CN109102146B - Electric power system risk assessment acceleration method based on multi-parameter linear programming - Google Patents
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Abstract
The invention discloses a risk assessment acceleration method for an electric power system based on multi-parameter linear programming, which is characterized by establishing a load shedding minimum optimization model corresponding to the running state of the electric power system and a multi-parameter linear programming model corresponding to the load shedding minimum optimization model; establishing a line state dictionary set; searching and matching a key judgment area set in a corresponding line state dictionary set element aiming at the running state of the power system obtained by a certain sampling; if the matching is unsuccessful, adopting optimization software to solve an optimization model with the minimum load shedding loss corresponding to the running state, and establishing a new key judgment area by using a solving result; and if the matching is successful, directly calculating the load loss of each node corresponding to the running state by using the characteristic information of the corresponding key judgment area. The method improves the risk evaluation efficiency of the power system, improves the risk evaluation speed of the power grid, and provides decision reference and support for power grid planning and operating personnel.
Description
Technical Field
The invention relates to the technical field of electric power system analysis, in particular to an electric power system risk assessment acceleration method based on multi-parameter linear programming.
Background
There is a great deal of uncertainty in today's power systems. The summation and interaction of these uncertainties makes the power system operate at risk in real time. For example, the possibility of a generator outage in a power plant may result in insufficient system-wide power generation capacity, thereby resulting in a forced outage; the transmission line has the probability of exiting operation, so that the load flow transfer occurs in the whole system, the transmission distribution of power is influenced, and the load requirement can not be met; the interaction of various uncertainties is more likely to cause major accidents.
Therefore, risk assessment needs to be performed on the power system, that is, modeling is performed on relevant uncertainty in the power system through a probability method, and power failure risk of the whole system is assessed according to uncertainty modeling results. Risk assessment can be divided into long-term and medium-short term assessment according to time scale. The long-term risk assessment result has an instructive effect on the planning of the power system, and the medium-term and short-term risk assessment can assist the power system scheduling operator to make a decision, so that the operation safety of the power system is improved.
In the risk evaluation process of the power system, a large number of samples need to be generated for simulating the randomness of the future power system, and the load shedding condition of each sample is judged by applying the optimal power flow method, so that the calculation amount is large, and the application of the risk evaluation method in an actual power grid is restricted due to the slow analysis speed. To overcome this difficulty, a number of methods have been proposed to improve the efficiency of the assessment. In the sampling technology, various high-efficiency sampling technologies including truncation sampling, hierarchical sampling, state space pruning, intelligent search, importance sampling, cross entropy method-based and the like are provided, so that the sampling efficiency is improved, the randomness of the power system is simulated by fewer samples, and better convergence is obtained. The risk assessment process is expedited by reducing the number of samples required. The existing methods and techniques are lacking in the evaluation techniques of the samples. Some methods based on machine learning techniques such as artificial neural networks, support vector machines, etc. have been proposed, but they are all based on the assumption that system branches (transmission lines, cables, transformers, and transmission equipment connecting two busbars are all defined as "branches") will not exit the operation, which is not in line with reality.
Therefore, an accelerating method for risk assessment of an electric power system is desired to solve the problem that the calculation efficiency of the risk assessment method of the electric power system in the prior art is low.
Disclosure of Invention
The invention aims to provide a power system risk assessment acceleration method based on multi-parameter linear programming.
The invention relates to a risk assessment acceleration method of an electric power system based on multi-parameter linear programming, which defines a power transmission line, a cable, a transformer and power transmission equipment for connecting two buses as branches; defining various power generation equipment in the system as a unit; the generator set and the branch circuit of the power system are collectively called as elements; defining all buses in the power system as nodes; defining a key distinguishing area set of the same multi-parameter planning problem as a key distinguishing area set, wherein each key distinguishing area in the key distinguishing area set is an element of the key distinguishing area set; the method comprises the steps of defining a set for storing a plurality of line states and corresponding power system parameters and key distinguishing area sets of the line states as a line state dictionary set, wherein each line state in the line state dictionary set and the corresponding power system parameters and key distinguishing area sets thereof are used as one element of the line state dictionary set.
