CN111950900B - Power system source network load storage intrinsic safety risk assessment method - Google Patents
Power system source network load storage intrinsic safety risk assessment method Download PDFInfo
- Publication number
- CN111950900B CN111950900B CN202010805156.2A CN202010805156A CN111950900B CN 111950900 B CN111950900 B CN 111950900B CN 202010805156 A CN202010805156 A CN 202010805156A CN 111950900 B CN111950900 B CN 111950900B
- Authority
- CN
- China
- Prior art keywords
- load
- power
- risk
- index
- power system
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000012502 risk assessment Methods 0.000 title claims abstract description 21
- 238000005070 sampling Methods 0.000 claims abstract description 30
- 238000004364 calculation method Methods 0.000 claims abstract description 28
- 238000004146 energy storage Methods 0.000 claims abstract description 28
- 238000007599 discharging Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 35
- 238000010248 power generation Methods 0.000 claims description 22
- 238000009434 installation Methods 0.000 claims description 8
- 238000013278 delphi method Methods 0.000 claims description 6
- 238000005315 distribution function Methods 0.000 claims description 5
- 230000005611 electricity Effects 0.000 claims description 5
- 239000002699 waste material Substances 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 3
- 238000007726 management method Methods 0.000 abstract description 2
- 238000011156 evaluation Methods 0.000 description 22
- 230000008569 process Effects 0.000 description 6
- 230000005855 radiation Effects 0.000 description 5
- 238000000342 Monte Carlo simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a power system safety technology, in particular to a power system source network charge storage intrinsic safety risk assessment method, which comprises the steps of firstly, respectively carrying out probability modeling on wind power, photovoltaic output and load to describe random fluctuation of the wind power, photovoltaic output and load, and providing a charging and discharging strategy of energy storage; secondly, carrying out load flow calculation on the power system by adopting a probability load flow method based on Latin hypercube sampling; and finally, establishing safety and economic risk indexes, establishing a system risk calculation method by adopting a subjective weighting method, and evaluating the intrinsic safety level of the source network charge storage of the power system. The influence of source, load and storage changes on risks is discussed in different scenes, the intrinsic safety risk of the source network load storage of the power system can be scientifically and systematically estimated, decision references are provided for risk management and control of the power department, and the intrinsic safety level of the power system is improved.
Description
Technical Field
The invention belongs to the technical field of power system safety, and particularly relates to an intrinsic safety risk assessment method for power system source network charge storage.
Background
The safety of the power grid is related to the national energy safety. With the increasing level of electrification, the demands of users for power supply reliability are gradually increasing. However, renewable energy permeability in an electric power system is increased increasingly, an energy supply side presents multi-mainbody, and flexible thermal power resources are called to stabilize the fluctuation of new energy; the adoption of new technologies such as regional interconnection, flexible and straight of the power grid complicates the topological structure; the flexible load participates in the response of the demand side, and the load side actively tracks the power generation output; the energy storage technology is vigorously developed, a large amount of energy storage is connected into a power grid, and the charging and discharging are random. The source side, the network side, the load side and the energy storage side of the power system all show certain uncertainty, the running state is increasingly complex, and the safe and stable running of the system is threatened. Intrinsic safety is an inherent capability of preventing and resisting accident risks, namely, through harmony, reliability and unification of factors such as people, objects, systems and regulations, the risk factors are controlled, and the aim of safety intrinsically and durably is gradually achieved. Moreover, current researches on risk assessment of power systems are mainly focused on static and transient security risks of large power systems in the power market environment. Based on the intrinsic safety concept of the electric power system, the risk assessment of the electric power system is changed from the safety assessment to the design of a problem solution, so that reasonable risk assessment is required to be carried out on a planning scheme, and the overall safety of the system is improved.
Disclosure of Invention
The invention aims to provide an intrinsic safety assessment method for source network charge storage of an electric power system.
In order to achieve the above purpose, the invention adopts the following technical scheme: an intrinsic safety risk assessment method for power system source network charge storage comprises the following steps:
Step 1, modeling uncertainty factors of power supply, load and energy storage links in a power system;
step 2, carrying out load flow calculation on the power system by adopting a probability load flow method;
Step 3, establishing an intrinsic safety risk assessment index system of power system source network charge storage, and dividing the intrinsic safety risk assessment index system into safety risk and economic risk indexes;
and 4, comprehensively calculating the intrinsic risk by adopting an AHP-Delphi weighting method, and evaluating the intrinsic safety level of the source network load storage of the power system.
