CN115906610A - Distributed power supply site selection planning method considering line faults and power grid toughness - Google Patents
Distributed power supply site selection planning method considering line faults and power grid toughness Download PDFInfo
- Publication number
- CN115906610A CN115906610A CN202211268026.5A CN202211268026A CN115906610A CN 115906610 A CN115906610 A CN 115906610A CN 202211268026 A CN202211268026 A CN 202211268026A CN 115906610 A CN115906610 A CN 115906610A
- Authority
- CN
- China
- Prior art keywords
- power supply
- distributed power
- line
- formula
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The distributed power supply site selection planning method considering the line fault and the power grid toughness comprises the following steps: a line fault model under the influence of typhoon load action is considered, and the influence condition of typhoon on line meteorological load is analyzed; calculating the fault probability of the line by combining the load capacity which can be actually borne by the line; selecting a fault scene by using the system information entropy, determining the fault scale and the occurrence probability of the fault scale possibly caused by typhoon, and establishing a distributed power supply site selection model based on the calculated line fault probability; and solving a distributed power supply site selection planning model considering both the line fault rate and the toughness of the power grid by using a multi-target particle swarm algorithm. According to the method, aiming at different typical scenes which may appear, the distributed power supply site selection position which meets various constraints and maximizes the toughness of the power grid can be obtained by using the method.
Description
Technical Field
The invention relates to the technical field of site selection optimization of a power distribution network, in particular to a distributed power supply site selection planning method considering line faults and toughness of the power distribution network.
Background
In recent years, severe power system failure events are caused by extreme events (natural disasters, network attacks, and the like), particularly, global climate is increasingly severe, and large-area power failure accidents caused by natural disasters cause great influence on society and economy. The power distribution network is used as a power system network for directly distributing electric energy to users in a power system, has the characteristics of complex network structure, wide distribution range, poor safety environment and the like, and is the weakest part of the power system, so that the power distribution network has more and more attention to the disaster coping capability. Based on this, the term "toughness" was introduced to evaluate the ability of the power distribution network to withstand disasters and to recover quickly to a desired level in extreme disaster conditions.
The traditional recovery strategy of the power distribution network mainly comprises the steps of line element reinforcement before disaster, power distribution network reconstruction after disaster and distribution and scheduling measures of schedulable power supplies and maintenance personnel. With the continuous deep research on distributed power supplies, micro-grids and electric vehicles at home and abroad, more and more scholars begin to recover power loss loads by using the distributed power supplies, the electric vehicles and the micro-grids, and as faults caused by extreme disasters generally have the characteristics of disconnection from an upstream power grid, large influence range, more power loss loads and the like, the tough power distribution network makes higher requirements on disaster models, evaluation indexes and recovery strategies.
Related researches related to the improvement of the toughness of the power grid in the prior art mainly comprise:
chinese patent 'a transmission line and energy storage planning system collaborative planning modeling method considering elastic power grid resilience improvement' (application number: 202210398654.9) for improving elastic power system resilience, a three-layer robust collaborative planning model is provided: the first layer of model is a mathematical model for selecting an electric power system operator to determine the capacity expansion scheme and construction cost of the power transmission network and the energy storage system and the operation cost of the electric power system; the second layer model is a mathematical model of the predicted values of the damage quantities of the power transmission line and the generator under extreme natural disasters; the third-layer model is a mathematical model for selecting the operation cost and the load shedding cost brought by the power system operator when the worst scene of the extreme natural disaster is dealt with by optimizing scheduling and load shedding, and the system operation scheme meeting the requirements is obtained by optimizing and solving the model, so that the system operation cost is minimized.
Chinese patent 'a high elasticity electric network source net load storage multi-element cooperative optimization control method' (application number: 202111001591.0) discloses a high elasticity electric network source net load storage multi-element cooperative optimization control method, firstly, a multi-element cooperative optimization control model; analyzing the response proceeding characteristics of the demand side, and establishing a demand side response scheduling model to realize coordinated scheduling and effective interaction of both supply and demand sides of the power grid; analyzing the characteristic of the energy storage unit, establishing a model and analyzing the side constraint quantity of the power grid; establishing a MOPSO-based collaborative optimization control method based on the control model, and establishing a target function and multi-objective optimization model; the source network load storage collaborative optimization scheduling problem is efficiently solved through the MOPSO, and the feasibility of realizing multi-objective optimization scheduling is better ensured by setting an updating strategy of a non-inferior solution set, so that various resources are reasonably scheduled for the source network load storage, and the economical efficiency of power grid operation is improved.
