h (S)_ p and C h (S)_ b The calculation process is as follows:

[0099]

[0100]

[0101] Among them, Φ i represents the heat load of node i, t fault Represents the fault duration. c w Represents the specific heat of water. represents the mass flow through node i. and Represents the water supply temperature and outlet temperature of node i. b Represents the heat loss coefficient of the building. Represents the lower limit of the calculated heat loss of the building, which is mentioned in formula (6) and sgn(·) represents a sign function, that is, when z>0, sgn(z)=1; when z≤0, sgn(z)=0.

[0102] 2.2 Reliability index extraction method

[0103] The embodiment of the present invention adopts Figure 4 The algorithm shown is used to monitor the reliability of the urban energy system. The specific process is as follows:

[0104] 1) Input the city energy system configuration data, failure rate and maintenance rate, electrical load and thermal load, and load reduction cost;

[0105] 2) Assuming that all equipment is in normal operation, the energy hub model and the traditional electricity-gas-heat steady-state network model are used to solve the steady-state power flow of the urban energy system and obtain the initial operating conditions of the electricity / gas / heating system coupled by the energy center;

[0106] Among them, the initial operating conditions include: pipeline water flow rate, temperature, exchange power between systems, etc. The pipeline water flow rate and other variables are used to determine the pipeline temperature and delay length, and then obtain a quasi-steady-state simulation model. When it comes to specific implementation, the embodiments of the present invention will not go into details.

[0107] 3) Combining the urban energy system structure and state enumeration technology, the fault set required for urban energy supply reliability monitoring is obtained;

[0108] 4) Input the conditions before and after the heating system failure into Figure 4 The quasi-steady-state simulation module on the right obtains the status of the electric-gas-heat system before and after the failure, as well as the temperature change process of the heating system and the building;

[0109] 5) Based on the human body's indoor temperature comfort and safety constraints, construct Figure 3 The generalized load loss is shown as a piecewise metering weight, and the equivalent load loss integrating energy supply and human body temperature acceptance is calculated using equations (9) and (10);

[0110] 6) Finally, combining different fault probabilities and load losses, the reliability index, i.e., the expected short supply energy service, is calculated using equations (7) and (8).

[0111] To summarize, the embodiments of the present invention realize, through the above steps, taking into account the adjustable capabilities of different types of resources and the nonlinear effects of different indoor temperatures on users in the optimization scheduling of the urban energy supply system, so as to improve the reliability monitoring accuracy and the rationality of the results, and fill the lack of consideration for user life safety under the influence of low temperature environment in traditional urban energy supply reliability monitoring.

[0112] Example 3

[0113] The feasibility of the solutions of Embodiments 1 and 2 of the present invention is verified by combining specific experiments, as described below:

[0114] The application scenario selects a simple urban energy system (system structure as Figure 5 The given model is verified by using the simulation results shown in Figure 2. The verification is divided into two parts: the impact of pipeline transmission delay and building thermal dynamics on energy services, and the impact of pipeline transmission delay and building thermal dynamics on urban energy supply reliability.

[0115] 1) Impact of pipeline transmission delay and building thermal dynamics on energy services

[0116] In order to highlight the impact of pipeline transmission delay and building thermal dynamics, Figure 5The study takes the failure of pipeline No. 13-14 in the heating system of the central heating system as an example. The failure started 1 hour later. After the failure, the dispatching system adjusted the mass flow of the heating network based on the steady-state flow optimization results to minimize heat loss. The failure was cleared within 6 hours and the mass flow returned to the value before the failure. The results are shown in Figure 6 shown.

[0117] From the perspective of thermal services, the first thing to observe is the change in building temperature. Figure 6 As shown in the figure, most node temperatures remained within the comfort zone during the entire fault period, but there were also some buildings where the temperature was below the minimum allowable temperature. Although from the perspective of the overall energy supply, this low temperature does not cause a large amount of heat load loss, the potential personal injury that the low temperature may cause to indoor occupants means a significant loss for energy services. In addition, due to the effect of thermal inertia, although the fault was restored in the 6th hour, the temperature of the building was still below the lower temperature limit for a period of time. This means further losses for energy services, and some faults have caused the low temperature time to exceed 2 hours.

[0118] Due to the different lengths of the paths between the heat load and the heat source, Figure 7 Various transmission delays are observed in the fault. Sudden changes in node temperature are observed at different times 1 hour after the fault starts. Similar behavior can be seen in the fault recovery phase, resulting in complex changes in node temperature. Since the length of the pipeline is limited, transmission delays do not have a significant impact on thermal services from an energy perspective.

[0119] 2) Impact of pipeline transmission delay and building thermal dynamics on urban energy supply reliability

[0120] Using the proposed monitoring method, the reliability of the urban energy system is compared under the following three scenarios:

[0121] (1) Energy supply reliability based on steady-state analysis model;

[0122] (2) Energy supply reliability considering heat transfer delay;

[0123] (3) Energy supply reliability taking into account both transmission delay and building thermal inertia.

[0124] The heat load loss coefficient is 1 and 3 outside the comfort zone and safety zone respectively. The results are shown in Table 1.

[0125] Table 1 Reliability index

[0126]

[0127] In scenario 2, the EENS of the heating system increases significantly. This is mainly due to the continuous heat load loss caused by the transition period of water temperature after the fault and the fault recovery period. In the initial stage of the fault, the water flow cannot change the temperature immediately, and the heating system will continue to heat the users at the water temperature before optimization until the transition process is over. Similar situations also exist in the fault recovery period, as shown above. Figure 6 and Figure 7 As shown in the figure, the whole process will bring additional load loss, which will lead to an increase in EESNS.

[0128] In scenario 3, considering the thermal inertia of the building and the human comfort range of the room temperature, its EESNS is much smaller than that in scenario 2. In this case, even if the heating is interrupted, the indoor temperature can still be maintained above the minimum comfort level for a period of time, which indicates that a short-term slight loss of heat supply will not cause actual user heat load loss. For the case where the room temperature is below the safe level, the EESNS will increase significantly, reflecting the decrease in system reliability caused by the local loss of heating.

[0129] Example 4

[0130] The modified design of the embodiment of the present invention includes the following aspects:

[0131] 1. Change of simulation algorithm: The embodiment of the present invention provides a new urban energy system reliability simulation method. According to the different structures and multi-time scale characteristics of the urban electricity-gas-heat integrated energy system, a universal quasi-steady-state mathematical model is constructed to obtain the corresponding simulation method. The simulation algorithm should be included in the scope of the present invention.

[0132] 2. Change of reliability index: If the indoor temperature is lower than the safe level, the impact on the people in the building will far exceed the cost of the missing heating itself. Therefore, it is necessary to introduce a new load loss description index. How to formulate the corresponding equivalent load loss weight according to the change in the temperature drop level caused by the lack of heating should also be included in the scope of this invention.

[0133] 3. Changes in reliability monitoring methods: The embodiment of the present invention proposes a reliability monitoring method based on a quasi-steady-state model, which fully considers the influence of pipeline transmission delay and building thermal inertia as well as human body temperature comfort and safety range, and recommends a targeted reliability improvement strategy. This method should also be included in the scope of the present invention.

[0134] Unless otherwise specified, the models of the components in the embodiments of the present invention are not limited, and any device that can perform the above functions may be used.

[0135] Those skilled in the art can understand that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0136] 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, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the reliability of urban energy supply taking into account the delay of the heating network and the tolerance of residents, characterized in that: The method comprises: The energy hub model and the traditional electricity-gas-heat steady-state network model are used to solve the steady-state power flow of the urban energy system and obtain the initial operating conditions of the electricity / gas / heating system coupled by the energy center; According to the urban energy system structure and the initial operating conditions, a quasi-steady-state model of the urban energy system is constructed; Combining the urban energy system structure and state enumeration technology, the system fault state space set required for urban energy supply reliability monitoring is obtained; The conditions before and after the heating system failure are input into the quasi-steady-state simulation model of the urban energy system to obtain the state of the electricity-gas-heat system before and after the failure, as well as the temperature change process of the heating system and the building; According to the human body's indoor temperature comfort and safety constraints, the generalized load loss segmentation weight is constructed, and the equivalent load loss integrating energy supply and human body temperature acceptance is calculated; Combining the probability of occurrence of different faults and load losses, the reliability index, i.e. the expected shortfall energy service, is calculated; Among them, the quasi-steady-state simulation model of the urban energy system is: Among them, x e ,x g ,x h Represent the state variables of the electricity, gas and heating systems respectively, y e ,y g ,y h Represent the algebraic variables of electricity, gas and heating systems respectively, u eh represents the control variable of the coupling unit described by the energy hub, f e (·),f g (·) represent the dynamic models of electricity and gas, respectively, and f hh (·) and f ht (·) represents the hydraulic and thermal subsystem models of the district heating system, and Represents the thermal dynamic variables and transmission delay dynamic variables of the heating system; The reliability index is: Among them, P(S) is the probability of state S, n s is the number of faulty components in state S, n is the total number of components, U i is the unavailability rate of component i in state S, S x is the system fault state space set, C(S) represents the load loss in state S, T0 is the time interval, and EEENS is the reliability analysis index of the urban integrated energy system; The equivalent load loss of the calculation integrating energy supply and human body temperature acceptance is: Among them, Φ i represents the heat load of node i, t fault represents the fault duration, c w represents the specific heat of water, represents the mass flow through node i, and represents the water supply temperature and outlet temperature of node i, R b represents the heat loss coefficient of the building, represents the lower limit of the calculated heat loss of the building, sgn(·) represents the sign function, c heat Represents the segment heat loss coefficient, T b is the temperature of node b before the fault, C h (S) _p Represents the heat loss caused by the delay in heating pipeline transmission, C h (S) _b Represents the heat loss caused by the indoor temperature.

2. A method for monitoring urban energy supply reliability taking into account heating network delay and residents' tolerance according to claim 1, characterized in that: The segment heat loss coefficient c heat for: in, and Represent the minimum and maximum values ​​of building comfort temperature, T b and represents the building temperature suitable for residents' living and its safety lower limit, c mior ,c low and c high They represent the heat loss weights under different impacts on the reliability of urban energy supply.

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