CN117810990A - Method for improving new energy bearing capacity based on distribution network two-line three-station structure - Google Patents
Method for improving new energy bearing capacity based on distribution network two-line three-station structure Download PDFInfo
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Abstract
The invention belongs to the technical field of new energy sources of power grids, and relates to a method for improving the bearing capacity of new energy sources based on a two-wire three-station structure of a distribution network. It comprises the following steps: step S1, obtaining the topological structure and node information of a target power grid, obtaining the connection information of a line through topological analysis, and selecting a calculation section of a distributed photovoltaic bearing capacity tide; step S2, according to the topological structure, node information and line connection information of the power grid; establishing a bearing capacity model, and determining the maximum new energy bearing capacity of the bearing capacity model and a corresponding topological structure; and step S3, completing reliability analysis of the topological structure according to the maximum new energy bearing capacity of the topological structure. The method comprehensively considers the voltage stability and line overload problems of the system, improves the bearing capacity of the system through structural adjustment, provides feasible technical support for efficient access of new energy in the power distribution network, and ensures safe and stable operation of the system.
Description
Technical Field
The invention belongs to the technical field of new energy sources of power grids, and relates to a method for improving the bearing capacity of new energy sources based on a two-wire three-station structure of a distribution network.
Background
The inherent randomness and fluctuation of the new energy output represented by photovoltaic power generation, the high uncertainty of the high-proportion new energy output brings new problems to power grid planning and operation. Because the output power of the distributed photovoltaic power supply has randomness, volatility and non-schedulable characteristics, the power quality problems such as voltage fluctuation and flicker, harmonic pollution, voltage out-of-limit and the like are easily caused, economic loss is brought to power users of the power distribution network, and meanwhile, the safe and stable operation of the photovoltaic system is jeopardized.
Inaccurate carrying capacity can lead to unnecessary delay of engineering analysis or prevent photovoltaic power generation equipment from being installed in a practically suitable area, meanwhile, the quality of the carrying capacity of a power grid is greatly influenced by voltage fluctuation, and life and property safety of a user can be influenced when voltage out-of-limit caused by the voltage fluctuation is serious.
Therefore, a method for improving the bearing capacity of new energy based on a two-line three-station structure of a distribution network is needed, which is used for calculating the bearing capacity problem of a distributed photovoltaic access power grid, and improving the bearing capacity of the power grid from suppressing voltage out-of-limit caused by voltage fluctuation.
Disclosure of Invention
The invention provides a method for improving the bearing capacity of new energy based on a two-wire three-station structure of a distribution network, which overcomes the defects of the prior art in the background art.
In order to achieve the above purpose, the method for improving the new energy bearing capacity based on the two-wire three-station structure of the distribution network adopts the following technical scheme:
step S1, obtaining the topological structure and node information of a target power grid, obtaining the connection information of a line through topological analysis, and selecting a calculation section of a distributed photovoltaic bearing capacity tide;
step S2, according to the topological structure, node information and line connection information of the power grid; establishing a bearing capacity model, and determining the maximum new energy bearing capacity of the bearing capacity model and a corresponding topological structure;
and step S3, completing reliability analysis of the topological structure according to the maximum new energy bearing capacity of the topological structure.
The specific steps of selecting the calculated section of the distributed photovoltaic load capacity tide in the step S1 are as follows:
firstly, selecting load data of a typical load day time interval and whole-day whole-point photovoltaic power generation power data;
then, calculating the power difference delta P between the load data corresponding to each moment in the typical load day time interval and the photovoltaic power generation power through a formula (1),
ΔP＝|P pv -P load | (1)
wherein P is pv The photovoltaic power generation power at a certain moment; p (P) load The load power at a certain moment;
and finally, taking the moment corresponding to the maximum value of the power difference between the load data and the photovoltaic power generation power as a tide calculation section to be selected for the distributed photovoltaic bearing capacity of the power distribution network in the evaluation.
The step of establishing the bearing capacity model in the step S2 specifically includes:
the method aims at maximizing the bearing capacity of the distributed new energy, establishes corresponding constraint and comprises the following objective functions:
wherein K is PV An access node set representing a distributed new energy source;representing the maximum access capacity of the distributed new energy of the node i;
the corresponding constraint comprises three aspects of constraint, namely photovoltaic power generation constraint, tide balance constraint, line switch constraint and safety constraint.