Chinese patent ' comprehensive evaluation method and system for toughness of power distribution network ' (application number: 202111059384.0) ' discloses a comprehensive evaluation method and system for power distribution network, which focuses three functions of distribution network situation perception, disturbance coping and self-improvement capacity aiming at six categories of key characteristics of a tough power grid, can establish a more comprehensive and refined comprehensive evaluation system under the toughness requirement, and improves the accuracy and reliability of an evaluation result.
The related patents consider the elasticity improvement method of the power transmission network under natural disasters, consider the source network load storage multivariate cooperative optimization control, but do not consider the scene complexity of large-scale faults caused by natural disasters, and also do not consider the influence of the line fault rate on a distributed power source site selection model, the change of a topological structure of the power distribution network and other conditions, and the actual conditions of the model are different after the model is established and high-risk-low-probability events have great influence on the power distribution network.
The existing research fault rate evaluation model is mainly a historical data statistical model, the historical data is required to be detailed to support joint probability distribution of multidimensional random variables, but the disaster causing mechanism and details cannot be reflected, and the requirement of a tough power grid on long-term data macroscopically, the influence of natural disasters on a large area of the power grid and the evolution situation of a disaster situation in a short period are difficult to perform statistical analysis.
The existing literature aims at the research of a location and volume model of a distributed power supply, and most of the research focuses on the improvement of an algorithm and the uncertainty of power generation and power load of the distributed power supply. Meanwhile, the large-scale faults caused by natural disasters are rarely considered in the literature, and the influence of the line fault rate on a distributed power supply site selection model, the change of a topological structure of a power distribution network and the like are not considered.
Disclosure of Invention
Aiming at the problem of site selection optimization of distributed power supplies in power distribution network planning, the influence of system element fault rate on site selection of the distributed power supplies under the typhoon background is analyzed in the planning stage, the invention provides the site selection planning method of the distributed power supplies considering line faults and power grid toughness, and the site selection position of the distributed power supplies meeting various constraints and maximizing the power grid toughness can be obtained by the method aiming at different possible typical scenes.
The technical scheme adopted by the invention is as follows:
the distributed power supply site selection planning method considering line faults and power grid toughness comprises the following steps of:
step 1: a line fault model under the influence of typhoon load action is considered, and the influence condition of typhoon on line meteorological load is analyzed;
step 2: calculating the fault probability of the line by combining the load capacity which can be actually borne by the line;
and step 3: selecting a fault scene by using the system information entropy, determining the fault scale possibly caused by typhoon and the occurrence probability of the fault scene, and establishing a distributed power supply address model based on the line fault probability calculated in the step 2;
and 4, step 4: and solving the distributed power supply site selection planning model which gives consideration to the line fault rate and the toughness of the power grid by using a multi-target particle swarm algorithm.
In the step 1, the wind speed and the wind direction of a certain point in the influence range are determined according to a Batts wind field model, and the expression is as follows:
in formula (1): v represents the wind speed at the point, and the wind direction is clockwise tangential direction of a circle tangent line taking the center of the typhoon as a circular point; r m The distance between the maximum wind speed point and the center of the typhoon is V Rm Representing; r is the distance between the line and the center of the typhoon, x is an empirical coefficient, and the value range is [0.5]。
Determining line load, wind load N 1 Gravity load N 2 Respectively expressed as:
in formula (2): d is the outer diameter of the lead; theta is an included angle between the wind direction and the line;
N 2 ＝m l g (3)
in formula (3): m is l Is the weight of the wire; g is the gravitational acceleration.
The tension of the highest suspension point of the wire has the following stress expression:
in the formula (4), T represents the horizontal stress of the lowest point of the arc sag of the wire; beta represents the included angle between the connecting line of the suspension points at the two ends of the lead and the horizontal plane; l gv The horizontal distance between the lowest point of the sag and the highest point of the wire suspension; n is the comprehensive load borne by the lead;
the stress of the wire section at the suspension point is:
in formula (5), S l Representing the cross-sectional area of the wire; t is g Representing the tension at the wire suspension point.