The electric power system risk assessment acceleration method based on multi-parameter linear programming is characterized by comprising the following steps of:
step 1: in a time period needing risk assessment, randomly sampling the operating state of the power system by adopting a Monte Carlo method, wherein the operating state of the power system comprises the following steps: the normal or outage state of each branch, the normal or outage state of the generator set and the load size of each node of the power system, and establishing an optimization model with minimum load shedding loss and a multi-parameter planning model corresponding to the optimization model with minimum load shedding loss for the operation state of the power system;
step 2: aiming at the sampling state of the power system branch with the sampling probability greater than a certain threshold value, establishing a line state dictionary set, wherein each element in the line state dictionary set corresponds to the sampling state of the power system branch, and storing a transfer distribution factor matrix and key discrimination area set characteristic information of the power system branch sampling state;
and step 3: searching and matching the sampled running state of the power system with elements in the line state dictionary set, if the matching is unsuccessful, turning to the step 4, and if the matching is successful, turning to the step 5;
and 4, step 4: solving the optimization model with the minimum load shedding loss corresponding to the sampled running state of the power system to obtain the load loss of each node, and turning to the step 6;
and 5: matching the unit state and the load level state in the current sample with elements in a key discrimination area set corresponding to a line state dictionary set, if the matching is unsuccessful, solving an optimization model with minimum load shedding loss corresponding to the running state of the power system to obtain the load loss of each node, calculating the key discrimination area corresponding to the running state of the power system, adding the area into the corresponding key discrimination area set in the elements of the line state dictionary set, and recording the characteristic information of the key discrimination area set; if the matching is successful, directly calculating the load loss of each node corresponding to the running state of the power system by adopting the characteristic information of the key distinguishing area corresponding to the corresponding key distinguishing area set, and turning to the step 6;
step 6: and (4) obtaining the next running state obtained by sampling, and turning to the step 3 until the risk evaluation calculation of the power system is completed.
Preferably, the establishing of the optimization model with the minimum load shedding loss for the operation state of the power system in the step 1 specifically includes:
establishing an optimization model satisfying the multi-parameter linear programming assumption and minimizing the load shedding loss, wherein the optimization model satisfies the multi-parameter linear programming assumption, and is represented by a formula (4):
in the objective function, DdCutting a load vector for each node, wherein P is active power injection of each node; c. C1And c2Is the corresponding weight vector; constraint part, 1TX P-0 represents the power balance constraint of the full system, i.e. the sum of the injected power of the full system is 0 without considering the grid loss;
in the middle, G is a transfer distribution factor matrix, and G multiplied by P is line active power flow and line power flow receivingP lineAnd
constraining;
Preferably, the multi-parameter planning model in step 1 specifically includes: constructing a parameter vector in a multi-parameter linear program as in equation (5):
expressing the change of the unit state and the change of the load level through the change of a parameter theta, establishing the unit state and the load change under the determined line state, and using a multi-parameter linear programming model for power grid risk assessment as shown in a formula (6):
preferably, the step 2 specifically includes:
the line state dictionary set is established for all line states of which the exit operation number is less than or equal to alpha, namely indexes are established in the line state dictionary set when accidents are serious to N-alpha, wherein N is the total number of the lines in the system, alpha is the exit operation number of the lines, and the total number of the line states stored in the line state dictionary set is formula (7):
storing all line states and related parameters in the line state dictionary set; wherein, the relevant parameters include: the parameters of the power system include transfer distribution factor matrix G and line transmission capacity limit in the current line state
AndPline; the multi-parameter linear programming solving technology comprises the step of storing corresponding key discrimination area information.
Preferably, the step 2 specifically includes the step of storing all the line states and related parameters in the line state dictionary set:
in the line state dictionary set, the information of each line state is stored through two parts, including numbers and contents; in the number set, the number of the current line state is stored, the number is realized by adopting binary coding, and each bit is the running state of the corresponding line; in the content set, storing a characteristic matrix corresponding to the current line state and used for judging the system load shedding, including
And
preferably, the step 5 of matching the unit state and the load level state with the elements in the key discrimination area set corresponding to the line state dictionary set specifically includes:
matching a key discrimination region set, wherein each element in the key discrimination region set comprises two characteristics, one is an attribute representing the range of a key discrimination region, and the other is shown as a formula (8); and the other is used for representing the mapping relation from the parameter vector to the optimal solution in the key discriminant region, and the formula (9) is as follows:
wherein the content of the first and second substances,
and
the method comprises the steps of obtaining a key distinguishing area feature matrix when a key distinguishing area is created;
when matching, firstly, the corresponding parameter of the current sample is calculated according to the formula (5)
Then, sequentially judging whether theta belongs to the key distinguishing area or not according to the characteristic parameters of each element in the key distinguishing area set; the judgment method is as follows: computing vectors
Judging whether all components are less than 0; if the judgment is true for a certain key judgment area, the fact that theta belongs to the key judgment area is indicated; if the two elements are not satisfied, the matching is not successful, and theta does not belong to any element in the key distinguishing area set;
directly calculating the load loss amount of each node, and directly using the mapping relation of the key distinguishing region where theta is located when the key distinguishing region is successfully matched, wherein each component of x (theta) in the formula (9) is the load loss amount of each node corresponding to the running state;
running optimization to obtain the load loss of each node, updating a key discrimination area set, and solving the linear programming problem shown in the formula (10) when the matching of the key discrimination areas is unsuccessful to obtain the load shedding of each node;
then, screening Lagrange multipliers of each constraint in the optimization model, forming all the constraints with the Lagrange multipliers larger than 0 into a set, and respectively forming corresponding parts of constraint conditions into a set
And
a matrix; all the constraints with Lagrange multipliers equal to 0 are combined into a set, and the corresponding parts of the constraint conditions are respectively combined into a set
And
a matrix;
according to what is obtained
And
the matrix is used for calculating the characteristic information of the corresponding key distinguishing area by applying formulas (8) and (9); and adding a corresponding key distinguishing area set in elements of the line state dictionary set to the newly obtained key distinguishing area, and recording the feature information of the key distinguishing area represented by the formula (8) and the formula (9).
The risk evaluation acceleration method based on the multi-parameter linear programming power system disclosed by the invention establishes a new sample evaluation method on the basis of the existing power system risk evaluation, the method applies the multi-parameter linear programming theory and the line state matching, and the multi-parameter linear programming method is used for evaluating the sample, so that compared with the existing method based on solving the optimal power flow model, the method has the advantage that the calculation efficiency is greatly improved. In addition, the method has no limitation on the sampling technology, so that the method has good applicability, and the calculation efficiency can be further improved by applying an efficient sampling method. By applying the method, the rapid evaluation of the risk of the power system can be realized, the application of the risk evaluation in a large-scale complex power system is promoted, the method can play a corresponding role on both the planning and operation sides of the power grid, and plays a decision reference and support role in the planning and operation of the power grid
Drawings
Fig. 1 is a flowchart of a risk assessment method for an electric power system using multi-parameter linear programming according to the present invention.
Fig. 2 is a schematic diagram of a line state dictionary set proposed in the present invention.
Fig. 3 is a network topology diagram of the IEEE RTS-79 power system in the present embodiment.
Fig. 4 shows the effect of the proposed method of the present invention on the IEEE RTS-79 power system in this embodiment.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are only some, but not all embodiments of the invention. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the 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.