The photovoltaic power generation constraint is as follows:
in the method, in the process of the invention,and->Respectively representing active power and reactive power of the distributed photovoltaic accessed by the node i;and->And respectively representing upper limits of active power and reactive power of the distributed photovoltaic accessed by the node i.
The flow balance constraint and the line switch constraint are as follows:
in the method, in the process of the invention,and->Representing the active and reactive loads on node i, respectively; p (P) ik And Q ik Active and reactive power respectively flowing from node i to node k; p (P) ji And Q ji Active power and reactive power respectively flow from node j to node i; r is (r) ij And x ij The resistance and reactance between the node i and the node j are respectively; i ij The current amplitude of the branch between the node i and the node j; u (U) i And U j The voltage amplitudes at node i and node j.
Wherein S is ij Representing the network topology switch state, where 1 represents closed and 0 represents open. The adjustment of the switch state is used as a part of an optimization model to find the optimal distribution network topology structure, so that the bearing capacity of the new energy is maximized.
The safety constraint is as follows:
U min,i ≤U i ≤U max,i (6)
I ij ≤I max,ij (7)
in U i For the voltage amplitude of node i, U max,i And U min,i Representing the upper and lower limits of the voltage amplitude of the node i; i max,ij Representing the maximum current carrying capacity of the line between nodes i and j.
The reliability analysis of step S3 is specifically as follows;
s3.1, inputting system initial state data to form system information;
and (3) inputting and reading information of network topology, electric load, air load, element parameters and wind-light output, setting simulation time duration N=100, and setting system initial time t=0, wherein all elements are in a normal state, and establishing system initial state data.
S3.2, sampling the system state;
carrying out random sampling on the load, the photovoltaic output and the element state by a sequential Monte Carlo method;
randomly extracting the running time of each element under the normal working stateLet t=min (TTF), R 1 Is in interval [0,1 ]]Uniformly extracting distributed random numbers, wherein lambda is the failure rate, and selecting an element with the minimum TTF as a failure element;
step S3.3, randomly extracting repair time of the failed componentTTR is taken as the duration of system failure at the same time, R 2 Is in interval [0,1 ]]Uniformly extracting the random numbers distributed in the matrix, wherein mu is the repair rate of the matrix;
s3.4, calculating wind-light output and load power at the moment t, judging connectivity of the power distribution network, performing fault traversal search, analyzing the load of the power distribution network affected by a fault element, and determining whether the power supply, the switch and the power failure type of a load point exist or not; accumulating the power failure time and the power failure times of the load points respectively;
step S3.5, the system simulation advances along with time, t=T+T+TTR, whether the simulation time T meets 1 year or not is judged, namely 8760 hours, if 1 year is reached, N=N+1, t=t-8760, and otherwise S3.2 is entered;
s3.6, calculating a reliability index;
calculating N > Nmax; if the condition that the sampling is finished immediately is met, otherwise, the step S3.2 is entered;
and S3.7, outputting a system reliability index.
The wind-light output and the load power at the t moment in the step S3.4 have the following three models;
a probability model of solar illumination intensity distribution, a photovoltaic power output power model and a load model.
The probability model of the solar illumination intensity distribution is as follows:
wherein r is the actual light intensity, r m For maximum light intensity, Γ is gamma function, and α, β are bata distribution shape parameters.
The photovoltaic power output power model is as follows:
the output power of the photovoltaic power supply is mainly composed of the number M of battery components on a battery board and the size A of the area of each component m Photoelectric conversion efficiency eta of each component m M= (1, 2.). M) is determined. Firstly, the total area A of the cell panel matrix is obtained, and the total photoelectric conversion efficiency eta of the matrix is obtained.
The total output power of this solar cell matrix is then:
P＝r·A·η (13)
after knowing the probability density distribution function of the light intensity, the probability density distribution function of the photovoltaic unit power generation also obeys Beta distribution, which is expressed as:
wherein R is m The photovoltaic cell's maximum output power is this period.
The load model is:
the load model obeys normal distribution;
wherein P is l Mu, for load power PL For the average load, σ PL Is the standard variation of the load power.
The reliability index in step S3.7 is specifically as follows;
EENS = total insufficient power of the system = Σl i U i (19)
Wherein lambda is i Annual average outage frequency for load point i; u (U) i The annual average power failure time for load point i is at; n (N) i The number of users for load point i; l (L) i The average power of the load point i.