In the step 2, whether the line breaks or not depends on the stress borne by the section of the line and the strength of the material of the lead. Therefore, a component function can be set, the line fault rate is described according to the load effect and the material strength of the line component, and when the component function is larger than 0, the component is in a reliable operation state, and the expression is as follows:
p r ＝P{(R-S)>0} (6)
in the formula (6), S is the stress on the element; and R is the ultimate strength of the element material.
In the step 3, the ultimate strength of the wire material is a random variable, and according to the regulation of the unified design standard for reliability of building structures, the probability distribution of the ultimate strength of the wire material can be represented by a normal distribution function, so that the probability of unreliable operation of the line is obtained:
in the formula (7), mu and delta are respectively the mean value and the standard deviation of the tensile strength of the wire; σ represents the magnitude of the stress to which the wire is subjected.
According to the probability of unreliable operation of the line, the system entropy value under the scene is obtained:
in the formula (8), omega B Representing a set of distribution lines; p is a radical of formula i，t Is the failure rate of element i at time t; z is a radical of formula i，t Indicating whether element i fails at time t; omega i Representing the component value weight. And selecting a scene with the system entropy value in a proper range as a typical scene of the distributed power supply addressing model for optimization solution.
In the step 3, due to the characteristics of large influence effect, large occurrence probability and the like of the typical scene selected by the system entropy, the fault scale and the occurrence probability possibly caused by typhoon need to be determined, and the typical scene loaded with the characteristics needs to be screened out. The statistical analysis of the typical scene shows the system fault number by the system fault line number, and takes the proportion of the same system fault number to the total scene number as the occurrence probability as the basis for selecting the typical fault scene.
In step 3, a distributed power source addressing model considering the line fault rate is established, which specifically includes:
the objective function of the distributed power supply addressing model considering the failure rate of the element is expressed as follows:
in the formula (9), y i A decision variable of 0-1 is used for indicating whether the node i builds the distributed power supply or not, and if the node i builds the distributed power supply, y i Taking 1, otherwise, taking 0; s and L are respectively a scene set and a node set; omega i Is the weight coefficient of the load i;
for node i to restore state in scene s, if the load is powered on->
Otherwise it is 0.
In the formula (10), P DG Representing the total power supply active power of the distributed power supply; c DG Represents the total cost of installing the distributed power supply; eta i Representing the annual average cost coefficient of the ith distributed power supply; c IC Representing the unit capacity cost of the distributed power supply; c MT Representing the maintenance cost per capacity of the distributed power supply.
Normalizing two target functions, wherein the reference value of N is the function value when all loads are in a power supply state, and C DG Is the absolute value of the difference between the maximum and minimum values of the expected investment, and the expression is as follows:
in the formula (11), N' represents a reference value of the objective function N; s is a fault scene set; l is a system node set; omega i Is the weight coefficient of the load i.
C 'of formula (12)' DG Representing an objective function C DG A reference value of (d); c DGmax And C DGmin Respectively representing the upper limit and the lower limit of the total cost of the distributed power supply for planned investment.
The distributed power supply site selection model constraint condition comprises the following steps:
restraining one: the number of distributed power supplies is constrained by the formula:
wherein L represents the set of all distributed power source candidate nodes; p represents the maximum number of distributed power sources that can be built.
And (2) constraining: the power flow constraint has the formula as follows:
in the formulae (14) and (15), P i 、Q i Respectively representing active power and reactive power injected into the node i; v i 、V j Respectively representing node voltages of nodes i and j; g ij 、B ij Respectively the real part and the imaginary part of the nodal admittance matrix.
And (3) constraining: the distributed power supply outputs active power and reactive power constraints, and the formula is as follows:
P DGmin ≤P DGi(t) ≤P DGmax (16)
Q DGmin ≤Q DGi(t) ≤Q DGmax (17)
in the formulae (16) and (17), P DGi(t) For the active power of distributed power supply at node i at time t, P DGmax 、P DGmin Respectively outputting the upper limit and the lower limit of active power of the distributed power supply; q DGmax 、Q DGmin The upper limit and the lower limit of the active power output by the distributed power supply are respectively.
And (4) constraint: node voltage constraint, whose formula is:
V imin ≤V i(t) ≤V imax (18)
in the equation (18), the node voltage constraint represents all the node voltages V i(t) Must be maintained within a specific range, and the node voltage amplitude is set to [0.9,1.1%]In between.