The invention provides a risk assessment method of an electric power system applying multi-parameter linear programming, wherein a power transmission line, a cable, a transformer and power transmission equipment for connecting two buses are defined as a branch circuit; defining various power generation equipment in the system as a unit; the generator set and the branch circuit of the power system are collectively called as 'elements'; defining all buses in the power system as nodes; defining a key discrimination area set of the same multi-parameter planning problem as a key discrimination area set, wherein each key discrimination area in the key discrimination area set is an element of the key discrimination area set; the method comprises the steps of defining and storing a plurality of line states and a set of power system parameters and a key distinguishing area set corresponding to each line state as a line state dictionary set, wherein each line state in the line state dictionary set and a corresponding power system parameter and key distinguishing area set thereof are used as one element of the line state dictionary set. The flow chart of the implementation of the method is shown in FIG. 1, and the detailed steps of the method are as follows:
1) in a time period needing risk assessment, randomly sampling a series of operating states of the power system by adopting a Monte Carlo method, wherein each operating state comprises a normal or outage state of each branch, a normal or outage state of a generator set and the load of each node of the power system; establishing an optimization model with minimum load shedding loss and a corresponding multi-parameter planning model for each running state of the power system; the method specifically comprises the following steps:
establishing an optimal direct current power flow model with the minimum tangential load loss as shown in a formula (1):
where c is the cost vector of the load shedding. In the method, a constant vector is taken here. The objective function of the model is to minimize the sum of the load-shedding costs or load-shedding quantities on all nodes.
The first line represents the system-wide power balance constraint, i.e. the sum of the system-wide injected powers is 0 without considering the grid loss. And the second row applies the transfer distribution factor matrix G to construct an equality relation between the line power flow and the node injection power. Method for calculating injection power of third behavior node, PgenFor the output of the generator, W is a unit node connection matrix, and if a unit j is connected to a node i, W isi,j1 is ═ 1; otherwise W i,j0. At each node, a generatorThe generated power is W multiplied by PgenThe load demand is D, considering the load shedding DdThe real load on the node is then D-Dd. And for each node, the power generated by the generator is subtracted from the real load of the node, and the power P injected into the power grid is obtained. The injected power can be positive, namely active power is actually injected into the system; it may also be negative, i.e. active from the grid.
The inequality constraint part, the fourth line is the output constraint of the generator, wherein
And the upper limit of the output of the generator. Transmission constraints of the fifth behavioural line, i.e. the power flow on the line cannot exceed the line transmission limitsP lineAnd
and the sixth action is load shedding constraint, namely the load shedding quantity cannot exceed the maximum load demand value of the node, and meanwhile, negative load shedding cannot be carried out.
The above model cannot apply a multi-parameter linear programming method in many cases, and therefore needs to be improved. In order to solve the above multi-solution problem and thus apply the multi-parameter linear programming technique, the objective function is improved based on the above model, as shown in formula (2):
min z＝c1 T×Dd+c2 T×P (2)
explicitly increasing node injection power P in the objective function and applying vector c1，c2To respectively express the load shedding quantity DdAnd the weight of the node injection power P in the objective function. The vector c is determined by the following method1，c2The value of each component, as in equation (3):
where M is a constant large relative to the number of system nodes, which ensures the modulusThe degree of accuracy of the pattern. At the same time, c1And c2Different nodes have different weights in the model, and when the generation power injection and load shedding distribution are considered, priorities exist among the different nodes, so that the problem of multiple solutions possibly occurring in the original model is avoided.
Thereafter, the model is subjected to the following reduction and variable replacement. With DdAnd P is used as a decision variable, all other intermediate variables in the model are expressed through the decision variable, the constraint condition is simplified, and the simplified risk assessment model is obtained, such as a formula (4).
The parameter vector in the multi-parameter linear programming is constructed as shown in the formula (5).
Both changes in the state of the unit and changes in the load level can be represented by changes in the parameter theta. And (3) after the parameter representation is applied to determine the unit and load changes in the line state, modifying the model into a matrix form like (6) to obtain a multi-parameter linear programming model for power grid risk assessment.
In fact, the actual physical meaning of the parameter θ in the model is the upper limit of active injection at each node after the fleet state and load level are determined.
2) Aiming at a power system branch sampling state with a high sampling probability, establishing a line state dictionary set, wherein each element in the line state dictionary set corresponds to one power system branch sampling state, and storing characteristic information such as a transfer distribution factor matrix and a key judgment area set corresponding to the state; the method specifically comprises the following steps:
and establishing a line state dictionary set. In the line state dictionary set, a certain number of line states are stored. Although for a power system with m transmission lines, it may occur that the line status is as high as 2mBut the probability of each state appearing in the sample is not uniform and varies greatly. In fact, because the reliability of the transmission line is high in reality and the probability of occurrence of many line states is extremely low, the line conditions of most samples can be covered by only considering the line states with high occurrence probability and storing the line states in the line state dictionary set. For example, the line states of which all lines exit the operation number is less than or equal to alpha are considered in the line state dictionary set, so that accidents serious to N-alpha (where N is the total number of lines in the system, and alpha is the number of lines exiting the operation) can be considered by the line state dictionary set, and the probability of accidents serious to N-alpha occurring in the power grid is low. In this case, the number of line states stored in the line state dictionary set is:
for each line state in the line state dictionary set of the line state dictionary set, the relevant parameters are stored. In terms of power system parameters, including transfer distribution factor matrix G in current line state, line transmission capacity limit
AndPand (4) line. Meanwhile, in the aspect of multi-parameter linear programming solving technology, corresponding key distinguishing area information is stored.
In the line state dictionary set, information of each line state is actually stored through two parts. Its form is similar to a binary data structure, divided into "number" and "content". In the numbering set, the number of the current line state is stored, which can be realized by a string of binary codes, wherein each bit is the running state of the corresponding line. In the content set, relevant parameters under the current line state are stored, and specific contents include parameters of the power system and parameters required by multi-parameter linear programming solution, and a schematic diagram of the parameter set is shown in fig. 2. The data structure of the line state dictionary set is similar to a dictionary.
3) Aiming at the running state of a certain sampled power system, searching and matching the line running state of the power system with elements in a line state dictionary set, if the matching is unsuccessful, turning to the step 4), and if the matching is successful, turning to the step 5); the method specifically comprises the following steps:
when the line state in the sample is matched with the elements in the line state dictionary set, the line state of the sample is only required to be compiled into corresponding binary codes, 0 on each bit represents that the line exits from operation, and 1 represents that the line is normal. And after the binary codes are obtained, comparing the binary codes with the numbers in the number set in the line state dictionary set. If the binary code is completely the same as a certain number in the content set, the matching is successful; otherwise, the matching is unsuccessful.
4) Adopting optimization software to solve the optimization model which is corresponding to the running state and has the minimum load shedding loss to obtain the load loss of each node, and turning to the step 6);
5) matching the unit state and the load level state in the current sample with elements in a key discrimination area set corresponding to a line state dictionary set, if the matching is unsuccessful, solving an optimization model with the minimum load shedding loss corresponding to the running state by adopting optimization software to obtain the load loss of each node, calculating the key discrimination area corresponding to the running state, adding the area into a corresponding key discrimination area set in the elements of the line state dictionary set, and recording the characteristic information of the area; and if the matching is successful, directly calculating the load loss of each node corresponding to the running state by adopting the characteristic information of the corresponding key distinguishing area in the corresponding key distinguishing area set.
Turning to step 6); the method specifically comprises the following steps:
5.1) Key discriminating region set matching
Each element in the key discrimination region set comprises two characteristics, one is an attribute representing the range of the key discrimination region, as shown in formula (8); and the other is used for representing the mapping relation from the parameter vector to the optimal solution in the key discrimination region, as shown in formula (9).
Wherein the content of the first and second substances,
and
is the matrix obtained when creating the critical discrimination area.
When matching, firstly, the parameters corresponding to the current sample are constructed according to the formula (5)
Then, sequentially judging whether theta belongs to the key distinguishing area according to the range attribute of each element in the key distinguishing area set, wherein the judging mode is as follows: calculate correspondences
It is determined whether all of its components are less than 0. If the judgment is true for a certain key judgment area, the fact that theta belongs to the key judgment area is indicated; if the two elements are not satisfied, the matching is not successful, and theta does not belong to any element in the key distinguishing area set.
5.2) directly calculating the load loss of each node
When the key distinguishing area is successfully matched, directly using the mapping relation of the key distinguishing area where theta is located, as shown in formula (9):
and each component of x (theta) is the load loss amount of each node corresponding to the running state.
5.3) operating optimization to obtain the load loss of each node and updating the key discrimination area set
When the matching of the key distinguishing area is unsuccessful, the loss load of each node cannot be calculated by directly using the characteristic information of the key distinguishing area. The optimization problem shown in formula (6) is formally expressed as follows:
and solving the linear programming problem shown in the formula (10) by using optimization software to obtain the load shedding amount of each node. Meanwhile, a Lagrange multiplier for each constraint in the optimization model is obtained. All the constraints with Lagrange multipliers larger than 0 are combined into a set, and the corresponding parts of the constraint conditions are respectively combined into a set
And
a matrix; all the constraints with Lagrange multipliers equal to 0 are combined into a set, and the corresponding parts of the constraint conditions are respectively combined into a set
And
and (4) matrix.
According to what is obtained
And
the matrix calculates feature information corresponding to the key discrimination region using equations (8) and (9). And adding the newly obtained key discrimination area into a corresponding key discrimination area set in the element of the line state dictionary set, and recording the feature information of the key discrimination area represented by the formula (8) and the formula (9).
6) And acquiring the next operation state obtained by sampling, and turning to the step 3) until the risk evaluation calculation of the power system is completed.
According to the risk assessment method for the power system, the efficiency of traditional power system risk assessment can be obviously improved, computing resources are saved, rapid assessment of the risk of the power system is promoted, and reference and decision support are provided for power grid operation and planning personnel.
Example 2:
the method for evaluating the risk of the power system applying the multi-parameter linear programming is explained by taking an IEEE reliability standard test power system (IEEE RTS-79) as an example, and the effect realized by the method is verified. The IEEE RTS-79 power system comprises 24 nodes, 32 generator sets and 38 branches, the highest load is 2850MW, and the installed capacity is 3405 MW. The network topology of the IEEE RTS-79 power system is shown in FIG. 3, the generator parameters are shown in Table 1, the load ratio of each node is shown in Table 2, and the branch (line and transformer) parameters are shown in Table 3.
TABLE 1IEEE RTS-79 Generator set reliability data
TABLE 2IEEE RTS-79 node load ratios
TABLE 3IEEE RTS-79 Branch (line and Transformer) parameters
By applying the method provided by the invention, a corresponding program is written on MATLAB R2017b, Cplex version 12.4 is called to solve the optimization problem, and the risk evaluation is carried out on the IEEE RTS-79 system. The computing device of the application is: the notebook computer is a thinkpa T470 notebook computer which is provided with an Intel i7-7500U processor, a 16GB memory and runs a Windows10 professional edition operating system. In the sampling stage of risk assessment, the basic Monte Carlo sampling method is directly adopted. Of course, more advanced and efficient sampling methods will provide better improvements in performance.
In the line state dictionary set, the considered line states include two types of states, N and N-1. Therefore, when a sample with the line state of N or N-1 is obtained in the sampling process, the evaluation method based on the multi-parameter linear programming can play a role; and when the line state of the sample is not the condition, solving a linear programming problem to evaluate the sample.
Meanwhile, in order to illustrate the effectiveness of the method, a traditional method for solving the optimal direct current power flow model is used as a contrast of the method. Fig. 4 shows the comparison of the two methods in terms of the calculated speed. It can be seen that the evaluation samples and the evaluation time consumption in the method basically form a linear relationship, and most samples can be solved by using a multi-parameter linear programming method in the evaluation process. As can be seen from fig. 4, the computational efficiency of the method is improved by about 25 times over the IEEE RTS-79 system compared to the conventional method.
Two independent monte carlo analyses were performed using the present method and the conventional method, respectively, with a relative error of 1% as a convergence target, and the results are shown in the table below.
TABLE 4 IEEE RTS-79 Risk assessment results
By comparison, the risk assessment method for the power system based on the multi-parameter linear programming is feasible, efficient and accurate. The method can effectively evaluate the risk of the power system, and can obtain great efficiency improvement from a sample evaluation level on the premise of ensuring the precision.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. 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 (4)
1. The electric power system risk assessment acceleration method based on multi-parameter linear programming is characterized by comprising the following steps of:
step 1: in a time period needing risk assessment, randomly sampling the operating state of the power system by adopting a Monte Carlo method, wherein the operating state of the power system comprises the following steps: the normal or outage state of each branch, the normal or outage state of the generator set and the load size of each node of the power system, and establishing an optimization model with minimum load shedding loss and a multi-parameter planning model corresponding to the optimization model with minimum load shedding loss for the operation state of the power system; the establishing of the optimization model with the minimum load shedding loss for the operation state of the power system specifically includes:
establishing an optimization model satisfying the multi-parameter linear programming assumption and minimizing the load shedding loss, wherein the optimization model satisfies the multi-parameter linear programming assumption, and is represented by a formula (4):
in the objective function, DdCutting a load vector for each node, wherein P is active power injection of each node; c. C1And c2Is the corresponding weight vector; constraint part, 1TX P-0 represents the power balance constraint of the full system, i.e. the sum of the injected power of the full system is 0 without considering the grid loss;
in the middle, G is a transfer distribution factor matrix, and G multiplied by P is line active power flow and line power flow receivingP lineAnd
constraining;
a power constraint is injected for the node,
the upper limit of the output of the generator is D, the load of each node is D, W is a unit node connection matrix, and if a unit j is connected to a node i, W isi,j1 is ═ 1; otherwise Wi,j0; for each node, the power generated by the generator is subtracted from the real load of the node to obtain the power P injected into the power grid; d is not less than 0dD is equal to or less than the load shedding constraint, namely the load shedding amount is not negative, and the size of the load shedding amount cannot exceed the maximum value of the load demand of the node; wherein
Limiting the output upper limit of the generator and the transmission capacity of the line
AndP line；
step 2: aiming at the sampling state of the power system branch with the sampling probability greater than a certain threshold value, establishing a line state dictionary set, wherein each element in the line state dictionary set corresponds to the sampling state of the power system branch, and storing a transfer distribution factor matrix and key discrimination area set characteristic information of the power system branch sampling state; the specific steps of all the line states and related parameters in the line state dictionary set are as follows:
in the line state dictionary set, the information of each line state is stored through two parts, including numbers and contents; in the number set, the number of the current line state is stored, the number is realized by adopting binary coding, and each bit is the running state of the corresponding line; in the content set, storing a characteristic matrix corresponding to the current line state and used for judging the system load shedding, including
And
and step 3: searching and matching the sampled running state of the power system with elements in the line state dictionary set, if the matching is unsuccessful, turning to the step 4, and if the matching is successful, turning to the step 5;
and 4, step 4: solving the optimization model with the minimum load shedding loss corresponding to the sampled running state of the power system to obtain the load loss of each node, and turning to the step 6;
and 5: matching the unit state and the load level state in the current sample with elements in a key discrimination area set corresponding to a line state dictionary set, if the matching is unsuccessful, solving an optimization model with minimum load shedding loss corresponding to the running state of the power system to obtain the load loss of each node, calculating the key discrimination area corresponding to the running state of the power system, adding the area into the corresponding key discrimination area set in the elements of the line state dictionary set, and recording the characteristic information of the key discrimination area set; if the matching is successful, directly calculating the load loss of each node corresponding to the running state of the power system by adopting the characteristic information of the key distinguishing area corresponding to the corresponding key distinguishing area set, and turning to the step 6;
step 6: and (4) obtaining the next running state obtained by sampling, and turning to the step 3 until the risk evaluation calculation of the power system is completed.
2. The electric power system risk assessment acceleration method based on multi-parameter linear programming according to claim 1, characterized in that: the multi-parameter planning model in the step 1 specifically includes: constructing a parameter vector in a multi-parameter linear program as in equation (5):
expressing the change of the unit state and the change of the load level through the change of a parameter theta, establishing the unit state and the load change under the determined line state, and using a multi-parameter linear programming model for power grid risk assessment as shown in a formula (6):
3. the electric power system risk assessment acceleration method based on multi-parameter linear programming according to claim 2, characterized in that: the step 2 specifically comprises:
the line state dictionary set is established for all line states of which the exit operation number is less than or equal to alpha, namely indexes are established in the line state dictionary set when accidents are serious to N-alpha, wherein N is the total number of the lines in the system, alpha is the exit operation number of the lines, and the total number of the line states stored in the line state dictionary set is formula (7):
storing all line states and related parameters in the line state dictionary set; wherein, the relevant parameters include: the parameters of the power system include transfer distribution factor matrix G and line transmission capacity in the current line stateLimiting
AndPline; the multi-parameter linear programming solving technology comprises the step of storing corresponding key discrimination area information.
4. The electric power system risk assessment acceleration method based on multi-parameter linear programming according to claim 3, characterized in that: the step 5 of matching the unit state and the load level state with the elements in the key discrimination area set corresponding to the line state dictionary set specifically includes:
matching a key discrimination region set, wherein each element in the key discrimination region set comprises two characteristics, one is an attribute representing the range of a key discrimination region, and the other is shown as a formula (8); and the other is used for representing the mapping relation from the parameter vector to the optimal solution in the key discriminant region, and the formula (9) is as follows:
wherein the content of the first and second substances,
and
the method comprises the steps of obtaining a key distinguishing area feature matrix when a key distinguishing area is created;
when matching, firstly, the corresponding parameter of the current sample is calculated according to the formula (5)
Then, according to the feature parameter of each element in the key distinguishing region setCounting, and sequentially judging whether theta belongs to the key judgment area; the judgment method is as follows: computing vectors
Judging whether all components are less than 0; if the judgment is true for a certain key judgment area, the fact that theta belongs to the key judgment area is indicated; if the two elements are not satisfied, the matching is not successful, and theta does not belong to any element in the key distinguishing area set;
directly calculating the load loss amount of each node, and directly using the mapping relation of the key distinguishing region where theta is located when the key distinguishing region is successfully matched, wherein each component of x (theta) in the formula (9) is the load loss amount of each node corresponding to the running state;
running optimization to obtain the load loss of each node, updating a key discrimination area set, and solving the linear programming problem shown in the formula (10) when the matching of the key discrimination areas is unsuccessful to obtain the load shedding of each node;
then, screening Lagrange multipliers of each constraint in the optimization model, forming all the constraints with the Lagrange multipliers larger than 0 into a set, and respectively forming corresponding parts of constraint conditions into a set
And
a matrix; all the constraints with Lagrange multipliers equal to 0 are combined into a set, and the corresponding parts of the constraint conditions are respectively combined into a set
And
a matrix;
according to what is obtained
And
the matrix is used for calculating the characteristic information of the corresponding key distinguishing area by applying formulas (8) and (9); and adding a corresponding key distinguishing area set in elements of the line state dictionary set to the newly obtained key distinguishing area, and recording the feature information of the key distinguishing area represented by the formula (8) and the formula (9).
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103366220A (en) * | 2012-04-06 | 2013-10-23 | 华东电力试验研究院有限公司 | Evaluation method of operational risk of electric system |
| CN103426056A (en) * | 2013-07-18 | 2013-12-04 | 清华大学 | Power system weak link identification method based on risk evaluation |
| CN105226643A (en) * | 2015-09-23 | 2016-01-06 | 清华大学 | Under security constraint, power system operation simulation model generates and method for solving fast |
Family Cites Families (1)
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|---|---|---|---|---|
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-
2018
- 2018-06-29 CN CN201810698644.0A patent/CN109102146B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103366220A (en) * | 2012-04-06 | 2013-10-23 | 华东电力试验研究院有限公司 | Evaluation method of operational risk of electric system |
| CN103426056A (en) * | 2013-07-18 | 2013-12-04 | 清华大学 | Power system weak link identification method based on risk evaluation |
| CN105226643A (en) * | 2015-09-23 | 2016-01-06 | 清华大学 | Under security constraint, power system operation simulation model generates and method for solving fast |
Non-Patent Citations (2)
| Title |
|---|
| Probabilistic Forecasting of Real-Time LMP and Network Congestion;Yuting Ji等;《IEEE TRANSACTIONS ON POWER SYSTEMS》;20160718;第32卷(第2期);全文 * |
| 计及电力安全事故风险的电网动态分区与切负荷协调控制研究;李兆伟等;《电力系统保护与控制》;20170131;第45卷(第1期);全文 * |
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