In the above power system source network load storage intrinsic safety risk assessment method, the power link uncertainty factor modeling in step 1 includes fan output and photovoltaic output; the load link uncertainty factor modeling comprises load with fluctuation and interruptible load participating in demand side response; the energy storage link uncertainty factor modeling comprises a charging and discharging strategy of energy storage.
In the above power system source network load storage intrinsic safety risk assessment method, the probability power flow method in step 2 adopts a Monte Carlo simulation method based on Latin hypercube sampling; the specific steps of the power flow calculation of the power system are as follows:
Step 2.1, inputting original data of a system to be evaluated, including grid frame structure and line parameters, the number, capacity and installation sites of a traditional generator set and new energy, load access conditions, energy storage capacity, installation sites, maximum charge and discharge power and interruptible load duty ratio;
Step 2.2, modeling the active power and the load uncertainty of the new energy;
2.3, extracting a wind power and photovoltaic output sequence by using a Latin hypercube sampling method within a given iteration number to generate a load sequence, thereby forming an energy storage running state;
Step 2.4, alternating current power flow calculation is carried out, and a calculation result is recorded;
Step 2.5, fitting the probability density distribution function of the line work by using difittool in a Matlab toolbox to obtain a proper probability distribution curve and related parameters;
And 2.6, obtaining various risk losses and probabilities according to the statistical value and the processing value of the probability power flow calculation result, and calculating various risk indexes.
In the above power system source network load storage intrinsic safety risk assessment method, the safety risk index in step 3 includes a node voltage out-of-limit risk index and a line power flow out-of-limit risk index; the economic risk index comprises a cut load loss risk index, a new energy power-off loss risk index and a unit rescheduling risk index.
In the above power system source network load storage intrinsic safety risk assessment method, the specific steps of comprehensively calculating the intrinsic risk by the AHP-Delphi weighting method in the step 4 are as follows:
Step 4.1, comparing indexes of each layer in pairs, and establishing a judgment matrix according to a judgment table;
Step 4.2, inviting a plurality of experts to carry out judgment matrix assignment according to the Delphi method;
step 4.3, carrying out averaging treatment on the effective judgment matrix to form an optimal judgment matrix;
Step 4.4, carrying out consistency test on the optimal judgment matrix, and further determining the weight of each index;
And 4.5, linearly weighting each index, and calculating the safety risk and the economic risk.
The invention has the beneficial effects that: 1. the invention establishes a multi-index system for evaluating the intrinsic safety risk of the power system, and the evaluation result is more overall and comprehensive. 2. According to the invention, an analytic hierarchy process and a Delphi method are introduced to convert single qualitative evaluation into multi-dimensional qualitative evaluation, and the evaluation result is more accurate and reliable. 3. The method provided by the invention has the advantages of strong feasibility and higher precision, and is beneficial to improving the capability of the power grid for resisting faults such as broken lines and the like and reducing the power grid risk caused by uncertainty factors.
Drawings
FIG. 1 is a flow chart of a method for evaluating intrinsic safety risk of source network charge storage of a power system according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a power system probabilistic load flow calculation according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an intrinsic safety risk indicator system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Under the background that the development of the source network charge storage brings a series of risks to the power system, the embodiment provides an intrinsic safety risk assessment method for the source network charge storage of the power system in consideration of the threat of the safe and stable operation of the power system. Firstly, probability modeling is carried out on wind power, photovoltaic output and load respectively to describe random fluctuation of the wind power, the photovoltaic output and the load, and a charging and discharging strategy of energy storage is provided; secondly, carrying out load flow calculation on the power system by adopting a probability load flow method based on Latin hypercube sampling; and finally, establishing safety and economic risk indexes, establishing a system risk calculation method by adopting a subjective weighting method, and evaluating the intrinsic safety level of the source network charge storage of the power system. The influence of source, load and storage changes on risks is discussed in different scenes, the intrinsic safety risk of the source network load storage of the power system can be scientifically and systematically estimated, decision references are provided for risk management and control of the power department, and the intrinsic safety level of the power system is improved.
The embodiment is realized by the following technical scheme, and the method for evaluating the intrinsic safety of the source network charge storage of the power system comprises the following steps:
s1, modeling uncertainty factors in links such as power supply, load, energy storage and the like in a power system;
S2, carrying out load flow calculation on the power system by adopting a probability load flow method;
s3, establishing an intrinsic safety risk assessment index system of the power system source network load storage, and dividing the intrinsic safety risk assessment index system into safety risk indexes and economic risk indexes;
and S4, comprehensively calculating the intrinsic risk by adopting an AHP-Delphi weighting method, and evaluating the intrinsic safety level of the source network load storage of the power system.