And (5) constraint: a line transmission limit constraint, whose formula is:
S i ≤S imax (19)
in the formula (19), S i Representing the transmission power of the line; s imax Representing the limit of the amount of line transmission power.
In the step 4, the optimization solution of the distributed power source address model is performed by using a multi-target particle swarm algorithm based on the improved population updating and fitness strategy, and the method specifically comprises the following steps:
s4.1: by taking a distributed power supply installation node as the position of a particle, a particle initial position x with the dimension of 90 × 4 and an element value interval of [0, 33] and a velocity v with the dimension of 90 × 4 and each element value of 0 or 1 are randomly generated within a constraint condition range, and the particle population size pop =90 and the maximum iteration number gen =100 are set.
S4.2: and calculating the network loss by adopting a forward-backward substitution load flow calculation method, and calculating the current particle adaptive value.
The iterative formula of the step (n + 1) of the power distribution network power flow forward-backward flow replacement algorithm is as follows:
the forward calculation formula of the node i is as follows:
in the formula: n is the number of iterations; r is ki Is branch k i The impedance of (a);
and &>
Is branch k i Power loss; />
And
for flowing through branch k i Of the power of (c). P Di And Q Di The load at node i disregarding the load voltage characteristic.
The back-stepping calculation formula of the voltage of the node i is as follows:
in the formula
Is branch k i Current; />
Is the conjugate of the complex voltage of node k; (r) ki +jx ki ) Is branch k i The impedance of (c).
On the basis of node layering, the iterative process of the power distribution network forward-backward flow-replacing algorithm is as follows:
(1) Initialization: given distribution feeder root node voltage V r And assigning V to the voltages of other nodes (0) ，n＝0；
(2) Forward calculation: calculating voltage drop from the last layer to the father node according to the given voltage and power of the child node, and then calculating the power distribution of each branch by using formulas (1) and (2);
(3) And (3) performing backward substitution calculation, starting from the root node, performing backward substitution calculation layer by layer on the child nodes according to the load power of the father node by using formulas (3) and (4), and solving the voltage distribution V of the nodes (n+1) ；
Network loss S i ＝U i *I i
In the formula of U i And I i Are respectively node voltage phasor U i And node injection current phasor I i Conjugation of (2)
In a typical sceneTotal cost of construction and operation C DG And the weighted load power supply N (objective function) is the current particle adaptive value.
S4.3: performing iterative optimization: and updating the particle speed v and the position x according to an improved iteration formula for the calculated pbest and gbest values.
pbest represents the historical optimality of individuals, each individual is changed continuously in the evolution process, if more excellent individuals appear in the continuous evolution process, pbest is updated, otherwise, pbest = N '-C' DG 。
After each evolution of all the individuals, an optimal individual, namely gbest, needs to be selected. In multisubjects, non-dominant individuals, i.e., individuals not dominated by any other individual, are necessarily the best shown in the current population, whereas non-dominant individuals are typically more than one. Therefore, all non-dominant individuals are first picked and put into a set gbest.
Improved iterative formula:
ω (t) is the inertial weight; c. C 1t 、c 2t Is a learning factor; r is 1 、r 2 Is [0,1 ]]A random number in between. v. of ki And x ki Respectively representing the particle speed of the particle i at the kth iteration and the current position of the particle;
and &>
Respectively representing the individual optimal position and the global optimal position of the particle i at the k-1 th iteration.
S4.4: recalculating the adaptive value, and deleting the inferior solution with low fitness in the new population to ensure that the number of individuals of the new population does not exceed the maximum capacity of the new population; obtaining a current global optimal solution gbest according to the result; checking whether the maximum iteration number is reached, and if not, returning to S4.3 to continue the calculation.
According to the condition that the gbest in S4.3 is a set of historical optimal solutions pbest, wherein the global optimal solution can be used as the gbest = N '-C' DG And (6) obtaining.
The invention discloses a distributed power supply site selection planning method considering line faults and power grid toughness, which has the following technical effects:
1) According to the method, firstly, a fault rate model aiming at a typhoon disaster-causing mechanism is established, so that the cause-effect inertia of the power grid fault caused by typhoon can be clearly reflected, and various sensitivity analyses can be realized; identifying parameters according to the effective historical data; and then, evaluating the fault rate of the line in real time according to the actual evolution information of the external environment.