The method for improving the new energy bearing capacity based on the two-wire three-station structure of the distribution network is introduced, so that the traditional planning thought of the distribution network is effectively broken, and the inherent defect of the traditional wiring mode is avoided; the flexibility and the reliability of the distribution network are improved, and the method has important practical value and reference significance for the construction of the distribution network.
Drawings
FIG. 1 is a flow chart of the overall method of the present invention;
FIG. 2 is a detailed flow chart of the method for establishing the bearing capacity model and reliability analysis according to the present invention;
FIG. 3 is a single-chain topology according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a two-wire three-station double-chain power supply network according to an embodiment of the invention;
fig. 5 is a wiring diagram of a two-wire three-station double-chain power supply system according to an embodiment of the invention.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
The invention will be further described with reference to the accompanying drawings. The following are only preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
The method for improving the new energy bearing capacity based on the two-line three-station structure of the distribution network comprises the following steps:
step S1, obtaining the topological structure and node information of a target power grid, obtaining the connection information of a line through topological analysis, and selecting a calculation section of a distributed photovoltaic bearing capacity tide;
step S2, according to the topological structure, node information and line connection information of the power grid; establishing a bearing capacity model, and determining the maximum new energy bearing capacity of the bearing capacity model and a corresponding topological structure;
and step S3, completing reliability analysis of the topological structure according to the maximum new energy bearing capacity of the topological structure.
The specific steps of selecting the calculated section of the distributed photovoltaic load capacity tide in the step S1 are as follows:
firstly, selecting load data of a typical load day time interval and whole-day whole-point photovoltaic power generation power data;
then, calculating the power difference delta P between the load data corresponding to each moment in the typical load day time interval and the photovoltaic power generation power through a formula (1),
ΔP＝|P pv -P load | (1)
wherein P is pv The photovoltaic power generation power at a certain moment; p (P) load The load power at a certain moment;
and finally, taking the moment corresponding to the maximum value of the power difference between the load data and the photovoltaic power generation power as a tide calculation section to be selected for the distributed photovoltaic bearing capacity of the power distribution network in the evaluation.
The step of establishing the bearing capacity model in the step S2 specifically includes:
the method aims at maximizing the bearing capacity of the distributed new energy, establishes corresponding constraint and comprises the following objective functions:
wherein K is PV An access node set representing a distributed new energy source;representing the maximum access capacity of the distributed new energy of the node i;
the corresponding constraint comprises three aspects of constraint, namely photovoltaic power generation constraint, tide balance constraint, line switch constraint and safety constraint.
The photovoltaic power generation constraint is as follows:
in the method, in the process of the invention,and->Respectively representing active power and reactive power of the distributed photovoltaic accessed by the node i;and->And respectively representing upper limits of active power and reactive power of the distributed photovoltaic accessed by the node i.
The flow balance constraint and the line switch constraint are as follows:
in the method, in the process of the invention,and->Representing the active and reactive loads on node i, respectively; p (P) ik And Q ik Active and reactive power respectively flowing from node i to node k; p (P) ji And Q ji Active power and reactive power respectively flow from node j to node i; r is (r) ij And x ij The resistance and reactance between the node i and the node j are respectively; i ij The current amplitude of the branch between the node i and the node j; u (U) i And U j The voltage amplitudes at node i and node j.
Wherein S is ij Representing the network topology switch state, where 1 represents closed and 0 represents open. The adjustment of the switch state is used as a part of an optimization model to find the optimal distribution network topology structure, so that the bearing capacity of the new energy is maximized.
The safety constraint is as follows:
U min,i ≤U i ≤U max,i (6)
I ij ≤I max,ij (7)
in U i For the voltage amplitude of node i, U max,i And U min,i Representing the upper and lower limits of the voltage amplitude of the node i; i max,ij Representing the maximum current carrying capacity of the line between nodes i and j.
The reliability analysis of step S3 is specifically as follows;
s3.1, inputting system initial state data to form system information;
and (3) inputting and reading information of network topology, electric load, air load, element parameters and wind-light output, setting simulation time duration N=100, and setting system initial time t=0, wherein all elements are in a normal state, and establishing system initial state data.
S3.2, sampling the system state;
carrying out random sampling on the load, the photovoltaic output and the element state by a sequential Monte Carlo method;
randomly extracting the running time of each element under the normal working stateLet t=min (TTF), R 1 Is in interval [0,1 ]]Uniformly extracting distributed random numbers, wherein lambda is the failure rate, and selecting an element with the minimum TTF as a failure element;
step S3.3, randomly extracting repair time of the failed componentTTR is taken as the duration of system failure at the same time, R 2 Is in interval [0,1 ]]Uniformly extracting the random numbers distributed in the matrix, wherein mu is the repair rate of the matrix;
s3.4, calculating wind-light output and load power at the moment t, judging connectivity of the power distribution network, performing fault traversal search, analyzing the load of the power distribution network affected by a fault element, and determining whether the power supply, the switch and the power failure type of a load point exist or not; accumulating the power failure time and the power failure times of the load points respectively;
step S3.5, the system simulation advances along with time, t=t+T+TTR, whether the simulation time T meets 1 year or not is judged, namely 8760 hours, if 1 year is reached, N=N+1, t=t-8760, and otherwise S3.2 is entered;
s3.6, calculating a reliability index;
calculating N > Nmax; if the condition that the sampling is finished immediately is met, otherwise, the step S3.2 is entered;
and S3.7, outputting a system reliability index.
The wind-light output and the load power at the t moment in the step S3.4 have the following three models;
a probability model of solar illumination intensity distribution, a photovoltaic power output power model and a load model.
The probability model of the solar illumination intensity distribution is as follows:
wherein r is the actual light intensity, r m For maximum light intensity, Γ is gamma function, and α, β are bata distribution shape parameters.
The photovoltaic power output power model is as follows:
the output power of the photovoltaic power supply is mainly composed of the number M of battery components on a battery board and the size A of the area of each component m Photoelectric conversion efficiency eta of each component m M= (1, 2.). M) is determined. Firstly, the total area A of the cell panel matrix is obtained, and the total photoelectric conversion efficiency eta of the matrix is obtained.
The total output power of this solar cell matrix is then:
P＝r·A·η (13)
after knowing the probability density distribution function of the light intensity, the probability density distribution function of the photovoltaic unit power generation also obeys Beta distribution, which is expressed as:
wherein R is m The photovoltaic cell's maximum output power is this period.
The load model is:
the load model obeys normal distribution;
wherein P is l Mu, for load power PL For the average load, σ PL Is the standard variation of the load power.
The reliability index in step S3.7 is specifically as follows;
EENS = total insufficient power of the system = Σl i U i (19)
Wherein lambda is i Annual average outage frequency for load point i; u (U) i The annual average power failure time for load point i is at; n (N) i The number of users for load point i; l (L) i The average power of the load point i.
Examples
Referring to the drawings, the two-line three-station structure in the method for improving the bearing capacity of the new energy based on the two-line three-station structure of the distribution network takes 2 220 kilovolt substations as an upper power supply, and each 220 kilovolt bus passes through 1 back 110 kilovolt line and is provided with 1 or 2 110 kilovolt main substations; namely, a 2-seat 220 kilovolt transformer substation is used as a power supply side and is electrically connected with a load side through a link.
Embodiments of the present invention are further described below with reference to fig. 4 and 5.
The structure definition of the existing double-chain power supply network structure of two-end power supply, two-line three-station is as follows:
(1) Power supply side: two ends are powered, a level A (220 kilovolt transformer substation) is provided with 2 seats (transformer substation numbers A1 and A2), a single station is provided with 2 back 110 kilovolt outgoing lines (2 outgoing line circuit breakers are provided with numbers 1 and 2), and 4 back outgoing lines are provided;
(2) Load side: the structure schematic diagram of a double-chain power supply network of 'three stations', wherein 'three stations', a class B (110 kilovolt transformer substation) 3 (transformer substation numbers B1, B2 and B3), a single station 4-turn 110 kilovolt outgoing line (loop in and loop out, power supply and feed can be mutually converted) 'two-end power supply, two-line three-station' and 'three-station' is shown in figure 5.
(3) And (3) link: "two-wire" refers to both L1 and L2 links. L1 is composed of 4 lines (L1-1, L1-2, L1-3, L1-4) and 8 circuit breakers at two ends; l1-1 refers to the lines A1-1 to B1-1, L1-2 refers to the lines B1-2 to B2-1, L1-3 refers to the lines B2-2 to B3-1, and L1-4 refers to the lines B3-2 to A2-1. L2 is composed of 4 lines (L2-1, L2-2, L2-3, L2-4) and 8 circuit breakers at two ends; l2-1 refers to the lines A1-2 to B1-3, L2-2 refers to the lines B1-4 to B2-3, L2-3 refers to the lines B2-4 to B3-3, and L2-4 refers to the lines B3-4 to A2-2.
As shown in fig. 1, the technical scheme flow of the method for improving the bearing capacity of the new energy based on the two-wire three-station structure of the distribution network is as follows:
firstly, data collection and preprocessing are carried out, the topological structure and node information of a target power grid are obtained, connection information of a line is obtained through topological analysis, a proper power flow calculation section is selected, load data and photovoltaic power generation power data are compared when the load data and the photovoltaic power generation power data are typically in line with 8-13 days, and the moment with the maximum corresponding power difference value between the load data and the photovoltaic power generation power data is used as the power flow calculation section for evaluating the distributed photovoltaic bearing capacity of the power distribution network;
and then, a binary variable switch is connected to the structure of the figure 3 and the structure of the figure 4 to flexibly adjust the topological structure, the maximum new energy bearing capacity is taken as a target, a bearing capacity optimization model is established by taking node power balance, voltage safety, line load, photovoltaic power generation and a switching state as constraint conditions, and the new energy bearing capacity and the corresponding topological structure under the structure are calculated.
Finally, the reliability index and the new energy bearing capacity of the optimal topological structure in fig. 3 are calculated, and compared with the reliability index and the new energy bearing capacity of the optimal topological structure in fig. 4, the method can prove that the two-line three-station structure based on the distribution network is a method for effectively improving the new energy bearing capacity.
Claims (12)
1. The method for improving the bearing capacity of the new energy based on the two-wire three-station structure of the distribution network is characterized by comprising the following steps of:
step S1, obtaining the topological structure and node information of a target power grid, obtaining the connection information of a line through topological analysis, and selecting a calculation section of a distributed photovoltaic bearing capacity tide;
step S2, according to the topological structure, node information and line connection information of the power grid; establishing a bearing capacity model, and determining the maximum new energy bearing capacity of the bearing capacity model and a corresponding topological structure;
and step S3, completing reliability analysis of the topological structure according to the maximum new energy bearing capacity of the topological structure.
2. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 1, which is characterized by comprising the following steps:
the specific steps of selecting the calculated section of the distributed photovoltaic load capacity tide in the step S1 are as follows:
firstly, selecting load data of a typical load day time interval and whole-day whole-point photovoltaic power generation power data;
then, calculating the power difference delta P between the load data corresponding to each moment in the typical load day time interval and the photovoltaic power generation power through a formula (1),
ΔP＝|P pv -P load | (1)
wherein P is pv The photovoltaic power generation power at a certain moment; p (P) load The load power at a certain moment;
and finally, taking the moment corresponding to the maximum value of the power difference between the load data and the photovoltaic power generation power as a tide calculation section to be selected for the distributed photovoltaic bearing capacity of the power distribution network in the evaluation.
3. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 1, which is characterized by comprising the following steps:
the step of establishing the bearing capacity model in the step S2 specifically includes:
the method aims at maximizing the bearing capacity of the distributed new energy, establishes corresponding constraint and comprises the following objective functions:
wherein K is PV An access node set representing a distributed new energy source;representing the maximum access capacity of the distributed new energy of the node i;
the corresponding constraint comprises three aspects of constraint, namely photovoltaic power generation constraint, tide balance constraint, line switch constraint and safety constraint.
4. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 3, which is characterized by comprising the following steps:
the photovoltaic power generation constraint is as follows:
in the method, in the process of the invention,and->Respectively representing active power and reactive power of the distributed photovoltaic accessed by the node i; />And (3) withAnd respectively representing upper limits of active power and reactive power of the distributed photovoltaic accessed by the node i.
5. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 3, which is characterized by comprising the following steps:
the flow balance constraint and the line switch constraint are as follows:
in the method, in the process of the invention,and->Representing the active and reactive loads on node i, respectively; p (P) ik And Q ik Active and reactive power respectively flowing from node i to node k; p (P) ji And Q ji Active power and reactive power respectively flow from node j to node i; r is (r) ij And x ij The resistance and reactance between the node i and the node j are respectively; i ij The current amplitude of the branch between the node i and the node j; u (U) i And U j The voltage amplitude of the node i and the node j;
wherein S is ij Representing the network topology switch state, where 1 represents closed and 0 represents open. The adjustment of the switch state is used as a part of an optimization model to find the optimal distribution network topology structureAnd the bearing capacity of new energy is maximized.
6. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 3, which is characterized by comprising the following steps:
the safety constraint is as follows:
U min,i ≤U i ≤U max,i (6)
I ij ≤I max,ij (7)
in U i For the voltage amplitude of node i, U max,i And U min,i Representing the upper and lower limits of the voltage amplitude of the node i; i max,ij Representing the maximum current carrying capacity of the line between nodes i and j.
7. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 1, which is characterized by comprising the following steps:
the reliability analysis of step S3 is specifically as follows;
s3.1, inputting system initial state data to form system information;
inputting and reading information of network topology, electric load, air load, element parameters and wind-light output, setting simulation time duration N=100, system initial time t=0, wherein all elements are in a normal state, and establishing system initial state data;
s3.2, sampling the system state;
carrying out random sampling on the load, the photovoltaic output and the element state by a sequential Monte Carlo method;
randomly extracting the running time of each element under the normal working stateLet t=min (TTF), R 1 Is in interval [0,1 ]]Uniformly extracting distributed random numbers, wherein lambda is the failure rate, and selecting an element with the minimum TTF as a failure element;
step S3.3, randomly extracting repair time of the failed componentTTR is taken as the duration of system failure at the same time, R 2 Is in interval [0,1 ]]Uniformly extracting the random numbers distributed in the matrix, wherein mu is the repair rate of the matrix;
s3.4, calculating wind-light output and load power at the moment t, judging connectivity of the power distribution network, performing fault traversal search, analyzing the load of the power distribution network affected by a fault element, and determining whether the power supply, the switch and the power failure type of a load point exist or not; accumulating the power failure time and the power failure times of the load points respectively;
step S3.5, the system simulation advances along with time, t=t+T+TTR, whether the simulation time T meets 1 year or not is judged, namely 8760 hours, if 1 year is reached, N=N+1, t=t-8760, and otherwise S3.2 is entered;
s3.6, calculating a reliability index;
calculating N > Nmax; if the condition that the sampling is finished immediately is met, otherwise, the step S3.2 is entered;
and S3.7, outputting a system reliability index.
8. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 7, which is characterized by comprising the following steps:
the wind-light output and the load power at the t moment in the step S3.4 have the following three models;
a probability model of solar illumination intensity distribution, a photovoltaic power output power model and a load model.
9. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 8, which is characterized by comprising the following steps:
the probability model of the solar illumination intensity distribution is as follows:
wherein r is the actual light intensity, r m For maximum light intensity, Γ is gamma function, and α, β are bata distribution shape parameters.
10. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 8, which is characterized by comprising the following steps:
the photovoltaic power output power model is as follows:
the output power of the photovoltaic power supply is mainly composed of the number M of battery components on a battery board and the size A of the area of each component m Photoelectric conversion efficiency eta of each component m M= (1, 2.). M) determined; firstly, solving the total area A of the cell panel matrix, wherein the total photoelectric conversion efficiency eta of the matrix;
the total output power of this solar cell matrix is then:
P＝r·A·η (13)
after knowing the probability density distribution function of the light intensity, the probability density distribution function of the photovoltaic unit power generation also obeys Beta distribution, which is expressed as:
wherein R is m The photovoltaic cell's maximum output power is this period.
11. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 8, which is characterized by comprising the following steps:
the load model is:
the load model obeys normal distribution;
wherein P is l Mu, for load power PL For the average load, σ PL Is the standard variation of the load power.
12. The method for improving the bearing capacity of new energy based on the two-wire three-station structure of the distribution network according to claim 7, which is characterized by comprising the following steps:
the reliability index in step S3.7 is specifically as follows;
EENS = total insufficient power of the system = Σl i U i (19)
Wherein lambda is i Annual average outage frequency for load point i; u (U) i The annual average power failure time for load point i is at; n (N) i The number of users for load point i; l (L) i The average power of the load point i.
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