CN108062633A - A kind of power distribution network methods of risk assessment under distributed generation resource Thief zone - Google Patents

Abstract

The invention discloses a distribution network risk assessment method under the high penetration of distributed power sources, which considers the characteristics of the access of large-scale distributed power sources, and performs risk assessment on the distribution network. The steps are: 1) Consider the output of distributed power sources 2) Establish a line outage model based on historical line fault information; 3) Consider the probability and consequences of distribution network operation risks under the high penetration of distributed Risk assessment index system for operational reliability and adequacy; 4) Using Monte Carlo simulation-particle swarm-breadth search comprehensive algorithm to solve the distribution network risk assessment model under the high penetration of distributed power generation, and determine the risk assessment index value, Complete the distribution network risk assessment under the high penetration of distributed power.

Description

A distribution network risk assessment method under the high penetration of distributed generation

technical field

The invention belongs to the field of distribution network risk assessment, and in particular relates to a distribution network risk assessment method under the high penetration of distributed power sources.

Background technique

In recent years, with the increasingly severe primary energy situation and the continuous growth of electricity demand, countries around the world are actively developing and utilizing alternative new energy sources. More and more attention has been paid to the access of distributed energy sources, mainly wind power and photovoltaic power generation, to the distribution network, and it has become a research hotspot at home and abroad. The access of large-scale distributed power supply has the advantages of energy saving and emission reduction, environmental pollution reduction, line loss reduction, power quality improvement and power supply reliability improvement, etc., but at the same time, its power output is greatly affected by the climate environment, which has obvious intermittent, Randomness and volatility will affect the normal operation of the power system.

Risk assessment of distribution network with distributed generation can effectively improve the safety and power supply reliability of distribution network with distributed generation, and avoid major safety problems. At present, there are few researches on the risk assessment of distribution network with distributed energy at home and abroad, and there are certain difficulties. First of all, due to the large number of components in the distribution network containing distributed energy, risk assessment involves a large number of component parameters and operating data, so the workload of grid equivalent processing and calculation and analysis is relatively large. In addition, the calculation of the risk index system is closely related to the power flow state of the distribution network, so whether the state of the power grid can reflect the operating characteristics has a greater impact on the risk assessment results. At present, the research on the risk assessment of distribution network with distributed energy resources lacks consideration of the influence of the uncertainty of distributed energy generation and the high penetration rate on the safe operation of distribution network. Aiming at the above problems, this paper proposes a distribution network risk assessment method under the high penetration of distributed generation.

Contents of the invention

The purpose of the present invention is to consider the influence of the uncertainty of distributed energy generation and the high penetration rate on the safe operation of the distribution network, and carry out risk assessment on the distribution network.

In order to solve the above technical problems, the present invention provides a distribution network risk assessment method under the high penetration of distributed power sources, including the following steps:

Step 1. Consider the fluctuation and intermittency of distributed power output, and establish a distributed power output model;

Step 2, establishing a line outage model according to line history fault information;

Step 3. Consider the probability and consequences of distribution network operation risk under the high penetration of distributed power generation, and establish a risk assessment index system that considers the reliability and adequacy of distribution network operation;

Step 4. Use Monte Carlo simulation-particle swarm-breadth search comprehensive algorithm to solve the distribution network risk assessment model under the high penetration of distributed generation, determine the risk assessment index value, and complete the distribution network under the high penetration of distributed generation risk assessment.

Further, in step 1, the distributed power generation output model includes a wind power generation output model and a photovoltaic power generation output model.

Further, in step 1, first establish a wind speed model and a sunshine model, and then establish a wind power generation output model and a photovoltaic power generation output model according to the relationship between wind speed and fan output, sunshine and photovoltaic power generation output.

Further, in step 1, the wind speed model adopts the Weibull distribution model, the sunshine model adopts the Beta distribution model, and the parameters of the models are obtained by calculating historical wind speed information and historical sunshine information.

Further, in step 2, the line outage model adopts a two-state model of an element, and the element has a running state and a fault state.

Further, in step 3, the reliability index includes the load point reliability index and the system reliability index, the load point reliability index includes the average failure rate, the average power outage duration and the annual average power outage duration, and the system reliability index includes the system average power outage Frequency, system average power outage duration and average power supply availability rate, the adequacy index includes the severity of load loss probability, the severity of power shortage, and the degree of loss of important loads.

Further, in Step 4, the solution of the distribution network risk assessment model under the high penetration of distributed generation includes the following steps:

(1) Basic data input, the basic data includes line parameters, power supply parameters, load parameters and distributed power supply parameters, etc.;

(2) Set the total simulation time, and initialize the risk assessment indicators and simulation parameters to 0;

(3) Generate a random number between (0,1) for each component, and calculate the running time and repair time of each component;

(4) Generate a random number between (0,1) corresponding to the distributed power supply, and calculate the wind speed and sunshine intensity, and then calculate the output of wind power generation and photovoltaic power generation;

(5) Select the component i with the smallest running time as the faulty component for each sampling, and accumulate its running time TTF(i) and repair time TTR(i) to the simulation time t, that is, t= TTF(i)+ TTR( i); Here, the value of i is a natural number other than 0;

(6) Use the breadth search algorithm to analyze the connectivity of the distribution network, and obtain the initial connectivity matrix A after the fault;

(7) Disconnect the faulty branch and use the particle swarm algorithm to divide the distribution network into islands;

(8) Use the breadth search algorithm to analyze the connectivity of the distribution network again, and obtain the connectivity matrix B after islanding;

(9) Compare A in step (6) and B in step (8), and classify the load points. If both A and B are connected to the power supply, there will be no power failure; if A is connected and B is not connected, the power will be cut off. , the outage time is the repair time TTR(i) of component i, and the simulation parameters are calculated according to the classification; the A is the initial connectivity matrix A after the fault, and B is the connectivity matrix after the islanding;

(10) Determine whether the simulation time t is greater than the total simulation time, if the simulation time t is greater than or equal to the total simulation time, then go to step (11); if the simulation time t is less than the total simulation time, go to step (3);

(11) Calculate the risk assessment index value according to the simulation parameters.

Further, in Step 4, the connectivity analysis steps of the breadth search algorithm are as follows:

(L1) Input the start point and end point, and add the start point to the set Q;

(L2) Take the node Vn from the set Q described in step (L1) in turn, and judge whether the set Q is empty at this time. If the set Q is empty, then output the starting point and the end point are not connected; if the set Q is not empty, then Go to the next step (L3);

(L3) Find out all the adjacent nodes Vw in the node Vn taken out in step (L2) that are not included in the set Q, judge whether the adjacent node Vw has an end point, if the adjacent node Vw has an end point, output the starting point It is connected with the end point; if the adjacent node Vw has no end point, add the adjacent node Vw to the set Q, and turn to step (L2).

Further, in Step 4, the steps for the particle swarm optimization algorithm to divide the distribution network into islands are as follows:

(G1) Initially generate particles randomly, initialize the number of populations, and initialize the number of iterations at the same time;

(G2) Connectivity analysis using breadth search algorithm;

(G3) According to the connectivity analysis results of step (G2), calculate the fitness value of the distribution network. If there is an island or the power constraint is not satisfied, the fitness value is set to negative infinity;

(G4) After updating the group optimal and individual optimal, update the velocity and position of the particle;

(G5) Judging whether the number of iterations set in step (G1) has been reached, if yes, output the optimal solution, if not, go to step (G2).

Compared with the prior art, the present invention has the following significant advantages: (1) The present invention considers the impact of the uncertainty of distributed energy generation and the high penetration rate on the safe operation of the distribution network, and the evaluation results are more accurate and effective; (2) ) The present invention uses the breadth search algorithm to analyze the connectivity of the distribution network after the fault and island division, and can quickly judge the connectivity of the distribution network, and then calculate the risk index; (3) The present invention uses the particle swarm algorithm to carry out Island division, the convergence speed is fast, and the rapid division of islands can be realized.