2) In order to research the line fault rate under the typhoon disaster, a fault rate mechanism model based on the typhoon disaster characteristics and the line element material characteristics is provided, and the typhoon disaster has the characteristics of wide influence range, long duration and the like. The invention provides a batts wind field model for simulating wind power and wind direction in a typhoon passing process, and the influence of the failure rate of system elements on the site selection of a distributed power supply under the typhoon background is considered.
3) The invention provides a method for selecting fault scenes by utilizing system information entropy through calculating the line fault probability, determining the fault scale possibly caused by typhoon and the occurrence probability thereof, and providing a distributed power supply site selection planning strategy based on the line fault rate.
4) The invention utilizes multi-target particle swarm algorithm, and has the advantages that: the method has the advantages that the method does not depend on problem information, real numbers are adopted for solving, the algorithm has strong universality, the principle is simple, the realization is easy, the parameters needing to be adjusted are few, the convergence speed is high, the requirement on the memory of a computer is not high, and the global optimum value can be found more easily due to the leap property of the particle swarm algorithm and cannot be trapped in local optimum.
Drawings
FIG. 1 is a schematic view of a wire suspension point
Fig. 2 is a structural diagram of an IEEE-33 node system according to the present invention.
Fig. 3 is a time-varying fault rate diagram of the line 1 and the line 16 according to the present invention.
FIG. 4 is an entropy probability distribution diagram according to the present invention
FIG. 5 is a non-cracking set diagram of a distributed power addressing model according to the present invention
Fig. 6 is a system performance diagram under various scenarios according to the present invention.
Fig. 7 (a) is a power distribution network division structure diagram of a scene 2 addressing scheme according to the present invention;
fig. 7 (b) is a power distribution network division structure diagram of the site selection scheme of scene 3 according to the present invention;
in fig. 7 (a) and 7 (b), the expression of each marker is as follows:
a third order load;
secondary load; />
A first-level load;
Detailed Description
Aiming at the defects of the prior art in the background art and combining the actual situation that the power grid in part of coastal areas of China is affected by typhoon as a main disaster all the year round in recent years, the invention provides a distributed power supply site selection planning method considering line faults and power grid toughness. On the basis, a fault scene is selected by using the system information entropy, the fault scale and the occurrence probability of the fault scene possibly caused by typhoon are determined, a distributed power supply site selection planning strategy is proposed based on the line fault rate, and a distributed power supply site selection planning model which gives consideration to the line fault rate and the power grid toughness is solved by using a multi-target particle swarm optimization. And finally, in order to prove the effectiveness of the distributed power supply constant-volume site selection planning based on the fault rate of the line element, a proper toughness evaluation index is provided by combining the load recovery quantity and the load weight, and the toughness evaluation index is compared and analyzed with the traditional power grid toughness improvement method.
FIG. 1 is a schematic diagram of a suspension point of a wire, the stress at the highest suspension point is obtained by applying wind load and gravity load to the wire, and whether the wire breaks can be determined by comparing the stress applied to the cross section of the wire and the material strength of the wire. The method comprises the following steps: the present invention employs an algorithm for improving the IEEE-33 node system, as shown in fig. 2, in which the geographical orientation of each feeder is consistent with that shown en route. Wherein, the total load demand of the node system is 3715kW.
Step two: taking lines 1 and 16 as examples, the relationship curve between the failure rate and the wind speed when a typhoon passes through is shown in fig. 3, and the starting time is the typhoon landing time. It is found that the entropy of the system information is distributed between [4, 18], as shown in fig. 4, and therefore, the selection of the typical fault scenario will be set in this interval.
Step three: and regarding the selected scene in the second step as a basic example, modeling and solving the example by using the distributed power supply address selection model provided by the second step to obtain a non-inferior solution set of the distributed power supply address selection model, as shown in fig. 5. Wherein the maximum value of the normalized objective function is the optimal solution.
Step four: fig. 6 depicts the failure recovery process in four cases:
scene 1: the method is characterized in that the method does not contain the operation condition of the distribution network of the distributed power supply, line fault repair can not be carried out under the typhoon condition, and the line fault repair can be carried out sequentially according to the element fault sequence after the typhoon passes through the environment.
Scene 2: and considering the recovery process of the power distribution network containing the distributed power supply under the line fault rate, the access nodes of the distributed power supply are 6, 13, 18, 19 and 33, and the power supply of the load is ensured.
Scene 3: a restoration process for a power distribution network comprising distributed power sources, the access of which is located at nodes 6, 16, 20, 24, 30.