Moreover, uncertainty factors for modeling in the power link are fan output and photovoltaic output; the uncertainty factors for modeling in the load link are fluctuating load and interruptible load participating in the response of the demand side; the uncertainty factor for modeling in the energy storage link is the charge-discharge strategy of the energy storage.
Moreover, the probability tide method adopts a Monte Carlo simulation method based on Latin hypercube sampling; the specific steps of the power flow calculation of the power system are as follows:
s2.1, inputting original data of a system to be evaluated: grid frame structure and line parameters, the number, capacity and installation place of the traditional generator set and new energy, load access condition, energy storage capacity, installation place, maximum charge and discharge power, interruptible load duty ratio and the like;
S2.2, modeling the active power and the load uncertainty of the new energy;
s2.3, extracting a wind power and photovoltaic output sequence by using a Latin hypercube sampling method within a given iteration number to generate a load sequence, thereby forming an energy storage running state;
S2.4, alternating current power flow calculation is carried out, and a calculation result is recorded;
S2.5, fitting a line active probability density distribution function by using difittool in a Matlab toolbox to obtain a proper probability distribution curve and related parameters;
S2.6, according to the statistical value and the processing value of the probability power flow calculation result, obtaining various risk losses and probabilities, and calculating various risk indexes.
The safety risk index comprises a node voltage out-of-limit risk index and a line power flow out-of-limit risk index; the economic risk index comprises a cut load loss risk index, a new energy power-off loss risk index and a unit rescheduling risk index.
Moreover, the AHP-Delphi weighting method comprehensively calculates the intrinsic risk as follows:
Comparing indexes of each layer pairwise, and establishing a judgment matrix according to a judgment table;
Inviting a plurality of experts to carry out judgment matrix assignment according to the Delphi method;
averaging the effective judgment matrix to form an optimal judgment matrix;
Consistency test is carried out on the optimal judgment matrix, and then each index weight is determined;
And linearly weighting each index, and calculating the safety risk and the economic risk.
In specific implementation, as shown in fig. 1, a method for evaluating intrinsic safety of source network charge storage of an electric power system includes the following steps:
S1: modeling uncertainty factors in links such as power supply, load, energy storage and the like in a power system;
modeling of uncertainty factors in the power link in S1 is as follows:
fan output model:
establishing a probability model of wind speed by using Weibull distribution, wherein the probability density function expression is as follows:
Wherein v is the measured wind speed of the wind farm construction site; k is a shape parameter, c is a scale parameter, and the values of the shape parameter and the scale parameter depend on local wind speed change characteristics.
Active output of wind turbine generator system:
Wherein v i、vr、vo is the cut-in wind speed, rated wind speed and cut-out wind speed of the wind driven generator respectively; p r is the rated power of the fan; A. b, C are corresponding characteristic parameters, and the values of the characteristic parameters depend on the wind power resource characteristics of the wind farm construction site:
Photovoltaic output model:
The output power of photovoltaic power generation is related to the intensity of solar radiation, and the probability density function is as follows:
Wherein r is the intensity of solar radiation; r max is the maximum radiation intensity; alpha and Beta are Beta distribution shape parameters respectively; Γ is a Gamma function. The alpha and beta parameters are obtained by the expected mu and standard deviation sigma of the ratio of the measured light intensity value r to the local maximum light intensity r max:
General mathematical expression of the above model:
Wherein R i is the actual solar radiation intensity at a certain moment; r r is the magnitude of the optical radiation corresponding to the rated value of the active output of the photovoltaic cell, and the value is generally 1000 (W/m 2); in general, the value of R c is set to 150 (W/m 2);Pn is the rated power of photovoltaic power generation.
Modeling of uncertainty factors in the load link in the S1 is as follows:
Uncertainty load model:
the load fluctuation is described by a normal distribution, and the probability density distribution is that:
In the method, in the process of the invention, Is the standard deviation of the load; /(I)Is the load average.
Node load at a certain time t:
Interruptible load model:
a load of a certain proportion is set.
Modeling of uncertainty factors in the energy storage link in the S1 is as follows:
the generated active power and the initial stored electric quantity of the device are in a linear relation:
Wherein P rel (t) is the active electric quantity which can be released in the t time period; e sto (t) is the initial electricity storage capacity of the battery energy storage in the t time period; e min is the minimum residual electric quantity allowed by the energy storage device; delta T is the sampling period; p dch,max is the maximum charge-discharge power of the energy storage device. Initial charge amount E sto (t+1) of the t+1 th period device:
Wherein P bat (t) is the charge/discharge power of the battery in the t-th period. And (3) in the system at the moment t, the surplus power generated by new energy sources such as wind power, photovoltaic and the like is recorded as follows:
ΔGwind(t)＝PWTG(t)-PL(t)·ηwind
Wherein Δg wind (t) is the new energy excess power at time t; p WTG (t) and P L (t) are respectively the actual output and real-time load value of the new energy at the moment t; η wind is the installed ratio of new energy of the system.