Description of drawings

The present invention will be described in further detail below in conjunction with accompanying drawing:

Fig. 1 is the flow chart of the distribution network risk assessment method under the distributed power generation high penetration of the present invention;

Fig. 2 is a flow chart of the algorithm for solving the distribution network risk assessment model under the high penetration of distributed power sources in the present invention;

Fig. 3 is the flow chart of connectivity analysis algorithm of breadth search algorithm among the present invention;

Fig. 4 is a flow chart of dividing the distribution network into islands by the particle swarm optimization algorithm in the present invention.

Detailed ways

The present invention provides a distribution network risk assessment method under the high penetration of distributed power sources. The flow chart of the method is shown in Figure 1, which includes the following steps:

Step 1. Consider the fluctuation and intermittency of distributed power output, and establish a distributed power output model;

Step 2, establishing a line outage model according to line history fault information;

Step 3. Consider the probability and consequences of distribution network operation risk under the high penetration of distributed power generation, and establish a risk assessment index system that considers the reliability and adequacy of distribution network operation;

Step 4. Use Monte Carlo simulation-particle swarm-breadth search comprehensive algorithm to solve the distribution network risk assessment model including distributed power output model and line outage model under the high penetration of distributed power, and determine the risk assessment index value. Complete the distribution network risk assessment under the high penetration of distributed power.

Further, in step 1, the distributed power generation output model includes a wind power generation output model and a photovoltaic power generation output model.

Further, in step 1, first establish a wind speed model and a sunshine model, and then establish a wind power generation output model and a photovoltaic power generation output model according to the relationship between wind speed and wind power generation output, sunshine and photovoltaic power generation output.

Further, in step 1, the wind speed model adopts the Weibull distribution model, the sunshine model adopts the Beta distribution model, and the parameters of the models are obtained by calculating historical wind speed information and historical sunshine information.

The Weibull wind speed distribution model is as follows:

(1)

In the formula, and represent the shape parameter and scale parameter in the Weibull distribution, respectively, is the wind speed.

and The average wind speed can be obtained from historical wind speed statistics and wind speed sample standard deviation Obtained, the calculation formula is as follows:

(2)

(3)

(4)

(5)

In the above formula: is the first of the historical wind speed statistics sample value; is the sample size.

Furthermore, according to the following relationship between wind speed and wind power output, the wind power output model shown in the following formula can be established:

(6)

In the formula, represents the wind speed, Indicates the output of wind power generation, , and Respectively represent cut-in wind speed, cut-out wind speed and rated wind speed, Indicates the rated output of wind power generation.

The Beta sunshine distribution model is as follows:

(7)

In the formula: is the sunlight intensity, is the maximum sunshine intensity value in the statistical time period, and the unit is W/m ² ; a and b are the shape parameters of the Beta distribution, which can be determined by the average value of sunshine intensity in a certain period of time and variance Calculated, the calculation formula is as follows:

(8)

(9)

Furthermore, according to the following relationship between sunshine and photovoltaic power generation output, the photovoltaic power generation output model shown in the following formula can be established:

(10)

In the formula: r represents the light intensity, Indicates the output of photovoltaic power generation, , Respectively represent the light-receiving area and photoelectric conversion efficiency.

Further, in step 2, the line outage model adopts the two-state model of the element, that is, the element only has the running state and the fault state.

The time when the component is in the running state is the average continuous working time TTF, and the time when the component is in the fault state is the average repair time TTR. The cumulative distribution functions of the average continuous working time TTF and the average repair time TTR are respectively:

(11)

(12)

In the formula, is a random number between (0,1), is the component failure rate, is the repair rate of the component.

From the above, it can be seen that the average continuous working time TTF and average repair time TTR of the components can be calculated by the following formulas respectively, so that the operating state sequence of the components during the simulation process can be obtained, which is the line outage model.

(13)

(14)

Further, in step 3, the reliability index includes the load point reliability index and the system reliability index, and the load point reliability evaluation index includes:

average failure rate

it represents a load point Number of outages during a year due to failure of distribution network components.

Average annual power outage duration

It represents the total number of times that load point i is out of power within a year. Calculated as follows:

(15)

In the formula, Indicates the duration of the jth power outage at load point i, is the average failure rate of load point i.

Average outage duration

It represents the average time required for each power outage at load point i from the beginning to the restoration of power supply. Calculated as follows:

(16)

In the formula, is the annual average power outage duration of load point i, is the average failure rate of load point i.

System reliability indicators include:

① System average power outage frequency index

It represents the average number of power outages per user in the system within a specified time.

(17)

In the formula: is the number of users at load point j, is the average failure rate of load point j.

System average outage duration indicator

It represents the average outage duration for users operating on the system over a period of one year.

(18)

In the formula, Indicates the annual average power outage duration of load point j, is the number of users at load point j.

Average Power Supply Availability Index

It represents the ratio of the number of hours a user is supplied with electricity to the total hours of electricity required by the user in a year.

(19)

In the formula, Indicates the annual average power outage duration of load point j, is the number of users at load point j.

Adequacy indicators include:

Loss of Load Probability Severity

(20)

In the formula: M is the total sampling times of the system state; is the load loss times of the system; is the allowable load loss probability limit.

Low battery severity

(twenty one)

In the formula: In order to cause the load to lose power and, is the allowable power shortage limit; M is the total number of sampling times of the system state.

Important load loss degree

(twenty two)

In the formula: is the sum of important load losses, n is the total number of important loads, , is the load weight and load capacity of important loads.

Further, in Step 4, the Monte Carlo simulation-particle swarm-breadth search comprehensive algorithm flow chart for solving the distribution network risk assessment model under the high penetration of distributed power sources is shown in Figure 2, which includes the following steps:

(1) Basic data input

Line parameters: number of components , the repair rate of each element ,failure rate ;

Load parameter: load number , the average load of each load, the number of users , the total number of important loads n, the load weight of important loads and load capacity ;

Power parameters: number of power supplies , the rated capacity of each power supply;

Distributed Power Parameters: Number of Wind Power Generation , the number of photovoltaic power generation , the parameters of each wind power generation include c, k and rated capacity obtained from the historical wind speed information according to formula (2)-(5) , cut-in wind speed , cut out wind speed , rated wind speed , the parameters of each photovoltaic power generation are a, b and r _max , the light-receiving area of photovoltaic power generation and the photoelectric conversion efficiency obtained by processing the historical illumination information according to formulas (8) and (9). , ;

Other data: Permissible bounds for loss of load probability , the allowable power shortage limit ;

(2) Set the total simulation time T _max , initialize the index system including the average failure rate , Average annual power outage duration , average outage duration , System average power outage frequency index , System average outage duration index , Average power supply availability index , Loss of load probability severity , Insufficient battery severity , the degree of important load loss is 0, set the simulation parameters, including simulation time T, total sampling times M of the system, failure times m of load points, failure time t, important load loss of the system , load loss , The number of times of load loss J is 0;

(3) produce Random numbers between (0,1), and one-to-one correspondence Calculate the average running time TTF and average repair time TTR of each element through the line parameters in step (1) and the line outage model shown in formulas (13) and (14);

(4) produce + Random numbers between (0,1), and one-to-one correspondence wind power, According to the distributed power generation parameters input in step (1) and formulas (1) and (7), the wind speed value of each wind power generation and the sunshine intensity value of each photovoltaic power generation are obtained, and then according to formula (6) , (10) Obtain the output of each wind power generation and the output of each photovoltaic power generation ;

(5) Select the component i with the smallest average running time TTF as the faulty component for each sampling, and accumulate its running time TTF(i) and repair time TTR(i) to the simulation time T, that is, T= TTF(i)+ TTR(i), while updating the number of simulations M=M+1;

(6) Use the breadth search algorithm to analyze the connectivity of the distribution network, and obtain the initial connectivity matrix A after the fault;

(7) Disconnect the fault branch, according to the load parameters and power parameters input in step (1) and the output of each wind power generation obtained in step (4) and the output of each photovoltaic power generation , using the particle swarm algorithm to divide the distribution network into islands;

(8) Use the breadth search algorithm to analyze the connectivity of the distribution network again, and obtain the connectivity matrix B after islanding;

(9) Compare the matrix A and B to judge the condition of the load point. For the load point, if the load point in A and B is connected to the power supply, there will be no power failure; if the load point in A and B is not connected, the power will be cut off and the load point will be accumulated The number of failures m=m+1, the failure time t=t+TTR. After the load point is judged, for the system, calculate and accumulate the load loss , important load loss , The number of times of load loss J;

(10) Determine whether the simulation time T is greater than the total simulation time T _max , if so, go to (11), if not, go to (3);

(11) Calculate the average failure rate of the initialization index system according to the data in formulas (15)-(22) and step (9) , Average annual power outage duration , average outage duration , System average power outage frequency index , System average outage duration index , Average power supply availability index , Loss of load probability severity , Insufficient battery severity , the degree of important load loss .

Further, in step 4, the flow chart of the breadth search algorithm for connectivity analysis is shown in Figure 3, and the steps are as follows:

(1) Input the start point and end point, and add the start point to the set Q;

(2) Take out the nodes Vn from the set Q one by one, judge whether Q is empty at this time, if so, output the start and end points are not connected, if not, go to (3);

(3) Find out all the adjacent nodes Vw of Vn that are not included in Q, judge whether Vw has an end point, if so, output the start and end points connected, if not, add Vw to Q, and go to (2).

Further, in Step 4, the flow chart of the particle swarm optimization algorithm for islanding the distribution network is shown in Figure 4, and the steps are as follows:

(1) Randomly generate initial particles, initialize the population number to 50, and initialize the number of iterations at the same time;

(2) Connectivity analysis using breadth search algorithm;

(3) According to the connectivity analysis results, calculate the fitness value of the distribution network. If there is an island or if the power constraint is not satisfied, the fitness value is set to negative infinity;

(4) Update the group optimal and individual optimal, and update the speed and position of the particles;

(5) Judging whether the number of iterations set in step (1) has been reached, if so, output the optimal solution, if not, go to (2).

Claims (9)

1. the power distribution network methods of risk assessment under a kind of distributed generation resource Thief zone, considers the access of large-scale distributed power supply Feature carries out risk assessment, which is characterized in that specific steps to power distribution network：

Step 1: considering fluctuation and intermittence that distributed generation resource is contributed, distributed generation resource output model is established；

Step 2: line outage model is established according to circuit historical failure information；

Step 3: considering the probability and consequence of power distribution network operation risk under distributed generation resource Thief zone, establish and consider power distribution network fortune Row reliability and abundance Risk Assessment Index System；

Step 4: using Monte Carlo simulation-population-breadth first search's integration algorithm to the distribution under distributed generation resource Thief zone Net risk evaluation model is solved, and determines risk assessment desired value, completes the power distribution network risk under distributed generation resource Thief zone Assessment.

2. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step In rapid one, the distributed generation resource output model includes wind power generation output model and photovoltaic generation output model.

3. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1 or 2, feature exist In, Wind speed model and sunlight model are initially set up, is contributed further according to wind speed and wind turbine, the relation that sunshine and photovoltaic generation are contributed, Establish wind power generation output model and photovoltaic generation output model.

4. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 3, which is characterized in that wind Fast model uses Weibull distributed models, and sunlight model uses Beta distributed models, and the parameter of model passes through historical wind speed information It is acquired with the calculating of history sunshine information.

5. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step In rapid two, the line outage model uses two state models of element, and the element has running status and fault status.

6. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step In rapid three, the reliability index includes load point reliability index and Reliability Index, load point reliability index bag Failure rate, System average interruption duration and annual interruption duration, Reliability Index is included to be averaged including system Power failure frequency, system System average interruption duration and availability of averagely powering, it is tight that the abundance index includes load-loss probability Severe, not enough power supply severity and the important load extent of damage.

7. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 1, which is characterized in that step In rapid four, the power distribution network risk evaluation model solution under the distributed generation resource Thief zone comprises the steps of：

（1）Master data inputs, and the basic data includes line parameter circuit value, power parameter, load parameter and distributed generation resource ginseng Number etc.；

（2）Emulation total time is set, and it is 0 to initialize risk assessment index and simulation parameter；

（3）Each element is generated（0,1）Between random number, and calculate run time and the repair time of each element；

（4）It generates corresponding with distributed generation resource（0,1）Between random number, and calculation of wind speed and intensity of sunshine, and then calculate Wind power generation output and photovoltaic generation are contributed；

（5）The element i of run time minimum is chosen as the fault element sampled every time, and by its running time T TF (i) and is repaiied Multiple time TTR (i) is accumulate to simulation time t, i.e. t=TTF (i)+TTR (i)；The value of i is 0 natural number herein；

（6）Connectivity analysis is carried out to power distribution network with breadth first search method, obtains initial connective matrix A after failure；

（7）Fault branch is disconnected, isolated island division is carried out to distribution with particle cluster algorithm；

（8）Connectivity analysis is carried out to power distribution network with breadth first search method again, obtains connective matrix B after isolated island division；

（9）Comparison step（6）In A and step（8）In B, load point is classified, is connected in A, B with power supply, no Have a power failure；It is connected in A, it is disconnected in B, have a power failure, power off time is element i repair time TTR（i）, calculated according to classification situation Simulation parameter；The A is initial connective matrix A after failure, and B is connective matrix after isolated island division；

（10）Judge whether simulation time t is more than emulation total time, if simulation time t is more than or equal to emulation total time, be transferred to Step（11）；If simulation time t is less than emulation total time, step is transferred to（3）；

（11）According to simulation parameter calculation risk evaluation index value.

8. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 7, which is characterized in that adopt The step of carrying out connectivity analysis with the breadth first search method is as follows：

（L1）Beginning and end is inputted, and starting point is added in set Q；

（L2）From step（L1）Node Vn is taken out successively in the set Q, judges whether set Q is at this time empty, if set Q is Sky then exports beginning and end and does not connect；If set Q is not sky, next step is transferred to（L3）；

（L3）Find out step（L2）In adjacent node Vw being not included in set Q all in the node Vn that takes out, judge phase Whether neighbors Vw has terminal, if adjacent node Vw has terminal, exports beginning and end connection；If adjacent node Vw is without eventually Adjacent node Vw is added in set Q, and is transferred to step by point（L2）.

9. the power distribution network methods of risk assessment under distributed generation resource Thief zone according to claim 7, which is characterized in that adopt The step of carrying out isolated island division to distribution with the particle cluster algorithm is as follows：

（G1）It is random to be initially generated particle, population number is initialized, while initializes iterations；

（G2）Connectivity analysis is carried out using breadth first search method；

（G3）According to step（G2）Connectivity analysis as a result, calculate power distribution network fitness value, if to be unsatisfactory for if there are isolated islands Power constraint, fitness value are set to negative infinite；

（G4）Update group it is optimal and individual it is optimal after, the speed of more new particle and position；

（G5）Judge whether to reach step（G1）The iterations of middle setting, if so, output optimal solution, if it is not, being transferred to step （G2）.