Scene 4: the fault recovery process of the distributed power distribution network is not included, and the recovery process of the fault element is selected according to the minimized load loss, namely, the fault line is repaired according to the sequence of the line 9, the line 19, the line 30, the line 14 and the line 16.
Wherein, the system performance SP represents the ratio of the total power of the system after being affected by the disaster to the total power in normal operation.
Through analyzing the load loss area under each fault scene, a corresponding toughness evaluation result can be obtained, as shown in table 1:
TABLE 1 evaluation results of toughness under different scenes
When the distributed power supply addresses the failure rate of the line element, the distributed power supply access node can supply power to the nodes 10, 11, 12, 13 and 14 after the failure of the distributed power supply at 7h S9 as shown in FIG. 7 (a) compared with the scenario 3.
If the distributed power supply is connected to the power grid under the condition that the fault rate of the line element is not considered, the system has a fault at the S19 position of 9.25h, the distributed power supply supplies power for the nodes 19, 20, 21 and 22, and the toughness of the power distribution network which is not connected to the distributed power supply is improved. The distribution network structure is shown in fig. 7 (b). Therefore, the toughness of the power grid can be effectively improved by considering the distributed power source location strategy after the line fault rate is considered.
After the failed component repair strategy that minimizes the load-miss area is applied, the system provides 15% more power after the first component repair than the system in scenario 1, compared to the load-loss case of scenario 1, which is improved. Therefore, the toughness of the power grid can be improved to a certain extent by optimizing the fault element repairing strategy.
As can be seen from the data in table 1, for the original power distribution network, the load power supply amount is only 75.9% of the normal level after being affected by typhoon, after the post-disaster repair strategy is improved, the toughness of the power grid is slightly improved to 82.4%, the toughness of the power grid can be effectively improved to 87.0% after the distributed power supply is added, and the toughness of the power grid can be further improved to 91.9% by the site selection optimization strategy after the fault rate of the line is considered.
Claims (7)
1. The distributed power supply site selection planning method considering the line fault and the toughness of the power grid is characterized by comprising the following steps of:
step 1: a line fault model under the influence of typhoon load action is considered, and the influence condition of typhoon on line meteorological load is analyzed;
step 2: calculating the fault probability of the line by combining the load capacity which can be actually borne by the line;
and step 3: selecting a fault scene by using the system information entropy, determining the fault scale possibly caused by typhoon and the occurrence probability of the fault scene, and establishing a distributed power supply address model based on the line fault probability calculated in the step 2;
and 4, step 4: and solving the distributed power supply site selection planning model which gives consideration to the line fault rate and the toughness of the power grid by using a multi-target particle swarm algorithm.
2. The distributed power supply site selection planning method considering line faults and power grid toughness as claimed in claim 1, wherein: in the step 1, the wind speed and the wind direction of a certain point in the influence range are determined according to a Batts wind field model, and the expression is as follows:
in formula (1): v represents the wind speed at the point, and the wind direction is clockwise tangential direction of a circle tangent line taking the center of the typhoon as a circular point; r is m The distance between the maximum wind speed point and the center of the typhoon is V Rm Represents; r is the distance between the line and the center of the typhoon; x is an empirical coefficient;
determining line load, wind load N 1 Gravity load N 2 Respectively expressed as:
in the formula (2): d is the outer diameter of the lead; theta is an included angle between the wind direction and the line;
N 2 ＝m l g (3)
in formula (3): m is a unit of l Is the weight of the wire; g is the acceleration of gravity;
the tension of the highest suspension point of the wire has the following stress expression:
in the formula (4), T represents the horizontal stress of the lowest point of the sag of the lead; beta represents the included angle between the connecting line of the suspension points at the two ends of the lead and the horizontal plane; l gv The horizontal distance between the lowest point of the sag and the highest point of the wire suspension; n is the comprehensive load borne by the lead;
the stress of the wire section at the suspension point is:
in formula (5), S l Representing the cross-sectional area of the wire; t is g Representing the tension at the wire suspension point.
3. The distributed power supply site selection planning method considering line faults and power grid toughness as claimed in claim 1, wherein: in the step 2, an element function is set, the line fault rate is described according to the load effect and the material strength of the line element, when the element function is greater than 0, the element is in a reliable operation state, and the expression is as follows:
p r ＝P{(R-S)>0} (6)
in the formula (6), S is the stress applied to the device; and R is the ultimate strength of the element material.
