CN117422299A - Risk assessment method for power transmission and transformation system containing multi-state wind power plant - Google Patents
Risk assessment method for power transmission and transformation system containing multi-state wind power plant Download PDFInfo
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Abstract
The invention provides a power transmission and transformation system risk assessment method comprising a multi-state wind power plant, which comprises the following steps: the method comprises the steps of constructing a wind turbine generator power model, discretizing a wind turbine generator output power curve to obtain the probability of corresponding fan capacity outage rate at random wind speed, constructing a risk assessment model of a power transmission and transformation combined system, performing risk assessment calculation on a bus element in the power transmission system equivalent to a transformer substation, sampling by using double-layer Monte Carlo, combining the obtained power transmission and transformation system element operation state and wind power plant output to form the operation state of the whole system, calculating the probability and load shedding condition of various states of the power transmission and transformation system according to the system operation state formed by sampling, and forming the reliability index of the power transmission and transformation combined system by statistics. The method can reflect the influence of the multi-state characteristics of the wind turbine generator on the risk assessment of the power transmission and transformation combined system during large-scale wind power grid connection, and can make up for the gap that no effective method exists at present for quantitative calculation of the risk of the power transmission and transformation combined system under the wind power grid connection.
Description
Technical Field
The invention relates to the technical field of risk assessment of power systems, in particular to a risk assessment method of a power transmission and transformation system containing a multi-state wind power plant
Background
With the increase of traditional energy cost and the further implementation of the 'two carbon' strategic goal, the new energy permeability represented by wind power at the power supply side in the current power grid is continuously increased. The transformer substation is one of key components in the power grid, a main wiring of the transformer substation is directly connected with a power transmission line of the power grid, a primary equipment fault in the transformer substation can cause partial power interruption of the substation and structural change of a regional power grid, the risk of power failure of the regional power grid is increased, and the safety of a power transmission and transformation combined system is influenced. After large-scale wind power is connected into a power grid, the output power of the wind power plant has multi-state characteristics due to the rapid fluctuation and unpredictability of the wind speed, and the uncertainty of a power system is further increased due to large-scale grid connection, so that a new problem is brought to risk assessment of a power transmission and transformation combined system. The main problem is that the existing method cannot combine the multi-state characteristic of the output power of the wind power plant with the fault characteristic of the power transmission and transformation combined system, and cannot reflect the influence on the reliability of the power transmission and transformation combined system after the large-scale wind power plant is connected, so that the risk assessment research of the power transmission and transformation system containing the multi-state wind power plant is very necessary.
At present, in the establishment of a reliability model of a wind turbine generator, most of the reliability model only comprises an off-line state and an operating state, or a fixed fan derating coefficient is used for representing the derating state, and the reliability model of the fan cannot reflect the multi-state characteristic of the output power of the fan when the wind speed is positioned between the cut-in wind speed and the cut-out wind speed. Aiming at the risk assessment field of the power system, the current research generally considers element faults in the power generation and transmission combined system or only considers power transmission line faults, and cannot reflect the influence of multi-state characteristics of the wind turbine generator on the risk assessment of the power transmission and transformation combined system when large-scale wind power grid connection is realized.
Disclosure of Invention
Aiming at the problems, the invention provides a power transmission and transformation system risk assessment method containing a multi-state wind power plant, which reflects the influence of multi-state characteristics of a wind turbine generator on power transmission and transformation combined system risk assessment when large-scale wind power is connected.