When the new energy source surplus power delta G wind (t) is more than 0, storing and charging:
when the new energy surplus power delta G wind (t) is less than 0, the energy storage discharges:
wherein, P ch,max,Pdch,max is the maximum charge and discharge power of the stored energy.
S2: carrying out load flow calculation on the power system by adopting a probability load flow method;
the principle of Latin hypercube sampling of the probability flow calculation method in S2 is as follows:
Let X 1,X2,…,XK be the K inputs in the study subject, and all obey a certain probability distribution, wherein the cumulative probability distribution function of any random variable X k:
Yk＝Dk(Xk)
Latin hypercube sampling is performed on all input random variables: the vertical axis of the curve Y k＝Dk(Xk) is equally divided into N layers which are not overlapped with each other, and since the value of Y k ranges from 0 to 1.0, each interval has the same probability of 1/N. Randomly extracting a point in each interval, generally selecting a midpoint as a sampling point, and then obtaining a sampling value of X k by using an inverse function of Y k＝Dk(Xk), wherein the expression of an nth sampling value in a sampling sequence is as follows:
The sampling sequence is subjected to decorrelation, and a K multiplied by N order sequential matrix H is randomly generated, wherein each row in the matrix determines the position of the element arrangement corresponding to the initial sampling sequence X so as to ensure the independence among variables. Let a K x N order matrix have been generated, where the elements of each row are a random arrangement of 1,2, …, N, then iterate in the forward and reverse directions:
Forward iteration:
for j＝2,3,…,k
for j＝1,2,…,j-1
Hk←takeout(Hk,Hj)
Hk←rank(Hk)
Reverse iteration:
for j＝K-1,K-2,…,1
for j＝K,K-1,…,j+1
Hk←takeout(Hk,Hj)
Hk←rank(Hk)
Wherein, the symbol is assigned; takeout (H k,Hj) represents the residual of developing linear regression H k＝a+bHj on vector H k,Hj; rank (H k) represents the ordering of elements in a pair from small to large. The forward and reverse alternate iterations are performed until the root mean square value ρ rms representing the matrix-column correlation becomes stable or a preset number of iterations is reached.
The specific steps of carrying out load flow calculation on the power system by adopting the probability load flow method in the S2 are as follows:
S2.1, inputting original data of a system to be evaluated: grid frame structure and line parameters, the number, capacity and installation place of the traditional generator set and new energy, load access condition, energy storage capacity, installation place, maximum charge and discharge power, interruptible load duty ratio and the like;
s2.2, modeling the active power and the load uncertainty of the new energy;
S2.3, extracting a wind power and photovoltaic output sequence by using a Latin hypercube sampling method within a given iteration number to generate a load sequence, thereby forming an energy storage running state;
S2.4, alternating current power flow calculation is carried out, and a calculation result is recorded;
S2.5, fitting the probability density distribution function of the line work by using difittool in a Matlab tool box to obtain a proper probability distribution curve and related parameters.
S2.6, according to the statistical value and the processing value of the probability power flow calculation result, obtaining each risk loss and probability, and calculating each risk index.
As shown in fig. 2, S3: establishing an intrinsic safety risk assessment index system of a power system source network charge storage, and dividing the intrinsic safety risk assessment index system into safety risk indexes and economic risk indexes;
And S3, the security risk index comprises a node voltage out-of-limit risk index and a line power flow out-of-limit risk index.
Node voltage out-of-limit risk indicator:
wherein P (U i) is the possibility of voltage out-of-limit of a certain node; f (U i) is the severity of the voltage violation.
Sampling to obtain a sample matrix U of voltages from the probability distribution of the voltages, wherein ,U＝U1,U2,...,Un,Ui＝Ui1,Ui2,...,UiN(i＝1,2,...,n). is the out-of-limit probability of the voltage of the node i:
Where N is the number of grid nodes, N 1 is the number of node voltage out-of-limit times, and N 2 is the total number of samples.
The problem of voltage out-of-limit can be divided into overvoltage and low voltage. Overvoltage severity function:
F(V)＝eα-1
α＝V-1
Where V is the per unit value of the node voltage.
Line tide out-of-limit risk index:
Wherein P (P ij) is the probability of active power out-of-limit of a certain line; f (P ij) is the severity of the active out-of-limit.
Line activity is considered to be 0.9P N for heavy loads and over P N for severe overload, so P max takes 0.9P N. Probability of line occurrence of power flow out-of-limit risk:
Wherein N 1 is the total number of times that the active power of the line ij is 0.9 times greater than the rated capacity thereof; n 2 is the total number of cycles calculated for the power flow.
F(Z)＝eβ-1
β＝Z-Zmax
Where Z max is the maximum value of the current that the line is allowed to flow through.
And S3, the economic risk index comprises a load shedding loss risk index, a new energy power discarding loss risk index and a unit rescheduling risk index.
Cut load loss risk index:
the system adopts power failure and power supply shortage measures of loads due to the shortage of power generation capacity or out-of-limit of line tide, so that corresponding load shedding loss is caused. Cut load loss risk index:
LLR＝LLPSevLL
Wherein U L,i is the system load loss state in the ith sampling state, and is a variable of 0-1, when 1 is taken, the load is required to be cut, and when 0 is taken, the load is not required to be cut; n is the total sampling times; c L_L is total loss of cut load, C ope is total gain of system power generation, and C ope comprises power generation gain C 1 of a new energy unit and power generation gain C 2.Ccom of a traditional water and fire motor set for fixed compensation to be paid to a user side according to a signed interruptible user protocol in consideration of sharing the cut load loss to the power supply side by an electric company; c L is unit power outage compensation of normal user load, lambda is the proportion of interruptible load, and P L_L,i is the total load quantity which should be cut off in the ith sampling.
New energy waste loss risk index:
when the line tide is out of limit or the system load level is lower, new energy needs to be cut off to generate electricity, and the electricity loss risk index is abandoned:
PLR＝PLPSevPL
Wherein U P,i is the system power loss state in the ith sampling state, is a variable of 0-1, and represents that power generation is needed to be abandoned when 1 is taken, and represents that power generation is not needed to be abandoned when 0 is taken; n is the total sampling times; p P_L,i is the total wind and light power generation amount discarded in the ith sampling state, and the new energy discarded amount is the product of the total cut load amount of the system and the installed ratio of the new energy; c waste is the unit of power generation loss, and C 1 is the total power generation yield of the new energy unit.
And rescheduling risk indexes of the unit:
In order to stabilize the fluctuation of the output of the new energy, the active output of the flexibly adjustable thermal generator and the like is regulated through rescheduling, so that a certain cost risk is generated. Rescheduling risk indicator for generator:
RLR＝RLP·SevRL
Wherein, C 2 is the power generation income of the traditional water-fire motor group, and C re is the rescheduling cost of the generator:
Wherein N G is the total number of generators; f Gi is the generator rescheduling cost of generator i:
FGi(PGik,PGi0)＝aΔi(PGik-PGi0)2
wherein a Δi is a given constant coefficient corresponding to the readjustment cost coefficient of the generator i; p Gi0,PGik is the active force of generator i in normal and fault states k (k+.0), respectively.
As shown in fig. 3, S4: determining index weights of all levels of evaluation index layers of a coordinated level evaluation index system by adopting a subjective weight method combining an analytic hierarchy process and a Delphi process;
and S4, determining the index weight of each level of evaluation index layer of the coordination level evaluation index system, wherein the specific weight determining method comprises the following steps:
S4.1, establishing a judgment matrix layer by layer according to the thought of the analytic hierarchy process;
The importance of each layer of indexes relative to the indexes of the upper layer of the indexes is compared pairwise by adopting a three-scale method, and a judgment matrix of an index system I-level evaluation index layer is established according to a judgment principle:
Wherein n is the index number of participation empowerment of an evaluation index layer of an evaluation index system, and the specific meaning of omega ij is as follows: under the condition that an index i of an index layer of an evaluation index system I and an index g of an upper layer corresponding to the index j, namely an index g of an l-1 layer, are taken as evaluation target conditions, the index i and the index j respectively have the effect on the index g of a comparison result, and omega ij＝1/ωji;
the judgment principle of the influence of each layer of index on the upper layer index is as follows:
When the influence degree of the index i on the index g is higher than the index j, namely the index i is more important than the index j, omega ij is more than 1, and the larger the value of omega ij is, the higher the importance degree is; when the influence degree of the index i on the index g is lower than the index j, namely the index i is less important than the index j, omega ij is smaller than 1, and the smaller the value of omega ij is, the lower the importance degree is; when index i is equally important as compared to index j, ω ij =1.
Where i, j=1, 2, …, n (l) and i+notej, g=1, 2, …, n (l-1);
S4.2, according to the judgment matrix of the index system I-level evaluation index layer, the weight of each index under the analytic hierarchy process is obtained.
