CN112528481A - Modeling and analyzing method for random dynamic process of thermoelectric coupling system under extremely cold disaster - Google Patents
Modeling and analyzing method for random dynamic process of thermoelectric coupling system under extremely cold disaster Download PDFInfo
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Abstract
The invention provides a modeling and analyzing method for a thermoelectric coupling system random dynamic process under an extremely cold disaster, which comprises the following steps: establishing a Markov state space of the thermoelectric coupling system under the extreme cold disaster according to the natural environment state and the equipment states of different types of equipment; analyzing the transfer process of the Markov state space to obtain the state transfer probability of the thermoelectric coupling system between adjacent moments; based on the transition probability of the state of the thermoelectric coupling system between adjacent moments, the occurrence probability of the state of the thermoelectric coupling system transitioning from the initial state at the beginning of the disaster to the current moment is calculated. The invention establishes the Markov state space of the thermoelectric coupling system under the extreme cold disaster according to the environment state of the extreme cold weather and various equipment states, calculates the transition probability of the state of the thermoelectric coupling system between adjacent moments by analyzing the transition process of the Markov state space, and can accurately know the occurrence probability of the state of the thermoelectric coupling system at any moment based on the transition process.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to a modeling and analyzing method for a thermoelectric coupling system random dynamic process under an extremely cold disaster.
Background
Under the impact of an extremely cold disaster, a cascading failure process may occur in a thermoelectric coupling system, and a large-area energy supply interruption accident occurs. Firstly, severe weather conditions such as strong wind, snowstorm, low temperature and the like in an extremely cold disaster can cause damage to a power transmission line, a heat supply pipeline and a unit, so that a topological structure of a power supply and heat supply network is damaged, and energy supply interruption occurs on a load side; secondly, because the regional distribution network and the heat supply network are closely coupled through CHP units, heat pumps and other equipment, mutual influence exists in the trend state. After a single electric power or thermal power equipment breaks down and stops running, the distribution of thermoelectric coupling tide can be changed, so that overload phenomena occur in other power supply circuits and thermal power pipe networks, further cascading failure events are induced, and the range of power failure and heat failure accidents is expanded.
The safety and the stability of the thermoelectric coupling system are seriously threatened by extremely cold disasters, the occurrence and the development mechanism of the thermoelectric coupling fault under the disasters are researched, and the fault state of the thermoelectric coupling system under the extremely cold disasters at any moment needs to be known in order to better realize pre-disaster prevention and post-disaster recovery.
Disclosure of Invention
In order to solve the problems in the background art, embodiments of the present invention provide a modeling and analyzing method for a random dynamic process of a thermoelectric coupling system in an extreme cold disaster, which can accurately calculate a state occurrence probability of the thermoelectric coupling system at each time in the extreme cold disaster.
The embodiment of the invention provides a modeling and analyzing method for a thermoelectric coupling system random dynamic process under an extremely cold disaster, which comprises the following steps:
establishing a Markov state space of the thermoelectric coupling system under the extreme cold disaster according to the natural environment state and the equipment states of different types of equipment;
analyzing the transfer process of the Markov state space to obtain the state transfer probability of the thermoelectric coupling system between adjacent moments;
based on the transition probability of the state of the thermoelectric coupling system between adjacent moments, the occurrence probability of the state of the thermoelectric coupling system from the initial state of the beginning of the disaster to the current moment is calculated.
According to the modeling and analyzing method for the random dynamic process of the thermoelectric coupling system under the extreme cold disaster, the Markov state space of the thermoelectric coupling system under the extreme cold disaster is established according to the environment state of the extreme cold weather and the states of various devices, the state occurrence probability of the thermoelectric coupling system at any moment is calculated by analyzing the transition process of the Markov state space, and the state occurrence probability of the thermoelectric coupling system at any moment can be accurately known based on the Markov state space transition process.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a modeling and analyzing method for a stochastic dynamic process of a thermoelectric coupling system in an extremely cold disaster according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a transition process of a Markov state space according to an embodiment of the present invention;
fig. 3 is a diagram illustrating an overall transition process of the markov state space according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a modeling and analyzing method for a thermoelectric coupling system random dynamic process under an extremely cold disaster is provided, which includes:
establishing a Markov state space of the thermoelectric coupling system under the extreme cold disaster according to the natural environment state and the equipment states of different types of equipment;
analyzing the transfer process of the Markov state space to obtain the state transfer probability of the thermoelectric coupling system between adjacent moments;
and calculating the occurrence probability of the thermoelectric coupling system from the initial state of the beginning of the disaster to the current state of the disaster on the basis of the state transition probability of the thermoelectric coupling system between adjacent moments.
It can be understood that under the weather of the extreme cold disaster, according to the natural environment state and the equipment states of different types of equipment, a markov state space of the thermoelectric coupling system under the extreme cold disaster can be set up, and the markov state space describes the state transition process of the thermoelectric coupling system under the extreme cold disaster along with the time.
And analyzing the transfer process of the Markov state space based on the established Markov state space of the thermoelectric coupling system to obtain the state transfer probability of the thermoelectric coupling system between adjacent moments. According to the transition process of the Markov state space, the occurrence probability of the state of the thermoelectric coupling system from the initial state of the beginning of the disaster to the current time is calculated, and the occurrence probability of the state of the thermoelectric coupling system at any time in the extremely cold disaster can be accurately known.
As a possible embodiment, the natural environment state is used to describe intensity information of the extreme cold disaster at each time. Because the process of strong wind, snowfall and low temperature is usually accompanied in the process of generating the extremely cold weather, the intensity of the extremely cold disaster can be described through the wind speed, the snowfall amount and the temperature at the current moment. Due to the fact that the influence range of extremely cold weather is generally large, compared with the scale of the researched thermocouple system, the difference of the disaster intensity along with the space change can be approximately ignored. Therefore, the intensity of the extreme cold weather in the disaster center at time t is defined as the natural environment state and is written as:
wherein, ω istIs the wind speed (m/s), pitIn terms of snowfall (mm), κtThe ambient temperature (. degree. C.).
In practice, the wind speed, the amount of snowfall, and the outside air temperature are physical quantities that can be continuously changed. Since the number of markov state quantities is limited, discretization of the natural environment state quantities is required. Considering that the environmental quantity reflects an average state over a period of time, the system is not significantly affected when the variable fluctuates by a small amplitude around a certain value. In this document, the numerical values of the environmental quantities are all approximated to integers, and since the change interval of the environmental quantities during the disaster is limited, the number of state quantities is also limited.
As a possible implementation manner, according to the type of the device affected by the disaster in the thermoelectric coupling system, the embodiment of the present invention mainly defines several device states, which mainly include a line state, a load state, a unit state, an energy storage state, and a device performance state.
The line state description describes the working states of the power grid line and the heat supply network pipeline, and is divided into normal operation and shutdown operation, so that the working states can be represented by 1-0 variables. The line state shifts from normal to outage, which may be caused by direct failure due to a disaster, or may be caused by indirect influences such as outage of an upstream line connected thereto.
The line state is defined as:
wherein,
and
respectively representing the operating states of the grid line and the heat supply network pipes.
The load state describes the loss degree of the load in the electric heating coupling system, and reflects the damage degree of the disaster on the electric heating coupling system from a macroscopic level. The embodiment of the invention uses the load loss proportion as the load state, namely the proportion of the load nodes with energy supply interruption to the total load nodes.
Defining the load state as:
wherein,
m load states corresponding to different degrees of load loss, gammaDThe element in (1) will be in the interval [0,1 ]]Equally dividing the interval into m sub-intervals;
each subinterval having a width Δ γDAnd satisfies the following conditions:
load state
Indicating that the degree of load loss is in the interval [ i.DELTA.gamma ]D,(i+1)·ΔγD) And (4) the following steps.
The unit state describes the amount of fuel left on the unit side and available for power generation and heat generation, and after an extremely cold disaster occurs, the road transportation capacity is reduced or even interrupted, so that the continuous operation capacity of the distributed unit (such as a heat source, a CHP unit and the like) depends on the amount of fuel stored before the disaster.
For the distributed unit G, the remaining available fuel quantity of the distributed unit at the time t is as follows:
wherein,
the total amount of fuel reserved before the disaster on the unit side is represented;
since the number of Markov states is limited, it is necessary to divide the interval
Discretization is as follows:
when the distributed unit G is in the state of
When the fuel quantity remaining in the distributed unit G is
In the meantime.
The stored energy state describes the remaining amount of movable stored energy. In the thermoelectric coupling system studied, the movable energy storage mainly comprises two types, namely an emergency power supply vehicle and heating materials.
The emergency power supply vehicle generally realizes energy storage through a battery, and the battery energy storage residual capacity state at a node j at the time t is as follows:
wherein,
representing the total electric energy reserved by the emergency power supply train at the node j before the disaster;
is an integer representing the number of emergency powered vehicles at node j, EVehRepresenting the power capacity of a single power supply vehicle.
The heating materials mainly take household fuel oil or charcoal as a main form, and the total energy of the available heating materials at a node j at the moment t is defined as follows:
wherein,
the total amount of heating materials allocated at the node j before the disaster occurs;
similar to the unit state, for intervals
And
discretizing to respectively obtain
Each width is
Is a sub-interval of
Has a width of
The subintervals of (a) correspond to different markov states, respectively.
The energy storage state is uniformly expressed as:
device performance describes the effect of a low temperature environment on the performance of the device. For the thermoelectric coupling system under study, the device performance is mainly reflected in the capacity performance of battery energy storage and the energy efficiency coefficient of the heat pump. Defining the capacity performance state of the battery energy storage at the moment t as
Wherein
Representing the ratio of the battery energy storage capacity at a given ambient temperature relative to the normal ambient temperature (25 c). It is also possible to define the energy efficiency state of the heat pump as
Representing the ratio of heat pump heating capacity to consumed electrical energy at ambient temperature at time t.
To simplify the problem, the embodiment of the present invention sets the external environment temperature at each time to an integer value, thereby obtaining the temperature corresponding to each temperature
And
and the value is obtained, thereby realizing the discretization of the performance state of the equipment.
The device performance state is defined as:
wherein, the capacity performance state of the battery energy storage at the time t is
Representing the ratio of the battery energy storage capacity at a given ambient temperature relative to the normal ambient temperature; the energy efficiency state of the heat pump is
Representing the ratio of heat pump heating capacity to consumed electrical energy at ambient temperature at time t.
As a possible implementation, it is understood that during the occurrence of extremely cold weather, the intensity of the disaster and the operating state of the system change gradually over time, often accompanied by processes of different time scales. For example, the intensity of a disaster typically varies slowly over time, and weather conditions may fluctuate around a value over time. In contrast, the change in the power flow distribution in a thermoelectric coupled system is much more rapid and may fluctuate continuously in a shorter time. The embodiment of the invention provides a concept of long time scale and short time scale, thereby realizing the description of different state change processes.
The Markov state space includes a Markov state space of a long timescale and a Markov state space of a short timescale, wherein one long timescale includes a plurality of short timescales.
In the embodiment of the invention, the long-time scale is one hour as a step length and is used for describing the evolution process of the disaster intensity in the extremely cold weather and the macroscopic disaster condition of the thermocouple system along with the time, and the process can be regarded as a time sequence consisting of continuous steady-state sections. The intensity of the extremely cold disaster can be given hourly weather data by weather forecast, such as average temperature, wind speed, precipitation and the like per hour; and the macroscopic disaster condition of the thermoelectric coupling system can be described by using the load state provided above, and the overall load loss ratio is used for carrying out quantitative evaluation on the disaster condition.
Based on the above analysis, the markov state space defining the long time scale is:
wherein, thRepresenting the time of day of the long timescale, the long timescale process describes the final steady state that the thermoelectric coupling system reaches within each one hour period. During adjacent time periods, the state of the thermoelectric coupling system may undergo a series of rapidly changing processes, which are described as markov processes on a short time scale.
Under the condition that the intensity of the extremely cold disaster is not changed, the tidal current distribution of the thermoelectric coupling system can still be changed. In contrast, the change process of the power flow distribution is faster, and the change of the system state related to the change process needs to be described through a short time scale.
The short timescale describes a continuous process of change of the system state in relation to the power flow over an hour. The natural disaster causes the trend in the system to change by causing the performance reduction of equipment or line fault; after the power flow is redistributed, a new fault may be caused due to the power out-of-limit, so that the power flow is redistributed again. This dynamic process may be repeated several times until the redistributed power flows are within the normal operating range, and the system reaches a final stable state at the current disaster intensity, and no new fault occurs.
For the power grid, the dynamic (transient) process is very fast, and the duration is usually only a few milliseconds or even shorter; the dynamic process of the heat supply network is longer than that of the power network, and the duration is generally several minutes. Thus, when performing state analysis on a short time scale, the grid typically uses a steady state model, while the heat supply network may select a static or dynamic model depending on the particular problem under study. The step length of a short time scale is set to be 5 minutes, and in each short time step length, the system organizes load flow calculation according to the current state to obtain line power, unit or stored energy output and the like. It is considered here that the flow redistribution process can be completed in steps of a short time scale and a new fault that may occur is given according to the flow result, so as to enter the next round of calculation.
Based on the above analysis, the markov state space for the short timescale is defined as:
comprises a line, a unit and an energy storage state, wherein tmRepresenting the time of day on a short timescale.
The Markov state space of the long time scale describes a macroscopic state of the thermoelectric coupling system influenced by a disaster, the Markov state space of the short time scale reflects the internal trend relation of equipment in the thermoelectric coupling system, and the Markov state space of the long time scale and the Markov state space of the short time scale jointly form a complete Markov state space.
As a possible implementation, analyzing the transition process of the markov state space to obtain the state transition probability of the thermocouple system between adjacent time instants includes:
calculating the line state transition probability between every two adjacent short time scales for a plurality of short time scales between two adjacent long time scales;
and calculating the line state transition probabilities of two adjacent long time scales according to the line state transition probabilities between every two adjacent short time scales.
It can be understood that, for the established Markov state space of the thermocouple system under the extreme cold disaster, the transition process of the Markov state space is analyzed, and the Markov state transition process gives the probability of state transition of the system between adjacent moments. In the actual disaster occurrence process, the change of each state has chronological sequence.
As can be seen from fig. 2, a long timescale includes a plurality of short timescales, and the plurality of times corresponding to the long timescale in fig. 2 are thTime-1, thTime t andh+1 time instant, wherein th-1 time and thA long time scale, t, is formed between the momentshTime t andhand forming a next long time scale between +1 moments to form two adjacent long time scales, and analyzing the transition process of the Markov state space, namely analyzing the probability of state transition of the thermocouple system between the adjacent scales.
As shown in FIG. 2, tm,1The time instant represents the first short timescale time instant between two adjacent long timescales, which include n short timescales.
Wherein t is the first short timescalem,1Line state transition probability of time of day
Including grid line outage probability
And/or probability of outage of heat supply network pipeline
If there is only heat in the thermoelectric coupling systemIf the network is connected, the line state transition probability only comprises the shutdown probability of the heat supply network pipeline; if the thermoelectric coupling system only has a power grid, the line state transition probability only comprises the power grid line outage probability; if the thermoelectric coupling system includes both the heat supply network and the power grid, the line state transition probability herein includes both the heat supply network pipeline outage probability and the power grid line outage probability.
It can be seen that in the long time scale, thAnd the equipment performance and the line outage probability in the thermoelectric coupling system are determined at the moment given disaster intensity. On the basis of the above, natural disasters can be caused
The probability of (2) causes a direct line fault, corresponds to the change of the line state in a short time scale, and further influences the unit and the energy storage state, so that the power flow of the thermoelectric coupling system is redistributed. Then, the power out-of-limit line is used again
And (4) the probability of (2) is cut off, and the state transition process of the next round is carried out.
On the premise of not considering the human intervention on the equipment performance, the Markov state space in the long time scale finally reflects the macroscopic load loss degree of the system, and the state transition probability thereof
Can be given by:
wherein n is the state transition times of the short time scale in each long time scale.
As a possible implementation, in an extremely cold disaster, the devices in the thermoelectric coupling system are the most immediate subject of the disaster. Under the impact of complex meteorological conditions such as strong wind, snowstorm, low-temperature freezing and the like, the operation state of equipment in the thermoelectric coupling system can be obviously changed. Among them, equipment failure and deterioration of the operation performance are major factors that cause a change in the operation state. These factors are the way in which an extremely cold disaster affects the thermocouple system, and the embodiments of the present invention divide it into two types, direct and indirect. The direct influence refers to line faults or equipment performance degradation directly caused by strong wind, snow fall and freezing in extremely cold disasters; the indirect influence is equipment failure caused by power flow transfer in the system on the basis of the direct influence, such as cascading failure condition in the system.
For thermal networks, the pipes are generally buried in the ground and are therefore not subject to erosion by high winds and snowfall, but freezing at low temperatures causes the soil layer around the pipes to shrink, thereby creating abnormal stresses that squeeze the pipes. Moreover, in the long-term use process of the pipeline, the aging corrosion of the materials and the looseness of the connection parts can reduce the extrusion resistance of the pipeline, and finally the pipeline is cracked. The burst probability of a pipe is directly related to the outside ambient temperature and is therefore a result of the direct effect of extreme coldness. In addition, the energy efficiency coefficient of the heat pump unit is obviously reduced along with the reduction of the external environment temperature.
For the power grid, the results of direct influence of disasters mainly include power grid line breakage, pole tower collapse and battery energy storage capacity reduction. The overhead line and the tower of the power grid are directly exposed to the external environment, the ice-coated and snow-accumulated loads on the line and the tower are gradually increased along with the increase of time, the wind area is also gradually increased, and the probability of breakage and collapse gradually rises along with the disaster process under the superposition of strong wind; the performance of a battery energy storage device is directly related to the ambient temperature, and when the ambient temperature decreases, the capacity of the battery energy storage device will significantly decrease.
Aiming at the direct influence of the disaster on the heat supply network and the power grid, the line fault probability is introduced below.
Whether the heat supply network pipeline or the power grid line is adopted, the fault condition under the disaster belongs to a small probability event. Correlation studies are typically summarized from statistical data from historical observations, empirical models, or are based on simulation results in the laboratory. Starting from a general rule of material strength, the probability of equipment failure and the natural disaster strength in actual engineering are given to accord with lognormal distribution:
wherein d isSIndicating a fault condition, SdThe parameters of the engineering requirements are represented,
is the median value of the engineering demand parameter at which the equipment reaches the fault condition threshold,
the standard deviation of the natural logarithm of the engineering demand parameter when the equipment reaches the fault state threshold value, and psi represents a standard normal cumulative distribution function.
The formula (15) can be directly applied to describing the relationship between the intensity of the extremely cold disaster and the failure probability of heat supply network pipelines, power grid lines and towers.
Based on equation (15), for a heat supply network, the failure probability model of the heat supply pipeline is:
wherein, PpipeIndicates the failure probability of the freezing burst of the central heating pipeline, wherein kappa is the actual environment temperature and kappacDenotes the temperature at which the soil freezes and begins to stress the heat supply pipe, κdThe lowest ambient temperature corresponding to the maximum stress that the heat supply network pipes can bear.
For the power grid, the fault probability model of the power grid tower is as follows:
wherein, PpoleThe fault probability of the collapse of the power grid tower is shown, and omega is the actual position near the towerWind speed, ωcRepresenting a minimum wind speed, omega, that poses a threat to the mastdThe maximum wind speed that the tower can bear.
For a power grid line, under an extremely cold disaster, a circuit fault probability empirical formula based on accumulated snow and ice is as follows:
in the formula,
representing the probability of a line fault caused by snow icing, pi representing the actual snow icing thickness of the line, piDThe nominal ice coating thickness of the line is shown, and the value of pi depends on the snowfall amount at each moment from the extremely cold disaster.
The empirical formula of the probability of line fault based on strong wind is as follows:
wherein,
representing the probability of a line fault caused by a strong wind, omega representing the actual wind speed in the vicinity of the overhead line of the grid, omegaDIndicating the rated wind speed that the line can withstand.
Whether the accumulated snow is covered with ice or the strong wind exists, the power grid line can be in failure when any one of the factors reaches the limit, and therefore the failure probability of the power grid line
Depending on the maximum values given by equation (18) and equation (19), i.e.:
the outage probability of the power grid line can be caused by collapse of a power grid tower, a fault of the power grid line and the fault of the power grid tower and can be caused by the collapse of the power grid tower and the fault of the power grid line, so that the outage probability of the power grid line at the moment t can be obtained
Comprises the following steps:
wherein n isgIn order to support the number of towers of the overhead line, all the towers have the same probability distribution.
It can be understood that, from the process analysis of the state transition of fig. 2, the probability of the state transition of the line in two adjacent long time scales is:
wherein n is the state transition times of a short time scale in a long time scale.
The markov process presented in figure 2 describes the markov state transition only once. In fact, thState of the moment
May be from th-1A wide variety of state transitions at time. Therefore, in order to calculate the occurrence probability of the system state at a certain time, it is necessary to analyze all markov state transition paths from the system initial state at which the disaster starts to the current time.
As shown in FIG. 3, the system passes t from the initial state before the disaster startsh1,th2,...,thkTransfer to the current moment of investigation thk. Wherein
Represents an initial markov state prior to occurrence of a disaster;
represents th1A long timescale state of time, wherein the load state is
At each moment in time the load state of the system is from
Respectively corresponding to m different load states in the formula, the load loss grade of which is
To
Gradually increasing.
Representing the probability of a state transition, where the subscript denotes the time from th0Time th1At time, the superscript indicates the transition from initial state 0 to load state
Also for
Then it indicates that time is from th0Time th1At the moment, the load state is from
Is transferred to
It can be seen that the state transition process during an extreme cold disaster has a distinct directional characteristic, i.e., the load state transitions to a state with the same or higher load loss level as time goes on. This is in contrast to the actual natural disaster, which does not send human resources to rob due to the harsh environmentThe modified condition is consistent.
According to the state transition relationship shown in the figure, the state occurrence probability at each moment is as follows:
wherein k is 1,2d，tdIn order to be able to continue the disaster for a long time,
represents thkThe system state at the moment is
Moreover, when k is 1, there is
The probability of occurrence of the Markov state at each time can be calculated in sequence according to equation (23).
According to the modeling and analyzing method for the random dynamic process of the thermoelectric coupling system under the extreme cold disaster, the Markov state space of the thermoelectric coupling system under the extreme cold disaster is established according to the environment state of the extreme cold weather and the states of various devices, the state occurrence probability of the thermoelectric coupling system at any moment is calculated by analyzing the transition process of the Markov state space, and the state occurrence probability of the thermoelectric coupling system at any moment can be accurately known based on the Markov state space transition process.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A modeling and analyzing method for a random dynamic process of a thermoelectric coupling system under an extremely cold disaster is characterized by comprising the following steps:
establishing a Markov state space of the thermoelectric coupling system under the extreme cold disaster according to the natural environment state and the equipment states of different types of equipment;
analyzing the transfer process of the Markov state space to obtain the transfer probability of the state of the thermoelectric coupling system between adjacent moments;
based on the transition probability of the state of the thermoelectric coupling system between adjacent moments, the occurrence probability of the state of the thermoelectric coupling system from the initial state of the beginning of the disaster to the current moment is calculated.
2. The modeling and analyzing method for the stochastic dynamic process of the thermoelectric coupling system under the extremely cold disaster as claimed in claim 1, wherein the natural environment state is the extremely cold weather intensity of the disaster center at time t:
wherein, ω istIs the wind speed (m/s), pitIn terms of snowfall (mm), κtThe ambient temperature (. degree. C.).
3. The method for modeling and analyzing stochastic dynamic processes of thermoelectric coupling systems in extreme cold disasters according to claim 1, wherein the equipment states comprise line states, load states, unit states, energy storage states, and equipment performance states.
4. The method for modeling and analyzing stochastic dynamic processes of thermoelectric coupling systems in extreme cold disasters according to claim 3, wherein the line state is expressed as:
wherein,
and
respectively representing the running states of the power grid line and the heat supply network pipeline;
the load state is represented as:
wherein,
m load states corresponding to different degrees of load loss, gammaDThe element in (1) will be in the interval [0,1 ]]Equally dividing the interval into m sub-intervals;
each subinterval having a width Δ γDAnd satisfies the following conditions:
load state
Indicating that the degree of load loss is in the interval [ i.DELTA.gamma ]D,(i+1)·ΔγD) Internal;
for the distributed unit G, the remaining available fuel quantity of the distributed unit at the time t is as follows:
wherein,
the total amount of fuel reserved before the disaster on the unit side is represented;
section of will
Discretization is as follows:
when the distributed unit G is in the state of
When the fuel quantity remaining in the distributed unit G is
To (c) to (d);
the energy storage state describes the residual amount of movable energy storage, and in the thermoelectric coupling system, the movable energy storage comprises two types of emergency power supply and heating materials;
the battery energy storage residual capacity state at the node j at the time t is as follows:
wherein,
representing the total electrical energy reserved by the emergency power supply train at node j before the disaster,
is an integer representing the number of emergency powered vehicles at node j, EVehRepresenting the electric energy capacity of a single power supply vehicle;
defining the total energy of available heating materials at a node j at the time t as follows:
wherein,
the total amount of heating materials allocated at the node j before the disaster occurs;
for interval
And
discretizing to respectively obtain
Each width is
Is a sub-interval of
Has a width of
A sub-interval of (a);
uniformly expressing the energy storage state as:
the device performance state is represented as:
wherein, the capacity performance state of the battery energy storage at the time t is
Representing the ratio of the battery energy storage capacity at a given ambient temperature relative to the normal ambient temperature; the energy efficiency state of the heat pump is
Representing the ratio of heat pump heating capacity to consumed electrical energy at ambient temperature at time t.
5. The modeling and analysis method for stochastic dynamic processes of thermoelectric coupling systems in extreme cold disasters according to claim 3, wherein the Markov state space comprises a Markov state space of long timescale and a Markov state space of short timescale, wherein one long timescale comprises a plurality of short timescales;
the markov state space for the long time scale may be represented as:
wherein, thTime of day representing a long timescale;
the markov state space of the short timescale can be represented as:
wherein, tmRepresenting the time of day on a short timescale.
6. The method for modeling and analyzing stochastic dynamic processes of thermoelectric coupling systems under extreme cold disasters according to claim 4, wherein analyzing the transition process of the Markov state space to obtain the state transition probability of the thermoelectric coupling systems between adjacent moments comprises:
calculating the line state transition probability between every two adjacent short time scales for a plurality of short time scales between two adjacent long time scales;
and calculating the line state transition probabilities of two adjacent long time scales according to the line state transition probabilities between every two adjacent short time scales.
7. The method for modeling and analyzing stochastic dynamic processes of thermoelectric coupling systems under extremely cold disasters according to claim 5, wherein the calculating the line state transition probability between every two adjacent short time scales comprises:
for long time scale th-1Time t andh+1t between momentshTime of day, t of the first short timescale thereinm,1Line state transition probability of time of day
Including grid line outage probability
And/or probability of outage of heat supply network pipeline
Wherein the probability of the heat supply network pipeline being shut down
Can be expressed as:
wherein,
indicates the failure probability of the freezing burst of the central heating pipeline, wherein kappa is the actual environment temperature and kappacDenotes the temperature at which the soil freezes and begins to stress the heat supply pipe, κdThe minimum environmental temperature corresponding to the maximum stress that the heat supply network pipeline can bear;
probability of outage of grid line
Can be expressed as:
wherein n isgTo support the number of towers of an overhead line, it is here assumed that each tower has the same probability distribution,
is the probability of the grid line fault at time th,
is tAnd h, the fault probability of the power grid tower.
8. The modeling and analyzing method for the stochastic dynamic process of the thermoelectric coupling system under the extremely cold disaster as recited in claim 7, wherein the failure probability of the power grid tower is:
wherein,
the fault probability of collapse of the power grid tower is shown, wherein omega is the actual wind speed near the tower, and omega iscRepresenting a minimum wind speed, omega, that poses a threat to the mastdThe maximum wind speed that the tower can bear.
9. The modeling and analyzing method for the stochastic dynamic process of the thermoelectric coupling system under the extremely cold disaster as claimed in claim 7, wherein the grid line fault probability is:
wherein,
for the probability of grid line faults caused by snow-covered ice,
the fault probability of the power grid line caused by strong wind is shown;
the probability of the power grid line fault caused by the accumulated snow covered ice is as follows:
in the long time scale, thThe disaster intensity is given at any moment, the equipment performance and the line outage probability in the system are also determined, and on the basis, natural disasters are caused
The probability of (2) causes a direct line fault corresponding to a change in line state in a short time scale and further affects the set and energy storage state to redistribute the system power flow, and then the power off-limit line is replaced with the power off-limit line
And (4) the probability of (2) is cut off, and the state transition process of the next round is carried out.
10. The modeling and analyzing method for the stochastic dynamic process of the thermoelectric coupling system under the extremely cold disaster as claimed in claim 6, wherein the transition probabilities of the two adjacent long-time-scale line states are as follows:
wherein n is the state transition times of a short time scale in a long time scale.
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