A power transmission and transformation system risk assessment method with a multi-state wind power plant comprises the following steps:
step (1), constructing a power model of the wind turbine generator: establishing a two-parameter Siterbuh distribution wind speed model f (v, i), and estimating the two-parameter SiterbuhThe probability density function is integrated in the (0, v) interval to obtain F (v, i), and the expression v of random wind speed is obtained through inverse function transformation j Carrying the generated random wind speed into a wind turbine generator power characteristic expression to obtain a wind turbine generator power sequence P corresponding to the random wind speed sequence wt (v)；
Step (2), discretizing an output power curve of the wind turbine generator: according to the power sequence P of the wind turbine generator wt (v) Rated capacity of fanGenerating capacity outage rate P corresponding to each power value of wind turbine generator cap Dispersing the output power curve of the wind turbine generator into corresponding fan capacity outage rate at each random wind speed by using a state reduction method, and obtaining the probability of corresponding fan capacity outage rate at the random wind speed according to a two-parameter Siweibull probability formula>
Step (3), constructing a risk assessment model of the power transmission and transformation combined system: modeling a transformer substation to be researched, generating a topological node diagram according to a main wiring diagram of the transformer substation, wherein the topological node diagram is composed of nodes and branches, each branch represents actual primary equipment of the transformer substation, and the primary equipment mainly comprises a breaker, a disconnecting switch, a transformer and a bus in the transformer substation; analyzing faults of internal elements of the transformer substation to generate a corrected topological node adjacency matrix C ij After correction, node adjacency matrix C ij Generating an reachable matrix P among nodes according to a reachable matrix algorithm, judging the communication relation between load nodes in the transformer substation and connecting nodes inside and outside the transformer substation according to the reachable matrix P, and further performing risk assessment calculation on the transformer substation equivalent as a bus element in the power transmission system;
step (4), calculating the load reduction amount of the transformer substation and the regional power grid caused by the system element fault, and constructing a power transmission and transformation combined system risk assessment index, wherein the power transmission and transformation combined system risk assessment index comprises a load reduction probability PLC, an expected load shedding frequency EFLC and an expected EENS with insufficient electric quantity;
step (5), double-layer Monte Carlo sampling is carried out: the outer layer samples the fault of each branch in the topological node diagram of the power transmission and transformation combined system sequentially, and if the fault of an element in the transformer substation is extracted, the transformer substation is equivalent according to the reachable matrix P; the inner layer samples the output power of the discretized wind turbine generator, adopts a roulette selection algorithm, and according to the capacity outage rate P of the discretized wind turbine generator cap And its corresponding probabilitySampling multi-state output of a wind power plant, and combining the sampled operating states of elements of the power transmission and transformation system and the output of the wind power plant to form the operating state of the whole system;
step (6), when the state duration of the element is extracted within the simulation period, the outer layer extracts the running state of a system by using a time sequence Monte Carlo sampling method in each iteration, the inner layer extracts the output of the wind power plant once by using a roulette selection algorithm, and the cut load caused by the system power flow out-of-limit and node voltage out-of-limit is eliminated in the running state of each system by using an alternating current optimal power flow model; calculating various states of the power transmission and transformation system according to the system running states formed by sampling, counting the occurrence probability of each system state and the load reduction conditions of the internal and regional power grids of the transformer substation, substituting the probability into the constructed risk assessment index PLC, EFLC, EENS of the power transmission and transformation combined system, and finally counting the risk index of each sampling within the simulation period to form the risk value of the power transmission and transformation combined system.
Further, the step (1) specifically includes:
1) The method comprises the steps of constructing a two-parameter Siterweibull distribution wind speed model, wherein the probability density function is as follows:
where i is the seasonal parameter (i=1, 2,3, 4), k i C is the shape parameter in the ith season i For the scale parameter under the ith season, v i The actual wind speed value in the ith season, and the values of the parameters k and c are obtained by fitting the historical wind speed data by adopting a maximum likelihood estimation method;
2) Integrating the two-parameter Siterweibull probability density function after parameter estimation in the (0, v) interval:
obtaining an expression of the random wind speed through inverse function transformation:
wherein U is a uniform random variable between (0, 1);
3) Carrying the generated random wind speed into a wind turbine generator power characteristic expression to obtain a wind turbine generator power sequence corresponding to the random wind speed sequence:
wherein V represents the actual wind speed, V in And V out Represents the cut-in wind speed and cut-out wind speed of the wind turbine generator, V r For rated wind speed of wind turbine generator, P wt And P r wt The actual power and rated power of the wind turbine generator are taken as A, B, C, D, and the power characteristic curve parameter of the wind turbine generator is taken as A, B, C, D.
Further, the step (2) specifically includes:
defining the capacity outage rate of the wind turbine generator system:
when P cap When=0, the full-power state of the wind turbine generator is P cap When the value is 100%, the operation state of the wind turbine generator is 0<P cap <100％When the wind turbine generator is in the derated state;
obtaining a fan capacity outage table under the specified derating state quantity by using the following state subtraction method:
wherein m is i ，m j The capacity outage rates for states i and j are specified for state reduction,for corresponding capacity outage rate m i ，m j Probability of->The probability of corresponding fan capacity outage rate at random wind speeds.
Further, the step (3) specifically includes:
analyzing faults of internal elements of the transformer substation, generating a corrected topological node adjacency matrix, wherein elements in the adjacency matrix are defined as follows:
regarding the faulty substation element as a branch-free between nodes;
generating an inter-node reachable matrix according to a reachable matrix algorithm on the basis of the modified node adjacent matrix, wherein for a system containing n nodes, the reachable matrix P is expressed in the following form:
wherein P is nn Indicating whether a connection relationship exists between the nth node and the nth node, if so, the connection relationship is 1, otherwise, the connection relationship is 0.
And judging the communication relation between the load nodes in the transformer substation and the connection nodes inside and outside the transformer substation according to the reachability matrix, and further performing risk assessment calculation on the transformer substation equivalent as one bus element in the power transmission system.
Further, the step (4) specifically includes:
(1) Load shedding probability PLC (probability of load curtailments)
Wherein P is i 、P j And P k Respectively representing the probability of occurrence of the system states i, j and k; s represents a system state set with load reduction in a transformer substation to be evaluated, G represents a system state set with load reduction caused by topology change of a regional power grid, and W represents a system state set with load reduction simultaneously occurring in the transformer substation and the regional power grid;
(2) Desired load shedding frequency EFLC (expected frequency of load curtailments)
Wherein F is i 、F j And F k Indicating the frequencies leaving the system states i, j and k, respectively;
(3) EENS (expected energy not supplied) for short power supply
Wherein C is i And C j Respectively representing the load loss in the transformer substation and the load loss quantity of the regional power grid caused by element failure, T a Is the duration of the simulation years.
Further, the step (5) specifically includes:
1) Setting reliability parameters of elements and maximum simulation years N of Monte Carlo y And the current iteration number k, assuming that the current system elements are in a normal running state, sampling the duration of the running state and the off-line state of the elements based on a sequential Monte Carlo method:
wherein D is n Is the state duration of the nth element, lambda n Is the failure rate or repair rate of the nth element, U n Is [0,1 ]]Random numbers uniformly distributed in intervals;
in simulation period N y Repeatedly sampling the state duration time of the elements, and forming a time sequence state transfer process of the system according to the time sequence state transfer process of each element;
2) According to a roulette selection algorithm, converting the probability of each capacity outage rate corresponding to the capacity outage table into the segment length of a 0-1 interval:
wherein P (x) k ) To correspond to the probability of capacity outage rate, Q (x i ) To accumulate probabilities, the segment lengths corresponding to the 0-1 interval are used.
Further, the alternating current optimal power flow model in the step (6) is specifically as follows:
objective function:
the constraint conditions are as follows:
P LDi -P Ci ＝P i
0≤P Ci ≤P LDi
wherein N is the number of nodes in the system, P Ci For load shedding amount on node i, P i 、Q i Respectively the active and reactive injection values on the node i, P LDi PG is the total load of node i i 、Respectively the active output of the generator set on the node i, the maximum value and the minimum value thereof, QG i 、/>Respectively the reactive output of the generator set on the node i, the maximum value and the minimum value thereof, and TP l For the actual transmission capacity of the line, this value is smaller than +.>In addition node voltage V i The minimum voltage at this node is required +.>And maximum voltage->Between them.
The invention has the following beneficial effects:
(1) The reliability model of the wind turbine can reflect the multi-state characteristics of the output power of the wind turbine.
(2) The risk assessment model of the power transmission and transformation combined system can reflect the influence of the multi-state characteristics of the wind turbine generator on the risk assessment of the power transmission and transformation combined system during large-scale wind power grid connection.
(3) The risk assessment method for the power transmission and transformation system with the multi-state wind power plant can make up for the lack of quantitative calculation of the risk of the power transmission and transformation combined system under the wind power grid by using the existing effective method.
Drawings
Fig. 1 is a schematic flow chart of a risk assessment method for a power transmission and transformation system including a multi-state wind farm according to an embodiment of the present invention;
FIG. 2 is a four season twenty derated state capacity outage table for a wind turbine according to an embodiment of the present invention;
FIG. 3 is a diagram of an IEEE-RTS6 system in accordance with an embodiment of the present invention;
fig. 4 is a topology node diagram of an IEEE-RTS6 system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, an embodiment of the present invention provides a risk assessment method for a power transmission and transformation system including a multi-state wind farm, including the following steps:
(1) The method for constructing the power model of the wind turbine specifically comprises the following steps:
1) Constructing a two-parameter Siweibull distribution wind speed model, wherein the probability density function is
Where i is the seasonal parameter (i=1, 2,3, 4), k i C is the shape parameter in the ith season i For the scale parameter under the ith season, v i Is the actual wind speed value in the ith season. The values of the parameters k and c are obtained by fitting historical wind speed data by adopting a maximum likelihood estimation method.
2) Integrating the two-parameter Siterweibull probability density function after parameter estimation in the (0, v) interval:
obtaining an expression of the random wind speed through inverse function transformation:
wherein U is a uniform random variable between (0, 1).
3) Carrying the generated random wind speed into a wind turbine generator power characteristic expression to obtain a wind turbine generator power sequence corresponding to the random wind speed sequence:
where V represents the actual wind speed,V in and V out Represents the cut-in wind speed and cut-out wind speed of the wind turbine generator, V r For rated wind speed of wind turbine generator, P wt And P r wt The actual power and rated power of the wind turbine generator are taken as A, B, C, D, and the power characteristic curve parameter of the wind turbine generator is taken as A, B, C, D.
(2) Discretizing an output power curve of the wind turbine, and defining the capacity outage rate of the wind turbine:
when P cap When=0, the full-power state of the wind turbine generator is P cap When the value is 100%, the operation state of the wind turbine generator is 0<P cap <And when the total power is 100%, the power is in a derated state of the wind turbine.
The fan capacity outage table in the specified derated state quantity can be obtained by using the following state subtraction method:
wherein m is i ，m j Specifying capacity outage rates for states i and j for state reduction, P ( m i) ,P ( m j) For corresponding capacity outage rate m i ，m j Probability of P ( p cap) The probability of corresponding fan capacity outage rate at random wind speeds.
(3) The method comprises the steps of constructing a risk assessment model of a power transmission and transformation combined system, modeling a transformer substation to be researched, generating a topological node diagram according to a main wiring diagram of the transformer substation, wherein the diagram is composed of nodes and branches, each branch represents actual primary equipment of the transformer substation, and the primary equipment mainly comprises a breaker, a disconnecting switch, a transformer and a bus in the transformer substation. Analyzing faults of internal elements of the transformer substation, generating a corrected topological node adjacency matrix, wherein elements in the adjacency matrix are defined as follows:
for a faulty substation element, it is considered as no branch between nodes.
Generating an inter-node reachable matrix according to a reachable matrix algorithm on the basis of the modified node adjacent matrix, wherein for a system containing n nodes, the reachable matrix P is expressed in the following form:
wherein P is nn Indicating whether a connection relationship exists between the nth node and the nth node, if so, the connection relationship is 1, otherwise, the connection relationship is 0.
And judging the communication relation between the load nodes in the transformer substation and the connection nodes inside and outside the transformer substation according to the reachability matrix, and further performing risk assessment calculation on the transformer substation equivalent as one bus element in the power transmission system.
(4) The power transmission and transformation combined system risk assessment index is constructed, and specifically comprises the following steps:
1) Load shedding probability PLC (probability of load curtailments)
Wherein P is i 、P j And P k Respectively representing the probability of occurrence of the system states i, j and k; s represents a set of system states with load shedding within the substation to be evaluated, G represents a set of system states with load shedding due to topology changes of the regional power grid,w represents a system state set of load shedding in the substation and the regional power grid at the same time.
2) Desired load shedding frequency EFLC (expected frequency of load curtailments)
Wherein F is i 、F j And F k The frequencies leaving the system states i, j and k are shown, respectively.
3) EENS (expected energy not supplied) for short power supply
Wherein C is i And C j Respectively representing the load loss in the transformer substation and the load loss quantity of the regional power grid caused by element failure, T a Is the duration of the simulation years.
(5) Double layer monte carlo sampling is performed: the outer layer samples the branch faults in the topological node diagram sequentially, if the branch faults are extracted to the element faults in the transformer substation, the transformer substation is equivalent according to the reachable matrix, and a power transmission and transformation combined system is formed; and the inner layer samples the output power of the discretized wind turbine generator, and adopts a roulette selection algorithm to sample the multi-state output of the wind farm according to the corresponding probability of the shutdown rate of each capacity of the wind turbine generator. The operation state of the whole system is formed by combining the operation state of the components of the power transmission and transformation system obtained by sampling and the output of a wind farm, and the specific steps are as follows:
1) Setting reliability parameters of elements and maximum simulation years N of Monte Carlo y And the current iteration number k, assuming that the current system elements are in a normal running state, sampling the duration of the two states (running, off-line) of the elements based on the sequential Monte Carlo method.
Wherein D is n Is the state duration of the nth element, lambda n Is the failure rate or repair rate of the nth element, U n Is [0,1 ]]Random numbers with uniformly distributed intervals.
In simulation period N y The state duration sampling of the elements is repeated, and the time sequence state transition process of the system is formed according to the time sequence state transition process of each element.
2) Converting the probability of each capacity outage rate corresponding to the capacity outage table of the fan of claim 2 into a segment length of 0-1 interval according to a roulette selection algorithm:
wherein P (x) k ) To correspond to the probability of capacity outage rate, Q (x i ) To accumulate probabilities, the segment lengths corresponding to the 0-1 interval are used.
(6) In simulation period N y When the state duration of the inner extraction element is kept, the outer layer extracts the running state of a system by using a time sequence Monte Carlo sampling method in each iteration, the inner layer extracts the output of the primary wind power plant by using a roulette selection algorithm, and the following alternating current optimal power flow model is used for solving the cut load caused by the out-of-limit of the system power flow and the out-of-limit of the node voltage in each running state of the system:
objective function:
the constraint conditions are as follows:
P LDi -P Ci ＝P i
0≤P Ci ≤P LDi
wherein N is the number of nodes in the system, P Ci For load shedding amount on node i, P i 、Q i Respectively the active and reactive injection values on the node i, P LDi PG is the total load of node i i 、Respectively the active output of the generator set on the node i, the maximum value and the minimum value thereof, QG i 、/>Respectively the reactive output of the generator set on the node i, the maximum value and the minimum value thereof, and TP l For the actual transmission capacity of the line, this value is smaller than +.>In addition node voltage V i The minimum voltage at this node is required +.>And maximum voltage->Between them.
And calculating the occurrence probability and load shedding conditions of various states of the power transmission and transformation system according to the system running state formed by sampling, and counting to form a reliability index PLC, EFLC, EENS of the power transmission and transformation combined system.
Taking an improved IEEE-RBTS6 node test system as an example, the power transmission and transformation combined system risk assessment model containing the multi-state wind power plant is utilized to carry out comparison analysis research on power transmission and transformation system risk assessment indexes under different wind power plant access scenes.
The IEEE-RBTS6 node test system has 6 nodes in total, 9 power transmission lines, the capacity of a conventional unit of the system is 240MW, the total load is 185MW, the main wiring of a transformer station where the BUS4 node is located is unfolded, as shown in fig. 3, one wind power station is planned to be added, an MRBTS system is formed, 4 scenes shown in the following table 1 are considered, and the access mode of the wind power station is equal capacity to replace the conventional unit.
Table 1 wind farm access position and installed capacity under different scenes of MRBTS system
Setting the simulation year to 300 years, performing calculation analysis based on wind speed data of a wind power plant in 2022 years at a certain place, obtaining fan power sequences of the wind power plant in different seasons according to the original wind speed data by using the step (1), and reducing the fan power sequences to a four-season twenty-derated state shown in fig. 2 by using the state reduction method of the step (2).
And (3) forming a topological node diagram of the power transmission and transformation combined system to be evaluated according to the step (3), as shown in fig. 4. Sequentially sampling topological faults of each branch of the system by utilizing the outer layer of the step (5), if the topological faults are extracted to an internal fault element of the transformer substation, generating an reachable matrix among nodes according to the step (3), judging the communication relation between the load in the substation and the connection point outside the substation, and performing risk assessment on the transformer substation equivalent as one bus element in the power transmission system; the inner layer samples using a roulette selection algorithm based on the twenty derated state of the wind farm.
And (3) selecting spring as a testing season, and calculating to obtain power transmission and transformation combined system risk indexes containing multi-state wind power stations in different scenes by utilizing the steps (4) and (6), wherein the power transmission and transformation combined system risk indexes are shown in a table 2.
TABLE 2 risk indicators of MRBTS systems in different scenes
From the above table, the scenario a is a risk index of the power transmission and transformation combined system when the wind farm is accessed, the wind farm access form of the present patent is that the capacity of the conventional unit of the accessed bus is replaced by the equal capacity, and the output power of the wind turbine unit is in the derated running state, so the risk index of the scenario b, c, d, e is increased compared with the scenario a.
And the comparison of risk indexes of the power transmission and transformation combined system with different wind power replacing conventional power supply capacities is considered, and compared with the scene b and the scene e, the scene c is compared with the scene d, and the risk indexes are increased because the conventional units with more capacities are replaced by the wind power units.
Considering the power transmission and transformation combined system of different wind power access nodes, wind power stations with wind power capacity of 15MW and 30MW are selected to be accessed to the node 1 and the node 2, and as can be seen from the table 2, the risk index after the wind power is accessed to the node 2 is higher than that of the node 1 under the condition that the same wind power station replaces the conventional power capacity, so that the wind power station is more suitable for being accessed to the node 1.
The foregoing is merely illustrative embodiments of the present invention, and the present invention is not limited thereto, and any changes or substitutions that may be easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (7)
1. The power transmission and transformation system risk assessment method with the multi-state wind power plant is characterized by comprising the following steps of:
step (1), constructing a power model of the wind turbine generator: establishing a two-parameter Siterbubal distribution wind speed model F (v, i), integrating the two-parameter Siterbubal probability density function after parameter estimation in a (0, v) interval to obtain F (v, i), and obtaining an expression v of random wind speed through inverse function transformation j Carrying the generated random wind speed into a wind turbine generator power characteristic expression to obtain a wind turbine generator power sequence P corresponding to the random wind speed sequence wt (v)；
Step (2), discretizing an output power curve of the wind turbine generator: according to the power sequence P of the wind turbine generator wt (v) Rated capacity of fanGenerating capacity outage rate P corresponding to each power value of wind turbine generator cap The output power curve of the wind turbine generator is discretized into corresponding fan capacity outage rate under each random wind speed by using a state subtraction method, and meanwhile, the probability P of the corresponding fan capacity outage rate under the random wind speed is obtained according to a two-parameter Siweibull probability formula ( p cap) ；
Step (3), constructing a risk assessment model of the power transmission and transformation combined system: modeling a transformer substation to be researched, generating a topological node diagram according to a main wiring diagram of the transformer substation, wherein the topological node diagram is composed of nodes and branches, each branch represents actual primary equipment of the transformer substation, and the primary equipment mainly comprises a breaker, a disconnecting switch, a transformer and a bus in the transformer substation; analyzing faults of internal elements of the transformer substation to generate a corrected topological node adjacency matrix C ij After correction, node adjacency matrix C ij Generating an reachable matrix P among nodes according to a reachable matrix algorithm, judging the communication relation between load nodes in the transformer substation and connecting nodes inside and outside the transformer substation according to the reachable matrix P, and further performing risk assessment calculation on the transformer substation equivalent as a bus element in the power transmission system;
step (4), calculating the load reduction amount of the transformer substation and the regional power grid caused by the system element fault, and constructing a power transmission and transformation combined system risk assessment index, wherein the power transmission and transformation combined system risk assessment index comprises a load reduction probability PLC, an expected load shedding frequency EFLC and an expected EENS with insufficient electric quantity;
step (5), double-layer Monte Carlo sampling is carried out: the outer layer samples the fault of each branch in the topological node diagram of the power transmission and transformation combined system sequentially, and if the fault of an element in the transformer substation is extracted, the transformer substation is equivalent according to the reachable matrix P; the inner layer samples the output power of the discretized wind turbine generator, adopts a roulette selection algorithm, and according to the capacity outage rate P of the discretized wind turbine generator cap And its corresponding probability P (P) cap ) Sampling multi-state output of a wind power plant, and combining the sampled operating states of elements of the power transmission and transformation system and the output of the wind power plant to form the operating state of the whole system;
step (6), when the state duration of the element is extracted within the simulation period, the outer layer extracts the running state of a system by using a time sequence Monte Carlo sampling method in each iteration, the inner layer extracts the output of the wind power plant once by using a roulette selection algorithm, and the cut load caused by the system power flow out-of-limit and node voltage out-of-limit is eliminated in the running state of each system by using an alternating current optimal power flow model; calculating various states of the power transmission and transformation system according to the system running states formed by sampling, counting the occurrence probability of each system state and the load reduction conditions of the internal and regional power grids of the transformer substation, substituting the probability into the constructed risk assessment index PLC, EFLC, EENS of the power transmission and transformation combined system, and finally counting the risk index of each sampling within the simulation period to form the risk value of the power transmission and transformation combined system.
2. A power transmission and transformation system risk assessment method comprising a multi-state wind farm as claimed in claim 1, wherein: the step (1) specifically comprises the following steps:
1) The method comprises the steps of constructing a two-parameter Siterweibull distribution wind speed model, wherein the probability density function is as follows:
where i is the seasonal parameter (i=1, 2,3, 4), k i C is the shape parameter in the ith season i For the scale parameter under the ith season, v i The actual wind speed value in the ith season, and the values of the parameters k and c are obtained by fitting the historical wind speed data by adopting a maximum likelihood estimation method;
2) Integrating the two-parameter Siterweibull probability density function after parameter estimation in the (0, v) interval:
obtaining an expression of the random wind speed through inverse function transformation:
wherein U is a uniform random variable between (0, 1);
3) Carrying the generated random wind speed into a wind turbine generator power characteristic expression to obtain a wind turbine generator power sequence corresponding to the random wind speed sequence:
wherein V represents the actual wind speed, V in And V out Represents the cut-in wind speed and cut-out wind speed of the wind turbine generator, V r For rated wind speed of wind turbine generator, P wt And P r wt The actual power and rated power of the wind turbine generator are taken as A, B, C, D, and the power characteristic curve parameter of the wind turbine generator is taken as A, B, C, D.
3. A power transmission and transformation system risk assessment method comprising a multi-state wind farm as claimed in claim 1, wherein: the step (2) specifically comprises:
defining the capacity outage rate of the wind turbine generator system:
when P cap When=0, the full-power state of the wind turbine generator is P cap When the value is 100%, the operation state of the wind turbine generator is 0<P cap <When 100%, the state is the derated state of the wind turbine generator;
obtaining a fan capacity outage table under the specified derating state quantity by using the following state subtraction method:
wherein m is i ，m j Specifying capacity outage rates for states i and j for state reduction, P ( m i) ,P ( m j) For corresponding capacity outage rate m i ，m j Probability of P ( p cap) The probability of corresponding fan capacity outage rate at random wind speeds.