And (3) adopting a feature vector method, firstly solving the maximum feature value lambda max(l) of the judgment matrix of the index system I-level evaluation index layer, and then solving the feature vector w (l) corresponding to the maximum feature value lambda max(l) to obtain the weight vector w (l)＝(w1(l),w2(l),…,wn(l). Normalization of w (l) The relative weight vector w (l)'＝(w1(l)',w2(l)',…,wn(l)' of a certain level of evaluation index relative to the previous level of evaluation index is obtained;
S4.3, introducing a Delphi method to optimize the weighting method to form an optimal judgment matrix;
according to the Delphi method, inviting m experts to assign values to the judgment matrix to obtain m different judgment matrices of an index system I-level evaluation index layer Let the weight vector obtained by the judgment matrix assigned by the kth expert be w (l) (k)＝(w1(l) (k),w2(l) (k),…,wn(l) (k)), k=1, 2, …, m (l).
The relative consistency of the judgment matrix of the ith expert is defined as the consistency with the judgment matrix of other experts, namely whether the judgment matrix can represent the opinion of most of the experts. The calculation formula is as follows: Normalization of S i(l) -
The expression of the optimal judgment matrix of the index system I-level evaluation index layer is as follows:
and S4.4, carrying out consistency test on the optimal judgment matrix of the index system I-level evaluation index layer, and further determining each index weight after the Delphi optimization by adopting the eigenvector method described in S5.2.
The consistency test method comprises the following steps: calculating the maximum eigenvalue lambda max(l) * of the optimal judgment matrix, and introducing compatibility indexesAnd checking and judging the consistency of the matrix.
Generally, when CI (l) is less than 1.0, the consistency of the judgment matrix is considered acceptable; when CI (l) is greater than or equal to 1.0, repeatedly executing S4.1 to S4.4, reconstructing a judgment matrix and calculating weights until CI (l) is less than 1.0, namely passing the consistency check.
The weight vector of the index of the corresponding index system I-level evaluation index layer index relative to the index of the I-1 level evaluation index layer index is w (l) *＝(w1(l) *,w2(l) *,…,wn(l) *).
And S4.5, linearly weighting each index according to each index weight determined in the step S4.4, and calculating the safety risk and the economic risk.
The comprehensive security risk value of the system is the sum of products of all security risk index values and corresponding weights, and the economic risk calculation method is the same. Calculating the comprehensive safety risk and the economic risk of the system:
RS＝α1·RU+α2·RP
RE＝β1·LLR+β2·PLR+β3·RLR
Wherein, alpha 1,α2 is the weight value occupied by each security index; beta 1,β2,β3 is the weight value of each economic index.
It should be understood that parts of the specification not specifically set forth herein are all prior art.
While particular embodiments of the present invention have been described above with reference to the accompanying drawings, it will be understood by those skilled in the art that these are by way of example only, and that various changes and modifications may be made to these embodiments without departing from the principles and spirit of the invention. The scope of the invention is limited only by the appended claims.
Claims (3)
1. The method for evaluating the intrinsic safety risk of the source network charge storage of the power system is characterized by comprising the following steps of:
Step 1, modeling uncertainty factors of power supply, load and energy storage links in a power system;
step 2, carrying out load flow calculation on the power system by adopting a probability load flow method;
The specific steps of the power flow calculation of the power system are as follows:
Step 2.1, inputting original data of a system to be evaluated, including grid frame structure and line parameters, the number, capacity and installation sites of a traditional generator set and new energy, load access conditions, energy storage capacity, installation sites, maximum charge and discharge power and interruptible load duty ratio;
Step 2.2, modeling the active power and the load uncertainty of the new energy;
2.3, extracting a wind power and photovoltaic output sequence by using a Latin hypercube sampling method within a given iteration number to generate a load sequence, thereby forming an energy storage running state;
Step 2.4, alternating current power flow calculation is carried out, and a calculation result is recorded;
Step 2.5, fitting the probability density distribution function of the line work by using difittool in a Matlab toolbox to obtain a proper probability distribution curve and related parameters;
Step 2.6, according to the statistical value and the processing value of the probability power flow calculation result, obtaining various risk losses and probabilities, and calculating various risk indexes;
Step 3, establishing an intrinsic safety risk assessment index system of power system source network charge storage, and dividing the intrinsic safety risk assessment index system into safety risk and economic risk indexes;
The economic risk index comprises a load shedding loss risk index, a new energy power-off loss risk index and a unit rescheduling risk index;
Cut load loss risk index:
LLR＝LLPSevLL
Wherein U L,i is the system load loss state in the ith sampling state, and is a variable of 0-1, when 1 is taken, the load is required to be cut, and when 0 is taken, the load is not required to be cut; n is the total sampling times; c L_L is total loss of cut load, C ope is total gain of system power generation, and C ope comprises power generation gain C 1 of a new energy unit and power generation gain C 2;Ccom of a traditional water and fire motor set for fixed compensation to be paid to a user side according to a signed interruptible user protocol in consideration of sharing the cut load loss to the power supply side by an electric company; c L is unit power failure compensation of common user load, lambda is the proportion occupied by the interruptible load, and P L_L,i is the total load quantity which should be cut off in the ith sampling;
new energy waste loss risk index:
when the line tide is out of limit or the system load level is lower, new energy needs to be cut off to generate electricity, and the electricity loss risk index is abandoned:
PLR＝PLPSevPL
Wherein U P,i is the system power loss state in the ith sampling state, is a variable of 0-1, and represents that power generation is needed to be abandoned when 1 is taken, and represents that power generation is not needed to be abandoned when 0 is taken; n is the total sampling times; p P_L,i is the total wind and light power generation amount discarded in the ith sampling state, and the new energy discarded amount is the product of the total cut load amount of the system and the installed ratio of the new energy; c waste is the unit waste power generation loss, and C 1 is the total power generation yield of the new energy unit;
and rescheduling risk indexes of the unit:
RLR＝RLP·SevRL
Wherein, C 2 is the power generation income of the traditional water-fire motor group, and C re is the rescheduling cost of the generator:
Wherein N G is the total number of generators; f Gi is the generator rescheduling cost of generator i:
FGi(PGik,PGi0)＝aΔi(PGik-PGi0)2
Wherein a Δi is a given constant coefficient corresponding to the readjustment cost coefficient of the generator i; p Gi0,PGik is the active force of generator i in normal and fault states k (k not equal to 0), respectively;
and 4, comprehensively calculating the intrinsic risk by adopting an AHP-Delphi weighting method, and evaluating the intrinsic safety level of the source network load storage of the power system.
2. The method for evaluating intrinsic safety risk of power system source network charge storage according to claim 1, wherein the modeling of the uncertainty factors of the power link in step 1 comprises fan output and photovoltaic output; the load link uncertainty factor modeling comprises load with fluctuation and interruptible load participating in demand side response; the energy storage link uncertainty factor modeling comprises a charging and discharging strategy of energy storage.
3. The method for evaluating the intrinsic safety risk of the source network charge storage of the power system according to claim 1, wherein the specific steps of comprehensively calculating the intrinsic risk by the AHP-Delphi weighting method in the step 4 are as follows:
Step 4.1, comparing indexes of each layer in pairs, and establishing a judgment matrix according to a judgment table;
Step 4.2, inviting a plurality of experts to carry out judgment matrix assignment according to the Delphi method;
step 4.3, carrying out averaging treatment on the effective judgment matrix to form an optimal judgment matrix;
Step 4.4, carrying out consistency test on the optimal judgment matrix, and further determining the weight of each index;
And 4.5, linearly weighting each index, and calculating the safety risk and the economic risk.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010805156.2A CN111950900B (en) | 2020-08-12 | 2020-08-12 | Power system source network load storage intrinsic safety risk assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010805156.2A CN111950900B (en) | 2020-08-12 | 2020-08-12 | Power system source network load storage intrinsic safety risk assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111950900A CN111950900A (en) | 2020-11-17 |
| CN111950900B true CN111950900B (en) | 2024-05-14 |
Family
ID=73332730
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010805156.2A Active CN111950900B (en) | 2020-08-12 | 2020-08-12 | Power system source network load storage intrinsic safety risk assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111950900B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112819648B (en) * | 2020-12-29 | 2024-02-13 | 湖南大学 | Power dispatching operation instruction safety verification method and system |
| CN113128071B (en) * | 2021-05-08 | 2024-02-09 | 南京工程学院 | Reliability evaluation method for power generation system containing photovoltaic power generation |
| CN113705986B (en) * | 2021-08-12 | 2023-11-07 | 武汉大学 | Method for constructing core backbone network frame based on risk theory |
| CN113807658A (en) * | 2021-08-18 | 2021-12-17 | 国网湖南省电力有限公司 | Resource aggregation potential evaluation method and device based on source network load storage collaborative interaction scheme |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014190759A1 (en) * | 2013-05-27 | 2014-12-04 | 国家电网公司 | Svc compensation strategy optimization method |
| CN108898287A (en) * | 2018-06-11 | 2018-11-27 | 国网江西省电力有限公司电力科学研究院 | The grid-connected power distribution network operation risk assessment method of large-scale photovoltaic |
| CN110334914A (en) * | 2019-06-12 | 2019-10-15 | 国网经济技术研究院有限公司 | A kind of source net lotus storage coordination level evaluation method based on risk thought |
| CN110599006A (en) * | 2019-08-25 | 2019-12-20 | 南京理工大学 | Power distribution network operation risk assessment method based on scene analysis |
-
2020
- 2020-08-12 CN CN202010805156.2A patent/CN111950900B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014190759A1 (en) * | 2013-05-27 | 2014-12-04 | 国家电网公司 | Svc compensation strategy optimization method |
| CN108898287A (en) * | 2018-06-11 | 2018-11-27 | 国网江西省电力有限公司电力科学研究院 | The grid-connected power distribution network operation risk assessment method of large-scale photovoltaic |
| CN110334914A (en) * | 2019-06-12 | 2019-10-15 | 国网经济技术研究院有限公司 | A kind of source net lotus storage coordination level evaluation method based on risk thought |
| CN110599006A (en) * | 2019-08-25 | 2019-12-20 | 南京理工大学 | Power distribution network operation risk assessment method based on scene analysis |
Non-Patent Citations (1)
| Title |
|---|
| 储能装置运行策略及运行特性对微电网可靠性的影响;陈丽丽;牟龙华;许旭锋;刘爽;;电力自动化设备(第07期);70-76 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111950900A (en) | 2020-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111950900B (en) | Power system source network load storage intrinsic safety risk assessment method | |
| Khatod et al. | Analytical approach for well-being assessment of small autonomous power systems with solar and wind energy sources | |
| Salkuti | Day-ahead thermal and renewable power generation scheduling considering uncertainty | |
| Khatod et al. | Evolutionary programming based optimal placement of renewable distributed generators | |
| Karaki et al. | Probabilistic performance assessment of autonomous solar-wind energy conversion systems | |
| Ahmed et al. | Probabilistic generation model for optimal allocation of wind DG in distribution systems with time varying load models | |
| Karaki et al. | Probabilistic performance assessment of wind energy conversion systems | |
| Abid et al. | Optimal planning of multiple renewable energy-integrated distribution system with uncertainties using artificial hummingbird algorithm | |
| Silva et al. | Stochastic assessment of the impact of photovoltaic distributed generation on the power quality indices of distribution networks | |
| EP2537222A2 (en) | Energy generating system and control thereof | |
| Paliwal | Reliability constrained planning and sensitivity analysis for Solar-Wind-Battery based Isolated Power System | |
| Oladeji et al. | Security constrained optimal placement of renewable energy sources distributed generation for modern grid operations | |
| Khasanov et al. | Optimal planning DG and BES units in distribution system consideringuncertainty of power generation and time-varying load | |
| CN112821463B (en) | Active power distribution network multi-target day-ahead optimization scheduling method based on wind and light randomness | |
| CN104636993B (en) | Power distribution system reliability algorithm | |
| CN112600244A (en) | Photovoltaic power station voltage control method based on neural network | |
| Khan et al. | Contingency analysis of a power grid with the participation of utility-scale solar PV units: a case study from Sarawak, Malaysia | |
| Kafle et al. | Reliability Analysis Techniques in Distribution System: A Comprehensive Review | |
| Thorat et al. | Optimization of fuel cost incorporating with wind, solar PV and Electric vehicle energy sources using improved artificial bee colony algorithm | |
| CN114970096A (en) | Power system flexibility quantitative evaluation method and system based on probability optimal power flow | |
| Kayalvizhi et al. | Dispatchable DG planning in distribution networks considering costs | |
| Barton et al. | Energy storage and its use with wind power | |
| Prashanti et al. | Impact of energy storage integration on composite system reliability | |
| Hemmati et al. | Uncertainty management in power system operation decision making | |
| Kazeem et al. | Optimal placement of distributed generation in power distribution systems using neuro-genetic Algorithm |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210926 Address after: 100031 No. 86 West Chang'an Avenue, Beijing, Xicheng District Applicant after: STATE GRID CORPORATION OF CHINA Applicant after: STATE GRID SHANDONG ELECTRIC POWER COMPANY YANTAI POWER SUPPLY Co. Applicant after: State Grid Shandong Yantai Fushan District Power Supply Co. Address before: 264000 Jiefang Road 158, Zhifu District, Yantai, Shandong Applicant before: STATE GRID SHANDONG ELECTRIC POWER COMPANY YANTAI POWER SUPPLY Co. Applicant before: State Grid Shandong Yantai Fushan District Power Supply Co. Applicant before: Longkou Power Supply Company of State Grid Shandong Electric Power Company |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |