CN111798111A - Comprehensive energy system energy supply reliability assessment method and computer system - Google Patents
Comprehensive energy system energy supply reliability assessment method and computer system Download PDFInfo
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Abstract
The invention relates to a method for evaluating the energy supply reliability of an integrated energy system and a computer system, wherein the method comprises the following steps: acquiring original parameters and a topological structure of a comprehensive energy system to be evaluated; acquiring a simulation model of the comprehensive energy system to be evaluated based on a sequential Monte Carlo method; performing topology analysis on the system state of the simulation model by adopting a node marking method to obtain a plurality of connected domains; and respectively carrying out state analysis on each connected domain, and carrying out reliability evaluation on the comprehensive energy system to be evaluated by adopting an energy supply reliability index system based on an energy utilization purpose and a user position. Compared with the prior art, the method can consider the energy use purposes and user standpoints of different types of users, has the advantages of high evaluation accuracy and the like, and ensures the safe and efficient operation of the comprehensive energy system.
Description
Technical Field
The invention relates to the field of comprehensive energy, in particular to a method for evaluating the energy supply reliability of a comprehensive energy system and a computer system.
Background
With the increasing global energy crisis and the increasing environmental problems, the traditional high-carbon energy development and utilization method will be difficult to continue. The american college jirimy, rifugin, in the third industrial revolution, states that the industrial model based on the large-scale utilization of fossil fuels laid down by the second industrial revolution is moving to the end. In recent years, information support technologies represented by big data, cloud computing, the internet of things, and the mobile internet, and energy technologies represented by renewable energy, distributed power generation, energy storage, micro-grids, electric vehicles, and the like have been rapidly developed, and energy internet characterized by deep fusion of information technologies and energy technologies has become a focus of academic, industrial, and government attention.
Under the background of energy internet, a regional comprehensive energy system which considers the coupling of various energy sources and aims at improving the energy utilization efficiency and fully utilizing renewable energy sources becomes a research hotspot at home and abroad. The regional comprehensive energy system can break through the existing modes of independent planning, independent design and independent operation of various traditional energy supply systems for power supply, gas supply, heat supply, cold supply and the like, and can organically coordinate and optimize the links of production, transmission, distribution, conversion, storage, consumption and the like of various energy sources in the planning, design, construction and operation stages.
With the development of economic society, people have higher and higher requirements on the reliability of energy supply, the guarantee of the energy supply reliability of the comprehensive energy systems such as electricity, gas, heat, cold and the like is particularly important, and the development of reliability evaluation research of the comprehensive energy systems has very important significance. Therefore, how to evaluate the reliability of the integrated energy system to ensure the safe and efficient operation of the integrated energy system is a problem to be solved by those skilled in the art. The existing comprehensive energy system reliability evaluation method does not establish a complete reliability index system, and cannot well take the user energy purpose and the energy utilization standpoint into account.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method and a computer system for evaluating the energy supply reliability of an integrated energy system with high evaluation accuracy.
The purpose of the invention can be realized by the following technical scheme:
an energy supply reliability assessment method for an integrated energy system comprises the following steps:
acquiring original parameters and a topological structure of a comprehensive energy system to be evaluated;
acquiring a simulation model of the comprehensive energy system to be evaluated based on a sequential Monte Carlo method;
performing topology analysis on the system state of the simulation model by adopting a node marking method to obtain a plurality of connected domains;
and respectively carrying out state analysis on each connected domain, and carrying out reliability evaluation on the comprehensive energy system to be evaluated by adopting an energy supply reliability index system based on an energy utilization purpose and a user position.
Further, the energy supply reliability index system based on energy use purpose and user position comprises a load point reliability index and a system level reliability index.
Further, the load point reliability index includes a load point failure rate, a load point average outage duration, a load point average annual outage duration, and a load point average annual thermal comfort time, wherein,
the load point failure rate is expressed as:
in the formula, λα,jIs the failure rate of the alpha subsystem load node j, T is the total simulation time,
the total failure times of the alpha subsystem load node j in the whole simulation time period;
the load point average outage duration is expressed as:
in the formula, gammaα,jFor the average outage duration of the alpha subsystem load node j,
the fault time of the alpha subsystem load node j in the ith simulation is shown, and N is the number of system random states in the whole simulation period;
the annual average outage duration at load point is expressed as:
Uα,j＝λα,jγα,j
in the formula of Uα,jThe annual average outage duration of the alpha subsystem load node j;
the load point annual average thermal comfort time is expressed as:
in the formula of Uh,jMean thermal comfort time per year at jth load point of thermodynamic system, Fh,j(Xi) Is in a random state X with the systemiCorresponding thermal comfort coefficient of 0 or 1, tiIs a system random state XiThe duration of time.
Further, the thermal comfort coefficient Fh,j(Xi) Is determined by the following formula:
where PMV is the predicted average heat sensation index.
Further, the system-level reliability indicators include an average frequency of system outages, an average duration of system outages, an average reliability of system power, an undersupply expectation, and a system hot user satisfaction, wherein,
system average outage frequency SAIFIαExpressed as:
in the formula, the subscript alpha represents the energy type of the subsystem, Nα,jThe number of users, N, of the load node j of the alpha subsystemαIs the total number of load nodes of the alpha subsystem, lambdaα,jThe failure rate of the alpha subsystem load node j is shown;
system average outage duration SAIDIαExpressed as:
in the formula of Uα,jThe annual average outage duration of the alpha subsystem load node j;
average energy supply reliability ASAI of systemαExpressed as:
system starvation expected EPNSαExpressed as:
in the formula, Cα,j(Xi) Is the system in state XiLower alpha subsystem load node j load reduction amount, tiIs a system random state XiDuration, T is total simulation time, and N is the number of system random states in the whole simulation period;
system hot user satisfaction PhExpressed as:
in the formula, NhIs the total number of heat load nodes, Nh,jIs the number of users, U, of the load node j of the thermal subsystemh,jRefers to the average thermal comfort time per year at the jth load point of the thermodynamic system.
Further, in the sequential monte carlo method, all elements adopt a working-fault two-state model, the fault-free working time TTF and the repair time TTR of each element are random variables that obey exponential distribution, and a system running state sequence is obtained based on the random variables.
Further, the node marking method distinguishes the connected domains by sequentially searching and marking the numbers of the nodes at the two ends of the branch.
Further, the performing the state analysis on each connected domain specifically includes:
if the energy supply amount in the region is 0, all loads in the region cannot be supplied with energy;
if the energy source supply amount in the region is not zero and is more than or equal to the load amount, all loads in the region are supplied with normal energy without load reduction;
and if the energy supply amount in the region is not zero and is less than the load amount, reducing the load in the region until the energy supply amount is greater than or equal to the load amount.
Furthermore, a load reduction strategy based on consideration of thermal inertia is adopted to reduce the loads in the region, different reduction modes are adopted for different loads, and the method is strong in pertinence and high in effectiveness.
The invention also provides a computer system for evaluating the energy supply reliability of the integrated energy system, which comprises a processor and a memory, wherein the memory stores a computer program, and the processor calls the computer program to realize the steps of the method for evaluating the energy supply reliability of the integrated energy system.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention establishes the energy supply reliability index system of the comprehensive energy system based on the energy utilization purpose and the user position, can perform more comprehensive and complete evaluation on the comprehensive energy system, and has high evaluation accuracy so as to ensure the safe and efficient operation of the comprehensive energy system.
2. The method has the advantages that the heat load characteristic and the user comfort level are considered, the load reduction strategy considering the heat inertia of the load is provided, the energy supply reliability evaluation flow of the comprehensive energy system is formed on the basis of the sequential Monte Carlo method, the energy consumption purpose and the user position of different types of users can be considered, the accuracy of evaluation results is further improved, the comprehensive energy system is convenient to use, and the energy supply reliability is improved.
Drawings
FIG. 1 is a schematic diagram of a sequential Monte Carlo simulation of the present invention;
FIG. 2 is a flow chart of a node marking method of the present invention;
FIG. 3 is a schematic view of an evaluation process of the present invention;
FIG. 4 is a block diagram of an energy supply system of the integrated energy system according to an embodiment of the present invention;
FIG. 5 is a diagram of the Energy hub I structure in an embodiment of the present invention;
FIG. 6 is a block diagram of the Energy hub II according to an embodiment of the present invention;
FIG. 7 illustrates a system level reliability index convergence rate in an embodiment of the present invention;
FIG. 8 is a graph of the mean values of the indicators at different simulation years according to an embodiment of the present invention;
FIG. 9 is a graph of standard deviation of indicators for different simulation years in an embodiment of the present invention;
FIG. 10 is a schematic diagram of the load point reliability indicator of the distribution system in an embodiment of the present invention, where (10a) is the load point failure rate, (10b) is the load point average outage duration, and (10c) is the load point average annual outage duration;
FIG. 11 is a schematic diagram of the load point reliability index of the natural gas system in an embodiment of the present invention, where (11a) is the load point failure rate, (11b) is the load point average outage duration, and (11c) is the load point average annual outage duration;
fig. 12 is a schematic diagram of the reliability index of the load point of the thermodynamic system in the embodiment of the present invention, where (12a) is the fault rate of the load point, (12b) is the average outage duration of the load point, (12c) is the average annual outage duration of the load point, and (12d) is the average annual thermal comfort time of the load point.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in fig. 3, the invention provides a method for evaluating the reliability of energy supply of an integrated energy system, comprising the following steps: acquiring original parameters and a topological structure of a comprehensive energy system to be evaluated; acquiring a simulation model of the comprehensive energy system to be evaluated based on a sequential Monte Carlo method; performing topology analysis on the system state of the simulation model by adopting a node marking method to obtain a plurality of connected domains; and respectively carrying out state analysis on each connected domain, and carrying out reliability evaluation on the comprehensive energy system to be evaluated by adopting an energy supply reliability index system based on an energy utilization purpose and a user position.
(1) Energy supply reliability index system based on energy use purpose and user position
The energy supply reliability index system established by the invention comprises a load point reliability index and a system level reliability index, and is specifically shown in table 1.
TABLE 1 energy supply reliability index system for integrated energy system
1) Load point reliability index
a. Failure rate of load point lambda (order/a)
In the formula, λα,jIs the failure rate of the alpha subsystem load node j, T is the total simulation time,
the total failure number of the alpha subsystem load node j in the whole simulation time period.
For electric and gas loads, if the energy which can be supplied by the system in the current time period is lower than the load demand of the load point, the power supply or gas supply fault is considered to occur. For the heat load, the load temperature can be calculated by equations (14) to (18), and if the temperature in the current period is lower than the lowest temperature allowed, the load point is considered to have a heat supply fault.
b. Average outage duration gamma (h/times) at load point
In the formula, gammaα,jFor the average outage duration of the alpha subsystem load node j,
and N is the number of system random states in the whole simulation period.
c. Mean annual outage duration U (h/a) of load point
Uα,j＝λα,jγα,j(3)
In the formula of Uα,jThe annual average outage duration for the alpha subsystem load node j.
d. Year average thermal comfort time U of load pointh(h/a)
In the formula of Uh,jMean thermal comfort time per year at jth load point of thermodynamic system, Fh,j(Xi) Is determined by the following formula:
2) system level reliability index
a. System average outage frequency index (SAIFI) (sub/user. a)
In the formula, the subscript alpha represents the energy type of the subsystem, Nα,jThe number of users, N, of the load node j of the alpha subsystemαIs the total number of the load nodes of the alpha subsystem.
b. System average outage duration index (SAIDI) (h/user. a)
c. Average power supply reliability index (ASAI) of system
d. Expected Energy Not Supply (EENS) (MWh/a)
In the formula, Cα,j(Xi) Is the system in state XiLower alpha subsystem load node j load reduction amount, tiIs a system random state XiThe duration of time.
e. System hot user satisfaction Ph
System hot user satisfaction PhRefers to the probability that a thermodynamic system user is in thermal comfort during the year.
In the formula, NhIs the total number of heat load nodes, Nh,jThe number of the users of the load node j of the heating power subsystem is.
The user thermal comfort is calculated as follows.
Since the user's perception of the comfort level of the temperature is ambiguous, i.e., the user does not feel a significant difference when the indoor temperature varies within a certain range, the heating load demand curve of the user is an interval. Thermal user requirements for the quality of the thermal environment are generally characterized by thermal comfort, and a predicted mean thermal sensation index (PMV) is selected to quantify the effect of temperature on the thermal comfort of the user. The PMV index is a comprehensive evaluation index taking a basic equation of human body heat balance and the grade of psychophysiological subjective thermal sensation as a starting point and considering a plurality of relevant factors of human body thermal sensation and comfort sensation. The PMV index indicates the average index of the population for seven levels of heat sensation voting, and the correspondence between human body sensation and PMV index is shown in table 2.
TABLE 2 correspondence of human perception to PMV index
The PMV value can be obtained by the following formula:
wherein M is the energy metabolism rate of the human body, W is the mechanical power of the human body, fclThe ratio of the area of the clothes covered by the human body to the exposed area, hcIs the surface heat transfer coefficient, PaIs the partial pressure of water vapor of the air surrounding the human body, tnIs the temperature of the air surrounding the human body, trIs the mean radiant temperature, tclIs the temperature of the outer surface of the garment.
According to the design code of heating, ventilation and air conditioning of civil buildings, the PMV is preferably between-1 and + 1. When heating in winter, energy saving is considered as much as possible under the condition of meeting thermal comfort from the principle of energy saving, so a colder environment is selected, and the PMV is preferably between [ -1, 0 ].
(2) Sequential Monte Carlo simulation model
Assuming that all elements of the system adopt a working-fault two-state model, the fault rate and the repair rate of the elements are respectively constant lambda and mu, the Time To Failure (TTF) and the repair time (TTR) of the elements are random variables which obey exponential distribution, sampling can be sequentially carried out according to the formula (12) and the formula (13) to form an element running state duration time sequence, and the system running state sequence can be obtained by synthesizing all the elements.
In the formula (I), the compound is shown in the specification,1and2is [0, 1]]Uniformly distributed random numbers.
Assuming that a simple system is composed of two elements A, B, the determination of the state transition process of the whole system through simulation of the state sequences of the two elements is shown in fig. 1. In the figure, the "1" state indicates the operation state of the element, and the "0" state indicates the shutdown state of the element. "00", "01", "10", and "11" indicate a system combination state composed of two elements.
(3) Integrated energy system topology analysis
The topological analysis is carried out on the system state obtained by Monte Carlo simulation, and the main task is to analyze how many subsystems the nodes of the system are connected by branches and energy conversion equipment. The method adopts a node marking method to carry out the connectivity identification of the system. The node marking method classifies each node in the system, nodes belonging to the same area (namely, a path exists between the nodes and can be communicated) are classified into one class, the nodes are distinguished in a communicated domain by sequentially searching and marking the serial numbers of the nodes at two ends of a branch, and the searching times are only the total number of the branch.
For a system with N nodes and M branches, matrix A is usedM×2And recording the physical connection relationship between the branches and the nodes, namely if the ith branch is connected between the nodes j and k, determining that A (i,1) is j and A (i,2) is k. Using a matrix CN×1The area to which the node belongs is recorded, that is, if the ith node is located in area 1, C (i,1) is 1. The specific implementation of the node marking method includes the following steps, and the flowchart is shown in fig. 2.
Step 1: input node number N, branch number M and matrix AM×2. Initialization CN×1The matrix is zero, i is 1, and x is 0(x represents the number of regions).
Step 2: if C (a (i,1),1) and C (a (i,2),1) are both 0, that is, the ith branch is not communicated with the existing region, x is x +1, C (a (i,1),1) is x, C (a (i,2),1) is x, that is, the number of independent regions is increased by 1, and the regions to which nodes at two ends of the branch belong are x, then step 5 is performed; if one of C (a (i,1),1) and C (a (i,2),1) is 0, that is, there is a node at both ends of the branch and only one node is communicated with the existing area, classifying the node with the area of 0 in the nodes at both ends of the current branch as the area of the other node, where C (a (i,1),1) is C (a (i,1),1) + C (a (i,2),1), C (a (i,2),1) is C (a (i,1),1), and go to step 5; if C (a (i,1),1) and C (a (i,2),1) are not 0 and are not equal, that is, the branch connects two independent areas, the number of areas is decreased by 1, and the area to which the node belongs is updated, big max { C (a (i,1),1), C (a (i,2),1 }, sma max { C (a (i,1),1), C (a (i,2),1) }, x-1, k-1, and step 3 is turned.
And step 3: if C (k,1) > big, C (k,1) ═ C (k,1) -1; if C (k,1) ═ big, then C (k,1) ═ sma.
And 4, step 4: and k is equal to k +1, if k is less than or equal to N, turning to the third step 3, otherwise, turning to the third step 5.
And 5: if i is equal to i +1, turning to the step 2 if i is equal to or less than M, and otherwise, turning to the step 6.
Step 6: and outputting the matrix C.
(3) Thermal inertia of load
The thermal inertia of the building heating system reflects the capability of the building maintenance structure to maintain the current room temperature, and is related to factors such as the area of the building envelope structure, the indoor and outdoor temperature and the like. Heat loss from building heating systems is a very complex process involving convection, conduction and radiation. When the room temperature of a heating user changes within an allowable range, the heat loss of the heating user can adopt a steady-state simple algorithm, and the heat loss mainly comprises three parts, namely building envelope heat load, cold air infiltration heat load and ventilation heat load, which are respectively expressed as follows:
in the formula (I), the compound is shown in the specification,
building envelope thermal load corresponding to heating user for t time period node k, SkThe area of the building enclosure structure corresponding to the heating user for the node k, FeThe heat conduction coefficient of the building envelope structure corresponding to the heating user is the node k,
for the time period t, the node k corresponds to the indoor temperature of the heating user,
for the time period t, the node k corresponds to the outdoor temperature of the heating user,
the correction coefficient of the floor height of the heating user corresponding to the node k in the period t,
the building orientation correction coefficient of the heating user corresponding to the node k in the period t,
the cold air infiltration heat load of a heating user corresponding to a node k in the t period is shown, Num is the air change times per hour, VkThe volume of the building enclosure structure corresponding to the heating user for the node k, cpIs the constant pressure specific heat capacity of the cold air,
for the time period t of the outdoor air density,
and Ven is the ventilation volume per hour, wherein the ventilation heat load of the heating user corresponding to the node k in the period t is shown.
When the dynamic change process of the room temperature is considered, the indoor temperature is taken as a state quantity, and an equation representing the thermal inertia of the heating load can be expressed as follows:
in the formula, Q is the heat load power of the heating user corresponding to the node k in the period of t, CMSpecific heat capacity of indoor air, MkThe node k corresponds to the indoor air quality of the heating user,
and the node k corresponds to the indoor temperature of the heating user in the period of t + 1.
(4) Load shedding strategy taking into account thermal inertia of load
When the comprehensive energy system fails to completely meet all load requirements, load reduction is required to ensure normal energy supply of residual loads. The priority of load shedding is determined here by defining a load shedding factor. The load reduction coefficient is defined by comprehensively considering the load importance degree and the position as shown in the following formula:
in the formula, alphaiIs the importance coefficient of the load point i, LCiIs an importance factor, beta, of the load point iiThe coefficient is reduced for the position of the load point i.
Is a load point i and an electrical (gas, heat) source point sjThe minimum distance of the load point is related to the topological structure of the system and is not related to the length of the branch, and the distance between two adjacent load points is 1. N is a radical ofsIs the total number of electric (gas, heat) source points. I isLiThe smaller the value of the reduction coefficient for the load point i, the higher the priority of reducing the load at the load point.
Based on the load reduction coefficient, a load reduction strategy considering load thermal inertia is set as follows:
and carrying out topology analysis on the system state obtained by Monte Carlo simulation, carrying out state analysis on n connected domains obtained after the topology analysis, if the energy supply in the ith connected domain cannot completely meet all load energy requirements, sequentially reducing the load according to a load reduction coefficient, and carrying out power balance analysis again after the load reduction work of one node is finished until the energy supply amount in the ith connected domain is more than or equal to the load amount.
For electrical and gas loads, the load at the load point is clipped to zero each time. For the thermal load, when the load at a certain load point is to be reduced, firstly, assuming that the load at the load point is reduced to zero, calculating the temperature at the next moment through equations (14) to (18), and reducing the load at the load point to zero if the temperature at the next moment is higher than the set lowest allowable temperature; and if the temperature at the next moment is lower than the set lowest allowable temperature, setting the temperature at the next moment as the lowest allowable temperature, and calculating the load power through the expressions (14) to (18) so as to obtain the load reduction power.
(5) Comprehensive energy system energy supply reliability assessment process
Fig. 3 shows a flow chart of main steps of evaluating the energy supply reliability of an integrated energy system based on sequential monte carlo simulation, which specifically includes:
step 1: inputting original parameters and topological structure of the system.
Step 2: data initialization, determining simulation years, initializing system simulation time, load point failure times and the like.
And step 3: and randomly generating random numbers which are uniform with the number of the elements, and solving the fault-free working time TTF of each element according to the random numbers.
And step 3: and selecting the element with the minimum TTF as a fault element, and accumulating the simulation time.
And 4, step 4: then, a random number is generated, and the repair time of the fault element is calculated.
And 5: and carrying out topology analysis on the system state by adopting a node marking method.
Step 6: respectively carrying out state analysis on the n connected domains obtained after the topology analysis:
if the energy source supply amount in the region is 0, the state of the region is judged as follows: all loads in the region cannot be supplied with energy;
if the energy source supply amount in the region is not zero and is not less than the load amount, the state of the region is determined as follows: all loads in the region are supplied with normal energy, and load reduction is not needed;
if the energy supply amount in the region is not zero and is less than the load amount, it is necessary to reduce the load in the region until the energy supply amount is equal to or greater than the load amount based on a load reduction strategy in which thermal inertia is considered.
And 7: for the heat load node, the next time temperature is calculated.
And 8: and (4) judging whether the simulation year reaches, if so, executing the step 9, and if not, returning to the step 3.
And step 9: and calculating a load point reliability index and a system level reliability index.
Step 10: and evaluating the energy supply reliability of the comprehensive energy system based on the calculation result of the step 9.
Examples
(1) Parameter setting
1) Overview of the System
As shown in fig. 4, the energy types in the system of the present embodiment include three types, i.e., electricity, natural gas, and heat. The power system is an IEEE 33 node system, the natural gas system is an 11 node system, and the thermal system is a 32 node system. Two Energy hubs are included in the example system. Node EB24, node GB10 and node HB3 are connected via Energy hub I. The Energy hub I inputs electric power and natural gas, and outputs heat Energy to supply Energy for a thermodynamic system load. Energy hub II connects node GB11 and node HB6, which provide natural gas and thermal Energy, respectively, to Energy hub II.
2) Parameters of power distribution system
TABLE 3 distribution system node data
TABLE 4 distribution system line data
3) Natural gas system parameters
TABLE 5 Natural gas System node data
Table 6 natural gas system pipeline data
4) Thermodynamic system parameters
TABLE 7 thermodynamic system node data
TABLE 8 thermodynamic system pipeline data
5) Energy hub parameters
The system shown in FIG. 4 includes two Energy hubs (Energy hubs), Energy hub I and Energy hub II. The Energy hub I structure is shown in FIG. 5.
Wherein T is a transformer, AC is an air conditioning system, and GF is a gas boiler.
The Energy hub I parameter matrix is shown as follows:
the structure of Energy hub II is shown in FIG. 6.
Wherein HE is an electric heater, and MT is a micro gas turbine.
The Energy hub II parameter matrix is shown as follows:
6) system component reliability parameter
The reliability evaluation of the integrated Energy system Energy supply system shown in fig. 4 was performed in consideration of the power distribution system main feeder, distribution line, gas source, natural gas pipeline, heat source, thermodynamic system pipeline, and Energy conversion equipment failure in Energy hub, and the system element reliability parameters are shown in table 9.
TABLE 9 System component reliability parameters
7) Heat load related parameter
Assuming that the heat loads in the embodiments are all the heat loads of the heating building, the relevant parameters in the thermal inertia equation are shown in table 10, and the relevant parameters in the PMV equation are shown in table 11.
TABLE 10 thermal inertia equation-related parameters for heating buildings
TABLE 11 PMV equation-related parameters
(2) EXAMPLES results
1) Index convergence analysis
Since the energy supply reliability of the integrated energy system is evaluated by specifying the simulation age, the simulation convergence is verified first. For sequential monte carlo simulations, the convergence rate β of the statistical indicator can be calculated according to the following equation:
in the formula, X is a certain statistical index, σ (X) is the standard deviation of X, E (X) is the mean value of X, and N is the number of years of simulation.
Since the SAIFI calculation is required in this embodimenteThe convergence rates of all the system-level reliability indexes calculated according to equation 22 after waiting for 13 system-level reliability indexes are shown in fig. 7. As can be seen from the figure, EPNSg、SAIDIg、SAIFIgAnd SAIDIeFour indices converge slowly, EPNS among themgThe index converges slowest.
On this basis, the simulation convergence is verified from two aspects: the first aspect is that the simulation is carried out for a plurality of times in the same simulation year, and the second aspect is that the results of different simulation years are compared and verified. Setting simulation years of 1000 years, 2000 years and 3000 years respectively, performing ten times of simulation respectively, and selecting EPNSg、SAIDIg、SAIFIgAnd SAIDIeThe results of the four representative system-level reliability indicators with slower convergence are shown in table 12, table 13, and table 14. The mean and standard deviation of ten simulations under different simulation years are shown in fig. 8 and 9.
Table 12 calculation results of representative system-level reliability indexes when the simulation year limit is 1000 years
TABLE 13 calculation results of representative system-level reliability indicators for 2000 year simulation
TABLE 14 calculation result of representative system-level reliability index when simulation year is 3000 years
Through table 12, table13. As can be seen from Table 14, FIG. 8 and FIG. 9, EPNS simulates multiple times for different simulation yearsgThe standard deviation of the index was always the largest, indicating that the convergence rate was the slowest, and matched the results of fig. 7. When the simulation year limit is increased from 1000 years to 2000 years and then to 3000 years, the statistical standard deviation of the system reliability index is gradually reduced, and the index convergence is gradually improved. When the simulation age limit is 3000 years, the statistical standard deviations of the system reliability indexes are all small, wherein the EPNS with the slowest convergence speedgThe index, the statistical standard deviation of which is below 0.9, reaches a better convergence level. In addition, by comparing the mean values of the system reliability index calculation results under different simulation years, it can be seen that the mean values of the index calculation results are basically consistent when the simulation years are 1000 years, 2000 years and 3000 years. Therefore, the simulation age can be set to 3000 years, at which time the system reliability index has better convergence.
2) Energy supply reliability index calculation result
In view of the relatively low probability of multiple system failures, only single system failures are considered here, including power distribution system main feeder, distribution lines, gas sources, natural gas pipelines, heat sources, thermal system pipelines, and Energy conversion equipment failures in Energy hubs. Based on sequential Monte Carlo simulation, the simulation year is 3000 years, the lowest temperature allowed by the heat load is 18 degrees, the highest temperature is 25 degrees, the operation condition of the system shown in FIG. 4 is simulated, and energy supply reliability indexes including a load point reliability index and a system level reliability index are calculated.
The calculation results of the reliability indexes of the load points of the power distribution system are shown in fig. 10, wherein (10a) is the fault rate of the load points, (10b) is the average outage duration of the load points, and (10c) is the annual average outage duration of the load points. From (10a), since the load power of node 1 is 0, the failure rate thereof is 0, and the failure rates of all other load points are below 0.4 times/a. In addition, the failure rate of the load points is gradually increased from the node 1 to the node 18, from the node 19 to the node 22 and from the node 23 to the node 33, and the failure rate of the load points is gradually increased from the head end of the feeder to the tail end of the feeder according to the energy supply characteristics of the radial distribution network. From (10b), it can be seen that the average outage duration of all load points of the power distribution system is about 8 h/time, and is matched with the given reliability parameters (the repair time of the main feeder of the power distribution system is 2h, and the repair time of the distribution line is 8 h). From (10c), the annual average outage duration of all the load points is less than 3h/a, the overall rule of the annual average outage duration of the load points is consistent with the overall rule of the fault rates of the load points, and the annual average outage duration of the load points gradually increases from the head end of the feeder line to the tail end of the feeder line.
The calculation results of the reliability index of the load point of the natural gas system are shown in fig. 11, wherein (11a) is the fault rate of the load point, (11b) is the average outage duration of the load point, and (11c) is the average annual outage duration of the load point. First, it can be seen from the figure that the load point failure rate, average outage duration, and average annual outage duration from node 2 to node 8 are all equal due to the particular topology of the natural gas system. The natural gas system in fig. 4 is a ring-tree topology structure, which has both ring and tree shapes, the pipeline connection mode from the node 2 to the node 8 is ring shape, and when only the faults of the gas source and the natural gas pipeline are considered, the faults from the node 2 to the node 8 are all caused by the fault of the gas source or the fault of the pipeline 1, so that the reliability indexes of the load points are equal.
As can be seen from (11a), the node 1 failure rate is 0, since the node 1 load is 0; the failure rate of all load points of the system is less than 0.35 times/a; the failure rate of the load point from the node 9 to the node 11 is gradually increased, because the pipeline connection mode from the node 9 to the node 11 is in a single branch shape, the farther away from the air source, the lower the load reliability; and the load point failure rate of nodes 9, 10, 11 is greater than from node 2 to node 8 because, in addition to an air supply failure or a pipeline 1 failure causing a load point 9, 10, 11 failure, a pipeline 12, 13, 14 failure also causes a load point 9, 10, 11 failure.
From (11b), it can be seen that the average outage duration of all the other load points is 25 h/time to 31 h/time except that the average outage duration of the node 1 is 0, and the average outage duration of all the other load points is consistent with the given reliability parameters (the gas source restoration time is 30h, and the natural gas pipeline restoration time is 20 h). Further, the average outage duration of the load points decreases from node 9 to node 11, and the average outage duration of the load points of nodes 9, 10, 11 is less than that of nodes 2 to 8. The reason for this is as follows: by analyzing the fault reasons of the load points, except that the fault of the gas source or the fault of the pipeline 1 can cause the faults from the node 2 to the node 11, the fault of the pipeline 12 can cause the faults of the load points 9, 10 and 11, the fault of the pipeline 13 can cause the faults of the load points 10 and 11, and the fault of the pipeline 14 can cause the fault of the load point 11, wherein in the setting of the reliability parameters, the gas source repairing time is longer than the gas pipeline repairing time. In the simulation time period, the total failure time and the total failure times of the nodes 9, 10 and 11 are sequentially increased and are all larger than the nodes 2 to 8, but the increase speed of the total failure time is smaller than the increase speed of the total failure times, so that the average outage duration time of the load points from the node 9 to the node 11 is gradually reduced and is all smaller than the average outage duration time of the nodes 2 to 8.
From (11c), the annual average outage duration of all load points of the system is below 9h/a, and the overall law of the annual average outage duration of the load points is consistent with the overall law of the fault rates of the load points.
The calculation results of the reliability index of the load point of the thermodynamic system are shown in fig. 12, where (12a) is the fault rate of the load point, (12b) is the average outage duration of the load point, (12c) is the average annual outage duration of the load point, and (12d) is the average annual thermal comfort time of the load point. First, a simple analysis is made on the thermodynamic system in fig. 4, which has three heat sources, and Energy hub I can also provide part of the heat Energy for the thermodynamic system, and the topological structure of the thermodynamic system is also in a ring branch shape, and has a ring shape and a branch shape. And analyzing the reliability index of the load point of the thermodynamic system on the basis. First, it can be seen from the figure that, for a node with a load power of 0, the load point reliability indexes are all 0. Although the load power of the node 3 is not 0, the failure rate, the average outage duration and the annual average outage duration are all 0, and the analysis reason is that the node 3 and the Energy hub I-Lh1And the Energy hub I is connected with the node 3, so that the Energy supply reliability of the node is improved.
From (12a), the failure rate of all load points of the thermodynamic system is below 0.4 times/a. The failure rates of the nodes 8, 21, and 24 are high because the load reduction coefficients of the nodes 8, 21, and 24 are small, and the smaller the load reduction coefficient is, the higher the priority is for reduction when load reduction is performed, and therefore the failure rates of these three nodes are high. From (12b), it can be seen that the average outage duration of all load points of the system is about 30 h/time, which is consistent with the given reliability parameters (the heat source restoration time is 30h, and the thermal pipeline restoration time is 25 h). From (12c), the annual average outage duration of all load points of the system is less than 14h/a, and the overall law of the annual average outage duration of the load points is basically consistent with the overall law of the fault rates of the load points. From (12d), it can be seen that the annual average thermal comfort time for all load points of the system is above 8700 h/a.
The load point reliability indexes of the three independent load nodes output by the Energy hub are shown in table 15. By analyzing the Energy hub I and Energy hub II structures, the reliability of the output electrical load node of the Energy hub I is related to the input electrical Energy of the Energy hub I and the reliability of the transformer in the Energy hub I; the output electrical load node of the Energy hub II has reliability related to the reliability of the input natural gas of the Energy hub II and the reliability of the micro gas turbine in the Energy hub II; the reliability of the output heat load node of the Energy hub II is related to the input natural gas and input heat Energy of the Energy hub II, the reliability of a micro gas turbine and a heat exchanger in the Energy hub II.
As can be seen from Table 15, Energy hub I-Le1Node, Energy hub II-Le2Node and Energy hub II-Lh2The load point failure rate and the annual average outage duration of the node are gradually increased, which shows that Energy hub I-Le1Highest node reliability, Energy hub II-Le2Second node, Energy hub II-Lh2The node reliability is the worst. Wherein Energy hub II-Lh2The reason for the larger failure rate of the nodes and the larger annual average outage duration is related to the larger failure rate of the micro gas turbine and the heat exchanger in the Energy hub II and the lower reliability of the heat Energy input of the Energy hub II. Furthermore, Energy hub II-Lh2The average thermal comfort time of the node year is 8688.5183h/a, and the thermal satisfaction is still at a higher level.
TABLE 15 Energy hub output load Point reliability index
The system level reliability index for the integrated energy system power system shown in fig. 4 is shown in table 16. As can be seen from the table, the average energy supply reliability of the three energy subsystems is over 0.999, and the system reliability level is high. The system average power failure frequency of the power distribution system, the natural gas system and the thermodynamic system is gradually increased, the system average power failure duration is gradually increased, the system average power supply reliability is gradually reduced, the system supply shortage expectation is gradually increased, the reliability level of the power distribution system is the highest, and the reliability level of the thermodynamic system is the lowest when the natural gas system is the next. In addition, for the thermodynamic system, the system thermal user satisfaction is 0.996278, which is at a high satisfaction level. In conclusion, the energy supply reliability of the integrated energy system shown in fig. 4 is at a high level.
TABLE 16 System level reliability index
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. The method for evaluating the energy supply reliability of the integrated energy system is characterized by comprising the following steps of:
acquiring original parameters and a topological structure of a comprehensive energy system to be evaluated;
acquiring a simulation model of the comprehensive energy system to be evaluated based on a sequential Monte Carlo method;
performing topology analysis on the system state of the simulation model by adopting a node marking method to obtain a plurality of connected domains;
and respectively carrying out state analysis on each connected domain, and carrying out reliability evaluation on the comprehensive energy system to be evaluated by adopting an energy supply reliability index system based on an energy utilization purpose and a user position.
2. The method according to claim 1, wherein the energy supply reliability index system based on energy usage goals and user standpoints comprises a load point reliability index and a system level reliability index.
3. The method according to claim 2, wherein the load point reliability indicators include a load point failure rate, a load point average outage duration, a load point average annual outage duration, and a load point average annual thermal comfort time, wherein,
the load point failure rate is expressed as:
in the formula, λα,jIs the failure rate of the alpha subsystem load node j, T is the total simulation time,
the total failure times of the alpha subsystem load node j in the whole simulation time period;
the load point average outage duration is expressed as:
in the formula, gammaα,jFor the average outage duration of the alpha subsystem load node j,
the fault time of the alpha subsystem load node j in the ith simulation is shown, and N is the number of system random states in the whole simulation period;
the annual average outage duration at load point is expressed as:
Uα,j＝λα,jγα,j
in the formula of Uα,jThe annual average outage duration of the alpha subsystem load node j;
the load point annual average thermal comfort time is expressed as:
in the formula of Uh,jMean thermal comfort time per year at jth load point of thermodynamic system, Fh,j(Xi) Is in a random state X with the systemiCorresponding thermal comfort coefficient of 0 or 1, tiIs a system random state XiThe duration of time.
4. The method according to claim 3, wherein the thermal comfort factor F is a measure of the reliability of the energy supply of the integrated energy systemh,j(Xi) Is determined by the following formula:
where PMV is the predicted average heat sensation index.
5. The method according to claim 2, wherein the system level reliability indicators comprise average system outage frequency, average system outage duration, average system energy reliability, expected system under-supply, and system hot user satisfaction, wherein,
system average outage frequency SAIFIαExpressed as:
in the formula, the subscript alpha represents the energy type of the subsystem, Nα,jThe number of users, N, of the load node j of the alpha subsystemαIs the total number of load nodes of the alpha subsystem, lambdaα,jThe failure rate of the alpha subsystem load node j is shown;
system average outage duration SAIDIαExpressed as:
in the formula of Uα,jThe annual average outage duration of the alpha subsystem load node j;
average energy supply reliability ASAI of systemαExpressed as:
system starvation expected EPNSαExpressed as:
in the formula, Cα,j(Xi) Is the system in state XiLower alpha subsystem load node j load reduction amount, tiIs a system random state XiDuration, T is total simulation time, and N is the number of system random states in the whole simulation period;
system hot user satisfaction PhExpressed as:
in the formula, NhIs the total number of heat load nodes, Nh,jIs the number of users, U, of the load node j of the thermal subsystemh,jRefers to the jth thermodynamic systemThe load point averages the thermal comfort time per year.
6. The method for evaluating the reliability of energy supply of the integrated energy system according to claim 1, wherein in the sequential monte carlo method, all elements adopt a working-fault two-state model, the fault-free working time TTF and the repair time TTR of each element are random variables which are subject to exponential distribution, and the system operation state sequence is obtained based on the random variables.
7. The method for evaluating the energy supply reliability of the integrated energy system according to claim 1, wherein the node marking method is used for distinguishing the connected domains by sequentially searching and marking the numbers of the nodes at the two ends of the branch.
8. The method for evaluating reliability of energy supply of integrated energy system according to claim 1, wherein the analyzing the state of each connected domain comprises:
if the energy supply amount in the region is 0, all loads in the region cannot be supplied with energy;
if the energy source supply amount in the region is not zero and is more than or equal to the load amount, all loads in the region are supplied with normal energy without load reduction;
and if the energy supply amount in the region is not zero and is less than the load amount, reducing the load in the region until the energy supply amount is greater than or equal to the load amount.
9. The method of claim 8, wherein the load shedding strategy based on thermal inertia is applied to shed loads in the area.
10. An integrated energy system energy supply reliability assessment computer system, comprising a processor and a memory, wherein the memory stores a computer program, characterized in that the processor calls the computer program to implement the steps of the integrated energy system energy supply reliability assessment method according to any one of claims 1 to 9.
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