4. A power transmission and transformation system risk assessment method comprising a multi-state wind farm as claimed in claim 1, wherein: the step (3) specifically comprises:
analyzing faults of internal elements of the transformer substation, generating a corrected topological node adjacency matrix, wherein elements in the adjacency matrix are defined as follows:
regarding the faulty substation element as a branch-free between nodes;
generating an inter-node reachable matrix according to a reachable matrix algorithm on the basis of the modified node adjacent matrix, wherein for a system containing n nodes, the reachable matrix P is expressed in the following form:
wherein P is nn Indicating whether a connection relationship exists between the nth node and the nth node, if so, the connection relationship is 1, otherwise, the connection relationship is 0.
And judging the communication relation between the load nodes in the transformer substation and the connection nodes inside and outside the transformer substation according to the reachability matrix, and further performing risk assessment calculation on the transformer substation equivalent as one bus element in the power transmission system.
5. A power transmission and transformation system risk assessment method comprising a multi-state wind farm as claimed in claim 1, wherein: the step (4) specifically comprises:
(1) Load shedding probability PLC (probability ofload curtailments)
Wherein P is i 、P j And P k Respectively representing the probability of occurrence of the system states i, j and k; s represents a system state set with load reduction in a transformer substation to be evaluated, G represents a system state set with load reduction caused by topology change of a regional power grid, and W represents a system state set with load reduction simultaneously occurring in the transformer substation and the regional power grid;
(2) Desired load shedding frequency EFLC (expected frequency ofload curtailments)
Wherein F is i 、F j And F k Indicating the frequencies leaving the system states i, j and k, respectively;
(3) EENS (expected energy not supplied) for short power supply
Wherein C is i And C j Respectively representing the load loss in the transformer substation and the load loss quantity of the regional power grid caused by element failure, T a Is the duration of the simulation years.
6. A power transmission and transformation system risk assessment method comprising a multi-state wind farm as claimed in claim 1, wherein: the step (5) specifically comprises:
1) Setting reliability parameters of elements and maximum simulation years N of Monte Carlo y And the current iteration number k, assuming that the current system elements are in a normal running state, sampling the duration of the running state and the off-line state of the elements based on a sequential Monte Carlo method:
wherein D is n Is the state duration of the nth element, lambda n Is the failure rate or repair rate of the nth element, U n Is [0,1 ]]Random numbers uniformly distributed in intervals;
in simulation period N y Repeatedly sampling the state duration time of the elements, and forming a time sequence state transfer process of the system according to the time sequence state transfer process of each element;
2) According to a roulette selection algorithm, converting the probability of each capacity outage rate corresponding to the capacity outage table into the segment length of a 0-1 interval:
wherein P (x) k ) To correspond to the probability of capacity outage rate, Q (x i ) To accumulate probabilities, the segment lengths corresponding to the 0-1 interval are used.
7. A power transmission and transformation system risk assessment method comprising a multi-state wind farm as claimed in claim 1, wherein: the alternating current optimal power flow model in the step (6) is specifically as follows:
objective function:
the constraint conditions are as follows:
P LDi -P Ci ＝P i
0≤P Ci ≤P LDi
|TP l |≤TP l max l∈L
V i min ≤V i ≤V i max
wherein N is the number of nodes in the system, P Ci For load shedding amount on node i, P i 、Q i Respectively the active and reactive injection values on the node i, P LDi PG is the total load of node i i 、Respectively the active output of the generator set on the node i, the maximum value and the minimum value thereof, QG i 、/>Respectively the reactive output of the generator set on the node i, the maximum value and the minimum value thereof, and TP l For the actual transmission capacity of the line, this value is smaller than +.>In addition node voltage V i The minimum voltage at this node is required +.>And maximum voltage->Between them.
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| CN117972451B (en) * | 2024-03-28 | 2024-06-11 | 国网安徽省电力有限公司电力科学研究院 | GIS isolating switch switching position confirmation method |
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