4. The distributed power supply siting planning method considering the line fault and the power grid toughness as claimed in claim 1, wherein in the step 3, the ultimate strength of the wire material is a random variable, and the probability distribution of the ultimate strength of the wire material can be represented by a normal distribution function, so as to obtain the probability of unreliable operation of the line:
in the formula (7), mu and delta are respectively the mean value and the standard deviation of the tensile strength of the wire; sigma represents the stress applied to the lead;
according to the probability of unreliable operation of the line, the system entropy value under the scene is obtained:
in the formula (8), omega B Representing a distribution line set; p is a radical of i,t Is the failure rate of element i at time t; z is a radical of formula i,t Indicating whether element i fails at time t; omega i Representing the component value weight.
5. The distributed power supply site selection planning method considering the line fault and the power grid toughness as claimed in claim 4, wherein in the step 3, a distributed power supply site selection model considering the line fault rate is established, and the method specifically comprises the following steps:
the distributed power supply addressing model objective function considering the element failure rate is expressed as follows:
in the formula (9), y i Is a 0-1 decision variable, tableIndicating whether the node i builds the distributed power supply or not, and if the node i builds the distributed power supply, y i Taking 1, and taking 0 otherwise; s and L are respectively a scene set and a node set; omega i A weight coefficient for load i;
for node i to restore state in scene s, if the load is powered on>
Otherwise, the value is 0;
in the formula (10), P DG Representing the total power supply active power of the distributed power supply; c DG Represents the total cost of installing the distributed power supply; eta i Representing the annual average cost coefficient of the ith distributed power supply; c IC Representing a unit capacity cost of the distributed power supply; c MT Represents a unit capacity maintenance cost of the distributed power supply;
normalizing two target functions, wherein the reference value of N is the function value when all loads are in a power supply state, and C DG Is the absolute value of the difference between the maximum and minimum values of the expected investment, and the expression is as follows:
in the formula (11), N' represents a reference value of the objective function N; s is a fault scene set; l is a system node set; omega i Is the weight coefficient of the load i;
c 'of formula (12)' DG Representing an objective function C DG A reference value of (d); c DGmax And C DGmin Respectively representing the upper limit and the lower limit of the total cost of the distributed power supply for planned investment.
6. The distributed power supply site selection planning method considering the line fault and the power grid toughness as claimed in claim 5, wherein the distributed power supply site selection model constraint conditions comprise:
restraining one: the number of distributed power supplies is constrained by the formula:
wherein L represents the set of all distributed power source candidate nodes; p represents the maximum number of distributed power sources which can be built;
and (2) constraining: the power flow constraint has the formula as follows:
in the formulae (14) and (15), P i 、Q i Respectively representing active power and reactive power injected into the node i; v i 、V j Respectively representing node voltages of nodes i and j; g ij 、B ij Respectively the real part and the imaginary part of the node admittance matrix;
and (3) constraining: the distributed power supply outputs active power and reactive power constraints, and the formula is as follows:
P DGmin ≤P DGi(t) ≤P DGmax (16)
Q DGmin ≤Q DGi(t) ≤Q DGmax (17)
in the formulae (16) and (17), P DGi(t) For distributed power at node i at time tActive power, P DGmax 、P DGmin Respectively outputting the upper limit and the lower limit of active power of the distributed power supply; q DGmax 、Q DGmin Respectively outputting the upper limit and the lower limit of active power of the distributed power supply;
and (4) constraining: node voltage constraint, whose formula is:
V imin ≤V i(t) ≤V imax (18)
in equation (18), the node voltage constraint represents all the node voltages V i(t) Must be maintained within a specific range, and the node voltage amplitude is set to 0.9,1.1]In the middle of;
and (5) constraint: a line transmission limit constraint, which is formulated as:
S i ≤S imax (19)
in the formula (19), S i Representing the transmission power of the line; s imax Representing the limit of the amount of line transmission power.
7. The distributed power supply site selection planning method considering the line fault and the power grid toughness as claimed in claim 1, wherein in the step 4, a multi-objective particle swarm algorithm based on improved population updating and fitness strategy is adopted to carry out optimization solution on the distributed power supply site selection model, and the method specifically comprises the following steps:
s4.1: randomly generating the initial position and the speed of a particle swarm within a constraint condition range by taking a distributed power supply installation node as the position of a particle, and setting the size of the particle swarm and the maximum iteration number;
s4.2: calculating network loss by adopting a forward-backward substitution load flow calculation method, and calculating a current particle adaptive value;
s4.3: performing iterative optimization: updating the particle velocity v and the position x of the calculated pbest and gbest values according to an improved iteration formula;
s4.4: recalculating the adaptive value, and deleting the inferior solution with low fitness in the new population to ensure that the number of individuals of the new population does not exceed the maximum capacity of the new population; according to the result, obtaining the current global optimal solution gbest; checking whether the maximum iteration number is reached, and if not, returning to S4.3 to continue the calculation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211268026.5A CN115906610A (en) | 2022-10-17 | 2022-10-17 | Distributed power supply site selection planning method considering line faults and power grid toughness |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211268026.5A CN115906610A (en) | 2022-10-17 | 2022-10-17 | Distributed power supply site selection planning method considering line faults and power grid toughness |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115906610A true CN115906610A (en) | 2023-04-04 |
Family
ID=86477367
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211268026.5A Pending CN115906610A (en) | 2022-10-17 | 2022-10-17 | Distributed power supply site selection planning method considering line faults and power grid toughness |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115906610A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117691598A (en) * | 2024-02-04 | 2024-03-12 | 华北电力大学 | Electric heating energy network toughness assessment method and system in extreme weather |
-
2022
- 2022-10-17 CN CN202211268026.5A patent/CN115906610A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117691598A (en) * | 2024-02-04 | 2024-03-12 | 华北电力大学 | Electric heating energy network toughness assessment method and system in extreme weather |
| CN117691598B (en) * | 2024-02-04 | 2024-04-12 | 华北电力大学 | Electric heating energy network toughness assessment method and system in extreme weather |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112217202B (en) | Distributed new energy, energy storage and power distribution network planning method considering flexibility investment | |
| Ding et al. | Long-term reserve expansion of power systems with high wind power penetration using universal generating function methods | |
| Zhang et al. | Spatial–temporal reliability and damage assessment of transmission networks under hurricanes | |
| EP2537222A2 (en) | Energy generating system and control thereof | |
| CN115310378A (en) | Power grid toughness evaluation and differentiation planning method under extreme typhoon disaster | |
| CN104866919A (en) | Multi-target planning method for power grid of wind farms based on improved NSGA-II | |
| CN111313420B (en) | Power distribution network elastic lifting method and system based on multi-energy coordination in extreme weather | |
| Al Karim et al. | A machine learning based optimized energy dispatching scheme for restoring a hybrid microgrid | |
| CN115906610A (en) | Distributed power supply site selection planning method considering line faults and power grid toughness | |
| Sameie et al. | A simulation-based Data Envelopment Analysis (DEA) model to evaluate wind plants locations | |
| CN115879833A (en) | Double-layer power distribution network toughness evaluation method and system considering disaster response and recovery | |
| CN113239512A (en) | Toughness-considered screening method and system for AC/DC power distribution network planning scheme | |
| CN116014790A (en) | Elasticity evaluation method for power distribution network containing distributed energy sources | |
| CN116187165A (en) | Power grid elasticity improving method based on improved particle swarm optimization | |
| CN110188998B (en) | Evaluation method for time sequence construction reliability of wind turbine generator and energy storage of power distribution network | |
| CN115809836B (en) | Method for planning toughness of power distribution network by considering distributed energy storage emergency power supply capacity | |
| CN111027855A (en) | Power system risk control method considering power transmission line meteorological disaster fault probability | |
| CN113408132B (en) | Reliability evaluation method for regional autonomous power grid considering stochastic fault scene | |
| CN114268124B (en) | Distributed power supply credible capacity assessment method based on equal power supply reliability | |
| CN112152210B (en) | Optimization method and device of power distribution network system | |
| Hou et al. | Cable connection scheme optimization for offshore wind farm considering wake effect | |
| Henry et al. | New trends for the assessment of power system security under uncertainty | |
| CN114266508A (en) | Correction algorithm for power distribution network power supply reliability evaluation index weight | |
| CN110518633B (en) | Power grid node new energy admission capacity obtaining method considering line capacity adequacy | |
| CN112182952A (en) | Multi-objective optimization scheduling method for improving elasticity of power system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |